xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

9aad78ebd9487f88e4eaa3d2967893534725787e

46,918

py

Python

tests/unit/test_connection.py

trir262/oneview-python

4636af9bc04cf651da779ccf9b5b20705683a56c

[ "Apache-2.0" ]

null

tests/unit/test_connection.py

trir262/oneview-python

4636af9bc04cf651da779ccf9b5b20705683a56c

[ "Apache-2.0" ]

null

tests/unit/test_connection.py

trir262/oneview-python

4636af9bc04cf651da779ccf9b5b20705683a56c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ### # (C) Copyright [2020] Hewlett Packard Enterprise Development LP # # 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 ssl import unittest import mmap import os import shutil import os.path from mock import patch, call, Mock, ANY from http.client import HTTPSConnection, BadStatusLine, HTTPException from hpeOneView.connection import connection from hpeOneView.exceptions import HPEOneViewException class ConnectionTest(unittest.TestCase): def setUp(self): self.host = '127.0.0.1' self.connection = connection(self.host, 800) self.accept_language_header = { 'Accept-Language': 'en_US' } self.default_headers = { 'X-API-Version': 800, 'Accept': 'application/json', 'Content-Type': 'application/json' } self.default_headers_with_etag_validation_off = { 'X-API-Version': 800, 'Accept': 'application/json', 'Content-Type': 'application/json', 'If-Match': '*' } self.merged_headers = { 'X-API-Version': 800, 'Accept': 'application/json', 'Content-Type': 'application/json', 'Accept-Language': 'en_US' } self.request_body = {"request body": "content"} self.response_body = {"response body": "content", "message": "An error occurred."} self.dumped_request_body = json.dumps(self.request_body.copy()) self.expected_response_body = self.response_body.copy() def __make_http_response(self, status): mock_response = Mock(status=status) mock_response.read.return_value = json.dumps(self.response_body).encode('utf-8') if status == 200 or status == 202: mock_response.getheader.return_value = '/task/uri' return mock_response def __create_fake_mapped_file(self): mock_mapped_file = Mock() mock_mapped_file.tell.side_effect = [0, 1048576, 2097152, 2621440] # 0, 1MB, 2MB 2.5MB mock_mapped_file.size.return_value = 2621440 # 2.5MB mock_mapped_file.read.side_effect = ['data chunck 1', 'data chunck 2', 'data chunck 3'] return mock_mapped_file def __prepare_connection_to_post_multipart(self, response_status=200): fake_connection = Mock() fake_connection.getresponse.return_value.read.return_value = json.dumps(self.response_body).encode('utf-8') fake_connection.getresponse.return_value.status = response_status self.connection.get_connection = Mock() self.connection.get_connection.return_value = fake_connection self.connection._open = Mock() self.connection._headers['auth'] = 'LTIxNjUzMjc0OTUzzHoF7eEkZLEUWVA-fuOZP4VGA3U8e67E' encode_multipart = "multipart/form-data; boundary=----------ThIs_Is_tHe_bouNdaRY_$" self.connection.encode_multipart_formdata = Mock() self.connection.encode_multipart_formdata.return_value = encode_multipart def test_default_headers(self): self.assertEqual(self.default_headers, self.connection._headers) def test_default_headers_when_etag_validation_is_disabled(self): self.connection.disable_etag_validation() self.assertEqual(self.default_headers_with_etag_validation_off, self.connection._headers) def test_default_headers_when_etag_validation_is_enabled(self): self.connection.enable_etag_validation() self.assertEqual(self.default_headers, self.connection._headers) def test_default_headers_when_etag_validation_is_disabled_and_enabled(self): self.connection.disable_etag_validation() self.connection.enable_etag_validation() self.assertEqual(self.default_headers, self.connection._headers) def test_default_headers_when_etag_validation_is_enabled_and_disabled(self): self.connection.enable_etag_validation() self.connection.disable_etag_validation() self.assertEqual(self.default_headers_with_etag_validation_off, self.connection._headers) def test_headers_with_api_version_800(self): self.connection = connection(self.host, 800) expected_headers = self.default_headers.copy() expected_headers['X-API-Version'] = 800 self.assertEqual(expected_headers, self.connection._headers) @patch.object(connection, 'get') def test_headers_with_default_api_version_800(self, mock_get): self.connection = connection(self.host) self.connection._apiVersion = None mock_get.side_effect = [{'minimumVersion': 400, 'currentVersion': 1800}] expected_version = self.connection.get_default_api_version() self.assertEqual(expected_version, 1800) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_when_status_is_202_and_task_contains_taskState(self, mock_response, mock_request): mock_request.return_value = {} fake_task = {"taskState": "Completed"} response = Mock(status=202) response.read.return_value = json.dumps(fake_task).encode('utf-8') response.getheader.return_value = '' mock_response.return_value = response task, body = self.connection.post('/path', self.request_body) self.assertEqual(task, fake_task) self.assertEqual(body, fake_task) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_when_status_is_202_and_response_is_not_a_task(self, mock_response, mock_request): mock_request.return_value = {} response = Mock(status=202) response.read.return_value = json.dumps(self.response_body).encode('utf-8') response.getheader.return_value = '' mock_response.return_value = response task, body = self.connection.post('/path', self.request_body) self.assertEqual(task, None) self.assertEqual(body, self.response_body) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_should_do_rest_call_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) self.connection.post('/path', self.request_body) mock_request.assert_called_once_with('POST', '/path', self.dumped_request_body, self.default_headers) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_should_do_rest_calls_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.post('/path', self.request_body) expected_calls = [call('POST', '/path', self.dumped_request_body, self.default_headers), call('GET', '/task/uri', '', self.default_headers)] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_should_send_merged_headers_when_headers_provided(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.post('/path', self.request_body, custom_headers=self.accept_language_header) expected_calls = [call('POST', ANY, ANY, self.merged_headers), ANY] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_should_return_body_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) result = self.connection.post('/path', self.response_body, custom_headers=self.accept_language_header) expected_result = (None, self.expected_response_body) self.assertEqual(expected_result, result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_should_return_tuple_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) result = self.connection.post('/path', self.response_body, custom_headers=self.accept_language_header) expected_result = (self.expected_response_body, self.expected_response_body) self.assertEqual(result, expected_result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_should_raise_exception_when_status_internal_error(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=400) try: self.connection.post('/path', self.request_body) except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_post_should_raise_exception_when_status_not_found(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=404) try: self.connection.post('/path', self.request_body) except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_put_should_do_rest_call_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) self.connection.put('/path', self.request_body) mock_request.assert_called_once_with('PUT', '/path', self.dumped_request_body, self.default_headers) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_put_should_do_rest_calls_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.put('/path', self.request_body) expected_calls = [call('PUT', '/path', self.dumped_request_body, self.default_headers), call('GET', '/task/uri', '', self.default_headers)] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_put_should_send_merged_headers_when_headers_provided(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.put('/path', self.request_body, custom_headers=self.accept_language_header) expected_calls = [call('PUT', ANY, ANY, self.merged_headers), ANY] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_put_should_return_body_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) result = self.connection.put('/path', self.response_body, custom_headers=self.accept_language_header) expected_result = (None, self.expected_response_body) self.assertEqual(result, expected_result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_put_should_return_tuple_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) result = self.connection.put('/path', self.response_body, custom_headers=self.accept_language_header) expected_result = (self.expected_response_body, self.expected_response_body) self.assertEqual(result, expected_result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_put_should_raise_exception_when_status_internal_error(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=400) try: self.connection.put('/path', self.request_body) except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_put_should_raise_exception_when_status_not_found(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=404) try: self.connection.put('/path', self.request_body) except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_patch_should_do_rest_call_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) self.connection.patch('/path', self.request_body) mock_request.assert_called_once_with('PATCH', '/path', self.dumped_request_body, self.default_headers) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_patch_should_do_rest_calls_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.patch('/path', self.request_body) expected_calls = [call('PATCH', '/path', self.dumped_request_body, self.default_headers), call('GET', '/task/uri', '', self.default_headers)] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_patch_should_send_merged_headers_when_headers_provided(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.patch('/path', self.request_body, custom_headers=self.accept_language_header) expected_calls = [call('PATCH', ANY, ANY, self.merged_headers), ANY] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_patch_should_return_body_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) result = self.connection.patch('/path', self.response_body, custom_headers=self.accept_language_header) expected_result = (None, self.expected_response_body) self.assertEqual(result, expected_result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_patch_should_return_tuple_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) result = self.connection.patch('/path', self.response_body, custom_headers=self.accept_language_header) expected_result = (self.expected_response_body, self.expected_response_body) self.assertEqual(result, expected_result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_patch_should_raise_exception_when_status_internal_error(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=400) try: self.connection.patch('/path', self.request_body) except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_patch_should_raise_exception_when_status_not_found(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=404) try: self.connection.patch('/path', self.request_body) except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_delete_should_do_rest_calls_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) self.connection.delete('/path') mock_request.assert_called_once_with('DELETE', '/path', json.dumps({}), self.default_headers) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_delete_should_do_rest_calls_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.delete('/path') expected_calls = [call('DELETE', '/path', json.dumps({}), self.default_headers), call('GET', '/task/uri', '', self.default_headers)] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_delete_should_send_merged_headers_when_headers_provided(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) self.connection.delete('/path', custom_headers=self.accept_language_header) expected_calls = [call('DELETE', ANY, ANY, self.merged_headers), ANY] self.assertEqual(expected_calls, mock_request.call_args_list) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_delete_should_return_body_when_status_ok(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=200) result = self.connection.delete('/path', custom_headers=self.accept_language_header) expected_result = (None, self.expected_response_body) self.assertEqual(result, expected_result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_delete_should_return_tuple_when_status_accepted(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=202) result = self.connection.delete('/path', custom_headers=self.accept_language_header) expected_result = (self.expected_response_body, self.expected_response_body) self.assertEqual(result, expected_result) @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_delete_should_raise_exception_when_status_internal_error(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=400) try: self.connection.delete('/path') except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(HTTPSConnection, 'request') @patch.object(HTTPSConnection, 'getresponse') def test_delete_should_raise_exception_when_status_not_found(self, mock_response, mock_request): mock_request.return_value = {} mock_response.return_value = self.__make_http_response(status=404) try: self.connection.delete('/path', self.request_body) except HPEOneViewException as e: self.assertEqual(e.oneview_response, self.expected_response_body) else: self.fail() @patch.object(connection, 'do_http') def test_task_in_response_body_without_202_status(self, mock_do_http): # create the return values mockedResponse = type('mockResponse', (), {'status': 200})() mockedTaskBody = {'category': 'tasks'} # set-up the mock mock_do_http.return_value = (mockedResponse, mockedTaskBody) # call the method we are testing (testTask, testBody) = self.connection._connection__do_rest_call('PUT', '/rest/test', '{ "body": "test" }', None) # verify the result self.assertEqual(mockedTaskBody, testTask) self.assertEqual(mockedTaskBody, testBody) @patch.object(connection, 'do_http') def test_do_rest_call_with_304_status(self, mock_do_http): mockedResponse = type('mockResponse', (), {'status': 304})() mock_do_http.return_value = (mockedResponse, '{ "body": "test" }') (testTask, testBody) = self.connection._connection__do_rest_call('PUT', '/rest/test', '{ "body": "test" }', None) self.assertIsNone(testTask) self.assertEqual(testBody, {"body": "test"}) @patch.object(connection, 'do_http') def test_do_rest_call_with_304_status_and_invalid_json(self, mock_do_http): mockedResponse = type('mockResponse', (), {'status': 304})() mock_do_http.return_value = (mockedResponse, 111) (testTask, testBody) = self.connection._connection__do_rest_call('PUT', '/rest/test', 111, None) self.assertIsNone(testTask) self.assertEqual(testBody, 111) @patch('time.sleep') @patch.object(connection, 'get_connection') def test_download_to_stream_when_status_ok(self, mock_get_conn, mock_sleep): mock_conn = Mock() # First attempt: Error, second attempt: successful connection mock_get_conn.side_effect = [BadStatusLine(0), mock_conn] mock_response = mock_conn.getresponse.return_value # Stops at the fourth read call mock_response.read.side_effect = ['111', '222', '333', None] mock_response.status = 200 mock_stream = Mock() result = self.connection.download_to_stream(mock_stream, '/rest/download.zip', custom_headers={'custom': 'custom'}) self.assertTrue(result) mock_stream.write.assert_has_calls([call('111'), call('222'), call('333')]) @patch.object(connection, 'get_connection') def test_download_to_stream_handling_of_status_302(self, mock_get_conn): # Mocking two responses as the first response would be redirect status 302 with header # having location to download. Second response would have 200 mock_redirect_resp = Mock(status=302) mock_redirect_resp.getheader.return_value = "/redirect/download.zip" mock_resp = Mock(status=200) mock_resp.read.side_effect = ["Something", None] mock_conn = Mock() mock_get_conn.return_value = mock_conn mock_conn.getresponse.side_effect = [mock_redirect_resp, mock_resp] mock_stream = Mock() result = self.connection.download_to_stream(mock_stream, '/rest/download.zip') mock_redirect_resp.getheader.assert_has_calls([call('Location')]) mock_stream.write.assert_has_calls([call("Something")]) self.assertTrue(result) @patch('time.sleep') @patch.object(connection, 'get_connection') def test_download_to_stream_when_error_status_with_response_body(self, mock_get_conn, mock_sleep): mock_conn = Mock() mock_get_conn.return_value = mock_conn mock_response = mock_conn.getresponse.return_value mock_response.read.return_value = json.dumps('error message').encode('utf-8') mock_response.status = 500 mock_stream = Mock() try: self.connection.download_to_stream(mock_stream, '/rest/download.zip') except HPEOneViewException as e: self.assertEqual(e.msg, 'error message') else: self.fail() @patch('time.sleep') @patch.object(connection, 'get_connection') def test_download_to_stream_when_error_status_with_decode_error(self, mock_get_conn, mock_sleep): mock_conn = Mock() mock_get_conn.return_value = mock_conn mock_response = mock_conn.getresponse.return_value mock_response.read.return_value = json.dumps('error message').encode('utf-8') mock_response.read.decode.side_effect = UnicodeDecodeError('sn33af', b"", 42, 43, 'ths239sn') mock_response.status = 500 mock_stream = Mock() try: self.connection.download_to_stream(mock_stream, '/rest/download.zip') except HPEOneViewException as e: self.assertEqual(e.msg, 'error message') else: self.fail() @patch('time.sleep') @patch.object(connection, 'get_connection') def test_download_to_stream_when_error_status_with_empty_body(self, mock_get_conn, mock_sleep): mock_conn = Mock() mock_get_conn.return_value = mock_conn mock_response = mock_conn.getresponse.return_value mock_response.read.return_value = json.dumps('').encode('utf-8') mock_response.status = 500 mock_stream = Mock() try: self.connection.download_to_stream(mock_stream, '/rest/download.zip') except HPEOneViewException as e: self.assertEqual(e.msg, 'Error 500') else: self.fail() @patch.object(connection, 'get_connection') def test_download_to_stream_with_timeout_error(self, mock_get_connection): mock_conn = mock_get_connection.return_value = Mock() mock_response = Mock() mock_conn.getresponse.side_effect = [HTTPException('timed out'), mock_response] mock_stream = Mock() with self.assertRaises(HPEOneViewException) as context: resp, body = self.connection.download_to_stream(mock_stream, '/rest/download.zip') self.assertTrue('timed out' in context.exception.msg) @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') def test_post_multipart_should_put_request(self, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart() mock_mmap.return_value = self.__create_fake_mapped_file() self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") internal_conn = self.connection.get_connection.return_value internal_conn.putrequest.assert_called_once_with('POST', '/rest/resources/') @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') def test_post_multipart_should_put_headers(self, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart() mock_mmap.return_value = self.__create_fake_mapped_file() mock_path_size.return_value = 2621440 # 2.5 MB self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") expected_putheader_calls = [ call('uploadfilename', 'archive.zip'), call('auth', 'LTIxNjUzMjc0OTUzzHoF7eEkZLEUWVA-fuOZP4VGA3U8e67E'), call('Content-Type', 'multipart/form-data; boundary=----------ThIs_Is_tHe_bouNdaRY_$'), call('Content-Length', 2621440), call('X-API-Version', 800)] internal_conn = self.connection.get_connection.return_value internal_conn.putheader.assert_has_calls(expected_putheader_calls) @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') def test_post_multipart_should_read_file_in_chunks_of_1mb(self, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart() mock_mmap.return_value = self.__create_fake_mapped_file() self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") expected_mmap_read_calls = [ call(1048576), call(1048576), call(1048576)] mock_mmap.return_value.read.assert_has_calls(expected_mmap_read_calls) @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') def test_post_multipart_should_send_file_in_chuncks_of_1mb(self, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart() mock_mmap.return_value = self.__create_fake_mapped_file() self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") expected_conn_send_calls = [ call('data chunck 1'), call('data chunck 2'), call('data chunck 3')] internal_conn = self.connection.get_connection.return_value internal_conn.send.assert_has_calls(expected_conn_send_calls) @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') def test_post_multipart_should_remove_temp_encoded_file(self, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart() mock_mmap.return_value = self.__create_fake_mapped_file() self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") mock_rm.assert_called_once_with('/a/path/filename.zip.b64') @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') def test_post_multipart_should_raise_exception_when_response_status_400(self, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart(response_status=400) mock_mmap.return_value = self.__create_fake_mapped_file() try: self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") except HPEOneViewException as e: self.assertEqual(e.msg, "An error occurred.") else: self.fail() @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') def test_post_multipart_should_return_response_and_body_when_response_status_200(self, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart() mock_mmap.return_value = self.__create_fake_mapped_file() response, body = self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") self.assertEqual(body, self.expected_response_body) self.assertEqual(response.status, 200) @patch.object(mmap, 'mmap') @patch.object(shutil, 'copyfileobj') @patch.object(os.path, 'getsize') @patch.object(os, 'remove') @patch.object(json, 'loads') def test_post_multipart_should_handle_json_load_exception(self, mock_json_loads, mock_rm, mock_path_size, mock_copy, mock_mmap): self.__prepare_connection_to_post_multipart() mock_mmap.return_value = self.__create_fake_mapped_file() mock_json_loads.side_effect = ValueError("Invalid JSON") response, body = self.connection.post_multipart(uri='/rest/resources/', fields=None, files="/a/path/filename.zip", baseName="archive.zip") self.assertTrue(body) self.assertEqual(response.status, 200) @patch.object(connection, 'post_multipart') def test_post_multipart_with_response_handling_when_status_202_without_task(self, mock_post_multipart): mock_response = Mock(status=202) mock_response.getheader.return_value = None mock_post_multipart.return_value = mock_response, "content" task, body = self.connection.post_multipart_with_response_handling("uri", "filepath", "basename") self.assertFalse(task) self.assertEqual(body, "content") @patch.object(connection, 'post_multipart') @patch.object(connection, 'get') def test_post_multipart_with_response_handling_when_status_202_with_task(self, mock_get, mock_post_multipart): fake_task = {"category": "tasks"} mock_response = Mock(status=202) mock_response.getheader.return_value = "/rest/tasks/taskid" mock_post_multipart.return_value = mock_response, "content" mock_get.return_value = fake_task task, body = self.connection.post_multipart_with_response_handling("uri", "filepath", "basename") self.assertEqual(task, fake_task) self.assertEqual(body, "content") @patch.object(connection, 'post_multipart') def test_post_multipart_with_response_handling_when_status_200_and_body_is_task(self, mock_post_multipart): fake_task = {"category": "tasks"} mock_post_multipart.return_value = Mock(status=200), fake_task task, body = self.connection.post_multipart_with_response_handling("uri", "filepath", "basename") self.assertEqual(task, fake_task) self.assertEqual(body, fake_task) @patch.object(connection, 'post_multipart') def test_post_multipart_with_response_handling_when_status_200_and_body_is_not_task(self, mock_post_multipart): mock_post_multipart.return_value = Mock(status=200), "content" task, body = self.connection.post_multipart_with_response_handling("uri", "filepath", "basename") self.assertFalse(task) self.assertEqual(body, "content") @patch.object(connection, 'get_connection') def test_do_http_with_invalid_unicode(self, mock_get_connection): mock_conn = mock_get_connection.return_value = Mock() mock_conn.getresponse.return_value = Mock() mock_conn.getresponse.return_value.read.side_effect = UnicodeDecodeError("utf8", b"response", 0, 4, "reason") _, body = self.connection.do_http('POST', '/rest/test', 'body') self.assertEqual(body, '') mock_conn.request.assert_called_once_with('POST', '/rest/test', 'body', {'Content-Type': 'application/json', 'X-API-Version': 800, 'Accept': 'application/json'}) mock_conn.close.assert_called_once() @patch.object(connection, 'get_connection') def test_do_http_with_invalid_json_return(self, mock_get_connection): mock_conn = mock_get_connection.return_value = Mock() mock_conn.getresponse.return_value = Mock() mock_conn.getresponse.return_value.read.return_value = b"response data" resp, body = self.connection.do_http('POST', '/rest/test', 'body') self.assertEqual(body, 'response data') mock_conn.request.assert_called_once_with('POST', '/rest/test', 'body', {'Content-Type': 'application/json', 'X-API-Version': 800, 'Accept': 'application/json'}) mock_conn.close.assert_called_once() @patch.object(connection, 'get_connection') def test_do_http_with_bad_status_line(self, mock_get_connection): mock_conn = mock_get_connection.return_value = Mock() # First attempt: Error, second attempt: successful response mock_response = Mock() mock_conn.getresponse.side_effect = [BadStatusLine(0), mock_response] # Stops at the fourth read call mock_response.read.return_value = b"response data" mock_response.status = 200 with patch('time.sleep'): resp, body = self.connection.do_http('POST', '/rest/test', 'body') self.assertEqual(body, 'response data') mock_conn.request.assert_called_with('POST', '/rest/test', 'body', {'Content-Type': 'application/json', 'X-API-Version': 800, 'Accept': 'application/json'}) mock_conn.close.assert_has_calls([call(), call()]) @patch.object(connection, 'get_connection') def test_do_http_with_timeout_error(self, mock_get_connection): mock_conn = mock_get_connection.return_value = Mock() mock_response = Mock() mock_conn.getresponse.side_effect = [HTTPException('timed out'), mock_response] with self.assertRaises(HPEOneViewException) as context: resp, body = self.connection.do_http('POST', '/rest/test', 'body') self.assertTrue('timed out' in context.exception.msg) @patch.object(connection, 'get') def test_get_by_uri(self, mock_get): uri = "/rest/uri" self.connection.get_by_uri(uri) mock_get.assert_called_once_with(uri) def test_make_url(self): url = self.connection.make_url('/test/path') self.assertEqual(url, url) @patch.object(connection, 'get') @patch.object(connection, 'post') def test_login(self, mock_post, mock_get): mock_get.side_effect = [{'minimumVersion': 800, 'currentVersion': 1000}] mock_post.return_value = {'cat': 'task'}, {'sessionID': '123'} self.connection.login({}) self.assertEqual(self.connection.get_session_id(), '123') self.assertEqual(self.connection.get_session(), True) @patch.object(connection, 'get') def test_login_catches_exceptions_as_hpeOneView(self, mock_get): mock_get.side_effect = [Exception('test')] with self.assertRaises(HPEOneViewException): self.connection.login({}) @patch.object(connection, 'get') @patch.object(connection, 'post') def test_login_with_exception_in_post(self, mock_post, mock_get): mock_get.side_effect = [{'minimumVersion': 800, 'currentVersion': 1000}] mock_post.side_effect = HPEOneViewException("Failed") self.assertRaises(HPEOneViewException, self.connection.login, {}) @patch.object(connection, 'get') @patch.object(connection, 'put') def test_login_sessionID(self, mock_put, mock_get): mock_get.side_effect = [{'minimumVersion': 800, 'currentVersion': 1000}] mock_put.return_value = {'cat': 'task'}, {'sessionID': '123'} self.connection.login({"sessionID": "123"}) self.assertEqual(self.connection.get_session_id(), '123') self.assertEqual(self.connection.get_session(), True) @patch.object(connection, 'get') @patch.object(connection, 'put') def test_login_username_password_sessionID(self, mock_put, mock_get): mock_get.side_effect = [{'minimumVersion': 800, 'currentVersion': 1000}] mock_put.return_value = {'cat': 'task'}, {'sessionID': '123'} self.connection.login({"userName": "administrator", "password": "", "sessionID": "123"}) self.assertEqual(self.connection.get_session_id(), '123') self.assertEqual(self.connection.get_session(), True) @patch.object(connection, 'get') @patch.object(connection, 'put') def test_login_with_exception_in_put(self, mock_put, mock_get): mock_get.side_effect = [{'minimumVersion': 800, 'currentVersion': 400}] mock_put.side_effect = HPEOneViewException("Failed") self.assertRaises(HPEOneViewException, self.connection.login, {"sessionID": "123"}) @patch.object(connection, 'get') @patch.object(connection, 'put') def test_login_with_exception_in_put_username_password_sessionID(self, mock_put, mock_get): mock_get.side_effect = [{'minimumVersion': 800, 'currentVersion': 400}] mock_put.side_effect = HPEOneViewException("Failed") self.assertRaises(HPEOneViewException, self.connection.login, {"userName": "administrator", "password": "", "sessionID": "123"}) @patch.object(connection, 'get') def test_validate_version_exceeding_minimum(self, mock_get): self.connection._apiVersion = 800 mock_get.side_effect = [{'minimumVersion': 400, 'currentVersion': 400}] self.assertRaises(HPEOneViewException, self.connection.validateVersion) @patch.object(connection, 'get') def test_validate_version_exceeding_current(self, mock_get): self.connection._apiVersion = 400 mock_get.side_effect = [{'minimumVersion': 800, 'currentVersion': 400}] self.assertRaises(HPEOneViewException, self.connection.validateVersion) @patch.object(shutil, 'copyfileobj') @patch.object(connection, '_open') def test_encode_multipart_formdata(self, mock_open, mock_copyfileobj): mock_in = Mock() mock_out = Mock() mock_open.side_effect = [mock_in, mock_out] self.connection.encode_multipart_formdata('', "/a/path/filename.zip", 'filename.zip') mock_open.assert_has_calls([call('/a/path/filename.zip', 'rb'), call('/a/path/filename.zip.b64', 'wb')]) mock_out.write.assert_has_calls( [call(bytearray(b'------------ThIs_Is_tHe_bouNdaRY_$\r\n')), call(bytearray( b'Content-Disposition: form-data; name="file"; filename="filename.zip"\r\n')), call(bytearray(b'Content-Type: application/octet-stream\r\n')), call(bytearray(b'\r\n')), call(bytearray(b'\r\n')), call(bytearray(b'------------ThIs_Is_tHe_bouNdaRY_$--\r\n')), call(bytearray(b'\r\n'))]) mock_in.close.assert_called_once() mock_out.close.assert_called_once() def test_get_connection_ssl_trust_all(self): conn = self.connection.get_connection() self.assertEqual(conn.host, '127.0.0.1') self.assertEqual(conn.port, 443) self.assertEqual(conn._context.protocol, ssl.PROTOCOL_TLSv1_2) def test_get_connection_ssl_trust_all_with_proxy(self): self.connection.set_proxy('10.0.0.1', 3128) conn = self.connection.get_connection() self.assertEqual(conn.host, '10.0.0.1') self.assertEqual(conn.port, 3128) self.assertEqual(conn._context.protocol, ssl.PROTOCOL_TLSv1_2) @patch.object(ssl.SSLContext, 'load_verify_locations') def test_get_connection_trusted_ssl_bundle_with_proxy(self, mock_lvl): self.connection.set_proxy('10.0.0.1', 3128) self.connection.set_trusted_ssl_bundle('/test') conn = self.connection.get_connection() self.assertEqual(conn.host, '10.0.0.1') self.assertEqual(conn.port, 3128) self.assertEqual(conn._context.protocol, ssl.PROTOCOL_TLSv1_2) @patch.object(ssl.SSLContext, 'load_verify_locations') def test_get_connection_trusted_ssl_bundle(self, mock_lvl): self.connection.set_trusted_ssl_bundle('/test') conn = self.connection.get_connection() self.assertEqual(conn.host, '127.0.0.1') self.assertEqual(conn.port, 443) self.assertEqual(conn._context.protocol, ssl.PROTOCOL_TLSv1_2) if __name__ == '__main__': unittest.main()

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120

0.671427

5,383

46,918

5.514211

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0.048917

0.052555

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0.121673

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null

0

1

0

null

0

6

9ac0858db080ee521a93e453e909a3341da4d7af

33

py

Python

models/ops/depthconv/functions/__init__.py

aksh1501/DepthAware_CNN_edit

3f6a859ef3f3b7bba1201dc087c860a22d1cd258

[ "MIT" ]

null

models/ops/depthconv/functions/__init__.py

aksh1501/DepthAware_CNN_edit

3f6a859ef3f3b7bba1201dc087c860a22d1cd258

[ "MIT" ]

null

models/ops/depthconv/functions/__init__.py

aksh1501/DepthAware_CNN_edit

3f6a859ef3f3b7bba1201dc087c860a22d1cd258

[ "MIT" ]

null

from .depthconv import depth_conv

33

0.878788

5

33

5.6

1

0

0.090909

33

1

33

0.933333

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1

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0

null

0

1

0

1

0

1

0

6

9ad569d64c7bfa36518a7bd5bad6bfcea1e5fef2

93

py

Python

Test.py

csy1993/PythonLeetcode

98fd9b1639626459fbf81bf94727775d39248dde

[ "Apache-2.0" ]

null

Test.py

csy1993/PythonLeetcode

98fd9b1639626459fbf81bf94727775d39248dde

[ "Apache-2.0" ]

null

Test.py

csy1993/PythonLeetcode

98fd9b1639626459fbf81bf94727775d39248dde

[ "Apache-2.0" ]

null

""" * @File: Test.py * @Author: CSY - 25809 * @Date: 2019/8/27 - 19:11 * @Project: Test """

13.285714

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0.537634

14

93

3.571429

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0.204301

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null

0

1

0

6

b1360c5575518d5b9e61c15fa171a87b9d334abe

81

py

Python

tests/conftest.py

ketgo/nameko-mongoengine

94b4a41dfa845cec8b48f874c0b64658a1c4bef6

[ "Apache-2.0" ]

2

2019-12-06T17:51:44.000Z

2020-02-20T22:38:38.000Z

tests/conftest.py

ketgo/nameko-mongoengine

94b4a41dfa845cec8b48f874c0b64658a1c4bef6

[ "Apache-2.0" ]

1

2022-03-07T02:32:45.000Z

2022-03-07T06:45:43.000Z

tests/conftest.py

ketgo/nameko-mongoengine

94b4a41dfa845cec8b48f874c0b64658a1c4bef6

[ "Apache-2.0" ]

null

# Nameko requires eventlet monkey patch import eventlet eventlet.monkey_patch()

16.2

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10

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6.6

0.6

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81

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null

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6

b1389bfb3d0cad3b7d6d35484fe3d0d5d3308972

39

py

Python

hbussi/sunnify.py

hannahbus/hbussi

31fbbf433a4511191850bd0221ec279a775e0cf0

[ "MIT" ]

1

2021-05-20T08:01:40.000Z

hbussi/sunnify.py

hannahbus/hbussi

31fbbf433a4511191850bd0221ec279a775e0cf0

[ "MIT" ]

null

hbussi/sunnify.py

hannahbus/hbussi

31fbbf433a4511191850bd0221ec279a775e0cf0

[ "MIT" ]

null

def sunshine(): return('whatever')

19.5

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0

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1

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6

b14aa378e6f860dc5390afde3148152e61315014

63,331

py

Python

autotest/smoother_tests.py

jbellino-usgs/pyemu

abcff190f517ac068298e5bbefea7026046d4830

[ "BSD-3-Clause" ]

null

autotest/smoother_tests.py

jbellino-usgs/pyemu

abcff190f517ac068298e5bbefea7026046d4830

[ "BSD-3-Clause" ]

null

autotest/smoother_tests.py

jbellino-usgs/pyemu

abcff190f517ac068298e5bbefea7026046d4830

[ "BSD-3-Clause" ]

null

import os if not os.path.exists("temp"): os.mkdir("temp") def henry_setup(): import os import pyemu pst = pyemu.Pst(os.path.join("smoother","henry_pc","pest.pst")) par = pst.parameter_data par.loc[:,"parlbnd"] = 20.0 par.loc[:,"parubnd"] = 2000.0 par.loc["mult1","parlbnd"] = 0.9 par.loc["mult1","parubnd"] = 1.1 # obs = pst.observation_data # head_groups = obs.groupby(obs.apply(lambda x: x.obgnme=="head" and x.weight>0.0, axis=1)).groups[True] # obs.loc[head_groups,"weight"] = 1.0 # conc_groups = obs.groupby(obs.apply(lambda x: x.obgnme=="conc" and x.weight>0.0, axis=1)).groups[True] # obs.loc[conc_groups,"weight"] = 0.5 pst.pestpp_options["sweep_parameter_csv_file"] = "sweep_in.csv" pst.write(pst.filename.replace("pest.pst","henry.pst")) def henry(): import os import pyemu os.chdir(os.path.join("smoother", "henry_pc")) csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst(os.path.join("henry.pst")) es = pyemu.EnsembleSmoother(pst, num_slaves=15,verbose="ies.log") es.initialize(210, init_lambda=1.0) for i in range(10): es.update(lambda_mults=[0.2,5.0],run_subset=45) os.chdir(os.path.join("..", "..")) def henry_plot(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages import pandas as pd from pyemu import Pst d = os.path.join("smoother","henry_pc") pst = Pst(os.path.join(d,"henry.pst")) plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) par_files = [os.path.join(d,f) for f in os.listdir(d) if "parensemble." in f and ".png" not in f] par_dfs = [pd.read_csv(par_file,index_col=0).apply(np.log10) for par_file in par_files] par_names = ["mult1"] mx = (pst.parameter_data.loc[:,"parubnd"] * 1.1).apply(np.log10) mn = (pst.parameter_data.loc[:,"parlbnd"] * 0.9).apply(np.log10) obj_df = pd.read_csv(os.path.join(d,"henry.pst.iobj.csv"),index_col=0) real_cols = [col for col in obj_df.columns if col.startswith("0")] obj_df.loc[:,real_cols] = obj_df.loc[:,real_cols].apply(np.log10) obj_df.loc[:,"mean"] = obj_df.loc[:,"mean"].apply(np.log10) obj_df.loc[:, "std"] = obj_df.loc[:, "std"].apply(np.log10) fig = plt.figure(figsize=(20, 10)) ax = plt.subplot(111) axt = plt.twinx() obj_df.loc[:, real_cols].plot(ax=ax, lw=0.5, color="0.5", alpha=0.5, legend=False) ax.plot(obj_df.index, obj_df.loc[:, "mean"], 'b', lw=2.5,marker='.',markersize=5) #ax.fill_between(obj_df.index, obj_df.loc[:, "mean"] - (1.96 * obj_df.loc[:, "std"]), # obj_df.loc[:, "mean"] + (1.96 * obj_df.loc[:, "std"]), # facecolor="b", edgecolor="none", alpha=0.25) axt.plot(obj_df.index,obj_df.loc[:,"lambda"],"k",dashes=(2,1),lw=2.5) ax.set_ylabel("log$_10$ phi") axt.set_ylabel("lambda") ax.set_title("total runs:{0}".format(obj_df.total_runs.max())) plt.savefig(os.path.join(plt_dir,"iobj.pdf")) plt.close() with PdfPages(os.path.join(plt_dir,"parensemble.pdf")) as pdf: for par_file,par_df in zip(par_files,par_dfs): print(par_file) fig = plt.figure(figsize=(20,10)) plt.figtext(0.5,0.975,par_file,ha="center") axes = [plt.subplot(1,1,i+1) for i in range(len(par_names))] for par_name,ax in zip(par_names,axes): mean = par_df.loc[:,par_name].mean() std = par_df.loc[:,par_name].std() par_df.loc[:,par_name].hist(ax=ax,edgecolor="none", alpha=0.5,grid=False) ax.set_yticklabels([]) ax.set_title("{0}, {1:6.2f}".\ format(par_name,10.0**mean)) ax.set_xlim(mn[par_name],mx[par_name]) ylim = ax.get_ylim() if "mult1" in par_name: val = np.log10(1.0) else: val = np.log10(200.0) ticks = ["{0:2.1f}".format(x) for x in 10.0**ax.get_xticks()] ax.set_xticklabels(ticks,rotation=90) ax.plot([val,val],ylim,"k-",lw=2.0) ax.plot([mean,mean],ylim,"b-",lw=1.5) ax.plot([mean+(2.0*std),mean+(2.0*std)],ylim,"b--",lw=1.5) ax.plot([mean-(2.0*std),mean-(2.0*std)],ylim,"b--",lw=1.5) pdf.savefig() plt.close() obs_files = [os.path.join(d,f) for f in os.listdir(d) if "obsensemble." in f and ".png" not in f] obs_dfs = [pd.read_csv(obs_file) for obs_file in obs_files] #print(obs_files) #mx = max([obs_df.obs.max() for obs_df in obs_dfs]) #mn = min([obs_df.obs.min() for obs_df in obs_dfs]) #print(mn,mx) obs_names = pst.nnz_obs_names obs_names.extend(["pd_one","pd_ten","pd_half"]) print(len(obs_names)) #print(obs_files) obs_dfs = [obs_df.loc[:,obs_names] for obs_df in obs_dfs] mx = {obs_name:max([obs_df.loc[:,obs_name].max() for obs_df in obs_dfs]) for obs_name in obs_names} mn = {obs_name:min([obs_df.loc[:,obs_name].min() for obs_df in obs_dfs]) for obs_name in obs_names} with PdfPages(os.path.join(plt_dir,"obsensemble.pdf")) as pdf: for obs_file,obs_df in zip(obs_files,obs_dfs): fig = plt.figure(figsize=(30,20)) plt.figtext(0.5,0.975,obs_file,ha="center") print(obs_file) axes = [plt.subplot(8,5,i+1) for i in range(len(obs_names))] for ax,obs_name in zip(axes,obs_names): mean = obs_df.loc[:,obs_name].mean() std = obs_df.loc[:,obs_name].std() obs_df.loc[:,obs_name].hist(ax=ax,edgecolor="none", alpha=0.5,grid=False) ax.set_yticklabels([]) #print(ax.get_xlim(),mn[obs_name],mx[obs_name]) ax.set_title("{0}, {1:6.2f}:{2:6.2f}".format(obs_name,mean,std)) ax.set_xlim(mn[obs_name],mx[obs_name]) #ax.set_xlim(0.0,20.0) ylim = ax.get_ylim() oval = pst.observation_data.loc[obs_name,"obsval"] ax.plot([oval,oval],ylim,"k-",lw=2) ax.plot([mean,mean],ylim,"b-",lw=1.5) ax.plot([mean+(2.0*std),mean+(2.0*std)],ylim,"b--",lw=1.5) ax.plot([mean-(2.0*std),mean-(2.0*std)],ylim,"b--",lw=1.5) ax.set_xticklabels([]) pdf.savefig() plt.close() def freyberg_check_phi_calc(): import os import pandas as pd import pyemu import shutil os.chdir(os.path.join("smoother","freyberg")) xy = pd.read_csv("freyberg.xy") csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst(os.path.join("freyberg.pst")) dia_parcov = pyemu.Cov.from_parameter_data(pst,sigma_range=6.0) nothk_names = [pname for pname in pst.adj_par_names if "hk" not in pname] parcov_nothk = dia_parcov.get(row_names=nothk_names) gs = pyemu.utils.geostats.read_struct_file(os.path.join("template","structure.dat")) print(gs.variograms[0].a,gs.variograms[0].contribution) #gs.variograms[0].a *= 10.0 #gs.variograms[0].contribution *= 10.0 gs.nugget = 0.0 print(gs.variograms[0].a,gs.variograms[0].contribution) full_parcov = gs.covariance_matrix(xy.x,xy.y,xy.name) parcov = parcov_nothk.extend(full_parcov) #print(parcov.to_pearson().x[-1,:]) pst.observation_data.loc[:,"weight"] /= 10.0 #pst.write("temp.pst") obscov = pyemu.Cov.from_observation_data(pst) es = pyemu.EnsembleSmoother(pst,parcov=parcov,obscov=obscov,num_slaves=1, verbose=True) es.initialize(num_reals=3) print(es.parensemble.loc[:,"hkr00c07"]) pst.parameter_data.loc[:,"parval1"] = es.parensemble.iloc[0,:] pst.observation_data.loc[pst.nnz_obs_names,"obsval"] = es.obsensemble_0.loc[0,pst.nnz_obs_names] pst.control_data.noptmax = 0 if os.path.exists("temp"): shutil.rmtree("temp") shutil.copytree("template","temp") pst.write(os.path.join("temp","temp.pst")) os.chdir("temp") os.system("pestpp temp.pst") os.chdir("..") p = pyemu.Pst(os.path.join("temp","temp.pst")) print(p.phi) os.chdir(os.path.join("..","..")) def freyberg(): import os import pandas as pd import pyemu os.chdir(os.path.join("smoother","freyberg")) if not os.path.exists("freyberg.xy"): import flopy ml = flopy.modflow.Modflow.load("freyberg.nam",model_ws="template", load_only=[]) xy = pd.DataFrame([(x,y) for x,y in zip(ml.sr.xcentergrid.flatten(),ml.sr.ycentergrid.flatten())], columns=['x','y']) names = [] for i in range(ml.nrow): for j in range(ml.ncol ): names.append("hkr{0:02d}c{1:02d}".format(i,j)) xy.loc[:,"name"] = names xy.to_csv("freyberg.xy") else: xy = pd.read_csv("freyberg.xy") csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst(os.path.join("freyberg.pst")) dia_parcov = pyemu.Cov.from_parameter_data(pst,sigma_range=6.0) nothk_names = [pname for pname in pst.adj_par_names if "hk" not in pname] parcov_nothk = dia_parcov.get(row_names=nothk_names) gs = pyemu.utils.geostats.read_struct_file(os.path.join("template","structure.dat")) print(gs.variograms[0].a,gs.variograms[0].contribution) #gs.variograms[0].a *= 10.0 #gs.variograms[0].contribution *= 10.0 gs.nugget = 0.0 print(gs.variograms[0].a,gs.variograms[0].contribution) full_parcov = gs.covariance_matrix(xy.x,xy.y,xy.name) parcov = parcov_nothk.extend(full_parcov) #print(parcov.to_pearson().x[-1,:]) parcov.to_binary("freyberg_prior.jcb") parcov.to_ascii("freyberg_prior.cov") return pst.observation_data.loc[:,"weight"] /= 10.0 pst.write("temp.pst") obscov = pyemu.Cov.from_obsweights(os.path.join("temp.pst")) es = pyemu.EnsembleSmoother(pst,parcov=parcov,obscov=obscov,num_slaves=20, verbose=True) #gs.variograms[0].a=10000 #gs.variograms[0].contribution=0.01 #gs.variograms[0].anisotropy = 10.0 # pp_df = pyemu.utils.gw_utils.pp_file_to_dataframe("points1.dat") # parcov_hk = gs.covariance_matrix(pp_df.x,pp_df.y,pp_df.name) # parcov_full = parcov_hk.extend(parcov_rch) es.initialize(100,init_lambda=100.0,enforce_bounds="reset") for i in range(10): es.update(lambda_mults=[0.01,0.2,5.0,100.0],run_subset=20) os.chdir(os.path.join("..","..")) def freyberg_condor(): import os import pandas as pd import pyemu os.chdir(os.path.join("smoother","freyberg")) if not os.path.exists("freyberg.xy"): import flopy ml = flopy.modflow.Modflow.load("freyberg.nam",model_ws="template", load_only=[]) xy = pd.DataFrame([(x,y) for x,y in zip(ml.sr.xcentergrid.flatten(),ml.sr.ycentergrid.flatten())], columns=['x','y']) names = [] for i in range(ml.nrow): for j in range(ml.ncol ): names.append("hkr{0:02d}c{1:02d}".format(i,j)) xy.loc[:,"name"] = names xy.to_csv("freyberg.xy") else: xy = pd.read_csv("freyberg.xy") csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst(os.path.join("freyberg.pst")) dia_parcov = pyemu.Cov.from_parameter_data(pst,sigma_range=6.0) nothk_names = [pname for pname in pst.adj_par_names if "hk" not in pname] parcov_nothk = dia_parcov.get(row_names=nothk_names) gs = pyemu.utils.geostats.read_struct_file(os.path.join("template","structure.dat")) print(gs.variograms[0].a,gs.variograms[0].contribution) #gs.variograms[0].a *= 10.0 #gs.variograms[0].contribution *= 10.0 gs.nugget = 0.0 print(gs.variograms[0].a,gs.variograms[0].contribution) full_parcov = gs.covariance_matrix(xy.x,xy.y,xy.name) parcov = parcov_nothk.extend(full_parcov) #print(parcov.to_pearson().x[-1,:]) pst.observation_data.loc[:,"weight"] /= 10.0 pst.write("temp.pst") obscov = pyemu.Cov.from_obsweights(os.path.join("temp.pst")) es = pyemu.EnsembleSmoother(pst,parcov=parcov,obscov=obscov,num_slaves=20, verbose=True,submit_file="freyberg.sub") #gs.variograms[0].a=10000 #gs.variograms[0].contribution=0.01 #gs.variograms[0].anisotropy = 10.0 # pp_df = pyemu.utils.gw_utils.pp_file_to_dataframe("points1.dat") # parcov_hk = gs.covariance_matrix(pp_df.x,pp_df.y,pp_df.name) # parcov_full = parcov_hk.extend(parcov_rch) es.initialize(300,init_lambda=10000.0,enforce_bounds="reset") for i in range(10): es.update(lambda_mults=[0.2,5.0],run_subset=40) os.chdir(os.path.join("..","..")) def freyberg_pars_to_array(par_df): import numpy as np #print(par_df.index) real_col = par_df.columns[0] hk_names = par_df.index.map(lambda x:x.startswith("hk")) hk_df = par_df.loc[hk_names,:] hk_df.loc[:,"row"] = hk_df.index.map(lambda x: int(x[3:5])) hk_df.loc[:,"column"] = hk_df.index.map(lambda x: int(x[-2:])) nrow,ncol = hk_df.row.max() + 1, hk_df.column.max() + 1 arr = np.zeros((nrow,ncol)) - 999.0 for r,c,v in zip(hk_df.row,hk_df.column,hk_df.loc[:,real_col]): arr[r-1,c-1] = v arr = np.ma.masked_where(arr==-999.,arr) return arr def freyberg_plot_par_seq(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages import pandas as pd from pyemu import Pst d = os.path.join("smoother","freyberg") pst = Pst(os.path.join(d,"freyberg.pst")) plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) par_files = [os.path.join(d,f) for f in os.listdir(d) if "parensemble." in f and ".png" not in f] par_dfs = [pd.read_csv(par_file,index_col=0).apply(np.log10) for par_file in par_files] #par_names = list(par_dfs[0].columns) par_names = ["rch_1","rch_2"] mx = (pst.parameter_data.loc[:,"parubnd"] * 1.1).apply(np.log10) mn = (pst.parameter_data.loc[:,"parlbnd"] * 0.9).apply(np.log10) f_count = 0 for par_file,par_df in zip(par_files,par_dfs): #print(par_file) fig = plt.figure(figsize=(4.5,3.5)) plt.figtext(0.5,0.95,"iteration {0}".format(f_count),ha="center") axes = [plt.subplot(3,4,i+1) for i in range(12)] arrs = [] for ireal in range(12): arrs.append(freyberg_pars_to_array(par_df.iloc[[ireal],:].T)) amx = max([arr.max() for arr in arrs]) amn = max([arr.min() for arr in arrs]) for ireal,arr in enumerate(arrs): axes[ireal].imshow(arr,vmax=amx,vmin=amn,interpolation="nearest") axes[ireal].set_xticklabels([]) axes[ireal].set_yticklabels([]) plt.savefig(os.path.join(plt_dir,"par_{0:03d}.png".format(f_count))) f_count += 1 plt.close() bdir = os.getcwd() os.chdir(plt_dir) #os.system("ffmpeg -r 1 -i par_%03d.png -vcodec libx264 -pix_fmt yuv420p freyberg_pars.mp4") os.system("ffmpeg -r 2 -i par_%03d.png -loop 0 -final_delay 100 -y freyberg_pars.gif") os.chdir(bdir) def freyberg_plot_obs_seq(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages import pandas as pd from pyemu import Pst d = os.path.join("smoother","freyberg") pst = Pst(os.path.join(d,"freyberg.pst")) plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) obs_files = [os.path.join(d,f) for f in os.listdir(d) if "obsensemble." in f and ".png" not in f] obs_dfs = [pd.read_csv(obs_file) for obs_file in obs_files] obs_names = pst.nnz_obs_names obs_names.extend(pst.pestpp_options["forecasts"].split(',')[:-1]) print(obs_names) print(len(obs_names)) #print(obs_files) obs_dfs = [obs_df.loc[:,obs_names] for obs_df in obs_dfs] mx = {obs_name:max([obs_df.loc[:,obs_name].max() for obs_df in obs_dfs]) for obs_name in obs_names} mn = {obs_name:min([obs_df.loc[:,obs_name].min() for obs_df in obs_dfs]) for obs_name in obs_names} f_count = 0 for obs_df in obs_dfs[1:]: fig = plt.figure(figsize=(4.5,3.5)) plt.figtext(0.5,0.95,"iteration {0}".format(f_count),ha="center",fontsize=8) #print(obs_file) axes = [plt.subplot(3,4,i+1) for i in range(len(obs_names))] for ax,obs_name in zip(axes,obs_names): mean = obs_df.loc[:,obs_name].mean() std = obs_df.loc[:,obs_name].std() obs_df.loc[:,obs_name].hist(ax=ax,edgecolor="none", alpha=0.25,grid=False) ax.set_yticklabels([]) #print(ax.get_xlim(),mn[obs_name],mx[obs_name]) ax.set_title(obs_name,fontsize=6) ttl = ax.title ttl.set_position([.5, 1.00]) ax.set_xlim(mn[obs_name],mx[obs_name]) #ax.set_xlim(0.0,20.0) ylim = ax.get_ylim() oval = pst.observation_data.loc[obs_name,"obsval"] ax.plot([oval,oval],ylim,"k--",lw=0.5) #ax.plot([mean,mean],ylim,"b-",lw=0.5) #ax.plot([mean+(2.0*std),mean+(2.0*std)],ylim,"b--",lw=0.5) #ax.plot([mean-(2.0*std),mean-(2.0*std)],ylim,"b--",lw=0.5) ax.set_xticks([]) ax.set_yticks([]) plt.savefig(os.path.join(plt_dir,"obs_{0:03d}.png".format(f_count))) f_count += 1 plt.close() bdir = os.getcwd() os.chdir(plt_dir) #os.system("ffmpeg -r 1 -i obs_%03d.png -vcodec libx264 -pix_fmt yuv420p freyberg_obs.mp4") os.system("ffmpeg -r 2 -i obs_%03d.png -loop 0 -final_delay 100 -y freyberg_obs.gif") os.chdir(bdir) def freyberg_plot_iobj(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages import pandas as pd from pyemu import Pst d = os.path.join("smoother","freyberg") pst = Pst(os.path.join(d,"freyberg.pst")) plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) obj_df = pd.read_csv(os.path.join(d, "freyberg.pst.iobj.csv"), index_col=0) real_cols = [col for col in obj_df.columns if col.startswith("0")] obj_df.loc[:, real_cols] = obj_df.loc[:, real_cols].apply(np.log10) obj_df.loc[:, "mean"] = obj_df.loc[:, "mean"].apply(np.log10) obj_df.loc[:, "std"] = obj_df.loc[:, "std"].apply(np.log10) fig = plt.figure(figsize=(10, 5)) ax = plt.subplot(111) obj_df.index = obj_df.total_runs obj_df.loc[:, real_cols].plot(ax=ax, lw=0.5, color="0.5", alpha=0.5, legend=False) ax.plot(obj_df.index, obj_df.loc[:, "mean"], 'b', lw=2.5, marker='.', markersize=5) # ax.fill_between(obj_df.index, obj_df.loc[:, "mean"] - (1.96 * obj_df.loc[:, "std"]), # obj_df.loc[:, "mean"] + (1.96 * obj_df.loc[:, "std"]), # facecolor="b", edgecolor="none", alpha=0.25) #axt = plt.twinx() #axt.plot(obj_df.index, obj_df.loc[:, "lambda"], "k", dashes=(2, 1), lw=2.5) pobj_df = pd.read_csv(os.path.join(d,"pest_master","freyberg.iobj"),index_col=0) #print(pobj_df.total_phi) #print(pobj_df.model_runs_completed) ax.plot(pobj_df.model_runs_completed.values,pobj_df.total_phi.apply(np.log10).values,"m",lw=2.5) #pobj_reg_df = pd.read_csv(os.path.join(d,"pest_master_reg","freyberg_reg.iobj"),index_col=0) #ax.plot(pobj_reg_df.model_runs_completed.values,pobj_reg_df.measurement_phi.apply(np.log10).values,"m",lw=2.5) ax.set_ylabel("log$_{10}$ $\phi$") #axt.set_ylabel("lambda") ax.set_xlabel("total runs") ax.grid() #ax.set_title("EnsembleSmoother $\phi$ summary; {0} realizations in ensemble".\ # format(obj_df.shape[1]-7)) #ax.set_xticks(obj_df.index.values) #ax.set_xticklabels(["{0}".format(tr) for tr in obj_df.total_runs]) plt.savefig(os.path.join(plt_dir, "iobj.png")) plt.close() def freyberg_plot(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages import pandas as pd from pyemu import Pst d = os.path.join("smoother","freyberg") pst = Pst(os.path.join(d,"freyberg.pst")) plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) obj_df = pd.read_csv(os.path.join(d, "freyberg.pst.iobj.csv"), index_col=0) real_cols = [col for col in obj_df.columns if col.startswith("0")] obj_df.loc[:, real_cols] = obj_df.loc[:, real_cols].apply(np.log10) obj_df.loc[:, "mean"] = obj_df.loc[:, "mean"].apply(np.log10) obj_df.loc[:, "std"] = obj_df.loc[:, "std"].apply(np.log10) fig = plt.figure(figsize=(20, 10)) ax = plt.subplot(111) obj_df.loc[:, real_cols].plot(ax=ax, lw=0.5, color="0.5", alpha=0.5, legend=False) ax.plot(obj_df.index, obj_df.loc[:, "mean"], 'b', lw=2.5, marker='.', markersize=5) ax.set_xticks(obj_df.index.values) ax.set_xticklabels(["{0}".format(tr) for tr in obj_df.total_runs]) # ax.fill_between(obj_df.index, obj_df.loc[:, "mean"] - (1.96 * obj_df.loc[:, "std"]), # obj_df.loc[:, "mean"] + (1.96 * obj_df.loc[:, "std"]), # facecolor="b", edgecolor="none", alpha=0.25) axt = plt.twinx() axt.plot(obj_df.index, obj_df.loc[:, "lambda"], "k", dashes=(2, 1), lw=2.5) ax.set_ylabel("log$_10$ $\phi$") axt.set_ylabel("lambda") ax.set_xlabel("total runs") ax.set_title("EnsembleSmoother $\phi$ summary; {0} realizations in ensemble".\ format(obj_df.shape[1]-7)) plt.savefig(os.path.join(plt_dir, "iobj.pdf")) plt.close() par_files = [os.path.join(d,f) for f in os.listdir(d) if "parensemble." in f and ".png" not in f] par_dfs = [pd.read_csv(par_file,index_col=0).apply(np.log10) for par_file in par_files] #par_names = list(par_dfs[0].columns) par_names = ["rch_1","rch_2"] mx = (pst.parameter_data.loc[:,"parubnd"] * 1.1).apply(np.log10) mn = (pst.parameter_data.loc[:,"parlbnd"] * 0.9).apply(np.log10) with PdfPages(os.path.join(plt_dir,"parensemble.pdf")) as pdf: for par_file,par_df in zip(par_files,par_dfs): #print(par_file) fig = plt.figure(figsize=(20,10)) plt.figtext(0.5,0.975,par_file,ha="center") axes = [plt.subplot(2,6,i+1) for i in range(12)] arrs = [] for ireal in range(10): arrs.append(freyberg_pars_to_array(par_df.iloc[[ireal],:].T)) amx = max([arr.max() for arr in arrs]) amn = max([arr.min() for arr in arrs]) for ireal,arr in enumerate(arrs): axes[ireal].imshow(arr,vmax=amx,vmin=amn,interpolation="nearest") for par_name,ax in zip(par_names,axes[-2:]): mean = par_df.loc[:,par_name].mean() std = par_df.loc[:,par_name].std() par_df.loc[:,par_name].hist(ax=ax,edgecolor="none", alpha=0.25,grid=False) ax.set_yticklabels([]) ax.set_title("{0}, {1:6.2f}".\ format(par_name,10.0**mean)) ax.set_xlim(mn[par_name],mx[par_name]) ylim = ax.get_ylim() if "stage" in par_name: val = np.log10(1.5) else: val = np.log10(2.5) ticks = ["{0:2.1f}".format(x) for x in 10.0**ax.get_xticks()] ax.set_xticklabels(ticks,rotation=90) ax.plot([val,val],ylim,"k-",lw=2.0) ax.plot([mean,mean],ylim,"b-",lw=1.5) ax.plot([mean+(2.0*std),mean+(2.0*std)],ylim,"b--",lw=1.5) ax.plot([mean-(2.0*std),mean-(2.0*std)],ylim,"b--",lw=1.5) pdf.savefig() plt.close() obs_files = [os.path.join(d,f) for f in os.listdir(d) if "obsensemble." in f and ".png" not in f] obs_dfs = [pd.read_csv(obs_file) for obs_file in obs_files] obs_names = pst.nnz_obs_names obs_names.extend(pst.pestpp_options["forecasts"].split(',')[:-1]) print(obs_names) print(len(obs_names)) #print(obs_files) obs_dfs = [obs_df.loc[:,obs_names] for obs_df in obs_dfs] mx = {obs_name:max([obs_df.loc[:,obs_name].max() for obs_df in obs_dfs]) for obs_name in obs_names} mn = {obs_name:min([obs_df.loc[:,obs_name].min() for obs_df in obs_dfs]) for obs_name in obs_names} with PdfPages(os.path.join(plt_dir,"obsensemble.pdf")) as pdf: for obs_file,obs_df in zip(obs_files,obs_dfs): fig = plt.figure(figsize=(30,40)) plt.figtext(0.5,0.975,obs_file,ha="center") print(obs_file) axes = [plt.subplot(3,4,i+1) for i in range(len(obs_names))] for ax,obs_name in zip(axes,obs_names): mean = obs_df.loc[:,obs_name].mean() std = obs_df.loc[:,obs_name].std() obs_df.loc[:,obs_name].hist(ax=ax,edgecolor="none", alpha=0.25,grid=False) ax.set_yticklabels([]) #print(ax.get_xlim(),mn[obs_name],mx[obs_name]) ax.set_title("{0}, {1:6.2f}:{2:6.2f}".format(obs_name,mean,std)) ax.set_xlim(mn[obs_name],mx[obs_name]) #ax.set_xlim(0.0,20.0) ylim = ax.get_ylim() oval = pst.observation_data.loc[obs_name,"obsval"] ax.plot([oval,oval],ylim,"k-",lw=2) ax.plot([mean,mean],ylim,"b-",lw=1.5) ax.plot([mean+(2.0*std),mean+(2.0*std)],ylim,"b--",lw=1.5) ax.plot([mean-(2.0*std),mean-(2.0*std)],ylim,"b--",lw=1.5) pdf.savefig() plt.close() def chenoliver_setup(): import pyemu os.chdir(os.path.join("smoother","chenoliver")) in_file = os.path.join("par.dat") tpl_file = in_file+".tpl" out_file = os.path.join("obs.dat") ins_file = out_file+".ins" pst = pyemu.pst_utils.pst_from_io_files(tpl_file,in_file,ins_file,out_file) par = pst.parameter_data par.loc[:,"partrans"] = "none" par.loc[:,"parval1"] = -2.0 par.loc[:,"parubnd"] = 20.0 par.loc[:,"parlbnd"] = -20.0 obs = pst.observation_data obs.loc[:,"obsval"] = 48.0 obs.loc[:,"weight"] = 1.0 pst.model_command = ["python chenoliver.py"] pst.control_data.noptmax = 0 pst.pestpp_options["sweep_parameter_csv_file"] = os.path.join("sweep_in.csv") pst.write(os.path.join("chenoliver.pst")) os.chdir(os.path.join("..","..")) def chenoliver_func_plot(ax=None): def func(par): return ((7.0/12.0) * par**3) - ((7.0/2.0) * par**2) + (8.0 * par) import numpy as np import matplotlib.pyplot as plt par = np.arange(-5.0,10.0,0.1) obs = func(par) if ax is None: fig = plt.figure(figsize=(10,5)) ax = plt.subplot(111) ax.plot(par,obs,"0.5",dashes=(3,2),lw=4.0) ax.scatter(-2.0,func(-2.0),marker='^',s=175,color="b",label="prior mean",zorder=4) ax.scatter(5.9,func(5.9),marker='*',s=175,color="m",label="posterior mean",zorder=4) ax.set_xlabel("parameter value") ax.set_ylabel("observation value") ax.grid() plt.savefig(os.path.join("smoother","chenoliver","function.png")) #plt.show() def chenoliver_plot_sidebyside(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Rectangle as rect import pandas as pd d = os.path.join("smoother","chenoliver") bins = 20 plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) obs_files = [os.path.join(d,f) for f in os.listdir(d) if "obsensemble." in f and ".png" not in f] obs_dfs = [pd.read_csv(obs_file) for obs_file in obs_files] #print(obs_files) omx = max([obs_df.obs.max() for obs_df in obs_dfs]) omn = min([obs_df.obs.min() for obs_df in obs_dfs]) par_files = [os.path.join(d,f) for f in os.listdir(d) if "parensemble." in f and ".png" not in f] par_dfs = [pd.read_csv(par_file) for par_file in par_files] #mx = max([par_df.par.max() for par_df in par_dfs]) #mn = min([par_df.par.min() for par_df in par_dfs]) pmx = 7 pmn = -5 figsize = (10,3) fcount = 1 for pdf, odf in zip(par_dfs,obs_dfs[1:]): fig = plt.figure(figsize=figsize) plt.figtext(0.5,0.95,"iteration {0}".format(fcount),ha="center",fontsize=8) #axp = plt.subplot(1,3,1) #axo = plt.subplot(1,3,2) #axf = plt.subplot(1,3,3) axp = plt.axes((0.05,0.075,0.25,0.825)) axo = plt.axes((0.375,0.075,0.25,0.825)) axf = plt.axes((0.7,0.075,0.25,0.825)) chenoliver_func_plot(axf) pdf.par.hist(ax=axp,bins=bins,edgecolor="none",grid=False) odf.obs.hist(ax=axo,bins=bins,edgecolor="none",grid=False) axf.scatter(pdf.par.values,odf.obs.values,marker='.',color="c",s=100) ylim = axf.get_ylim() r = rect((0.0,ylim[0]),4,ylim[1]-ylim[0],facecolor='0.5',alpha=0.25) axf.add_patch(r) axp.set_yticks([]) axo.set_yticks([]) ylim = axp.get_ylim() axp.plot([5.9,5.9],ylim,"k--") r = rect((0.0,ylim[0]),4,ylim[1]-ylim[0],facecolor='0.5',alpha=0.25) axp.add_patch(r) ylim = axo.get_ylim() axo.plot([48,48],ylim,"k--") axp.set_xlim(pmn,pmx) axo.set_xlim(omn,omx) axp.set_title("parameter",fontsize=6) axo.set_title("observation",fontsize=6) axf.set_ylabel("") axf.set_xlabel("") axf.set_title("par vs obs",fontsize=6) plt.savefig(os.path.join(plt_dir,"sbs_{0:03d}.png".format(fcount))) #plt.tight_layout() plt.close(fig) fcount += 1 #if fcount > 15: # break bdir = os.getcwd() os.chdir(plt_dir) #os.system("ffmpeg -r 6 -i sbs_%03d.png -vcodec libx264 -pix_fmt yuv420p chenoliver.mp4") os.system("ffmpeg -r 2 -i sbs_%03d.png -loop 0 -final_delay 100 -y chenoliver.gif") os.chdir(bdir) def chenoliver_obj_plot(): import os import numpy as np import matplotlib.pyplot as plt import pandas as pd d = os.path.join("smoother","chenoliver") plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) obj_df = pd.read_csv(os.path.join(d,"chenoliver.pst.iobj.csv"),index_col=0) real_cols = [col for col in obj_df.columns if col.startswith("0")] obj_df.loc[:,real_cols] = obj_df.loc[:,real_cols].apply(np.log10) obj_df.loc[:,"mean"] = obj_df.loc[:,"mean"].apply(np.log10) obj_df.loc[:, "std"] = obj_df.loc[:, "std"].apply(np.log10) real_cols = [col for col in obj_df.columns if col.startswith("0")] #obj_df.loc[:, real_cols] = obj_df.loc[:, real_cols].apply(np.log10) #obj_df.loc[:, "mean"] = obj_df.loc[:, "mean"].apply(np.log10) #obj_df.loc[:, "std"] = obj_df.loc[:, "std"].apply(np.log10) fig = plt.figure(figsize=(10, 5)) ax = plt.subplot(111) obj_df.loc[:, real_cols].plot(ax=ax, lw=0.5, color="0.5", alpha=0.5, legend=False) ax.plot(obj_df.index, obj_df.loc[:, "mean"], 'b', lw=1.5, marker='.', markersize=5,label="ensemble mean") ax.set_ylabel("log$_{10}$ $\phi$") ax.set_xlabel("iteration") pobj_df = pd.read_csv(os.path.join(d,"pest","chenoliver.iobj"),index_col=0) ax.plot(pobj_df.index,pobj_df.total_phi.apply(np.log10),"m",lw=2.5,label="pest++") #ax.legend(loc="upper left") ax.grid() plt.savefig(os.path.join(plt_dir, "iobj.png")) plt.close() def chenoliver_plot(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages import pandas as pd d = os.path.join("smoother","chenoliver") bins = 20 plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) obs_files = [os.path.join(d,f) for f in os.listdir(d) if "obsensemble." in f and ".png" not in f] obs_dfs = [pd.read_csv(obs_file) for obs_file in obs_files] #print(obs_files) mx = max([obs_df.obs.max() for obs_df in obs_dfs]) mn = min([obs_df.obs.min() for obs_df in obs_dfs]) #print(mn,mx) with PdfPages(os.path.join(plt_dir,"obsensemble.pdf")) as pdf: for obs_file,obs_df in zip(obs_files,obs_dfs): #fig = plt.figure(figsize=(10,10)) ax = plt.subplot(111) obs_df.loc[:,["obs"]].hist(ax=ax,bins=bins,edgecolor="none") ax.set_xlim(mn,mx) ax.set_title("{0}".format(obs_file)) #plt.savefig(os.path.join(plt_dir,os.path.split(obs_file)[-1]+".png")) #plt.close("all") pdf.savefig() plt.close() par_files = [os.path.join(d,f) for f in os.listdir(d) if "parensemble." in f and ".png" not in f] par_dfs = [pd.read_csv(par_file) for par_file in par_files] #mx = max([par_df.par.max() for par_df in par_dfs]) #mn = min([par_df.par.min() for par_df in par_dfs]) mx = 7 mn = -5 with PdfPages(os.path.join(plt_dir,"parensemble.pdf")) as pdf: for par_file in par_files: par_df = pd.read_csv(par_file) fig = plt.figure(figsize=(10,10)) ax = plt.subplot(111) par_df.loc[:,["par"]].hist(ax=ax,bins=bins,edgecolor="none") #ax.set_xlim(-10,10) ax.set_xlim(mn,mx) ax.set_xticks(np.arange(mn,mx+0.25,0.25)) ax.set_xticklabels(["{0:2.2f}".format(x) for x in np.arange(mn,mx+0.25,0.25)], rotation=90) ax.set_title("{0}".format(par_file)) #plt.savefig(os.path.join(plt_dir,os.path.split(par_file)[-1]+".png")) #plt.close("all") pdf.savefig() plt.close() def chenoliver(): import os import numpy as np import pyemu os.chdir(os.path.join("smoother","chenoliver")) csv_files = [f for f in os.listdir('.') if f.endswith(".csv") and "bak" not in f] [os.remove(csv_file) for csv_file in csv_files] parcov = pyemu.Cov(x=np.ones((1,1)),names=["par"],isdiagonal=True) pst = pyemu.Pst("chenoliver.pst") #obscov = pyemu.Cov(x=np.ones((1,1))*16.0,names=["obs"],isdiagonal=True) obscov = pyemu.Cov(x=np.ones((1,1)),names=["obs"],isdiagonal=True) num_reals = 100 es = pyemu.EnsembleSmoother(pst,parcov=parcov,obscov=obscov, num_slaves=15,verbose=True) es.initialize(num_reals=num_reals,enforce_bounds=None,init_lambda=10.0) for it in range(25): es.update(use_approx=False) os.chdir(os.path.join("..","..")) def chenoliver_existing(): import os import numpy as np import pyemu os.chdir(os.path.join("smoother","chenoliver")) csv_files = [f for f in os.listdir('.') if f.endswith(".csv") and "bak" not in f] [os.remove(csv_file) for csv_file in csv_files] parcov = pyemu.Cov(x=np.ones((1,1)),names=["par"],isdiagonal=True) pst = pyemu.Pst("chenoliver.pst") obscov = pyemu.Cov(x=np.ones((1,1))*16.0,names=["obs"],isdiagonal=True) #obscov = pyemu.Cov(x=np.ones((1,1))*16.0,names=["obs"],isdiagonal=True) num_reals = 100 es = pyemu.EnsembleSmoother(pst,parcov=parcov,obscov=obscov, num_slaves=10,verbose=True) es.initialize(num_reals=num_reals,enforce_bounds=None) obs1 = es.obsensemble.copy() es.parensemble_0.to_csv("paren.csv") es.obsensemble_0.to_csv("obsen.csv") #es = pyemu.EnsembleSmoother(pst,parcov=parcov,obscov=obscov, # num_slaves=1,verbose=True) es.initialize(parensemble="paren.csv",obsensemble="obsen.csv") obs2 = es.obsensemble.copy() print(obs1.shape,obs2.shape) print(obs1) print(obs2) assert (obs1 - obs2).loc[:,"obs"].sum() == 0.0 for it in range(40): es.update(lambda_mults=[0.1,1.0,10.0],use_approx=False,run_subset=30) os.chdir(os.path.join("..","..")) def chenoliver_condor(): import os import numpy as np import pyemu os.chdir(os.path.join("smoother","chenoliver")) csv_files = [f for f in os.listdir('.') if f.endswith(".csv") and "bak" not in f] [os.remove(csv_file) for csv_file in csv_files] parcov = pyemu.Cov(x=np.ones((1,1)),names=["par"],isdiagonal=True) pst = pyemu.Pst("chenoliver.pst") obscov = pyemu.Cov(x=np.ones((1,1))*16.0,names=["obs"],isdiagonal=True) num_reals = 100 es = pyemu.EnsembleSmoother(pst,parcov=parcov,obscov=obscov, num_slaves=10,verbose=True, submit_file="chenoliver.sub") es.initialize(num_reals=num_reals,enforce_bounds=None) for it in range(40): es.update(lambda_mults=[1.0],use_approx=True) os.chdir(os.path.join("..","..")) def tenpar_test(): import os import numpy as np import pandas as pd import flopy import pyemu os.chdir(os.path.join("smoother", "10par_xsec")) #bak_obj = pd.read_csv("iobj.bak",skipinitialspace=True) #bak_obj_act = pd.read_csv("iobj.actual.bak") bak_upgrade = pd.read_csv("upgrade_1.bak") csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst("10par_xsec.pst") par = pst.parameter_data par.loc["stage", "partrans"] = "fixed" v = pyemu.utils.ExpVario(contribution=0.25, a=60.0) gs = pyemu.utils.GeoStruct(variograms=[v], transform="log") par = pst.parameter_data k_names = par.loc[par.parnme.apply(lambda x: x.startswith('k')), "parnme"] sr = flopy.utils.SpatialReference(delc=[10], delr=np.zeros((10)) + 10.0) cov = gs.covariance_matrix(sr.xcentergrid[0, :], sr.ycentergrid[0, :], k_names) obs = pst.observation_data obs.loc["h01_09", "weight"] = 100.0 obs.loc["h01_09", 'obgnme'] = "lt_test" obs.loc["h01_09", 'obsval'] = 2.0 es = pyemu.EnsembleSmoother(pst, parcov=cov, num_slaves=10, port=4005, verbose=True, drop_bad_reals=14000.) lz = es.get_localizer().to_dataframe() # the k pars upgrad of h01_04 and h01_06 are localized upgrad_pars = [pname for pname in lz.columns if "_" in pname and \ int(pname.split('_')[1]) > 4] lz.loc["h01_04", upgrad_pars] = 0.0 upgrad_pars = [pname for pname in lz.columns if '_' in pname and \ int(pname.split('_')[1]) > 6] lz.loc["h01_06", upgrad_pars] = 0.0 lz = pyemu.Matrix.from_dataframe(lz).T es.initialize(parensemble="10par_xsec.pe.bak",obsensemble="10par_xsec.oe.bak", restart_obsensemble="10par_xsec.oe.restart.bak",init_lambda=10000.0) # just for force full upgrade testing for es.iter_num = 2 es.update(lambda_mults=[.1, 1000.0],calc_only=True,use_approx=False,localizer=lz) #obj = pd.read_csv("10par_xsec.pst.iobj.csv") #obj_act = pd.read_csv("10par_xsec.pst.iobj.actual.csv") upgrade = pd.read_csv("10par_xsec.pst.upgrade_1.0003.csv") os.chdir(os.path.join("..", "..")) # for b,n in zip([bak_obj,bak_obj_act,bak_upgrade],[obj,obj_act,upgrade]): # print(b,n) # d = b - n # print(d.max(),d.min()) d = (bak_upgrade - upgrade).apply(np.abs) assert d.max().max() < 1.0e-6 def tenpar_fixed(): import os import numpy as np import flopy import pyemu os.chdir(os.path.join("smoother","10par_xsec")) csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst("10par_xsec.pst") par = pst.parameter_data par.loc["stage","partrans"] = "fixed" v = pyemu.utils.ExpVario(contribution=0.25,a=60.0) gs = pyemu.utils.GeoStruct(variograms=[v],transform="log") par = pst.parameter_data k_names = par.loc[par.parnme.apply(lambda x: x.startswith('k')),"parnme"] sr = flopy.utils.SpatialReference(delc=[10],delr=np.zeros((10))+10.0) cov = gs.covariance_matrix(sr.xcentergrid[0,:],sr.ycentergrid[0,:],k_names) es = pyemu.EnsembleSmoother(pst,parcov=cov, num_slaves=10,port=4005,verbose=True, drop_bad_reals=14000.) lz = es.get_localizer().to_dataframe() #the k pars upgrad of h01_04 and h01_06 are localized upgrad_pars = [pname for pname in lz.columns if "_" in pname and\ int(pname.split('_')[1]) > 4] lz.loc["h01_04",upgrad_pars] = 0.0 upgrad_pars = [pname for pname in lz.columns if '_' in pname and \ int(pname.split('_')[1]) > 6] lz.loc["h01_06", upgrad_pars] = 0.0 lz = pyemu.Matrix.from_dataframe(lz).T print(lz) es.initialize(num_reals=100,init_lambda=10000.0) for it in range(1): #es.update(lambda_mults=[0.1,1.0,10.0],localizer=lz,run_subset=20) #es.update(lambda_mults=[0.1,1.0,10.0],run_subset=30) es.update(lambda_mults=[.1,1000.0]) os.chdir(os.path.join("..","..")) def tenpar(): import os import numpy as np import flopy import pyemu os.chdir(os.path.join("smoother","10par_xsec")) csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst("10par_xsec.pst") dia_parcov = pyemu.Cov.from_parameter_data(pst,sigma_range=6.0) v = pyemu.utils.ExpVario(contribution=0.25,a=60.0) gs = pyemu.utils.GeoStruct(variograms=[v],transform="log") par = pst.parameter_data k_names = par.loc[par.parnme.apply(lambda x: x.startswith('k')),"parnme"] sr = flopy.utils.SpatialReference(delc=[10],delr=np.zeros((10))+10.0) full_cov = gs.covariance_matrix(sr.xcentergrid[0,:],sr.ycentergrid[0,:],k_names) dia_parcov.drop(list(k_names),axis=1) cov = dia_parcov.extend(full_cov) es = pyemu.EnsembleSmoother("10par_xsec.pst",parcov=cov, num_slaves=10,port=4005,verbose=True, drop_bad_reals=14000.) lz = es.get_localizer().to_dataframe() #the k pars upgrad of h01_04 and h01_06 are localized upgrad_pars = [pname for pname in lz.columns if "_" in pname and\ int(pname.split('_')[1]) > 4] lz.loc["h01_04",upgrad_pars] = 0.0 upgrad_pars = [pname for pname in lz.columns if '_' in pname and \ int(pname.split('_')[1]) > 6] lz.loc["h01_06", upgrad_pars] = 0.0 lz = pyemu.Matrix.from_dataframe(lz).T print(lz) es.initialize(num_reals=100,init_lambda=10000.0) for it in range(1): #es.update(lambda_mults=[0.1,1.0,10.0],localizer=lz,run_subset=20) #es.update(lambda_mults=[0.1,1.0,10.0],run_subset=30) es.update(lambda_mults=[.1,1000.0]) os.chdir(os.path.join("..","..")) def tenpar_opt(): import os import numpy as np import pandas as pd import matplotlib.pyplot as plt import flopy import pyemu os.chdir(os.path.join("smoother","10par_xsec")) csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] [os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst("10par_xsec.pst") dia_parcov = pyemu.Cov.from_parameter_data(pst,sigma_range=6.0) v = pyemu.utils.ExpVario(contribution=0.25,a=60.0) gs = pyemu.utils.GeoStruct(variograms=[v],transform="log") par = pst.parameter_data k_names = par.loc[par.parnme.apply(lambda x: x.startswith('k')),"parnme"] sr = flopy.utils.SpatialReference(delc=[10],delr=np.zeros((10))+10.0) full_cov = gs.covariance_matrix(sr.xcentergrid[0,:],sr.ycentergrid[0,:],k_names) dia_parcov.drop(list(k_names),axis=1) cov = dia_parcov.extend(full_cov) obs = pst.observation_data # obs.loc["h01_02","weight"] = 10.0 # obs.loc["h01_02","obgnme"] = "lt_test" # obs.loc["h01_02", "obsval"] = 1.0 obs.loc["h01_09","weight"] = 100.0 obs.loc["h01_09",'obgnme'] = "lt_test" obs.loc["h01_09", 'obsval'] = 2.0 print(obs) #return() pst.write("10par_xsec_opt.pst") pst.write(os.path.join("template","10par_xsec_opt.pst")) es = pyemu.EnsembleSmoother("10par_xsec_opt.pst",parcov=cov, num_slaves=10,port=4005,verbose=True) lz = es.get_localizer().to_dataframe() #the k pars upgrad of h01_04 and h01_06 are localized upgrad_pars = [pname for pname in lz.columns if "_" in pname and\ int(pname.split('_')[1]) > 4] lz.loc["h01_04",upgrad_pars] = 0.0 upgrad_pars = [pname for pname in lz.columns if '_' in pname and \ int(pname.split('_')[1]) > 6] lz.loc["h01_06", upgrad_pars] = 0.0 lz = pyemu.Matrix.from_dataframe(lz).T print(lz) mc = pyemu.MonteCarlo(pst=pst,parcov=cov) mc.draw(300,obs=True) es.initialize(parensemble=mc.parensemble,obsensemble=mc.obsensemble,init_lambda=10000.0) niter=20 for it in range(niter): #es.update(lambda_mults=[0.1,1.0,10.0],localizer=lz,run_subset=20) #es.update(lambda_mults=[0.1,1.0,10.0],run_subset=30) es.update(lambda_mults=[.1,1.0,10.0],run_subset=30) oe_ieq = pd.read_csv("10par_xsec_opt.pst.obsensemble.{0:04d}.csv".format(niter)) #obs.loc["h01_09","weight"] = 0.0 es = pyemu.EnsembleSmoother("10par_xsec.pst", parcov=cov, num_slaves=10, port=4005, verbose=True) lz = es.get_localizer().to_dataframe() # the k pars upgrad of h01_04 and h01_06 are localized upgrad_pars = [pname for pname in lz.columns if "_" in pname and \ int(pname.split('_')[1]) > 4] lz.loc["h01_04", upgrad_pars] = 0.0 upgrad_pars = [pname for pname in lz.columns if '_' in pname and \ int(pname.split('_')[1]) > 6] lz.loc["h01_06", upgrad_pars] = 0.0 lz = pyemu.Matrix.from_dataframe(lz).T print(lz) es.initialize(parensemble=mc.parensemble,obsensemble=mc.obsensemble, init_lambda=10000.0) for it in range(niter): # es.update(lambda_mults=[0.1,1.0,10.0],localizer=lz,run_subset=20) # es.update(lambda_mults=[0.1,1.0,10.0],run_subset=30) es.update(lambda_mults=[.1, 1.0,10.0], run_subset=30) oe_base = pd.read_csv("10par_xsec.pst.obsensemble.{0:04d}.csv".format(niter)) for oname in obs.obsnme: ax = plt.subplot(111) oe_base.loc[:,oname].hist(bins=20, ax=ax, color="0.5", alpha=0.54) oe_ieq.loc[:,oname].hist(bins=20,ax=ax,color="b",alpha=0.5) ax.set_xlim(oe_ieq.loc[:,oname].min()*0.75,oe_ieq.loc[:,oname].max() * 1.25) plt.savefig(oname+".png") plt.close("all") #oe_base.to_csv("base.csv") #oe_ieq.to_csv("ieq.csv") os.chdir(os.path.join("..","..")) def plot_10par_opt_traj(): import numpy as np import pandas as pd from matplotlib.backends.backend_pdf import PdfPages import matplotlib.pyplot as plt import pyemu d = os.path.join("smoother","10par_xsec") case1 = "10par_xsec.pst" case2 = "10par_xsec_opt.pst" files = os.listdir(d) case1_oes = [f for f in files if case1 in f and "obsensemble" in f] case2_oes = [f for f in files if case2 in f and "obsensemble" in f] case1_oes = [pd.read_csv(os.path.join(d,f)) for f in case1_oes] case2_oes = [pd.read_csv(os.path.join(d,f)) for f in case2_oes] case1_pes = [f for f in files if case1 in f and "parensemble" in f] case2_pes = [f for f in files if case2 in f and "parensemble" in f] case1_pes = [pd.read_csv(os.path.join(d, f)) for f in case1_pes] case2_pes = [pd.read_csv(os.path.join(d, f)) for f in case2_pes] print(case1_oes) print(case2_oes) pst = pyemu.Pst(os.path.join(d,"10par_xsec.pst")) with PdfPages("traj.pdf") as pdf: for oname in pst.observation_data.obsnme: #dfs1 = [c.loc[:,[oname]] for c in case1_oes] df1 = pd.concat([c.loc[:,[oname]] for c in case1_oes],axis=1) df2 = pd.concat([c.loc[:, [oname]] for c in case2_oes], axis=1) df1.columns = np.arange(df1.shape[1]) df2.columns = np.arange(df2.shape[1]) fig = plt.figure(figsize=(10,5)) ax = plt.subplot(111) [ax.plot(df1.columns,df1.loc[i,:],color='0.5',lw=0.2) for i in df1.index] [ax.plot(df2.columns, df2.loc[i, :], color='b', lw=0.2) for i in df2.index] ax.set_title(oname) pdf.savefig() plt.close(fig) for pname in pst.parameter_data.parnme: #dfs1 = [c.loc[:,[oname]] for c in case1_oes] df1 = pd.concat([c.loc[:,[pname]] for c in case1_pes],axis=1) df2 = pd.concat([c.loc[:, [pname]] for c in case2_pes], axis=1) df1.columns = np.arange(df1.shape[1]) df2.columns = np.arange(df2.shape[1]) fig = plt.figure(figsize=(10,5)) ax = plt.subplot(111) [ax.plot(df1.columns,df1.loc[i,:],color='0.5',lw=0.2) for i in df1.index] [ax.plot(df2.columns, df2.loc[i, :], color='b', lw=0.2) for i in df2.index] ax.set_title(pname) pdf.savefig() plt.close(fig) def tenpar_restart(): import os import numpy as np import flopy import pyemu os.chdir(os.path.join("smoother","10par_xsec")) pst = pyemu.Pst("10par_xsec.pst") dia_parcov = pyemu.Cov.from_parameter_data(pst,sigma_range=6.0) v = pyemu.utils.ExpVario(contribution=0.25,a=60.0) gs = pyemu.utils.GeoStruct(variograms=[v],transform="log") par = pst.parameter_data k_names = par.loc[par.parnme.apply(lambda x: x.startswith('k')),"parnme"] sr = flopy.utils.SpatialReference(delc=[10],delr=np.zeros((10))+10.0) full_cov = gs.covariance_matrix(sr.xcentergrid[0,:],sr.ycentergrid[0,:],k_names) dia_parcov.drop(list(k_names),axis=1) cov = dia_parcov.extend(full_cov) es = pyemu.EnsembleSmoother("10par_xsec.pst",parcov=cov, num_slaves=10,port=4005,verbose=True) lz = es.get_localizer().to_dataframe() #the k pars upgrad of h01_04 and h01_06 are localized upgrad_pars = [pname for pname in lz.columns if "_" in pname and\ int(pname.split('_')[1]) > 4] lz.loc["h01_04",upgrad_pars] = 0.0 upgrad_pars = [pname for pname in lz.columns if '_' in pname and \ int(pname.split('_')[1]) > 6] lz.loc["h01_06", upgrad_pars] = 0.0 lz = pyemu.Matrix.from_dataframe(lz).T print(lz) es.initialize(parensemble="par_start.csv",obsensemble="obs_start.csv", restart_obsensemble="obs_restart.csv",init_lambda=10000.0) for it in range(1): #es.update(lambda_mults=[0.1,1.0,10.0],localizer=lz,run_subset=20) es.update(lambda_mults=[0.1,1.0,10.0],run_subset=30) os.chdir(os.path.join("..","..")) def tenpar_failed_runs(): import os import numpy as np import pyemu os.chdir(os.path.join("smoother","10par_xsec")) #csv_files = [f for f in os.listdir('.') if f.endswith(".csv")] #[os.remove(csv_file) for csv_file in csv_files] pst = pyemu.Pst("10par_xsec.pst") dia_parcov = pyemu.Cov.from_parameter_data(pst,sigma_range=6.0) v = pyemu.utils.ExpVario(contribution=0.25,a=60.0) gs = pyemu.utils.GeoStruct(variograms=[v],transform="log") par = pst.parameter_data k_names = par.loc[par.parnme.apply(lambda x: x.startswith('k')),"parnme"] sr = pyemu.utils.SpatialReference(delc=[10],delr=np.zeros((10))+10.0) full_cov = gs.covariance_matrix(sr.xcentergrid[0,:],sr.ycentergrid[0,:],k_names) dia_parcov.drop(list(k_names),axis=1) cov = dia_parcov.extend(full_cov) es = pyemu.EnsembleSmoother("10par_xsec.pst",parcov=cov, num_slaves=2, verbose=True) lz = es.get_localizer().to_dataframe() #the k pars upgrad of h01_04 and h01_06 are localized upgrad_pars = [pname for pname in lz.columns if "_" in pname and\ int(pname.split('_')[1]) > 4] lz.loc["h01_04",upgrad_pars] = 0.0 upgrad_pars = [pname for pname in lz.columns if '_' in pname and \ int(pname.split('_')[1]) > 6] lz.loc["h01_06", upgrad_pars] = 0.0 lz = pyemu.Matrix.from_dataframe(lz).T print(lz) #es.initialize(num_reals=10,init_lambda=10000.0) es.initialize(parensemble="par_start.csv",obsensemble="obs_start.csv") for it in range(10): #es.update(lambda_mults=[0.1,1.0,10.0],localizer=lz,run_subset=20) #es.update(lambda_mults=[0.1,1.0,10.0],run_subset=7) es.update(use_approx=False,lambda_mults=[0.1,1.0,10.0]) os.chdir(os.path.join("..","..")) def tenpar_plot(): import os import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages import pandas as pd from pyemu import Pst d = os.path.join("smoother","10par_xsec") pst = Pst(os.path.join(d,"10par_xsec.pst")) plt_dir = os.path.join(d,"plot") if not os.path.exists(plt_dir): os.mkdir(plt_dir) par_files = [os.path.join(d,f) for f in os.listdir(d) if "parensemble." in f and ".png" not in f] par_dfs = [pd.read_csv(par_file,index_col=0) for par_file in par_files] par_names = list(par_dfs[0].columns) #mx = (pst.parameter_data.loc[:,"parubnd"] * 1.1) #mn = (pst.parameter_data.loc[:,"parlbnd"] * 0.9) mx = max([pdf.max().max() for pdf in par_dfs]) num_reals_plot = 12 plot_rows = 2 plot_cols = 6 assert plot_rows * plot_cols == num_reals_plot figsize = (20,10) with PdfPages(os.path.join(plt_dir,"parensemble_reals.pdf")) as pdf: for par_file,par_df in zip(par_files,par_dfs): #print(par_file) fig = plt.figure(figsize=figsize) plt.figtext(0.5,0.975,par_file,ha="center") axes = [plt.subplot(plot_rows,plot_cols,i+1) for i in range(num_reals_plot)] for ireal in range(num_reals_plot): real_df = par_df.iloc[ireal,:] #print(real_df) real_df.plot(kind="bar",ax=axes[ireal]) axes[ireal].set_ylim(0,mx.max()) pdf.savefig() plt.close() obj_df = pd.read_csv(os.path.join(d,"10par_xsec.pst.iobj.csv"),index_col=0) real_cols = [col for col in obj_df.columns if col.startswith("0")] obj_df.loc[:,real_cols] = obj_df.loc[:,real_cols].apply(np.log10) obj_df.loc[:,"mean"] = obj_df.loc[:,"mean"].apply(np.log10) obj_df.loc[:, "std"] = obj_df.loc[:, "std"].apply(np.log10) fig = plt.figure(figsize=(20, 10)) ax = plt.subplot(111) axt = plt.twinx() obj_df.loc[:, real_cols].plot(ax=ax, lw=0.5, color="0.5", alpha=0.5, legend=False) ax.plot(obj_df.index, obj_df.loc[:, "mean"], 'b', lw=2.5,marker='.',markersize=5) #ax.fill_between(obj_df.index, obj_df.loc[:, "mean"] - (1.96 * obj_df.loc[:, "std"]), # obj_df.loc[:, "mean"] + (1.96 * obj_df.loc[:, "std"]), # facecolor="b", edgecolor="none", alpha=0.25) axt.plot(obj_df.index,obj_df.loc[:,"lambda"],"k",dashes=(2,1),lw=2.5) ax.set_ylabel("log$_10$ phi") axt.set_ylabel("lambda") ax.set_title("total runs:{0}".format(obj_df.total_runs.max())) plt.savefig(os.path.join(plt_dir,"iobj.pdf")) plt.close() mx = (pst.parameter_data.loc[:,"parubnd"] * 1.1) mn = (pst.parameter_data.loc[:,"parlbnd"] * 0.9) with PdfPages(os.path.join(plt_dir,"parensemble.pdf")) as pdf: for par_file,par_df in zip(par_files,par_dfs): print(par_file) fig = plt.figure(figsize=(20,10)) plt.figtext(0.5,0.975,par_file,ha="center") axes = [plt.subplot(2,6,i+1) for i in range(len(par_names))] for par_name,ax in zip(par_names,axes): mean = par_df.loc[:,par_name].mean() std = par_df.loc[:,par_name].std() par_df.loc[:,par_name].hist(ax=ax,edgecolor="none", alpha=0.5,grid=False) ax.set_yticklabels([]) ax.set_title("{0}, {1:6.2f}".\ format(par_name,10.0**mean)) ax.set_xlim(mn[par_name],mx[par_name]) ylim = ax.get_ylim() if "stage" in par_name: val = 1.5 else: val = 2.5 ticks = ["{0:2.1f}".format(x) for x in ax.get_xticks()] ax.set_xticklabels(ticks,rotation=90) ax.plot([val,val],ylim,"k-",lw=2.0) ax.plot([mean,mean],ylim,"b-",lw=1.5) ax.plot([mean+(2.0*std),mean+(2.0*std)],ylim,"b--",lw=1.5) ax.plot([mean-(2.0*std),mean-(2.0*std)],ylim,"b--",lw=1.5) pdf.savefig() plt.close() obs_files = [os.path.join(d,f) for f in os.listdir(d) if "obsensemble." in f and ".png" not in f] obs_dfs = [pd.read_csv(obs_file) for obs_file in obs_files] #print(obs_files) #mx = max([obs_df.obs.max() for obs_df in obs_dfs]) #mn = min([obs_df.obs.min() for obs_df in obs_dfs]) #print(mn,mx) obs_names = ["h01_04","h01_06","h01_08","h02_08"] #print(obs_files) obs_dfs = [obs_df.loc[:,obs_names] for obs_df in obs_dfs] mx = {obs_name:max([obs_df.loc[:,obs_name].max() for obs_df in obs_dfs]) for obs_name in obs_names} mn = {obs_name:min([obs_df.loc[:,obs_name].min() for obs_df in obs_dfs]) for obs_name in obs_names} with PdfPages(os.path.join(plt_dir,"obsensemble.pdf")) as pdf: for obs_file,obs_df in zip(obs_files,obs_dfs): fig = plt.figure(figsize=(10,10)) plt.figtext(0.5,0.975,obs_file,ha="center") print(obs_file) axes = [plt.subplot(2,2,i+1) for i in range(len(obs_names))] for ax,obs_name in zip(axes,obs_names): mean = obs_df.loc[:,obs_name].mean() std = obs_df.loc[:,obs_name].std() obs_df.loc[:,obs_name].hist(ax=ax,edgecolor="none", alpha=0.5,grid=False) ax.set_yticklabels([]) #print(ax.get_xlim(),mn[obs_name],mx[obs_name]) ax.set_title("{0}, {1:6.2f}:{2:6.2f}".format(obs_name,mean,std)) #ax.set_xlim(mn[obs_name],mx[obs_name]) ax.set_xlim(0.0,20.0) ylim = ax.get_ylim() oval = pst.observation_data.loc[obs_name,"obsval"] ax.plot([oval,oval],ylim,"k-",lw=2) ax.plot([mean,mean],ylim,"b-",lw=1.5) ax.plot([mean+(2.0*std),mean+(2.0*std)],ylim,"b--",lw=1.5) ax.plot([mean-(2.0*std),mean-(2.0*std)],ylim,"b--",lw=1.5) pdf.savefig() plt.close() def setup_lorenz(): import os import shutil import pandas as pd import pyemu state_file = "lorenz.dat" d = os.path.join("smoother", "lorenz","template") dt = 1.0 prev = [1.0,1.0,1.05,dt] if os.path.exists(d): shutil.rmtree(d) #os.mkdir(d) os.makedirs(d) df = pd.DataFrame({"variable":['x','y','z','dt']},index=['x','y','z','dt']) df.loc[:,"prev"] = prev df.loc[:,"new"] = prev df.to_csv(os.path.join(d,state_file),sep=' ',index=False) df.loc[:,"prev"] = df.variable.apply(lambda x: "~ {0} ~".format(x)) with open(os.path.join(d,state_file+".tpl"),'w') as f: f.write("ptf ~\n") df.to_csv(f,sep=' ',index=False) with open(os.path.join(d,state_file+".ins"),'w') as f: f.write("pif ~\nl1\n") for v in df.variable: f.write("l1 w !prev_{0}! !{0}!\n".format(v)) with open(os.path.join(d,"forward_run.py"),'w') as f: f.write("import os\nimport numpy as np\nimport pandas as pd\n") f.write("sigma,rho,beta = 10.0,28.0,2.66667\n") f.write("df = pd.read_csv('{0}',delim_whitespace=True,index_col=0)\n".format(state_file)) f.write("x,y,z,dt = df.loc[:,'prev'].values\n") f.write("df.loc['x','new'] = sigma * (y - x)\n") f.write("df.loc['y','new'] = (rho * x) - y - (x * z)\n") f.write("df.loc['z','new'] = (x * y) - (beta * z)\n") f.write("df.loc[:,'new'] *= dt\n") f.write("df.to_csv('{0}',sep=' ')\n".format(state_file)) #with open(os.path.join(d,"par.tpl"),'w') as f: # f.write("ptf ~\n") # f.write("dum ~ dum ~\n") base_dir = os.getcwd() os.chdir(d) pst = pyemu.Pst.from_io_files(*pyemu.helpers.parse_dir_for_io_files('.')) os.chdir(base_dir) pst.parameter_data.loc[:,"parval1"] = prev pst.parameter_data.loc['y',"parlbnd"] = -40.0 pst.parameter_data.loc['y', "parubnd"] = 40.0 pst.parameter_data.loc['x', "parlbnd"] = -40.0 pst.parameter_data.loc['x', "parubnd"] = 40.0 pst.parameter_data.loc['z', "parlbnd"] = 0.0 pst.parameter_data.loc['z', "parubnd"] = 50.0 pst.parameter_data.loc[:,"partrans"] = "none" pst.parameter_data.loc['dt','partrans'] = 'fixed' pst.observation_data.loc[:,"weight"] = 0.0 pst.observation_data.loc[['x','y','z'],'weight'] = 1.0 pst.model_command = "python forward_run.py" pst.pestpp_options["lambda_scale_fac"] = 1.0 pst.pestpp_options["upgrade_augment"] = "false" pst.control_data.noptmax = 10 pst.write(os.path.join(d,"lorenz.pst")) print(pst.parameter_data) pyemu.helpers.run("pestpp lorenz.pst",cwd=d) if __name__ == "__main__": #setup_lorenz() #henry_setup() #henry() #henry_plot() #freyberg() #freyberg_plot() #freyberg_plot_iobj() #freyberg_plot_par_seq() #freyberg_plot_obs_seq() #chenoliver_func_plot() #chenoliver_plot_sidebyside() #chenoliver_obj_plot() #chenoliver_setup() #chenoliver_condor() #chenoliver() #chenoliver_existing() #chenoliver_plot() #chenoliver_func_plot() #chenoliver_plot_sidebyside() #chenoliver_obj_plot() #tenpar_fixed() #tenpar() tenpar_test() #tenpar_opt() #plot_10par_opt_traj() #tenpar_restart() #tenpar_plot() #tenpar_failed_runs() #freyberg() #freyberg_check_phi_calc() #freyberg_condor() #freyberg_plot() #freyberg_plot_iobj() #freyberg_plotuse_iobj() #freyberg_plot_par_seq() #freyberg_plot_obs_seq()

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0.225214

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0

null

0

1

0

null

0

6

b15ad7c8867d0b7d2b1e32a9bfa6e737732cebcc

225

py

Python

tests/unit/conftest.py

Egnod/sitri

ca974cce9041bea8296284b0ca67d970a6e072cf

[ "MIT" ]

11

2020-12-16T07:00:29.000Z

2021-05-25T16:24:50.000Z

tests/unit/conftest.py

Egnod/sitri

ca974cce9041bea8296284b0ca67d970a6e072cf

[ "MIT" ]

6

2019-10-08T22:55:21.000Z

2019-10-11T19:29:53.000Z

tests/unit/conftest.py

Egnod/sitri

ca974cce9041bea8296284b0ca67d970a6e072cf

[ "MIT" ]

2

2019-10-10T12:09:50.000Z

2019-10-10T23:52:38.000Z

import pytest from sitri import Sitri from sitri.providers.contrib.system import SystemConfigProvider @pytest.fixture(scope="module") def test_sitri(): return Sitri(config_provider=SystemConfigProvider(prefix="test"))

22.5

69

0.808889

27

225

6.666667

0.62963

0.1

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0.097778

225

9

70

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0

1

0

null

0

1

0

1

0

6

b16142150dd443a20d5c47f8a101609b68b27c43

161

py

Python

scripts/generate_multiple.py

j1nma/Automaton-Off-Lattice

55a73ffbd75251d822c037c7f048c4299cda46c1

[ "MIT" ]

null

scripts/generate_multiple.py

j1nma/Automaton-Off-Lattice

55a73ffbd75251d822c037c7f048c4299cda46c1

[ "MIT" ]

null

scripts/generate_multiple.py

j1nma/Automaton-Off-Lattice

55a73ffbd75251d822c037c7f048c4299cda46c1

[ "MIT" ]

1

2020-04-19T02:11:09.000Z

from functions import generate_multiple_files numbers = [40,100,4000,10000] i = 0 for x in numbers: i += 1 generate_multiple_files(numbers[i-1], 20.0)

17.888889

47

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27

161

4.111111

0.666667

0.288288

0.378378

0.504505

0

0.151515

0.180124

161

8

48

20.125

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0

1

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false

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null

1

0

1

0

1

0

null

0

6

b1646421e1bafc53e901f1d8608a804dcc96e96d

6,452

py

Python

src/tests/test_get_redemption_request.py

andela/andela-societies-backend

b8382f308449a08e5c7bda46c6deabe597cc2e25

[ "MIT" ]

1

2018-09-13T16:33:20.000Z

src/tests/test_get_redemption_request.py

jonathankamau/andela-societies-backend

b8382f308449a08e5c7bda46c6deabe597cc2e25

[ "MIT" ]

6

2019-03-11T17:50:27.000Z

2019-08-26T11:00:40.000Z

src/tests/test_get_redemption_request.py

jonathankamau/andela-societies-backend

b8382f308449a08e5c7bda46c6deabe597cc2e25

[ "MIT" ]

9

2019-01-09T12:23:12.000Z

2021-05-28T04:58:31.000Z

"""Test suite for Point Redemption Module.""" import json import uuid from .points_redemption_base_test_case_setup import PointRedemptionBaseTestCase class GetRedemptionRequest(PointRedemptionBaseTestCase): def test_get_all_redemption_requests(self): """Test retrieval of Redemption Requests.""" response = self.client.get("api/v1/societies/redeem", headers=self.society_president, content_type='application/json') message = "fetched successfully" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 200) def test_get_all_redemption_requests_by_cio(self): """Test retrieval of Redemption Requests.""" response = self.client.get("api/v1/societies/redeem?paginate=false", headers=self.cio, content_type='application/json') message = "fetched successfully" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 200) def test_get_existing_redemption_requests_by_id(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem/{self.redemp_req.uuid}", headers=self.society_president, content_type='application/json') message = "fetched successfully" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 200) def test_get_existing_redemption_requests_by_name(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem?name={self.redemp_req.name}", headers=self.society_president, content_type='application/json') message = "fetched successfully" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 200) def test_get_existing_redemption_requests_by_society(self): """Test retrieval of Redemption Requests.""" self.test_user.society.save() response = self.client.get( f"api/v1/societies/redeem?society={self.test_user.society.name}", headers=self.society_president, content_type='application/json') message = "fetched successfully" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 200) def test_get_existing_redemption_requests_by_status(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem?status={self.redemp_req.status}", headers=self.society_president, content_type='application/json') message = "fetched successfully" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 200) def test_get_existing_redemption_requests_by_center(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem?center={self.redemp_req.center.name}", headers=self.society_president, content_type='application/json') message = "fetched successfully" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 200) def test_get_non_existing_redemption_requests_by_id(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem/{str(uuid.uuid4())}", headers=self.society_president, content_type='application/json') message = "does not exist" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 404) def test_get_non_existing_redemption_requests_by_name(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem?name={str(uuid.uuid4())}", headers=self.society_president, content_type='application/json') message = "Resources were not found." response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 404) def test_get_non_existing_redemption_requests_by_society(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem?society={str(uuid.uuid4())}", headers=self.society_president, content_type='application/json') message = f'not found' response_details = json.loads(response.data) self.assertTrue(response_details["message"].find(message)) self.assertEqual(response.status_code, 400) def test_get_non_existing_redemption_requests_by_status(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem?status={str(uuid.uuid4())}", headers=self.society_president, content_type='application/json') message = "Resources were not found." response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 404) def test_get_non_existing_redemption_requests_by_center(self): """Test retrieval of Redemption Requests.""" response = self.client.get( f"api/v1/societies/redeem?center={str(uuid.uuid4())}", headers=self.society_president, content_type='application/json') message = "not found" response_details = json.loads(response.data) self.assertIn(message, response_details["message"]) self.assertEqual(response.status_code, 400)

40.074534

79

0.66522

705

6,452

5.88227

0.103546

0.104172

0.028937

0.05498

0.911743

0.899204

0.888112

0.884254

0.872679

0

0.010649

0.228611

6,452

160

80

40.325

0.822584

0.07858

0

0.693694

0

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0.097976

0

0.216216

1

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false

0

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0.144144

0

null

0

1

0

null

0

6

b186f1b58985529834d28c5e2b50f32009b6c36e

1,970

py

Python

sarna/core/roles.py

rsrdesarrollo/sarna

0c1f44e06a932520b70e505585a5469b77f6302e

[ "MIT" ]

25

2019-03-11T22:42:52.000Z

2022-03-15T09:49:15.000Z

sarna/core/roles.py

hackingmess/sarna

0c1f44e06a932520b70e505585a5469b77f6302e

[ "MIT" ]

14

2019-01-08T08:35:51.000Z

2022-03-11T23:30:28.000Z

sarna/core/roles.py

hackingmess/sarna

0c1f44e06a932520b70e505585a5469b77f6302e

[ "MIT" ]

12

2019-07-26T05:38:32.000Z

2022-03-29T09:54:49.000Z

from functools import wraps from flask_login import login_required from werkzeug.exceptions import abort from sarna.model.enums import UserType valid_auditors = {UserType.manager, UserType.trusted_auditor, UserType.auditor} valid_trusted = {UserType.manager, UserType.trusted_auditor} valid_managers = {UserType.manager} valid_admins = {UserType.admin} def admin_required(func): from sarna.core.auth import current_user needs_accounts = valid_admins setattr(func, 'needs_accounts', needs_accounts) @wraps(func) @login_required def decorated_view(*args, **kwargs): if current_user.user_type not in needs_accounts: abort(403) else: return func(*args, **kwargs) return decorated_view def manager_required(func): from sarna.core.auth import current_user needs_accounts = valid_managers setattr(func, 'needs_accounts', needs_accounts) @wraps(func) @login_required def decorated_view(*args, **kwargs): if current_user.user_type not in needs_accounts: abort(403) else: return func(*args, **kwargs) return decorated_view def trusted_required(func): from sarna.core.auth import current_user needs_accounts = valid_trusted setattr(func, 'needs_accounts', needs_accounts) @wraps(func) @login_required def decorated_view(*args, **kwargs): if current_user.user_type not in needs_accounts: abort(403) else: return func(*args, **kwargs) return decorated_view def auditor_required(func): from sarna.core.auth import current_user needs_accounts = valid_auditors setattr(func, 'needs_accounts', needs_accounts) @wraps(func) @login_required def decorated_view(*args, **kwargs): if current_user.user_type not in needs_accounts: abort(403) else: return func(*args, **kwargs) return decorated_view

24.936709

79

0.694416

241

1,970

5.443983

0.174274

0.158537

0.04878

0.064024

0.791921

0.735518

0

0.007848

0.223858

1,970

78

80

25.25641

0.850229

0

0.714286

0

0.028426

0

1

0.142857

false

0

0.142857

0

0.428571

0

null

0

1

0

1

0

null

0

6

4934a572b7f8cbb4408575a9c61f5cbb5aef7bc6

16,168

py

Python

lifelist/api/tests/test_api.py

andela-mnzomo/life-list

28a7fa9d16e2b322e4a1bce269dbe7331e783534

[ "Unlicense" ]

3

2017-08-17T07:12:03.000Z

2017-10-18T11:13:44.000Z

lifelist/api/tests/test_api.py

andela-mnzomo/life-list

28a7fa9d16e2b322e4a1bce269dbe7331e783534

[ "Unlicense" ]

1

2018-05-30T14:38:52.000Z

lifelist/api/tests/test_api.py

andela-mnzomo/life-list

28a7fa9d16e2b322e4a1bce269dbe7331e783534

[ "Unlicense" ]

null

from django.core.urlresolvers import reverse from rest_framework import status from rest_framework.test import APIRequestFactory, APITestCase from django.contrib.auth.models import User from api.models import Bucketlist, Item class TestBase(APITestCase): """ Base configurations for the tests """ # Get authentication token def get_token(self): """ Returns authentication token """ url = reverse("api-login") self.user = {"username": "testuser", "password": "testpassword"} response = self.client.post(url, data=self.user) token = str(response.data.get("token")) return token def setUp(self): # Add test user url = reverse("user-list") self.user = {"username": "testuser", "email": "testuser@email.com", "password": "testpassword"} response = self.client.post(url, data=self.user) self.test_user_id = str(response.data["id"]) # Add first test bucket list url = reverse("bucketlist-list") self.bucketlist = {"title": "The List of Awesome", "description": "Awesome things!", "created_by": self.test_user_id} self.client.credentials(HTTP_AUTHORIZATION="Token " + self.get_token()) response = self.client.post(url, data=self.bucketlist) self.first_bucketlist_id = str(response.data["id"]) # Add second test bucket list self.bucketlist = {"title": "Knowledge Goals", "description": "Things to learn", "created_by": self.test_user_id} response = self.client.post(url, data=self.bucketlist) self.second_bucketlist_id = str(response.data["id"]) # Add first test bucket list item url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/" self.item = {"title": "Swim with dolphins", "description": "Swim with dolphins in Watamu"} response = self.client.post(url, data=self.item) self.first_item_id = str(response.data["id"]) # Add first second bucket list item url = "/api/v1/bucketlists/" + self.second_bucketlist_id + "/items/" self.item = {"title": "Visit all continents", "description": "Within 5 years"} response = self.client.post(url, data=self.item) self.second_item_id = str(response.data["id"]) class TestAuth(TestBase): """ Test user registration and login """ def test_registration(self): """ Test user registration """ url = reverse("user-list") self.user = {"username": "testuser2", "email": "testuser2@email.com", "password": "testpassword"} response = self.client.post(url, data=self.user) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(User.objects.count(), 2) self.assertTrue("testuser2" in response.data["username"]) self.assertTrue("testuser2@email.com" in response.data["email"]) def test_login(self): """ Test user login """ url = reverse("api-login") self.user = {"username": "testuser", "password": "testpassword"} response = self.client.post(url, data=self.user) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_invalid_credentials(self): """ Test that users cannot login with invalid credentials """ # Invalid username url = reverse("api-login") self.user = {"username": "invalid", "password": "testpassword"} response = self.client.post(url, data=self.user) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) # Invalid password url = reverse("api-login") self.user = {"username": "testuser", "password": "invalid"} response = self.client.post(url, data=self.user) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) class TestBucketlists(TestBase): """ Test operations on bucketlists """ def test_no_token_bucketlist(self): """ Test that user cannot add a bucket list without an authentication token """ url = reverse("bucketlist-list") self.bucketlist = {"title": "The List of Awesome", "description": "Awesome things I want to do", "created_by": self.test_user_id} self.client.credentials() response = self.client.post(url, data=self.bucketlist) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) self.assertTrue("Authentication credentials were not provided" in response.data["detail"]) def test_invalid_token_bucketlist(self): """ Test that user cannot add a bucket list with an invalid token """ url = reverse("bucketlist-list") self.bucketlist = {"title": "The List of Awesome", "description": "Awesome things I want to do", "created_by": self.test_user_id} invalid_token = "1234" self.client.credentials(HTTP_AUTHORIZATION="Token " + invalid_token) response = self.client.post(url, data=self.bucketlist) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) self.assertTrue("Invalid token" in response.data["detail"]) def test_add_bucketlist(self): """ Test that user can add a bucket list """ url = reverse("bucketlist-list") self.bucketlist = {"title": "Adventure!", "description": "Adventurous stuff", "created_by": self.test_user_id} response = self.client.post(url, data=self.bucketlist) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(Bucketlist.objects.count(), 3) self.assertTrue("Adventure!" in response.data["title"]) self.assertTrue("Adventurous stuff" in response.data["description"]) def test_delete_bucketlist(self): """ Test deletion of bucket lists """ url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/" response = self.client.delete(url) # Only one bucket list remains self.assertEqual(Bucketlist.objects.count(), 1) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) def test_edit_bucketlist(self): """ Test editing of bucket lists """ self.bucketlist = {"title": "Mission Multilinguist", "description": "Languages to learn"} url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/" response = self.client.put(url, data=self.bucketlist) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertTrue("Mission Multilinguist" in response.data["title"]) self.assertTrue("Languages to learn" in response.data["description"]) def test_get_bucketlists(self): """ Test that all bucket lists are displayed """ url = reverse("bucketlist-list") response = self.client.get(url) self.assertEqual(response.status_code, status.HTTP_200_OK) bucketlist1 = response.data[0] bucketlist2 = response.data[1] # Both bucket lists are displayed self.assertEqual(bucketlist1.get("title"), "The List of Awesome") self.assertEqual(bucketlist2.get("title"), "Knowledge Goals") def test_get_bucketlist(self): """ Test that specified bucket list is displayed """ # Get first bucket list url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/" response = self.client.get(url) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data.get("title"), "The List of Awesome") # Get second bucket list url = "/api/v1/bucketlists/" + self.second_bucketlist_id + "/" response = self.client.get(url) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data.get("title"), "Knowledge Goals") def test_get_nonexistent_bucketlist(self): """ Test that specifying a bucket list with invalid id will throw an error """ url = "/api/v1/bucketlists/1234/" response = self.client.get(url) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) self.assertTrue("Not found" in response.data["detail"]) def test_unauthorized_access_bucketlist(self): """ Test that users cannot edit or delete another user's bucket lists """ # Register a new user url = reverse("user-list") self.user = {"username": "testuser2", "email": "testuser2@email.com", "password": "testpassword"} self.client.post(url, data=self.user) # Log new user in and obtain their token url = reverse("api-login") self.user = {"username": "testuser2", "password": "testpassword"} response = self.client.post(url, data=self.user) token = str(response.data.get("token")) # Cannot edit bucket list self.bucketlist = {"title": "Mission Multilinguist", "description": "Languages to learn"} url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/" self.client.credentials(HTTP_AUTHORIZATION="Token " + token) response = self.client.put(url, data=self.bucketlist) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.assertTrue("You do not have permission to perform this action" in response.data["detail"]) # Cannot delete bucket list url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/" response = self.client.delete(url) # Number of bucket lists remains the same self.assertEqual(Bucketlist.objects.count(), 2) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) class TestItems(TestBase): """ Test operations on bucket list items""" def test_no_token_item(self): """ Test that user cannot add a bucket list item without an authentication token """ url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/" self.item = {"title": "Learn Japanese", "description": "To fluency!", "item_bucketlist_id": self.first_bucketlist_id} self.client.credentials() response = self.client.post(url, data=self.item) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) self.assertTrue("Authentication credentials were not provided" in response.data["detail"]) def test_invalid_token_item(self): """ Test that user cannot add a bucket list item with an invalid token """ url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/" self.item = {"title": "Learn Japanese", "description": "To fluency!", "item_bucketlist_id": self.first_bucketlist_id} invalid_token = "1234" self.client.credentials(HTTP_AUTHORIZATION="Token " + invalid_token) response = self.client.post(url, data=self.item) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) self.assertTrue("Invalid token" in response.data["detail"]) def test_add_item(self): """ Test that user can add a bucket list item""" url = "/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/" self.item = {"title": "Learn Japanese", "description": "To fluency!"} response = self.client.post(url, data=self.item) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(Item.objects.count(), 3) self.assertTrue("Learn Japanese" in response.data["title"]) self.assertTrue("To fluency!" in response.data["description"]) def test_delete_item(self): """ Test deletion of bucket list items """ url = ("/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/" + self.first_item_id + "/") response = self.client.delete(url) self.assertEqual(Item.objects.count(), 1) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) def test_edit_item(self): """ Test editing of bucket list items """ self.bucketlist = {"title": "Learn Spanish", "description": "To fluency!"} url = ("/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/" + self.first_item_id + "/") response = self.client.put(url, data=self.bucketlist) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertTrue("Learn Spanish" in response.data["title"]) self.assertTrue("To fluency!" in response.data["description"]) def test_get_item(self): """ Test that specified bucket list item is displayed """ # Get first bucket list item url = ("/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/" + self.first_item_id + "/") response = self.client.get(url) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data.get("title"), "Swim with dolphins") # Get second bucket list item url = ("/api/v1/bucketlists/" + self.second_bucketlist_id + "/items/" + self.second_item_id + "/") response = self.client.get(url) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data.get("title"), "Visit all continents") def test_get_nonexistent_bucketlist(self): """ Test that specifying a bucket list with invalid id will throw an error """ url = ("/api/v1/bucketlists/" + self.first_bucketlist_id + "/items/1234/") response = self.client.get(url) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) self.assertTrue("Not found" in response.data["detail"]) def test_unauthorized_access_item(self): """ Test that users cannot edit or delete another user's bucket list items """ # Register a new user url = reverse("user-list") self.user = {"username": "testuser2", "email": "testuser2@email.com", "password": "testpassword"} self.client.post(url, data=self.user) # Log new user in and obtain their token url = reverse("api-login") self.user = {"username": "testuser2", "password": "testpassword"} response = self.client.post(url, data=self.user) token = str(response.data.get("token")) # Cannot edit bucket list item self.item = {"title": "Learn Japanese", "description": "To fluency!"} url = ("/api/v1/bucketlists/" + self.second_bucketlist_id + "/items/" + self.second_item_id + "/") self.client.credentials(HTTP_AUTHORIZATION="Token " + token) response = self.client.put(url, data=self.item) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.assertTrue("You do not have permission to perform this action" in response.data["detail"]) # Cannot delete bucket list item url = ("/api/v1/bucketlists/" + self.second_bucketlist_id + "/items/" + self.second_item_id + "/") response = self.client.delete(url) # Number of bucket list items remains the same self.assertEqual(Item.objects.count(), 2) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)

44.786704

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null

0

1

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null

0

6

496e764d0d7f0bbc002a0dba85baa37dbd909e4d

21

py

Python

comma/__init__.py

zbanks/comma

75f77d659a47a777b6790b2e47114a0355bbf0cc

[ "MIT" ]

1

2020-06-15T02:22:14.000Z

comma/__init__.py

zbanks/comma

75f77d659a47a777b6790b2e47114a0355bbf0cc

[ "MIT" ]

null

comma/__init__.py

zbanks/comma

75f77d659a47a777b6790b2e47114a0355bbf0cc

[ "MIT" ]

null

from .comma import *

10.5

20

0.714286

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21

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21

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1

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null

0

1

0

1

0

1

0

6

49880fbc6c6fb0176a5db60138e603e1cd701057

173

py

Python

checks/check.py

signalfx/alert-assessor

9376793c9e9c1ebf3f98e5e0b5df2e695cb495aa

[ "Apache-2.0" ]

3

2020-05-07T17:58:41.000Z

2021-06-10T19:58:46.000Z

checks/check.py

signalfx/alert-assessor

9376793c9e9c1ebf3f98e5e0b5df2e695cb495aa

[ "Apache-2.0" ]

3

2019-09-13T16:07:52.000Z

2020-06-24T20:14:23.000Z

checks/check.py

signalfx/alert-assessor

9376793c9e9c1ebf3f98e5e0b5df2e695cb495aa

[ "Apache-2.0" ]

3

2019-08-29T09:14:08.000Z

2021-12-20T09:29:04.000Z

import re class Check: def __init__(self): pass class RuleCheck: RE_USES_PARAMETER_VARS = re.compile("\{\{\S*\}\}") def __init__(self): pass

13.307692

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173

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0.25

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null

0

1

0

1

0

1

0

6

b8fa07b1f35fe7db1f7c55005fa49e658e44e8cc

1,760

py

Python

app/src/main/python/cve-2020-0796-scanner.py

lionche/KillNet

e5ec7a744c74fecc4bf480cb2474e387cba23d54

[ "MIT" ]

1

2021-12-01T03:22:55.000Z

app/src/main/python/cve-2020-0796-scanner.py

lionche/KillNet

e5ec7a744c74fecc4bf480cb2474e387cba23d54

[ "MIT" ]

null

app/src/main/python/cve-2020-0796-scanner.py

lionche/KillNet

e5ec7a744c74fecc4bf480cb2474e387cba23d54

[ "MIT" ]

null

import struct import socket def scannerIp(stringIp): sock = socket.socket(socket.AF_INET) sock.settimeout(3) sock.connect((stringIp, 445)) packet = b'\x00\x00\x00\xc0\xfeSMB@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1f\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00$\x00\x08\x00\x01\x00\x00\x00\x7f\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00x\x00\x00\x00\x02\x00\x00\x00\x02\x02\x10\x02"\x02$\x02\x00\x03\x02\x03\x10\x03\x11\x03\x00\x00\x00\x00\x01\x00&\x00\x00\x00\x00\x00\x01\x00 \x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\n\x00\x00\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00' sock.send(packet) length = sock.recv(4) # print(length) nb, = struct.unpack(">I", length) # print(nb) result = sock.recv(nb) # if not result[68:70] == b"\x11\x03": # # print("Not vulnerable") # if not result[70:72] == b"\x02\x00": # print("Not vulnerable") # print("Vulnerable") # if result[68:70] == b"\x11\x03": # exit("vulnerable") # if result[70:72] == b"\x02\x00": # exit("vulnerable") # exit("Not Vulnerable") # print(result[68:70]) # print(result[70:72]) # ifVulnerable = False if result[68:72] != b"\x11\x03\x02\x00": print("Not Vulnerable") ifVulnerable = False return False else: print("vulnerable") ifVulnerable = True return True return False exit() # scannerIp()

41.904762

768

0.639773

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1,760

3.629032

0.158065

0.741333

0.992

1.173333

0.505778

0.455111

0.394667

0.384

0.36

0

0.285135

0.159091

1,760

42

769

41.904762

0.475

0.217614

0

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0

0.047619

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0

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0

null

1

0

1

0

1

0

1

null

0

6

7707094cf29645fbcec0a5b4291ed680144ef744

53

py

Python

example/diff_imports/import_from_module.py

DKorytkin/pytest-never-sleep

e655fbff4d51b8a7e41a56e584dae55013f7160f

[ "MIT" ]

null

example/diff_imports/import_from_module.py

DKorytkin/pytest-never-sleep

e655fbff4d51b8a7e41a56e584dae55013f7160f

[ "MIT" ]

2

2021-05-19T07:55:13.000Z

2021-05-21T09:49:05.000Z

example/diff_imports/import_from_module.py

DKorytkin/pytest-never-sleep

e655fbff4d51b8a7e41a56e584dae55013f7160f

[ "MIT" ]

null

import time def do_some_stuff(): time.sleep(1)

8.833333

20

0.679245

9

53

3.777778

0.888889

0

0.02381

0.207547

53

5

21

10.6

0.785714

0

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

77112182a8c37f277bd2bb6200addd0506822e1c

11,720

py

Python

constructor_io/modules/browse.py

Constructor-io/constructorio-python

ff4f068816f51914893c6c40093f5a0503cbf1a3

[ "MIT" ]

null

constructor_io/modules/browse.py

Constructor-io/constructorio-python

ff4f068816f51914893c6c40093f5a0503cbf1a3

[ "MIT" ]

8

2015-10-05T03:28:37.000Z

2021-11-17T17:23:41.000Z

constructor_io/modules/browse.py

Constructor-io/constructorio-python

ff4f068816f51914893c6c40093f5a0503cbf1a3

[ "MIT" ]

4

2016-05-12T06:16:19.000Z

2018-02-21T18:49:20.000Z

'''Browse Module''' from time import time from urllib.parse import quote, urlencode import requests as r from constructor_io.helpers.exception import ConstructorException from constructor_io.helpers.utils import (clean_params, create_auth_header, create_request_headers, create_shared_query_params, throw_http_exception_from_response) def _create_browse_url(prefix, parameters, user_parameters, options, omit_timestamp = False): # pylint: disable=too-many-branches '''Create URL from supplied filter name, filter value, and parameters''' query_params = create_shared_query_params(options, parameters, user_parameters) if parameters: if parameters.get('item_ids'): query_params['ids'] = parameters.get('item_ids') if not omit_timestamp: query_params['_dt'] = int(time()*1000.0) query_params = clean_params(query_params) query_string = urlencode(query_params, doseq=True) return f'{options.get("service_url")}/{prefix}?{query_string}' # pylint: disable=line-too-long class Browse: '''Browse Class''' def __init__(self, options): self.__options = options or {} def get_browse_results(self, filter_name, filter_value, parameters=None, user_parameters=None): ''' Retrieve browse results from API :param str filter_name: Filter name to display results from :param str filter_value: Filter value to display results from :param dict parameters: Additional parameters to refine result set :param int parameters.page: The page number of the results :param int parameters.results_per_page: The number of results per page to return :param dict parameters.filters: Filters used to refine results :param str parameters.sort_by: The sort method for results :param str parameters.sort_order: The sort order for results :param str parameters.section: Section name for results :param dict parameters.fmt_options: The format options used to refine result groups :param list parameters.hidden_fields: Hidden metadata fields to return :param dict user_parameters: Parameters relevant to the user request :param int user_parameters.session_id: Session ID, utilized to personalize results :param str user_parameters.client_id: Client ID, utilized to personalize results :param str user_parameters.user_id: User ID, utilized to personalize results :param str user_parameters.segments: User segments :param dict user_parameters.test_cells: User test cells :param str user_parameters.user_ip: Origin user IP, from client :param str user_parameters.user_agent: Origin user agent, from client :return: dict ''' if not filter_name or not isinstance(filter_name, str): raise ConstructorException('filter_name is a required parameter of type string') if not filter_value or not isinstance(filter_value, str): raise ConstructorException('filter_value is a required parameter of type string') if not parameters: parameters = {} if not user_parameters: user_parameters = {} url_prefix = f'browse/{quote(filter_name)}/{quote(filter_value)}' request_url = _create_browse_url( url_prefix, parameters, user_parameters, self.__options ) requests = self.__options.get('requests') or r response = requests.get( request_url, auth=create_auth_header(self.__options), headers=create_request_headers(self.__options, user_parameters) ) if not response.ok: throw_http_exception_from_response(response) json = response.json() json_response = json.get('response') if json_response: if json_response.get('results') or json_response.get('results') == []: result_id = json.get('result_id') if result_id: for result in json_response.get('results'): result['result_id'] = result_id return json raise ConstructorException('get_browse_results response data is malformed') def get_browse_results_for_item_ids(self, item_ids, parameters=None, user_parameters=None): ''' Retrieve browse results from API using item ID's :param list item_ids: Item ID's of results to get results for :param dict parameters: Additional parameters to refine result set :param int parameters.page: The page number of the results :param int parameters.results_per_page: The number of results per page to return :param dict parameters.filters: Filters used to refine results :param str parameters.sort_by: The sort method for results :param str parameters.sort_order: The sort order for results :param str parameters.section: Section name for results :param dict parameters.fmt_options: The format options used to refine result groups :param list parameters.hidden_fields: Hidden metadata fields to return :param dict user_parameters: Parameters relevant to the user request :param int user_parameters.session_id: Session ID, utilized to personalize results :param str user_parameters.client_id: Client ID, utilized to personalize results :param str user_parameters.user_id: User ID, utilized to personalize results :param str user_parameters.segments: User segments :param dict user_parameters.test_cells: User test cells :param str user_parameters.user_ip: Origin user IP, from client :param str user_parameters.user_agent: Origin user agent, from client :return: dict ''' if not item_ids or not isinstance(item_ids, list): raise ConstructorException('item_ids is a required parameter of type list') if not parameters: parameters = {} if not user_parameters: user_parameters = {} url_prefix = 'browse/items' request_url = _create_browse_url( url_prefix, { **parameters, 'item_ids': item_ids}, user_parameters, self.__options ) requests = self.__options.get('requests') or r response = requests.get( request_url, auth=create_auth_header(self.__options), headers=create_request_headers(self.__options, user_parameters) ) if not response.ok: throw_http_exception_from_response(response) json = response.json() json_response = json.get('response') if json_response: if json_response.get('results') or json_response.get('results') == []: result_id = json.get('result_id') if result_id: for result in json_response.get('results'): result['result_id'] = result_id return json raise ConstructorException('get_browse_results_for_item_ids response data is malformed') def get_browse_groups(self, parameters=None, user_parameters=None): ''' Retrieve groups from API :param dict parameters: Additional parameters to refine result set :param dict parameters.filters: Filters used to refine results :param dict parameters.fmt_options: The format options used to refine result groups :param int parameters.fmt_options.groups_max_depth: The maximum depth of the hierarchy group structure # pylint: disable=line-too-long :param dict user_parameters: Parameters relevant to the user request :param int user_parameters.session_id: Session ID, utilized to personalize results :param str user_parameters.client_id: Client ID, utilized to personalize results :param str user_parameters.user_id: User ID, utilized to personalize results :param str user_parameters.segments: User segments :param dict user_parameters.test_cells: User test cells :param str user_parameters.user_ip: Origin user IP, from client :param str user_parameters.user_agent: Origin user agent, from client :return: dict ''' if not parameters: parameters = {} if not user_parameters: user_parameters = {} url_prefix = 'browse/groups' request_url = _create_browse_url( url_prefix, parameters, user_parameters, self.__options, True ) requests = self.__options.get('requests') or r response = requests.get( request_url, auth=create_auth_header(self.__options), headers=create_request_headers(self.__options, user_parameters) ) if not response.ok: throw_http_exception_from_response(response) json = response.json() json_response = json.get('response') if json_response: if json_response.get('groups') or json_response.get('groups') == []: return json raise ConstructorException('get_browse_groups response data is malformed') def get_browse_facets(self, parameters=None, user_parameters=None): ''' Retrieve facets from API :param dict parameters: Additional parameters to refine result set :param dict parameters.page: The page number of the results :param dict parameters.results_per_page: The number of results per page to return :param dict parameters.fmt_options: The format options used to refine result groups :param int parameters.fmt_options.show_hidden_facets: Include facets configured as hidden :param int parameters.fmt_options.show_protected_facets: Include facets configured as protected # pylint: disable=line-too-long :param dict user_parameters: Parameters relevant to the user request :param int user_parameters.session_id: Session ID, utilized to personalize results :param str user_parameters.client_id: Client ID, utilized to personalize results :param str user_parameters.user_id: User ID, utilized to personalize results :param str user_parameters.segments: User segments :param dict user_parameters.test_cells: User test cells :param str user_parameters.user_ip: Origin user IP, from client :param str user_parameters.user_agent: Origin user agent, from client :return: dict ''' if not parameters: parameters = {} if not user_parameters: user_parameters = {} url_prefix = 'browse/facets' request_url = _create_browse_url( url_prefix, parameters, user_parameters, self.__options, True ) requests = self.__options.get('requests') or r response = requests.get( request_url, auth=create_auth_header(self.__options), headers=create_request_headers(self.__options, user_parameters) ) if not response.ok: throw_http_exception_from_response(response) json = response.json() json_response = json.get('response') if json_response: if json_response.get('facets') or json_response.get('facets') == []: return json raise ConstructorException('get_browse_facets response data is malformed')

41.122807

142

0.664164

1,406

11,720

5.319346

0.100285

0.101083

0.03209

0.058831

0.814949

0.789009

0.765343

0.739537

0.733654

0.719882

0

0.000584

0.270051

11,720

284

143

41.267606

0.873641

0.409898

0

0.64539

0

0.10716

0.021049

0

1

0.042553

false

0

0.035461

0

0.120567

0

null

0

1

0

null

0

6

771255520ef922971e08ab1f3a6fea3938b0c54c

2,605

py

Python

tests/testMapping.py

shimpe/pyvectortween

aff071180474739060ec2d3102c39c8e73510988

[ "MIT" ]

6

2017-05-17T23:34:41.000Z

2022-03-15T17:12:19.000Z

tests/testMapping.py

shimpe/pyvectortween

aff071180474739060ec2d3102c39c8e73510988

[ "MIT" ]

null

tests/testMapping.py

shimpe/pyvectortween

aff071180474739060ec2d3102c39c8e73510988

[ "MIT" ]

null

from vectortween.Mapping import Mapping def test_linlin(): clip = True noclip = False table = ( # inputs, expected output ([0, 0, 0, 0, 0], 0), ([0, 0, 0, 0, 0, clip], 0), ([1, 0, 0, 0, 0], None), ([1, 0, 0, 0, 0, noclip], None), ([1, 0, 2, 0, 100], 50), ([-1, 0, -2, 0, 100], 50), ([-2, 0, -2, 0, 100], 100), ([6, 5, 10, 50, 100], 60), ([2, 0, 1, 0, 100, noclip], 200), ([2, 0, 1, 0, 100, clip], 100), ([-2, 0, -1, 0, -100, noclip], -200), ([2, 0, 1, 0, -100, clip], -100), ([2, 1, 10, 1, 100], 12), ([2, 10, 1, 1, 100], 89), ([2, 10, 1, 100, 1], 12), ([2, 1, 10, 100, 1], 89), ([-2, -1, -10, 1, 100], 12), ([-2, -10, -1, 1, 100], 89), ([-2, -10, -1, 100, 1], 12), ([-2, -1, -10, 100, 1], 89), ([2, 1, 10, -1, -100], -12), ([2, 10, 1, -1, -100], -89), ([2, 10, 1, -100, -1], -12), ([2, 1, 10, -100, -1], -89), ) for test in table: assert Mapping.linlin(*test[0]) == test[1] def test_linexp(): clip = True noclip = False table = ( # inputs, expected output ([0, 0, 0, 0, 0], None), ([0, 0, 0, 0, 0, clip], None), ([1, 0, 0, 0, 0], None), ([1, 0, 0, 0, 0, noclip], None), ([1, 0, 2, 0, 100], None), ([1, 1, 10, 1, 100], 1), ([2, 1, 10, 1, 100], 1.6681005372000588), ([8, 1, 10, 1, 100], 35.938136638046274), ([8, 1, 10, -1, -100], -35.938136638046274), ([11, 1, 10, -1, -100], -100), ([11, 1, 10, -1, -100, noclip], -166.81005372000593), ([-2, -1, -10, -1, -100], -1.6681005372000588), ([-2, -10, -1, -1, -100], -59.94842503189409), ([-2, -10, -1, -100, -1], -1.6681005372000592), ([-2, -1, -10, -100, -1], -59.948425031894104), ([2, 1, 10, 1, 100], 1.6681005372000588), ([2, 10, 1, 1, 100], 59.94842503189409), ([2, 10, 1, 100, 1], 1.6681005372000592), ([2, 1, 10, 100, 1], 59.948425031894104), ([-2, -1, -10, 1, 100], 1.6681005372000588), ([-2, -10, -1, 1, 100], 59.94842503189409), ([-2, -10, -1, 100, 1], 1.6681005372000592), ([-2, -1, -10, 100, 1], 59.948425031894104), ([2, 1, 10, -1, -100], -1.6681005372000588), ([2, 10, 1, -1, -100], -59.94842503189409), ([2, 10, 1, -100, -1], -1.6681005372000592), ([2, 1, 10, -100, -1], -59.948425031894104), ) for test in table: assert Mapping.linexp(*test[0]) == test[1]

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0.413436

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1

0

1

0

null

0

6

77433b7438319111901c902898b32a94e99e6bc5

1,523

py

Python

tests/test_metric_optimization_tf.py

junpenglao/pysaliency

2b243a26086669bf089391a8cc9cd5d80a718188

[ "MIT" ]

118

2015-12-29T20:52:24.000Z

2022-03-14T20:57:30.000Z

tests/test_metric_optimization_tf.py

junpenglao/pysaliency

2b243a26086669bf089391a8cc9cd5d80a718188

[ "MIT" ]

20

2016-10-13T09:25:56.000Z

2021-12-01T03:06:55.000Z

tests/test_metric_optimization_tf.py

junpenglao/pysaliency

2b243a26086669bf089391a8cc9cd5d80a718188

[ "MIT" ]

35

2015-12-23T09:11:24.000Z

2022-02-27T03:44:17.000Z

import numpy as np import pytest # from pysaliency.metric_optimization_tf import maximize_expected_sim @pytest.mark.skip("tensorflow <2.0 not available for new python versions, need to upgrade to tensorflow 2 in pysaliency") def test_maximize_expected_sim_decay_1overk(): density = np.ones((20, 20)) density[6:17, 8:12] = 20 density[2:4, 18:18] = 30 density /= density.sum() log_density = np.log(density) saliency_map, score = maximize_expected_sim( log_density=log_density, kernel_size=1, train_samples_per_epoch=1000, val_samples=1000, max_iter=100 ) np.testing.assert_allclose(score, -0.8202789932489393, rtol=5e-7) # need bigger tolerance to handle differences between CPU and GPU @pytest.mark.skip("tensorflow <2.0 not available for new python versions, need to upgrade to tensorflow 2 in pysaliency") def test_maximize_expected_sim_decay_on_plateau(): density = np.ones((20, 20)) density[6:17, 8:12] = 20 density[2:4, 18:18] = 30 density /= density.sum() log_density = np.log(density) saliency_map, score = maximize_expected_sim( log_density=log_density, kernel_size=1, train_samples_per_epoch=1000, val_samples=1000, max_iter=100, backlook=1, min_iter=10, learning_rate_decay_scheme='validation_loss', ) np.testing.assert_allclose(score, -0.8203513294458387, rtol=5e-7) # need bigger tolerance to handle differences between CPU and GPU

32.404255

136

0.701248

219

1,523

4.666667

0.388128

0.078278

0.092955

0.046967

0.816047

0.759296

0

0.092639

0.206172

1,523

46

137

33.108696

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0.114286

0

null

0

1

0

null

0

6

774d01fb4eaf23896e03b57bf10213cdaf92039a

5,839

py

Python

tests/ci/unit_tests/release_flow/test_npm_ci.py

Food-X-Technologies/foodx_devops_tools

57d1bf1304d9c9a386eaffa427f9eb36c410c350

[ "MIT" ]

3

2021-06-23T20:53:43.000Z

2022-01-26T14:19:43.000Z

tests/ci/unit_tests/release_flow/test_npm_ci.py

Food-X-Technologies/foodx_devops_tools

57d1bf1304d9c9a386eaffa427f9eb36c410c350

[ "MIT" ]

33

2021-08-09T15:44:51.000Z

2022-03-03T18:28:02.000Z

tests/ci/unit_tests/release_flow/test_npm_ci.py

Food-X-Technologies/foodx_devops_tools

57d1bf1304d9c9a386eaffa427f9eb36c410c350

[ "MIT" ]

1

2021-06-23T20:53:52.000Z

# Copyright (c) 2021 Food-X Technologies # # This file is part of foodx_devops_tools. # # You should have received a copy of the MIT License along with # foodx_devops_tools. If not, see <https://opensource.org/licenses/MIT>. from foodx_devops_tools.release_flow_entry import release_flow from tests.ci.support.click_runner import click_runner # noqa: F401 class TestNpmSubcommand: MOCK_PACKAGE_JSON_CONTENT = """{ "author": "FoodX", "description": "some package", "keywords": [ "foodx" ], "license": "SEE LICENSE IN LICENSE", "name": "@foodx/some-package", "version": "0.0.0+local" } """ def test_npm_id_nonrelease(self, click_runner, mocker): mock_input = [ "npm", "id", "package.json", "refs/tags/3.14.159-alpha.13", "abc123def", ] mocker.patch( "foodx_devops_tools.release_flow._simple_ci_release_id.acquire_post_data", return_value=("3.1.4", "26"), ) result = click_runner.invoke(release_flow, mock_input) assert result.exit_code == 0 assert result.output == "3.1.4-post.26.abc123d" def test_npm_id_release(self, click_runner, mocker): mock_input = [ "npm", "id", "package.json", "refs/tags/3.14.159", "abc123def", ] mocker.patch( "foodx_devops_tools.release_flow._simple_ci_release_id.acquire_post_data", return_value=("3.1.4", "26"), ) result = click_runner.invoke(release_flow, mock_input) assert result.exit_code == 0 assert result.output == "3.14.159" def test_npm_package_nonrelease(self, click_runner, mocker): mock_input = [ "npm", "package", "package.json", "refs/tags/3.14.159-alpha.13", "abc123def", ] mocker.patch( "foodx_devops_tools.release_flow._simple_ci_release_id.acquire_post_data", return_value=("3.1.4", "26"), ) with click_runner.isolated_filesystem(): with open("package.json", mode="w") as f: f.write(self.MOCK_PACKAGE_JSON_CONTENT) result = click_runner.invoke(release_flow, mock_input) assert result.exit_code == 0 assert result.output == "foodx-some-package-3.1.4-post.26.abc123d.tgz" def test_npm_package_release(self, click_runner, mocker): mock_input = [ "npm", "package", "package.json", "refs/tags/3.14.159", "abc123def", ] mocker.patch( "foodx_devops_tools.release_flow._simple_ci_release_id.acquire_post_data", return_value=("3.1.4", "26"), ) with click_runner.isolated_filesystem(): with open("package.json", mode="w") as f: f.write(self.MOCK_PACKAGE_JSON_CONTENT) result = click_runner.invoke(release_flow, mock_input) assert result.exit_code == 0 assert result.output == "foodx-some-package-3.14.159.tgz" def test_main_branch(self, click_runner, mocker): mock_arguments = [ "npm", "package", "package.json", "refs/heads/main", "123abc", ] mocker.patch( "foodx_devops_tools.release_flow.npm_ci.apply_package_release_id", return_value="@some-group/this-package", ) mocker.patch( "foodx_devops_tools.release_flow._simple_ci_release_id.acquire_post_data", return_value=("3.1.4", "26"), ) with click_runner.isolated_filesystem(): with open("package.json", mode="w") as f: f.write(self.MOCK_PACKAGE_JSON_CONTENT) result = click_runner.invoke(release_flow, mock_arguments) assert result.exit_code == 0 assert ( result.output == "some-group-this-package-3.1.4-post.26.123abc.tgz" ) def test_release_tag(self, click_runner, mocker): mock_arguments = [ "npm", "package", "package.json", "refs/tags/3.14.159", "123abc", ] mocker.patch( "foodx_devops_tools.release_flow.npm_ci.apply_package_release_id", return_value="@some-group/this-package", ) mocker.patch( "foodx_devops_tools.release_flow._simple_ci_release_id.acquire_post_data", return_value=("3.1.4", "26"), ) with click_runner.isolated_filesystem(): with open("package.json", mode="w") as f: f.write(self.MOCK_PACKAGE_JSON_CONTENT) result = click_runner.invoke(release_flow, mock_arguments) assert result.exit_code == 0 assert result.output == "some-group-this-package-3.14.159.tgz" def test_dryrun_tag(self, click_runner, mocker): mock_arguments = [ "npm", "package", "package.json", "refs/tags/3.14.159-dryrun45", "123abc", ] mocker.patch( "foodx_devops_tools.release_flow.npm_ci.apply_package_release_id", return_value="@some-group/this-package", ) mocker.patch( "foodx_devops_tools.release_flow._simple_ci_release_id.acquire_post_data", return_value=("3.1.4", "26"), ) with click_runner.isolated_filesystem(): with open("package.json", mode="w") as f: f.write(self.MOCK_PACKAGE_JSON_CONTENT) result = click_runner.invoke(release_flow, mock_arguments) assert result.exit_code == 0 assert result.output == "some-group-this-package-3.14.159-dryrun45.tgz"

32.620112

86

0.584004

695

5,839

4.628777

0.159712

0.071806

0.064657

0.078645

0.83525

0.826857

0.815045

0.807274

0.796705

0

0.040146

0.296112

5,839

178

87

32.803371

0.742579

0.038191

0

0.668919

0

0.006757

0.292974

0.193652

0

0.094595

1

0.047297

false

0

0.013514

0

0.074324

0

null

0

1

0

null

0

6

6272e7e485e16c1f10471bf63c8e9bc275f7a9f7

84,151

py

Python

TP_model/fit_and_forecast/generate_posterior.py

djmorris7/covid19-forecasting-aus

789bd40637738292b7a77103cbae636c177c2479

[ "MIT" ]

1

2021-10-12T10:25:31.000Z

TP_model/fit_and_forecast/generate_posterior.py

djmorris7/covid19-forecasting-aus

789bd40637738292b7a77103cbae636c177c2479

[ "MIT" ]

null

TP_model/fit_and_forecast/generate_posterior.py

djmorris7/covid19-forecasting-aus

789bd40637738292b7a77103cbae636c177c2479

[ "MIT" ]

null

######### imports ######### from ast import arg from datetime import timedelta import sys sys.path.insert(0, "TP_model") sys.path.insert(0, "TP_model/fit_and_forecast") from Reff_constants import * from Reff_functions import * import glob import os from sys import argv import arviz as az import seaborn as sns import matplotlib.pyplot as plt import numpy as np import pandas as pd import matplotlib from math import ceil import pickle from cmdstanpy import CmdStanModel matplotlib.use("Agg") from params import ( truncation_days, start_date, third_start_date, alpha_start_date, omicron_start_date, omicron_only_date, omicron_dominance_date, pop_sizes, num_forecast_days, get_all_p_detect_old, get_all_p_detect, ) def process_vax_data_array( data_date, third_states, third_end_date, variant="Delta", print_latest_date_in_ts=False, ): """ Processes the vaccination data to an array for either the Omicron or Delta strain. """ # Load in vaccination data by state and date vaccination_by_state = pd.read_csv( "data/vaccine_effect_timeseries_" + data_date.strftime("%Y-%m-%d") + ".csv", parse_dates=["date"], ) # there are a couple NA's early on in the time series but is likely due to slightly # different start dates vaccination_by_state.fillna(1, inplace=True) vaccination_by_state = vaccination_by_state.loc[ vaccination_by_state["variant"] == variant ] vaccination_by_state = vaccination_by_state[["state", "date", "effect"]] if print_latest_date_in_ts: # display the latest available date in the NSW data (will be the same date between states) print( "Latest date in vaccine data is {}".format( vaccination_by_state[vaccination_by_state.state == "NSW"].date.values[-1] ) ) # Get only the dates we need + 1 (this serves as the initial value) vaccination_by_state = vaccination_by_state[ ( vaccination_by_state.date >= pd.to_datetime(third_start_date) - timedelta(days=1) ) & (vaccination_by_state.date <= third_end_date) ] vaccination_by_state = vaccination_by_state[ vaccination_by_state["state"].isin(third_states) ] # Isolate fitting states vaccination_by_state = vaccination_by_state.pivot( index="state", columns="date", values="effect" ) # Convert to matrix form # If we are missing recent vaccination data, fill it in with the most recent available data. latest_vacc_data = vaccination_by_state.columns[-1] if latest_vacc_data < pd.to_datetime(third_end_date): vaccination_by_state = pd.concat( [vaccination_by_state] + [ pd.Series(vaccination_by_state[latest_vacc_data], name=day) for day in pd.date_range(start=latest_vacc_data, end=third_end_date) ], axis=1, ) # Convert to simple array only useful to pass to stan (index 1 onwards) vaccination_by_state_array = vaccination_by_state.iloc[:, 1:].to_numpy() return vaccination_by_state_array def get_data_for_posterior(data_date): """ Read in the various datastreams and combine the samples into a dictionary that we then dump to a pickle file. """ print("Performing inference on state level Reff") data_date = pd.to_datetime(data_date) # Define data date print("Data date is {}".format(data_date.strftime("%d%b%Y"))) fit_date = pd.to_datetime(data_date - timedelta(days=truncation_days)) print("Last date in fitting {}".format(fit_date.strftime("%d%b%Y"))) # * Note: 2020-09-09 won't work (for some reason) # read in microdistancing survey data surveys = pd.DataFrame() path = "data/md/Barometer wave*.csv" for file in glob.glob(path): surveys = surveys.append(pd.read_csv(file, parse_dates=["date"])) surveys = surveys.sort_values(by="date") print("Latest Microdistancing survey is {}".format(surveys.date.values[-1])) surveys["state"] = surveys["state"].map(states_initials).fillna(surveys["state"]) surveys["proportion"] = surveys["count"] / surveys.respondents surveys.date = pd.to_datetime(surveys.date) always = surveys.loc[surveys.response == "Always"].set_index(["state", "date"]) always = always.unstack(["state"]) # If you get an error here saying 'cannot create a new series when the index is not unique', # then you have a duplicated md file. idx = pd.date_range("2020-03-01", pd.to_datetime("today")) always = always.reindex(idx, fill_value=np.nan) always.index.name = "date" # fill back to earlier and between weeks. # Assume survey on day x applies for all days up to x - 6 always = always.fillna(method="bfill") # assume values continue forward if survey hasn't completed always = always.fillna(method="ffill") always = always.stack(["state"]) # Zero out before first survey 20th March always = always.reset_index().set_index("date") always.loc[:"2020-03-20", "count"] = 0 always.loc[:"2020-03-20", "respondents"] = 0 always.loc[:"2020-03-20", "proportion"] = 0 always = always.reset_index().set_index(["state", "date"]) survey_X = pd.pivot_table( data=always, index="date", columns="state", values="proportion" ) survey_counts_base = ( pd.pivot_table(data=always, index="date", columns="state", values="count") .drop(["Australia", "Other"], axis=1) .astype(int) ) survey_respond_base = ( pd.pivot_table(data=always, index="date", columns="state", values="respondents") .drop(["Australia", "Other"], axis=1) .astype(int) ) # read in and process mask wearing data mask_wearing = pd.DataFrame() path = "data/face_coverings/face_covering_*_.csv" for file in glob.glob(path): mask_wearing = mask_wearing.append(pd.read_csv(file, parse_dates=["date"])) mask_wearing = mask_wearing.sort_values(by="date") print("Latest Mask wearing survey is {}".format(mask_wearing.date.values[-1])) mask_wearing["state"] = ( mask_wearing["state"].map(states_initials).fillna(mask_wearing["state"]) ) mask_wearing["proportion"] = mask_wearing["count"] / mask_wearing.respondents mask_wearing.date = pd.to_datetime(mask_wearing.date) mask_wearing_always = mask_wearing.loc[ mask_wearing.face_covering == "Always" ].set_index(["state", "date"]) mask_wearing_always = mask_wearing_always.unstack(["state"]) idx = pd.date_range("2020-03-01", pd.to_datetime("today")) mask_wearing_always = mask_wearing_always.reindex(idx, fill_value=np.nan) mask_wearing_always.index.name = "date" # fill back to earlier and between weeks. # Assume survey on day x applies for all days up to x - 6 mask_wearing_always = mask_wearing_always.fillna(method="bfill") # assume values continue forward if survey hasn't completed mask_wearing_always = mask_wearing_always.fillna(method="ffill") mask_wearing_always = mask_wearing_always.stack(["state"]) # Zero out before first survey 20th March mask_wearing_always = mask_wearing_always.reset_index().set_index("date") mask_wearing_always.loc[:"2020-03-20", "count"] = 0 mask_wearing_always.loc[:"2020-03-20", "respondents"] = 0 mask_wearing_always.loc[:"2020-03-20", "proportion"] = 0 mask_wearing_X = pd.pivot_table( data=mask_wearing_always, index="date", columns="state", values="proportion" ) mask_wearing_counts_base = pd.pivot_table( data=mask_wearing_always, index="date", columns="state", values="count" ).astype(int) mask_wearing_respond_base = pd.pivot_table( data=mask_wearing_always, index="date", columns="state", values="respondents" ).astype(int) df_Reff = pd.read_csv( "results/EpyReff/Reff_delta" + data_date.strftime("%Y-%m-%d") + "tau_4.csv", parse_dates=["INFECTION_DATES"], ) df_Reff["date"] = df_Reff.INFECTION_DATES df_Reff["state"] = df_Reff.STATE df_Reff_omicron = pd.read_csv( "results/EpyReff/Reff_omicron" + data_date.strftime("%Y-%m-%d") + "tau_4.csv", parse_dates=["INFECTION_DATES"], ) df_Reff_omicron["date"] = df_Reff_omicron.INFECTION_DATES df_Reff_omicron["state"] = df_Reff_omicron.STATE # relabel some of the columns to avoid replication in the merged dataframe col_names_replace = { "mean": "mean_omicron", "lower": "lower_omicron", "upper": "upper_omicron", "top": "top_omicron", "bottom": "bottom_omicron", "std": "std_omicron", } df_Reff_omicron.rename(col_names_replace, axis=1, inplace=True) # read in NNDSS/linelist data # If this errors it may be missing a leading zero on the date. df_state = read_in_cases( case_file_date=data_date.strftime("%d%b%Y"), apply_delay_at_read=True, apply_inc_at_read=True, ) # save the case file for convenience df_state.to_csv("results/cases_" + data_date.strftime("%Y-%m-%d") + ".csv") df_Reff = df_Reff.merge( df_state, how="left", left_on=["state", "date"], right_on=["STATE", "date_inferred"], ) # how = left to use Reff days, NNDSS missing dates # merge in the omicron stuff df_Reff = df_Reff.merge( df_Reff_omicron, how="left", left_on=["state", "date"], right_on=["state", "date"], ) df_Reff["rho_moving"] = df_Reff.groupby(["state"])["rho"].transform( lambda x: x.rolling(7, 1).mean() ) # minimum number of 1 # some days have no cases, so need to fillna df_Reff["rho_moving"] = df_Reff.rho_moving.fillna(method="bfill") # counts are already aligned with infection date by subtracting a random incubation period df_Reff["local"] = df_Reff.local.fillna(0) df_Reff["imported"] = df_Reff.imported.fillna(0) ######### Read in Google mobility results ######### sys.path.insert(0, "../") df_google = read_in_google(moving=True, moving_window=7) # df_google = read_in_google(moving=False) df = df_google.merge(df_Reff[[ "date", "state", "mean", "lower", "upper", "top", "bottom", "std", "mean_omicron", "lower_omicron", "upper_omicron", "top_omicron", "bottom_omicron", "std_omicron", "rho", "rho_moving", "local", "imported", ]], on=["date", "state"], how="inner", ) ######### Create useable dataset ######### # ACT and NT not in original estimates, need to extrapolated sorting keeps consistent # with sort in data_by_state # Note that as we now consider the third wave for ACT, we include it in the third # wave fitting only! states_to_fit_all_waves = sorted( ["NSW", "VIC", "QLD", "SA", "WA", "TAS", "ACT", "NT"] ) first_states = sorted(["NSW", "VIC", "QLD", "SA", "WA", "TAS"]) fit_post_March = True ban = "2020-03-20" first_end_date = "2020-03-31" # data for the first wave first_date_range = { "NSW": pd.date_range(start="2020-03-01", end=first_end_date).values, "QLD": pd.date_range(start="2020-03-01", end=first_end_date).values, "SA": pd.date_range(start="2020-03-01", end=first_end_date).values, "TAS": pd.date_range(start="2020-03-01", end=first_end_date).values, "VIC": pd.date_range(start="2020-03-01", end=first_end_date).values, "WA": pd.date_range(start="2020-03-01", end=first_end_date).values, } # Second wave inputs sec_states = sorted([ "NSW", # "VIC", ]) sec_start_date = "2020-06-01" sec_end_date = "2021-01-19" # choose dates for each state for sec wave sec_date_range = { "NSW": pd.date_range(start="2020-06-01", end="2021-01-19").values, # "VIC": pd.date_range(start="2020-06-01", end="2020-10-28").values, } # Third wave inputs third_states = sorted([ "NSW", "VIC", "ACT", "QLD", "SA", "TAS", # "NT", "WA", ]) # Subtract the truncation days to avoid right truncation as we consider infection dates # and not symptom onset dates third_end_date = data_date - pd.Timedelta(days=truncation_days) # choose dates for each state for third wave # Note that as we now consider the third wave for ACT, we include it in # the third wave fitting only! third_date_range = { "ACT": pd.date_range(start="2021-08-15", end=third_end_date).values, "NSW": pd.date_range(start="2021-06-25", end=third_end_date).values, # "NT": pd.date_range(start="2021-12-20", end=third_end_date).values, "QLD": pd.date_range(start="2021-07-30", end=third_end_date).values, "SA": pd.date_range(start="2021-12-10", end=third_end_date).values, "TAS": pd.date_range(start="2021-12-20", end=third_end_date).values, "VIC": pd.date_range(start="2021-07-10", end=third_end_date).values, "WA": pd.date_range(start="2022-01-01", end=third_end_date).values, } fit_mask = df.state.isin(first_states) if fit_post_March: fit_mask = (fit_mask) & (df.date >= start_date) fit_mask = (fit_mask) & (df.date <= first_end_date) second_wave_mask = df.state.isin(sec_states) second_wave_mask = (second_wave_mask) & (df.date >= sec_start_date) second_wave_mask = (second_wave_mask) & (df.date <= sec_end_date) # Add third wave stuff here third_wave_mask = df.state.isin(third_states) third_wave_mask = (third_wave_mask) & (df.date >= third_start_date) third_wave_mask = (third_wave_mask) & (df.date <= third_end_date) predictors = mov_values.copy() # predictors.extend(['driving_7days','transit_7days','walking_7days','pc']) # remove residential to see if it improves fit # predictors.remove("residential_7days") df["post_policy"] = (df.date >= ban).astype(int) dfX = df.loc[fit_mask].sort_values("date") df2X = df.loc[second_wave_mask].sort_values("date") df3X = df.loc[third_wave_mask].sort_values("date") dfX["is_first_wave"] = 0 for state in first_states: dfX.loc[dfX.state == state, "is_first_wave"] = ( dfX.loc[dfX.state == state] .date.isin(first_date_range[state]) .astype(int) .values ) df2X["is_sec_wave"] = 0 for state in sec_states: df2X.loc[df2X.state == state, "is_sec_wave"] = ( df2X.loc[df2X.state == state] .date.isin(sec_date_range[state]) .astype(int) .values ) # used to index what dates are featured in omicron AND third wave omicron_date_range = pd.date_range(start=omicron_start_date, end=third_end_date) df3X["is_third_wave"] = 0 for state in third_states: df3X.loc[df3X.state == state, "is_third_wave"] = ( df3X.loc[df3X.state == state] .date.isin(third_date_range[state]) .astype(int) .values ) # condition on being in third wave AND omicron df3X.loc[df3X.state == state, "is_omicron_wave"] = ( ( df3X.loc[df3X.state == state].date.isin(omicron_date_range) * df3X.loc[df3X.state == state].date.isin(third_date_range[state]) ) .astype(int) .values ) data_by_state = {} sec_data_by_state = {} third_data_by_state = {} for value in ["mean", "std", "local", "imported"]: data_by_state[value] = pd.pivot( dfX[["state", value, "date"]], index="date", columns="state", values=value, ).sort_index(axis="columns") # account for dates pre pre second wave if df2X.loc[df2X.state == sec_states[0]].shape[0] == 0: print("making empty") sec_data_by_state[value] = pd.DataFrame(columns=sec_states).astype(float) else: sec_data_by_state[value] = pd.pivot( df2X[["state", value, "date"]], index="date", columns="state", values=value, ).sort_index(axis="columns") # account for dates pre pre third wave if df3X.loc[df3X.state == third_states[0]].shape[0] == 0: print("making empty") third_data_by_state[value] = pd.DataFrame(columns=third_states).astype( float ) else: third_data_by_state[value] = pd.pivot( df3X[["state", value, "date"]], index="date", columns="state", values=value, ).sort_index(axis="columns") # now add in the summary stats for Omicron Reff for value in ["mean_omicron", "std_omicron"]: if df3X.loc[df3X.state == third_states[0]].shape[0] == 0: print("making empty") third_data_by_state[value] = pd.DataFrame(columns=third_states).astype( float ) else: third_data_by_state[value] = pd.pivot( df3X[["state", value, "date"]], index="date", columns="state", values=value, ).sort_index(axis="columns") # FIRST PHASE mobility_by_state = [] mobility_std_by_state = [] count_by_state = [] respond_by_state = [] mask_wearing_count_by_state = [] mask_wearing_respond_by_state = [] include_in_first_wave = [] # filtering survey responses to dates before this wave fitting survey_respond = survey_respond_base.loc[: dfX.date.values[-1]] survey_counts = survey_counts_base.loc[: dfX.date.values[-1]] mask_wearing_respond = mask_wearing_respond_base.loc[: dfX.date.values[-1]] mask_wearing_counts = mask_wearing_counts_base.loc[: dfX.date.values[-1]] for state in first_states: mobility_by_state.append(dfX.loc[dfX.state == state, predictors].values / 100) mobility_std_by_state.append( dfX.loc[dfX.state == state, [val + "_std" for val in predictors]].values / 100 ) count_by_state.append(survey_counts.loc[start_date:first_end_date, state].values) respond_by_state.append(survey_respond.loc[start_date:first_end_date, state].values) mask_wearing_count_by_state.append( mask_wearing_counts.loc[start_date:first_end_date, state].values ) mask_wearing_respond_by_state.append( mask_wearing_respond.loc[start_date:first_end_date, state].values ) include_in_first_wave.append( dfX.loc[dfX.state == state, "is_first_wave"].values ) # SECOND PHASE sec_mobility_by_state = [] sec_mobility_std_by_state = [] sec_count_by_state = [] sec_respond_by_state = [] sec_mask_wearing_count_by_state = [] sec_mask_wearing_respond_by_state = [] include_in_sec_wave = [] # filtering survey responses to dates before this wave fitting survey_respond = survey_respond_base.loc[: df2X.date.values[-1]] survey_counts = survey_counts_base.loc[: df2X.date.values[-1]] mask_wearing_respond = mask_wearing_respond_base.loc[: df2X.date.values[-1]] mask_wearing_counts = mask_wearing_counts_base.loc[: df2X.date.values[-1]] for state in sec_states: sec_mobility_by_state.append( df2X.loc[df2X.state == state, predictors].values / 100 ) sec_mobility_std_by_state.append( df2X.loc[df2X.state == state, [val + "_std" for val in predictors]].values / 100 ) sec_count_by_state.append( survey_counts.loc[sec_start_date:sec_end_date, state].values ) sec_respond_by_state.append( survey_respond.loc[sec_start_date:sec_end_date, state].values ) sec_mask_wearing_count_by_state.append( mask_wearing_counts.loc[sec_start_date:sec_end_date, state].values ) sec_mask_wearing_respond_by_state.append( mask_wearing_respond.loc[sec_start_date:sec_end_date, state].values ) include_in_sec_wave.append(df2X.loc[df2X.state == state, "is_sec_wave"].values) # THIRD WAVE third_mobility_by_state = [] third_mobility_std_by_state = [] third_count_by_state = [] third_respond_by_state = [] third_mask_wearing_count_by_state = [] third_mask_wearing_respond_by_state = [] include_in_third_wave = [] include_in_omicron_wave = [] # filtering survey responses to dates before this wave fitting survey_respond = survey_respond_base.loc[: df3X.date.values[-1]] survey_counts = survey_counts_base.loc[: df3X.date.values[-1]] mask_wearing_respond = mask_wearing_respond_base.loc[: df3X.date.values[-1]] mask_wearing_counts = mask_wearing_counts_base.loc[: df3X.date.values[-1]] for state in third_states: third_mobility_by_state.append( df3X.loc[df3X.state == state, predictors].values / 100 ) third_mobility_std_by_state.append( df3X.loc[df3X.state == state, [val + "_std" for val in predictors]].values / 100 ) third_count_by_state.append( survey_counts.loc[third_start_date:third_end_date, state].values ) third_respond_by_state.append( survey_respond.loc[third_start_date:third_end_date, state].values ) third_mask_wearing_count_by_state.append( mask_wearing_counts.loc[third_start_date:third_end_date, state].values ) third_mask_wearing_respond_by_state.append( mask_wearing_respond.loc[third_start_date:third_end_date, state].values ) include_in_third_wave.append( df3X.loc[df3X.state == state, "is_third_wave"].values ) include_in_omicron_wave.append( df3X.loc[df3X.state == state, "is_omicron_wave"].values ) # policy boolean flag for after travel ban in each wave policy = dfX.loc[ dfX.state == first_states[0], "post_policy" ] # this is the post ban policy policy_sec_wave = [1] * df2X.loc[df2X.state == sec_states[0]].shape[0] policy_third_wave = [1] * df3X.loc[df3X.state == third_states[0]].shape[0] # read in the vaccination data delta_vaccination_by_state_array = process_vax_data_array( data_date=data_date, third_states=third_states, third_end_date=third_end_date, variant="Delta", print_latest_date_in_ts=True, ) omicron_vaccination_by_state_array = process_vax_data_array( data_date=data_date, third_states=third_states, third_end_date=third_end_date, variant="Omicron", ) # Make state by state arrays state_index = {state: i + 1 for i, state in enumerate(states_to_fit_all_waves)} # dates to apply alpha in the second wave (this won't allow for VIC to be added as # the date_ranges are different) apply_alpha_sec_wave = ( sec_date_range["NSW"] >= pd.to_datetime(alpha_start_date) ).astype(int) omicron_start_day = ( pd.to_datetime(omicron_start_date) - pd.to_datetime(third_start_date) ).days omicron_only_day = ( pd.to_datetime(omicron_only_date) - pd.to_datetime(third_start_date) ).days heterogeneity_start_day = ( pd.to_datetime("2021-08-20") - pd.to_datetime(third_start_date) ).days # number of days we fit the average VE over tau_vax_block_size = 3 # get pop size array pop_size_array = [] for s in states_to_fit_all_waves: pop_size_array.append(pop_sizes[s]) p_detect = get_all_p_detect_old( states=third_states, end_date=third_end_date, num_days=df3X.loc[df3X.state == "NSW"].shape[0], ) df_p_detect = pd.DataFrame(p_detect, columns=third_states) df_p_detect["date"] = third_date_range["NSW"] df_p_detect.to_csv("results/CA_" + data_date.strftime("%Y-%m-%d") + ".csv") # p_detect = get_all_p_detect( # end_date=third_end_date, # num_days=df3X.loc[df3X.state == "NSW"].shape[0], # ) # input data block for stan model input_data = { "j_total": len(states_to_fit_all_waves), "N_first": dfX.loc[dfX.state == first_states[0]].shape[0], "K": len(predictors), "j_first": len(first_states), "Reff": data_by_state["mean"].values, "mob": mobility_by_state, "mob_std": mobility_std_by_state, "sigma2": data_by_state["std"].values ** 2, "policy": policy.values, "local": data_by_state["local"].values, "imported": data_by_state["imported"].values, "N_sec": df2X.loc[df2X.state == sec_states[0]].shape[0], "j_sec": len(sec_states), "Reff_sec": sec_data_by_state["mean"].values, "mob_sec": sec_mobility_by_state, "mob_sec_std": sec_mobility_std_by_state, "sigma2_sec": sec_data_by_state["std"].values ** 2, "policy_sec": policy_sec_wave, "local_sec": sec_data_by_state["local"].values, "imported_sec": sec_data_by_state["imported"].values, "apply_alpha_sec": apply_alpha_sec_wave, "N_third": df3X.loc[df3X.state == "NSW"].shape[0], "j_third": len(third_states), "Reff_third": third_data_by_state["mean"].values, "Reff_omicron": third_data_by_state["mean_omicron"].values, "mob_third": third_mobility_by_state, "mob_third_std": third_mobility_std_by_state, "sigma2_third": third_data_by_state["std"].values ** 2, "sigma2_omicron": third_data_by_state["std_omicron"].values ** 2, "policy_third": policy_third_wave, "local_third": third_data_by_state["local"].values, "imported_third": third_data_by_state["imported"].values, "count_md": count_by_state, "respond_md": respond_by_state, "count_md_sec": sec_count_by_state, "respond_md_sec": sec_respond_by_state, "count_md_third": third_count_by_state, "respond_md_third": third_respond_by_state, "count_masks": mask_wearing_count_by_state, "respond_masks": mask_wearing_respond_by_state, "count_masks_sec": sec_mask_wearing_count_by_state, "respond_masks_sec": sec_mask_wearing_respond_by_state, "count_masks_third": third_mask_wearing_count_by_state, "respond_masks_third": third_mask_wearing_respond_by_state, "map_to_state_index_first": [state_index[state] for state in first_states], "map_to_state_index_sec": [state_index[state] for state in sec_states], "map_to_state_index_third": [state_index[state] for state in third_states], "total_N_p_sec": sum([sum(x) for x in include_in_sec_wave]).item(), "total_N_p_third": sum([sum(x) for x in include_in_third_wave]).item(), "include_in_first": include_in_first_wave, "include_in_sec": include_in_sec_wave, "include_in_third": include_in_third_wave, "pos_starts_sec": np.cumsum([sum(x) for x in include_in_sec_wave]).astype(int).tolist(), "pos_starts_third": np.cumsum( [sum(x) for x in include_in_third_wave] ).astype(int).tolist(), "ve_delta_data": delta_vaccination_by_state_array, "ve_omicron_data": omicron_vaccination_by_state_array, "omicron_start_day": omicron_start_day, "omicron_only_day": omicron_only_day, "include_in_omicron": include_in_omicron_wave, "total_N_p_third_omicron": int(sum([sum(x) for x in include_in_omicron_wave]).item()), "pos_starts_third_omicron": np.cumsum( [sum(x) for x in include_in_omicron_wave] ).astype(int).tolist(), 'tau_vax_block_size': tau_vax_block_size, 'total_N_p_third_blocks': int( sum([int(ceil(sum(x)/tau_vax_block_size)) for x in include_in_third_wave]) ), 'pos_starts_third_blocks': np.cumsum( [int(ceil(sum(x)/tau_vax_block_size)) for x in include_in_third_wave] ).astype(int), 'total_N_p_third_omicron_blocks': int( sum([int(ceil(sum(x)/tau_vax_block_size)) for x in include_in_omicron_wave]) ), 'pos_starts_third_omicron_blocks': np.cumsum( [int(ceil(sum(x)/tau_vax_block_size)) for x in include_in_omicron_wave] ).astype(int), "pop_size_array": pop_size_array, "heterogeneity_start_day": heterogeneity_start_day, "p_detect": p_detect, } # dump the dictionary to a json file with open("results/stan_input_data.pkl", "wb") as f: pickle.dump(input_data, f) return None def run_stan( data_date, num_chains=4, num_samples=1000, num_warmup_samples=500, max_treedepth=12, ): """ Read the input_data.json in and run the stan model. """ data_date = pd.to_datetime(data_date) # read in the input data as a dictionary with open("results/stan_input_data.pkl", "rb") as f: input_data = pickle.load(f) # make results and figs dir figs_dir = ( "figs/stan_fit/stan_fit_" + data_date.strftime("%Y-%m-%d") + "/" ) results_dir = ( "results/" + data_date.strftime("%Y-%m-%d") + "/" ) os.makedirs(figs_dir, exist_ok=True) os.makedirs(results_dir, exist_ok=True) # path to the stan model # basic model with a switchover between Reffs # rho_model_gamma = "TP_model/fit_and_forecast/stan_models/TP_switchover.stan" # mixture model with basic susceptible depletion # rho_model_gamma = "TP_model/fit_and_forecast/stan_models/TP_gamma_mix.stan" # model that has a switchover but incorporates a waning in infection acquired immunity rho_model_gamma = "TP_model/fit_and_forecast/stan_models/TP_switchover_waning_infection.stan" # model that incorporates a waning in infection acquired immunity but is coded as a mixture # rho_model_gamma = "TP_model/fit_and_forecast/stan_models/TP_gamma_mix_waning_infection.stan" # model that has a switchover but incorporates a waning in infection acquired immunity # rho_model_gamma = "TP_model/fit_and_forecast/stan_models/TP_switchover_waning_infection_single_md.stan" # compile the stan model model = CmdStanModel(stan_file=rho_model_gamma) # obtain a posterior sample from the model conditioned on the data fit = model.sample( chains=num_chains, iter_warmup=num_warmup_samples, iter_sampling=num_samples, data=input_data, max_treedepth=max_treedepth, refresh=10 ) # display convergence diagnostics for the current run print("===========") print(fit.diagnose()) print("===========") # save output file to fit.save_csvfiles(dir=results_dir) df_fit = fit.draws_pd() df_fit.to_csv( results_dir + "posterior_sample_" + data_date.strftime("%Y-%m-%d") + ".csv" ) # output a set of diagnostics filename = ( figs_dir + "fit_summary_all_parameters" + data_date.strftime("%Y-%m-%d") + ".csv" ) # save a summary file for all parameters; this involves ESS and ESS/s as well as summary stats fit_summary = fit.summary() fit_summary.to_csv(filename) # now save a small summary to easily view key parameters pars_of_interest = ["bet[" + str(i + 1) + "]" for i in range(5)] pars_of_interest = pars_of_interest + ["R_Li[" + str(i + 1) + "]" for i in range(8)] pars_of_interest = pars_of_interest + [ "R_I", "R_L", "theta_md", "theta_masks", "sig", "voc_effect_alpha", "voc_effect_delta", "voc_effect_omicron", ] pars_of_interest = pars_of_interest + [ col for col in df_fit if "phi" in col and "simplex" not in col ] # save a summary for ease of viewing # output a set of diagnostics filename = ( figs_dir + "fit_summary_main_parameters" + data_date.strftime("%Y-%m-%d") + ".csv" ) fit_summary.loc[pars_of_interest].to_csv(filename) return None def plot_and_save_posterior_samples(data_date): """ Runs the full suite of plotting. """ data_date = pd.to_datetime(data_date) # Define data date figs_dir = ( "figs/stan_fit/stan_fit_" + data_date.strftime("%Y-%m-%d") + "/" ) # read in the posterior sample samples_mov_gamma = pd.read_csv( "results/" + data_date.strftime("%Y-%m-%d") + "/posterior_sample_" + data_date.strftime("%Y-%m-%d") + ".csv" ) # * Note: 2020-09-09 won't work (for some reason) ######### Read in microdistancing (md) surveys ######### surveys = pd.DataFrame() path = "data/md/Barometer wave*.csv" for file in glob.glob(path): surveys = surveys.append(pd.read_csv(file, parse_dates=["date"])) surveys = surveys.sort_values(by="date") print("Latest Microdistancing survey is {}".format(surveys.date.values[-1])) surveys["state"] = surveys["state"].map(states_initials).fillna(surveys["state"]) surveys["proportion"] = surveys["count"] / surveys.respondents surveys.date = pd.to_datetime(surveys.date) always = surveys.loc[surveys.response == "Always"].set_index(["state", "date"]) always = always.unstack(["state"]) # If you get an error here saying 'cannot create a new series when the index is not unique', # then you have a duplicated md file. idx = pd.date_range("2020-03-01", pd.to_datetime("today")) always = always.reindex(idx, fill_value=np.nan) always.index.name = "date" # fill back to earlier and between weeks. # Assume survey on day x applies for all days up to x - 6 always = always.fillna(method="bfill") # assume values continue forward if survey hasn't completed always = always.fillna(method="ffill") always = always.stack(["state"]) # Zero out before first survey 20th March always = always.reset_index().set_index("date") always.loc[:"2020-03-20", "count"] = 0 always.loc[:"2020-03-20", "respondents"] = 0 always.loc[:"2020-03-20", "proportion"] = 0 always = always.reset_index().set_index(["state", "date"]) survey_X = pd.pivot_table( data=always, index="date", columns="state", values="proportion" ) survey_counts_base = ( pd.pivot_table(data=always, index="date", columns="state", values="count") .drop(["Australia", "Other"], axis=1) .astype(int) ) survey_respond_base = ( pd.pivot_table(data=always, index="date", columns="state", values="respondents") .drop(["Australia", "Other"], axis=1) .astype(int) ) ## read in and process mask wearing data mask_wearing = pd.DataFrame() path = "data/face_coverings/face_covering_*_.csv" for file in glob.glob(path): mask_wearing = mask_wearing.append(pd.read_csv(file, parse_dates=["date"])) mask_wearing = mask_wearing.sort_values(by="date") print("Latest Mask wearing survey is {}".format(mask_wearing.date.values[-1])) mask_wearing["state"] = ( mask_wearing["state"].map(states_initials).fillna(mask_wearing["state"]) ) mask_wearing["proportion"] = mask_wearing["count"] / mask_wearing.respondents mask_wearing.date = pd.to_datetime(mask_wearing.date) mask_wearing_always = mask_wearing.loc[ mask_wearing.face_covering == "Always" ].set_index(["state", "date"]) mask_wearing_always = mask_wearing_always.unstack(["state"]) idx = pd.date_range("2020-03-01", pd.to_datetime("today")) mask_wearing_always = mask_wearing_always.reindex(idx, fill_value=np.nan) mask_wearing_always.index.name = "date" # fill back to earlier and between weeks. # Assume survey on day x applies for all days up to x - 6 mask_wearing_always = mask_wearing_always.fillna(method="bfill") # assume values continue forward if survey hasn't completed mask_wearing_always = mask_wearing_always.fillna(method="ffill") mask_wearing_always = mask_wearing_always.stack(["state"]) # Zero out before first survey 20th March mask_wearing_always = mask_wearing_always.reset_index().set_index("date") mask_wearing_always.loc[:"2020-03-20", "count"] = 0 mask_wearing_always.loc[:"2020-03-20", "respondents"] = 0 mask_wearing_always.loc[:"2020-03-20", "proportion"] = 0 mask_wearing_X = pd.pivot_table( data=mask_wearing_always, index="date", columns="state", values="proportion" ) mask_wearing_counts_base = pd.pivot_table( data=mask_wearing_always, index="date", columns="state", values="count" ).astype(int) mask_wearing_respond_base = pd.pivot_table( data=mask_wearing_always, index="date", columns="state", values="respondents" ).astype(int) df_Reff = pd.read_csv( "results/EpyReff/Reff_delta" + data_date.strftime("%Y-%m-%d") + "tau_4.csv", parse_dates=["INFECTION_DATES"], ) df_Reff["date"] = df_Reff.INFECTION_DATES df_Reff["state"] = df_Reff.STATE df_Reff_omicron = pd.read_csv( "results/EpyReff/Reff_omicron" + data_date.strftime("%Y-%m-%d") + "tau_4.csv", parse_dates=["INFECTION_DATES"], ) df_Reff_omicron["date"] = df_Reff_omicron.INFECTION_DATES df_Reff_omicron["state"] = df_Reff_omicron.STATE # relabel some of the columns to avoid replication in the merged dataframe col_names_replace = { "mean": "mean_omicron", "lower": "lower_omicron", "upper": "upper_omicron", "top": "top_omicron", "bottom": "bottom_omicron", "std": "std_omicron", } df_Reff_omicron.rename(col_names_replace, axis=1, inplace=True) # read in NNDSS/linelist data # If this errors it may be missing a leading zero on the date. df_state = read_in_cases( case_file_date=data_date.strftime("%d%b%Y"), apply_delay_at_read=True, apply_inc_at_read=True, ) df_Reff = df_Reff.merge( df_state, how="left", left_on=["state", "date"], right_on=["STATE", "date_inferred"], ) # how = left to use Reff days, NNDSS missing dates # merge in the omicron stuff df_Reff = df_Reff.merge( df_Reff_omicron, how="left", left_on=["state", "date"], right_on=["state", "date"], ) df_Reff["rho_moving"] = df_Reff.groupby(["state"])["rho"].transform( lambda x: x.rolling(7, 1).mean() ) # minimum number of 1 # some days have no cases, so need to fillna df_Reff["rho_moving"] = df_Reff.rho_moving.fillna(method="bfill") # counts are already aligned with infection date by subtracting a random incubation period df_Reff["local"] = df_Reff.local.fillna(0) df_Reff["imported"] = df_Reff.imported.fillna(0) ######### Read in Google mobility results ######### sys.path.insert(0, "../") df_google = read_in_google(moving=True) df = df_google.merge( df_Reff[ [ "date", "state", "mean", "lower", "upper", "top", "bottom", "std", "mean_omicron", "lower_omicron", "upper_omicron", "top_omicron", "bottom_omicron", "std_omicron", "rho", "rho_moving", "local", "imported", ] ], on=["date", "state"], how="inner", ) # ACT and NT not in original estimates, need to extrapolated sorting keeps consistent # with sort in data_by_state # Note that as we now consider the third wave for ACT, we include it in the third # wave fitting only! states_to_fit_all_waves = sorted( ["NSW", "VIC", "QLD", "SA", "WA", "TAS", "ACT", "NT"] ) first_states = sorted(["NSW", "VIC", "QLD", "SA", "WA", "TAS"]) fit_post_March = True ban = "2020-03-20" first_end_date = "2020-03-31" # data for the first wave first_date_range = { "NSW": pd.date_range(start="2020-03-01", end=first_end_date).values, "QLD": pd.date_range(start="2020-03-01", end=first_end_date).values, "SA": pd.date_range(start="2020-03-01", end=first_end_date).values, "TAS": pd.date_range(start="2020-03-01", end=first_end_date).values, "VIC": pd.date_range(start="2020-03-01", end=first_end_date).values, "WA": pd.date_range(start="2020-03-01", end=first_end_date).values, } # Second wave inputs sec_states = sorted([ 'NSW', # 'VIC', ]) sec_start_date = "2020-06-01" sec_end_date = "2021-01-19" # choose dates for each state for sec wave sec_date_range = { "NSW": pd.date_range(start="2020-06-01", end="2021-01-19").values, # "VIC": pd.date_range(start="2020-06-01", end="2020-10-28").values, } # Third wave inputs third_states = sorted([ "NSW", "VIC", "ACT", "QLD", "SA", "TAS", # "NT", "WA", ]) # Subtract the truncation days to avoid right truncation as we consider infection dates # and not symptom onset dates third_end_date = data_date - pd.Timedelta(days=truncation_days) # choose dates for each state for third wave # Note that as we now consider the third wave for ACT, we include it in # the third wave fitting only! third_date_range = { "ACT": pd.date_range(start="2021-08-15", end=third_end_date).values, "NSW": pd.date_range(start="2021-06-25", end=third_end_date).values, # "NT": pd.date_range(start="2021-12-20", end=third_end_date).values, "QLD": pd.date_range(start="2021-07-30", end=third_end_date).values, "SA": pd.date_range(start="2021-12-10", end=third_end_date).values, "TAS": pd.date_range(start="2021-12-20", end=third_end_date).values, "VIC": pd.date_range(start="2021-07-10", end=third_end_date).values, "WA": pd.date_range(start="2022-01-01", end=third_end_date).values, } fit_mask = df.state.isin(first_states) if fit_post_March: fit_mask = (fit_mask) & (df.date >= start_date) fit_mask = (fit_mask) & (df.date <= first_end_date) second_wave_mask = df.state.isin(sec_states) second_wave_mask = (second_wave_mask) & (df.date >= sec_start_date) second_wave_mask = (second_wave_mask) & (df.date <= sec_end_date) # Add third wave stuff here third_wave_mask = df.state.isin(third_states) third_wave_mask = (third_wave_mask) & (df.date >= third_start_date) third_wave_mask = (third_wave_mask) & (df.date <= third_end_date) predictors = mov_values.copy() # predictors.extend(['driving_7days','transit_7days','walking_7days','pc']) # remove residential to see if it improves fit # predictors.remove("residential_7days") df["post_policy"] = (df.date >= ban).astype(int) dfX = df.loc[fit_mask].sort_values("date") df2X = df.loc[second_wave_mask].sort_values("date") df3X = df.loc[third_wave_mask].sort_values("date") dfX["is_first_wave"] = 0 for state in first_states: dfX.loc[dfX.state == state, "is_first_wave"] = ( dfX.loc[dfX.state == state] .date.isin(first_date_range[state]) .astype(int) .values ) df2X["is_sec_wave"] = 0 for state in sec_states: df2X.loc[df2X.state == state, "is_sec_wave"] = ( df2X.loc[df2X.state == state] .date.isin(sec_date_range[state]) .astype(int) .values ) # used to index what dates are also featured in omicron omicron_date_range = pd.date_range(start=omicron_start_date, end=third_end_date) df3X["is_third_wave"] = 0 for state in third_states: df3X.loc[df3X.state == state, "is_third_wave"] = ( df3X.loc[df3X.state == state] .date.isin(third_date_range[state]) .astype(int) .values ) # condition on being in third wave AND omicron df3X.loc[df3X.state == state, "is_omicron_wave"] = ( ( df3X.loc[df3X.state == state].date.isin(omicron_date_range) * df3X.loc[df3X.state == state].date.isin(third_date_range[state]) ) .astype(int) .values ) data_by_state = {} sec_data_by_state = {} third_data_by_state = {} for value in ["mean", "std", "local", "imported"]: data_by_state[value] = pd.pivot( dfX[["state", value, "date"]], index="date", columns="state", values=value ).sort_index(axis="columns") # account for dates pre pre second wave if df2X.loc[df2X.state == sec_states[0]].shape[0] == 0: print("making empty") sec_data_by_state[value] = pd.DataFrame(columns=sec_states).astype(float) else: sec_data_by_state[value] = pd.pivot( df2X[["state", value, "date"]], index="date", columns="state", values=value, ).sort_index(axis="columns") # account for dates pre pre third wave if df3X.loc[df3X.state == third_states[0]].shape[0] == 0: print("making empty") third_data_by_state[value] = pd.DataFrame(columns=third_states).astype( float ) else: third_data_by_state[value] = pd.pivot( df3X[["state", value, "date"]], index="date", columns="state", values=value, ).sort_index(axis="columns") # now add in the summary stats for Omicron Reff for value in ["mean_omicron", "std_omicron"]: if df3X.loc[df3X.state == third_states[0]].shape[0] == 0: print("making empty") third_data_by_state[value] = pd.DataFrame(columns=third_states).astype( float ) else: third_data_by_state[value] = pd.pivot( df3X[["state", value, "date"]], index="date", columns="state", values=value, ).sort_index(axis="columns") # FIRST PHASE mobility_by_state = [] mobility_std_by_state = [] count_by_state = [] respond_by_state = [] mask_wearing_count_by_state = [] mask_wearing_respond_by_state = [] include_in_first_wave = [] # filtering survey responses to dates before this wave fitting survey_respond = survey_respond_base.loc[: dfX.date.values[-1]] survey_counts = survey_counts_base.loc[: dfX.date.values[-1]] mask_wearing_respond = mask_wearing_respond_base.loc[: dfX.date.values[-1]] mask_wearing_counts = mask_wearing_counts_base.loc[: dfX.date.values[-1]] for state in first_states: mobility_by_state.append(dfX.loc[dfX.state == state, predictors].values / 100) mobility_std_by_state.append( dfX.loc[dfX.state == state, [val + "_std" for val in predictors]].values / 100 ) count_by_state.append(survey_counts.loc[start_date:first_end_date, state].values) respond_by_state.append(survey_respond.loc[start_date:first_end_date, state].values) mask_wearing_count_by_state.append( mask_wearing_counts.loc[start_date:first_end_date, state].values ) mask_wearing_respond_by_state.append( mask_wearing_respond.loc[start_date:first_end_date, state].values ) include_in_first_wave.append( dfX.loc[dfX.state == state, "is_first_wave"].values ) # SECOND PHASE sec_mobility_by_state = [] sec_mobility_std_by_state = [] sec_count_by_state = [] sec_respond_by_state = [] sec_mask_wearing_count_by_state = [] sec_mask_wearing_respond_by_state = [] include_in_sec_wave = [] # filtering survey responses to dates before this wave fitting survey_respond = survey_respond_base.loc[: df2X.date.values[-1]] survey_counts = survey_counts_base.loc[: df2X.date.values[-1]] mask_wearing_respond = mask_wearing_respond_base.loc[: df2X.date.values[-1]] mask_wearing_counts = mask_wearing_counts_base.loc[: df2X.date.values[-1]] for state in sec_states: sec_mobility_by_state.append( df2X.loc[df2X.state == state, predictors].values / 100 ) sec_mobility_std_by_state.append( df2X.loc[df2X.state == state, [val + "_std" for val in predictors]].values / 100 ) sec_count_by_state.append( survey_counts.loc[sec_start_date:sec_end_date, state].values ) sec_respond_by_state.append( survey_respond.loc[sec_start_date:sec_end_date, state].values ) sec_mask_wearing_count_by_state.append( mask_wearing_counts.loc[sec_start_date:sec_end_date, state].values ) sec_mask_wearing_respond_by_state.append( mask_wearing_respond.loc[sec_start_date:sec_end_date, state].values ) include_in_sec_wave.append(df2X.loc[df2X.state == state, "is_sec_wave"].values) # THIRD WAVE third_mobility_by_state = [] third_mobility_std_by_state = [] third_count_by_state = [] third_respond_by_state = [] third_mask_wearing_count_by_state = [] third_mask_wearing_respond_by_state = [] include_in_third_wave = [] include_in_omicron_wave = [] # filtering survey responses to dates before this wave fitting survey_respond = survey_respond_base.loc[: df3X.date.values[-1]] survey_counts = survey_counts_base.loc[: df3X.date.values[-1]] mask_wearing_respond = mask_wearing_respond_base.loc[: df3X.date.values[-1]] mask_wearing_counts = mask_wearing_counts_base.loc[: df3X.date.values[-1]] for state in third_states: third_mobility_by_state.append( df3X.loc[df3X.state == state, predictors].values / 100 ) third_mobility_std_by_state.append( df3X.loc[df3X.state == state, [val + "_std" for val in predictors]].values / 100 ) third_count_by_state.append( survey_counts.loc[third_start_date:third_end_date, state].values ) third_respond_by_state.append( survey_respond.loc[third_start_date:third_end_date, state].values ) third_mask_wearing_count_by_state.append( mask_wearing_counts.loc[third_start_date:third_end_date, state].values ) third_mask_wearing_respond_by_state.append( mask_wearing_respond.loc[third_start_date:third_end_date, state].values ) include_in_third_wave.append( df3X.loc[df3X.state == state, "is_third_wave"].values ) include_in_omicron_wave.append( df3X.loc[df3X.state == state, "is_omicron_wave"].values ) # Make state by state arrays state_index = {state: i for i, state in enumerate(states_to_fit_all_waves)} # get pop size array pop_size_array = [] for s in states_to_fit_all_waves: pop_size_array.append(pop_sizes[s]) # First phase # rho calculated at data entry if isinstance(df_state.index, pd.MultiIndex): df_state = df_state.reset_index() states = sorted(["NSW", "QLD", "VIC", "TAS", "SA", "WA", "ACT", "NT"]) fig, ax = plt.subplots(figsize=(24, 9), ncols=len(states), sharey=True) states_to_fitd = {state: i + 1 for i, state in enumerate(first_states)} for i, state in enumerate(states): if state in first_states: dates = df_Reff.loc[ (df_Reff.date >= start_date) & (df_Reff.state == state) & (df_Reff.date <= first_end_date) ].date rho_samples = samples_mov_gamma[ [ "brho[" + str(j + 1) + "," + str(states_to_fitd[state]) + "]" for j in range(dfX.loc[dfX.state == first_states[0]].shape[0]) ] ] ax[i].plot(dates, rho_samples.median(), label="fit", color="C0") ax[i].fill_between( dates, rho_samples.quantile(0.25), rho_samples.quantile(0.75), color="C0", alpha=0.4, ) ax[i].fill_between( dates, rho_samples.quantile(0.05), rho_samples.quantile(0.95), color="C0", alpha=0.4, ) else: sns.lineplot( x="date_inferred", y="rho", data=df_state.loc[ (df_state.date_inferred >= start_date) & (df_state.STATE == state) & (df_state.date_inferred <= first_end_date) ], ax=ax[i], color="C1", label="data", ) sns.lineplot( x="date", y="rho", data=df_Reff.loc[ (df_Reff.date >= start_date) & (df_Reff.state == state) & (df_Reff.date <= first_end_date) ], ax=ax[i], color="C1", label="data", ) sns.lineplot( x="date", y="rho_moving", data=df_Reff.loc[ (df_Reff.date >= start_date) & (df_Reff.state == state) & (df_Reff.date <= first_end_date) ], ax=ax[i], color="C2", label="moving", ) dates = dfX.loc[dfX.state == first_states[0]].date ax[i].tick_params("x", rotation=90) ax[i].xaxis.set_major_locator(plt.MaxNLocator(4)) ax[i].set_title(state) ax[0].set_ylabel("Proportion of imported cases") plt.legend() plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "rho_first_phase.png", dpi=144 ) # Second phase if df2X.shape[0] > 0: fig, ax = plt.subplots( figsize=(24, 9), ncols=len(sec_states), sharey=True, squeeze=False ) states_to_fitd = {state: i + 1 for i, state in enumerate(sec_states)} pos = 0 for i, state in enumerate(sec_states): # Google mobility only up to a certain date, so take only up to that value dates = df2X.loc[ (df2X.state == state) & (df2X.is_sec_wave == 1) ].date.values rho_samples = samples_mov_gamma[ [ "brho_sec[" + str(j + 1) + "]" for j in range( pos, pos + df2X.loc[df2X.state == state].is_sec_wave.sum() ) ] ] pos = pos + df2X.loc[df2X.state == state].is_sec_wave.sum() ax[0, i].plot(dates, rho_samples.median(), label="fit", color="C0") ax[0, i].fill_between( dates, rho_samples.quantile(0.25), rho_samples.quantile(0.75), color="C0", alpha=0.4, ) ax[0, i].fill_between( dates, rho_samples.quantile(0.05), rho_samples.quantile(0.95), color="C0", alpha=0.4, ) sns.lineplot( x="date_inferred", y="rho", data=df_state.loc[ (df_state.date_inferred >= sec_start_date) & (df_state.STATE == state) & (df_state.date_inferred <= sec_end_date) ], ax=ax[0, i], color="C1", label="data", ) sns.lineplot( x="date", y="rho", data=df_Reff.loc[ (df_Reff.date >= sec_start_date) & (df_Reff.state == state) & (df_Reff.date <= sec_end_date) ], ax=ax[0, i], color="C1", label="data", ) sns.lineplot( x="date", y="rho_moving", data=df_Reff.loc[ (df_Reff.date >= sec_start_date) & (df_Reff.state == state) & (df_Reff.date <= sec_end_date) ], ax=ax[0, i], color="C2", label="moving", ) dates = dfX.loc[dfX.state == sec_states[0]].date ax[0, i].tick_params("x", rotation=90) ax[0, i].xaxis.set_major_locator(plt.MaxNLocator(4)) ax[0, i].set_title(state) ax[0, 0].set_ylabel("Proportion of imported cases") plt.legend() plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "rho_sec_phase.png", dpi=144 ) df_rho_third_all_states = pd.DataFrame() df_rho_third_tmp = pd.DataFrame() # Third phase if df3X.shape[0] > 0: fig, ax = plt.subplots( figsize=(9, 24), nrows=len(third_states), sharex=True, squeeze=False ) states_to_fitd = {state: i + 1 for i, state in enumerate(third_states)} pos = 0 for i, state in enumerate(third_states): # Google mobility only up to a certain date, so take only up to that value dates = df3X.loc[ (df3X.state == state) & (df3X.is_third_wave == 1) ].date.values rho_samples = samples_mov_gamma[ [ "brho_third[" + str(j + 1) + "]" for j in range( pos, pos + df3X.loc[df3X.state == state].is_third_wave.sum() ) ] ] pos = pos + df3X.loc[df3X.state == state].is_third_wave.sum() df_rho_third_tmp = rho_samples.T df_rho_third_tmp["date"] = dates df_rho_third_tmp["state"] = state df_rho_third_all_states = pd.concat([df_rho_third_all_states, df_rho_third_tmp]) ax[i, 0].plot(dates, rho_samples.median(), label="fit", color="C0") ax[i, 0].fill_between( dates, rho_samples.quantile(0.25), rho_samples.quantile(0.75), color="C0", alpha=0.4, ) ax[i, 0].fill_between( dates, rho_samples.quantile(0.05), rho_samples.quantile(0.95), color="C0", alpha=0.4, ) sns.lineplot( x="date_inferred", y="rho", data=df_state.loc[ (df_state.date_inferred >= third_start_date) & (df_state.STATE == state) & (df_state.date_inferred <= third_end_date) ], ax=ax[i, 0], color="C1", label="data", ) sns.lineplot( x="date", y="rho", data=df_Reff.loc[ (df_Reff.date >= third_start_date) & (df_Reff.state == state) & (df_Reff.date <= third_end_date) ], ax=ax[i, 0], color="C1", label="data", ) sns.lineplot( x="date", y="rho_moving", data=df_Reff.loc[ (df_Reff.date >= third_start_date) & (df_Reff.state == state) & (df_Reff.date <= third_end_date) ], ax=ax[i, 0], color="C2", label="moving", ) dates = dfX.loc[dfX.state == third_states[0]].date ax[i, 0].tick_params("x", rotation=90) ax[i, 0].xaxis.set_major_locator(plt.MaxNLocator(4)) ax[i, 0].set_title(state) ax[i, 0].set_ylabel("Proportion of imported cases") plt.legend() plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "rho_third_phase.png", dpi=144, ) df_rho_third_all_states.to_csv( "results/" + data_date.strftime("%Y-%m-%d") + "/rho_samples" + data_date.strftime("%Y-%m-%d") + ".csv" ) # plotting fig, ax = plt.subplots(figsize=(12, 9)) # sample from the priors for RL and RI samples_mov_gamma["R_L_prior"] = np.random.gamma( 1.8 * 1.8 / 0.05, 0.05 / 1.8, size=samples_mov_gamma.shape[0] ) samples_mov_gamma["R_I_prior"] = np.random.gamma( 0.5 ** 2 / 0.2, 0.2 / 0.5, size=samples_mov_gamma.shape[0] ) samples_mov_gamma["R_L_national"] = np.random.gamma( samples_mov_gamma.R_L.values ** 2 / samples_mov_gamma.sig.values, samples_mov_gamma.sig.values / samples_mov_gamma.R_L.values, ) sns.violinplot( x="variable", y="value", data=pd.melt( samples_mov_gamma[[ col for col in samples_mov_gamma if "R" in col and col not in ("R_I0", "R_I0_omicron") ]] ), ax=ax, cut=0, ) ax.set_yticks( [1], minor=True, ) ax.set_yticks([0, 2, 3], minor=False) ax.set_yticklabels([0, 2, 3], minor=False) ax.set_ylim((0, 3)) # state labels in alphabetical ax.set_xticklabels( [ "R_I", "R_I_omicron", "R_L0 mean", "R_L0 ACT", "R_L0 NSW", "R_L0 NT", "R_L0 QLD", "R_L0 SA", "R_L0 TAS", "R_L0 VIC", "R_L0 WA", "R_L0 prior", "R_I prior", "R_L0 national", ] ) ax.set_xlabel("") ax.set_ylabel("Effective reproduction number") ax.tick_params("x", rotation=90) ax.yaxis.grid(which="minor", linestyle="--", color="black", linewidth=2) plt.tight_layout() plt.savefig(figs_dir + data_date.strftime("%Y-%m-%d") + "R_priors.png", dpi=144) # Making a new figure that doesn't include the priors fig, ax = plt.subplots(figsize=(12, 9)) small_plot_cols = ["R_Li[" + str(i) + "]" for i in range(1, 9)] + ["R_I"] sns.violinplot( x="variable", y="value", data=pd.melt(samples_mov_gamma[small_plot_cols]), ax=ax, cut=0, ) ax.set_yticks( [1], minor=True, ) ax.set_yticks([0, 2, 3], minor=False) ax.set_yticklabels([0, 2, 3], minor=False) ax.set_ylim((0, 3)) # state labels in alphabetical ax.set_xticklabels( [ "$R_L0$ ACT", "$R_L0$ NSW", "$R_L0$ NT", "$R_L0$ QLD", "$R_L0$ SA", "$R_L0$ TAS", "$R_L0$ VIC", "$R_L0$ WA", "$R_I$", ] ) ax.tick_params("x", rotation=90) ax.set_xlabel("") ax.set_ylabel("Effective reproduction number") ax.yaxis.grid(which="minor", linestyle="--", color="black", linewidth=2) plt.tight_layout() plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "R_priors_(without_priors).png", dpi=288, ) # Making a new figure that doesn't include the priors fig, ax = plt.subplots(figsize=(12, 9)) samples_mov_gamma["voc_effect_third_prior"] = np.random.gamma( 1.5 * 1.5 / 0.05, 0.05 / 1.5, size=samples_mov_gamma.shape[0] ) small_plot_cols = [ "voc_effect_third_prior", "voc_effect_delta", "voc_effect_omicron", ] sns.violinplot( x="variable", y="value", data=pd.melt(samples_mov_gamma[small_plot_cols]), ax=ax, cut=0, ) ax.set_yticks([1], minor=True) # ax.set_yticks([0, 0.5, 1, 1.5, 2, 2.5, 3], minor=False) # ax.set_yticklabels([0, 0.5, 1, 1.5, 2, 2.5, 3], minor=False) # ax.set_ylim((0, 1)) # state labels in alphabetical ax.set_xticklabels(["VoC (prior)", "VoC (Delta)", "VoC (Omicron)"]) # ax.tick_params('x', rotation=90) ax.set_xlabel("") ax.set_ylabel("value") ax.yaxis.grid(which="minor", linestyle="--", color="black", linewidth=2) plt.tight_layout() plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "voc_effect_posteriors.png", dpi=288, ) posterior = samples_mov_gamma[["bet[" + str(i + 1) + "]" for i in range(len(predictors))]] split = True md = "power" # samples_mov_gamma.md.values posterior.columns = [val for val in predictors] long = pd.melt(posterior) fig, ax2 = plt.subplots(figsize=(12, 9)) ax2 = sns.violinplot(x="variable", y="value", data=long, ax=ax2, color="C0") ax2.plot([0] * len(predictors), linestyle="dashed", alpha=0.6, color="grey") ax2.tick_params(axis="x", rotation=90) ax2.set_title("Coefficients of mobility indices") ax2.set_xlabel("Social mobility index") ax2.set_xticklabels([var[:-6] for var in predictors]) ax2.set_xticklabels( [ "Retail and Recreation", "Grocery and Pharmacy", "Parks", "Transit Stations", "Workplaces", "Residential", ] ) ax2.tick_params("x", rotation=15) plt.tight_layout() plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "mobility_posteriors.png", dpi=288, ) # plot the TP's RL_by_state = { state: samples_mov_gamma["R_Li[" + str(i + 1) + "]"].values for state, i in state_index.items() } ax3 = predict_plot( samples_mov_gamma, df.loc[(df.date >= start_date) & (df.date <= first_end_date)], moving=True, grocery=True, rho=first_states, ) for ax in ax3: for a in ax: a.set_ylim((0, 2.5)) a.set_xlim((pd.to_datetime(start_date), pd.to_datetime(first_end_date))) plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "Reff_first_phase.png", dpi=144, ) if df2X.shape[0] > 0: df["is_sec_wave"] = 0 for state in sec_states: df.loc[df.state == state, "is_sec_wave"] = ( df.loc[df.state == state] .date.isin(sec_date_range[state]) .astype(int) .values ) # plot only if there is second phase data - have to have second_phase=True ax4 = predict_plot( samples_mov_gamma, df.loc[(df.date >= sec_start_date) & (df.date <= sec_end_date)], moving=True, grocery=True, rho=sec_states, second_phase=True, ) for ax in ax4: for a in ax: a.set_ylim((0, 2.5)) plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "Reff_sec_phase.png", dpi=144 ) # remove plots from memory fig.clear() plt.close(fig) # Load in vaccination data by state and date vaccination_by_state = pd.read_csv( "data/vaccine_effect_timeseries_" + data_date.strftime("%Y-%m-%d") + ".csv", parse_dates=["date"], ) # there are a couple NA's early on in the time series but is likely due to slightly # different start dates vaccination_by_state.fillna(1, inplace=True) # we take the whole set of estimates up to the end of the forecast period # (with 10 days padding which won't be used in the forecast) vaccination_by_state = vaccination_by_state[ ( vaccination_by_state.date >= pd.to_datetime(third_start_date) - timedelta(days=1) ) & ( vaccination_by_state.date <= pd.to_datetime(data_date) + timedelta(days=num_forecast_days + 10) ) ] vaccination_by_state_delta = vaccination_by_state.loc[ vaccination_by_state["variant"] == "Delta" ][["state", "date", "effect"]] vaccination_by_state_omicron = vaccination_by_state.loc[ vaccination_by_state["variant"] == "Omicron" ][["state", "date", "effect"]] vaccination_by_state_delta = vaccination_by_state_delta.pivot( index="state", columns="date", values="effect" ) # Convert to matrix form vaccination_by_state_omicron = vaccination_by_state_omicron.pivot( index="state", columns="date", values="effect" ) # Convert to matrix form # If we are missing recent vaccination data, fill it in with the most recent available data. latest_vacc_data = vaccination_by_state_omicron.columns[-1] if latest_vacc_data < pd.to_datetime(third_end_date): vaccination_by_state_delta = pd.concat( [vaccination_by_state_delta] + [ pd.Series(vaccination_by_state_delta[latest_vacc_data], name=day) for day in pd.date_range(start=latest_vacc_data, end=third_end_date) ], axis=1, ) vaccination_by_state_omicron = pd.concat( [vaccination_by_state_omicron] + [ pd.Series(vaccination_by_state_omicron[latest_vacc_data], name=day) for day in pd.date_range(start=latest_vacc_data, end=third_end_date) ], axis=1, ) # get the dates for vaccination dates = vaccination_by_state_delta.columns third_days = {k: v.shape[0] for (k, v) in third_date_range.items()} third_days_cumulative = np.append([0], np.cumsum([v for v in third_days.values()])) delta_ve_idx_ranges = { k: range(third_days_cumulative[i], third_days_cumulative[i + 1]) for (i, k) in enumerate(third_days.keys()) } third_days_tot = sum(v for v in third_days.values()) # construct a range of dates for omicron which starts at the maximum of the start date # for that state or the Omicron start date third_omicron_date_range = { k: pd.date_range( start=max(v[0], pd.to_datetime(omicron_start_date)), end=v[-1] ).values for (k, v) in third_date_range.items() } third_omicron_days = {k: v.shape[0] for (k, v) in third_omicron_date_range.items()} third_omicron_days_cumulative = np.append( [0], np.cumsum([v for v in third_omicron_days.values()]) ) omicron_ve_idx_ranges = { k: range(third_omicron_days_cumulative[i], third_omicron_days_cumulative[i + 1]) for (i, k) in enumerate(third_omicron_days.keys()) } third_omicron_days_tot = sum(v for v in third_omicron_days.values()) # extrac the samples delta_ve_samples = samples_mov_gamma[ ["ve_delta[" + str(j + 1) + "]" for j in range(third_days_tot)] ].T omicron_ve_samples = samples_mov_gamma[ ["ve_omicron[" + str(j + 1) + "]" for j in range(third_omicron_days_tot)] ].T # now we plot and save the adjusted ve time series to be read in by the forecasting plot_adjusted_ve( data_date, samples_mov_gamma, states, vaccination_by_state_delta, third_states, third_date_range, delta_ve_samples, delta_ve_idx_ranges, figs_dir, "delta", ) plot_adjusted_ve( data_date, samples_mov_gamma, states, vaccination_by_state_omicron, third_states, third_omicron_date_range, omicron_ve_samples, omicron_ve_idx_ranges, figs_dir, "omicron", ) if df3X.shape[0] > 0: df["is_third_wave"] = 0 for state in third_states: df.loc[df.state == state, "is_third_wave"] = ( df.loc[df.state == state] .date.isin(third_date_range[state]) .astype(int) .values ) # plot only if there is third phase data - have to have third_phase=True ax4 = macro_factor_plots( samples_mov_gamma, df.loc[(df.date >= third_start_date) & (df.date <= third_end_date)], ) # by states.... for ax in ax4: for a in ax: a.set_ylim((0, 1.25)) # a.set_xlim((start_date,end_date)) plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "macro_factor_comp.png", dpi=144, ) # remove plots from memory fig.clear() plt.close(fig) df["is_third_wave"] = 0 for state in third_states: df.loc[df.state == state, "is_third_wave"] = ( df.loc[df.state == state] .date.isin(third_date_range[state]) .astype(int) .values ) # plot only if there is third phase data - have to have third_phase=True ax4 = predict_plot( samples_mov_gamma, df.loc[(df.date >= third_start_date) & (df.date <= third_end_date)], moving=True, grocery=True, rho=third_states, third_phase=True, ) # by states.... for ax in ax4: for a in ax: a.set_ylim((0, 2.5)) # a.set_xlim((start_date,end_date)) plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "Reff_third_phase_combined.png", dpi=144, ) # remove plots from memory fig.clear() plt.close(fig) # plot only if there is third phase data - have to have third_phase=True ax4 = predict_plot( samples_mov_gamma, df.loc[(df.date >= third_start_date) & (df.date <= third_end_date)], moving=True, grocery=True, rho=third_states, third_phase=True, third_plot_type="delta" ) # by states.... for ax in ax4: for a in ax: a.set_ylim((0, 2.5)) # a.set_xlim((start_date,end_date)) plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "Reff_third_phase_delta.png", dpi=144, ) # remove plots from memory fig.clear() plt.close(fig) for param in ("micro", "macro", "susceptibility"): # plot only if there is third phase data - have to have third_phase=True ax4 = predict_multiplier_plot( samples_mov_gamma, df.loc[(df.date >= third_start_date) & (df.date <= third_end_date)], param=param, ) # by states.... for ax in ax4: for a in ax: if param == "macro": a.set_ylim((0, 1.25)) else: a.set_ylim((0, 1.1)) plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + param + "_factor.png", dpi=144, ) # remove plots from memory fig.clear() plt.close(fig) if df3X.shape[0] > 0: df["is_omicron_wave"] = 0 for state in third_states: df.loc[df.state == state, "is_omicron_wave"] = ( df.loc[df.state == state] .date.isin(third_omicron_date_range[state]) .astype(int) .values ) # plot only if there is third phase data - have to have third_phase=True ax4 = predict_plot( samples_mov_gamma, df.loc[(df.date >= omicron_start_date) & (df.date <= third_end_date)], moving=True, grocery=True, rho=third_states, third_phase=True, third_plot_type="omicron" ) # by states.... for ax in ax4: for a in ax: a.set_ylim((0, 2.5)) # a.set_xlim((start_date,end_date)) plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "Reff_third_phase_omicron.png", dpi=144, ) # remove plots from memory fig.clear() plt.close(fig) # plot the omicron proportion # create a range of dates from the beginning of Omicron to use for producing the Omicron # proportion omicron_date_range = pd.date_range( omicron_start_date, pd.to_datetime(data_date) + timedelta(45) ) prop_omicron_to_delta = np.array([]) # create array of times to plot against t = np.tile(range(len(omicron_date_range)), (samples_mov_gamma.shape[0], 1)).T fig, ax = plt.subplots(figsize=(15, 12), nrows=4, ncols=2, sharex=True, sharey=True) for (i, state) in enumerate(third_states): m0 = np.tile(samples_mov_gamma.loc[:, "m0[" + str(i + 1) + "]"], (len(omicron_date_range), 1)) m1 = np.tile(samples_mov_gamma.loc[:, "m1[" + str(i + 1) + "]"], (len(omicron_date_range), 1)) # m1 = 1.0 r = np.tile(samples_mov_gamma.loc[:, "r[" + str(i + 1) + "]"], (len(omicron_date_range), 1)) tau = np.tile(samples_mov_gamma.loc[:, "tau[" + str(i + 1) + "]"] , (len(omicron_date_range), 1)) omicron_start_date_tmp = max( pd.to_datetime(omicron_start_date), third_date_range[state][0] ) omicron_date_range_tmp = pd.date_range( omicron_start_date_tmp, third_date_range[state][-1] ) # if state in {"TAS", "WA", "NT"}: # prop_omicron_to_delta_tmp = m1 # else: # prop_omicron_to_delta_tmp = m0 + (m1 - m0) / (1 + np.exp(-r * (t - tau))) prop_omicron_to_delta_tmp = m0 + (m1 - m0) / (1 + np.exp(-r * (t - tau))) ax[i // 2, i % 2].plot( omicron_date_range, np.median(prop_omicron_to_delta_tmp, axis=1), ) ax[i // 2, i % 2].fill_between( omicron_date_range, np.quantile(prop_omicron_to_delta_tmp, 0.05, axis=1), np.quantile(prop_omicron_to_delta_tmp, 0.95, axis=1), alpha=0.2, ) ax[i // 2, i % 2].axvline( omicron_date_range_tmp[0], ls="--", c="k", lw=1 ) ax[i // 2, i % 2].axvline( omicron_date_range_tmp[-1], ls="--", c="k", lw=1 ) ax[i // 2, i % 2].set_title(state) ax[i // 2, i % 2].xaxis.set_major_locator(plt.MaxNLocator(3)) ax[i // 2, 0].set_ylabel("Proportion of Omicron\ncases to Delta") if len(prop_omicron_to_delta) == 0: prop_omicron_to_delta = prop_omicron_to_delta_tmp[:, -len(omicron_date_range_tmp):] else: prop_omicron_to_delta = np.hstack( ( prop_omicron_to_delta, prop_omicron_to_delta_tmp[:, -len(omicron_date_range_tmp):], ) ) fig.tight_layout() plt.savefig( figs_dir + data_date.strftime("%Y-%m-%d") + "omicron_proportion.png", dpi=144 ) # need to rotate to put into a good format prop_omicron_to_delta = prop_omicron_to_delta.T df_prop_omicron_to_delta = pd.DataFrame( prop_omicron_to_delta, columns=[ "prop_omicron_to_delta." + str(i+1) for i in range(prop_omicron_to_delta.shape[1]) ] ) df_prop_omicron_to_delta.to_csv( "results/" + data_date.strftime("%Y-%m-%d") + "/prop_omicron_to_delta" + data_date.strftime("%Y-%m-%d") + ".csv" ) # saving the final processed posterior samples to h5 for generate_RL_forecasts.py var_to_csv = predictors samples_mov_gamma[predictors] = samples_mov_gamma[ ["bet[" + str(i + 1) + "]" for i in range(len(predictors))] ] # var_to_csv = [ # "R_I", # "R_I_omicron", # "R_L", # "sig", # "theta_masks", # "theta_md", # "voc_effect_alpha", # "voc_effect_delta", # "voc_effect_omicron", # "sus_dep_factor", # ] var_to_csv = [ "R_I", "R_I_omicron", "R_L", "sig", "theta_masks", "theta_md", "voc_effect_alpha", "voc_effect_delta", "voc_effect_omicron", ] var_to_csv = var_to_csv + [col for col in samples_mov_gamma if "phi" in col] var_to_csv = ( var_to_csv + predictors + ["R_Li[" + str(i + 1) + "]" for i in range(len(states_to_fit_all_waves))] ) var_to_csv = var_to_csv + ["ve_delta[" + str(j + 1) + "]" for j in range(third_days_tot)] var_to_csv = var_to_csv + [ "ve_omicron[" + str(j + 1) + "]" for j in range(third_omicron_days_tot) ] var_to_csv = var_to_csv + ["r[" + str(j + 1) + "]" for j in range(len(third_states))] var_to_csv = var_to_csv + ["tau[" + str(j + 1) + "]" for j in range(len(third_states))] var_to_csv = var_to_csv + ["m0[" + str(j + 1) + "]" for j in range(len(third_states))] var_to_csv = var_to_csv + ["m1[" + str(j + 1) + "]" for j in range(len(third_states))] # save the posterior samples_mov_gamma[var_to_csv].to_hdf( "results/" + data_date.strftime("%Y-%m-%d") + "/soc_mob_posterior" + data_date.strftime("%Y-%m-%d") + ".h5", key="samples", ) return None def main(data_date, run_flag=0): """ Runs the stan model in parts to cut down on memory. The run_flag enables us to run components of the model as required and has the following settings: run_flag=0 (default) : Run full inference and plotting procedures. run_flag=1 : Generate the data, save it. run_flag=2 : Using the data from 1, run the inference. run_flag=3 : Run plotting methods. """ if run_flag in (0, 1): get_data_for_posterior(data_date=data_date) if run_flag in (0, 2): num_chains = 4 num_warmup_samples = 500 num_samples = 1000 max_treedepth = 12 run_stan( data_date=data_date, num_chains=num_chains, num_samples=num_samples, num_warmup_samples=num_warmup_samples, max_treedepth=max_treedepth, ) if run_flag in (0, 3): # remove the susceptibility depletion term from Reff for strain in ("Delta", "Omicron"): # remove_sus_from_Reff(strain=strain, data_date=data_date) remove_sus_with_waning_from_Reff(strain=strain, data_date=data_date) plot_and_save_posterior_samples(data_date=data_date) return None if __name__ == "__main__": """ If we are running the script here (which is always) then this ensures things run appropriately. """ data_date = argv[1] try: run_flag = int(argv[2]) except: run_flag = 0 main(data_date, run_flag=run_flag)

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venv/lib/python3.8/site-packages/cachecontrol/__init__.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/cachecontrol/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

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19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/cachecontrol/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

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Python

ramda/split_when_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

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2018-08-06T08:44:58.000Z

2022-03-17T09:49:03.000Z

ramda/split_when_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

28

2019-06-17T11:09:52.000Z

2022-02-18T16:59:21.000Z

ramda/split_when_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

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2019-09-18T09:24:38.000Z

2021-07-21T08:40:23.000Z

from ramda import * from ramda.private.asserts import * def split_when_test(): assert_equal(split_when(equals(2), [1, 2, 3, 1, 2, 3]), [[1], [2, 3, 1, 2, 3]])

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docker_simple_backup/__main__.py

quanturium/docker-simple-backup

95cfcb2d1f74766ef204fc3bc7820305bfb3f57b

[ "MIT" ]

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docker_simple_backup/__main__.py

quanturium/docker-simple-backup

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docker_simple_backup/__main__.py

quanturium/docker-simple-backup

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boot.py

achomgbah/iot-device

f4e298c22ccdb9e265134f963a74b6110c807bd8

[ "Apache-2.0" ]

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boot.py

achomgbah/iot-device

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boot.py

achomgbah/iot-device

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null

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Python

dynamics/refine_prediction.py

dingmyu/VRDP

34c15866708f062a099b8b2cf1175adc9bae69a3

[ "MIT" ]

31

2021-10-30T01:57:11.000Z

2022-03-21T21:34:12.000Z

dynamics/refine_prediction.py

dingmyu/VRDP

34c15866708f062a099b8b2cf1175adc9bae69a3

[ "MIT" ]

2

2022-01-05T07:09:43.000Z

2022-01-06T10:58:12.000Z

dynamics/refine_prediction.py

dingmyu/VRDP

34c15866708f062a099b8b2cf1175adc9bae69a3

[ "MIT" ]

5

2021-12-04T16:07:41.000Z

2022-03-20T18:43:19.000Z

# -*- coding: utf-8 -*- # Author: Mingyu Ding # Time: 1/4/2021 12:44 PM # Copyright 2019. All Rights Reserved. # # 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 sys import time import json import numpy as np import torch from LBFGS import FullBatchLBFGS def get_2d_coor(x3d, y3d, z3d=0.2): cam_mat = np.array(((-207.8461456298828, 525.0000610351562, -120.00001525878906, 1200.0003662109375), (123.93595886230469, 1.832598354667425e-05, -534.663330078125, 799.9999389648438), (-0.866025447845459, -3.650024282819686e-08, -0.4999999701976776, 5.000000476837158), (0, 0, 0, 1))) pos_3d = np.array([[x3d], [y3d], [z3d], [1.0]], dtype=np.float32) uv = cam_mat[:3].dot(pos_3d) pos_2d = uv[:-1] / uv[-1] return pos_2d for process_index in range(int(sys.argv[1]), int(sys.argv[2])): object_dict = json.load(open(f'../data/object_dicts_with_physics/objects_{process_index:05d}.json')) output_dict = json.load(open(f'../data/object_simulated/sim_{process_index:05d}.json')) step_88 = output_dict['step_88'] print(f'===============start processing {process_index}==================') device = 'cpu' n_balls = len(object_dict) steps = 210 target_x = torch.zeros((128, n_balls, 2), dtype=torch.float32).to(device) + 1000 shapes = [] shape_dict = { 'sphere': 0, 'cube': 1, 'cylinder': 2 } for object_index, identity in enumerate(object_dict.keys()): locations = torch.tensor(object_dict[identity]['trajectory']).to(device) target_x[:locations.shape[0], object_index, :] = locations shapes.append(shape_dict[object_dict[identity]['shape']]) target_x = target_x[-40:-19] for object_index, identity in enumerate(object_dict.keys()): if target_x[0][object_index][0] > 500: target_x[0][object_index] = torch.tensor(step_88['x'][object_index]) shape = torch.tensor(shapes, dtype=torch.int8).to(device) angle0 = torch.tensor(step_88['angle'], dtype=torch.float32).to(device) angle0.requires_grad = True interval = 10 dt = 1/350 gravity = 9.806 radius = 0.2 inertia = 0.4 * 0.4 / 6 frictional = torch.tensor(0.03).to(device) frictional.requires_grad = True linear_damping = torch.tensor(0.06).to(device) linear_damping.requires_grad = True v0 = torch.tensor(step_88['v'], dtype=torch.float32).to(device) v0.requires_grad = True restitution = torch.tensor(step_88['restitution'], dtype=torch.float32).to(device) restitution.requires_grad = True mass = torch.tensor(step_88['mass'], dtype=torch.float32).to(device) mass.requires_grad = True def norm(vector, degree=2, dim=0): return torch.norm(vector, degree, dim=dim) def normalized(vector): return vector / norm(vector) def collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions): imp = torch.tensor([0.0, 0.0]).to(device) x_inc_contrib = torch.tensor([0.0, 0.0]).to(device) if i != j: dist = (x[t, i] + dt * v[t, i]) - (x[t, j] + dt * v[t, j]) dist_norm = norm(dist) rela_v = v[t, i] - v[t, j] if dist_norm < 2 * radius: dir = normalized(dist) projected_v = dir.dot(rela_v) if projected_v < 0: if i < j: repeat = False for item in collisions: if json.dumps(item).startswith(json.dumps([i, j])[:-1]): repeat = True if not repeat: collisions.append([i, j, round(t / 10.0)]) imp = -(1 + restitution[i] * restitution[j]) * (mass[j] / (mass[i] + mass[j])) * projected_v * dir toi = (dist_norm - 2 * radius) / min( -1e-3, projected_v) x_inc_contrib = min(toi - dt, 0) * imp x_inc[t + 1, i] += x_inc_contrib impulse[t + 1, i] += imp def sphere_collide_cube(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions): imp = torch.tensor([0.0, 0.0]).to(device) x_inc_contrib = torch.tensor([0.0, 0.0]).to(device) if i != j: rela_v = v[t, i] - v[t, j] pos_xy = x[t, i] - x[t, j] rotate_x = pos_xy.dot(torch.tensor([torch.cos(-angle[t, j]), -torch.sin(-angle[t, j])])) rotate_y = pos_xy.dot(torch.tensor([torch.sin(-angle[t, j]), torch.cos(-angle[t, j])])) moving_direction = torch.tensor([0.0, 0.0]) dist_norm = 0.0 collision = True if torch.abs(rotate_x) > 2 * radius: collision = False elif torch.abs(rotate_y) > 2 * radius: collision = False elif torch.abs(rotate_x) <= radius: if rotate_y > 0: moving_direction = torch.tensor([0.0, 1.0]) dist_norm = rotate_y elif rotate_y < 0: moving_direction = torch.tensor([0.0, -1.0]) dist_norm = - rotate_y elif torch.abs(rotate_y) <= radius: if rotate_x > 0: moving_direction = torch.tensor([1.0, 0.0]) dist_norm = rotate_x elif rotate_x < 0: moving_direction = torch.tensor([-1.0, 0.0]) dist_norm = - rotate_x elif (torch.abs(rotate_x) - radius) ** 2 + (torch.abs(rotate_y) - radius) ** 2 <= radius ** 2: if rotate_x > radius and rotate_y > radius: moving_direction = normalized(torch.tensor([rotate_x - radius, rotate_y - radius])) dist_norm = norm(torch.tensor([rotate_x - radius, rotate_y - radius])) + radius elif rotate_x < -radius and rotate_y > radius: moving_direction = normalized(torch.tensor([rotate_x + radius, rotate_y - radius])) dist_norm = norm(torch.tensor([rotate_x + radius, rotate_y - radius])) + radius elif rotate_x > radius and rotate_y < -radius: moving_direction = normalized(torch.tensor([rotate_x - radius, rotate_y + radius])) dist_norm = norm(torch.tensor([rotate_x - radius, rotate_y + radius])) + radius elif rotate_x < -radius and rotate_y < -radius: moving_direction = normalized(torch.tensor([rotate_x + radius, rotate_y + radius])) dist_norm = norm(torch.tensor([rotate_x + radius, rotate_y + radius])) + radius if collision: origin_dir = torch.tensor( [moving_direction.dot(torch.tensor([torch.cos(angle[t, j]), -torch.sin(angle[t, j])])), moving_direction.dot(torch.tensor([torch.sin(angle[t, j]), torch.cos(angle[t, j])]))] ) projected_v = origin_dir.dot(rela_v) if projected_v < 0: if i < j: repeat = False for item in collisions: if json.dumps(item).startswith(json.dumps([i, j])[:-1]): repeat = True if not repeat: collisions.append([i, j, round(t / 10.0)]) imp = -(1 + restitution[i] * restitution[j]) * (mass[j] / (mass[i] + mass[j])) * projected_v * origin_dir # 冲量，速度变化量 toi = (dist_norm - 2 * radius) / min( -1e-3, projected_v) x_inc_contrib = min(toi - dt, 0) * imp x_inc[t + 1, i] += x_inc_contrib impulse[t + 1, i] += imp def cube_collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions): imp = torch.tensor([0.0, 0.0]) x_inc_contrib = torch.tensor([0.0, 0.0]) a_rotate = 0.0 if i != j: rela_v = v[t, i] - v[t, j] pos_xy = x[t, j] - x[t, i] rotate_x = pos_xy.dot(torch.tensor([torch.cos(-angle[t, i]), -torch.sin(-angle[t, i])])) rotate_y = pos_xy.dot(torch.tensor([torch.sin(-angle[t, i]), torch.cos(-angle[t, i])])) moving_direction = torch.tensor([0.0, 0.0]) collision_direction = torch.tensor([0.0, 0.0]) dist_norm = 0.0 r_rotate = 0.0 rotate_dir = False collision = True if torch.abs(rotate_x) > 2 * radius: collision = False elif torch.abs(rotate_y) > 2 * radius: collision = False elif torch.abs(rotate_x) <= radius: if rotate_y > 0: moving_direction = torch.tensor([0.0, -1.0]) collision_direction = normalized(torch.tensor([-rotate_x, -radius])) dist_norm = rotate_y if rotate_x > 0: rotate_dir = 1 elif rotate_y < 0: moving_direction = torch.tensor([0.0, 1.0]) collision_direction = normalized(torch.tensor([-rotate_x, radius])) dist_norm = - rotate_y if rotate_x < 0: rotate_dir = 1 r_rotate = norm(torch.tensor([radius, rotate_x])) elif torch.abs(rotate_y) <= radius: if rotate_x > 0: moving_direction = torch.tensor([-1.0, 0.0]) collision_direction = normalized(torch.tensor([-radius, -rotate_y])) dist_norm = rotate_x if rotate_y < 0: rotate_dir = 1 elif rotate_x < 0: moving_direction = torch.tensor([1.0, 0.0]) collision_direction = normalized(torch.tensor([radius, -rotate_y])) dist_norm = - rotate_x if rotate_y > 0: rotate_dir = 1 r_rotate = norm(torch.tensor([radius, rotate_y])) elif (torch.abs(rotate_x) - radius) ** 2 + (torch.abs(rotate_y) - radius) ** 2 <= radius ** 2: if rotate_x > radius and rotate_y > radius: moving_direction = - normalized(torch.tensor([rotate_x - radius, rotate_y - radius])) collision_direction = normalized(torch.tensor([-1.0, -1.0])) dist_norm = norm(torch.tensor([rotate_x - radius, rotate_y - radius])) + radius if rotate_y > rotate_x: rotate_dir = 1 elif rotate_x < -radius and rotate_y > radius: moving_direction = - normalized(torch.tensor([rotate_x + radius, rotate_y - radius])) collision_direction = normalized(torch.tensor([1.0, -1.0])) dist_norm = norm(torch.tensor([rotate_x + radius, rotate_y - radius])) + radius if -rotate_x > rotate_y: rotate_dir = 1 elif rotate_x > radius and rotate_y < -radius: moving_direction = - normalized(torch.tensor([rotate_x - radius, rotate_y + radius])) collision_direction = normalized(torch.tensor([-1.0, 1.0])) dist_norm = norm(torch.tensor([rotate_x - radius, rotate_y + radius])) + radius if rotate_x > -rotate_y: rotate_dir = 1 elif rotate_x < -radius and rotate_y < -radius: moving_direction = - normalized(torch.tensor([rotate_x + radius, rotate_y + radius])) collision_direction = normalized(torch.tensor([1.0, 1.0])) dist_norm = norm(torch.tensor([rotate_x + radius, rotate_y + radius])) + radius if -rotate_y > -rotate_x: rotate_dir = 1 r_rotate = norm(torch.tensor([radius, radius])) if collision: origin_moving_dir = torch.tensor( [moving_direction.dot(torch.tensor([torch.cos(angle[t, i]), -torch.sin(angle[t, i])])), moving_direction.dot(torch.tensor([torch.sin(angle[t, i]), torch.cos(angle[t, i])]))] ) origin_collision_dir = torch.tensor( [collision_direction.dot(torch.tensor([torch.cos(angle[t, i]), -torch.sin(angle[t, i])])), collision_direction.dot(torch.tensor([torch.sin(angle[t, i]), torch.cos(angle[t, i])]))] ) projected_v = origin_moving_dir.dot(rela_v) if projected_v < 0: if i < j: repeat = False for item in collisions: if json.dumps(item).startswith(json.dumps([i, j])[:-1]): repeat = True if not repeat: collisions.append([i, j, round(t / 10.0)]) imp = -(1 + restitution[i] * restitution[j]) * (mass[j] / (mass[i] + mass[j])) * projected_v * origin_moving_dir toi = (dist_norm - 2 * radius) / min( -1e-3, projected_v) x_inc_contrib = min(toi - dt, 0) * imp f_rotate = (origin_moving_dir - origin_collision_dir.dot(origin_moving_dir) * origin_collision_dir).dot(-projected_v * origin_moving_dir) a_rotate = f_rotate * r_rotate / inertia if rotate_dir: a_rotate = -a_rotate x_inc[t + 1, i] += x_inc_contrib impulse[t + 1, i] += imp angle_impulse[t + 1, i] += a_rotate def collide(shape, x, v, x_inc, impulse, t, angle, angle_impulse, collisions): for i in range(n_balls): for j in range(i): if shape[i] != 1 and shape[j] != 1: collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) elif shape[i] != 1 and shape[j] == 1: sphere_collide_cube(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) elif shape[i] == 1 and shape[j] != 1: cube_collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) elif shape[i] == 1 and shape[j] == 1: collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) for i in range(n_balls): for j in range(i + 1, n_balls): if shape[i] != 1 and shape[j] != 1: collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) elif shape[i] != 1 and shape[j] == 1: sphere_collide_cube(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) elif shape[i] == 1 and shape[j] != 1: cube_collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) elif shape[i] == 1 and shape[j] == 1: collide_sphere(x, v, x_inc, impulse, t, i, j, angle, angle_impulse, collisions) def friction(shape, x, v, x_inc, impulse, v_old, t, i): if shape[i] == 0: if v_old[0] > 0.0: v[t, i][0] = max(0, v_old[0] - linear_damping * dt * v_old[0] * norm(v_old)) elif v_old[0] < 0.0: v[t, i][0] = min(0, v_old[0] - linear_damping * dt * v_old[0] * norm(v_old)) if v_old[1] > 0.0: v[t, i][1] = max(0, v_old[1] - linear_damping * dt * v_old[1] * norm(v_old)) elif v_old[1] < 0.0: v[t, i][1] = min(0, v_old[1] - linear_damping * dt * v_old[1] * norm(v_old)) else: if v_old[0] > 0.0: v[t, i][0] = max(0, v_old[0] - gravity * frictional * dt * normalized(v_old)[0] - linear_damping * dt * v_old[0] * norm(v_old)) elif v_old[0] < 0.0: v[t, i][0] = min(0, v_old[0] - gravity * frictional * dt * normalized(v_old)[0] - linear_damping * dt * v_old[0] * norm(v_old)) if v_old[1] > 0.0: v[t, i][1] = max(0, v_old[1] - gravity * frictional * dt * normalized(v_old)[1] - linear_damping * dt * v_old[1] * norm(v_old)) elif v_old[1] < 0.0: v[t, i][1] = min(0, v_old[1] - gravity * frictional * dt * normalized(v_old)[1] - linear_damping * dt * v_old[1] * norm(v_old)) def advance(shape, x, v, x_inc, impulse, t, angle, delta_angle, angle_impulse): for i in range(n_balls): v_old = v[t - 1, i] + impulse[t, i] friction(shape, x, v, x_inc, impulse, v_old, t, i) x[t, i] = x[t - 1, i] + dt * (v[t, i] + v_old)/2 + x_inc[t, i] delta_angle[t, i] = delta_angle[t - 1, i] + angle_impulse[t, i] if delta_angle[t, i] > 0.0: delta_angle[t, i] = max(0, delta_angle[t, i] - dt * gravity / 2) elif delta_angle[t, i] < 0.0: delta_angle[t, i] = min(0, delta_angle[t, i] + dt * gravity / 2) angle[t, i] = angle[t - 1, i] + dt * delta_angle[t, i] def init(): x = torch.zeros((steps, n_balls, 2), dtype=torch.float32).to(device) v = torch.zeros((steps, n_balls, 2), dtype=torch.float32).to(device) x_inc = torch.zeros((steps, n_balls, 2), dtype=torch.float32).to(device) impulse = torch.zeros((steps, n_balls, 2), dtype=torch.float32).to(device) angle = torch.zeros((steps, n_balls), dtype=torch.float32).to(device) delta_angle = torch.zeros((steps, n_balls), dtype=torch.float32).to(device) angle_impulse = torch.zeros((steps, n_balls), dtype=torch.float32).to(device) x[0, :] = target_x[0] v[0, :] = v0 angle[0, :] = angle0 return x, v, x_inc, impulse, angle, delta_angle, angle_impulse def closure(): optimizer.zero_grad() x, v, x_inc, impulse, angle, delta_angle, angle_impulse = init() loss = 0 collisions = [] for t in range(1, 210): collide(shape, x, v, x_inc, impulse, t - 1, angle, angle_impulse, collisions) advance(shape, x, v, x_inc, impulse, t, angle, delta_angle, angle_impulse) if t % interval == 0: loss += (((x[t, :] - target_x[int(t/interval), :]) * (target_x[int(t/interval), :] < 100)) ** 2).mean() return loss def init_inference(): x = torch.zeros((210, n_balls, 2), dtype=torch.float32).to(device) v = torch.zeros((210, n_balls, 2), dtype=torch.float32).to(device) x_inc = torch.zeros((210, n_balls, 2), dtype=torch.float32).to(device) impulse = torch.zeros((210, n_balls, 2), dtype=torch.float32).to(device) angle = torch.zeros((210, n_balls), dtype=torch.float32).to(device) delta_angle = torch.zeros((210, n_balls), dtype=torch.float32).to(device) angle_impulse = torch.zeros((210, n_balls), dtype=torch.float32).to(device) x[0, :] = target_x[0] v[0, :] = v0 angle[0, :] = angle0 return x, v, x_inc, impulse, angle, delta_angle, angle_impulse # if __name__ == '__main__': optimizer = FullBatchLBFGS([v0, mass, restitution]) start = time.time() loss = closure() loss.backward() for i in range(15): options = {'closure': closure, 'current_loss': loss, 'max_ls': 10} loss, _, lr, _, F_eval, G_eval, _, _ = optimizer.step(options) print(loss, lr, v0, mass, restitution) if loss < 0.0002 or lr == 0: break time_cost = time.time() - start print(f'----- learned, cost {time_cost}s') collisions = [] x, v, x_inc, impulse, angle, delta_angle, angle_impulse = init_inference() for t in range(1, 210): collide(shape, x, v, x_inc, impulse, t - 1, angle, angle_impulse, collisions) # 计算碰撞 advance(shape, x, v, x_inc, impulse, t, angle, delta_angle, angle_impulse) # 更新速度和位置 # ================================================================================== shapes = [] shape_dict = { 'sphere': 0, 'cube': 1, 'cylinder': 2 } reverse_shape_dict = { 0: 'sphere', 1: 'cube', 2: 'cylinder' } colors = [] materials = [] for object_index, identity in enumerate(object_dict.keys()): shapes.append(shape_dict[object_dict[identity]['shape']]) colors.append(object_dict[identity]['color']) materials.append(object_dict[identity]['material']) gt_objects = list(object_dict.keys()) old_collisions = output_dict['predictions'][0]['collisions'].copy() uniq_collisions = [] for item in old_collisions: if item['frame'] > 88: output_dict['predictions'][0]['collisions'].remove(item) print('remove collision', item['frame']) else: uniq_collisions.append([gt_objects.index(item['objects'][0]['color'] + item['objects'][0]['material'] + item['objects'][0]['shape']), gt_objects.index(item['objects'][1]['color'] + item['objects'][1]['material'] + item['objects'][1]['shape']), item['frame']]) for collision_index, item in enumerate(collisions): i, j, frame = item repeat = False for colli_item in uniq_collisions: if json.dumps(colli_item).startswith(json.dumps([i, j])[:-1]): repeat = True if not repeat: output_dict['predictions'][0]['collisions'].append({ 'frame': 88 + frame, 'objects': [{ 'color': colors[i], 'material': materials[i], 'shape': reverse_shape_dict[shapes[i]], }, { 'color': colors[j], 'material': materials[j], 'shape': reverse_shape_dict[shapes[j]], }] }) print('add collision', 88 + frame) output_dict['predictions'][0]['trajectory'] = output_dict['predictions'][0]['trajectory'][:18] print('keep trajectory from 0 to', output_dict['predictions'][0]['trajectory'][-1]['frame_index']) for frame_index, locations in enumerate(x): if frame_index % 50 == 20: frame_info = {'frame_index': 88 + frame_index // 10, 'objects': []} for object_index, location in enumerate(locations): xy = get_2d_coor(location[0].cpu().item(), location[1].cpu().item()) xy1 = get_2d_coor(location[0].cpu().item() + radius * 0.7071, location[1].cpu().item(), z3d=radius * (1 - 0.7071)) xy2 = get_2d_coor(location[0].cpu().item() - radius * 0.7071, location[1].cpu().item(), z3d=radius * (1 + 0.7071)) xy3 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item() + radius) xy4 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item() - radius) xy5 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item(), z3d=0) xy6 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item(), z3d=2 * radius) if (-10 < xy[0] < 490 and -10 < xy[1] < 330) \ or (0 < xy1[0] < 480 and 0 < xy1[1] < 320) \ or (0 < xy2[0] < 480 and 0 < xy2[1] < 320) \ or (0 < xy3[0] < 480 and 0 < xy3[1] < 320) \ or (0 < xy4[0] < 480 and 0 < xy3[1] < 320) \ or (0 < xy5[0] < 480 and 0 < xy3[1] < 320) \ or (0 < xy6[0] < 480 and 0 < xy4[1] < 320): frame_info['objects'].append({ 'x': float(xy[1]) / 3.2, 'y': float(xy[0]) / 3.2, 'color': colors[object_index], 'material': materials[object_index], 'shape': reverse_shape_dict[shapes[object_index]], }) output_dict['predictions'][0]['trajectory'].append(frame_info) print('add trajectory', frame_info['frame_index']) n_balls = len(object_dict) steps = 200 target_x = torch.zeros((128, n_balls, 2), dtype=torch.float32).to(device) + 1000 shapes = [] shape_dict = { 'sphere': 0, 'cube': 1, 'cylinder': 2 } for object_index, identity in enumerate(object_dict.keys()): locations = torch.tensor(object_dict[identity]['trajectory']).to(device) target_x[:locations.shape[0], object_index, :] = locations shapes.append(shape_dict[object_dict[identity]['shape']]) target_x = target_x[-20:] for object_index, identity in enumerate(object_dict.keys()): if target_x[0][object_index][0] > 500: target_x[0][object_index] = torch.tensor(x[-1].detach()[object_index]) shape = torch.tensor(shapes, dtype=torch.int8).to(device) angle0 = angle[-1].detach() angle0.requires_grad = True interval = 10 dt = 1/350 gravity = 9.806 radius = 0.2 inertia = 0.4 * 0.4 / 6 frictional = torch.tensor(0.03).to(device) frictional.requires_grad = True linear_damping = torch.tensor(0.06).to(device) linear_damping.requires_grad = True v0 = torch.tensor(v[-1].detach(), dtype=torch.float32).to(device) v0.requires_grad = True restitution = torch.tensor(restitution.detach(), dtype=torch.float32).to(device) restitution.requires_grad = True mass = torch.tensor(mass.detach(), dtype=torch.float32).to(device) mass.requires_grad = True def closure_108(): optimizer.zero_grad() x, v, x_inc, impulse, angle, delta_angle, angle_impulse = init() loss = 0 collisions = [] for t in range(1, 200): collide(shape, x, v, x_inc, impulse, t - 1, angle, angle_impulse, collisions) advance(shape, x, v, x_inc, impulse, t, angle, delta_angle, angle_impulse) if t % interval == 0: loss += (((x[t, :] - target_x[int(t/interval), :]) * (target_x[int(t/interval), :] < 100)) ** 2).mean() return loss def init_inference_108(): x = torch.zeros((780, n_balls, 2), dtype=torch.float32).to(device) v = torch.zeros((780, n_balls, 2), dtype=torch.float32).to(device) x_inc = torch.zeros((780, n_balls, 2), dtype=torch.float32).to(device) impulse = torch.zeros((780, n_balls, 2), dtype=torch.float32).to(device) angle = torch.zeros((780, n_balls), dtype=torch.float32).to(device) delta_angle = torch.zeros((780, n_balls), dtype=torch.float32).to(device) angle_impulse = torch.zeros((780, n_balls), dtype=torch.float32).to(device) x[0, :] = target_x[0] v[0, :] = v0 angle[0, :] = angle0 return x, v, x_inc, impulse, angle, delta_angle, angle_impulse optimizer = FullBatchLBFGS([v0, mass, restitution]) start = time.time() loss = closure_108() loss.backward() for i in range(15): options = {'closure': closure_108, 'current_loss': loss, 'max_ls': 10} loss, _, lr, _, F_eval, G_eval, _, _ = optimizer.step(options) print(loss, lr, v0, mass, restitution) if loss < 0.0002 or lr == 0: break time_cost = time.time() - start print(f'----- learned, cost {time_cost}s') collisions = [] x, v, x_inc, impulse, angle, delta_angle, angle_impulse = init_inference_108() for t in range(1, 780): collide(shape, x, v, x_inc, impulse, t - 1, angle, angle_impulse, collisions) advance(shape, x, v, x_inc, impulse, t, angle, delta_angle, angle_impulse) # ================================================================================== shapes = [] shape_dict = { 'sphere': 0, 'cube': 1, 'cylinder': 2 } reverse_shape_dict = { 0: 'sphere', 1: 'cube', 2: 'cylinder' } colors = [] materials = [] for object_index, identity in enumerate(object_dict.keys()): shapes.append(shape_dict[object_dict[identity]['shape']]) colors.append(object_dict[identity]['color']) materials.append(object_dict[identity]['material']) gt_objects = list(object_dict.keys()) old_collisions = output_dict['predictions'][0]['collisions'].copy() uniq_collisions = [] for item in old_collisions: if item['frame'] > 108: output_dict['predictions'][0]['collisions'].remove(item) print('remove collision', item['frame']) else: uniq_collisions.append([gt_objects.index(item['objects'][0]['color'] + item['objects'][0]['material'] + item['objects'][0]['shape']), gt_objects.index(item['objects'][1]['color'] + item['objects'][1]['material'] + item['objects'][1]['shape']), item['frame']]) for collision_index, item in enumerate(collisions): i, j, frame = item repeat = False for colli_item in uniq_collisions: if json.dumps(colli_item).startswith(json.dumps([i, j])[:-1]): repeat = True if not repeat: output_dict['predictions'][0]['collisions'].append({ 'frame': 108 + frame, 'objects': [{ 'color': colors[i], 'material': materials[i], 'shape': reverse_shape_dict[shapes[i]], }, { 'color': colors[j], 'material': materials[j], 'shape': reverse_shape_dict[shapes[j]], }] }) print('add collision', 108 + frame) output_dict['predictions'][0]['trajectory'] = output_dict['predictions'][0]['trajectory'][:22] print('keep trajectory from 0 to', output_dict['predictions'][0]['trajectory'][-1]['frame_index']) for frame_index, locations in enumerate(x): if frame_index % 50 == 20: frame_info = {'frame_index': 108 + frame_index // 10, 'objects': []} for object_index, location in enumerate(locations): xy = get_2d_coor(location[0].cpu().item(), location[1].cpu().item()) xy1 = get_2d_coor(location[0].cpu().item() + radius * 0.7071, location[1].cpu().item(), z3d=radius * (1 - 0.7071)) xy2 = get_2d_coor(location[0].cpu().item() - radius * 0.7071, location[1].cpu().item(), z3d=radius * (1 + 0.7071)) xy3 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item() + radius) xy4 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item() - radius) xy5 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item(), z3d=0) xy6 = get_2d_coor(location[0].cpu().item(), location[1].cpu().item(), z3d=2 * radius) if (-10 < xy[0] < 490 and -10 < xy[1] < 330) \ or (0 < xy1[0] < 480 and 0 < xy1[1] < 320) \ or (0 < xy2[0] < 480 and 0 < xy2[1] < 320) \ or (0 < xy3[0] < 480 and 0 < xy3[1] < 320) \ or (0 < xy4[0] < 480 and 0 < xy3[1] < 320) \ or (0 < xy5[0] < 480 and 0 < xy3[1] < 320) \ or (0 < xy6[0] < 480 and 0 < xy4[1] < 320): frame_info['objects'].append({ 'x': float(xy[1]) / 3.2, 'y': float(xy[0]) / 3.2, 'color': colors[object_index], 'material': materials[object_index], 'shape': reverse_shape_dict[shapes[object_index]], }) output_dict['predictions'][0]['trajectory'].append(frame_info) print('add trajectory', frame_info['frame_index']) json.dump(output_dict, open(f'../data/object_updated_results/sim_{process_index:05d}.json', 'w'))

47.50578

157

0.53124

4,242

32,874

3.955446

0.074022

0.048513

0.009834

0.033971

0.895524

0.880684

0.874426

0.866917

0.859825

0.855355

0

0.053278

0.316572

32,874

691

158

47.57453

0.693551

0.027925

0

0.691252

0

0.044158

0.006608

0

1

0.024014

false

0

0.010292

0.003431

0.048027

0.022298

0

null

0

1

0

null

0

6

65ea73b58981c99fbcec117ca6a7ec83ab6687fe

2,359

py

Python

model_test/evaluate_model.py

lover-520/wzm_landform_scene_model

1bc8894d99b76213ca2544e540dccab2ad52be00

[ "MIT" ]

4

2021-01-23T15:47:49.000Z

2021-05-05T17:03:12.000Z

model_test/evaluate_model.py

lover-520/wzm_landform_scene_model

1bc8894d99b76213ca2544e540dccab2ad52be00

[ "MIT" ]

null

model_test/evaluate_model.py

lover-520/wzm_landform_scene_model

1bc8894d99b76213ca2544e540dccab2ad52be00

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ @author: WZM @time: 2021/1/2 17:52 @function: 测试模型精度 """ from net.ouy_net import Network import numpy as np import torch import os def load_net(fname, net): import h5py h5f = h5py.File(fname, mode='r') for k, v in net.state_dict().items(): param = torch.from_numpy(np.asarray(h5f[k])) v.copy_(param) def evaluate_model(trained_model, data_loader, index): net = Network(index) load_net(trained_model, net) device = torch.device('cuda:0') if torch.cuda.is_available(): net = net.to(device) net.eval() count = 0 total = 0 lableresultpath = trained_model.replace(".h5", ".txt") if os.path.exists(lableresultpath): os.remove(lableresultpath) valid_loss = 0.0 for blob in data_loader: im_data = blob[0] dem_data = blob[2] img_data = blob[1] gt_data = blob[3].reshape((blob[3].shape[0], 1)) index = 61 pre_label = net(im_data, dem_data, img_data, index, gt_data) pre_label = pre_label.data.cpu().numpy() valid_loss += net.loss.item() label = pre_label.argmax(axis=1).flatten() num = len(label) for i in range(0, num): if gt_data[i] == label[i]: count = count + 1 total = total + 1 return 1.0 * count / total, valid_loss def evaluate_model1(net, data_loader, index): device = torch.device('cuda:0') if torch.cuda.is_available(): net = net.to(device) net.eval() count = 0 total = 0 # lableresultpath = trained_model.replace(".h5", ".txt") # if os.path.exists(lableresultpath): # os.remove(lableresultpath) valid_loss = 0.0 for blob in data_loader: im_data = blob[0] dem_data = blob[2] img_data = blob[1] gt_data = blob[3].reshape((blob[3].shape[0], 1)) index = 61 with torch.no_grad(): pre_label = net(im_data, dem_data, img_data, index, gt_data) pre_label = pre_label.data.cpu().numpy() valid_loss += net.loss.item() label = pre_label.argmax(axis=1).flatten() num = len(label) for i in range(0, num): if gt_data[i] == label[i]: count = count + 1 total = total + 1 return 1.0 * count / total, valid_loss

25.365591

72

0.578635

338

2,359

3.887574

0.269231

0.048706

0.039574

0.031963

0.732116

0

0.034503

0.28741

2,359

92

73

25.641304

0.747174

0.083934

0

0.71875

0

0.009302

0

1

0.046875

false

0

0.078125

0

0.15625

0

null

0

1

0

1

0

null

0

6

029f520d7ac712ac57449f8c27779487e0ce463b

27

py

Python

tests/block/test_block.py

huksley/notion-py

90c66891ed6b892c77befd8eeeba4cb637b008a9

[ "MIT" ]

58

2020-07-01T17:13:26.000Z

2022-03-16T16:02:01.000Z

tests/block/test_block.py

huksley/notion-py

90c66891ed6b892c77befd8eeeba4cb637b008a9

[ "MIT" ]

30

2020-07-02T09:28:05.000Z

2022-02-04T18:10:36.000Z

tests/block/test_block.py

huksley/notion-py

90c66891ed6b892c77befd8eeeba4cb637b008a9

[ "MIT" ]

10

2020-07-01T14:59:09.000Z

2021-11-28T07:57:47.000Z

def test_block(): pass

9

17

0.62963

4

27

4

1

0

0.259259

27

2

18

13.5

0.8

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

02af99284141051aff882684d3335e3e35b73d3f

1,594

py

Python

Abstraction/IApiClient.py

pjpmosteiro/DiscordGPT-3

2a64cb78debf4366d080163c38503b5f6443fbc6

[ "MIT" ]

49

2020-12-01T21:22:14.000Z

2022-03-19T03:01:29.000Z

Abstraction/IApiClient.py

pjpmosteiro/DiscordGPT-3

2a64cb78debf4366d080163c38503b5f6443fbc6

[ "MIT" ]

13

2021-01-27T08:04:14.000Z

2022-03-04T18:12:56.000Z

Abstraction/IApiClient.py

pjpmosteiro/DiscordGPT-3

2a64cb78debf4366d080163c38503b5f6443fbc6

[ "MIT" ]

19

2021-02-01T16:11:04.000Z

2022-02-15T20:51:16.000Z

# e4c6 ~ 2021 from abc import ABCMeta, abstractmethod from typing import Tuple class ApiClientInterface(metaclass=ABCMeta): @abstractmethod async def complete(self, prompt: Tuple[str], length: int, api_key: str, language: str, temperature: float) -> str: raise NotImplementedError @abstractmethod async def answer(self, question: Tuple[str], length: int, api_key: str, language: str, temperature: float) -> str: raise NotImplementedError @abstractmethod async def song(self, song_name: Tuple[str], user_name, length: int, api_key: str, language: str, temperature: float) -> str: raise NotImplementedError @abstractmethod async def headline(self, prompt: Tuple[str], length: int, api_key: str, language: str, temperature: float) -> str: raise NotImplementedError @abstractmethod async def sentiment(self, prompt: Tuple[str], api_key: str, language: str) -> str: raise NotImplementedError @abstractmethod async def emojify(self, prompt: Tuple[str], length: int, api_key: str, language: str, temperature: float) -> str: raise NotImplementedError @abstractmethod async def sarcastic_answer(self, prompt: Tuple[str], length: int, api_key: str, language: str, temperature: float) -> str: raise NotImplementedError @abstractmethod async def foulmouth_answer(self, prompt: Tuple[str], length: int, api_key: str, language: str, temperature: float) -> str: raise NotImplementedError

37.069767

118

0.67064

176

1,594

6.005682

0.210227

0.143803

0.166509

0.128666

0.77105

0.752129

0.705771

0

0.00493

0.236512

1,594

42

119

37.952381

0.863599

0.006901

0

0.633333

0

1

0

true

0

0.066667

0

0.1

0

null

0

1

0

null

0

1

0

6

b858b8a397097b2a1a231aeafd88656d0db76b2a

229

py

Python

jft/cfg.py

isimluk/jft

998a6d4248f6b4757f9e0cb281c9b2cb51d3558f

[ "Unlicense" ]

null

jft/cfg.py

isimluk/jft

998a6d4248f6b4757f9e0cb281c9b2cb51d3558f

[ "Unlicense" ]

null

jft/cfg.py

isimluk/jft

998a6d4248f6b4757f9e0cb281c9b2cb51d3558f

[ "Unlicense" ]

null

# Todo replace this with normal python config read from file config = { 'url': 'https://issues.redhat.com', 'username': 'me', 'password': 'hackmepls', } config['url'] = 'https://issues.stage.redhat.com'

22.9

60

0.606987

27

229

5.148148

0.740741

0.129496

0.201439

0.28777

0

0.218341

229

9

61

25.444444

0.776536

0.253275

0

0.526627

0

0.111111

0

1

0

false

0.166667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

b868ce889dbbe95c91d7fd7e3d71519821b4baf5

30

py

Python

Fundamentos/holaMundo.py

ricnef2121/python

9669921f3a9f9cafd1b40a17948c5dcfce60a1ac

[ "MIT" ]

null

Fundamentos/holaMundo.py

ricnef2121/python

9669921f3a9f9cafd1b40a17948c5dcfce60a1ac

[ "MIT" ]

null

Fundamentos/holaMundo.py

ricnef2121/python

9669921f3a9f9cafd1b40a17948c5dcfce60a1ac

[ "MIT" ]

null

print("hola mundo con python")

30

0.766667

5

30

4.6

1

0

0.1

30

1

30

0.851852

0

0.677419

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

b87caeda30534d7179124225dbef1779907127dd

10,858

py

Python

pysimplegui/DemoPrograms/Demo_Floating_Toolbar.py

konsan1101/py-etc

bcca13119b0d2453866988404fd1c4976f55d4d5

[ "MIT" ]

null

pysimplegui/DemoPrograms/Demo_Floating_Toolbar.py

konsan1101/py-etc

bcca13119b0d2453866988404fd1c4976f55d4d5

[ "MIT" ]

2

2020-06-06T00:30:56.000Z

2021-06-10T22:30:37.000Z

pysimplegui/DemoPrograms/Demo_Floating_Toolbar.py

konsan1101/py-etc

bcca13119b0d2453866988404fd1c4976f55d4d5

[ "MIT" ]

null

import PySimpleGUI as sg import sys ''' Example of borderless floating toolbar. ''' button_names = ('close', 'cookbook', 'cpu', 'github', 'pysimplegui', 'run', 'storage', 'timer') house64 = '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' cpu64 = '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' timer64 = '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' close64 = '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' def main(): def tbutton(image_data, key): return sg.Button(image_data=image_data, button_color=('white', 'black'), pad=(0,0), key=key) toolbar_buttons = [[tbutton(close64, '-CLOSE-'), tbutton(timer64, '-TIMER-'), tbutton(house64, '-HOUSE-'), tbutton(cpu64, '-CPU-') ]] # layout = toolbar_buttons layout = [[sg.Col(toolbar_buttons, background_color='black')]] window = sg.Window('Toolbar', layout, no_titlebar=True, grab_anywhere=True, background_color='black', margins=(0, 0)) # ---===--- Loop taking in user input --- # while True: button, value = window.read() print(button) if button == '-CLOSE-' or button is None: break # exit button clicked elif button == '-TIMER-': # add your call to launch a timer program print('Timer Button') elif button == '-CPU-': # add your call to launch a CPU measuring utility print('CPU Button') elif button == '-HOUSE-': print('Home Button') window.close() if __name__ == '__main__': main()

208.807692

3,208

0.910481

433

10,858

22.785219

0.815242

0.002737

0.004054

0.002635

0.004054

0

0.138283

0.042273

10,858

51

3,209

212.901961

0.810463

0.015933

0

0.125

0.899219

0.88341

0

1

0

1

0.0625

false

0

0.0625

0.03125

0.15625

0.125

0

1

null

0

1

0

1

null

1

0

6

b2258ea5d934f449177dc446ebc144260b1a63e7

54

py

Python

tests/core/test_import.py

jmcph4/py-snappy

1b254774f6c4daccba99114704cb9ecd589e6345

[ "MIT" ]

null

tests/core/test_import.py

jmcph4/py-snappy

1b254774f6c4daccba99114704cb9ecd589e6345

[ "MIT" ]

null

tests/core/test_import.py

jmcph4/py-snappy

1b254774f6c4daccba99114704cb9ecd589e6345

[ "MIT" ]

null

def test_import(): import py_snappy # noqa: F401

18

34

0.685185

8

54

4.375

0.875

0

0.071429

0.222222

54

2

35

27

0.761905

0.185185

0

1

0.5

true

0

1

0

1.5

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b248479baeb995fa954afc54dac6361c3ff3af5e

702

py

Python

tests/structures/test_set_comprehension.py

jacebrowning/voc

7bc84e8a870674d300ad5083748cf6b826e7fb68

[ "BSD-3-Clause" ]

850

2015-08-17T16:45:22.000Z

2019-03-24T07:50:15.000Z

tests/structures/test_set_comprehension.py

jacebrowning/voc

7bc84e8a870674d300ad5083748cf6b826e7fb68

[ "BSD-3-Clause" ]

506

2015-09-26T18:20:00.000Z

2019-03-19T18:16:18.000Z

tests/structures/test_set_comprehension.py

jacebrowning/voc

7bc84e8a870674d300ad5083748cf6b826e7fb68

[ "BSD-3-Clause" ]

670

2015-09-12T21:57:44.000Z

2019-03-19T13:15:33.000Z

from ..utils import TranspileTestCase class SetComprehensionTests(TranspileTestCase): def test_syntax(self): self.assertCodeExecution(""" x = [1, 2, 3, 4, 5] s = {v**2 for v in x} print(len(s)) print(1 in s) print(4 in s) print(9 in s) print(16 in s) print(25 in s) """) def test_method(self): self.assertCodeExecution(""" x = [1, 2, 3, 4, 5] s = set(v**2 for v in x) print(len(s)) print(1 in s) print(4 in s) print(9 in s) print(16 in s) print(25 in s) """)

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null

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1

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6

b24e02ec0148963d56df29dc7792f6de19d6d82d

124

py

Python

deep_image_compression/__init__.py

LichengXiao2017/deep-image-compression

cf6e5699bad4d7b4a0dd8db6da72aa0c56e3d1e4

[ "MIT" ]

9

2020-01-09T21:15:17.000Z

2022-02-08T12:41:54.000Z

deep_image_compression/__init__.py

LichengXiao2017/deep-image-compression

cf6e5699bad4d7b4a0dd8db6da72aa0c56e3d1e4

[ "MIT" ]

8

2019-10-15T23:50:03.000Z

2021-11-10T19:40:15.000Z

deep_image_compression/__init__.py

LichengXiao2017/enas-image-compression

cf6e5699bad4d7b4a0dd8db6da72aa0c56e3d1e4

[ "MIT" ]

3

2019-10-16T06:06:49.000Z

2020-07-06T15:02:09.000Z

from deep_image_compression.single_psnr import SingleEvaluator from deep_image_compression.batch_psnr import BatchEvaluator

41.333333

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a26522302af02e9c03efb369c14c4be8629d1885

79

py

Python

frontend/__init__.py

AzoeDesarrollos/PyMavisDatabase

bfcd0557f63a4d8a73f0f8e891c47b47a1de1b45

[ "MIT" ]

null

frontend/__init__.py

AzoeDesarrollos/PyMavisDatabase

bfcd0557f63a4d8a73f0f8e891c47b47a1de1b45

[ "MIT" ]

2

2019-10-05T14:20:11.000Z

2019-10-05T14:22:31.000Z

frontend/__init__.py

AzoeDesarrollos/PyMavisDatabase

bfcd0557f63a4d8a73f0f8e891c47b47a1de1b45

[ "MIT" ]

null

from .globals import Renderer, WidgetHandler from .globals.constantes import *

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null

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1

0

1

0

6

0c4566fa37e78a7c2104632f5b27d1e411598246

7,844

py

Python

models/norm_module.py

hassan-mahmood/Layout-Agnostic-Object-Alignment-and-Image-Generation

c526cb365b6fe383bb85423afcbc914e3e791790

[ "Apache-2.0" ]

1

2021-11-02T05:13:12.000Z

models/norm_module.py

hassan-mahmood/Layout-Agnostic-Object-Alignment-and-Image-Generation

c526cb365b6fe383bb85423afcbc914e3e791790

[ "Apache-2.0" ]

1

2021-12-17T14:29:18.000Z

baselines/arch/lostgans/norm_module.py

atmacvit/meronymnet

47e1a7caadc0f770439bb26a93b885f790f62804

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F # Adaptive instance normalization # modified from https://github.com/NVlabs/MUNIT/blob/d79d62d99b588ae341f9826799980ae7298da553/networks.py#L453-L482 class AdaptiveInstanceNorm2d(nn.Module): def __init__(self, num_features, num_w=512, eps=1e-5, momentum=0.1): super(AdaptiveInstanceNorm2d, self).__init__() self.num_features = num_features self.eps = eps self.momentum = momentum # just dummy buffers, not used self.register_buffer('running_mean', torch.zeros(num_features)) self.register_buffer('running_var', torch.ones(num_features)) # projection layer self.weight_proj = nn.Linear(num_w, num_features) self.bias_proj = nn.Linear(num_w, num_features) def forward(self, x, w): b, c = x.size(0), x.size(1) running_mean = self.running_mean.repeat(b) running_var = self.running_var.repeat(b) weight, bias = self.weight_proj(w).contiguous().view(-1) + 1, self.bias_proj(w).contiguous().view(-1) # Apply instance norm x_reshaped = x.contiguous().view(1, b * c, *x.size()[2:]) out = F.batch_norm( x_reshaped, running_mean, running_var, weight, bias, True, self.momentum, self.eps) return out.view(b, c, *x.size()[2:]) def __repr__(self): return self.__class__.__name__ + '(' + str(self.num_features) + ')' class SpatialAdaptiveInstanceNorm2d(nn.Module): def __init__(self, num_features, num_w=512, eps=1e-5, momentum=0.1): super(SpatialAdaptiveInstanceNorm2d, self).__init__() self.num_features = num_features self.eps = eps self.momentum = momentum # just dummy buffers, not used self.register_buffer('running_mean', torch.zeros(num_features)) self.register_buffer('running_var', torch.ones(num_features)) # projection layer self.weight_proj = nn.Linear(num_w, num_features) self.bias_proj = nn.Linear(num_w, num_features) def forward(self, x, w, bbox): b, c, h, w = x.size() running_mean = self.running_mean.repeat(b) running_var = self.running_var.repeat(b) return x class AdaptiveBatchNorm2d(nn.BatchNorm2d): def __init__(self, num_features, num_w=512, eps=1e-5, momentum=0.1, affine=False, track_running_stats=True): super(AdaptiveBatchNorm2d, self).__init__( num_features, eps, momentum, affine, track_running_stats ) # projection layer self.weight_proj = nn.Linear(num_w, num_features) self.bias_proj = nn.Linear(num_w, num_features) def forward(self, x, w): self._check_input_dim(x) exponential_average_factor = 0.0 if self.training and self.track_running_stats: self.num_batches_tracked += 1 if self.momentum is None: # use cumulative moving average exponential_average_factor = 1.0 / self.num_batches_tracked.item() else: # use exponential moving average exponential_average_factor = self.momentum output = F.batch_norm(x, self.running_mean, self.running_var, self.weight, self.bias, self.training or not self.track_running_stats, exponential_average_factor, self.eps) size = output.size() weight, bias = self.weight_proj(w) + 1, self.bias_proj(w) weight = weight.unsqueeze(-1).unsqueeze(-1).expand(size) bias = bias.unsqueeze(-1).unsqueeze(-1).expand(size) return weight * output + bias def __repr__(self): return self.__class__.__name__ + '(' + str(self.num_features) + ')' class SpatialAdaptiveBatchNorm2d(nn.BatchNorm2d): def __init__(self, num_features, num_w=512, eps=1e-5, momentum=0.1, affine=False, track_running_stats=True): super(SpatialAdaptiveBatchNorm2d, self).__init__( num_features, eps, momentum, affine, track_running_stats ) # projection layer self.weight_proj = nn.Linear(num_w, num_features) self.bias_proj = nn.Linear(num_w, num_features) def forward(self, x, vector, bbox): """ :param x: input feature map (b, c, h, w) :param vector: latent vector (b*o, dim_w) :param bbox: bbox map (b, o, h, w) :return: """ self._check_input_dim(x) exponential_average_factor = 0.0 if self.training and self.track_running_stats: self.num_batches_tracked += 1 if self.momentum is None: # use cumulative moving average exponential_average_factor = 1.0 / self.num_batches_tracked.item() else: # use exponential moving average exponential_average_factor = self.momentum output = F.batch_norm(x, self.running_mean, self.running_var, self.weight, self.bias, self.training or not self.track_running_stats, exponential_average_factor, self.eps) b, o, _, _ = bbox.size() _, _, h, w = x.size() bbox = F.interpolate(bbox, size=(h, w), mode='bilinear') # calculate weight and bias weight, bias = self.weight_proj(vector), self.bias_proj(vector) weight, bias = weight.view(b, o, -1), bias.view(b, o, -1) weight = torch.sum(bbox.unsqueeze(2) * weight.unsqueeze(-1).unsqueeze(-1), dim=1, keepdim=False) / \ (torch.sum(bbox.unsqueeze(2), dim=1, keepdim=False) + 1e-6) + 1 bias = torch.sum(bbox.unsqueeze(2) * bias.unsqueeze(-1).unsqueeze(-1), dim=1, keepdim=False) / \ (torch.sum(bbox.unsqueeze(2), dim=1, keepdim=False) + 1e-6) return weight * output + bias def __repr__(self): return self.__class__.__name__ + '(' + str(self.num_features) + ')' from .sync_batchnorm import SynchronizedBatchNorm2d class SpatialAdaptiveSynBatchNorm2d(nn.Module): def __init__(self, num_features, num_w=512, batchnorm_func=SynchronizedBatchNorm2d, eps=1e-5, momentum=0.1, affine=False, track_running_stats=True): super(SpatialAdaptiveSynBatchNorm2d, self).__init__() # projection layer self.num_features = num_features self.weight_proj = nn.utils.spectral_norm(nn.Linear(num_w, num_features)) self.bias_proj = nn.utils.spectral_norm(nn.Linear(num_w, num_features)) self.batch_norm2d = batchnorm_func(num_features, eps=eps, momentum=momentum, affine=affine) def forward(self, x, vector, bbox): """ :param x: input feature map (b, c, h, w) :param vector: latent vector (b*o, dim_w) :param bbox: bbox map (b, o, h, w) :return: """ # self._check_input_dim(x) output = self.batch_norm2d(x) b, o, bh, bw = bbox.size() _, _, h, w = x.size() if bh != h or bw != w: bbox = F.interpolate(bbox, size=(h, w), mode='bilinear') # calculate weight and bias weight, bias = self.weight_proj(vector), self.bias_proj(vector) weight, bias = weight.view(b, o, -1), bias.view(b, o, -1) weight = torch.sum(bbox.unsqueeze(2) * weight.unsqueeze(-1).unsqueeze(-1), dim=1, keepdim=False) / \ (torch.sum(bbox.unsqueeze(2), dim=1, keepdim=False) + 1e-6) + 1 bias = torch.sum(bbox.unsqueeze(2) * bias.unsqueeze(-1).unsqueeze(-1), dim=1, keepdim=False) / \ (torch.sum(bbox.unsqueeze(2), dim=1, keepdim=False) + 1e-6) return weight * output + bias def __repr__(self): return self.__class__.__name__ + '(' + str(self.num_features) + ')'

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0

null

0

1

0

null

0

6

a77d52eea372d1cbcf810c4f1e88cab29b5063d1

138

py

Python

pyaws/AWSLambda/__init__.py

mwozniczak/pyaws

af8f6d64ff47fd2ef2eb9fef25680e4656523fa3

[ "MIT" ]

null

pyaws/AWSLambda/__init__.py

mwozniczak/pyaws

af8f6d64ff47fd2ef2eb9fef25680e4656523fa3

[ "MIT" ]

null

pyaws/AWSLambda/__init__.py

mwozniczak/pyaws

af8f6d64ff47fd2ef2eb9fef25680e4656523fa3

[ "MIT" ]

null

""" Functional Utilities for AWS Lambda """ from pyaws.AWSLambda.lambda_utils import * from pyaws.AWSLambda.env import read_env_variable

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0.333333

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0.115942

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null

0

1

0

1

0

1

0

6

a78d1e20aa9e77cde2319c9822147f8c21a9c3a6

102

py

Python

pyxwb2/models/exceptions.py

minsis/pyxwb2

e3f9c898a5669b47bb5b8ab344fdcb37fc98d7f0

[ "MIT" ]

null

pyxwb2/models/exceptions.py

minsis/pyxwb2

e3f9c898a5669b47bb5b8ab344fdcb37fc98d7f0

[ "MIT" ]

null

pyxwb2/models/exceptions.py

minsis/pyxwb2

e3f9c898a5669b47bb5b8ab344fdcb37fc98d7f0

[ "MIT" ]

null

class PilotsMissingException(Exception): pass class FactionMissingException(Exception): pass

17

41

0.794118

8

102

10.125

0.625

0.320988

0

0.147059

102

6

42

17

0.931034

0

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0

1

0

true

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0

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0

1

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null

1

0

1

0

null

0

1

0

6

a7a02cef8d1a6a603b33213f6d3cd29588ccb9e6

20

py

Python

TailScout/tailscout_app/models/__init__.py

MihirSachdeva/IIT_Roorkee_India

3916e6a3f33596a6eae0ae6c1e38b70645196a49

[ "MIT" ]

2

2020-10-28T08:11:40.000Z

2020-12-07T14:29:12.000Z

TailScout/tailscout_app/models/__init__.py

MihirSachdeva/IIT_Roorkee_India

3916e6a3f33596a6eae0ae6c1e38b70645196a49

[ "MIT" ]

null

TailScout/tailscout_app/models/__init__.py

MihirSachdeva/IIT_Roorkee_India

3916e6a3f33596a6eae0ae6c1e38b70645196a49

[ "MIT" ]

1

2020-10-23T22:29:49.000Z

from .job import Job

20

0.8

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20

4

0.75

0

0.15

20

1

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0.941176

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0

true

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6

ac19e8c149b818b8fa44bd83af7e40e40a680b7a

26

py

Python

packages/pytea/pylib/torch/utils/__init__.py

lego0901/pytea

8ede650def2e68f4610ba816451d8b9e28f09f76

[ "MIT" ]

241

2021-03-19T01:11:44.000Z

2022-03-25T03:15:22.000Z

packages/pytea/pylib/torch/utils/__init__.py

lego0901/pytea

8ede650def2e68f4610ba816451d8b9e28f09f76

[ "MIT" ]

2

2021-02-26T08:16:04.000Z

2022-02-28T02:52:58.000Z

packages/pytea/pylib/torch/utils/__init__.py

lego0901/pytea

8ede650def2e68f4610ba816451d8b9e28f09f76

[ "MIT" ]

14

2021-01-08T02:22:58.000Z

2022-01-19T14:13:14.000Z

from . import data as data

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0.769231

5

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1

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1

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1

0

1

0

6

ac3b7eaded551b4a4b25c2f15b8752a231eba205

3,936

py

Python

character/migrations/0002_auto_20200920_2245.py

SamusChief/myth-caster-api

76a43f48b70c6a4b509c90757d7906689799cc25

[ "MIT" ]

null

character/migrations/0002_auto_20200920_2245.py

SamusChief/myth-caster-api

76a43f48b70c6a4b509c90757d7906689799cc25

[ "MIT" ]

null

character/migrations/0002_auto_20200920_2245.py

SamusChief/myth-caster-api

76a43f48b70c6a4b509c90757d7906689799cc25

[ "MIT" ]

1

2021-08-14T18:46:52.000Z

# Generated by Django 3.1.1 on 2020-09-20 22:45 from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('character', '0001_initial'), ] operations = [ migrations.AlterField( model_name='ancestry', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_ancestry_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='background', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_background_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='character', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_character_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='characterclass', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_characterclass_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='classandlevel', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_classandlevel_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='feature', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_feature_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='featuresatlevel', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_featuresatlevel_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='inventoryadventuringgear', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_inventoryadventuringgear_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='inventoryarmor', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_inventoryarmor_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='inventorytool', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_inventorytool_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='inventoryweapon', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_inventoryweapon_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='inventorywondrousitem', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_inventorywondrousitem_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='skillproficiency', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_skillproficiency_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='spellslotsatlevel', name='authorized_editors', field=models.ManyToManyField(blank=True, related_name='_spellslotsatlevel_authorized_editors_+', to=settings.AUTH_USER_MODEL), ), ]

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6

ac73383a1c4126e4d6b278860b91753dbc8f3340

109

py

Python

relbert/prompt/__init__.py

asahi417/relbert

cb718e40fb452e88ccae1c271ccdea25013791b1

[ "MIT" ]

17

2021-09-10T14:49:41.000Z

2022-01-26T13:18:02.000Z

relbert/prompt/__init__.py

asahi417/relbert

cb718e40fb452e88ccae1c271ccdea25013791b1

[ "MIT" ]

2

2021-11-14T07:47:36.000Z

2021-11-22T17:34:06.000Z

relbert/prompt/__init__.py

asahi417/relbert

cb718e40fb452e88ccae1c271ccdea25013791b1

[ "MIT" ]

1

2021-12-14T01:35:05.000Z

from .discrete_tuning import GradientTriggerSearch from .continuous_tuning import ContinuousTriggerEmbedding

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1

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6

ac862584b336e085680f1e3395d7b578190454ca

164

py

Python

open_alchemy/schemas/helpers/__init__.py

rgreinho/OpenAlchemy

23202bdecb94763d09b6d9e84eb9b29506c811ae

[ "Apache-2.0" ]

null

open_alchemy/schemas/helpers/__init__.py

rgreinho/OpenAlchemy

23202bdecb94763d09b6d9e84eb9b29506c811ae

[ "Apache-2.0" ]

53

2020-12-30T15:32:55.000Z

2022-03-31T10:07:00.000Z

open_alchemy/schemas/helpers/__init__.py

rgreinho/OpenAlchemy

23202bdecb94763d09b6d9e84eb9b29506c811ae

[ "Apache-2.0" ]

null

"""Helper functions for processing the schemas.""" from . import association from . import backref from . import clean from . import iterate from . import process

20.5

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1

0

1

0

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6

3bbd1ab196e422a5e1723896f596e8f23e9e2a99

233

py

Python

range-function/range.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

range-function/range.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

range-function/range.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

print('-------------------------------------------------------------------------') for i in range(1,10): print(i) print('thank you') print('-------------------------------------------------------------------------')

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6

ce09f6f5a72d97ff2e70c11ce694e0a8d62d266b

177

py

Python

Python/packages/databricks-test/tests/widgets_test.py

anandmrya/DataOps

1a671c707e27b30030687a2a88e5fa94374ce780

[ "MIT" ]

42

2019-12-04T04:10:53.000Z

2022-03-31T13:04:17.000Z

Python/packages/databricks-test/tests/widgets_test.py

anandmrya/DataOps

1a671c707e27b30030687a2a88e5fa94374ce780

[ "MIT" ]

2

2020-02-25T11:24:34.000Z

2020-03-05T06:12:59.000Z

Python/packages/databricks-test/tests/widgets_test.py

anandmrya/DataOps

1a671c707e27b30030687a2a88e5fa94374ce780

[ "MIT" ]

18

2020-01-25T06:25:08.000Z

2021-11-16T08:40:09.000Z

import databricks_test def test_widgets(): with databricks_test.session() as dbrickstest: # Run notebook dbrickstest.run_notebook(".", "widgets_notebook")

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0

1

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6

ce2937a1f28ddc9e0f27653d1d22065e6e2408a4

4,570

py

Python

test/unitTests/nodeTests/testFloatValueNode.py

pieter-hendriks/STL-monitoring

114b73b1f4b0687b11b8842b3c4a1c8af7b0d9df

[ "MIT" ]

null

test/unitTests/nodeTests/testFloatValueNode.py

pieter-hendriks/STL-monitoring

114b73b1f4b0687b11b8842b3c4a1c8af7b0d9df

[ "MIT" ]

null

test/unitTests/nodeTests/testFloatValueNode.py

pieter-hendriks/STL-monitoring

114b73b1f4b0687b11b8842b3c4a1c8af7b0d9df

[ "MIT" ]

null

import unittest from stl.tree import FloatValueNode from stl.signals import Signal, SignalList, BooleanSignal from typing import Iterable class FloatValueNodeTest(unittest.TestCase): def setUp(self): pass def testNegativeZeroValue(self): # Str because that's the data type the node gets values: Iterable[str] = ['-', '0'] # Create the node and read tokens node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [0, 0], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) def testFractionalZeroValue(self): # Str because that's the data type the node gets values: Iterable[str] = ['0', '.', '0'] # Create the node and read tokens node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [0, 0], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) def testNegativeFractionalZeroValue(self): # Str because that's the data type the node gets values: Iterable[str] = ['-', '0', '.', '0'] # Create the node and read tokens node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [0, 0], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) def testZeroValue(self): # Str because that's the data type the node gets values: Iterable[str] = ['0'] # Create the node and read tokens node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [0, 0], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) def testWholePositiveValue(self): # Str because that's the data type the node gets values: Iterable[str] = ['123'] # Create the node and read tokens node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [123, 123], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) def testFractionalPositiveValue(self): # Str because that's the data type the node gets values: str = ['123', '.', '456'] # Create the node and read tokens node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [123.456, 123.456], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) def testWholeNegativeValue(self): # Str because that's the data type the node gets values: Iterable[str] = ['-', '123'] # Create the node and read token node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [-123, -123], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) def testFractionalNegativeValue(self): # Str because that's the data type the node gets values: Iterable[str] = ['-', '123', '.', '456'] # Create the node and read token node: FloatValueNode = FloatValueNode() for value in values: node.processToken(value) # Create expected result and compare the two expectedSignal: Signal = Signal("ValueNodeSignal", [0, float('inf')], [-123.456, -123.456], [0, 0]) self.assertEqual(node.quantitativeValidate(None, None), expectedSignal) self.assertEqual(node.booleanValidate(None, None), expectedSignal) if __name__ == "__main__": unittest.main()

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null

0

1

0

null

0

6

020fa9ab5c5a52035dc50e6bc24f34f3cc0a8b7d

27

py

Python

gQiwiAPI/__init__.py

gnifajio/gQiwiAPI

bae74bf11c070410383146674a154c0ffd7b6f8c

[ "MIT" ]

null

gQiwiAPI/__init__.py

gnifajio/gQiwiAPI

bae74bf11c070410383146674a154c0ffd7b6f8c

[ "MIT" ]

null

gQiwiAPI/__init__.py

gnifajio/gQiwiAPI

bae74bf11c070410383146674a154c0ffd7b6f8c

[ "MIT" ]

null

from .API import Qiwi, Bill

27

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4.2

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0

null

0

1

0

1

0

1

0

6

022bf55dd2c1c11291d3282e22f643abfc8c38e5

4,845

py

Python

tests/validation/parameter/test_enum_validation.py

maroux/flex

dfd7c6d79d065d7ce1b0c799e51e9bb5292612b2

[ "MIT" ]

160

2015-01-15T05:36:44.000Z

2021-08-04T00:43:54.000Z

tests/validation/parameter/test_enum_validation.py

maroux/flex

dfd7c6d79d065d7ce1b0c799e51e9bb5292612b2

[ "MIT" ]

151

2015-01-20T16:45:36.000Z

2022-02-23T21:07:58.000Z

tests/validation/parameter/test_enum_validation.py

maroux/flex

dfd7c6d79d065d7ce1b0c799e51e9bb5292612b2

[ "MIT" ]

90

2015-01-20T11:19:36.000Z

2021-08-03T08:58:18.000Z

import pytest import os from flex.exceptions import ValidationError from flex.loading.schema.paths.path_item.operation.parameters import ( parameters_validator, ) from flex.validation.parameter import ( validate_parameters, ) from flex.constants import ( PATH, STRING, NUMBER, BOOLEAN, FLEX_DISABLE_X_NULLABLE ) from flex.error_messages import MESSAGES from tests.utils import assert_message_in_errors # # enum validation tests # @pytest.mark.parametrize( 'enum,value', ( ([True, False], 0), ([True, False], 1), ([True, False], ''), ([True, False], None), ([0, 1, 2, 3], True), ([0, 1, 2, 3], False), ([0, 1, 2, 3], '1'), ([0, 1, 2, 3], 4), ([0, 1, 2, 3], 1.0), ([0, 1, 2, 3], None), (['1', '2', 'a', 'b'], 'A'), (['1', '2', 'a', 'b'], 1), (['1', '2', 'a', 'b'], 2), (['1', '2', 'a', 'b'], None), ), ) def test_enum_validation_with_invalid_values(enum, value): parameters = parameters_validator([ { 'name': 'id', 'in': PATH, 'description': 'id', 'type': [STRING, NUMBER, BOOLEAN], 'required': True, 'enum': enum, }, ]) parameter_values = { 'id': value, } with pytest.raises(ValidationError) as err: validate_parameters(parameter_values, parameters, {}) assert_message_in_errors( MESSAGES['enum']['invalid'], err.value.detail, 'id.enum', ) @pytest.mark.parametrize( 'enum,value', ( ([True, False], True), ([True, False], False), ([0, 1, 2, 3], 3), ([0, 1, 2, 3], 1), (['1', '2', 'a', 'b'], 'a'), (['1', '2', 'a', 'b'], '1'), ), ) def test_enum_validation_with_allowed_values(enum, value): parameters = parameters_validator([ { 'name': 'id', 'in': PATH, 'description': 'id', 'type': [STRING, NUMBER, BOOLEAN], 'required': True, 'enum': enum, }, ]) parameter_values = { 'id': value, } validate_parameters(parameter_values, parameters, {}) @pytest.mark.parametrize( 'enum,value', ( ([True, False], True), ([True, False], None), ([0, 1, 2, 3], 1), ([0, 1, 2, 3], None), (['1', '2', 'a', 'b'], 'a'), (['1', '2', 'a', 'b'], None), ), ) def test_nullable_enum_validation_with_allowed_values(enum, value): parameters = parameters_validator([ { 'name': 'id', 'in': PATH, 'description': 'id', 'type': [STRING, NUMBER, BOOLEAN], 'required': True, 'enum': enum, 'x-nullable': True }, ]) parameter_values = { 'id': value, } validate_parameters(parameter_values, parameters, {}) @pytest.mark.parametrize( 'enum,value', ( ([True, False], None), ([0, 1, 2, 3], None), (['1', '2', 'a', 'b'], None), ), ) def test_nullable_enum_with_null_values_strict(enum, value, monkeypatch): parameters = parameters_validator([ { 'name': 'id', 'in': PATH, 'description': 'id', 'type': [STRING, NUMBER, BOOLEAN], 'required': True, 'enum': enum, 'x-nullable': True }, ]) parameter_values = { 'id': value, } monkeypatch.setattr(os, 'environ', {FLEX_DISABLE_X_NULLABLE: '1'}) with pytest.raises(ValidationError) as err: validate_parameters(parameter_values, parameters, {}) assert_message_in_errors( MESSAGES['enum']['invalid'], err.value.detail, 'id.enum', ) @pytest.mark.parametrize( 'enum,value', ( ([True, False], 0), ([True, False], 1), ([True, False], ''), ([0, 1, 2, 3], True), ([0, 1, 2, 3], False), ([0, 1, 2, 3], '1'), ([0, 1, 2, 3], 4), ([0, 1, 2, 3], 1.0), (['1', '2', 'a', 'b'], 'A'), (['1', '2', 'a', 'b'], 1), (['1', '2', 'a', 'b'], 2), ), ) def test_nullable_enum_with_invalid_values(enum, value): parameters = parameters_validator([ { 'name': 'id', 'in': PATH, 'description': 'id', 'type': [STRING, NUMBER, BOOLEAN], 'required': True, 'enum': enum, 'x-nullable': True }, ]) parameter_values = { 'id': value, } with pytest.raises(ValidationError) as err: validate_parameters(parameter_values, parameters, {}) assert_message_in_errors( MESSAGES['enum']['invalid'], err.value.detail, 'id.enum', )

23.519417

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null

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1

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null

0

6

65fe5a9b5f506d5fdb0dc3a647a62624dcb16a0a

42

py

Python

torchcv/engine/__init__.py

RJaikanth/torch-cv

8102aaae840b674389f09a01c5c45df559cb3819

[ "MIT" ]

1

2020-10-10T11:40:43.000Z

torchcv/engine/__init__.py

RJaikanth/torch-cv

8102aaae840b674389f09a01c5c45df559cb3819

[ "MIT" ]

null

torchcv/engine/__init__.py

RJaikanth/torch-cv

8102aaae840b674389f09a01c5c45df559cb3819

[ "MIT" ]

null

from .preprocess import PREPROCESS_ENGINE

21

41

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5

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7.2

0.8

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42

1

42

0.947368

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null

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1

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1

0

1

0

6

5a2263d0dbdf57a703525d78da00210c9495a79f

167

py

Python

pybamview/tests/__init__.py

mgymrek/pybamview

719c4251769510260d29287074845650b399a3d0

[ "MIT" ]

37

2015-01-26T01:06:57.000Z

2021-04-16T05:48:39.000Z

pybamview/tests/__init__.py

gymreklab/pybamview

719c4251769510260d29287074845650b399a3d0

[ "MIT" ]

10

2015-01-10T12:22:27.000Z

2018-11-17T09:13:07.000Z

pybamview/tests/__init__.py

gymreklab/pybamview

719c4251769510260d29287074845650b399a3d0

[ "MIT" ]

11

2015-01-21T12:58:14.000Z

2021-06-29T10:42:32.000Z

from os.path import dirname, join from pybamview.tests import __file__ as test_directory def test_data(path): return join(dirname(test_directory), 'data', path)

23.857143

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167

4.92

0.6

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167

6

55

27.833333

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false

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null

0

1

0

1

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6

5a6f2056accdd38a4839205c7ad9ea2cd73bbc6e

133

py

Python

src/cysofa/cypyx/test.py

MattTurnock/cysofa

15e95288937b765df561a65e24faf780f9e59bd4

[ "MIT" ]

null

src/cysofa/cypyx/test.py

MattTurnock/cysofa

15e95288937b765df561a65e24faf780f9e59bd4

[ "MIT" ]

null

src/cysofa/cypyx/test.py

MattTurnock/cysofa

15e95288937b765df561a65e24faf780f9e59bd4

[ "MIT" ]

1

2018-12-08T21:10:06.000Z

import os import sys #print(os.path.abspath('../../..')) def tester(): """ here are some things but idk """ return 0

14.777778

35

0.556391

18

133

4.111111

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0

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0.24812

133

9

36

14.777778

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0.473684

0

1

0.25

true

0

0.5

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null

0

1

0

1

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1

0

6

5a8b45683943b6267de692c63187c3fb09c21276

288

py

Python

rfdesigner/components/filter/__init__.py

fronzbot/python-rfdesigner

3e78722f030efc327a68945b6a09d7cdbf42734d

[ "Apache-2.0" ]

1

2022-01-28T17:50:08.000Z

rfdesigner/components/filter/__init__.py

fronzbot/python-rfdesigner

3e78722f030efc327a68945b6a09d7cdbf42734d

[ "Apache-2.0" ]

3

2020-06-02T17:23:12.000Z

2020-06-02T22:29:04.000Z

rfdesigner/components/filter/__init__.py

fronzbot/python-rfdesigner

3e78722f030efc327a68945b6a09d7cdbf42734d

[ "Apache-2.0" ]

null

"""Initialize the filter classes.""" from rfdesigner.components import Passive class LPF(Passive): """Representation of a low pass filter.""" class HPF(Passive): """Representation of a high pass filter.""" class BPF(Passive): """Representation of a band pass filter."""

19.2

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null

0

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1

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6

ced9a71142a99213ff9a0ccf1238f835fc74bd6f

102

py

Python

routes/api.py

RafaelGSS/pylack

a91a7b76102b60522176e47647744e8fb2421e61

[ "MIT" ]

2

2018-05-14T22:55:43.000Z

2018-05-16T12:57:52.000Z

routes/api.py

RafaelGSS/HappyAnalytics

a91a7b76102b60522176e47647744e8fb2421e61

[ "MIT" ]

null

routes/api.py

RafaelGSS/HappyAnalytics

a91a7b76102b60522176e47647744e8fb2421e61

[ "MIT" ]

null

from bootstrap.main_app import app @app.route('/api/v1') def api_index(): return 'Hello worlds!'

17

34

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35

17

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1

0.25

true

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null

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1

0

null

0

1

0

1

0

6

0c68789c65062d0defab5324e61177fe631829fe

170

py

Python

src/procmedia/help.py

miki164/procmedia

ebe2ed1a886c4cbe83bdf5e73f26386a602e4c0b

[ "MIT" ]

null

src/procmedia/help.py

miki164/procmedia

ebe2ed1a886c4cbe83bdf5e73f26386a602e4c0b

[ "MIT" ]

null

src/procmedia/help.py

miki164/procmedia

ebe2ed1a886c4cbe83bdf5e73f26386a602e4c0b

[ "MIT" ]

null

def show_help(): print("Procmedia help:") print("-detect path_to_media path_to_haarcascade Optional: output_name") print("Applies haarcascade to image/video")

42.5

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4

77

42.5

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true

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null

0

1

0

1

0

6

0c85f4634b88b7d12c9607fc258c8b78f29eaa3d

39

py

Python

tests/__init__.py

fullatron/generatER

df0d3b0d5cb34481cf358955116808ef170fd7e3

[ "MIT" ]

1

2021-03-24T13:22:23.000Z

tests/__init__.py

fullatron/generatER

df0d3b0d5cb34481cf358955116808ef170fd7e3

[ "MIT" ]

null

tests/__init__.py

fullatron/generatER

df0d3b0d5cb34481cf358955116808ef170fd7e3

[ "MIT" ]

1

2021-03-24T13:22:30.000Z

"""Unit test package for generater."""

19.5

38

0.692308

5

39

5.4

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1

39

0.794118

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null

0

1

0

6

0cc4f8b9fbcfcd3adad4c8c9c7c5c5743847ec1c

5,585

py

Python

integration/structure-local/test.py

xbabka01/retdec-regression-tests

1ac40cca5165740364e6f7fb72b20820eac9bc7c

[ "MIT" ]

8

2017-12-14T14:25:17.000Z

2019-03-09T03:29:12.000Z

integration/structure-local/test.py

xbabka01/retdec-regression-tests

1ac40cca5165740364e6f7fb72b20820eac9bc7c

[ "MIT" ]

10

2019-06-14T09:12:55.000Z

2021-10-01T12:15:43.000Z

integration/structure-local/test.py

xbabka01/retdec-regression-tests

1ac40cca5165740364e6f7fb72b20820eac9bc7c

[ "MIT" ]

8

2019-05-10T14:59:48.000Z

2022-03-07T16:34:23.000Z

from regression_tests import * class TestBase(Test): def test_check_function_fnc_basic_print(self): assert self.out_c.has_func('fnc_basic_print') assert self.out_c.funcs['fnc_basic_print'].return_type.is_int(32) assert self.out_c.funcs['fnc_basic_print'].param_count == 1 #ssert self.out_c.funcs['fnc_basic_print'].params[0].type.is_pointer() #assert self.out_c.funcs['fnc_basic_print'].params[0].type.point_type.is_struct() assert self.out_c.funcs['fnc_basic_print'].calls('printf') def test_check_function_fnc_basic(self): assert self.out_c.has_func('fnc_basic') assert self.out_c.funcs['fnc_basic'].return_type.is_void() assert self.out_c.funcs['fnc_basic'].param_count == 0 assert self.out_c.funcs['fnc_basic'].calls('malloc') assert self.out_c.funcs['fnc_basic'].calls('scanf') assert self.out_c.funcs['fnc_basic'].calls('printf') assert self.out_c.funcs['fnc_basic'].calls('fnc_basic_print') def test_check_function_fnc_complex_print(self): assert self.out_c.funcs['fnc_complex_print'].return_type.is_void() #assert self.out_c.funcs['fnc_complex_print'].param_count == 1 #assert self.out_c.funcs['fnc_complex_print'].params[0].type.is_pointer() #assert self.out_c.funcs['fnc_complex_print'].params[0].type.point_type.is_struct() assert self.out_c.funcs['fnc_complex_print'].calls('printf') assert self.out_c.funcs['fnc_complex_print'].has_any_for_loops() or self.out_c.funcs['fnc_complex_print'].has_any_while_loops() def test_check_function_fnc_complex(self): assert self.out_c.has_func('fnc_complex') assert self.out_c.funcs['fnc_complex'].return_type.is_int(32) assert self.out_c.funcs['fnc_complex'].param_count == 0 assert self.out_c.funcs['fnc_complex'].has_any_for_loops() or self.out_c.funcs['fnc_complex'].has_any_while_loops() assert self.out_c.funcs['fnc_complex'].calls('malloc') assert self.out_c.funcs['fnc_complex'].has_any_return_stmts() #assert self.out_c.funcs['fnc_complex'].has_return_stmts('return 0') def test_check_function_fnc_sasa_fill(self): assert self.out_c.has_func('fnc_sasa_fill') assert self.out_c.funcs['fnc_sasa_fill'].return_type.is_void() assert self.out_c.funcs['fnc_sasa_fill'].param_count == 1 #assert self.out_c.funcs['fnc_sasa_fill'].params[0].type.is_pointer() #assert self.out_c.funcs['fnc_sasa_fill'].params[0].type.point_type.is_pointer() #assert self.out_c.funcs['fnc_sasa_fill'].params[0].type.point_type.point_type.is_struct() #assert self.out_c.funcs['fnc_sasa_fill'].calls('malloc') def test_check_function_fnc_sasa_print(self): assert self.out_c.has_func('fnc_sasa_print') assert self.out_c.funcs['fnc_sasa_print'].return_type.is_void() assert self.out_c.funcs['fnc_sasa_print'].param_count == 1 #assert self.out_c.funcs['fnc_sasa_print'].params[0].type.is_pointer() #assert self.out_c.funcs['fnc_sasa_print'].params[0].type.point_type.is_struct() assert self.out_c.funcs['fnc_sasa_print'].calls('printf') assert self.out_c.funcs['fnc_sasa_print'].has_any_for_loops() or self.out_c.funcs['fnc_sasa_print'].has_any_while_loops() def test_check_function_fnc_sasa(self): assert self.out_c.has_func('fnc_sasa') assert self.out_c.funcs['fnc_sasa'].return_type.is_int(32) assert self.out_c.funcs['fnc_sasa'].param_count == 0 assert self.out_c.funcs['fnc_sasa'].calls('malloc') assert self.out_c.funcs['fnc_sasa'].calls('fnc_sasa_fill') assert self.out_c.funcs['fnc_sasa'].calls('fnc_sasa_print') assert self.out_c.funcs['fnc_sasa'].has_any_return_stmts() #assert self.out_c.funcs['fnc_sasa'].has_return_stmts('return 0') def test_check_function_main(self): assert self.out_c.has_func('main') assert self.out_c.funcs['main'].calls('fnc_basic') assert self.out_c.funcs['main'].calls('fnc_complex') assert self.out_c.funcs['main'].calls('fnc_sasa') assert self.out_c.funcs['main'].has_any_return_stmts() assert self.out_c.funcs['main'].has_return_stmts('return 0') def test_check_presence_of_literals(self): #assert self.out_c.has_string_literal("\\n") assert self.out_c.has_string_literal("%d\\n") assert self.out_c.has_string_literal("%d %d\\n") assert self.out_c.has_string_literal("%f %d %d\\n") assert self.out_c.has_string_literal("%d %d %f\\n") #assert self.out_c.has_string_literal("%c %d %f\\n") assert self.out_c.has_string_literal("%d %d %d %f\\n") class Test_2017(TestBase): settings_2017 = TestSettings( input=files_in_dir('2017-11-14'), ) #class Test_2015(TestBase): #settings_2015 = TestSettings( #input=files_in_dir('2015-03-30'), #) class TestRun(TestBase): def test_produce_expected_output(self): if not on_macos(): self.assert_c_produces_output_when_run( input='a 10 3.1415', expected_return_code=0, expected_output= '''97 10 3.140000 3.140000 10 97 123 97 3.140000 1 2 3 0.000000 3 4 5 4.140000 5 6 7 8.280001 7 8 9 12.420000 9 10 11 16.560001 123 456 0 55 65 1 65 75 2 75 85 3 85 95 4 95 105 5 105 115 6 115 125 7 125 135 8 135 145 9 145 155 ''' ) class Test_2018(TestBase): settings_2018 = TestSettings( input=files_in_dir('2018-09-17'), )

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0.711511

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0.050928

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5,585

155

136

36.032258

0.715796

0.200179

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null

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0

null

0

1

0

6

0cda3be79187411fb4467d1a002d751625da8f6b

36

py

Python

holistic_records/__init__.py

visinf/mnvi

654b68888f86e008c9b686950f7f3e493b47c011

[ "Apache-2.0" ]

null

holistic_records/__init__.py

visinf/mnvi

654b68888f86e008c9b686950f7f3e493b47c011

[ "Apache-2.0" ]

null

holistic_records/__init__.py

visinf/mnvi

654b68888f86e008c9b686950f7f3e493b47c011

[ "Apache-2.0" ]

1

2021-11-24T09:51:55.000Z

from .recorder import EpochRecorder

18

35

0.861111

4

36

7.75

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0

0.111111

36

1

36

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0

1

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0

null

0

1

0

1

0

1

0

6

0ce6a8dd77fb3fc28eb0b904592a01a777af62ba

83,091

py

Python

test/unit/test_direct_link_provider_v2.py

IBM/networking-services-python-sdk

a19e47db6a5971562a502982d69a5868997245f3

[ "Apache-2.0" ]

1

2022-03-26T18:20:42.000Z

test/unit/test_direct_link_provider_v2.py

IBM/networking-services-python-sdk

a19e47db6a5971562a502982d69a5868997245f3

[ "Apache-2.0" ]

null

test/unit/test_direct_link_provider_v2.py

IBM/networking-services-python-sdk

a19e47db6a5971562a502982d69a5868997245f3

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # (C) Copyright IBM Corp. 2021. # # 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. """ Unit Tests for DirectLinkProviderV2 """ from datetime import datetime, timezone from ibm_cloud_sdk_core.authenticators.no_auth_authenticator import NoAuthAuthenticator from ibm_cloud_sdk_core.utils import datetime_to_string, string_to_datetime import inspect import json import os import pytest import re import requests import responses import urllib from ibm_cloud_networking_services.direct_link_provider_v2 import * version = 'testString' _service = DirectLinkProviderV2( authenticator=NoAuthAuthenticator(), version=version ) _base_url = 'https://directlink.cloud.ibm.com/provider/v2' _service.set_service_url(_base_url) ############################################################################## # Start of Service: ProviderAPIs ############################################################################## # region class TestNewInstance(): """ Test Class for new_instance """ def test_new_instance(self): """ new_instance() """ os.environ['TEST_SERVICE_AUTH_TYPE'] = 'noAuth' service = DirectLinkProviderV2.new_instance( version=version, service_name='TEST_SERVICE', ) assert service is not None assert isinstance(service, DirectLinkProviderV2) def test_new_instance_without_authenticator(self): """ new_instance_without_authenticator() """ with pytest.raises(ValueError, match='authenticator must be provided'): service = DirectLinkProviderV2.new_instance( version=version, ) def test_new_instance_without_required_params(self): """ new_instance_without_required_params() """ with pytest.raises(TypeError, match='new_instance\$\$ missing \\d required positional arguments?: \'.*\''): service = DirectLinkProviderV2.new_instance() def test_new_instance_required_param_none(self): """ new_instance_required_param_none() """ with pytest.raises(ValueError, match='version must be provided'): service = DirectLinkProviderV2.new_instance( version=None, ) class TestListProviderGateways(): """ Test Class for list_provider_gateways """ def preprocess_url(self, request_url: str): """ Preprocess the request URL to ensure the mock response will be found. """ request_url = urllib.parse.unquote(request_url) # don't double-encode if already encoded request_url = urllib.parse.quote(request_url, safe=':/') if re.fullmatch('.*/+', request_url) is None: return request_url else: return re.compile(request_url.rstrip('/') + '/+') @responses.activate def test_list_provider_gateways_all_params(self): """ list_provider_gateways() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways') mock_response = '{"first": {"href": "https://directlink.cloud.ibm.com/provider/v2/gateways?limit=100"}, "limit": 100, "next": {"href": "https://directlink.cloud.ibm.com/provider/v2/gateways?start=8c4a91a3e2cbd233b5a5b33436855fc2&limit=100", "start": "8c4a91a3e2cbd233b5a5b33436855fc2"}, "total_count": 132, "gateways": [{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values start = 'testString' limit = 1 # Invoke method response = _service.list_provider_gateways( start=start, limit=limit, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 200 # Validate query params query_string = responses.calls[0].request.url.split('?',1)[1] query_string = urllib.parse.unquote_plus(query_string) assert 'start={}'.format(start) in query_string assert 'limit={}'.format(limit) in query_string def test_list_provider_gateways_all_params_with_retries(self): # Enable retries and run test_list_provider_gateways_all_params. _service.enable_retries() self.test_list_provider_gateways_all_params() # Disable retries and run test_list_provider_gateways_all_params. _service.disable_retries() self.test_list_provider_gateways_all_params() @responses.activate def test_list_provider_gateways_required_params(self): """ test_list_provider_gateways_required_params() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways') mock_response = '{"first": {"href": "https://directlink.cloud.ibm.com/provider/v2/gateways?limit=100"}, "limit": 100, "next": {"href": "https://directlink.cloud.ibm.com/provider/v2/gateways?start=8c4a91a3e2cbd233b5a5b33436855fc2&limit=100", "start": "8c4a91a3e2cbd233b5a5b33436855fc2"}, "total_count": 132, "gateways": [{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Invoke method response = _service.list_provider_gateways() # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 200 def test_list_provider_gateways_required_params_with_retries(self): # Enable retries and run test_list_provider_gateways_required_params. _service.enable_retries() self.test_list_provider_gateways_required_params() # Disable retries and run test_list_provider_gateways_required_params. _service.disable_retries() self.test_list_provider_gateways_required_params() @responses.activate def test_list_provider_gateways_value_error(self): """ test_list_provider_gateways_value_error() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways') mock_response = '{"first": {"href": "https://directlink.cloud.ibm.com/provider/v2/gateways?limit=100"}, "limit": 100, "next": {"href": "https://directlink.cloud.ibm.com/provider/v2/gateways?start=8c4a91a3e2cbd233b5a5b33436855fc2&limit=100", "start": "8c4a91a3e2cbd233b5a5b33436855fc2"}, "total_count": 132, "gateways": [{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Pass in all but one required param and check for a ValueError req_param_dict = { } for param in req_param_dict.keys(): req_copy = {key:val if key is not param else None for (key,val) in req_param_dict.items()} with pytest.raises(ValueError): _service.list_provider_gateways(**req_copy) def test_list_provider_gateways_value_error_with_retries(self): # Enable retries and run test_list_provider_gateways_value_error. _service.enable_retries() self.test_list_provider_gateways_value_error() # Disable retries and run test_list_provider_gateways_value_error. _service.disable_retries() self.test_list_provider_gateways_value_error() class TestCreateProviderGateway(): """ Test Class for create_provider_gateway """ def preprocess_url(self, request_url: str): """ Preprocess the request URL to ensure the mock response will be found. """ request_url = urllib.parse.unquote(request_url) # don't double-encode if already encoded request_url = urllib.parse.quote(request_url, safe=':/') if re.fullmatch('.*/+', request_url) is None: return request_url else: return re.compile(request_url.rstrip('/') + '/+') @responses.activate def test_create_provider_gateway_all_params(self): """ create_provider_gateway() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.POST, url, body=mock_response, content_type='application/json', status=201) # Construct a dict representation of a ProviderGatewayPortIdentity model provider_gateway_port_identity_model = {} provider_gateway_port_identity_model['id'] = 'fffdcb1a-fee4-41c7-9e11-9cd99e65c777' # Set up parameter values bgp_asn = 64999 customer_account_id = '4111d05f36894e3cb9b46a43556d9000' name = 'myGateway' port = provider_gateway_port_identity_model speed_mbps = 1000 bgp_cer_cidr = '10.254.30.78/30' bgp_ibm_cidr = '10.254.30.77/30' vlan = 10 check_only = 'testString' # Invoke method response = _service.create_provider_gateway( bgp_asn, customer_account_id, name, port, speed_mbps, bgp_cer_cidr=bgp_cer_cidr, bgp_ibm_cidr=bgp_ibm_cidr, vlan=vlan, check_only=check_only, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 201 # Validate query params query_string = responses.calls[0].request.url.split('?',1)[1] query_string = urllib.parse.unquote_plus(query_string) assert 'check_only={}'.format(check_only) in query_string # Validate body params req_body = json.loads(str(responses.calls[0].request.body, 'utf-8')) assert req_body['bgp_asn'] == 64999 assert req_body['customer_account_id'] == '4111d05f36894e3cb9b46a43556d9000' assert req_body['name'] == 'myGateway' assert req_body['port'] == provider_gateway_port_identity_model assert req_body['speed_mbps'] == 1000 assert req_body['bgp_cer_cidr'] == '10.254.30.78/30' assert req_body['bgp_ibm_cidr'] == '10.254.30.77/30' assert req_body['vlan'] == 10 def test_create_provider_gateway_all_params_with_retries(self): # Enable retries and run test_create_provider_gateway_all_params. _service.enable_retries() self.test_create_provider_gateway_all_params() # Disable retries and run test_create_provider_gateway_all_params. _service.disable_retries() self.test_create_provider_gateway_all_params() @responses.activate def test_create_provider_gateway_required_params(self): """ test_create_provider_gateway_required_params() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.POST, url, body=mock_response, content_type='application/json', status=201) # Construct a dict representation of a ProviderGatewayPortIdentity model provider_gateway_port_identity_model = {} provider_gateway_port_identity_model['id'] = 'fffdcb1a-fee4-41c7-9e11-9cd99e65c777' # Set up parameter values bgp_asn = 64999 customer_account_id = '4111d05f36894e3cb9b46a43556d9000' name = 'myGateway' port = provider_gateway_port_identity_model speed_mbps = 1000 bgp_cer_cidr = '10.254.30.78/30' bgp_ibm_cidr = '10.254.30.77/30' vlan = 10 # Invoke method response = _service.create_provider_gateway( bgp_asn, customer_account_id, name, port, speed_mbps, bgp_cer_cidr=bgp_cer_cidr, bgp_ibm_cidr=bgp_ibm_cidr, vlan=vlan, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 201 # Validate body params req_body = json.loads(str(responses.calls[0].request.body, 'utf-8')) assert req_body['bgp_asn'] == 64999 assert req_body['customer_account_id'] == '4111d05f36894e3cb9b46a43556d9000' assert req_body['name'] == 'myGateway' assert req_body['port'] == provider_gateway_port_identity_model assert req_body['speed_mbps'] == 1000 assert req_body['bgp_cer_cidr'] == '10.254.30.78/30' assert req_body['bgp_ibm_cidr'] == '10.254.30.77/30' assert req_body['vlan'] == 10 def test_create_provider_gateway_required_params_with_retries(self): # Enable retries and run test_create_provider_gateway_required_params. _service.enable_retries() self.test_create_provider_gateway_required_params() # Disable retries and run test_create_provider_gateway_required_params. _service.disable_retries() self.test_create_provider_gateway_required_params() @responses.activate def test_create_provider_gateway_value_error(self): """ test_create_provider_gateway_value_error() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.POST, url, body=mock_response, content_type='application/json', status=201) # Construct a dict representation of a ProviderGatewayPortIdentity model provider_gateway_port_identity_model = {} provider_gateway_port_identity_model['id'] = 'fffdcb1a-fee4-41c7-9e11-9cd99e65c777' # Set up parameter values bgp_asn = 64999 customer_account_id = '4111d05f36894e3cb9b46a43556d9000' name = 'myGateway' port = provider_gateway_port_identity_model speed_mbps = 1000 bgp_cer_cidr = '10.254.30.78/30' bgp_ibm_cidr = '10.254.30.77/30' vlan = 10 # Pass in all but one required param and check for a ValueError req_param_dict = { "bgp_asn": bgp_asn, "customer_account_id": customer_account_id, "name": name, "port": port, "speed_mbps": speed_mbps, } for param in req_param_dict.keys(): req_copy = {key:val if key is not param else None for (key,val) in req_param_dict.items()} with pytest.raises(ValueError): _service.create_provider_gateway(**req_copy) def test_create_provider_gateway_value_error_with_retries(self): # Enable retries and run test_create_provider_gateway_value_error. _service.enable_retries() self.test_create_provider_gateway_value_error() # Disable retries and run test_create_provider_gateway_value_error. _service.disable_retries() self.test_create_provider_gateway_value_error() class TestDeleteProviderGateway(): """ Test Class for delete_provider_gateway """ def preprocess_url(self, request_url: str): """ Preprocess the request URL to ensure the mock response will be found. """ request_url = urllib.parse.unquote(request_url) # don't double-encode if already encoded request_url = urllib.parse.quote(request_url, safe=':/') if re.fullmatch('.*/+', request_url) is None: return request_url else: return re.compile(request_url.rstrip('/') + '/+') @responses.activate def test_delete_provider_gateway_all_params(self): """ delete_provider_gateway() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways/testString') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.DELETE, url, body=mock_response, content_type='application/json', status=202) # Set up parameter values id = 'testString' # Invoke method response = _service.delete_provider_gateway( id, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 202 def test_delete_provider_gateway_all_params_with_retries(self): # Enable retries and run test_delete_provider_gateway_all_params. _service.enable_retries() self.test_delete_provider_gateway_all_params() # Disable retries and run test_delete_provider_gateway_all_params. _service.disable_retries() self.test_delete_provider_gateway_all_params() @responses.activate def test_delete_provider_gateway_value_error(self): """ test_delete_provider_gateway_value_error() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways/testString') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.DELETE, url, body=mock_response, content_type='application/json', status=202) # Set up parameter values id = 'testString' # Pass in all but one required param and check for a ValueError req_param_dict = { "id": id, } for param in req_param_dict.keys(): req_copy = {key:val if key is not param else None for (key,val) in req_param_dict.items()} with pytest.raises(ValueError): _service.delete_provider_gateway(**req_copy) def test_delete_provider_gateway_value_error_with_retries(self): # Enable retries and run test_delete_provider_gateway_value_error. _service.enable_retries() self.test_delete_provider_gateway_value_error() # Disable retries and run test_delete_provider_gateway_value_error. _service.disable_retries() self.test_delete_provider_gateway_value_error() class TestGetProviderGateway(): """ Test Class for get_provider_gateway """ def preprocess_url(self, request_url: str): """ Preprocess the request URL to ensure the mock response will be found. """ request_url = urllib.parse.unquote(request_url) # don't double-encode if already encoded request_url = urllib.parse.quote(request_url, safe=':/') if re.fullmatch('.*/+', request_url) is None: return request_url else: return re.compile(request_url.rstrip('/') + '/+') @responses.activate def test_get_provider_gateway_all_params(self): """ get_provider_gateway() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways/testString') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values id = 'testString' # Invoke method response = _service.get_provider_gateway( id, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 200 def test_get_provider_gateway_all_params_with_retries(self): # Enable retries and run test_get_provider_gateway_all_params. _service.enable_retries() self.test_get_provider_gateway_all_params() # Disable retries and run test_get_provider_gateway_all_params. _service.disable_retries() self.test_get_provider_gateway_all_params() @responses.activate def test_get_provider_gateway_value_error(self): """ test_get_provider_gateway_value_error() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways/testString') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values id = 'testString' # Pass in all but one required param and check for a ValueError req_param_dict = { "id": id, } for param in req_param_dict.keys(): req_copy = {key:val if key is not param else None for (key,val) in req_param_dict.items()} with pytest.raises(ValueError): _service.get_provider_gateway(**req_copy) def test_get_provider_gateway_value_error_with_retries(self): # Enable retries and run test_get_provider_gateway_value_error. _service.enable_retries() self.test_get_provider_gateway_value_error() # Disable retries and run test_get_provider_gateway_value_error. _service.disable_retries() self.test_get_provider_gateway_value_error() class TestUpdateProviderGateway(): """ Test Class for update_provider_gateway """ def preprocess_url(self, request_url: str): """ Preprocess the request URL to ensure the mock response will be found. """ request_url = urllib.parse.unquote(request_url) # don't double-encode if already encoded request_url = urllib.parse.quote(request_url, safe=':/') if re.fullmatch('.*/+', request_url) is None: return request_url else: return re.compile(request_url.rstrip('/') + '/+') @responses.activate def test_update_provider_gateway_all_params(self): """ update_provider_gateway() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways/testString') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.PATCH, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values id = 'testString' bgp_asn = 64999 bgp_cer_cidr = '169.254.0.10/30' bgp_ibm_cidr = '169.254.0.9/30' name = 'myNewGateway' speed_mbps = 1000 vlan = 10 # Invoke method response = _service.update_provider_gateway( id, bgp_asn=bgp_asn, bgp_cer_cidr=bgp_cer_cidr, bgp_ibm_cidr=bgp_ibm_cidr, name=name, speed_mbps=speed_mbps, vlan=vlan, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 200 # Validate body params req_body = json.loads(str(responses.calls[0].request.body, 'utf-8')) assert req_body['bgp_asn'] == 64999 assert req_body['bgp_cer_cidr'] == '169.254.0.10/30' assert req_body['bgp_ibm_cidr'] == '169.254.0.9/30' assert req_body['name'] == 'myNewGateway' assert req_body['speed_mbps'] == 1000 assert req_body['vlan'] == 10 def test_update_provider_gateway_all_params_with_retries(self): # Enable retries and run test_update_provider_gateway_all_params. _service.enable_retries() self.test_update_provider_gateway_all_params() # Disable retries and run test_update_provider_gateway_all_params. _service.disable_retries() self.test_update_provider_gateway_all_params() @responses.activate def test_update_provider_gateway_value_error(self): """ test_update_provider_gateway_value_error() """ # Set up mock url = self.preprocess_url(_base_url + '/gateways/testString') mock_response = '{"bgp_asn": 64999, "bgp_cer_cidr": "10.254.30.78/30", "bgp_ibm_asn": 13884, "bgp_ibm_cidr": "10.254.30.77/30", "bgp_status": "active", "change_request": {"type": "create_gateway"}, "created_at": "2019-01-01T12:00:00.000Z", "crn": "crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "customer_account_id": "4111d05f36894e3cb9b46a43556d9000", "id": "ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4", "name": "myGateway", "operational_status": "configuring", "port": {"id": "fffdcb1a-fee4-41c7-9e11-9cd99e65c777"}, "provider_api_managed": true, "speed_mbps": 1000, "type": "connect", "vlan": 10}' responses.add(responses.PATCH, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values id = 'testString' bgp_asn = 64999 bgp_cer_cidr = '169.254.0.10/30' bgp_ibm_cidr = '169.254.0.9/30' name = 'myNewGateway' speed_mbps = 1000 vlan = 10 # Pass in all but one required param and check for a ValueError req_param_dict = { "id": id, } for param in req_param_dict.keys(): req_copy = {key:val if key is not param else None for (key,val) in req_param_dict.items()} with pytest.raises(ValueError): _service.update_provider_gateway(**req_copy) def test_update_provider_gateway_value_error_with_retries(self): # Enable retries and run test_update_provider_gateway_value_error. _service.enable_retries() self.test_update_provider_gateway_value_error() # Disable retries and run test_update_provider_gateway_value_error. _service.disable_retries() self.test_update_provider_gateway_value_error() class TestListProviderPorts(): """ Test Class for list_provider_ports """ def preprocess_url(self, request_url: str): """ Preprocess the request URL to ensure the mock response will be found. """ request_url = urllib.parse.unquote(request_url) # don't double-encode if already encoded request_url = urllib.parse.quote(request_url, safe=':/') if re.fullmatch('.*/+', request_url) is None: return request_url else: return re.compile(request_url.rstrip('/') + '/+') @responses.activate def test_list_provider_ports_all_params(self): """ list_provider_ports() """ # Set up mock url = self.preprocess_url(_base_url + '/ports') mock_response = '{"first": {"href": "https://directlink.cloud.ibm.com/provider/v2/ports?limit=100"}, "limit": 100, "next": {"href": "https://directlink.cloud.ibm.com/provider/v2/ports?start=9d5a91a3e2cbd233b5a5b33436855ed1&limit=100", "start": "9d5a91a3e2cbd233b5a5b33436855ed1"}, "total_count": 132, "ports": [{"id": "01122b9b-820f-4c44-8a31-77f1f0806765", "label": "XCR-FRK-CS-SEC-01", "location_display_name": "Dallas 03", "location_name": "dal03", "provider_name": "provider_1", "supported_link_speeds": [21]}]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values start = 'testString' limit = 1 # Invoke method response = _service.list_provider_ports( start=start, limit=limit, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 200 # Validate query params query_string = responses.calls[0].request.url.split('?',1)[1] query_string = urllib.parse.unquote_plus(query_string) assert 'start={}'.format(start) in query_string assert 'limit={}'.format(limit) in query_string def test_list_provider_ports_all_params_with_retries(self): # Enable retries and run test_list_provider_ports_all_params. _service.enable_retries() self.test_list_provider_ports_all_params() # Disable retries and run test_list_provider_ports_all_params. _service.disable_retries() self.test_list_provider_ports_all_params() @responses.activate def test_list_provider_ports_required_params(self): """ test_list_provider_ports_required_params() """ # Set up mock url = self.preprocess_url(_base_url + '/ports') mock_response = '{"first": {"href": "https://directlink.cloud.ibm.com/provider/v2/ports?limit=100"}, "limit": 100, "next": {"href": "https://directlink.cloud.ibm.com/provider/v2/ports?start=9d5a91a3e2cbd233b5a5b33436855ed1&limit=100", "start": "9d5a91a3e2cbd233b5a5b33436855ed1"}, "total_count": 132, "ports": [{"id": "01122b9b-820f-4c44-8a31-77f1f0806765", "label": "XCR-FRK-CS-SEC-01", "location_display_name": "Dallas 03", "location_name": "dal03", "provider_name": "provider_1", "supported_link_speeds": [21]}]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Invoke method response = _service.list_provider_ports() # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 200 def test_list_provider_ports_required_params_with_retries(self): # Enable retries and run test_list_provider_ports_required_params. _service.enable_retries() self.test_list_provider_ports_required_params() # Disable retries and run test_list_provider_ports_required_params. _service.disable_retries() self.test_list_provider_ports_required_params() @responses.activate def test_list_provider_ports_value_error(self): """ test_list_provider_ports_value_error() """ # Set up mock url = self.preprocess_url(_base_url + '/ports') mock_response = '{"first": {"href": "https://directlink.cloud.ibm.com/provider/v2/ports?limit=100"}, "limit": 100, "next": {"href": "https://directlink.cloud.ibm.com/provider/v2/ports?start=9d5a91a3e2cbd233b5a5b33436855ed1&limit=100", "start": "9d5a91a3e2cbd233b5a5b33436855ed1"}, "total_count": 132, "ports": [{"id": "01122b9b-820f-4c44-8a31-77f1f0806765", "label": "XCR-FRK-CS-SEC-01", "location_display_name": "Dallas 03", "location_name": "dal03", "provider_name": "provider_1", "supported_link_speeds": [21]}]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Pass in all but one required param and check for a ValueError req_param_dict = { } for param in req_param_dict.keys(): req_copy = {key:val if key is not param else None for (key,val) in req_param_dict.items()} with pytest.raises(ValueError): _service.list_provider_ports(**req_copy) def test_list_provider_ports_value_error_with_retries(self): # Enable retries and run test_list_provider_ports_value_error. _service.enable_retries() self.test_list_provider_ports_value_error() # Disable retries and run test_list_provider_ports_value_error. _service.disable_retries() self.test_list_provider_ports_value_error() class TestGetProviderPort(): """ Test Class for get_provider_port """ def preprocess_url(self, request_url: str): """ Preprocess the request URL to ensure the mock response will be found. """ request_url = urllib.parse.unquote(request_url) # don't double-encode if already encoded request_url = urllib.parse.quote(request_url, safe=':/') if re.fullmatch('.*/+', request_url) is None: return request_url else: return re.compile(request_url.rstrip('/') + '/+') @responses.activate def test_get_provider_port_all_params(self): """ get_provider_port() """ # Set up mock url = self.preprocess_url(_base_url + '/ports/testString') mock_response = '{"id": "01122b9b-820f-4c44-8a31-77f1f0806765", "label": "XCR-FRK-CS-SEC-01", "location_display_name": "Dallas 03", "location_name": "dal03", "provider_name": "provider_1", "supported_link_speeds": [21]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values id = 'testString' # Invoke method response = _service.get_provider_port( id, headers={} ) # Check for correct operation assert len(responses.calls) == 1 assert response.status_code == 200 def test_get_provider_port_all_params_with_retries(self): # Enable retries and run test_get_provider_port_all_params. _service.enable_retries() self.test_get_provider_port_all_params() # Disable retries and run test_get_provider_port_all_params. _service.disable_retries() self.test_get_provider_port_all_params() @responses.activate def test_get_provider_port_value_error(self): """ test_get_provider_port_value_error() """ # Set up mock url = self.preprocess_url(_base_url + '/ports/testString') mock_response = '{"id": "01122b9b-820f-4c44-8a31-77f1f0806765", "label": "XCR-FRK-CS-SEC-01", "location_display_name": "Dallas 03", "location_name": "dal03", "provider_name": "provider_1", "supported_link_speeds": [21]}' responses.add(responses.GET, url, body=mock_response, content_type='application/json', status=200) # Set up parameter values id = 'testString' # Pass in all but one required param and check for a ValueError req_param_dict = { "id": id, } for param in req_param_dict.keys(): req_copy = {key:val if key is not param else None for (key,val) in req_param_dict.items()} with pytest.raises(ValueError): _service.get_provider_port(**req_copy) def test_get_provider_port_value_error_with_retries(self): # Enable retries and run test_get_provider_port_value_error. _service.enable_retries() self.test_get_provider_port_value_error() # Disable retries and run test_get_provider_port_value_error. _service.disable_retries() self.test_get_provider_port_value_error() # endregion ############################################################################## # End of Service: ProviderAPIs ############################################################################## ############################################################################## # Start of Model Tests ############################################################################## # region class TestModel_ProviderGateway(): """ Test Class for ProviderGateway """ def test_provider_gateway_serialization(self): """ Test serialization/deserialization for ProviderGateway """ # Construct dict forms of any model objects needed in order to build this model. provider_gateway_change_request_model = {} # ProviderGatewayChangeRequestProviderGatewayCreate provider_gateway_change_request_model['type'] = 'create_gateway' provider_gateway_port_reference_model = {} # ProviderGatewayPortReference provider_gateway_port_reference_model['id'] = 'fffdcb1a-fee4-41c7-9e11-9cd99e65c777' # Construct a json representation of a ProviderGateway model provider_gateway_model_json = {} provider_gateway_model_json['bgp_asn'] = 64999 provider_gateway_model_json['bgp_cer_cidr'] = '10.254.30.78/30' provider_gateway_model_json['bgp_ibm_asn'] = 13884 provider_gateway_model_json['bgp_ibm_cidr'] = '10.254.30.77/30' provider_gateway_model_json['bgp_status'] = 'active' provider_gateway_model_json['change_request'] = provider_gateway_change_request_model provider_gateway_model_json['created_at'] = "2019-01-01T12:00:00Z" provider_gateway_model_json['crn'] = 'crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4' provider_gateway_model_json['customer_account_id'] = '4111d05f36894e3cb9b46a43556d9000' provider_gateway_model_json['id'] = 'ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4' provider_gateway_model_json['name'] = 'myGateway' provider_gateway_model_json['operational_status'] = 'configuring' provider_gateway_model_json['port'] = provider_gateway_port_reference_model provider_gateway_model_json['provider_api_managed'] = True provider_gateway_model_json['speed_mbps'] = 1000 provider_gateway_model_json['type'] = 'connect' provider_gateway_model_json['vlan'] = 10 # Construct a model instance of ProviderGateway by calling from_dict on the json representation provider_gateway_model = ProviderGateway.from_dict(provider_gateway_model_json) assert provider_gateway_model != False # Construct a model instance of ProviderGateway by calling from_dict on the json representation provider_gateway_model_dict = ProviderGateway.from_dict(provider_gateway_model_json).__dict__ provider_gateway_model2 = ProviderGateway(**provider_gateway_model_dict) # Verify the model instances are equivalent assert provider_gateway_model == provider_gateway_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_model_json2 = provider_gateway_model.to_dict() assert provider_gateway_model_json2 == provider_gateway_model_json class TestModel_ProviderGatewayCollection(): """ Test Class for ProviderGatewayCollection """ def test_provider_gateway_collection_serialization(self): """ Test serialization/deserialization for ProviderGatewayCollection """ # Construct dict forms of any model objects needed in order to build this model. provider_gateway_collection_first_model = {} # ProviderGatewayCollectionFirst provider_gateway_collection_first_model['href'] = 'https://directlink.cloud.ibm.com/provider/v2/gateways?limit=100' provider_gateway_collection_next_model = {} # ProviderGatewayCollectionNext provider_gateway_collection_next_model['href'] = 'https://directlink.cloud.ibm.com/provider/v2/gateways?start=8c4a91a3e2cbd233b5a5b33436855fc2&limit=100' provider_gateway_collection_next_model['start'] = '8c4a91a3e2cbd233b5a5b33436855fc2' provider_gateway_change_request_model = {} # ProviderGatewayChangeRequestProviderGatewayCreate provider_gateway_change_request_model['type'] = 'create_gateway' provider_gateway_port_reference_model = {} # ProviderGatewayPortReference provider_gateway_port_reference_model['id'] = 'fffdcb1a-fee4-41c7-9e11-9cd99e65c777' provider_gateway_model = {} # ProviderGateway provider_gateway_model['bgp_asn'] = 64999 provider_gateway_model['bgp_cer_cidr'] = '10.254.30.78/30' provider_gateway_model['bgp_ibm_asn'] = 13884 provider_gateway_model['bgp_ibm_cidr'] = '10.254.30.77/30' provider_gateway_model['bgp_status'] = 'active' provider_gateway_model['change_request'] = provider_gateway_change_request_model provider_gateway_model['created_at'] = "2019-01-01T12:00:00Z" provider_gateway_model['crn'] = 'crn:v1:bluemix:public:directlink:dal03:a/4111d05f36894e3cb9b46a43556d9000::connect:ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4' provider_gateway_model['customer_account_id'] = '4111d05f36894e3cb9b46a43556d9000' provider_gateway_model['id'] = 'ef4dcb1a-fee4-41c7-9e11-9cd99e65c1f4' provider_gateway_model['name'] = 'myGateway' provider_gateway_model['operational_status'] = 'configuring' provider_gateway_model['port'] = provider_gateway_port_reference_model provider_gateway_model['provider_api_managed'] = True provider_gateway_model['speed_mbps'] = 1000 provider_gateway_model['type'] = 'connect' provider_gateway_model['vlan'] = 10 # Construct a json representation of a ProviderGatewayCollection model provider_gateway_collection_model_json = {} provider_gateway_collection_model_json['first'] = provider_gateway_collection_first_model provider_gateway_collection_model_json['limit'] = 100 provider_gateway_collection_model_json['next'] = provider_gateway_collection_next_model provider_gateway_collection_model_json['total_count'] = 132 provider_gateway_collection_model_json['gateways'] = [provider_gateway_model] # Construct a model instance of ProviderGatewayCollection by calling from_dict on the json representation provider_gateway_collection_model = ProviderGatewayCollection.from_dict(provider_gateway_collection_model_json) assert provider_gateway_collection_model != False # Construct a model instance of ProviderGatewayCollection by calling from_dict on the json representation provider_gateway_collection_model_dict = ProviderGatewayCollection.from_dict(provider_gateway_collection_model_json).__dict__ provider_gateway_collection_model2 = ProviderGatewayCollection(**provider_gateway_collection_model_dict) # Verify the model instances are equivalent assert provider_gateway_collection_model == provider_gateway_collection_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_collection_model_json2 = provider_gateway_collection_model.to_dict() assert provider_gateway_collection_model_json2 == provider_gateway_collection_model_json class TestModel_ProviderGatewayCollectionFirst(): """ Test Class for ProviderGatewayCollectionFirst """ def test_provider_gateway_collection_first_serialization(self): """ Test serialization/deserialization for ProviderGatewayCollectionFirst """ # Construct a json representation of a ProviderGatewayCollectionFirst model provider_gateway_collection_first_model_json = {} provider_gateway_collection_first_model_json['href'] = 'https://directlink.cloud.ibm.com/provider/v2/gateways?limit=100' # Construct a model instance of ProviderGatewayCollectionFirst by calling from_dict on the json representation provider_gateway_collection_first_model = ProviderGatewayCollectionFirst.from_dict(provider_gateway_collection_first_model_json) assert provider_gateway_collection_first_model != False # Construct a model instance of ProviderGatewayCollectionFirst by calling from_dict on the json representation provider_gateway_collection_first_model_dict = ProviderGatewayCollectionFirst.from_dict(provider_gateway_collection_first_model_json).__dict__ provider_gateway_collection_first_model2 = ProviderGatewayCollectionFirst(**provider_gateway_collection_first_model_dict) # Verify the model instances are equivalent assert provider_gateway_collection_first_model == provider_gateway_collection_first_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_collection_first_model_json2 = provider_gateway_collection_first_model.to_dict() assert provider_gateway_collection_first_model_json2 == provider_gateway_collection_first_model_json class TestModel_ProviderGatewayCollectionNext(): """ Test Class for ProviderGatewayCollectionNext """ def test_provider_gateway_collection_next_serialization(self): """ Test serialization/deserialization for ProviderGatewayCollectionNext """ # Construct a json representation of a ProviderGatewayCollectionNext model provider_gateway_collection_next_model_json = {} provider_gateway_collection_next_model_json['href'] = 'https://directlink.cloud.ibm.com/provider/v2/gateways?start=8c4a91a3e2cbd233b5a5b33436855fc2&limit=100' provider_gateway_collection_next_model_json['start'] = '8c4a91a3e2cbd233b5a5b33436855fc2' # Construct a model instance of ProviderGatewayCollectionNext by calling from_dict on the json representation provider_gateway_collection_next_model = ProviderGatewayCollectionNext.from_dict(provider_gateway_collection_next_model_json) assert provider_gateway_collection_next_model != False # Construct a model instance of ProviderGatewayCollectionNext by calling from_dict on the json representation provider_gateway_collection_next_model_dict = ProviderGatewayCollectionNext.from_dict(provider_gateway_collection_next_model_json).__dict__ provider_gateway_collection_next_model2 = ProviderGatewayCollectionNext(**provider_gateway_collection_next_model_dict) # Verify the model instances are equivalent assert provider_gateway_collection_next_model == provider_gateway_collection_next_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_collection_next_model_json2 = provider_gateway_collection_next_model.to_dict() assert provider_gateway_collection_next_model_json2 == provider_gateway_collection_next_model_json class TestModel_ProviderGatewayPortIdentity(): """ Test Class for ProviderGatewayPortIdentity """ def test_provider_gateway_port_identity_serialization(self): """ Test serialization/deserialization for ProviderGatewayPortIdentity """ # Construct a json representation of a ProviderGatewayPortIdentity model provider_gateway_port_identity_model_json = {} provider_gateway_port_identity_model_json['id'] = 'fffdcb1a-fee4-41c7-9e11-9cd99e65c777' # Construct a model instance of ProviderGatewayPortIdentity by calling from_dict on the json representation provider_gateway_port_identity_model = ProviderGatewayPortIdentity.from_dict(provider_gateway_port_identity_model_json) assert provider_gateway_port_identity_model != False # Construct a model instance of ProviderGatewayPortIdentity by calling from_dict on the json representation provider_gateway_port_identity_model_dict = ProviderGatewayPortIdentity.from_dict(provider_gateway_port_identity_model_json).__dict__ provider_gateway_port_identity_model2 = ProviderGatewayPortIdentity(**provider_gateway_port_identity_model_dict) # Verify the model instances are equivalent assert provider_gateway_port_identity_model == provider_gateway_port_identity_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_port_identity_model_json2 = provider_gateway_port_identity_model.to_dict() assert provider_gateway_port_identity_model_json2 == provider_gateway_port_identity_model_json class TestModel_ProviderGatewayPortReference(): """ Test Class for ProviderGatewayPortReference """ def test_provider_gateway_port_reference_serialization(self): """ Test serialization/deserialization for ProviderGatewayPortReference """ # Construct a json representation of a ProviderGatewayPortReference model provider_gateway_port_reference_model_json = {} provider_gateway_port_reference_model_json['id'] = 'fffdcb1a-fee4-41c7-9e11-9cd99e65c777' # Construct a model instance of ProviderGatewayPortReference by calling from_dict on the json representation provider_gateway_port_reference_model = ProviderGatewayPortReference.from_dict(provider_gateway_port_reference_model_json) assert provider_gateway_port_reference_model != False # Construct a model instance of ProviderGatewayPortReference by calling from_dict on the json representation provider_gateway_port_reference_model_dict = ProviderGatewayPortReference.from_dict(provider_gateway_port_reference_model_json).__dict__ provider_gateway_port_reference_model2 = ProviderGatewayPortReference(**provider_gateway_port_reference_model_dict) # Verify the model instances are equivalent assert provider_gateway_port_reference_model == provider_gateway_port_reference_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_port_reference_model_json2 = provider_gateway_port_reference_model.to_dict() assert provider_gateway_port_reference_model_json2 == provider_gateway_port_reference_model_json class TestModel_ProviderPort(): """ Test Class for ProviderPort """ def test_provider_port_serialization(self): """ Test serialization/deserialization for ProviderPort """ # Construct a json representation of a ProviderPort model provider_port_model_json = {} provider_port_model_json['id'] = '01122b9b-820f-4c44-8a31-77f1f0806765' provider_port_model_json['label'] = 'XCR-FRK-CS-SEC-01' provider_port_model_json['location_display_name'] = 'Dallas 03' provider_port_model_json['location_name'] = 'dal03' provider_port_model_json['provider_name'] = 'provider_1' provider_port_model_json['supported_link_speeds'] = [1000, 2000, 5000, 10000] # Construct a model instance of ProviderPort by calling from_dict on the json representation provider_port_model = ProviderPort.from_dict(provider_port_model_json) assert provider_port_model != False # Construct a model instance of ProviderPort by calling from_dict on the json representation provider_port_model_dict = ProviderPort.from_dict(provider_port_model_json).__dict__ provider_port_model2 = ProviderPort(**provider_port_model_dict) # Verify the model instances are equivalent assert provider_port_model == provider_port_model2 # Convert model instance back to dict and verify no loss of data provider_port_model_json2 = provider_port_model.to_dict() assert provider_port_model_json2 == provider_port_model_json class TestModel_ProviderPortCollection(): """ Test Class for ProviderPortCollection """ def test_provider_port_collection_serialization(self): """ Test serialization/deserialization for ProviderPortCollection """ # Construct dict forms of any model objects needed in order to build this model. provider_port_collection_first_model = {} # ProviderPortCollectionFirst provider_port_collection_first_model['href'] = 'https://directlink.cloud.ibm.com/provider/v2/ports?limit=100' provider_port_collection_next_model = {} # ProviderPortCollectionNext provider_port_collection_next_model['href'] = 'https://directlink.cloud.ibm.com/provider/v2/ports?start=9d5a91a3e2cbd233b5a5b33436855ed1&limit=100' provider_port_collection_next_model['start'] = '9d5a91a3e2cbd233b5a5b33436855ed1' provider_port_model = {} # ProviderPort provider_port_model['id'] = '01122b9b-820f-4c44-8a31-77f1f0806765' provider_port_model['label'] = 'XCR-FRK-CS-SEC-01' provider_port_model['location_display_name'] = 'Dallas 03' provider_port_model['location_name'] = 'dal03' provider_port_model['provider_name'] = 'provider_1' provider_port_model['supported_link_speeds'] = [1000, 2000, 5000, 10000] # Construct a json representation of a ProviderPortCollection model provider_port_collection_model_json = {} provider_port_collection_model_json['first'] = provider_port_collection_first_model provider_port_collection_model_json['limit'] = 100 provider_port_collection_model_json['next'] = provider_port_collection_next_model provider_port_collection_model_json['total_count'] = 132 provider_port_collection_model_json['ports'] = [provider_port_model] # Construct a model instance of ProviderPortCollection by calling from_dict on the json representation provider_port_collection_model = ProviderPortCollection.from_dict(provider_port_collection_model_json) assert provider_port_collection_model != False # Construct a model instance of ProviderPortCollection by calling from_dict on the json representation provider_port_collection_model_dict = ProviderPortCollection.from_dict(provider_port_collection_model_json).__dict__ provider_port_collection_model2 = ProviderPortCollection(**provider_port_collection_model_dict) # Verify the model instances are equivalent assert provider_port_collection_model == provider_port_collection_model2 # Convert model instance back to dict and verify no loss of data provider_port_collection_model_json2 = provider_port_collection_model.to_dict() assert provider_port_collection_model_json2 == provider_port_collection_model_json class TestModel_ProviderPortCollectionFirst(): """ Test Class for ProviderPortCollectionFirst """ def test_provider_port_collection_first_serialization(self): """ Test serialization/deserialization for ProviderPortCollectionFirst """ # Construct a json representation of a ProviderPortCollectionFirst model provider_port_collection_first_model_json = {} provider_port_collection_first_model_json['href'] = 'https://directlink.cloud.ibm.com/provider/v2/ports?limit=100' # Construct a model instance of ProviderPortCollectionFirst by calling from_dict on the json representation provider_port_collection_first_model = ProviderPortCollectionFirst.from_dict(provider_port_collection_first_model_json) assert provider_port_collection_first_model != False # Construct a model instance of ProviderPortCollectionFirst by calling from_dict on the json representation provider_port_collection_first_model_dict = ProviderPortCollectionFirst.from_dict(provider_port_collection_first_model_json).__dict__ provider_port_collection_first_model2 = ProviderPortCollectionFirst(**provider_port_collection_first_model_dict) # Verify the model instances are equivalent assert provider_port_collection_first_model == provider_port_collection_first_model2 # Convert model instance back to dict and verify no loss of data provider_port_collection_first_model_json2 = provider_port_collection_first_model.to_dict() assert provider_port_collection_first_model_json2 == provider_port_collection_first_model_json class TestModel_ProviderPortCollectionNext(): """ Test Class for ProviderPortCollectionNext """ def test_provider_port_collection_next_serialization(self): """ Test serialization/deserialization for ProviderPortCollectionNext """ # Construct a json representation of a ProviderPortCollectionNext model provider_port_collection_next_model_json = {} provider_port_collection_next_model_json['href'] = 'https://directlink.cloud.ibm.com/provider/v2/ports?start=9d5a91a3e2cbd233b5a5b33436855ed1&limit=100' provider_port_collection_next_model_json['start'] = '9d5a91a3e2cbd233b5a5b33436855ed1' # Construct a model instance of ProviderPortCollectionNext by calling from_dict on the json representation provider_port_collection_next_model = ProviderPortCollectionNext.from_dict(provider_port_collection_next_model_json) assert provider_port_collection_next_model != False # Construct a model instance of ProviderPortCollectionNext by calling from_dict on the json representation provider_port_collection_next_model_dict = ProviderPortCollectionNext.from_dict(provider_port_collection_next_model_json).__dict__ provider_port_collection_next_model2 = ProviderPortCollectionNext(**provider_port_collection_next_model_dict) # Verify the model instances are equivalent assert provider_port_collection_next_model == provider_port_collection_next_model2 # Convert model instance back to dict and verify no loss of data provider_port_collection_next_model_json2 = provider_port_collection_next_model.to_dict() assert provider_port_collection_next_model_json2 == provider_port_collection_next_model_json class TestModel_ProviderGatewayChangeRequestProviderGatewayCreate(): """ Test Class for ProviderGatewayChangeRequestProviderGatewayCreate """ def test_provider_gateway_change_request_provider_gateway_create_serialization(self): """ Test serialization/deserialization for ProviderGatewayChangeRequestProviderGatewayCreate """ # Construct a json representation of a ProviderGatewayChangeRequestProviderGatewayCreate model provider_gateway_change_request_provider_gateway_create_model_json = {} provider_gateway_change_request_provider_gateway_create_model_json['type'] = 'create_gateway' # Construct a model instance of ProviderGatewayChangeRequestProviderGatewayCreate by calling from_dict on the json representation provider_gateway_change_request_provider_gateway_create_model = ProviderGatewayChangeRequestProviderGatewayCreate.from_dict(provider_gateway_change_request_provider_gateway_create_model_json) assert provider_gateway_change_request_provider_gateway_create_model != False # Construct a model instance of ProviderGatewayChangeRequestProviderGatewayCreate by calling from_dict on the json representation provider_gateway_change_request_provider_gateway_create_model_dict = ProviderGatewayChangeRequestProviderGatewayCreate.from_dict(provider_gateway_change_request_provider_gateway_create_model_json).__dict__ provider_gateway_change_request_provider_gateway_create_model2 = ProviderGatewayChangeRequestProviderGatewayCreate(**provider_gateway_change_request_provider_gateway_create_model_dict) # Verify the model instances are equivalent assert provider_gateway_change_request_provider_gateway_create_model == provider_gateway_change_request_provider_gateway_create_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_change_request_provider_gateway_create_model_json2 = provider_gateway_change_request_provider_gateway_create_model.to_dict() assert provider_gateway_change_request_provider_gateway_create_model_json2 == provider_gateway_change_request_provider_gateway_create_model_json class TestModel_ProviderGatewayChangeRequestProviderGatewayDelete(): """ Test Class for ProviderGatewayChangeRequestProviderGatewayDelete """ def test_provider_gateway_change_request_provider_gateway_delete_serialization(self): """ Test serialization/deserialization for ProviderGatewayChangeRequestProviderGatewayDelete """ # Construct a json representation of a ProviderGatewayChangeRequestProviderGatewayDelete model provider_gateway_change_request_provider_gateway_delete_model_json = {} provider_gateway_change_request_provider_gateway_delete_model_json['type'] = 'delete_gateway' # Construct a model instance of ProviderGatewayChangeRequestProviderGatewayDelete by calling from_dict on the json representation provider_gateway_change_request_provider_gateway_delete_model = ProviderGatewayChangeRequestProviderGatewayDelete.from_dict(provider_gateway_change_request_provider_gateway_delete_model_json) assert provider_gateway_change_request_provider_gateway_delete_model != False # Construct a model instance of ProviderGatewayChangeRequestProviderGatewayDelete by calling from_dict on the json representation provider_gateway_change_request_provider_gateway_delete_model_dict = ProviderGatewayChangeRequestProviderGatewayDelete.from_dict(provider_gateway_change_request_provider_gateway_delete_model_json).__dict__ provider_gateway_change_request_provider_gateway_delete_model2 = ProviderGatewayChangeRequestProviderGatewayDelete(**provider_gateway_change_request_provider_gateway_delete_model_dict) # Verify the model instances are equivalent assert provider_gateway_change_request_provider_gateway_delete_model == provider_gateway_change_request_provider_gateway_delete_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_change_request_provider_gateway_delete_model_json2 = provider_gateway_change_request_provider_gateway_delete_model.to_dict() assert provider_gateway_change_request_provider_gateway_delete_model_json2 == provider_gateway_change_request_provider_gateway_delete_model_json class TestModel_ProviderGatewayChangeRequestProviderGatewayUpdateAttributes(): """ Test Class for ProviderGatewayChangeRequestProviderGatewayUpdateAttributes """ def test_provider_gateway_change_request_provider_gateway_update_attributes_serialization(self): """ Test serialization/deserialization for ProviderGatewayChangeRequestProviderGatewayUpdateAttributes """ # Construct dict forms of any model objects needed in order to build this model. provider_gateway_update_attributes_updates_item_model = {} # ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate provider_gateway_update_attributes_updates_item_model['speed_mbps'] = 500 # Construct a json representation of a ProviderGatewayChangeRequestProviderGatewayUpdateAttributes model provider_gateway_change_request_provider_gateway_update_attributes_model_json = {} provider_gateway_change_request_provider_gateway_update_attributes_model_json['type'] = 'update_attributes' provider_gateway_change_request_provider_gateway_update_attributes_model_json['updates'] = [provider_gateway_update_attributes_updates_item_model] # Construct a model instance of ProviderGatewayChangeRequestProviderGatewayUpdateAttributes by calling from_dict on the json representation provider_gateway_change_request_provider_gateway_update_attributes_model = ProviderGatewayChangeRequestProviderGatewayUpdateAttributes.from_dict(provider_gateway_change_request_provider_gateway_update_attributes_model_json) assert provider_gateway_change_request_provider_gateway_update_attributes_model != False # Construct a model instance of ProviderGatewayChangeRequestProviderGatewayUpdateAttributes by calling from_dict on the json representation provider_gateway_change_request_provider_gateway_update_attributes_model_dict = ProviderGatewayChangeRequestProviderGatewayUpdateAttributes.from_dict(provider_gateway_change_request_provider_gateway_update_attributes_model_json).__dict__ provider_gateway_change_request_provider_gateway_update_attributes_model2 = ProviderGatewayChangeRequestProviderGatewayUpdateAttributes(**provider_gateway_change_request_provider_gateway_update_attributes_model_dict) # Verify the model instances are equivalent assert provider_gateway_change_request_provider_gateway_update_attributes_model == provider_gateway_change_request_provider_gateway_update_attributes_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_change_request_provider_gateway_update_attributes_model_json2 = provider_gateway_change_request_provider_gateway_update_attributes_model.to_dict() assert provider_gateway_change_request_provider_gateway_update_attributes_model_json2 == provider_gateway_change_request_provider_gateway_update_attributes_model_json class TestModel_ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate(): """ Test Class for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate """ def test_provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_serialization(self): """ Test serialization/deserialization for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate """ # Construct a json representation of a ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate model provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_json = {} provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_json['bgp_asn'] = 64999 # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_json) assert provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model != False # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_dict = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_json).__dict__ provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model2 = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPASNUpdate(**provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_dict) # Verify the model instances are equivalent assert provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model == provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_json2 = provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model.to_dict() assert provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_json2 == provider_gateway_update_attributes_updates_item_provider_gateway_bgpasn_update_model_json class TestModel_ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate(): """ Test Class for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate """ def test_provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_serialization(self): """ Test serialization/deserialization for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate """ # Construct a json representation of a ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate model provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json = {} provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json['bgp_cer_cidr'] = '169.254.0.10/30' provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json['bgp_ibm_cidr'] = '169.254.0.9/30' # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json) assert provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model != False # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_dict = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json).__dict__ provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model2 = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayBGPIPUpdate(**provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_dict) # Verify the model instances are equivalent assert provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model == provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json2 = provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model.to_dict() assert provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json2 == provider_gateway_update_attributes_updates_item_provider_gateway_bgpip_update_model_json class TestModel_ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate(): """ Test Class for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate """ def test_provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_serialization(self): """ Test serialization/deserialization for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate """ # Construct a json representation of a ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate model provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_json = {} provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_json['speed_mbps'] = 500 # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_json) assert provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model != False # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_dict = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_json).__dict__ provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model2 = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewaySpeedUpdate(**provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_dict) # Verify the model instances are equivalent assert provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model == provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_json2 = provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model.to_dict() assert provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_json2 == provider_gateway_update_attributes_updates_item_provider_gateway_speed_update_model_json class TestModel_ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN(): """ Test Class for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN """ def test_provider_gateway_update_attributes_updates_item_provider_gateway_vlan_serialization(self): """ Test serialization/deserialization for ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN """ # Construct a json representation of a ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN model provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_json = {} provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_json['vlan'] = 10 # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_json) assert provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model != False # Construct a model instance of ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN by calling from_dict on the json representation provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_dict = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN.from_dict(provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_json).__dict__ provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model2 = ProviderGatewayUpdateAttributesUpdatesItemProviderGatewayVLAN(**provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_dict) # Verify the model instances are equivalent assert provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model == provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model2 # Convert model instance back to dict and verify no loss of data provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_json2 = provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model.to_dict() assert provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_json2 == provider_gateway_update_attributes_updates_item_provider_gateway_vlan_model_json # endregion ############################################################################## # End of Model Tests ##############################################################################

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null

0

6

4900366a8de4b64633258f8779ebc71d3304d130

83

py

Python

src/rectangle/utils/__init__.py

Jiongqi/RectAngle

558fa036d1b21b5ae0a556271ab674cd8ffe88b6

[ "MIT" ]

1

2021-04-23T01:00:53.000Z

src/rectangle/utils/__init__.py

Jiongqi/RectAngle

558fa036d1b21b5ae0a556271ab674cd8ffe88b6

[ "MIT" ]

null

src/rectangle/utils/__init__.py

Jiongqi/RectAngle

558fa036d1b21b5ae0a556271ab674cd8ffe88b6

[ "MIT" ]

3

2021-06-17T10:17:36.000Z

2021-06-24T19:07:05.000Z

from . import io from . import metrics from . import train from . import transforms

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1

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0

6

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72

py

Python

data structures/set py.py

hamzashabbir11/dataStructures

1918b4e7636aad3a40db9c1e7acea6a829f82671

[ "MIT" ]

null

data structures/set py.py

hamzashabbir11/dataStructures

1918b4e7636aad3a40db9c1e7acea6a829f82671

[ "MIT" ]

null

data structures/set py.py

hamzashabbir11/dataStructures

1918b4e7636aad3a40db9c1e7acea6a829f82671

[ "MIT" ]

null

l={1,2,3,4,5} a=set(['a','b','c']) print(l) print(a) print(l.union(a))

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20

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19

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f01a0cb917b8c8133b45022985b4def898b043cc

189

py

Python

tf2lib/__init__.py

jai-kannan1184/Cyc_Gan2

8f09307f644e49339f657635c353d98df8ae0131

[ "MIT" ]

null

tf2lib/__init__.py

jai-kannan1184/Cyc_Gan2

8f09307f644e49339f657635c353d98df8ae0131

[ "MIT" ]

null

tf2lib/__init__.py

jai-kannan1184/Cyc_Gan2

8f09307f644e49339f657635c353d98df8ae0131

[ "MIT" ]

1

2019-05-26T14:38:54.000Z

import tensorflow as tf from tf2lib.data import * from tf2lib.image import * from tf2lib.ops import * from tf2lib.utils import * tf.config.gpu.set_per_process_memory_growth(enabled=True)

21

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6

f02f4359fa1b1f4ab19e469b87a6232c3552a308

48

py

Python

docs/multiple-tests/max-line-length/src/file.py

codacy/codacy-pylint-python3

462614fbe679d2f7978dc3e74993099b4ef5c1c9

[ "Apache-2.0" ]

1

2021-02-02T06:57:31.000Z

docs/multiple-tests/max-line-length/src/file.py

itsMo07/codacy-pylint-python3

e25ddfcea787d790c7df05407966fadd6e0a209b

[ "Apache-2.0" ]

50

2019-08-14T16:14:45.000Z

2022-03-31T11:00:50.000Z

docs/multiple-tests/max-line-length/src/file.py

itsMo07/codacy-pylint-python3

e25ddfcea787d790c7df05407966fadd6e0a209b

[ "Apache-2.0" ]

5

2019-08-27T14:56:36.000Z

2021-02-02T06:48:30.000Z

def function(): print("A very long string")

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f05efd4b9b3caef4711de4ae644e75d0ef4a1753

323

py

Python

cellstates/__init__.py

pgrobecker/cellstates

0c50b9e444487e0e822541c4ad67b3bd92524210

[ "MIT" ]

4

2021-09-14T08:50:47.000Z

2021-09-18T19:43:15.000Z

cellstates/__init__.py

pgrobecker/cellstates

0c50b9e444487e0e822541c4ad67b3bd92524210

[ "MIT" ]

10

2021-02-20T21:01:12.000Z

2022-01-12T07:16:18.000Z

cellstates/__init__.py

pgrobecker/cellstates

0c50b9e444487e0e822541c4ad67b3bd92524210

[ "MIT" ]

1

2022-02-06T17:13:57.000Z

from .cluster import Cluster from .helpers import clusters_from_hierarchy, get_hierarchy_df, get_scipy_hierarchy, hierarchy_to_newick from .helpers import marker_score_table from .plotting import plot_hierarchy_scipy try: from .plotting import plot_hierarchy_ete3 except ImportError: pass from .run import run_mcmc

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py

Python

frille-lang/lib/python3.6/site-packages/pygraphviz/tests/test_readwrite.py

frillecode/CDS-spring-2021-language

a0b2116044cd20d4a34b98f23bd2663256c90c5d

[ "MIT" ]

null

frille-lang/lib/python3.6/site-packages/pygraphviz/tests/test_readwrite.py

frillecode/CDS-spring-2021-language

a0b2116044cd20d4a34b98f23bd2663256c90c5d

[ "MIT" ]

null

frille-lang/lib/python3.6/site-packages/pygraphviz/tests/test_readwrite.py

frillecode/CDS-spring-2021-language

a0b2116044cd20d4a34b98f23bd2663256c90c5d

[ "MIT" ]

null

from nose.tools import assert_equal from nose.tools import assert_true from nose.tools import assert_false import pygraphviz as pgv import os import tempfile import pathlib def test_readwrite(): A = pgv.AGraph(name="test graph") A.add_path([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) the_file = tempfile.NamedTemporaryFile(delete=False) fname = the_file.name # Make sure it can be opened for writing again on Win32 the_file.close() # Pass a string to trigger the code paths that close the newly created file handle A.write(fname) B = pgv.AGraph(fname) assert_equal(A, B) assert_true(B == A) assert_false(B is A) os.unlink(fname) def test_readwrite_pathobj(): A = pgv.AGraph(name="test graph") A.add_path([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) the_file = tempfile.NamedTemporaryFile(delete=False) fname = pathlib.Path(the_file.name) # Make sure it can be opened for writing again on Win32 the_file.close() # Pass a string to trigger the code paths that close the newly created file handle A.write(fname) B = pgv.AGraph(fname) assert_equal(A, B) assert_true(B == A) assert_false(B is A) os.unlink(fname)

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650c65fe09ef31d814cd0c277a374b45eb1d3ae9

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py

Python

tests/python/modules/config_loading/test__config_parser.py

dotmodules/dm

ec2ebf6c8b9ac707440a81d0f25003af6f0603c2

[ "MIT" ]

null

tests/python/modules/config_loading/test__config_parser.py

dotmodules/dm

ec2ebf6c8b9ac707440a81d0f25003af6f0603c2

[ "MIT" ]

null

tests/python/modules/config_loading/test__config_parser.py

dotmodules/dm

ec2ebf6c8b9ac707440a81d0f25003af6f0603c2

[ "MIT" ]

null

from typing import cast from unittest.mock import MagicMock import pytest from pytest_mock.plugin import MockerFixture from dotmodules.modules.loader import ConfigLoader, LoaderError from dotmodules.modules.parser import ConfigParser, ParserError @pytest.fixture def mock_loader(mocker: MockerFixture) -> MagicMock: # By default the type of a MagicMock object is Any. We want to narrow it # back to MagicMock.. return cast(MagicMock, mocker.MagicMock()) @pytest.fixture def parser(mocker: MockerFixture, mock_loader: MagicMock) -> ConfigParser: loader = cast(ConfigLoader, mock_loader) return ConfigParser(loader=loader) # ============================================================================= # LOW LEVEL PARSING METHODS # ============================================================================= class TestStringParsing: def test__valid_string_can_be_parsed( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "key" dummy_value = "some value" mock_loader.get.return_value = dummy_value result = parser._parse_string(key=dummy_key) assert result == dummy_value mock_loader.get.assert_called_with(key=dummy_key) def test__missing_key__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "invalid_key" mock_loader.get.side_effect = LoaderError("missing key") with pytest.raises(ParserError) as exception_info: parser._parse_string(key=dummy_key) expected = f"Mandatory '{dummy_key}' section is missing!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key=dummy_key) def test__missing_key__but_not_mandatory__empty_should_be_returned( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "invalid_key" mock_loader.get.side_effect = LoaderError("missing key") result = parser._parse_string(key=dummy_key, mandatory=False) assert result == "" mock_loader.get.assert_called_with(key=dummy_key) def test__empty_value__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_value = "" dummy_key = "key" mock_loader.get.return_value = dummy_value with pytest.raises(ParserError) as exception_info: parser._parse_string(key=dummy_key) expected = f"Empty value for section '{dummy_key}'!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key=dummy_key) def test__empty_value__but_not_mandatory__empty_should_be_returned( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_value = "" dummy_key = "invalid_key" mock_loader.get.return_value = dummy_value result = parser._parse_string(key=dummy_key, mandatory=False) assert result == "" mock_loader.get.assert_called_with(key=dummy_key) def test__non_string_value__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_value = ["this", "is", "not", "a", "string"] dummy_key = "key" mock_loader.get.return_value = dummy_value with pytest.raises(ParserError) as exception_info: parser._parse_string(key=dummy_key) expected = f"Value for section '{dummy_key}' should be a string, got '['this', 'is', 'not', 'a', 'string']'!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key=dummy_key) class TestBooleanParsing: def test__valid_boolean_can_be_parsed( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "key" dummy_value = True mock_loader.get.return_value = dummy_value result = parser._parse_boolean(key=dummy_key) assert result == dummy_value mock_loader.get.assert_called_with(key=dummy_key) def test__missing_key__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "invalid_key" mock_loader.get.side_effect = LoaderError("missing key") with pytest.raises(ParserError) as exception_info: parser._parse_boolean(key=dummy_key) expected = f"Mandatory '{dummy_key}' section is missing!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key=dummy_key) def test__missing_key__but_not_mandatory__false_should_be_returned( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "invalid_key" mock_loader.get.side_effect = LoaderError("missing key") result = parser._parse_boolean(key=dummy_key, mandatory=False) assert not result mock_loader.get.assert_called_with(key=dummy_key) def test__non_boolean_value__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_value = 42 dummy_key = "key" mock_loader.get.return_value = dummy_value with pytest.raises(ParserError) as exception_info: parser._parse_boolean(key=dummy_key) expected = f"Value for section '{dummy_key}' should be a boolean, got '42'!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key=dummy_key) class TestItemListParsing: def test__missing_key_should_be_converted_to_an_empty_list( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = {"irrelevant": "irrelevant"} mock_loader.get.side_effect = LoaderError("missing key") result = parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, ignoring # the mypy warning. expected_item=dummy_expected_item, # type: ignore ) assert result == [] mock_loader.get.assert_called_with(key=dummy_key) def test__none_value_should_be_converted_to_an_empty_list( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = {"irrelevant": "irrelevant"} mock_loader.get.return_value = None result = parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, ignoring # the mypy warning. expected_item=dummy_expected_item, # type: ignore ) assert result == [] mock_loader.get.assert_called_with(key=dummy_key) def test__empty_value_should_be_converted_to_an_empty_list( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = {"irrelevant": "irrelevant"} mock_loader.get.return_value = {} result = parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, ignoring # the mypy warning. expected_item=dummy_expected_item, # type: ignore ) assert result == [] mock_loader.get.assert_called_with(key=dummy_key) def test__not_a_list__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = "I am not a list" with pytest.raises(ParserError) as exception_info: parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, # ignoring the mypy warning. expected_item=dummy_expected_item, # type: ignore ) expected = "Invalid value for 'my_key'! It should contain a list of objects!" assert str(exception_info.value) == expected def test__not_a_list_of_dictionaries__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = [42, {"hello": "hello"}] with pytest.raises(ParserError) as exception_info: parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, # ignoring the mypy warning. expected_item=dummy_expected_item, # type: ignore ) expected = "Invalid value for 'my_key'! It should contain a list of objects!" assert str(exception_info.value) == expected def test__valid_item_can_be_parsed( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = [ { "field_1": "value_1", "field_2": 42, }, ] result = parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, ignoring # the mypy warning. expected_item=dummy_expected_item, # type: ignore ) assert result == [ { "field_1": "value_1", "field_2": 42, }, ] def test__multiple_valid_items_can_be_parsed( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = [ { "field_1": "value_1", "field_2": 42, }, { "field_1": "value_2", "field_2": 43, }, ] result = parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, ignoring # the mypy warning. expected_item=dummy_expected_item, # type: ignore ) assert result == [ { "field_1": "value_1", "field_2": 42, }, { "field_1": "value_2", "field_2": 43, }, ] def test__missing_key__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = [ { "field_1": "value_1", }, ] with pytest.raises(ParserError) as exception_info: parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, # ignoring the mypy warning. expected_item=dummy_expected_item, # type: ignore ) expected = ( "Missing mandatory field 'field_2' from section 'my_key' item at index 1!" ) assert str(exception_info.value) == expected def test__additional_key__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = [ { "field_1": "value_1", "field_2": 42, "extra_field": "uff", }, ] with pytest.raises(ParserError) as exception_info: parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, # ignoring the mypy warning. expected_item=dummy_expected_item, # type: ignore ) expected = ( "Unexpected field 'extra_field' found for section 'my_key' item at index 1!" ) assert str(exception_info.value) == expected def test__additional_keys__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = [ { "field_1": "value_1", "field_2": 42, "extra_field_1": "uff", "extra_field_2": "huff", }, ] with pytest.raises(ParserError) as exception_info: parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, # ignoring the mypy warning. expected_item=dummy_expected_item, # type: ignore ) expected = "Unexpected fields 'extra_field_1', 'extra_field_2' found for section 'my_key' item at index 1!" assert str(exception_info.value) == expected def test__invalid_value_type__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: dummy_key = "my_key" dummy_expected_item = { "field_1": "string", "field_2": 42, } mock_loader.get.return_value = [ { "field_1": "value_1", "field_2": "I am not an integer", }, ] with pytest.raises(ParserError) as exception_info: parser._parse_item_list( key=dummy_key, # We are testing here with a simplified expected item type, # ignoring the mypy warning. expected_item=dummy_expected_item, # type: ignore ) expected = "The value for field 'field_2' should be an int in section 'my_key' item at index 1!" assert str(exception_info.value) == expected # ============================================================================= # HIGHER LEVEL PARSING METHODS # ============================================================================= class TestNameParsing: def test__name_can_be_parsed( self, parser: ConfigParser, mocker: MockerFixture ) -> None: dummy_name = "my_name" mock_parse_string = mocker.patch.object(parser, "_parse_string") mock_parse_string.return_value = dummy_name result = parser.parse_name() assert result == dummy_name mock_parse_string.assert_called_with(key="name") class TestVersionParsing: def test__version_can_be_parsed( self, parser: ConfigParser, mocker: MockerFixture ) -> None: dummy_version = "my_version" mock_parse_string = mocker.patch.object(parser, "_parse_string") mock_parse_string.return_value = dummy_version result = parser.parse_version() assert result == dummy_version mock_parse_string.assert_called_with(key="version") class TestEnabledParsing: def test__enabled_flag_can_be_parsed( self, parser: ConfigParser, mocker: MockerFixture ) -> None: dummy_enabled_flag = True mock_parse_boolean = mocker.patch.object(parser, "_parse_boolean") mock_parse_boolean.return_value = dummy_enabled_flag result = parser.parse_enabled() assert result == dummy_enabled_flag mock_parse_boolean.assert_called_with(key="enabled") class TestDocumentationParsing: def test__documentation_can_be_parsed( self, parser: ConfigParser, mocker: MockerFixture ) -> None: dummy_documentation = "line1\nline2" mock_parse_string = mocker.patch.object(parser, "_parse_string") mock_parse_string.return_value = dummy_documentation result = parser.parse_documentation() assert result == [ "line1", "line2", ] mock_parse_string.assert_called_with(key="documentation", mandatory=False) class TestVariablesParsing: def test__missing_key_should_be_converted_to_dict( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.side_effect = LoaderError("missing key") result = parser.parse_variables() assert result == {} mock_loader.get.assert_called_with(key="variables") def test__empty_value_should_be_left_as_is( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.return_value = {} result = parser.parse_variables() assert result == {} mock_loader.get.assert_called_with(key="variables") def test__scalar_value__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.return_value = "I am a string" with pytest.raises(ParserError) as exception_info: parser.parse_variables() expected = "The 'variables' section should have the following syntax: 'VARIABLE_NAME' = ['var_1', 'var_2', ..] !" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key="variables") def test__non_string_key__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.return_value = { 42: ["non", "string", "key"], } with pytest.raises(ParserError) as exception_info: parser.parse_variables() expected = "The 'variables' section should only have string variable names!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key="variables") def test__non_compatible_variable__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.return_value = { "VARIABLE": {"this is not a list": 42}, } with pytest.raises(ParserError) as exception_info: parser.parse_variables() expected = "The 'variables' section should contain a single string or a list of strings for a variable name!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key="variables") def test__non_list_variable_value__should_be_converted_to_a_list( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.return_value = { "VARIABLE": "I am not a list", } result = parser.parse_variables() assert result == { "VARIABLE": ["I am not a list"], } mock_loader.get.assert_called_with(key="variables") def test__list_variable_values__should_be_left_as_is( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.return_value = { "VARIABLE": ["I", "am", "a", "list"], } result = parser.parse_variables() assert result == { "VARIABLE": ["I", "am", "a", "list"], } mock_loader.get.assert_called_with(key="variables") def test__non_string_items__error_should_be_raised( self, parser: ConfigParser, mock_loader: MagicMock ) -> None: mock_loader.get.return_value = { "VARIABLE": ["I am a string", 42], } with pytest.raises(ParserError) as exception_info: parser.parse_variables() expected = "The 'variables' section should contain a single string or a list of strings for a variable name!" assert str(exception_info.value) == expected mock_loader.get.assert_called_with(key="variables") class TestLinkParsing: def test__links_can_be_parsed( self, parser: ConfigParser, mocker: MockerFixture ) -> None: dummy_links = [ { "path_to_target": "my_path_to_target_1", "path_to_symlink": "my_path_to_symlink_1", "name": "my_name_1", }, { "path_to_target": "my_path_to_target_2", "path_to_symlink": "my_path_to_symlink_2", "name": "my_name_2", }, ] mock_parse_item_list = mocker.patch.object(parser, "_parse_item_list") mock_parse_item_list.return_value = dummy_links result = parser.parse_links() assert result == dummy_links mock_parse_item_list.assert_called_with( key="links", expected_item={ "path_to_target": "string", "path_to_symlink": "string", "name": "string", }, ) class TestHookParsing: def test__hooks_can_be_parsed( self, parser: ConfigParser, mocker: MockerFixture ) -> None: dummy_hooks = [ { "path_to_script": "my_path_to_script_1", "name": "my_name_1", "priority": 42, }, { "path_to_script": "my_path_to_script_2", "name": "my_name_2", "priority": 43, }, ] mock_parse_item_list = mocker.patch.object(parser, "_parse_item_list") mock_parse_item_list.return_value = dummy_hooks result = parser.parse_hooks() assert result == dummy_hooks mock_parse_item_list.assert_called_with( key="hooks", expected_item={ "path_to_script": "string", "name": "string", "priority": 0, }, )

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0

null

0

6

e8eaec138b53d04dc22c7cc543b0388e8a9e201b

1,610

py

Python

January9th/test_guessingGame.py

EricCharnesky/CIS2001-Winter2020

e51d967e97399248dc8b69aaed2d5ca8aee0cd6e

[ "MIT" ]

3

2020-01-06T23:21:36.000Z

2021-03-01T08:36:57.000Z

January9th/test_guessingGame.py

EricCharnesky/CIS2001-Winter2020

e51d967e97399248dc8b69aaed2d5ca8aee0cd6e

[ "MIT" ]

null

January9th/test_guessingGame.py

EricCharnesky/CIS2001-Winter2020

e51d967e97399248dc8b69aaed2d5ca8aee0cd6e

[ "MIT" ]

2

2020-01-21T16:00:03.000Z

2020-05-05T14:57:34.000Z

from unittest import TestCase from January9th import GuessingGame class TestGuessingGame(TestCase): def test_guess_guess_correctly_in_one_guess(self): # AAA # arrange - set up all variables magic_number = 4 expected_result = "You guessed it in 1 guesses!" max_number = 10 test_game = GuessingGame(max_number) test_game._magic_number = magic_number # act - call the code we are testing actual_result = test_game.guess(magic_number) # assert - did we get what we expected self.assertEqual(expected_result, actual_result) def test_guess_guess_too_low(self): # AAA # arrange - set up all variables magic_number = 4 expected_result = "Your guess was too low!" max_number = 10 test_game = GuessingGame(max_number) test_game._magic_number = max_number # act - call the code we are testing actual_result = test_game.guess(magic_number) # assert - did we get what we expected self.assertEqual(expected_result, actual_result) def test_guess_guess_too_high(self): # AAA # arrange - set up all variables magic_number = 4 expected_result = "Your guess was too high!" max_number = 10 test_game = GuessingGame(max_number) test_game._magic_number = magic_number # act - call the code we are testing actual_result = test_game.guess(max_number) # assert - did we get what we expected self.assertEqual(expected_result, actual_result)

27.288136

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0.65528

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1,610

4.849515

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0.11011

0.036036

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0.009607

0.28882

1,610

58

57

27.758621

0.862882

0.198758

0

0.592593

0

0.058731

0

0.111111

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0.111111

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0

0.074074

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0.222222

0

null

0

1

0

null

0

6

e8f2da97a055f7abd807f7756b5b0f170c97303b

318

py

Python

puma/logging/__init__.py

gift-surg/puma

58beae3459a0c8d96adfe9af323e26868428df4d

[ "Apache-2.0" ]

null

puma/logging/__init__.py

gift-surg/puma

58beae3459a0c8d96adfe9af323e26868428df4d

[ "Apache-2.0" ]

13

2020-05-04T14:14:58.000Z

2020-07-29T16:37:03.000Z

puma/logging/__init__.py

gift-surg/puma

58beae3459a0c8d96adfe9af323e26868428df4d

[ "Apache-2.0" ]

null

from puma.logging.log_level import LogLevel # noqa: F401 from puma.logging.logging import Logging # noqa: F401 from puma.logging.managed_process_log_queue import ManagedProcessLogQueue # noqa: F401 from puma.logging.child_process_logging.process_logging_mechanism import ProcessLoggingMechanism # noqa: F401, I100

63.6

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0.839623

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6.166667

0.404762

0.123552

0.23166

0.185328

0.266409

0

0.052632

0.103774

318

4

117

79.5

0.85614

0.154088

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true

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null

0

1

0

1

0

1

0

6

e8f3acadb0080e938b63a6f881fab49c52eba8e2

2,556

py

Python

epytope/Data/pssms/smmpmbec/mat/A_11_01_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smmpmbec/mat/A_11_01_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smmpmbec/mat/A_11_01_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

A_11_01_10 = {0: {'A': -0.451, 'C': 0.083, 'E': 0.511, 'D': 0.521, 'G': -0.164, 'F': 0.086, 'I': -0.067, 'H': 0.032, 'K': -0.193, 'M': -0.153, 'L': -0.026, 'N': 0.141, 'Q': -0.012, 'P': 0.593, 'S': -0.547, 'R': -0.252, 'T': -0.298, 'W': 0.303, 'V': -0.163, 'Y': 0.055}, 1: {'A': -0.376, 'C': 0.806, 'E': 0.116, 'D': 0.212, 'G': 0.06, 'F': 0.081, 'I': -0.571, 'H': 0.478, 'K': 0.518, 'M': -0.522, 'L': -0.348, 'N': 0.322, 'Q': -0.194, 'P': 0.147, 'S': -0.662, 'R': 0.9, 'T': -0.933, 'W': 0.333, 'V': -0.714, 'Y': 0.346}, 2: {'A': -0.058, 'C': -0.023, 'E': 0.292, 'D': 0.15, 'G': 0.203, 'F': -0.195, 'I': -0.416, 'H': 0.238, 'K': 0.357, 'M': -0.552, 'L': -0.152, 'N': -0.09, 'Q': 0.169, 'P': 0.119, 'S': -0.268, 'R': 0.502, 'T': 0.057, 'W': 0.005, 'V': -0.016, 'Y': -0.321}, 3: {'A': -0.053, 'C': -0.056, 'E': 0.243, 'D': 0.003, 'G': -0.021, 'F': -0.31, 'I': -0.048, 'H': 0.189, 'K': 0.253, 'M': -0.306, 'L': 0.058, 'N': -0.121, 'Q': 0.06, 'P': -0.013, 'S': -0.111, 'R': 0.199, 'T': 0.057, 'W': 0.001, 'V': 0.077, 'Y': -0.102}, 4: {'A': -0.2, 'C': -0.021, 'E': 0.281, 'D': 0.097, 'G': 0.095, 'F': -0.197, 'I': 0.099, 'H': -0.022, 'K': 0.184, 'M': -0.107, 'L': 0.06, 'N': -0.029, 'Q': 0.067, 'P': -0.012, 'S': -0.001, 'R': 0.008, 'T': -0.016, 'W': -0.213, 'V': 0.006, 'Y': -0.079}, 5: {'A': -0.065, 'C': 0.098, 'E': 0.073, 'D': 0.171, 'G': 0.145, 'F': -0.24, 'I': -0.102, 'H': 0.169, 'K': 0.091, 'M': -0.034, 'L': -0.068, 'N': 0.052, 'Q': 0.167, 'P': -0.078, 'S': -0.128, 'R': 0.105, 'T': -0.119, 'W': 0.042, 'V': -0.19, 'Y': -0.089}, 6: {'A': 0.057, 'C': 0.017, 'E': 0.262, 'D': 0.203, 'G': 0.085, 'F': -0.136, 'I': 0.007, 'H': -0.062, 'K': 0.153, 'M': -0.129, 'L': -0.157, 'N': -0.014, 'Q': 0.171, 'P': 0.075, 'S': -0.098, 'R': 0.042, 'T': -0.042, 'W': -0.138, 'V': 0.088, 'Y': -0.383}, 7: {'A': 0.149, 'C': -0.0, 'E': 0.087, 'D': 0.351, 'G': 0.381, 'F': -0.448, 'I': -0.253, 'H': 0.031, 'K': 0.424, 'M': -0.421, 'L': -0.338, 'N': 0.088, 'Q': 0.284, 'P': -0.057, 'S': 0.013, 'R': 0.405, 'T': 0.05, 'W': -0.215, 'V': -0.135, 'Y': -0.396}, 8: {'A': -0.009, 'C': 0.166, 'E': -0.001, 'D': 0.248, 'G': 0.192, 'F': -0.378, 'I': 0.15, 'H': -0.06, 'K': 0.13, 'M': 0.043, 'L': -0.107, 'N': 0.008, 'Q': 0.154, 'P': -0.188, 'S': -0.005, 'R': 0.136, 'T': -0.105, 'W': 0.019, 'V': 0.08, 'Y': -0.474}, 9: {'A': -0.012, 'C': 0.356, 'E': 0.571, 'D': 0.48, 'G': -0.007, 'F': 0.264, 'I': 0.154, 'H': 0.021, 'K': -2.185, 'M': 0.082, 'L': 0.261, 'N': 0.306, 'Q': 0.44, 'P': 0.592, 'S': 0.149, 'R': -1.268, 'T': 0.302, 'W': 0.228, 'V': 0.379, 'Y': -1.112}, -1: {'con': 5.01083}}

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0

1

null

0

1

0

1

0

null

0

6

33410e3feaa389d77854a91253a0989e09470f58

206

py

Python

dynamic_model/BoatDynamics.py

archipela-go/analysis

6932cc401713ca92c682984c6e7682f4966f6ba0

[ "MIT" ]

null

dynamic_model/BoatDynamics.py

archipela-go/analysis

6932cc401713ca92c682984c6e7682f4966f6ba0

[ "MIT" ]

null

dynamic_model/BoatDynamics.py

archipela-go/analysis

6932cc401713ca92c682984c6e7682f4966f6ba0

[ "MIT" ]

null

import numpy as np class BoatDynamics: def __init__(self): pass def calculate_wrench(self, state, control): pass def calculate_derivatives(self, state, control): pass

17.166667

52

0.650485

24

206

5.333333

0.625

0.109375

0.25

0.3125

0

0.281553

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11

53

18.727273

0.864865

0

0.375

0

1

0.375

false

0.375

0.125

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0.625

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0

1

0

null

0

1

0

1

0

1

0

6

334fd41d4b008afe76738cca56836090d1a89de7

418

py

Python

src/fiesta/core/patterns.py

lerooze/django-fiesta

d521f50bcdd3d40e91f0474ec2fa7e256758e0a5

[ "BSD-3-Clause" ]

null

src/fiesta/core/patterns.py

lerooze/django-fiesta

d521f50bcdd3d40e91f0474ec2fa7e256758e0a5

[ "BSD-3-Clause" ]

3

2019-10-29T23:31:01.000Z

2020-03-31T03:08:28.000Z

src/fiesta/core/patterns.py

lerooze/django-fiesta

d521f50bcdd3d40e91f0474ec2fa7e256758e0a5

[ "BSD-3-Clause" ]

null

import re # urn:sdmx:org.package-name.class-name=agency-id:(maintainable-parent-object-id[maintainable-parent-object-version].)?(container-object-id.)?object-id([object-version])? MAINTAINABLE = re.compile(r'(?P<object_id>\.*)(\[(?P<version>.*)\])?') PARENTABLE = re.compile(r'(?P<maintainable_parent_id>\.*)\[(?P<maintainable_parent_version>.*)\]\.(?P<container_id>.*?\.)?(?P<object_id>\.*)(\[(?P<version>.*)\])?')

59.714286

169

0.677033

55

418

5.018182

0.345455

0.144928

0.188406

0.123188

0

0.028708

418

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170

69.666667

0.679803

0.399522

0

0.333333

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0

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0

1

0

1

0

1

null

0

1

0

6

6839306a9b7cd2c8856e23bb1ee4244a3260c65e

57

py

Python

codingbat.com/List-1/rotate_left3.py

ahmedelq/PythonicAlgorithms

ce10dbb6e1fd0ea5c922a932b0f920236aa411bf

[ "MIT" ]

null

codingbat.com/List-1/rotate_left3.py

ahmedelq/PythonicAlgorithms

ce10dbb6e1fd0ea5c922a932b0f920236aa411bf

[ "MIT" ]

null

codingbat.com/List-1/rotate_left3.py

ahmedelq/PythonicAlgorithms

ce10dbb6e1fd0ea5c922a932b0f920236aa411bf

[ "MIT" ]

null

def rotate_left3(nums): return nums[1:] + nums[:1]

19

32

0.614035

9

57

3.777778

0.666667

0.294118

0

0.066667

0.210526

57

2

33

28.5

0.688889

0

1

0.5

false

0

0.5

1

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1

0

null

1

0

1

0

null

0

1

0

1

0

6

6881f916fb4b35ae85e614ac3a383995a6b6d6dd

292

py

Python

TimeWrapper_JE/venv/Lib/site-packages/tqdm/_tqdm.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

TimeWrapper_JE/venv/Lib/site-packages/tqdm/_tqdm.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

TimeWrapper_JE/venv/Lib/site-packages/tqdm/_tqdm.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

from warnings import warn from .std import * # NOQA from .std import __all__ # NOQA from .std import TqdmDeprecationWarning warn("This function will be removed in tqdm==5.0.0\n" "Please use `tqdm.std.*` instead of `tqdm._tqdm.*`", TqdmDeprecationWarning, stacklevel=2)

29.2

58

0.69863

40

292

4.975

0.6

0.105528

0.19598

0.170854

0

0.017094

0.19863

292

9

59

32.444444

0.833333

0.030822

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0.350554

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true

0

0.571429

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0.571429

0

null

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0

null

0

1

0

1

0

1

0

6

68a7c605d941b820d15e3b77e51e9d55d68946df

89

py

Python

sample_app/boards/admin.py

CCE-IT/cce-toolkit

a3dc470bd8fd3f01615ff57198dfefc88d3aa50c

[ "BSD-3-Clause" ]

8

2016-06-23T14:41:26.000Z

2018-07-06T17:54:08.000Z

sample_app/boards/admin.py

cceit/cce-toolkit

a3dc470bd8fd3f01615ff57198dfefc88d3aa50c

[ "BSD-3-Clause" ]

null

sample_app/boards/admin.py

cceit/cce-toolkit

a3dc470bd8fd3f01615ff57198dfefc88d3aa50c

[ "BSD-3-Clause" ]

null

from toolkit.helpers.admin import auto_admin_register auto_admin_register(__package__)

17.8

53

0.876404

12

89

5.833333

0.666667

0.257143

0.485714

0

0.078652

89

4

54

22.25

0.853659

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d7ace1e1f48842c36836976f75166b664d2dcc6c

31

py

Python

models/fixmatch/__init__.py

limberc/TorchSSL

b78918964bde9a91ba8bb5be58c2b238951949f8

[ "MIT" ]

null

models/fixmatch/__init__.py

limberc/TorchSSL

b78918964bde9a91ba8bb5be58c2b238951949f8

[ "MIT" ]

null

models/fixmatch/__init__.py

limberc/TorchSSL

b78918964bde9a91ba8bb5be58c2b238951949f8

[ "MIT" ]

null

from .fixmatch import FixMatch

15.5

30

0.83871

4

31

6.5

0.75

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d7ff1613c1f599210d19df9755cccd19f5ca8318

26

py

Python

python/testData/psi/SingleQuotedFStringInsideMultilineFStringTerminatedByLineBreakInExpressionInParentheses.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/psi/SingleQuotedFStringInsideMultilineFStringTerminatedByLineBreakInExpressionInParentheses.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/psi/SingleQuotedFStringInsideMultilineFStringTerminatedByLineBreakInExpressionInParentheses.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

s = f"""{f'{(1 + 2)}'}"""

13

17

0.192308

5

26

1

0.8

0

0.095238

0.192308

26

2

18

13

0.142857

0

0.555556

0

null

0

null

0

1

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0

1

0

1

0

null

0

1

0

6

d7ffe1a488769967ce950bc9e48437a7e95b1b34

33,585

py

Python

tests/unit/stream_alert_alert_processor/test_outputs/test_pagerduty.py

tuapuikia/streamalert

b1f733259aa051f8d533e7881018280fe77d7bda

[ "Apache-2.0" ]

null

tests/unit/stream_alert_alert_processor/test_outputs/test_pagerduty.py

tuapuikia/streamalert

b1f733259aa051f8d533e7881018280fe77d7bda

[ "Apache-2.0" ]

null

tests/unit/stream_alert_alert_processor/test_outputs/test_pagerduty.py

tuapuikia/streamalert

b1f733259aa051f8d533e7881018280fe77d7bda

[ "Apache-2.0" ]

null

""" Copyright 2017-present, Airbnb Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ # pylint: disable=protected-access,attribute-defined-outside-init from mock import patch, PropertyMock from moto import mock_s3, mock_kms from nose.tools import assert_equal, assert_false, assert_true from stream_alert.alert_processor.outputs.pagerduty import ( PagerDutyOutput, PagerDutyOutputV2, PagerDutyIncidentOutput ) from stream_alert_cli.helpers import put_mock_creds from tests.unit.stream_alert_alert_processor import ( ACCOUNT_ID, FUNCTION_NAME, KMS_ALIAS, REGION ) from tests.unit.stream_alert_alert_processor.helpers import get_alert, remove_temp_secrets @mock_s3 @mock_kms @patch('stream_alert.alert_processor.outputs.output_base.OutputDispatcher.MAX_RETRY_ATTEMPTS', 1) class TestPagerDutyOutput(object): """Test class for PagerDutyOutput""" DESCRIPTOR = 'unit_test_pagerduty' SERVICE = 'pagerduty' OUTPUT = ':'.join([SERVICE, DESCRIPTOR]) CREDS = {'url': 'http://pagerduty.foo.bar/create_event.json', 'service_key': 'mocked_service_key'} def setup(self): """Setup before each method""" self._dispatcher = PagerDutyOutput(REGION, ACCOUNT_ID, FUNCTION_NAME, None) remove_temp_secrets() output_name = self._dispatcher.output_cred_name(self.DESCRIPTOR) put_mock_creds(output_name, self.CREDS, self._dispatcher.secrets_bucket, REGION, KMS_ALIAS) def test_get_default_properties(self): """PagerDutyOutput - Get Default Properties""" props = self._dispatcher._get_default_properties() assert_equal(len(props), 1) assert_equal(props['url'], 'https://events.pagerduty.com/generic/2010-04-15/create_event.json') @patch('logging.Logger.info') @patch('requests.post') def test_dispatch_success(self, post_mock, log_mock): """PagerDutyOutput - Dispatch Success""" post_mock.return_value.status_code = 200 assert_true(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.post') def test_dispatch_failure(self, post_mock, log_mock): """PagerDutyOutput - Dispatch Failure, Bad Request""" post_mock.return_value.status_code = 400 assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') def test_dispatch_bad_descriptor(self, log_mock): """PagerDutyOutput - Dispatch Failure, Bad Descriptor""" assert_false( self._dispatcher.dispatch(get_alert(), ':'.join([self.SERVICE, 'bad_descriptor']))) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, 'bad_descriptor') @mock_s3 @mock_kms @patch('stream_alert.alert_processor.outputs.output_base.OutputDispatcher.MAX_RETRY_ATTEMPTS', 1) class TestPagerDutyOutputV2(object): """Test class for PagerDutyOutputV2""" DESCRIPTOR = 'unit_test_pagerduty-v2' SERVICE = 'pagerduty-v2' OUTPUT = ':'.join([SERVICE, DESCRIPTOR]) CREDS = {'url': 'http://pagerduty.foo.bar/create_event.json', 'routing_key': 'mocked_routing_key'} def setup(self): """Setup before each method""" self._dispatcher = PagerDutyOutputV2(REGION, ACCOUNT_ID, FUNCTION_NAME, None) remove_temp_secrets() output_name = self._dispatcher.output_cred_name(self.DESCRIPTOR) put_mock_creds(output_name, self.CREDS, self._dispatcher.secrets_bucket, REGION, KMS_ALIAS) def test_get_default_properties(self): """PagerDutyOutputV2 - Get Default Properties""" props = self._dispatcher._get_default_properties() assert_equal(len(props), 1) assert_equal(props['url'], 'https://events.pagerduty.com/v2/enqueue') @patch('logging.Logger.info') @patch('requests.post') def test_dispatch_success(self, post_mock, log_mock): """PagerDutyOutputV2 - Dispatch Success""" post_mock.return_value.status_code = 200 assert_true(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.post') def test_dispatch_failure(self, post_mock, log_mock): """PagerDutyOutputV2 - Dispatch Failure, Bad Request""" json_error = {'message': 'error message', 'errors': ['error1']} post_mock.return_value.json.return_value = json_error post_mock.return_value.status_code = 400 assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') def test_dispatch_bad_descriptor(self, log_mock): """PagerDutyOutputV2 - Dispatch Failure, Bad Descriptor""" assert_false( self._dispatcher.dispatch(get_alert(), ':'.join([self.SERVICE, 'bad_descriptor']))) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, 'bad_descriptor') #pylint: disable=too-many-public-methods @mock_s3 @mock_kms @patch('stream_alert.alert_processor.outputs.output_base.OutputDispatcher.MAX_RETRY_ATTEMPTS', 1) @patch('stream_alert.alert_processor.outputs.pagerduty.PagerDutyIncidentOutput.BACKOFF_MAX', 0) @patch('stream_alert.alert_processor.outputs.pagerduty.PagerDutyIncidentOutput.BACKOFF_TIME', 0) class TestPagerDutyIncidentOutput(object): """Test class for PagerDutyIncidentOutput""" DESCRIPTOR = 'unit_test_pagerduty-incident' SERVICE = 'pagerduty-incident' OUTPUT = ':'.join([SERVICE, DESCRIPTOR]) CREDS = {'api': 'https://api.pagerduty.com', 'token': 'mocked_token', 'service_name': 'mocked_service_name', 'service_id': 'mocked_service_id', 'escalation_policy': 'mocked_escalation_policy', 'escalation_policy_id': 'mocked_escalation_policy_id', 'email_from': 'email@domain.com', 'integration_key': 'mocked_key'} def setup(self): """Setup before each method""" self._dispatcher = PagerDutyIncidentOutput(REGION, ACCOUNT_ID, FUNCTION_NAME, None) self._dispatcher._base_url = self.CREDS['api'] remove_temp_secrets() output_name = self._dispatcher.output_cred_name(self.DESCRIPTOR) put_mock_creds(output_name, self.CREDS, self._dispatcher.secrets_bucket, REGION, KMS_ALIAS) def test_get_default_properties(self): """PagerDutyIncidentOutput - Get Default Properties""" props = self._dispatcher._get_default_properties() assert_equal(len(props), 1) assert_equal(props['api'], 'https://api.pagerduty.com') def test_get_endpoint(self): """PagerDutyIncidentOutput - Get Endpoint""" endpoint = self._dispatcher._get_endpoint(self.CREDS['api'], 'testtest') assert_equal(endpoint, 'https://api.pagerduty.com/testtest') @patch('requests.get') def test_check_exists_get_id(self, get_mock): """PagerDutyIncidentOutput - Check Exists Get ID""" # GET /check get_mock.return_value.status_code = 200 json_check = {'check': [{'id': 'checked_id'}]} get_mock.return_value.json.return_value = json_check checked = self._dispatcher._check_exists('filter', 'http://mock_url', 'check') assert_equal(checked, 'checked_id') @patch('requests.get') def test_check_exists_get_id_fail(self, get_mock): """PagerDutyIncidentOutput - Check Exists Get Id Fail""" get_mock.return_value.status_code = 200 get_mock.return_value.json.return_value = dict() checked = self._dispatcher._check_exists('filter', 'http://mock_url', 'check') assert_false(checked) @patch('requests.get') def test_check_exists_no_get_id(self, get_mock): """PagerDutyIncidentOutput - Check Exists No Get Id""" # GET /check get_mock.return_value.status_code = 200 json_check = {'check': [{'id': 'checked_id'}]} get_mock.return_value.json.return_value = json_check assert_true(self._dispatcher._check_exists('filter', 'http://mock_url', 'check', False)) @patch('requests.get') def test_user_verify_success(self, get_mock): """PagerDutyIncidentOutput - User Verify Success""" get_mock.return_value.status_code = 200 json_check = {'users': [{'id': 'verified_user_id'}]} get_mock.return_value.json.return_value = json_check user_verified = self._dispatcher._user_verify('valid_user') assert_equal(user_verified['id'], 'verified_user_id') assert_equal(user_verified['type'], 'user_reference') @patch('requests.get') def test_user_verify_fail(self, get_mock): """PagerDutyIncidentOutput - User Verify Fail""" get_mock.return_value.status_code = 200 json_check = {'not_users': [{'not_id': 'verified_user_id'}]} get_mock.return_value.json.return_value = json_check user_verified = self._dispatcher._user_verify('valid_user') assert_false(user_verified) @patch('requests.get') def test_policy_verify_success_no_default(self, get_mock): """PagerDutyIncidentOutput - Policy Verify Success (No Default)""" # GET /escalation_policies get_mock.return_value.status_code = 200 json_check = {'escalation_policies': [{'id': 'good_policy_id'}]} get_mock.return_value.json.return_value = json_check policy_verified = self._dispatcher._policy_verify('valid_policy', '') assert_equal(policy_verified['id'], 'good_policy_id') assert_equal(policy_verified['type'], 'escalation_policy_reference') @patch('requests.get') def test_policy_verify_success_default(self, get_mock): """PagerDutyIncidentOutput - Policy Verify Success (Default)""" # GET /escalation_policies type(get_mock.return_value).status_code = PropertyMock(side_effect=[200, 200]) json_check_bad = {'no_escalation_policies': [{'id': 'bad_policy_id'}]} json_check_good = {'escalation_policies': [{'id': 'good_policy_id'}]} get_mock.return_value.json.side_effect = [json_check_bad, json_check_good] policy_verified = self._dispatcher._policy_verify('valid_policy', 'default_policy') assert_equal(policy_verified['id'], 'good_policy_id') assert_equal(policy_verified['type'], 'escalation_policy_reference') @patch('requests.get') def test_policy_verify_fail_default(self, get_mock): """PagerDutyIncidentOutput - Policy Verify Fail (Default)""" # GET /not_escalation_policies type(get_mock.return_value).status_code = PropertyMock(side_effect=[400, 400]) json_check_bad = {'escalation_policies': [{'id': 'bad_policy_id'}]} json_check_bad_default = {'escalation_policies': [{'id': 'good_policy_id'}]} get_mock.return_value.json.side_effect = [json_check_bad, json_check_bad_default] assert_false(self._dispatcher._policy_verify('valid_policy', 'default_policy')) @patch('requests.get') def test_policy_verify_fail_no_default(self, get_mock): """PagerDutyIncidentOutput - Policy Verify Fail (No Default)""" # GET /not_escalation_policies get_mock.return_value.status_code = 200 json_check = {'not_escalation_policies': [{'not_id': 'verified_policy_id'}]} get_mock.return_value.json.return_value = json_check assert_false(self._dispatcher._policy_verify('valid_policy', 'default_policy')) @patch('requests.get') def test_service_verify_success(self, get_mock): """PagerDutyIncidentOutput - Service Verify Success""" # GET /services get_mock.return_value.status_code = 200 json_check = {'services': [{'id': 'verified_service_id'}]} get_mock.return_value.json.return_value = json_check service_verified = self._dispatcher._service_verify('valid_service') assert_equal(service_verified['id'], 'verified_service_id') assert_equal(service_verified['type'], 'service_reference') @patch('requests.get') def test_service_verify_fail(self, get_mock): """PagerDutyIncidentOutput - Service Verify Fail""" get_mock.return_value.status_code = 200 json_check = {'not_services': [{'not_id': 'verified_service_id'}]} get_mock.return_value.json.return_value = json_check assert_false(self._dispatcher._service_verify('valid_service')) @patch('requests.get') def test_item_verify_success(self, get_mock): """PagerDutyIncidentOutput - Item Verify Success""" # GET /items get_mock.return_value.status_code = 200 json_check = {'items': [{'id': 'verified_item_id'}]} get_mock.return_value.json.return_value = json_check item_verified = self._dispatcher._item_verify('valid_item', 'items', 'item_reference') assert_equal(item_verified['id'], 'verified_item_id') assert_equal(item_verified['type'], 'item_reference') @patch('requests.get') def test_item_verify_no_get_id_success(self, get_mock): """PagerDutyIncidentOutput - Item Verify No Get Id Success""" # GET /items get_mock.return_value.status_code = 200 json_check = {'items': [{'id': 'verified_item_id'}]} get_mock.return_value.json.return_value = json_check assert_true(self._dispatcher._item_verify('valid_item', 'items', 'item_reference', False)) @patch('requests.get') def test_priority_verify_success(self, get_mock): """PagerDutyIncidentOutput - Priority Verify Success""" priority_name = 'priority_name' # GET /priorities get_mock.return_value.status_code = 200 json_check = {'priorities': [{'id': 'verified_priority_id', 'name': priority_name}]} get_mock.return_value.json.return_value = json_check context = {'incident_priority': priority_name} priority_verified = self._dispatcher._priority_verify(context) assert_equal(priority_verified['id'], 'verified_priority_id') assert_equal(priority_verified['type'], 'priority_reference') @patch('requests.get') def test_priority_verify_fail(self, get_mock): """PagerDutyIncidentOutput - Priority Verify Fail""" # GET /priorities get_mock.return_value.status_code = 404 context = {'incident_priority': 'priority_name'} priority_not_verified = self._dispatcher._priority_verify(context) assert_equal(priority_not_verified, dict()) @patch('requests.get') def test_priority_verify_empty(self, get_mock): """PagerDutyIncidentOutput - Priority Verify Empty""" # GET /priorities get_mock.return_value.status_code = 200 json_check = {} get_mock.return_value.json.return_value = json_check context = {'incident_priority': 'priority_name'} priority_not_verified = self._dispatcher._priority_verify(context) assert_equal(priority_not_verified, dict()) @patch('requests.get') def test_priority_verify_not_found(self, get_mock): """PagerDutyIncidentOutput - Priority Verify Not Found""" # GET /priorities get_mock.return_value.status_code = 200 json_check = {'priorities': [{'id': 'verified_priority_id', 'name': 'not_priority_name'}]} get_mock.return_value.json.return_value = json_check context = {'incident_priority': 'priority_name'} priority_not_verified = self._dispatcher._priority_verify(context) assert_equal(priority_not_verified, dict()) @patch('requests.get') def test_priority_verify_invalid(self, get_mock): """PagerDutyIncidentOutput - Priority Verify Invalid""" # GET /priorities get_mock.return_value.status_code = 200 json_check = {'not_priorities': [{'id': 'verified_priority_id', 'name': 'priority_name'}]} get_mock.return_value.json.return_value = json_check context = {'incident_priority': 'priority_name'} priority_not_verified = self._dispatcher._priority_verify(context) assert_equal(priority_not_verified, dict()) @patch('requests.get') def test_incident_assignment_user(self, get_mock): """PagerDutyIncidentOutput - Incident Assignment User""" context = {'assigned_user': 'user_to_assign'} get_mock.return_value.status_code = 200 json_user = {'users': [{'id': 'verified_user_id'}]} get_mock.return_value.json.return_value = json_user assigned_key, assigned_value = self._dispatcher._incident_assignment(context) assert_equal(assigned_key, 'assignments') assert_equal(assigned_value[0]['assignee']['id'], 'verified_user_id') assert_equal(assigned_value[0]['assignee']['type'], 'user_reference') def test_incident_assignment_policy_no_default(self): """PagerDutyIncidentOutput - Incident Assignment Policy (No Default)""" context = {'assigned_policy_id': 'policy_id_to_assign'} assigned_key, assigned_value = self._dispatcher._incident_assignment(context) assert_equal(assigned_key, 'escalation_policy') assert_equal(assigned_value['id'], 'policy_id_to_assign') assert_equal(assigned_value['type'], 'escalation_policy_reference') @patch('requests.post') def test_add_note_incident_success(self, post_mock): """PagerDutyIncidentOutput - Add Note to Incident Success""" post_mock.return_value.status_code = 200 json_note = {'note': {'id': 'created_note_id'}} post_mock.return_value.json.return_value = json_note note_id = self._dispatcher._add_incident_note('incident_id', 'this is the note') assert_equal(note_id, 'created_note_id') @patch('requests.post') def test_add_note_incident_fail(self, post_mock): """PagerDutyIncidentOutput - Add Note to Incident Fail""" post_mock.return_value.status_code = 200 json_note = {'note': {'not_id': 'created_note_id'}} post_mock.return_value.json.return_value = json_note note_id = self._dispatcher._add_incident_note('incident_id', 'this is the note') assert_false(note_id) @patch('requests.post') def test_add_note_incident_bad_request(self, post_mock): """PagerDutyIncidentOutput - Add Note to Incident Bad Request""" post_mock.return_value.status_code = 400 json_note = {'note': {'id': 'created_note_id'}} post_mock.return_value.json.return_value = json_note note_id = self._dispatcher._add_incident_note('incident_id', 'this is the note') assert_false(note_id) @patch('requests.post') def test_add_note_incident_no_response(self, post_mock): """PagerDutyIncidentOutput - Add Note to Incident No Response""" post_mock.return_value.status_code = 200 json_note = {} post_mock.return_value.json.return_value = json_note note_id = self._dispatcher._add_incident_note('incident_id', 'this is the note') assert_false(note_id) @patch('requests.get') def test_item_verify_fail(self, get_mock): """PagerDutyIncidentOutput - Item Verify Fail""" # /not_items get_mock.return_value.status_code = 200 json_check = {'not_items': [{'not_id': 'verified_item_id'}]} get_mock.return_value.json.return_value = json_check item_verified = self._dispatcher._item_verify('http://mock_url', 'valid_item', 'items', 'item_reference') assert_false(item_verified) @patch('logging.Logger.info') @patch('requests.put') @patch('requests.post') @patch('requests.get') def test_dispatch_success_good_user(self, get_mock, post_mock, put_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Success, Good User""" # GET /users, /users json_user = {'users': [{'id': 'valid_user_id'}]} # GET /incidents json_lookup = {'incidents': [{'id': 'incident_id'}]} get_mock.return_value.status_code = 200 get_mock.return_value.json.side_effect = [json_user, json_user, json_lookup] # POST /incidents, /v2/enqueue, /incidents/incident_id/notes post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {'dedup_key': 'returned_dedup_key'} json_note = {'note': {'id': 'note_id'}} post_mock.return_value.json.side_effect = [json_incident, json_event, json_note] # PUT /incidents/indicent_id/merge put_mock.return_value.status_code = 200 ctx = {'pagerduty-incident': {'assigned_user': 'valid_user'}} assert_true(self._dispatcher.dispatch(get_alert(context=ctx), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.info') @patch('requests.put') @patch('requests.post') @patch('requests.get') def test_dispatch_success_good_policy(self, get_mock, post_mock, put_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Success, Good Policy""" # GET /users json_user = {'users': [{'id': 'user_id'}]} # GET /incidents json_lookup = {'incidents': [{'id': 'incident_id'}]} get_mock.return_value.status_code = 200 get_mock.return_value.json.side_effect = [json_user, json_lookup] # POST /incidents, /v2/enqueue, /incidents/incident_id/notes post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {'dedup_key': 'returned_dedup_key'} json_note = {'note': {'id': 'note_id'}} post_mock.return_value.json.side_effect = [json_incident, json_event, json_note] # PUT /incidents/indicent_id/merge put_mock.return_value.status_code = 200 ctx = {'pagerduty-incident': {'assigned_policy_id': 'valid_policy_id'}} assert_true(self._dispatcher.dispatch(get_alert(context=ctx), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.info') @patch('requests.put') @patch('requests.post') @patch('requests.get') def test_dispatch_success_with_priority(self, get_mock, post_mock, put_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Success With Priority""" # GET /priorities, /users json_user = {'users': [{'id': 'user_id'}]} json_priority = {'priorities': [{'id': 'priority_id', 'name': 'priority_name'}]} # GET /incidents json_lookup = {'incidents': [{'id': 'incident_id'}]} get_mock.return_value.status_code = 200 get_mock.return_value.json.side_effect = [json_user, json_priority, json_lookup] # POST /incidents, /v2/enqueue, /incidents/incident_id/notes post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {'dedup_key': 'returned_dedup_key'} json_note = {'note': {'id': 'note_id'}} post_mock.return_value.json.side_effect = [json_incident, json_event, json_note] # PUT /incidents/indicent_id/merge put_mock.return_value.status_code = 200 ctx = { 'pagerduty-incident': { 'assigned_policy_id': 'valid_policy_id', 'incident_priority': 'priority_name' } } assert_true(self._dispatcher.dispatch(get_alert(context=ctx), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.info') @patch('requests.put') @patch('requests.post') @patch('requests.get') def test_dispatch_success_bad_user(self, get_mock, post_mock, put_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Success, Bad User""" # GET /users, /users json_user = {'users': [{'id': 'user_id'}]} json_not_user = {'not_users': [{'id': 'user_id'}]} # GET /incidents json_lookup = {'incidents': [{'id': 'incident_id'}]} get_mock.return_value.status_code = 200 get_mock.return_value.json.side_effect = [json_user, json_not_user, json_lookup] # POST /incidents, /v2/enqueue, /incidents/incident_id/notes post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {'dedup_key': 'returned_dedup_key'} json_note = {'note': {'id': 'note_id'}} post_mock.return_value.json.side_effect = [json_incident, json_event, json_note] # PUT /incidents/indicent_id/merge put_mock.return_value.status_code = 200 ctx = {'pagerduty-incident': {'assigned_user': 'invalid_user'}} assert_true(self._dispatcher.dispatch(get_alert(context=ctx), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.info') @patch('requests.put') @patch('requests.post') @patch('requests.get') def test_dispatch_success_no_context(self, get_mock, post_mock, put_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Success, No Context""" # GET /users json_user = {'users': [{'id': 'user_id'}]} # GET /incidents json_lookup = {'incidents': [{'id': 'incident_id'}]} get_mock.return_value.status_code = 200 get_mock.return_value.json.side_effect = [json_user, json_lookup] # POST /incidents, /v2/enqueue, /incidents/incident_id/notes post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {'dedup_key': 'returned_dedup_key'} json_note = {'note': {'id': 'note_id'}} post_mock.return_value.json.side_effect = [json_incident, json_event, json_note] # PUT /incidents/indicent_id/merge put_mock.return_value.status_code = 200 assert_true(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.post') @patch('requests.get') def test_dispatch_failure_bad_everything(self, get_mock, post_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Failure: No User""" # GET /users, /users type(get_mock.return_value).status_code = PropertyMock(side_effect=[200, 400]) # Only set the return_value here since there will only be one successful call # that makes it to the point of calling the .json() method get_mock.return_value.json.return_value = {'users': [{'id': 'user_id'}]} # POST /incidents post_mock.return_value.status_code = 400 assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.info') @patch('requests.put') @patch('requests.post') @patch('requests.get') def test_dispatch_success_no_merge_response(self, get_mock, post_mock, put_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Success, No Merge Response""" # GET /users get_mock.return_value.status_code = 200 json_user = {'users': [{'id': 'user_id'}]} json_lookup = {'incidents': [{'id': 'existing_incident_id'}]} get_mock.return_value.json.side_effect = [json_user, json_lookup] # POST /incidents, /v2/enqueue post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {'dedup_key': 'returned_dedup_key'} post_mock.return_value.json.side_effect = [json_incident, json_event] # PUT /incidents/indicent_id/merge put_mock.return_value.status_code = 200 put_mock.return_value.json.return_value = {} ctx = {'pagerduty-incident': {'assigned_policy_id': 'valid_policy_id'}} assert_true(self._dispatcher.dispatch(get_alert(context=ctx), self.OUTPUT)) log_mock.assert_called_with('Successfully sent alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.post') @patch('requests.get') def test_dispatch_no_dispatch_no_incident_response(self, get_mock, post_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Failure, No Incident Response""" # /users get_mock.return_value.status_code = 200 json_user = {'users': [{'id': 'user_id'}]} get_mock.return_value.json.return_value = json_user # /incidents post_mock.return_value.status_code = 200 post_mock.return_value.json.return_value = {} assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.post') @patch('requests.get') def test_dispatch_no_dispatch_no_incident_event(self, get_mock, post_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Failure, No Incident Event""" # /users get_mock.return_value.status_code = 200 json_user = {'users': [{'id': 'user_id'}]} get_mock.return_value.json.return_value = json_user # /incidents, /v2/enqueue post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {} post_mock.return_value.json.side_effect = [json_incident, json_event] assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.post') @patch('requests.get') def test_dispatch_no_dispatch_no_incident_key(self, get_mock, post_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Failure, No Incident Key""" # /users get_mock.return_value.status_code = 200 json_user = {'users': [{'id': 'user_id'}]} get_mock.return_value.json.return_value = json_user # /incidents, /v2/enqueue post_mock.return_value.status_code = 200 json_incident = {'incident': {'id': 'incident_id'}} json_event = {'not_dedup_key': 'returned_dedup_key'} post_mock.return_value.json.side_effect = [json_incident, json_event] assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.post') @patch('requests.get') def test_dispatch_bad_dispatch(self, get_mock, post_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Failure, Bad Request""" # /users get_mock.return_value.status_code = 200 json_user = {'users': [{'id': 'user_id'}]} get_mock.return_value.json.return_value = json_user # /incidents post_mock.return_value.status_code = 400 assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') @patch('requests.get') def test_dispatch_bad_email(self, get_mock, log_mock): """PagerDutyIncidentOutput - Dispatch Failure, Bad Email""" # /users get_mock.return_value.status_code = 400 json_user = {'not_users': [{'id': 'no_user_id'}]} get_mock.return_value.json.return_value = json_user assert_false(self._dispatcher.dispatch(get_alert(), self.OUTPUT)) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, self.DESCRIPTOR) @patch('logging.Logger.error') def test_dispatch_bad_descriptor(self, log_mock): """PagerDutyIncidentOutput - Dispatch Failure, Bad Descriptor""" assert_false( self._dispatcher.dispatch(get_alert(), ':'.join([self.SERVICE, 'bad_descriptor']))) log_mock.assert_called_with('Failed to send alert to %s:%s', self.SERVICE, 'bad_descriptor')

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cc025368851be7c6700e770d03a4ecdc3909d95f

96

py

Python

venv/lib/python3.8/site-packages/poetry/core/poetry.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/poetry/core/poetry.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/poetry/core/poetry.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/b9/50/07/84f0fefd3cb7be3c2131dd2413ff1c70524175a05712a85693a2ff50e0

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1

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04398c6c38a9073774c593853860e8f661ae9c6e

205

py

Python

mak/libs/pyxx/cxx/grammar/__init__.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

4

2015-05-13T16:28:36.000Z

2017-05-24T15:34:14.000Z

mak/libs/pyxx/cxx/grammar/__init__.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

null

mak/libs/pyxx/cxx/grammar/__init__.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

1

2017-03-21T08:28:07.000Z

from . import basic from . import expression from . import statement from . import declaration from . import module from . import klass from . import overload from . import template from . import exception

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f0c7816363d0fd6be3744d4c8b5e81db366bef95

171

py

Python

tests/test_advanced/foo.py

thulsadum/configurator

251f260c74ea130a804e63da987431ec0f6e7f1a

[ "MIT" ]

null

tests/test_advanced/foo.py

thulsadum/configurator

251f260c74ea130a804e63da987431ec0f6e7f1a

[ "MIT" ]

null

tests/test_advanced/foo.py

thulsadum/configurator

251f260c74ea130a804e63da987431ec0f6e7f1a

[ "MIT" ]

null

from configurator import Config from .cfgctx import CFGCTX @Config("foo", "foo value", default='foo', arg_name='cfg', context=CFGCTX) def foo(*,cfg=None): return cfg

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9be08d45559e2f577ba6d664c87ff15e5c9733c0

121

py

Python

AI_Web/ChineseChess/views/chess_views.py

xwy27/ArtificialIntelligenceProjects

e2b0154f07d749084e2d670260fa82f8f5ea23ed

[ "MIT" ]

4

2018-12-19T14:10:56.000Z

2021-07-12T06:05:17.000Z

AI_Web/ChineseChess/views/chess_views.py

xwy27/ArtificialIntelligenceProjects

e2b0154f07d749084e2d670260fa82f8f5ea23ed

[ "MIT" ]

1

2019-08-06T01:57:41.000Z

AI_Web/ChineseChess/views/chess_views.py

xwy27/ArtificialIntelligenceProjects

e2b0154f07d749084e2d670260fa82f8f5ea23ed

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def chess(request): return render(request, "chess.html")

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py

Python

modules/db_migration/db_migration_to_s3/daily_migration.py

jindongyang94/docker-kubernetes-airflow

81f27f981e03e6e22cef14e3f7a2690353685e11

[ "Apache-2.0" ]

null

modules/db_migration/db_migration_to_s3/daily_migration.py

jindongyang94/docker-kubernetes-airflow

81f27f981e03e6e22cef14e3f7a2690353685e11

[ "Apache-2.0" ]

null

modules/db_migration/db_migration_to_s3/daily_migration.py

jindongyang94/docker-kubernetes-airflow

81f27f981e03e6e22cef14e3f7a2690353685e11

[ "Apache-2.0" ]

null

import subprocess import os import re import csv import contextlib from datetime import datetime, timedelta import time import progressbar import boto3 import psycopg2 from db_migration.db_migration_lib.helper import RDSHelper, S3Helper, PGHelper, DATALAKE_NAME, logger, DATABASE_TAGS, INSTANCE_TAGS, TABLE_TAGS """ The idea of this script is to find the respective database instances using Boto3, and then find the respective databases in the instance and finally find the respective tables in each database and do a iterative export and dump one table at a time to prevent overloading of memory. This process can be expedited by parallel processing but I am unsure of how to do so yet. Would figure out a way if this becomes a pertinent issue. Upload the file downloaded to s3 to the correct respective folders and buckets based on company name. It is important to note that the files with the same name would be replaced. This would help in not saving redundant files but might not be useful if we want to version. Since tables will never be able to be appended directly from s3, it does not make sense to load the entire csv all the time. Perhaps write another script to merge each csvs based on time periodically. S3 files would be named as follows: s3://{BucketName}/{InstanceName}/{DBName}/{TableName}/{TableName-TimeStamp}.csv # This method allows me to connect to export csv files for each table. # This method does not require the maintenance of a JSON file at all, just different AWS credentials # needed for different servers if different users have different access to the databases. """ # List Individual DBs instance and their respective Database List ----------------------------------------------- def describe_all_instances(): rds = RDSHelper() dbs = rds.describe_db_instances(filters=INSTANCE_TAGS) db_dictionary = {} for db in dbs: instance = db['DBInstanceIdentifier'] user = db['MasterUsername'] endpoint = db['Endpoint'] host = endpoint['Address'] port = endpoint['Port'] location = str(db['DBInstanceArn'].split(':')[3]) logger.info("Accessing instance %s ..." % instance) pg = PGHelper(dbname='postgres', host=host, port=port, user=user) con = pg.conn() cur = con.cursor() def extract_name_query(title, qry): logger.info('%s' % (title)) cur.execute(qry) results = cur.fetchall() result_names = list(map(lambda x: x[0], results)) return result_names # List all available databases in the same instance database_names = extract_name_query( 'Extracting databases...', 'SELECT * FROM pg_database') # Filtering available databases default_databases = ['postgres', 'rdsadmin', 'template1', 'template0'] database_names = list( filter(lambda x: x not in default_databases, database_names)) if DATABASE_TAGS: database_names = list( filter(lambda x: x in DATABASE_TAGS, database_names)) # Save all the information based on key: DBInstance, value: [db, [list of databases extracted from the instance]] db_dictionary[instance] = [db, database_names] return db_dictionary # Individual Company Database Migration ----------------------------------------------- def individual_company_migration(instance_details, database_name, table_filters): instance = instance_details['DBInstanceIdentifier'] user = instance_details['MasterUsername'] endpoint = instance_details['Endpoint'] host = endpoint['Address'] port = endpoint['Port'] location = str(instance_details['DBInstanceArn'].split(':')[3]) pg = PGHelper(dbname='postgres', host=host, port=port, user=user, type_db='prod') logger.info("Accessing %s ..." % database_name) con = pg.conn(database=database_name) cur = con.cursor() def extract_name_query(title, qry): logger.info('%s' % (title)) cur.execute(qry) results = cur.fetchall() result_names = list(map(lambda x: x[0], results)) return result_names # List all available tables in the same instance table_query = "SELECT table_name FROM information_schema.tables WHERE table_schema='public' AND table_type='BASE TABLE'" table_names = extract_name_query('Extracting tables...', table_query) # Filtering available tables if table_filters: table_names = list( filter(lambda x: x in table_names, table_names)) # We should also filter away those tables that does not start with hubble as well: ['delayed_jobs', 'ar_internal_metadata', 'schema_migrations', 'audits'] # We are going to remove hubble_safety_permit_logs as well as it is too big to be exported at the moment. misc_tables = ['delayed_jobs', 'ar_internal_metadata', 'schema_migrations', 'audits', 'hubble_safety_permit_logs'] table_names = list( filter(lambda x: x not in misc_tables, table_names) ) logger.info("Tables List: %s" % table_names) # for table_name in table_names: for j in range(len(table_names)): table_name = table_names[j] # # Rerun for the table when the exception fails # try: # Save individual tables to CSV first - as we are sending one table at a time, we can del the csv files # as soon as we have uploaded them logger.info("Accessing %s ..." % table_name) # We will save the time based on the latest commit time. Thus, there will be only one file for one table all time # However, they might be of different timestamp due to difference in commit time. s3 = S3Helper() # Extract latest timestamp separately here: # Use this query to extract the latest commit timestamp at that point of time extract_ts_query = "SELECT MAX(pg_xact_commit_timestamp(xmin)) FROM " + table_name + " WHERE pg_xact_commit_timestamp(xmin) IS NOT NULL;" cur.execute(extract_ts_query) latest_timestamp = str(cur.fetchone()[0]) # Define needed timestamp to set the csvname we are using. if latest_timestamp and latest_timestamp != 'None': logger.info ("Latest Commit Timestamp from PostGres is: %s" % latest_timestamp) latest_csvtimestamp = s3._convert_s3timestamp(latest_timestamp) # However, if there is no timestamp at all, then use 24 '0's as the default. else: logger.info ("No Commit Timestamp available in PostGres. Using default.") latest_csvtimestamp = '0' * 24 csvname = table_name + "-" + latest_csvtimestamp + ".csv" local_csvname = database_name + "-" + csvname # Respective paths needed full_folder_path = ("%s/%s/%s/%s") % (DATALAKE_NAME, instance, database_name, table_name) full_table_path = "%s/%s/%s/%s/%s" % (DATALAKE_NAME, instance, database_name, table_name, csvname) s3_path = ("s3://%s") % (full_table_path) # Grab the latest_timestamp from the folder. Ideally, there should only be one file under each table folder, but # we will still segregate them as such for easy referencing. table_timestamp = s3.latest_s3timestamp(full_folder_path) # If we could not get a proper timestamp from s3, it means there is no initial file. if not table_timestamp: logger.info ("No CSV found in the respective S3 folder. Exporting all rows from table %s to csv." % table_name) local_csvpath = '/tmp/' + local_csvname with open(local_csvpath, "w") as csvfile: # Get all of the rows and export them export_query = "COPY " + table_name + " TO STDOUT WITH CSV HEADER" cur.copy_expert(export_query, csvfile) else: logger.info ("CSV File found with Commit Timestamp: %s." % table_timestamp) # Since the timestamp is down to the last milisecond, it is almost impossible for it be miss any rows. # Thus, to save processing time, we share ignore any need to update the table csv if the timestamp is the same. table_csvtimestamp = s3._convert_s3timestamp(table_timestamp) if table_csvtimestamp == latest_csvtimestamp: logger.info ("The latest Commit Timestamp (%s) and the latest S3 Timestamp (%s) are the same. Proceeeding to next table." % (latest_timestamp, table_timestamp)) logger.info('\n') break # If timestamp is 0000.. , we should just use the min datetime to prevent error. if table_csvtimestamp == '0' * 24: table_timestamp = datetime.min # Get only the rows after the committed timestamp retrieved and append that to the current csv. # If there is no results, just go to the next table export_query = "SELECT * FROM " + table_name + " WHERE pg_xact_commit_timestamp(xmin) > %s " cur.execute(export_query, (table_timestamp,)) results = cur.fetchall() if not results: logger.info ("No new rows or updates from the current Database.") logger.info('\n') break # Download the file to local storage first, then utilizing it - always save it under /tmp/ directory # The file will also be deleted from s3 local_csvpath = s3.download_latest(full_folder_path, local_csvname) with open(local_csvpath, 'a') as csvfile: # Append by downloading the existing csv and append locally. logger.info ("Writing rows into current local CSV File...") for row in results: writer = csv.writer(csvfile) writer.writerow(row) # Upload the file to the respective bucket - Replacing or uploading uses the same function # This way of uploading would not resetting the entire path, so it is fine to not add a check. s3.create_folder(full_folder_path, location) s3.upload(local_csvpath, full_table_path) latest_timestamp = s3._convert_timestamp(latest_csvtimestamp) logger.info ('FILE PUT AT: %s with Latest Committed Time (%s)' % (s3_path, latest_timestamp)) # Deleting file from /tmp/ after use os.remove(local_csvpath) logger.info ('Local File Deleted: %s' % local_csvpath) logger.info('\n') break # except psycopg2.Error as e: # logger.error(e.pgerror) # logger.info("Retrying for %s table." % table_name) # logger.info('\n') # continue return # Full Program to Run Locally----------------------------------------------- def full_database_migration(instance_filters=None, database_filters=None, table_filters=None): """ -instance_filters (dict): for now it can be anything we are going to use to filter the instance: 1. db-cluster-id 2. db-instance-id A filter name and value pair that is used to return a more specific list of results from a describe operation. Filters can be used to match a set of resources by specific criteria, such as IDs. The filters supported by a describe operation are documented with the describe operation. E.g. [{"Name" :"tag:keyname", "Values":[""] }] - Must explicitly specify "Names" and "Values" pair. -database_filters (list): simply only append the database names to this list so we only access those databases. By default, it will access all -table_filters (list): simply only append table names to this list so we only export those tables. By default it will export all. """ # Initiate RDS instance helper to iterate through RDS rds = RDSHelper() dbs = rds.describe_db_instances(filters=instance_filters) logger.info ("Instances List: %s" % list(map(lambda x: x['DBInstanceIdentifier'], dbs))) for db in dbs: instance = db['DBInstanceIdentifier'] user = db['MasterUsername'] endpoint = db['Endpoint'] host = endpoint['Address'] port = endpoint['Port'] location = str(db['DBInstanceArn'].split(':')[3]) logger.info('instance: %s' % instance) logger.info('user: %s' % user) logger.info('endpoint: %s' % endpoint) logger.info('host: %s' % host) logger.info('port: %s' % port) logger.info('location: %s' % location) logger.info ("Accessing instance %s ..." % instance) pg = PGHelper(dbname='postgres', host=host, port=port, user=user) con = pg.conn() cur = con.cursor() def extract_name_query(title, qry): logger.info('%s' % (title)) cur.execute(qry) results = cur.fetchall() result_names = list(map(lambda x: x[0], results)) return result_names # List all available databases in the same instance database_names = extract_name_query( 'Extracting databases...', 'SELECT * FROM pg_database') # Filtering available databases default_databases = ['postgres', 'rdsadmin', 'template1', 'template0'] database_names = list( filter(lambda x: x not in default_databases, database_names)) if database_filters: database_names = list( filter(lambda x: x in database_filters, database_names)) logger.info("Databases List: %s" % database_names) # for i in progressbar.progressbar(range(len(database_names))): for database_name in database_names: # database_name = database_names[i] # Change database connection logger.info("Accessing %s ..." % database_name) con = pg.conn(database=database_name) cur = con.cursor() # List all available tables in the same instance table_query = "SELECT table_name FROM information_schema.tables WHERE table_schema='public' AND table_type='BASE TABLE'" table_names = extract_name_query('Extracting tables...', table_query) # Filtering available tables if table_filters: table_names = list( filter(lambda x: x in table_names, table_names)) # We should also filter away those tables that does not start with hubble as well: ['delayed_jobs', 'ar_internal_metadata', 'schema_migrations', 'audits'] # We are going to remove hubble_safety_permit_logs as well as it is too big to be exported at the moment. misc_tables = ['delayed_jobs', 'ar_internal_metadata', 'schema_migrations', 'audits', 'hubble_safety_permit_logs'] table_names = list( filter(lambda x: x not in misc_tables, table_names) ) logger.info("Tables List: %s" % table_names) progressbar.streams.wrap_stderr() # for table_name in table_names: for j in progressbar.progressbar(range(len(table_names))): table_name = table_names[j] # Rerun for the table when the exception fails while True: try: # Save individual tables to CSV first - as we are sending one table at a time, we can del the csv files # as soon as we have uploaded them logger.info("Accessing %s ..." % table_name) # We will save the time based on the latest commit time. Thus, there will be only one file for one table all time # However, they might be of different timestamp due to difference in commit time. s3 = S3Helper() # Extract latest timestamp separately here: # Use this query to extract the latest commit timestamp at that point of time extract_ts_query = "SELECT MAX(pg_xact_commit_timestamp(xmin)) FROM " + table_name + " WHERE pg_xact_commit_timestamp(xmin) IS NOT NULL;" cur.execute(extract_ts_query) latest_timestamp = str(cur.fetchone()[0]) # Define needed timestamp to set the csvname we are using. if latest_timestamp and latest_timestamp != 'None': logger.info ("Latest Commit Timestamp from PostGres is: %s" % latest_timestamp) latest_csvtimestamp = s3._convert_s3timestamp(latest_timestamp) # However, if there is no timestamp at all, then use 24 '0's as the default. else: logger.info ("No Commit Timestamp available in PostGres. Using default.") latest_csvtimestamp = '0' * 24 csvname = table_name + "-" + latest_csvtimestamp + ".csv" # Respective paths needed full_folder_path = ("%s/%s/%s/%s") % (DATALAKE_NAME, instance, database_name, table_name) full_table_path = "%s/%s/%s/%s/%s" % (DATALAKE_NAME, instance, database_name, table_name, csvname) s3_path = ("s3://%s") % (full_table_path) # Grab the latest_timestamp from the folder. Ideally, there should only be one file under each table folder, but # we will still segregate them as such for easy referencing. table_timestamp = s3.latest_s3timestamp(full_folder_path) # If we could not get a proper timestamp from s3, it means there is no initial file. if not table_timestamp: logger.info ("No CSV found in the respective S3 folder. Exporting all rows from table %s to csv." % table_name) local_csvpath = '/tmp/' + csvname with open(local_csvpath, "w") as csvfile: # Get all of the rows and export them export_query = "COPY " + table_name + " TO STDOUT WITH CSV HEADER" cur.copy_expert(export_query, csvfile) else: logger.info ("CSV File found with Commit Timestamp: %s." % table_timestamp) # Since the timestamp is down to the last milisecond, it is almost impossible for it be miss any rows. # Thus, to save processing time, we share ignore any need to update the table csv if the timestamp is the same. table_csvtimestamp = s3._convert_s3timestamp(table_timestamp) if table_csvtimestamp == latest_csvtimestamp: logger.info ("The latest Commit Timestamp (%s) and the latest S3 Timestamp (%s) are the same. Proceeeding to next table." % (latest_timestamp, table_timestamp)) logger.info('\n') break # If timestamp is 0000.. , we should just use the min datetime to prevent error. if table_csvtimestamp == '0' * 24: table_timestamp = datetime.min # Get only the rows after the committed timestamp retrieved and append that to the current csv. # If there is no results, just go to the next table export_query = "SELECT * FROM " + table_name + " WHERE pg_xact_commit_timestamp(xmin) > %s " cur.execute(export_query, (table_timestamp,)) results = cur.fetchall() if not results: logger.info ("No new rows or updates from the current Database.") logger.info('\n') break # Download the file to local storage first, then utilizing it - always save it under /tmp/ directory # The file will also be deleted from s3 local_csvpath = s3.download_latest(full_folder_path) with open(local_csvpath, 'a') as csvfile: # Append by downloading the existing csv and append locally. logger.info ("Writing rows into current local CSV File...") for row in results: writer = csv.writer(csvfile) writer.writerow(row) # Upload the file to the respective bucket - Replacing or uploading uses the same function # This way of uploading would not resetting the entire path, so it is fine to not add a check. s3.create_folder(full_folder_path, location) s3.upload(local_csvpath, full_table_path) latest_timestamp = s3._convert_timestamp(latest_csvtimestamp) logger.info ('FILE PUT AT: %s with Latest Committed Time (%s)' % (s3_path, latest_timestamp)) # Deleting file from /tmp/ after use os.remove(local_csvpath) logger.info ('Local File Deleted') logger.info('\n') break except psycopg2.Error as e: logger.error(e.pgerror) logger.info("Retrying for %s table." % table_name) logger.info('\n') continue # Handler to Accomodate to Lambda Context Manager----------------------------------------------- def handler(event=None, context=None): # Start Time start = time.time() # The tag or name of the instance we want to enter # test_server = 'arn:aws:rds:ap-southeast-1:160830294233:db:companya' instance_tags = INSTANCE_TAGS # The given companies # database_tags = ['companyaworkers'] database_tags = DATABASE_TAGS # The related modules needed # correct_tables = [] full_database_migration(instance_filters=instance_tags, database_filters=database_tags) end = time.time() seconds = end - start time_spent = str(timedelta(seconds=seconds)) logger.info("Time Spent on Script: %s" % time_spent) if __name__ == "__main__": handler()

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acac28bdd06a5c907dd20ecd81d9ab5dc4484d2f

258,357

py

Python

instances/passenger_demand/pas-20210422-1717-int1/34.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/34.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/34.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 19244 passenger_arriving = ( (5, 4, 5, 6, 3, 2, 1, 1, 4, 1, 0, 0, 0, 9, 3, 5, 2, 7, 3, 1, 1, 2, 1, 0, 0, 0), # 0 (4, 6, 10, 9, 2, 1, 1, 4, 1, 0, 1, 1, 0, 7, 1, 3, 2, 6, 3, 2, 2, 2, 1, 1, 1, 0), # 1 (5, 9, 8, 7, 5, 0, 0, 6, 1, 1, 0, 0, 0, 6, 6, 3, 2, 0, 3, 4, 2, 3, 6, 1, 0, 0), # 2 (5, 9, 3, 6, 7, 2, 1, 2, 4, 0, 0, 0, 0, 7, 5, 2, 6, 5, 1, 1, 1, 2, 3, 2, 0, 0), # 3 (5, 5, 2, 2, 6, 4, 6, 2, 0, 0, 1, 0, 0, 5, 4, 7, 0, 6, 1, 4, 1, 1, 1, 0, 0, 0), # 4 (7, 2, 8, 8, 4, 0, 2, 3, 0, 0, 1, 0, 0, 7, 5, 5, 3, 5, 6, 5, 2, 0, 3, 0, 1, 0), # 5 (9, 4, 6, 4, 6, 3, 2, 2, 3, 2, 0, 0, 0, 6, 7, 5, 5, 3, 4, 1, 1, 4, 6, 3, 0, 0), # 6 (9, 4, 3, 8, 8, 1, 1, 2, 3, 2, 0, 0, 0, 15, 7, 4, 4, 8, 3, 5, 2, 0, 2, 2, 2, 0), # 7 (6, 7, 7, 12, 6, 4, 3, 4, 4, 2, 0, 1, 0, 5, 6, 6, 5, 9, 7, 11, 3, 2, 3, 1, 1, 0), # 8 (7, 4, 9, 7, 2, 2, 2, 5, 4, 0, 1, 0, 0, 8, 5, 10, 6, 6, 5, 4, 1, 4, 4, 2, 1, 0), # 9 (12, 6, 10, 11, 5, 2, 1, 0, 1, 2, 2, 0, 0, 8, 8, 5, 5, 6, 7, 4, 0, 0, 0, 0, 0, 0), # 10 (4, 8, 0, 9, 2, 3, 6, 6, 5, 2, 2, 0, 0, 11, 10, 4, 6, 7, 3, 2, 2, 1, 3, 2, 1, 0), # 11 (9, 7, 6, 11, 8, 3, 2, 6, 5, 3, 0, 1, 0, 9, 10, 4, 7, 12, 4, 2, 1, 2, 3, 2, 1, 0), # 12 (5, 10, 9, 13, 5, 2, 6, 4, 3, 4, 2, 1, 0, 17, 10, 4, 2, 5, 6, 5, 1, 4, 2, 4, 0, 0), # 13 (10, 12, 6, 9, 7, 3, 4, 7, 4, 4, 3, 0, 0, 11, 8, 6, 6, 3, 6, 4, 3, 4, 2, 0, 3, 0), # 14 (8, 11, 10, 5, 10, 2, 5, 6, 4, 1, 1, 0, 0, 11, 11, 7, 2, 8, 5, 5, 4, 5, 0, 2, 1, 0), # 15 (7, 11, 10, 8, 6, 1, 3, 2, 2, 2, 1, 0, 0, 19, 6, 7, 6, 13, 7, 2, 2, 9, 1, 4, 0, 0), # 16 (9, 12, 4, 12, 13, 4, 2, 1, 2, 0, 0, 1, 0, 6, 10, 8, 5, 6, 6, 6, 1, 3, 2, 0, 1, 0), # 17 (12, 9, 8, 8, 5, 3, 6, 7, 2, 2, 4, 1, 0, 8, 12, 7, 3, 6, 4, 5, 2, 4, 0, 3, 0, 0), # 18 (15, 6, 10, 9, 10, 6, 5, 3, 3, 0, 0, 0, 0, 8, 8, 2, 5, 4, 7, 4, 2, 2, 4, 4, 1, 0), # 19 (14, 16, 9, 10, 3, 5, 1, 1, 3, 4, 3, 0, 0, 6, 13, 6, 2, 5, 7, 5, 1, 4, 2, 1, 0, 0), # 20 (5, 8, 3, 10, 10, 5, 2, 1, 5, 2, 0, 1, 0, 8, 10, 5, 6, 11, 3, 1, 0, 3, 4, 2, 1, 0), # 21 (6, 11, 14, 5, 6, 2, 3, 6, 4, 1, 1, 2, 0, 4, 6, 5, 7, 7, 7, 3, 7, 1, 2, 2, 2, 0), # 22 (9, 6, 3, 10, 4, 0, 3, 1, 4, 1, 1, 0, 0, 10, 8, 7, 1, 6, 7, 6, 3, 3, 3, 3, 1, 0), # 23 (9, 9, 9, 7, 13, 5, 1, 1, 4, 4, 2, 1, 0, 14, 6, 3, 4, 9, 8, 7, 3, 2, 2, 4, 0, 0), # 24 (19, 6, 5, 11, 8, 2, 7, 4, 4, 1, 2, 4, 0, 9, 13, 3, 8, 10, 4, 6, 2, 4, 3, 0, 2, 0), # 25 (14, 10, 10, 11, 11, 7, 6, 6, 5, 1, 1, 4, 0, 18, 6, 5, 4, 12, 4, 3, 4, 4, 5, 0, 2, 0), # 26 (14, 7, 6, 12, 3, 4, 4, 4, 3, 0, 2, 0, 0, 10, 7, 6, 7, 7, 6, 2, 9, 7, 5, 1, 0, 0), # 27 (11, 10, 5, 10, 10, 0, 2, 6, 10, 4, 0, 2, 0, 14, 5, 6, 5, 14, 7, 6, 2, 7, 3, 0, 1, 0), # 28 (10, 12, 10, 7, 7, 3, 8, 7, 4, 1, 0, 1, 0, 14, 10, 7, 4, 7, 5, 6, 1, 3, 4, 1, 1, 0), # 29 (8, 12, 6, 9, 8, 5, 8, 4, 5, 4, 2, 1, 0, 11, 12, 8, 8, 11, 3, 3, 2, 1, 7, 2, 2, 0), # 30 (11, 12, 8, 5, 3, 3, 3, 2, 7, 2, 0, 1, 0, 8, 7, 4, 4, 12, 5, 2, 1, 2, 1, 1, 2, 0), # 31 (5, 7, 9, 12, 10, 1, 2, 5, 6, 2, 1, 1, 0, 8, 7, 10, 8, 13, 5, 10, 4, 5, 5, 4, 0, 0), # 32 (13, 10, 13, 8, 7, 5, 3, 3, 5, 2, 1, 0, 0, 15, 12, 7, 8, 9, 8, 2, 3, 1, 2, 3, 0, 0), # 33 (3, 13, 8, 3, 5, 3, 3, 3, 3, 1, 3, 3, 0, 14, 5, 8, 9, 10, 4, 6, 0, 2, 4, 1, 0, 0), # 34 (11, 8, 9, 14, 9, 0, 7, 3, 3, 3, 1, 2, 0, 14, 11, 3, 3, 14, 6, 4, 3, 5, 4, 1, 0, 0), # 35 (12, 11, 11, 10, 3, 4, 7, 3, 6, 0, 3, 2, 0, 13, 7, 8, 2, 8, 7, 2, 2, 5, 2, 3, 0, 0), # 36 (12, 9, 10, 9, 10, 7, 7, 5, 3, 2, 1, 1, 0, 18, 12, 7, 4, 5, 5, 8, 1, 4, 2, 1, 3, 0), # 37 (8, 15, 9, 7, 5, 6, 4, 3, 7, 2, 0, 0, 0, 11, 5, 7, 5, 9, 6, 6, 3, 6, 2, 4, 1, 0), # 38 (10, 11, 12, 6, 7, 2, 6, 3, 2, 1, 1, 1, 0, 6, 10, 3, 6, 6, 3, 0, 3, 4, 4, 3, 0, 0), # 39 (9, 9, 8, 7, 9, 1, 6, 7, 10, 1, 2, 0, 0, 4, 11, 8, 6, 16, 3, 6, 1, 5, 5, 2, 0, 0), # 40 (6, 4, 7, 11, 11, 4, 3, 5, 5, 2, 1, 0, 0, 14, 4, 1, 11, 2, 5, 4, 3, 3, 4, 1, 3, 0), # 41 (8, 10, 14, 9, 3, 3, 3, 4, 4, 3, 1, 1, 0, 15, 16, 7, 5, 13, 4, 3, 1, 2, 2, 1, 0, 0), # 42 (11, 12, 7, 5, 7, 4, 4, 2, 1, 0, 2, 1, 0, 12, 3, 3, 7, 2, 0, 0, 2, 7, 3, 2, 0, 0), # 43 (15, 10, 15, 12, 6, 4, 6, 2, 4, 1, 0, 1, 0, 10, 4, 7, 5, 8, 1, 5, 3, 2, 2, 0, 0, 0), # 44 (11, 12, 3, 10, 11, 3, 5, 4, 2, 1, 3, 0, 0, 14, 12, 9, 7, 8, 8, 3, 1, 3, 3, 0, 1, 0), # 45 (8, 10, 6, 8, 9, 1, 2, 2, 3, 1, 1, 0, 0, 16, 8, 11, 5, 7, 1, 1, 1, 2, 2, 0, 1, 0), # 46 (10, 10, 15, 11, 10, 6, 1, 5, 3, 3, 3, 1, 0, 11, 7, 9, 10, 5, 3, 3, 5, 4, 5, 4, 1, 0), # 47 (11, 7, 10, 10, 9, 1, 1, 2, 10, 2, 2, 1, 0, 8, 1, 7, 5, 9, 4, 2, 1, 3, 4, 4, 1, 0), # 48 (13, 11, 8, 5, 5, 2, 5, 6, 2, 3, 1, 0, 0, 15, 9, 12, 6, 5, 7, 2, 2, 3, 2, 0, 3, 0), # 49 (10, 6, 3, 9, 10, 3, 5, 2, 1, 1, 2, 3, 0, 15, 9, 5, 7, 9, 4, 4, 1, 2, 2, 4, 0, 0), # 50 (10, 15, 9, 11, 9, 5, 2, 7, 7, 0, 1, 0, 0, 9, 11, 6, 8, 2, 6, 2, 2, 3, 3, 0, 0, 0), # 51 (11, 8, 10, 7, 10, 3, 3, 9, 7, 3, 1, 0, 0, 8, 10, 1, 2, 11, 5, 2, 2, 1, 3, 0, 5, 0), # 52 (9, 20, 5, 13, 6, 2, 2, 2, 1, 3, 1, 0, 0, 8, 6, 3, 8, 11, 6, 3, 3, 4, 6, 3, 0, 0), # 53 (10, 11, 8, 10, 6, 5, 6, 2, 6, 2, 1, 0, 0, 10, 12, 4, 4, 6, 5, 3, 3, 6, 5, 2, 0, 0), # 54 (4, 10, 13, 9, 9, 6, 2, 2, 3, 1, 1, 1, 0, 9, 10, 12, 5, 12, 1, 3, 0, 4, 4, 1, 2, 0), # 55 (6, 5, 7, 8, 10, 5, 5, 2, 5, 2, 2, 2, 0, 6, 8, 8, 4, 11, 7, 1, 3, 2, 2, 1, 2, 0), # 56 (10, 8, 7, 8, 8, 3, 3, 5, 5, 2, 0, 1, 0, 8, 10, 8, 9, 5, 10, 3, 0, 5, 3, 0, 0, 0), # 57 (13, 7, 11, 9, 10, 0, 4, 1, 3, 2, 1, 1, 0, 11, 8, 5, 6, 4, 4, 2, 3, 4, 3, 3, 0, 0), # 58 (6, 8, 12, 13, 10, 3, 4, 3, 3, 3, 1, 0, 0, 11, 16, 4, 0, 8, 4, 6, 1, 3, 4, 2, 1, 0), # 59 (11, 8, 5, 5, 6, 4, 2, 1, 3, 3, 1, 1, 0, 13, 2, 7, 5, 7, 2, 4, 3, 7, 3, 0, 1, 0), # 60 (15, 9, 15, 11, 4, 4, 4, 2, 7, 2, 3, 1, 0, 8, 8, 4, 7, 4, 4, 5, 6, 2, 3, 2, 1, 0), # 61 (6, 9, 11, 5, 12, 4, 2, 2, 5, 0, 1, 0, 0, 6, 9, 9, 4, 9, 3, 5, 3, 2, 3, 4, 2, 0), # 62 (13, 8, 6, 8, 9, 2, 3, 3, 2, 2, 3, 1, 0, 14, 8, 5, 5, 3, 6, 4, 2, 4, 1, 0, 1, 0), # 63 (8, 6, 14, 12, 10, 2, 3, 4, 5, 5, 1, 0, 0, 16, 9, 9, 8, 8, 4, 3, 2, 5, 3, 2, 0, 0), # 64 (14, 6, 10, 7, 10, 4, 3, 3, 2, 1, 1, 1, 0, 11, 8, 5, 3, 5, 5, 7, 1, 1, 2, 1, 1, 0), # 65 (12, 10, 7, 12, 6, 2, 2, 4, 6, 1, 0, 0, 0, 11, 9, 7, 1, 7, 4, 7, 1, 11, 6, 1, 0, 0), # 66 (12, 9, 6, 8, 6, 5, 0, 6, 3, 3, 1, 0, 0, 10, 6, 4, 10, 9, 3, 4, 4, 3, 0, 1, 0, 0), # 67 (10, 8, 4, 8, 8, 5, 5, 3, 4, 2, 2, 0, 0, 14, 8, 6, 5, 9, 8, 7, 5, 3, 2, 1, 0, 0), # 68 (12, 10, 5, 8, 11, 6, 3, 1, 5, 1, 3, 0, 0, 5, 7, 5, 5, 3, 6, 2, 5, 1, 6, 4, 1, 0), # 69 (7, 9, 6, 8, 11, 3, 3, 1, 9, 4, 2, 1, 0, 11, 11, 2, 5, 11, 5, 2, 3, 7, 3, 3, 1, 0), # 70 (9, 8, 8, 9, 11, 6, 3, 1, 4, 1, 2, 1, 0, 7, 8, 8, 7, 4, 8, 0, 3, 3, 1, 1, 0, 0), # 71 (7, 7, 5, 12, 11, 3, 6, 3, 4, 3, 2, 1, 0, 14, 11, 10, 5, 8, 11, 3, 3, 7, 1, 1, 0, 0), # 72 (15, 3, 14, 4, 3, 6, 2, 4, 1, 1, 2, 1, 0, 4, 12, 5, 7, 5, 5, 3, 3, 3, 6, 0, 1, 0), # 73 (10, 5, 6, 8, 3, 2, 5, 1, 4, 2, 1, 0, 0, 10, 9, 4, 1, 8, 4, 5, 4, 7, 4, 0, 2, 0), # 74 (10, 13, 11, 11, 5, 6, 3, 5, 2, 2, 2, 0, 0, 12, 8, 10, 7, 8, 3, 4, 1, 2, 6, 1, 0, 0), # 75 (9, 12, 18, 8, 9, 4, 4, 3, 3, 3, 2, 1, 0, 9, 11, 10, 4, 9, 3, 5, 1, 3, 2, 3, 0, 0), # 76 (10, 8, 12, 7, 10, 1, 4, 2, 3, 2, 3, 1, 0, 7, 7, 8, 7, 9, 3, 6, 5, 4, 5, 2, 2, 0), # 77 (11, 5, 8, 13, 7, 4, 5, 5, 5, 0, 2, 0, 0, 10, 10, 6, 4, 7, 3, 4, 4, 5, 0, 3, 0, 0), # 78 (8, 8, 9, 11, 8, 5, 5, 4, 3, 0, 1, 2, 0, 11, 8, 3, 2, 5, 7, 2, 3, 6, 2, 0, 0, 0), # 79 (7, 8, 10, 8, 6, 3, 3, 0, 3, 3, 2, 0, 0, 13, 4, 7, 4, 9, 2, 4, 1, 4, 0, 3, 0, 0), # 80 (10, 6, 6, 10, 5, 5, 4, 1, 4, 1, 3, 1, 0, 7, 5, 7, 4, 8, 7, 0, 3, 7, 2, 3, 0, 0), # 81 (4, 5, 11, 15, 7, 9, 2, 3, 7, 2, 1, 1, 0, 7, 9, 7, 3, 9, 3, 5, 1, 4, 4, 2, 2, 0), # 82 (13, 10, 7, 13, 6, 4, 1, 4, 2, 2, 2, 1, 0, 6, 10, 11, 4, 9, 5, 4, 1, 3, 2, 4, 1, 0), # 83 (13, 12, 9, 7, 3, 1, 4, 1, 2, 2, 0, 1, 0, 11, 7, 6, 2, 7, 1, 1, 2, 5, 1, 1, 2, 0), # 84 (9, 9, 10, 13, 4, 3, 7, 4, 7, 5, 0, 0, 0, 7, 6, 7, 10, 6, 3, 9, 5, 2, 1, 0, 0, 0), # 85 (13, 6, 12, 10, 9, 2, 6, 2, 7, 1, 5, 0, 0, 9, 7, 5, 4, 8, 10, 4, 1, 0, 5, 2, 0, 0), # 86 (9, 8, 10, 10, 4, 5, 5, 1, 6, 1, 0, 0, 0, 14, 5, 4, 6, 9, 5, 0, 1, 3, 3, 1, 1, 0), # 87 (6, 3, 7, 11, 7, 3, 7, 4, 3, 2, 3, 0, 0, 16, 8, 8, 2, 4, 7, 1, 5, 5, 4, 1, 0, 0), # 88 (10, 8, 7, 8, 8, 7, 0, 2, 3, 1, 0, 0, 0, 8, 7, 7, 6, 1, 4, 1, 2, 4, 6, 1, 1, 0), # 89 (21, 7, 8, 8, 14, 2, 3, 2, 10, 1, 1, 1, 0, 7, 9, 9, 4, 11, 2, 3, 3, 2, 1, 4, 0, 0), # 90 (11, 12, 10, 5, 6, 0, 5, 1, 1, 2, 1, 0, 0, 10, 8, 10, 7, 8, 6, 1, 1, 4, 3, 0, 0, 0), # 91 (4, 10, 5, 11, 4, 4, 5, 4, 7, 1, 1, 0, 0, 14, 5, 5, 8, 9, 2, 3, 2, 2, 2, 2, 1, 0), # 92 (9, 10, 11, 8, 5, 3, 7, 4, 2, 4, 2, 1, 0, 12, 10, 13, 4, 6, 3, 1, 0, 3, 5, 1, 1, 0), # 93 (9, 7, 13, 10, 6, 6, 4, 7, 1, 0, 1, 2, 0, 14, 7, 4, 4, 12, 4, 3, 1, 7, 2, 4, 1, 0), # 94 (7, 9, 6, 6, 15, 4, 7, 8, 5, 2, 2, 2, 0, 11, 9, 4, 1, 9, 4, 6, 2, 2, 3, 0, 1, 0), # 95 (5, 9, 9, 12, 5, 12, 2, 2, 0, 1, 2, 0, 0, 6, 6, 4, 6, 11, 1, 4, 3, 0, 5, 1, 1, 0), # 96 (8, 6, 14, 6, 6, 6, 2, 2, 6, 2, 1, 0, 0, 13, 7, 7, 4, 8, 4, 4, 6, 6, 2, 2, 2, 0), # 97 (9, 4, 6, 7, 17, 1, 5, 2, 4, 1, 1, 2, 0, 14, 13, 7, 6, 7, 2, 2, 0, 6, 0, 2, 0, 0), # 98 (11, 10, 13, 9, 8, 0, 3, 2, 6, 2, 1, 1, 0, 18, 6, 7, 4, 8, 6, 2, 3, 2, 4, 0, 1, 0), # 99 (11, 8, 2, 5, 6, 6, 6, 1, 4, 1, 0, 1, 0, 6, 3, 9, 3, 11, 3, 4, 3, 5, 3, 3, 0, 0), # 100 (10, 5, 13, 10, 5, 2, 4, 3, 3, 1, 1, 0, 0, 8, 10, 5, 2, 13, 3, 2, 2, 4, 3, 2, 2, 0), # 101 (10, 9, 7, 9, 6, 2, 1, 1, 5, 0, 1, 0, 0, 8, 9, 10, 4, 10, 3, 3, 0, 5, 2, 2, 1, 0), # 102 (18, 9, 13, 15, 10, 4, 1, 3, 5, 1, 1, 0, 0, 8, 6, 6, 6, 5, 4, 3, 4, 3, 5, 1, 0, 0), # 103 (9, 10, 6, 3, 7, 2, 3, 1, 2, 2, 2, 0, 0, 9, 7, 6, 3, 7, 6, 5, 3, 5, 3, 3, 0, 0), # 104 (14, 7, 3, 12, 10, 3, 2, 3, 2, 0, 0, 0, 0, 7, 6, 10, 7, 3, 5, 4, 1, 3, 5, 1, 2, 0), # 105 (8, 9, 12, 9, 8, 1, 4, 3, 2, 1, 1, 1, 0, 11, 10, 10, 4, 4, 3, 3, 4, 2, 4, 0, 0, 0), # 106 (8, 8, 10, 11, 9, 5, 2, 4, 2, 1, 2, 0, 0, 7, 8, 4, 7, 11, 5, 4, 1, 0, 3, 0, 0, 0), # 107 (7, 8, 7, 7, 5, 5, 2, 0, 2, 0, 1, 2, 0, 12, 6, 6, 4, 8, 3, 4, 5, 1, 0, 4, 1, 0), # 108 (9, 6, 11, 10, 8, 4, 6, 2, 5, 1, 5, 2, 0, 8, 11, 5, 7, 8, 2, 2, 1, 4, 1, 1, 1, 0), # 109 (8, 6, 13, 13, 5, 2, 3, 1, 5, 0, 0, 0, 0, 7, 16, 6, 7, 6, 5, 5, 1, 6, 4, 1, 0, 0), # 110 (11, 8, 7, 7, 5, 3, 5, 3, 5, 0, 0, 1, 0, 9, 9, 10, 7, 9, 2, 1, 3, 2, 4, 1, 2, 0), # 111 (4, 3, 9, 10, 9, 1, 4, 2, 1, 0, 1, 0, 0, 5, 11, 7, 4, 14, 3, 3, 2, 2, 3, 2, 1, 0), # 112 (6, 6, 7, 8, 11, 5, 5, 1, 3, 1, 0, 1, 0, 8, 9, 7, 4, 10, 6, 4, 2, 3, 1, 1, 1, 0), # 113 (10, 11, 6, 11, 10, 8, 1, 3, 5, 2, 1, 1, 0, 7, 9, 5, 5, 7, 1, 4, 2, 4, 2, 2, 0, 0), # 114 (12, 4, 8, 10, 8, 7, 4, 2, 5, 1, 1, 0, 0, 10, 10, 4, 4, 5, 1, 1, 2, 2, 2, 0, 1, 0), # 115 (6, 3, 6, 5, 9, 3, 1, 2, 2, 1, 2, 0, 0, 8, 5, 6, 4, 8, 1, 3, 4, 5, 3, 0, 0, 0), # 116 (8, 10, 13, 6, 9, 1, 2, 3, 6, 2, 1, 2, 0, 9, 5, 3, 3, 6, 3, 5, 2, 4, 1, 0, 0, 0), # 117 (4, 5, 5, 5, 9, 2, 6, 2, 5, 1, 2, 0, 0, 9, 4, 2, 4, 5, 4, 4, 1, 5, 4, 3, 0, 0), # 118 (11, 9, 2, 14, 8, 3, 1, 2, 3, 2, 3, 0, 0, 13, 8, 8, 5, 6, 1, 1, 3, 4, 1, 1, 1, 0), # 119 (7, 7, 14, 6, 5, 4, 1, 1, 5, 1, 2, 1, 0, 11, 11, 4, 5, 5, 1, 6, 1, 2, 2, 1, 1, 0), # 120 (8, 7, 5, 7, 11, 5, 2, 1, 3, 4, 1, 0, 0, 10, 4, 8, 3, 9, 0, 5, 4, 2, 6, 0, 0, 0), # 121 (6, 6, 7, 10, 5, 7, 3, 1, 4, 0, 1, 1, 0, 12, 5, 4, 2, 14, 4, 4, 1, 1, 3, 3, 0, 0), # 122 (6, 10, 6, 9, 3, 0, 1, 2, 2, 1, 0, 0, 0, 5, 7, 2, 5, 9, 0, 4, 3, 5, 0, 1, 0, 0), # 123 (6, 8, 7, 5, 2, 6, 3, 6, 3, 0, 1, 0, 0, 9, 7, 8, 0, 7, 7, 3, 2, 1, 0, 0, 2, 0), # 124 (15, 6, 9, 8, 8, 2, 2, 2, 4, 1, 1, 0, 0, 15, 9, 7, 4, 10, 5, 4, 0, 4, 3, 1, 1, 0), # 125 (10, 7, 7, 8, 8, 4, 2, 3, 6, 2, 1, 1, 0, 10, 5, 6, 2, 6, 4, 1, 3, 3, 2, 1, 1, 0), # 126 (7, 7, 6, 5, 6, 2, 2, 2, 3, 1, 0, 2, 0, 7, 7, 4, 1, 4, 3, 2, 3, 5, 2, 2, 2, 0), # 127 (7, 8, 6, 10, 5, 8, 3, 3, 8, 3, 0, 0, 0, 7, 5, 9, 5, 8, 7, 1, 5, 3, 5, 1, 1, 0), # 128 (13, 2, 4, 7, 5, 5, 3, 0, 2, 1, 3, 1, 0, 10, 10, 4, 6, 12, 2, 3, 0, 4, 2, 0, 0, 0), # 129 (10, 6, 6, 8, 5, 4, 4, 4, 7, 1, 1, 0, 0, 9, 5, 9, 3, 11, 1, 4, 4, 1, 3, 2, 0, 0), # 130 (7, 4, 7, 8, 6, 3, 1, 1, 4, 1, 2, 0, 0, 9, 5, 3, 3, 4, 2, 0, 2, 4, 2, 2, 0, 0), # 131 (9, 5, 9, 12, 8, 5, 1, 5, 5, 2, 0, 0, 0, 11, 4, 6, 4, 9, 4, 3, 1, 5, 3, 2, 0, 0), # 132 (13, 10, 6, 13, 2, 3, 6, 1, 4, 1, 1, 1, 0, 14, 5, 6, 7, 5, 3, 4, 1, 3, 2, 0, 0, 0), # 133 (9, 8, 6, 9, 9, 3, 5, 2, 0, 0, 2, 0, 0, 8, 10, 7, 4, 11, 5, 0, 4, 7, 0, 2, 0, 0), # 134 (8, 9, 10, 5, 11, 1, 4, 2, 4, 2, 1, 1, 0, 7, 9, 3, 5, 8, 4, 0, 2, 2, 0, 1, 1, 0), # 135 (7, 7, 11, 8, 9, 4, 1, 1, 1, 0, 1, 0, 0, 11, 5, 7, 4, 6, 0, 0, 1, 7, 2, 2, 1, 0), # 136 (7, 8, 11, 4, 7, 9, 0, 3, 4, 2, 0, 0, 0, 10, 7, 4, 1, 10, 1, 3, 4, 3, 4, 1, 1, 0), # 137 (12, 4, 5, 8, 3, 0, 3, 0, 3, 2, 1, 0, 0, 10, 5, 4, 6, 3, 2, 4, 3, 8, 2, 0, 0, 0), # 138 (17, 14, 11, 10, 7, 1, 1, 2, 5, 0, 1, 1, 0, 7, 6, 8, 2, 12, 1, 2, 2, 3, 3, 3, 1, 0), # 139 (2, 7, 4, 7, 7, 3, 5, 1, 1, 1, 1, 0, 0, 9, 6, 9, 3, 8, 4, 3, 2, 2, 2, 2, 1, 0), # 140 (10, 5, 7, 7, 6, 2, 4, 3, 3, 0, 0, 0, 0, 11, 5, 8, 5, 9, 7, 5, 2, 3, 3, 1, 1, 0), # 141 (12, 7, 7, 15, 4, 3, 0, 1, 1, 1, 1, 1, 0, 4, 5, 8, 10, 6, 2, 3, 3, 2, 2, 1, 0, 0), # 142 (7, 5, 5, 8, 4, 1, 2, 1, 5, 0, 0, 1, 0, 10, 8, 5, 1, 4, 3, 1, 2, 4, 3, 0, 0, 0), # 143 (9, 3, 5, 7, 9, 5, 2, 5, 2, 0, 1, 0, 0, 10, 4, 3, 1, 7, 1, 3, 0, 3, 5, 1, 0, 0), # 144 (6, 6, 2, 6, 5, 7, 3, 5, 3, 5, 0, 0, 0, 7, 9, 6, 3, 4, 6, 1, 0, 4, 2, 0, 0, 0), # 145 (9, 1, 8, 14, 9, 0, 3, 3, 3, 1, 0, 0, 0, 18, 9, 12, 2, 9, 4, 2, 2, 2, 1, 2, 0, 0), # 146 (8, 9, 5, 4, 5, 5, 2, 2, 1, 1, 1, 0, 0, 10, 6, 5, 1, 5, 3, 2, 6, 3, 3, 2, 0, 0), # 147 (8, 4, 4, 6, 5, 1, 4, 2, 3, 0, 0, 0, 0, 9, 8, 2, 6, 8, 4, 4, 4, 4, 5, 1, 0, 0), # 148 (7, 5, 11, 8, 6, 1, 6, 3, 3, 1, 0, 1, 0, 6, 7, 11, 3, 6, 1, 6, 2, 4, 5, 3, 0, 0), # 149 (7, 7, 8, 14, 6, 1, 0, 3, 3, 0, 0, 0, 0, 11, 2, 3, 6, 6, 6, 3, 2, 0, 4, 1, 0, 0), # 150 (12, 3, 4, 12, 2, 3, 2, 1, 3, 1, 1, 0, 0, 6, 13, 4, 6, 7, 4, 1, 2, 4, 2, 0, 1, 0), # 151 (4, 4, 11, 6, 13, 1, 2, 2, 9, 2, 0, 0, 0, 7, 3, 6, 3, 5, 1, 0, 1, 1, 3, 1, 0, 0), # 152 (7, 6, 12, 9, 10, 7, 2, 3, 3, 1, 0, 0, 0, 10, 4, 6, 5, 4, 1, 1, 3, 3, 4, 1, 1, 0), # 153 (9, 5, 4, 6, 5, 3, 3, 1, 7, 1, 0, 1, 0, 5, 9, 1, 1, 2, 4, 3, 3, 2, 1, 2, 0, 0), # 154 (9, 7, 8, 4, 6, 4, 0, 3, 4, 0, 1, 1, 0, 6, 6, 2, 8, 7, 2, 2, 8, 3, 2, 1, 2, 0), # 155 (3, 5, 6, 10, 4, 3, 1, 5, 4, 2, 2, 1, 0, 10, 6, 5, 1, 4, 3, 2, 1, 2, 3, 1, 0, 0), # 156 (6, 6, 8, 4, 3, 3, 4, 3, 2, 2, 0, 0, 0, 11, 3, 4, 4, 7, 3, 3, 3, 6, 2, 1, 1, 0), # 157 (4, 4, 4, 6, 5, 4, 4, 1, 5, 0, 2, 0, 0, 2, 7, 2, 6, 3, 6, 4, 1, 4, 4, 2, 0, 0), # 158 (6, 8, 8, 7, 6, 3, 3, 3, 2, 2, 0, 2, 0, 10, 6, 3, 3, 5, 3, 3, 1, 4, 1, 1, 1, 0), # 159 (6, 10, 5, 9, 7, 5, 3, 1, 5, 1, 1, 0, 0, 3, 4, 8, 1, 6, 1, 5, 4, 6, 3, 3, 1, 0), # 160 (9, 5, 8, 6, 4, 3, 4, 1, 5, 1, 0, 0, 0, 8, 7, 2, 4, 6, 3, 1, 1, 3, 2, 3, 0, 0), # 161 (5, 1, 8, 4, 8, 3, 2, 0, 8, 0, 2, 0, 0, 11, 8, 1, 4, 4, 4, 4, 5, 3, 1, 0, 1, 0), # 162 (12, 4, 3, 5, 4, 0, 1, 2, 3, 1, 0, 0, 0, 8, 7, 4, 3, 8, 4, 2, 2, 2, 0, 1, 0, 0), # 163 (12, 8, 5, 5, 7, 5, 2, 3, 1, 0, 0, 0, 0, 8, 9, 3, 0, 10, 2, 1, 4, 1, 2, 1, 1, 0), # 164 (9, 3, 7, 7, 8, 1, 3, 2, 4, 0, 2, 0, 0, 8, 8, 4, 4, 7, 6, 2, 0, 4, 4, 2, 0, 0), # 165 (7, 5, 5, 4, 4, 3, 4, 1, 4, 0, 2, 0, 0, 8, 6, 1, 1, 10, 1, 4, 1, 2, 1, 4, 1, 0), # 166 (4, 6, 5, 5, 6, 5, 1, 3, 2, 2, 0, 2, 0, 7, 11, 2, 4, 7, 1, 4, 1, 4, 1, 1, 0, 0), # 167 (10, 4, 9, 7, 6, 3, 1, 3, 4, 2, 0, 0, 0, 13, 9, 7, 5, 3, 2, 2, 0, 1, 4, 0, 0, 0), # 168 (6, 8, 4, 7, 4, 1, 1, 1, 2, 0, 0, 1, 0, 12, 4, 3, 2, 11, 0, 3, 2, 4, 3, 3, 1, 0), # 169 (2, 5, 2, 3, 7, 2, 2, 2, 2, 0, 0, 1, 0, 7, 3, 3, 3, 9, 5, 3, 2, 2, 2, 1, 0, 0), # 170 (5, 6, 4, 2, 4, 2, 1, 2, 2, 0, 1, 0, 0, 3, 5, 3, 2, 4, 3, 3, 0, 6, 1, 1, 0, 0), # 171 (4, 4, 5, 7, 3, 4, 1, 1, 0, 2, 0, 0, 0, 5, 2, 6, 8, 7, 4, 0, 1, 4, 3, 1, 0, 0), # 172 (6, 4, 7, 3, 2, 2, 1, 0, 1, 1, 1, 1, 0, 9, 9, 4, 0, 6, 1, 4, 0, 4, 1, 0, 0, 0), # 173 (6, 4, 2, 3, 7, 4, 1, 2, 0, 1, 1, 0, 0, 4, 8, 0, 2, 4, 2, 0, 0, 1, 0, 2, 0, 0), # 174 (4, 1, 7, 6, 4, 2, 2, 2, 3, 0, 0, 0, 0, 5, 4, 1, 0, 4, 4, 1, 2, 2, 0, 1, 0, 0), # 175 (6, 2, 4, 1, 7, 3, 2, 1, 3, 1, 0, 1, 0, 7, 5, 3, 4, 4, 2, 1, 1, 2, 1, 2, 0, 0), # 176 (1, 1, 7, 5, 3, 2, 4, 1, 0, 1, 1, 1, 0, 4, 0, 4, 1, 6, 2, 1, 0, 0, 0, 2, 0, 0), # 177 (4, 4, 3, 2, 7, 1, 0, 0, 2, 1, 0, 0, 0, 3, 2, 3, 4, 6, 1, 1, 0, 3, 1, 0, 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 = ( (5.020865578371768, 5.525288559693166, 5.211283229612507, 6.214667773863432, 5.554685607609612, 3.1386549320373387, 4.146035615373915, 4.653176172979423, 6.090099062168007, 3.9580150155223697, 4.205265163885603, 4.897915078306173, 5.083880212578363), # 0 (5.354327152019974, 5.890060694144759, 5.555346591330152, 6.625144253276616, 5.922490337474237, 3.3459835840425556, 4.419468941263694, 4.959513722905708, 6.492245326332909, 4.21898069227715, 4.483096135956131, 5.221216660814354, 5.419791647439855), # 1 (5.686723008979731, 6.253385170890979, 5.8980422855474135, 7.033987704664794, 6.288962973749744, 3.5524851145124448, 4.691818507960704, 5.264625247904419, 6.892786806877549, 4.478913775020546, 4.759823148776313, 5.543232652053055, 5.75436482820969), # 2 (6.016757793146562, 6.613820501936447, 6.238010869319854, 7.439576407532074, 6.652661676001902, 3.757340622585113, 4.962003641647955, 5.567301157494507, 7.290135160921093, 4.736782698426181, 5.0343484118273825, 5.862685684930461, 6.086272806254225), # 3 (6.343136148415981, 6.9699251992857745, 6.573892899703036, 7.840288641382569, 7.012144603796492, 3.9597312073986677, 5.2289436685084585, 5.866331861194915, 7.682702045582707, 4.991555897167679, 5.305574134590575, 6.178298392354764, 6.414188632939817), # 4 (6.66456271868351, 7.320257774943588, 6.9043289337525175, 8.234502685720393, 7.36596991669928, 4.158837968091214, 5.491557914725224, 6.160507768524592, 8.068899117981559, 5.242201805918663, 5.572402526547132, 6.488793407234148, 6.736785359632827), # 5 (6.979742147844666, 7.663376740914501, 7.227959528523866, 8.620596820049652, 7.712695774276043, 4.353842003800864, 5.7487657064812625, 6.4486192890024885, 8.447138035236815, 5.487688859352758, 5.833735797178282, 6.792893362476808, 7.052736037699606), # 6 (7.2873790797949685, 7.997840609203132, 7.543425241072635, 8.996949323874462, 8.050880336092554, 4.543924413665721, 5.999486369959585, 6.729456832147552, 8.815830454467644, 5.726985492143586, 6.088476155965268, 7.089320890990929, 7.360713718506519), # 7 (7.586178158429934, 8.322207891814099, 7.849366628454396, 9.361938476698928, 8.379081761714586, 4.7282662968238895, 6.2426392313431975, 7.001810807478725, 9.173388032793206, 5.959060138964774, 6.335525812389321, 7.376798625684702, 7.659391453419917), # 8 (7.874844027645085, 8.635037100752022, 8.144424247724704, 9.713942558027169, 8.69585821070791, 4.906048752413484, 6.47714361681512, 7.264471624514963, 9.518222427332674, 6.182881234489941, 6.573786975931678, 7.654049199466313, 7.947442293806162), # 9 (8.152081331335932, 8.934886748021516, 8.427238655939124, 10.051339847363288, 8.9997678426383, 5.076452879572607, 6.701918852558355, 7.516229692775211, 9.848745295205214, 6.397417213392714, 6.802161856073574, 7.919795245243952, 8.22353929103161), # 10 (8.416594713398005, 9.220315345627206, 8.696450410153215, 10.372508624211397, 9.289368817071534, 5.238659777439368, 6.915884264755916, 7.7558754217784145, 10.163368293529993, 6.601636510346719, 7.019552662296249, 8.17275939592581, 8.486355496462611), # 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127 (8.900977653256046, 7.272005133876735, 8.93656152298245, 10.420711509552332, 10.28338262435479, 5.583009403086944, 5.719885803137382, 5.917536562716062, 10.603844712130984, 5.6768871604896125, 6.641586913182724, 7.879713317158788, 9.178601957164537), # 128 (8.867045884450281, 7.234735243373241, 8.91520234433844, 10.390196911533382, 10.257387038233311, 5.572656809633695, 5.694841498757313, 5.90750959292626, 10.587570119817174, 5.656998392566545, 6.619661223023571, 7.855698288082636, 9.154748206457038), # 129 (8.832495959893366, 7.197311058785966, 8.893475406731179, 10.359227099549086, 10.230736244690213, 5.562194223323808, 5.669664319327063, 5.89761872414975, 10.571155732535, 5.636981477582757, 6.5975978088718445, 7.831399878523152, 9.130339756107748), # 130 (8.797291513808094, 7.159672197148127, 8.87134129883538, 10.327749089660475, 10.203400119968745, 5.55159404032328, 5.644308793374809, 5.88781751618415, 10.554552249386486, 5.616794557149185, 6.575348172904468, 7.806772118125624, 9.105346312528312), # 131 (8.76139618041726, 7.121758275492944, 8.848760609325746, 10.295709897928587, 10.175348540312154, 5.540828656798102, 5.618729449428725, 5.878059528827073, 10.537710369473654, 5.596395772876765, 6.552863817298364, 7.781769036535342, 9.079737582130376), # 132 (8.724773593943663, 7.083508910853635, 8.825693926876983, 10.263056540414452, 10.146551381963686, 5.529870468914266, 5.592880816016989, 5.868298321876132, 10.520580791898526, 5.575743266376432, 6.53009624423046, 7.756344663397592, 9.053483271325586), # 133 (8.687387388610095, 7.044863720263423, 8.802101840163804, 10.229736033179103, 10.116978521166592, 5.518691872837765, 5.566717421667779, 5.858487455128944, 10.503114215763128, 5.5547951792591235, 6.506996955877678, 7.730453028357666, 9.026553086525583), # 134 (8.649201198639354, 7.005762320755524, 8.777944937860909, 10.195695392283579, 10.08659983416412, 5.507265264734592, 5.540193794909268, 5.84858048838312, 10.48526134016948, 5.533509653135776, 6.483517454416942, 7.704048161060852, 8.99891673414202), # 135 (8.610178658254235, 6.966144329363159, 8.753183808643008, 10.160881633788906, 10.055385197199517, 5.495563040770739, 5.513264464269635, 5.838530981436277, 10.466972864219606, 5.511844829617322, 6.459609242025177, 7.677084091152441, 8.970543920586536), # 136 (8.570283401677534, 6.925949363119547, 8.72777904118481, 10.125241773756125, 10.023304486516034, 5.483557597112198, 5.485883958277055, 5.828292494086029, 10.448199487015533, 5.4897588503147015, 6.435223820879306, 7.649514848277719, 8.941404352270776), # 137 (8.529479063132047, 6.885117039057908, 8.701691224161017, 10.088722828246263, 9.990327578356919, 5.471221329924964, 5.458006805459704, 5.81781858612999, 10.428891907659281, 5.4672098568388465, 6.410312693156252, 7.621294462081978, 8.91146773560639), # 138 (8.487729276840568, 6.843586974211461, 8.67488094624634, 10.051271813320358, 9.956424348965415, 5.458526635375026, 5.429587534345759, 5.807062817365774, 10.409000825252871, 5.444155990800697, 6.38482736103294, 7.592376962210506, 8.880703777005019), # 139 (8.444997677025897, 6.801298785613425, 8.647308796115487, 10.012835745039444, 9.92156467458478, 5.445445909628379, 5.400580673463397, 5.795978747590996, 10.388476938898332, 5.420555393811186, 6.358719326686294, 7.562716378308592, 8.849082182878314), # 140 (8.40124789791083, 6.758192090297021, 8.61893536244316, 9.973361639464553, 9.885718431458253, 5.431951548851015, 5.370940751340795, 5.78451993660327, 10.36727094769768, 5.396366207481251, 6.331940092293238, 7.532266740021525, 8.816572659637913), # 141 (8.356443573718156, 6.714206505295466, 8.58972123390407, 9.93279651265672, 9.848855495829087, 5.418015949208927, 5.340622296506126, 5.772639944200211, 10.345333550752942, 5.371546573421828, 6.304441160030697, 7.500982076994594, 8.783144913695466), # 142 (8.310548338670674, 6.669281647641981, 8.559626999172925, 9.891087380676975, 9.810945743940529, 5.403611506868106, 5.3095798374875685, 5.760292330179432, 10.322615447166147, 5.3460546332438525, 6.276174032075593, 7.4688164188730894, 8.748768651462617), # 143 (8.263525826991184, 6.623357134369786, 8.528613246924428, 9.848181259586356, 9.771959052035829, 5.388710617994547, 5.277767902813299, 5.747430654338549, 10.29906733603931, 5.31984852855826, 6.247090210604851, 7.435723795302299, 8.713413579351014), # 144 (8.215339672902477, 6.576372582512099, 8.496640565833289, 9.804025165445895, 9.731865296358233, 5.3732856787542405, 5.245141021011493, 5.734008476475176, 10.274639916474454, 5.292886400975988, 6.217141197795395, 7.401658235927513, 8.6770494037723), # 145 (8.16595351062735, 6.528267609102142, 8.463669544574216, 9.758566114316626, 9.690634353150992, 5.35730908531318, 5.21165372061033, 5.719979356386927, 10.249283887573606, 5.2651263921079705, 6.186278495824149, 7.3665737703940195, 8.639645831138118), # 146 (8.1153309743886, 6.47898183117313, 8.42966077182191, 9.71175112225958, 9.648236098657351, 5.340753233837358, 5.177260530137981, 5.705296853871415, 10.22294994843879, 5.236526643565146, 6.154453606868036, 7.3304244283471105, 8.601172567860118), # 147 (8.063435698409021, 6.428454865758288, 8.394574836251083, 9.663527205335797, 9.604640409120561, 5.323590520492767, 5.1419159781226265, 5.689914528726257, 10.195588798172029, 5.207045296958447, 6.1216180331039824, 7.29316423943207, 8.561599320349941), # 148 (8.010231316911412, 6.37662632989083, 8.358372326536443, 9.613841379606303, 9.55981716078387, 5.3057933414453995, 5.105574593092441, 5.673785940749067, 10.167151135875338, 5.176640493898813, 6.08772327670891, 7.254747233294191, 8.520895795019237), # 149 (7.955681464118564, 6.323435840603979, 8.321013831352694, 9.562640661132138, 9.513736229890526, 5.287334092861249, 5.0681909035756005, 5.656864649737456, 10.137587660650752, 5.1452703759971765, 6.0527208398597425, 7.215127439578763, 8.479031698279647), # 150 (7.899749774253275, 6.268823014930954, 8.282459939374542, 9.50987206597433, 9.466367492683776, 5.268185170906305, 5.029719438100283, 5.639104215489043, 10.106849071600289, 5.112893084864478, 6.016562224733405, 7.174258887931072, 8.435976736542818), # 151 (7.842399881538343, 6.212727469904973, 8.242671239276701, 9.455482610193918, 9.417680825406869, 5.2483189717465635, 4.9901147251946645, 5.620458197801441, 10.07488606782597, 5.079466762111649, 5.979198933506821, 7.132095607996409, 8.391700616220398), # 152 (7.78359542019656, 6.155088822559256, 8.201608319733868, 9.399419309851933, 9.367646104303056, 5.2277078915480155, 4.949331293386919, 5.600880156472262, 10.041649348429823, 5.044949549349629, 5.940582468356916, 7.088591629420064, 8.346173043724027), # 153 (7.723300024450729, 6.095846689927024, 8.159231769420758, 9.34162918100941, 9.31623320561558, 5.206324326476654, 4.907323671205228, 5.580323651299123, 10.007089612513866, 5.009299588189353, 5.900664331460612, 7.043700981847325, 8.299363725465357), # 154 (7.6614773285236355, 6.034940689041495, 8.115502177012075, 9.282059239727378, 9.263412005587696, 5.184140672698471, 4.864046387177761, 5.558742242079636, 9.971157559180128, 4.972475020241754, 5.859396024994833, 6.997377694923482, 8.251242367856026), # 155 (7.598090966638081, 5.972310436935888, 8.070380131182526, 9.220656502066875, 9.209152380462648, 5.161129326379461, 4.8194539698327, 5.5360894886114185, 9.933803887530626, 4.934433987117773, 5.816729051136504, 6.949575798293822, 8.201778677307685), # 156 (7.533104573016862, 5.907895550643423, 8.023826220606818, 9.157367984088937, 9.153424206483685, 5.137262683685614, 4.773500947698219, 5.512318950692082, 9.894979296667389, 4.895134630428341, 5.772614912062549, 6.900249321603637, 8.150942360231976), # 157 (7.464680946405239, 5.840453120772258, 7.973591953902355, 9.089769581651243, 9.093681105870997, 5.11102447631711, 4.725106720927857, 5.485796952349372, 9.851662091599097, 4.8533659162911436, 5.7255957525389425, 6.847599564194339, 8.096485859415345), # 158 (7.382286766978402, 5.763065319599478, 7.906737818402988, 9.003977158788453, 9.015191309781628, 5.073689648007103, 4.668212763385716, 5.4472135327643825, 9.786427261222144, 4.802280994098745, 5.667416935618994, 6.781362523683108, 8.025427646920194), # 159 (7.284872094904309, 5.675096728540714, 7.821920957955888, 8.89857751040886, 8.916420131346795, 5.024341296047684, 4.602243748383784, 5.3955991895273465, 9.697425227228651, 4.741205651862893, 5.59725950860954, 6.700501948887847, 7.93642060889358), # 160 (7.17322205458596, 5.577120868080469, 7.720046971910309, 8.774572503756728, 8.798393124282113, 4.963577241570314, 4.527681446006876, 5.33160053310978, 9.585829766999018, 4.6706581931709374, 5.515741654599707, 6.605767468907571, 7.830374044819097), # 161 (7.048121770426357, 5.469711258703239, 7.602021459615496, 8.632964006076326, 8.662135842303204, 4.891995305706455, 4.445007626339809, 5.255864173983202, 9.452814657913637, 4.5911569216102315, 5.42348155667862, 6.497908712841293, 7.708197254180333), # 162 (6.9103563668284975, 5.353441420893524, 7.468750020420702, 8.474753884611934, 8.508673839125688, 4.810193309587572, 4.354704059467401, 5.169036722619125, 9.299553677352906, 4.503220140768125, 5.321097397935408, 6.3776753097880325, 7.570799536460879), # 163 (6.760710968195384, 5.228884875135821, 7.321138253675176, 8.300944006607818, 8.339032668465189, 4.718769074345129, 4.257252515474466, 5.071764789489069, 9.127220602697223, 4.407366154231968, 5.209207361459196, 6.245816888846803, 7.419090191144328), # 164 (6.599970698930017, 5.096615141914632, 7.160091758728169, 8.112536239308252, 8.154237884037324, 4.618320421110586, 4.153134764445822, 4.964694985064546, 8.93698921132698, 4.3041132655891134, 5.088429630339111, 6.10308307911662, 7.25397851771427), # 165 (6.428920683435397, 4.957205741714454, 6.9865161349289275, 7.910532449957501, 7.955315039557714, 4.509445171015408, 4.042832576466286, 4.848473919817077, 8.730033280622573, 4.193979778426912, 4.959382387664279, 5.950223509696501, 7.0763738156542955), # 166 (6.248346046114523, 4.811230195019787, 6.801316981626704, 7.695934505799843, 7.74328968874198, 4.392741145191058, 3.9268277216206746, 4.723748204218176, 8.5075265879644, 4.077483996332714, 4.822683816523827, 5.7879878096854585, 6.887185384447996), # 167 (6.059031911370395, 4.659262022315128, 6.605399898170748, 7.469744274079546, 7.519187385305742, 4.268806164768999, 3.805601969993804, 4.5911644487393595, 8.270642910732855, 3.955144222893872, 4.678952100006881, 5.617125608182511, 6.6873225235789615), # 168 (5.861763403606015, 4.501874744084979, 6.399670483910309, 7.232963622040883, 7.28403368296462, 4.138238050880695, 3.6796370916704917, 4.451369263852145, 8.020556026308338, 3.8274787616977366, 4.528805421202568, 5.438386534286672, 6.477694532530785), # 169 (5.657325647224384, 4.339641880813837, 6.185034338194635, 6.98659441692812, 7.038854135434233, 4.001634624657607, 3.549414856735553, 4.305009260028047, 7.7584397120712385, 3.6950059163316578, 4.372861963200016, 5.252520217096959, 6.259210710787055), # 170 (5.4465037666285, 4.173136952986201, 5.962397060372978, 6.731638525985535, 6.784674296430206, 3.8595937072311983, 3.4154170352738054, 4.152731047738583, 7.485467745401956, 3.5582439903829886, 4.211739909088348, 5.060276285712386, 6.032780357831365), # 171 (5.230082886221365, 4.002933481086569, 5.7326642497945866, 6.4690978164573965, 6.5225197196681535, 3.7127131197329337, 3.2781253973700655, 3.9951812374552707, 7.202813903680886, 3.41771128743908, 4.046057441956694, 4.862404369231971, 5.799312773147303), # 172 (5.00884813040598, 3.8296049855994423, 5.4967415058087115, 6.1999741555879755, 6.253415958863702, 3.5615906832942748, 3.1380217131091497, 3.8330064396496235, 6.911651964288422, 3.2739261110872815, 3.8764327448941778, 4.659654096754725, 5.5597172562184625), # 173 (4.783584623585344, 3.653724987009318, 5.2555344277646014, 5.9252694106215404, 5.978388567732466, 3.406824219046685, 2.9955877525758754, 3.6668532647931604, 6.613155704604964, 3.1274067649149466, 3.7034840009899277, 4.452775097379668, 5.314903106528433), # 174 (4.555077490162455, 3.4758670058006946, 5.009948615011508, 5.645985448802367, 5.698463099990069, 3.2490115481216284, 2.851305285855058, 3.497368323357396, 6.308498902010905, 2.9786715525094243, 3.5278293933330693, 4.242517000205814, 5.0657796235608075), # 175 (4.324111854540319, 3.296604562458073, 4.760889666898678, 5.363124137374725, 5.41466510935213, 3.0887504916505666, 2.705656083031515, 3.325198225813849, 5.998855333886642, 2.828238777458067, 3.35008710501273, 4.029629434332179, 4.813256106799174), # 176 (4.0914728411219325, 3.1165111774659513, 4.5092631827753635, 5.077687343582883, 5.128020149534273, 2.9266388707649633, 2.5591219141900625, 3.1509895826340326, 5.68539877761257, 2.6766267433482245, 3.1708753191180357, 3.8148620288577786, 4.5582418557271245), # 177 (3.8579455743102966, 2.9361603713088282, 4.255974761990814, 4.790676934671116, 4.8395537742521135, 2.7632745065962827, 2.4121845494155174, 2.9753890042894655, 5.3693030105690855, 2.52435375376725, 2.9908122187381125, 3.598964412881627, 4.301646169828252), # 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 = ( (5, 4, 5, 6, 3, 2, 1, 1, 4, 1, 0, 0, 0, 9, 3, 5, 2, 7, 3, 1, 1, 2, 1, 0, 0, 0), # 0 (9, 10, 15, 15, 5, 3, 2, 5, 5, 1, 1, 1, 0, 16, 4, 8, 4, 13, 6, 3, 3, 4, 2, 1, 1, 0), # 1 (14, 19, 23, 22, 10, 3, 2, 11, 6, 2, 1, 1, 0, 22, 10, 11, 6, 13, 9, 7, 5, 7, 8, 2, 1, 0), # 2 (19, 28, 26, 28, 17, 5, 3, 13, 10, 2, 1, 1, 0, 29, 15, 13, 12, 18, 10, 8, 6, 9, 11, 4, 1, 0), # 3 (24, 33, 28, 30, 23, 9, 9, 15, 10, 2, 2, 1, 0, 34, 19, 20, 12, 24, 11, 12, 7, 10, 12, 4, 1, 0), # 4 (31, 35, 36, 38, 27, 9, 11, 18, 10, 2, 3, 1, 0, 41, 24, 25, 15, 29, 17, 17, 9, 10, 15, 4, 2, 0), # 5 (40, 39, 42, 42, 33, 12, 13, 20, 13, 4, 3, 1, 0, 47, 31, 30, 20, 32, 21, 18, 10, 14, 21, 7, 2, 0), # 6 (49, 43, 45, 50, 41, 13, 14, 22, 16, 6, 3, 1, 0, 62, 38, 34, 24, 40, 24, 23, 12, 14, 23, 9, 4, 0), # 7 (55, 50, 52, 62, 47, 17, 17, 26, 20, 8, 3, 2, 0, 67, 44, 40, 29, 49, 31, 34, 15, 16, 26, 10, 5, 0), # 8 (62, 54, 61, 69, 49, 19, 19, 31, 24, 8, 4, 2, 0, 75, 49, 50, 35, 55, 36, 38, 16, 20, 30, 12, 6, 0), # 9 (74, 60, 71, 80, 54, 21, 20, 31, 25, 10, 6, 2, 0, 83, 57, 55, 40, 61, 43, 42, 16, 20, 30, 12, 6, 0), # 10 (78, 68, 71, 89, 56, 24, 26, 37, 30, 12, 8, 2, 0, 94, 67, 59, 46, 68, 46, 44, 18, 21, 33, 14, 7, 0), # 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125 (1187, 1057, 1060, 1120, 934, 436, 444, 390, 498, 203, 167, 85, 0, 1283, 1016, 786, 617, 977, 552, 455, 291, 439, 364, 199, 98, 0), # 126 (1194, 1064, 1066, 1125, 940, 438, 446, 392, 501, 204, 167, 87, 0, 1290, 1023, 790, 618, 981, 555, 457, 294, 444, 366, 201, 100, 0), # 127 (1201, 1072, 1072, 1135, 945, 446, 449, 395, 509, 207, 167, 87, 0, 1297, 1028, 799, 623, 989, 562, 458, 299, 447, 371, 202, 101, 0), # 128 (1214, 1074, 1076, 1142, 950, 451, 452, 395, 511, 208, 170, 88, 0, 1307, 1038, 803, 629, 1001, 564, 461, 299, 451, 373, 202, 101, 0), # 129 (1224, 1080, 1082, 1150, 955, 455, 456, 399, 518, 209, 171, 88, 0, 1316, 1043, 812, 632, 1012, 565, 465, 303, 452, 376, 204, 101, 0), # 130 (1231, 1084, 1089, 1158, 961, 458, 457, 400, 522, 210, 173, 88, 0, 1325, 1048, 815, 635, 1016, 567, 465, 305, 456, 378, 206, 101, 0), # 131 (1240, 1089, 1098, 1170, 969, 463, 458, 405, 527, 212, 173, 88, 0, 1336, 1052, 821, 639, 1025, 571, 468, 306, 461, 381, 208, 101, 0), # 132 (1253, 1099, 1104, 1183, 971, 466, 464, 406, 531, 213, 174, 89, 0, 1350, 1057, 827, 646, 1030, 574, 472, 307, 464, 383, 208, 101, 0), # 133 (1262, 1107, 1110, 1192, 980, 469, 469, 408, 531, 213, 176, 89, 0, 1358, 1067, 834, 650, 1041, 579, 472, 311, 471, 383, 210, 101, 0), # 134 (1270, 1116, 1120, 1197, 991, 470, 473, 410, 535, 215, 177, 90, 0, 1365, 1076, 837, 655, 1049, 583, 472, 313, 473, 383, 211, 102, 0), # 135 (1277, 1123, 1131, 1205, 1000, 474, 474, 411, 536, 215, 178, 90, 0, 1376, 1081, 844, 659, 1055, 583, 472, 314, 480, 385, 213, 103, 0), # 136 (1284, 1131, 1142, 1209, 1007, 483, 474, 414, 540, 217, 178, 90, 0, 1386, 1088, 848, 660, 1065, 584, 475, 318, 483, 389, 214, 104, 0), # 137 (1296, 1135, 1147, 1217, 1010, 483, 477, 414, 543, 219, 179, 90, 0, 1396, 1093, 852, 666, 1068, 586, 479, 321, 491, 391, 214, 104, 0), # 138 (1313, 1149, 1158, 1227, 1017, 484, 478, 416, 548, 219, 180, 91, 0, 1403, 1099, 860, 668, 1080, 587, 481, 323, 494, 394, 217, 105, 0), # 139 (1315, 1156, 1162, 1234, 1024, 487, 483, 417, 549, 220, 181, 91, 0, 1412, 1105, 869, 671, 1088, 591, 484, 325, 496, 396, 219, 106, 0), # 140 (1325, 1161, 1169, 1241, 1030, 489, 487, 420, 552, 220, 181, 91, 0, 1423, 1110, 877, 676, 1097, 598, 489, 327, 499, 399, 220, 107, 0), # 141 (1337, 1168, 1176, 1256, 1034, 492, 487, 421, 553, 221, 182, 92, 0, 1427, 1115, 885, 686, 1103, 600, 492, 330, 501, 401, 221, 107, 0), # 142 (1344, 1173, 1181, 1264, 1038, 493, 489, 422, 558, 221, 182, 93, 0, 1437, 1123, 890, 687, 1107, 603, 493, 332, 505, 404, 221, 107, 0), # 143 (1353, 1176, 1186, 1271, 1047, 498, 491, 427, 560, 221, 183, 93, 0, 1447, 1127, 893, 688, 1114, 604, 496, 332, 508, 409, 222, 107, 0), # 144 (1359, 1182, 1188, 1277, 1052, 505, 494, 432, 563, 226, 183, 93, 0, 1454, 1136, 899, 691, 1118, 610, 497, 332, 512, 411, 222, 107, 0), # 145 (1368, 1183, 1196, 1291, 1061, 505, 497, 435, 566, 227, 183, 93, 0, 1472, 1145, 911, 693, 1127, 614, 499, 334, 514, 412, 224, 107, 0), # 146 (1376, 1192, 1201, 1295, 1066, 510, 499, 437, 567, 228, 184, 93, 0, 1482, 1151, 916, 694, 1132, 617, 501, 340, 517, 415, 226, 107, 0), # 147 (1384, 1196, 1205, 1301, 1071, 511, 503, 439, 570, 228, 184, 93, 0, 1491, 1159, 918, 700, 1140, 621, 505, 344, 521, 420, 227, 107, 0), # 148 (1391, 1201, 1216, 1309, 1077, 512, 509, 442, 573, 229, 184, 94, 0, 1497, 1166, 929, 703, 1146, 622, 511, 346, 525, 425, 230, 107, 0), # 149 (1398, 1208, 1224, 1323, 1083, 513, 509, 445, 576, 229, 184, 94, 0, 1508, 1168, 932, 709, 1152, 628, 514, 348, 525, 429, 231, 107, 0), # 150 (1410, 1211, 1228, 1335, 1085, 516, 511, 446, 579, 230, 185, 94, 0, 1514, 1181, 936, 715, 1159, 632, 515, 350, 529, 431, 231, 108, 0), # 151 (1414, 1215, 1239, 1341, 1098, 517, 513, 448, 588, 232, 185, 94, 0, 1521, 1184, 942, 718, 1164, 633, 515, 351, 530, 434, 232, 108, 0), # 152 (1421, 1221, 1251, 1350, 1108, 524, 515, 451, 591, 233, 185, 94, 0, 1531, 1188, 948, 723, 1168, 634, 516, 354, 533, 438, 233, 109, 0), # 153 (1430, 1226, 1255, 1356, 1113, 527, 518, 452, 598, 234, 185, 95, 0, 1536, 1197, 949, 724, 1170, 638, 519, 357, 535, 439, 235, 109, 0), # 154 (1439, 1233, 1263, 1360, 1119, 531, 518, 455, 602, 234, 186, 96, 0, 1542, 1203, 951, 732, 1177, 640, 521, 365, 538, 441, 236, 111, 0), # 155 (1442, 1238, 1269, 1370, 1123, 534, 519, 460, 606, 236, 188, 97, 0, 1552, 1209, 956, 733, 1181, 643, 523, 366, 540, 444, 237, 111, 0), # 156 (1448, 1244, 1277, 1374, 1126, 537, 523, 463, 608, 238, 188, 97, 0, 1563, 1212, 960, 737, 1188, 646, 526, 369, 546, 446, 238, 112, 0), # 157 (1452, 1248, 1281, 1380, 1131, 541, 527, 464, 613, 238, 190, 97, 0, 1565, 1219, 962, 743, 1191, 652, 530, 370, 550, 450, 240, 112, 0), # 158 (1458, 1256, 1289, 1387, 1137, 544, 530, 467, 615, 240, 190, 99, 0, 1575, 1225, 965, 746, 1196, 655, 533, 371, 554, 451, 241, 113, 0), # 159 (1464, 1266, 1294, 1396, 1144, 549, 533, 468, 620, 241, 191, 99, 0, 1578, 1229, 973, 747, 1202, 656, 538, 375, 560, 454, 244, 114, 0), # 160 (1473, 1271, 1302, 1402, 1148, 552, 537, 469, 625, 242, 191, 99, 0, 1586, 1236, 975, 751, 1208, 659, 539, 376, 563, 456, 247, 114, 0), # 161 (1478, 1272, 1310, 1406, 1156, 555, 539, 469, 633, 242, 193, 99, 0, 1597, 1244, 976, 755, 1212, 663, 543, 381, 566, 457, 247, 115, 0), # 162 (1490, 1276, 1313, 1411, 1160, 555, 540, 471, 636, 243, 193, 99, 0, 1605, 1251, 980, 758, 1220, 667, 545, 383, 568, 457, 248, 115, 0), # 163 (1502, 1284, 1318, 1416, 1167, 560, 542, 474, 637, 243, 193, 99, 0, 1613, 1260, 983, 758, 1230, 669, 546, 387, 569, 459, 249, 116, 0), # 164 (1511, 1287, 1325, 1423, 1175, 561, 545, 476, 641, 243, 195, 99, 0, 1621, 1268, 987, 762, 1237, 675, 548, 387, 573, 463, 251, 116, 0), # 165 (1518, 1292, 1330, 1427, 1179, 564, 549, 477, 645, 243, 197, 99, 0, 1629, 1274, 988, 763, 1247, 676, 552, 388, 575, 464, 255, 117, 0), # 166 (1522, 1298, 1335, 1432, 1185, 569, 550, 480, 647, 245, 197, 101, 0, 1636, 1285, 990, 767, 1254, 677, 556, 389, 579, 465, 256, 117, 0), # 167 (1532, 1302, 1344, 1439, 1191, 572, 551, 483, 651, 247, 197, 101, 0, 1649, 1294, 997, 772, 1257, 679, 558, 389, 580, 469, 256, 117, 0), # 168 (1538, 1310, 1348, 1446, 1195, 573, 552, 484, 653, 247, 197, 102, 0, 1661, 1298, 1000, 774, 1268, 679, 561, 391, 584, 472, 259, 118, 0), # 169 (1540, 1315, 1350, 1449, 1202, 575, 554, 486, 655, 247, 197, 103, 0, 1668, 1301, 1003, 777, 1277, 684, 564, 393, 586, 474, 260, 118, 0), # 170 (1545, 1321, 1354, 1451, 1206, 577, 555, 488, 657, 247, 198, 103, 0, 1671, 1306, 1006, 779, 1281, 687, 567, 393, 592, 475, 261, 118, 0), # 171 (1549, 1325, 1359, 1458, 1209, 581, 556, 489, 657, 249, 198, 103, 0, 1676, 1308, 1012, 787, 1288, 691, 567, 394, 596, 478, 262, 118, 0), # 172 (1555, 1329, 1366, 1461, 1211, 583, 557, 489, 658, 250, 199, 104, 0, 1685, 1317, 1016, 787, 1294, 692, 571, 394, 600, 479, 262, 118, 0), # 173 (1561, 1333, 1368, 1464, 1218, 587, 558, 491, 658, 251, 200, 104, 0, 1689, 1325, 1016, 789, 1298, 694, 571, 394, 601, 479, 264, 118, 0), # 174 (1565, 1334, 1375, 1470, 1222, 589, 560, 493, 661, 251, 200, 104, 0, 1694, 1329, 1017, 789, 1302, 698, 572, 396, 603, 479, 265, 118, 0), # 175 (1571, 1336, 1379, 1471, 1229, 592, 562, 494, 664, 252, 200, 105, 0, 1701, 1334, 1020, 793, 1306, 700, 573, 397, 605, 480, 267, 118, 0), # 176 (1572, 1337, 1386, 1476, 1232, 594, 566, 495, 664, 253, 201, 106, 0, 1705, 1334, 1024, 794, 1312, 702, 574, 397, 605, 480, 269, 118, 0), # 177 (1576, 1341, 1389, 1478, 1239, 595, 566, 495, 666, 254, 201, 106, 0, 1708, 1336, 1027, 798, 1318, 703, 575, 397, 608, 481, 269, 118, 0), # 178 (1576, 1341, 1389, 1478, 1239, 595, 566, 495, 666, 254, 201, 106, 0, 1708, 1336, 1027, 798, 1318, 703, 575, 397, 608, 481, 269, 118, 0), # 179 ) passenger_arriving_rate = ( (5.020865578371768, 5.064847846385402, 4.342736024677089, 4.661000830397574, 3.7031237384064077, 1.8308820436884476, 2.0730178076869574, 1.938823405408093, 2.030033020722669, 0.9895037538805926, 0.7008775273142672, 0.4081595898588478, 0.0, 5.083880212578363, 4.489755488447325, 3.5043876365713356, 2.968511261641777, 4.060066041445338, 2.7143527675713304, 2.0730178076869574, 1.3077728883488913, 1.8515618692032039, 1.5536669434658585, 0.8685472049354179, 0.4604407133077639, 0.0), # 0 (5.354327152019974, 5.399222302966028, 4.629455492775127, 4.968858189957462, 3.948326891649491, 1.9518237573581576, 2.209734470631847, 2.066464051210712, 2.164081775444303, 1.0547451730692876, 0.7471826893260219, 0.4351013884011963, 0.0, 5.419791647439855, 4.786115272413158, 3.73591344663011, 3.164235519207862, 4.328163550888606, 2.8930496716949965, 2.209734470631847, 1.3941598266843982, 1.9741634458247455, 1.6562860633191545, 0.9258910985550255, 0.49083839117872996, 0.0), # 1 (5.686723008979731, 5.732269739983398, 4.915035237956178, 5.275490778498595, 4.192641982499829, 2.072282983465593, 2.345909253980352, 2.193593853293508, 2.297595602292516, 1.1197284437551367, 0.7933038581293855, 0.46193605433775464, 0.0, 5.75436482820969, 5.0812965977153, 3.9665192906469278, 3.3591853312654094, 4.595191204585032, 3.0710313946109116, 2.345909253980352, 1.480202131046852, 2.0963209912499146, 1.758496926166199, 0.9830070475912357, 0.5211154309075817, 0.0), # 2 (6.016757793146562, 6.062668793441743, 5.198342391099879, 5.579682305649055, 4.435107784001268, 2.191782029841316, 2.4810018208239777, 2.3197088156227115, 2.430045053640364, 1.1841956746065454, 0.8390580686378972, 0.4885571404108718, 0.0, 6.086272806254225, 5.374128544519589, 4.195290343189486, 3.5525870238196355, 4.860090107280728, 3.247592341871796, 2.4810018208239777, 1.5655585927437972, 2.217553892000634, 1.8598941018830188, 1.0396684782199759, 0.551151708494704, 0.0), # 3 (6.343136148415981, 6.389098099345293, 5.478244083085864, 5.880216481036927, 4.674763069197661, 2.3098432043158894, 2.6144718342542292, 2.444304942164548, 2.560900681860902, 1.24788897429192, 0.8842623557650959, 0.514858199362897, 0.0, 6.414188632939817, 5.6634401929918665, 4.42131177882548, 3.743666922875759, 5.121801363721804, 3.422026919030367, 2.6144718342542292, 1.6498880030827783, 2.3373815345988307, 1.9600721603456428, 1.095648816617173, 0.5808270999404813, 0.0), # 4 (6.66456271868351, 6.710236293698289, 5.753607444793765, 6.175877014290295, 4.910646611132853, 2.4259888147198754, 2.745778957362612, 2.566878236885247, 2.689633039327186, 1.310550451479666, 0.9287337544245222, 0.5407327839361791, 0.0, 6.736785359632827, 5.948060623297969, 4.64366877212261, 3.9316513544389973, 5.379266078654372, 3.593629531639346, 2.745778957362612, 1.7328491533713395, 2.4553233055664263, 2.058625671430099, 1.1507214889587531, 0.6100214812452991, 0.0), # 5 (6.979742147844666, 7.024762012504959, 6.023299607103222, 6.465447615037239, 5.141797182850695, 2.5397411688838374, 2.8743828532406313, 2.686924703751037, 2.8157126784122717, 1.3719222148381898, 0.9722892995297139, 0.5660744468730674, 0.0, 7.052736037699606, 6.22681891560374, 4.8614464976485685, 4.115766644514569, 5.631425356824543, 3.761694585251452, 2.8743828532406313, 1.8141008349170267, 2.5708985914253475, 2.1551492050124135, 1.2046599214206444, 0.6386147284095418, 0.0), # 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15 (9.5029653356198, 9.51563296645512, 8.159074056802854, 8.758623452831788, 6.981519607721555, 3.4405433400458514, 3.892870194481988, 3.6375347129591504, 3.8141310803694286, 1.8579694948654994, 1.3172204860774188, 0.7667619895570784, 0.0, 9.554781444523545, 8.434381885127861, 6.586102430387094, 5.5739084845964975, 7.628262160738857, 5.092548598142811, 3.892870194481988, 2.4575309571756083, 3.4907598038607777, 2.9195411509439295, 1.6318148113605708, 0.8650575424050111, 0.0), # 16 (9.663932709715075, 9.670211430531618, 8.291613663785293, 8.900929631461583, 7.097166527942559, 3.4964421923866666, 3.9560763548653552, 3.6965223351920073, 3.8760872738829946, 1.8881335782173672, 1.3386259463796333, 0.7792159954886714, 0.0, 9.710046977881415, 8.571375950375383, 6.693129731898166, 5.6644007346521, 7.752174547765989, 5.17513126926881, 3.9560763548653552, 2.4974587088476192, 3.5485832639712793, 2.9669765438205284, 1.6583227327570589, 0.8791101300483289, 0.0), # 17 (9.803108669450204, 9.802321051112584, 8.404887641924901, 9.022550393121959, 7.1965457544723925, 3.5442154824542103, 4.010095245116426, 3.746935177164692, 3.929037377492032, 1.9139132517608846, 1.3569199720900849, 0.7898597126920597, 0.0, 9.842743079009345, 8.688456839612655, 6.784599860450424, 5.741739755282652, 7.858074754984064, 5.245709248030569, 4.010095245116426, 2.531582487467293, 3.5982728772361963, 3.0075167977073205, 1.6809775283849802, 0.8911200955556896, 0.0), # 18 (9.919197858720699, 9.910640464202265, 8.497763122101317, 9.122269447440985, 7.2786960603549105, 3.5833855180790386, 4.054386528326697, 3.7882692428434357, 3.9724519435695926, 1.9350506241644574, 1.3719195981223131, 0.7985866939095915, 0.0, 9.951542799273696, 8.784453633005505, 6.859597990611565, 5.80515187249337, 7.944903887139185, 5.30357693998081, 4.054386528326697, 2.55956108434217, 3.6393480301774552, 3.0407564824803295, 1.6995526244202632, 0.9009673149274788, 0.0), # 19 (10.010904921422082, 9.993848305804882, 8.569107235194169, 9.198870504046766, 7.342656218633962, 3.613474607091719, 4.088409867587681, 3.8200205361944657, 4.005801524488732, 1.95128780409649, 1.3834418593898585, 0.805290491883616, 0.0, 10.035119190040824, 8.858195410719775, 6.9172092969492915, 5.853863412289469, 8.011603048977465, 5.348028750672252, 4.088409867587681, 2.5810532907797996, 3.671328109316981, 3.0662901680155894, 1.713821447038834, 0.9085316641640803, 0.0), # 20 (10.076934501449866, 10.050623211924679, 8.6177871120831, 9.251137272567364, 7.387465002353392, 3.6340050573228124, 4.1116249259908795, 3.84168506118401, 4.028556672622507, 1.9623669002253892, 1.39130379080626, 0.8098646593564828, 0.0, 10.092145302677078, 8.90851125292131, 6.9565189540313, 5.887100700676166, 8.057113345245014, 5.378359085657614, 4.1116249259908795, 2.5957178980877234, 3.693732501176696, 3.0837124241891223, 1.72355742241662, 0.91369301926588, 0.0), # 21 (10.115991242699579, 10.079643818565883, 8.642669883647738, 9.277853462630876, 7.41216118455705, 3.644499176602881, 4.1234913666278, 3.852758821778298, 4.040187940343971, 1.968030021219561, 1.3953224272850568, 0.8122027490705409, 0.0, 10.121294188548827, 8.934230239775948, 6.976612136425284, 5.904090063658682, 8.080375880687942, 5.393862350489617, 4.1234913666278, 2.6032136975734863, 3.706080592278525, 3.09261782087696, 1.7285339767295478, 0.9163312562332622, 0.0), # 22 (10.13039336334264, 10.083079961133974, 8.645769318701419, 9.281198109567903, 7.418488037355065, 3.6458333333333335, 4.124902001129669, 3.8539557613168727, 4.0416420781893, 1.9686980681298587, 1.3958263395269568, 0.8124914647157445, 0.0, 10.125, 8.93740611187319, 6.9791316976347835, 5.906094204389575, 8.0832841563786, 5.395538065843622, 4.124902001129669, 2.604166666666667, 3.7092440186775324, 3.0937327031893016, 1.729153863740284, 0.9166436328303613, 0.0), # 23 (10.141012413034153, 10.08107561728395, 8.645262345679013, 9.280786458333335, 7.422071742409901, 3.6458333333333335, 4.124126906318083, 3.852291666666667, 4.041447222222222, 1.968287654320988, 1.39577076318743, 0.8124238683127573, 0.0, 10.125, 8.936662551440328, 6.978853815937151, 5.904862962962962, 8.082894444444443, 5.393208333333334, 4.124126906318083, 2.604166666666667, 3.7110358712049507, 3.0935954861111123, 1.7290524691358027, 0.9164614197530866, 0.0), # 24 (10.15140723021158, 10.077124771376313, 8.644261545496114, 9.279972029320987, 7.4255766303963355, 3.6458333333333335, 4.122599451303155, 3.8490226337448563, 4.041062242798354, 1.96747970964792, 1.3956605665710604, 0.8122904282883707, 0.0, 10.125, 8.935194711172077, 6.978302832855302, 5.902439128943758, 8.082124485596708, 5.388631687242799, 4.122599451303155, 2.604166666666667, 3.7127883151981678, 3.0933240097736636, 1.728852309099223, 0.9161022519433014, 0.0), # 25 (10.161577019048034, 10.071287780064015, 8.642780635573846, 9.278764081790122, 7.429002578947403, 3.6458333333333335, 4.120343359154361, 3.8442103909465026, 4.0404920781893, 1.9662876771833566, 1.3954967473084758, 0.8120929736320684, 0.0, 10.125, 8.933022709952752, 6.977483736542379, 5.898863031550069, 8.0809841563786, 5.381894547325103, 4.120343359154361, 2.604166666666667, 3.7145012894737013, 3.0929213605967085, 1.7285561271147696, 0.915571616369456, 0.0), # 26 (10.171520983716636, 10.063624999999998, 8.640833333333333, 9.277171874999999, 7.432349465696142, 3.6458333333333335, 4.117382352941177, 3.837916666666667, 4.039741666666666, 1.9647250000000003, 1.3952803030303031, 0.8118333333333335, 0.0, 10.125, 8.930166666666667, 6.976401515151515, 5.894175, 8.079483333333332, 5.373083333333334, 4.117382352941177, 2.604166666666667, 3.716174732848071, 3.0923906250000006, 1.7281666666666669, 0.914875, 0.0), # 27 (10.181238328390501, 10.054196787837219, 8.638433356195703, 9.275204668209877, 7.4356171682756, 3.6458333333333335, 4.113740155733075, 3.830203189300412, 4.038815946502057, 1.9628051211705537, 1.3950122313671698, 0.8115133363816492, 0.0, 10.125, 8.926646700198141, 6.9750611568358485, 5.88841536351166, 8.077631893004114, 5.3622844650205765, 4.113740155733075, 2.604166666666667, 3.7178085841378, 3.091734889403293, 1.7276866712391405, 0.9140178898033837, 0.0), # 28 (10.19072825724275, 10.043063500228623, 8.635594421582077, 9.272871720679012, 7.438805564318813, 3.6458333333333335, 4.109440490599533, 3.821131687242798, 4.037719855967078, 1.9605414837677189, 1.3946935299497027, 0.811134811766499, 0.0, 10.125, 8.922482929431489, 6.973467649748514, 5.881624451303155, 8.075439711934155, 5.349584362139917, 4.109440490599533, 2.604166666666667, 3.7194027821594067, 3.0909572402263383, 1.7271188843164156, 0.9130057727480568, 0.0), # 29 (10.199989974446497, 10.03028549382716, 8.63233024691358, 9.270182291666666, 7.441914531458824, 3.6458333333333335, 4.104507080610022, 3.8107638888888884, 4.036458333333333, 1.957947530864198, 1.39432519640853, 0.8106995884773662, 0.0, 10.125, 8.917695473251028, 6.9716259820426485, 5.873842592592593, 8.072916666666666, 5.335069444444444, 4.104507080610022, 2.604166666666667, 3.720957265729412, 3.0900607638888897, 1.7264660493827162, 0.9118441358024693, 0.0), # 30 (10.209022684174858, 10.01592312528578, 8.62865454961134, 9.267145640432098, 7.444943947328672, 3.6458333333333335, 4.09896364883402, 3.799161522633745, 4.035036316872428, 1.9550367055326936, 1.3939082283742779, 0.8102094955037343, 0.0, 10.125, 8.912304450541077, 6.969541141871389, 5.865110116598079, 8.070072633744855, 5.318826131687243, 4.09896364883402, 2.604166666666667, 3.722471973664336, 3.0890485468107003, 1.7257309099222682, 0.910538465935071, 0.0), # 31 (10.217825590600954, 10.00003675125743, 8.624581047096479, 9.263771026234568, 7.447893689561397, 3.6458333333333335, 4.092833918340999, 3.7863863168724285, 4.033458744855967, 1.951822450845908, 1.3934436234775742, 0.8096663618350862, 0.0, 10.125, 8.906329980185948, 6.96721811738787, 5.8554673525377225, 8.066917489711933, 5.3009408436214, 4.092833918340999, 2.604166666666667, 3.7239468447806985, 3.0879236754115236, 1.7249162094192958, 0.909094250114312, 0.0), # 32 (10.226397897897897, 9.98268672839506, 8.620123456790123, 9.260067708333333, 7.450763635790041, 3.6458333333333335, 4.086141612200436, 3.7725000000000004, 4.031730555555555, 1.9483182098765437, 1.392932379349046, 0.8090720164609053, 0.0, 10.125, 8.899792181069957, 6.96466189674523, 5.84495462962963, 8.06346111111111, 5.2815, 4.086141612200436, 2.604166666666667, 3.7253818178950207, 3.086689236111112, 1.724024691358025, 0.9075169753086421, 0.0), # 33 (10.23473881023881, 9.963933413351622, 8.615295496113397, 9.256044945987654, 7.453553663647644, 3.6458333333333335, 4.078910453481805, 3.7575643004115222, 4.029856687242798, 1.9445374256973027, 1.3923754936193207, 0.8084282883706753, 0.0, 10.125, 8.892711172077426, 6.961877468096604, 5.833612277091907, 8.059713374485597, 5.260590020576132, 4.078910453481805, 2.604166666666667, 3.726776831823822, 3.085348315329219, 1.7230590992226795, 0.9058121284865113, 0.0), # 34 (10.242847531796807, 9.943837162780063, 8.610110882487428, 9.25171199845679, 7.456263650767246, 3.6458333333333335, 4.071164165254579, 3.741640946502058, 4.0278420781893, 1.9404935413808875, 1.3917739639190256, 0.807737006553879, 0.0, 10.125, 8.88510707209267, 6.958869819595128, 5.821480624142661, 8.0556841563786, 5.238297325102881, 4.071164165254579, 2.604166666666667, 3.728131825383623, 3.0839039994855972, 1.7220221764974855, 0.9039851966163696, 0.0), # 35 (10.250723266745005, 9.922458333333331, 8.604583333333334, 9.247078125, 7.45889347478189, 3.6458333333333335, 4.062926470588235, 3.724791666666667, 4.025691666666666, 1.9362000000000004, 1.391128787878788, 0.8070000000000002, 0.0, 10.125, 8.877, 6.95564393939394, 5.8086, 8.051383333333332, 5.214708333333334, 4.062926470588235, 2.604166666666667, 3.729446737390945, 3.0823593750000007, 1.7209166666666669, 0.9020416666666666, 0.0), # 36 (10.258365219256524, 9.89985728166438, 8.598726566072246, 9.242152584876543, 7.4614430133246135, 3.6458333333333335, 4.054221092552247, 3.707078189300412, 4.023410390946502, 1.931670244627344, 1.3904409631292352, 0.8062190976985216, 0.0, 10.125, 8.868410074683737, 6.952204815646175, 5.79501073388203, 8.046820781893004, 5.189909465020577, 4.054221092552247, 2.604166666666667, 3.7307215066623067, 3.080717528292182, 1.7197453132144491, 0.8999870256058529, 0.0), # 37 (10.265772593504476, 9.876094364426155, 8.592554298125286, 9.23694463734568, 7.46391214402846, 3.6458333333333335, 4.04507175421609, 3.6885622427983544, 4.021003189300411, 1.92691771833562, 1.3897114873009937, 0.8053961286389272, 0.0, 10.125, 8.859357415028198, 6.948557436504967, 5.780753155006859, 8.042006378600822, 5.163987139917697, 4.04507175421609, 2.604166666666667, 3.73195607201423, 3.078981545781894, 1.7185108596250571, 0.8978267604023779, 0.0), # 38 (10.272944593661986, 9.851229938271604, 8.586080246913582, 9.231463541666667, 7.466300744526468, 3.6458333333333335, 4.035502178649238, 3.6693055555555554, 4.0184750000000005, 1.9219558641975314, 1.3889413580246914, 0.8045329218106996, 0.0, 10.125, 8.849862139917693, 6.944706790123457, 5.765867592592593, 8.036950000000001, 5.137027777777778, 4.035502178649238, 2.604166666666667, 3.733150372263234, 3.07715451388889, 1.7172160493827164, 0.8955663580246914, 0.0), # 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174 (4.555077490162455, 3.18621142198397, 4.174957179176257, 4.2344890866017755, 3.7989753999933793, 1.8952567364042834, 1.425652642927529, 1.457236801398915, 2.102832967336968, 0.7446678881273562, 0.5879715655555117, 0.35354308335048457, 0.0, 5.0657796235608075, 3.8889739168553294, 2.939857827777558, 2.234003664382068, 4.205665934673936, 2.040131521958481, 1.425652642927529, 1.3537548117173452, 1.8994876999966896, 1.411496362200592, 0.8349914358352515, 0.28965558381672457, 0.0), # 175 (4.324111854540319, 3.0218875155865668, 3.9674080557488987, 4.0223431030310435, 3.609776739568087, 1.8017711201294973, 1.3528280415157574, 1.3854992607557703, 1.9996184446288805, 0.7070596943645169, 0.558347850835455, 0.33580245286101496, 0.0, 4.813256106799174, 3.693826981471164, 2.791739254177275, 2.1211790830935504, 3.999236889257761, 1.9396989650580787, 1.3528280415157574, 1.2869793715210696, 1.8048883697840434, 1.3407810343436815, 0.7934816111497798, 0.2747170468715061, 0.0), # 176 (4.0914728411219325, 2.856801912677122, 3.7577193189794698, 3.808265507687162, 3.4186800996895155, 1.7072060079462288, 1.2795609570950313, 1.3129123260975137, 1.8951329258708567, 0.6691566858370562, 0.528479219853006, 0.3179051690714816, 0.0, 4.5582418557271245, 3.496956859786297, 2.6423960992650297, 2.0074700575111684, 3.7902658517417134, 1.838077256536519, 1.2795609570950313, 1.2194328628187348, 1.7093400498447577, 1.269421835895721, 0.751543863795894, 0.25970926478882933, 0.0), # 177 (3.8579455743102966, 2.6914803403664256, 3.5466456349923448, 3.593007701003337, 3.226369182834742, 1.6119101288478317, 1.2060922747077587, 1.239745418453944, 1.7897676701896952, 0.6310884384418126, 0.49846870312301883, 0.299913701073469, 0.0, 4.301646169828252, 3.299050711808158, 2.4923435156150937, 1.8932653153254375, 3.5795353403793904, 1.7356435858355217, 1.2060922747077587, 1.1513643777484512, 1.613184591417371, 1.1976692336677792, 0.7093291269984691, 0.24468003094240237, 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), # 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159 (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), # 160 (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 33, # 1 )

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Chapter 05/apyori_ex.py

bpbpublications/Essentials-of-Deep-Learning-and-AI

6ef6a6958afe88c11b1bbb18932cc43df2d43b29

[ "MIT" ]

null

Chapter 05/apyori_ex.py

bpbpublications/Essentials-of-Deep-Learning-and-AI

6ef6a6958afe88c11b1bbb18932cc43df2d43b29

[ "MIT" ]

null

Chapter 05/apyori_ex.py

bpbpublications/Essentials-of-Deep-Learning-and-AI

6ef6a6958afe88c11b1bbb18932cc43df2d43b29

[ "MIT" ]

1

2021-11-29T10:18:57.000Z

import numpy as np import matplotlib.pyplot as plt import pandas as pd from apyori import apriori store_data = pd.read_csv('./store_data.csv') store_data.head(0)

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py

Python

arghandle/__init__.py

svaisakh/magnet

bff6748803ac8efd081f0ddbdca8b1743c674a14

[ "MIT" ]

343

2018-09-03T09:59:36.000Z

2022-02-08T11:32:34.000Z

arghandle/__init__.py

svaisakh/magnet

bff6748803ac8efd081f0ddbdca8b1743c674a14

[ "MIT" ]

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2018-09-04T07:03:11.000Z

2019-03-21T07:17:14.000Z

arghandle/__init__.py

MagNet-DL/magnet

bff6748803ac8efd081f0ddbdca8b1743c674a14

[ "MIT" ]

23

2018-09-03T19:12:04.000Z

2021-02-20T09:23:30.000Z

from arghandle.core import arghandle, args

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py

Python

flagging_site/blueprints/__init__.py

cameronreaves/flagging

412fae782ac38f971a1715aeb257a8ab10a9ad3a

[ "MIT" ]

null

flagging_site/blueprints/__init__.py

cameronreaves/flagging

412fae782ac38f971a1715aeb257a8ab10a9ad3a

[ "MIT" ]

null

flagging_site/blueprints/__init__.py

cameronreaves/flagging

412fae782ac38f971a1715aeb257a8ab10a9ad3a

[ "MIT" ]

null

from . import cyanobacteria from . import flagging

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aispace/layers/adapters/model_adapters.py

SmileGoat/AiSpace

35fc120667e4263c99b300815e0bf018f5064a40

[ "Apache-2.0" ]

32

2020-01-16T07:59:03.000Z

2022-03-31T09:24:00.000Z

aispace/layers/adapters/model_adapters.py

SmileGoat/AiSpace

35fc120667e4263c99b300815e0bf018f5064a40

[ "Apache-2.0" ]

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2020-06-05T03:27:06.000Z

2022-03-12T01:00:17.000Z

aispace/layers/adapters/model_adapters.py

SmileGoat/AiSpace

35fc120667e4263c99b300815e0bf018f5064a40

[ "Apache-2.0" ]

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2020-06-09T02:22:50.000Z

2021-07-19T06:07:07.000Z

# -*- coding: utf-8 -*- # @Time : 2019-11-28 13:57 # @Author : yingyuankai # @Email : yingyuankai@aliyun.com # @File : tf_model_adapters.py import re import numpy as np from collections import OrderedDict import tensorflow as tf __all__ = [ "tf_huggingface_bert_adapter", "tf_huggingface_ernie_adapter", "tf_huggingface_xlnet_adapter", "tf_huggingface_albert_chinese_adapter", "tf_huggingface_albert_chinese_google_adapter", "tf_huggingface_electra_adapter", "tf_huggingface_gpt2_adapter" ] def tf_huggingface_bert_adapter(hf_model_variables: list, init_checkpoint: str): """Build name to variable map from huggingface bert names to bert_wwm variables, and then set values for current model. :param hf_model_variables: :return: """ name_to_values = list() for item in hf_model_variables: var_name = item.name matched_name = re.match("^.*/(bert/.*):\\d+$", var_name) if matched_name is None: continue matched_name = matched_name.group(1) # for bert/encoder encoder_matched = re.match("^bert/encoder/layer_._\\d+.*$", matched_name) if encoder_matched is not None: matched_name = matched_name.replace("_._", "_") # for bert/embeddings if matched_name == "bert/embeddings/weight": matched_name = "bert/embeddings/word_embeddings" elif matched_name == "bert/embeddings/position_embeddings/embeddings": matched_name = "bert/embeddings/position_embeddings" elif matched_name == "bert/embeddings/token_type_embeddings/embeddings": matched_name = "bert/embeddings/token_type_embeddings" elif matched_name == "bert/embeddings/task_type_embeddings/embeddings": matched_name = "bert/embeddings/task_type_embeddings" value = tf.train.load_variable(init_checkpoint, matched_name) name_to_values.append((item, value)) tf.keras.backend.batch_set_value(name_to_values) def tf_huggingface_ernie_adapter(hf_model_variables: list, init_checkpoint: str): """Build name to variable map from huggingface bert names to bert_wwm variables, and then set values for current model. :param hf_model_variables: :return: """ name_to_values = list() for item in hf_model_variables: var_name = item.name matched_name = re.match("^.*/(ernie/.*):\\d+$", var_name) if matched_name is None: continue matched_name = matched_name.group(1) # for bert/encoder encoder_matched = re.match("^ernie/encoder/layer_._\\d+.*$", matched_name) if encoder_matched is not None: matched_name = matched_name.replace("_._", "_").replace("ernie", "bert") # for bert/embeddings if matched_name == "ernie/embeddings/weight": matched_name = "bert/embeddings/word_embeddings" elif matched_name == "ernie/embeddings/position_embeddings/embeddings": matched_name = "bert/embeddings/position_embeddings" elif matched_name == "ernie/embeddings/token_type_embeddings/embeddings": matched_name = "bert/embeddings/token_type_embeddings" elif matched_name == "ernie/embeddings/task_type_embeddings/embeddings": matched_name = "bert/embeddings/task_type_embeddings" matched_name = matched_name.replace("ernie", "bert") value = tf.train.load_variable(init_checkpoint, matched_name) name_to_values.append((item, value)) tf.keras.backend.batch_set_value(name_to_values) def tf_huggingface_xlnet_adapter(hf_model_variables: list, init_checkpoint: str): """Build name to variable map from huggingface xlnet names to xlnet_chinese variables, and then set values for current model. :param hf_model_variables: :return: """ name_to_values = list() r_r_bias_values = tf.train.load_variable(init_checkpoint, "model/transformer/r_r_bias") r_s_bias_values = tf.train.load_variable(init_checkpoint, "model/transformer/r_s_bias") r_w_bias_values = tf.train.load_variable(init_checkpoint, "model/transformer/r_w_bias") seg_embed_values = tf.train.load_variable(init_checkpoint, "model/transformer/seg_embed") for item in hf_model_variables: var_name = item.name matched_name = re.match("^.*/(xl_net/.*):\\d+$", var_name) if matched_name is None: continue matched_name = matched_name.group(1) # for bert/encoder encoder_matched = re.match("^xl_net/layer_._\\d+.*$", matched_name) if encoder_matched is not None: matched_name = matched_name.replace("_._", "_").\ replace("xl_net", "model/transformer").\ replace("layer_norm", "LayerNorm") i = int(re.match("^.*/layer_(\\d+).*$", matched_name).group(1)) # for r_r_bias r_r_bias_matched = re.match("^.*/r_r_bias$", matched_name) if r_r_bias_matched is not None: value = np.squeeze(r_r_bias_values[i]) name_to_values.append((item, value)) continue # for r_s_bias r_s_bias_matched = re.match("^.*/r_s_bias$", matched_name) if r_s_bias_matched is not None: value = np.squeeze(r_s_bias_values[i]) name_to_values.append((item, value)) continue # for r_w_bias r_w_bias_matched = re.match("^.*/r_w_bias$", matched_name) if r_w_bias_matched is not None: value = np.squeeze(r_w_bias_values[i]) name_to_values.append((item, value)) continue # for seq_embed seg_embed_matched = re.match("^.*/seg_embed$", matched_name) if seg_embed_matched is not None: value = np.squeeze(seg_embed_values[i]) name_to_values.append((item, value)) continue # for ending with kqvor kqvor_matched = re.match("^.*/[kqvor]$", matched_name) if kqvor_matched is not None: matched_name += "/kernel" # for bert/embeddings if matched_name == 'xl_net/word_embedding/weight': matched_name = "model/transformer/word_embedding/lookup_table" if matched_name.endswith("mask_emb"): matched_name = "model/transformer/mask_emb/mask_emb" value = tf.train.load_variable(init_checkpoint, matched_name) name_to_values.append((item, value)) tf.keras.backend.batch_set_value(name_to_values) def tf_huggingface_albert_chinese_adapter(hf_model_variables: list, init_checkpoint: str): """Build name to variable map from huggingface albert names to albert_chinese variables, and then set values for current model. brightmart version ref: https://github.com/brightmart/albert_zh :param hf_model_variables: :return: """ name_to_values = list() default_prefix = "bert/encoder/layer_shared/" default_var_name = "albert_brightmart" for item in hf_model_variables: var_name = item.name matched_name = re.match(f"^.*/({default_var_name}/.*):\\d+$", var_name) if matched_name is None: continue matched_name = matched_name.group(1) # for pooler if matched_name == f"{default_var_name}/pooler/bias": matched_name = "bert/pooler/dense/bias" elif matched_name == f"{default_var_name}/pooler/kernel": matched_name = "bert/pooler/dense/kernel" # for embeddings elif matched_name == f"{default_var_name}/embeddings/word_embeddings/weight": matched_name = "bert/embeddings/word_embeddings" elif matched_name == f"{default_var_name}/embeddings/position_embeddings/embeddings": matched_name = "bert/embeddings/position_embeddings" elif matched_name == f"{default_var_name}/embeddings/token_type_embeddings/embeddings": matched_name = "bert/embeddings/token_type_embeddings" elif matched_name == f"{default_var_name}/embeddings/LayerNorm/gamma": matched_name = "bert/embeddings/LayerNorm/gamma" elif matched_name == f"{default_var_name}/embeddings/LayerNorm/beta": matched_name = "bert/embeddings/LayerNorm/beta" # for encoder elif matched_name == f"{default_var_name}/embeddings/embedding_hidden_mapping_in": matched_name = "bert/embeddings/word_embeddings_2" # for transformer layers elif matched_name.endswith("ffn/kernel"): matched_name = f"{default_prefix}intermediate/dense/kernel" elif matched_name.endswith("ffn/bias"): matched_name = f"{default_prefix}intermediate/dense/bias" elif matched_name.endswith("ffn_output/kernel"): matched_name = f"{default_prefix}output/dense/kernel" elif matched_name.endswith("ffn_output/bias"): matched_name = f"{default_prefix}output/dense/bias" elif matched_name.endswith("full_layer_layer_norm/gamma"): matched_name = f"{default_prefix}output/LayerNorm/gamma" elif matched_name.endswith("full_layer_layer_norm/beta"): matched_name = f"{default_prefix}output/LayerNorm/beta" elif matched_name.endswith("attention/LayerNorm/gamma"): matched_name = f"{default_prefix}attention/output/LayerNorm/gamma" elif matched_name.endswith("attention/LayerNorm/beta"): matched_name = f"{default_prefix}attention/output/LayerNorm/beta" elif matched_name.find("attention/dense") != -1: matched_name = re.match("^.*attention/(.*)$", matched_name).group(1) matched_name = f"{default_prefix}attention/output/{matched_name}" elif matched_name.find("attention") != -1: matched_name = re.match("^.*attention/(.*)$", matched_name).group(1) matched_name = f"{default_prefix}attention/self/{matched_name}" # else: # continue value = tf.train.load_variable(init_checkpoint, matched_name) name_to_values.append((item, value)) tf.keras.backend.batch_set_value(name_to_values) def tf_huggingface_albert_chinese_google_adapter(hf_model_variables: list, init_checkpoint: str): """Build name to variable map from huggingface albert names to albert_chinese_google variables, and then set values for current model. :param hf_model_variables: :return: """ name_to_values = list() default_prefix = "bert/encoder/transformer/group_0/inner_group_0/" for item in hf_model_variables: var_name = item.name matched_name = re.match("^.*/(albert/.*):\\d+$", var_name) if matched_name is None: continue matched_name = matched_name.group(1) # for pooler if matched_name == "albert/pooler/bias": matched_name = "bert/pooler/dense/bias" elif matched_name == "albert/pooler/kernel": matched_name = "bert/pooler/dense/kernel" # for embeddings elif matched_name == "albert/embeddings/word_embeddings/weight": matched_name = "bert/embeddings/word_embeddings" elif matched_name == "albert/embeddings/position_embeddings/embeddings": matched_name = "bert/embeddings/position_embeddings" elif matched_name == "albert/embeddings/token_type_embeddings/embeddings": matched_name = "bert/embeddings/token_type_embeddings" elif matched_name == "albert/embeddings/LayerNorm/gamma": matched_name = "bert/embeddings/LayerNorm/gamma" elif matched_name == "albert/embeddings/LayerNorm/beta": matched_name = "bert/embeddings/LayerNorm/beta" # for encoder elif matched_name == "albert/encoder/embedding_hidden_mapping_in/kernel": matched_name = "bert/encoder/embedding_hidden_mapping_in/kernel" elif matched_name == "albert/encoder/embedding_hidden_mapping_in/bias": matched_name = "bert/encoder/embedding_hidden_mapping_in/bias" # for transformer layers elif matched_name.endswith("ffn/kernel"): matched_name = f"{default_prefix}ffn_1/intermediate/dense/kernel" elif matched_name.endswith("ffn/bias"): matched_name = f"{default_prefix}ffn_1/intermediate/dense/bias" elif matched_name.endswith("ffn_output/kernel"): matched_name = f"{default_prefix}ffn_1/intermediate/output/dense/kernel" elif matched_name.endswith("ffn_output/bias"): matched_name = f"{default_prefix}ffn_1/intermediate/output/dense/bias" elif matched_name.endswith("full_layer_layer_norm/gamma"): matched_name = f"{default_prefix}LayerNorm_1/gamma" elif matched_name.endswith("full_layer_layer_norm/beta"): matched_name = f"{default_prefix}LayerNorm_1/beta" elif matched_name.endswith("attention/LayerNorm/gamma"): matched_name = f"{default_prefix}LayerNorm/gamma" elif matched_name.endswith("attention/LayerNorm/beta"): matched_name = f"{default_prefix}LayerNorm/beta" elif matched_name.find("attention/dense") != -1: matched_name = re.match("^.*attention/(.*)$", matched_name).group(1) matched_name = f"{default_prefix}attention_1/output/{matched_name}" elif matched_name.find("attention") != -1: matched_name = re.match("^.*attention/(.*)$", matched_name).group(1) matched_name = f"{default_prefix}attention_1/self/{matched_name}" value = tf.train.load_variable(init_checkpoint, matched_name) name_to_values.append((item, value)) tf.keras.backend.batch_set_value(name_to_values) def tf_huggingface_electra_adapter(hf_model_variables: list, init_checkpoint: str): """Build name to variable map from huggingface electra names to electra variables, and then set values for current model. :param hf_model_variables: :return: """ name_to_values = list() for item in hf_model_variables: var_name = item.name matched_name = re.match("^.*/(electra/.*):\\d+$", var_name) if matched_name is None: continue matched_name = matched_name.group(1) # for bert/encoder encoder_matched = re.match("^electra/encoder/layer_._\\d+.*$", matched_name) if encoder_matched is not None: matched_name = matched_name.replace("_._", "_") # for bert/embeddings if matched_name == "electra/embeddings/weight": matched_name = "electra/embeddings/word_embeddings" elif matched_name == "electra/embeddings/position_embeddings/embeddings": matched_name = "electra/embeddings/position_embeddings" elif matched_name == "electra/embeddings/token_type_embeddings/embeddings": matched_name = "electra/embeddings/token_type_embeddings" elif matched_name == "electra/embeddings/task_type_embeddings/embeddings": matched_name = "electra/embeddings/task_type_embeddings" value = tf.train.load_variable(init_checkpoint, matched_name) name_to_values.append((item, value)) tf.keras.backend.batch_set_value(name_to_values) def tf_huggingface_gpt2_adapter(hf_model_variables: list, init_checkpoint: str): """Build name to variable map from huggingface gpt2 names to gpt2 variables, and then set values for current model. :param hf_model_variables: :return: """ model_gold = tf.keras.models.load_model(init_checkpoint) vars_gold = model_gold.trainable_variables vars_gold_refinded = {} name_to_values = list() for var in vars_gold: name, value = var.name, var.numpy() name = name.replace("kernel", "weight") name_pieces = name.split("/") prefix = "/".join(name_pieces[:3] + [name_pieces[-1]]) if name.endswith("bias:0"): value = np.reshape(value, [1, value.shape[0]]) # need merge if name.find("query_layer") != -1 or name.find("key_layer") != -1 or name.find("value_layer") != -1: if prefix not in vars_gold_refinded: vars_gold_refinded[prefix] = value else: vars_gold_refinded[prefix] = np.concatenate((vars_gold_refinded[prefix], value), axis=1) else: vars_gold_refinded[name] = value for item in hf_model_variables: var_name = item.name matched_name = re.match("^.*/(gpt2/.*)$", var_name) if matched_name is None: continue matched_name = matched_name.group(1) matched_name = matched_name.replace("gpt2", "gpt") name_pieces = matched_name.split("/") if name_pieces[1] == "wte": matched_name = "gpt/embedding/embeddings:0" elif name_pieces[1] == "wpe": matched_name = "position_embeddings:0" elif name_pieces[1] == "ln_f": matched_name = matched_name.replace(name_pieces[1], "LayerNorm_final_norm") elif name_pieces[1].startswith("h_._"): layer_name = name_pieces[1] layer_idx = int(layer_name.split("_._")[-1]) new_layer_name = f"layer{layer_idx:02}" matched_name = matched_name.replace(layer_name, new_layer_name) if len(name_pieces) >= 4: if name_pieces[2] == "attn": if name_pieces[3] == "c_attn": matched_name = matched_name.replace("/".join(name_pieces[2: 4]), "attention") elif name_pieces[3] == "c_proj": matched_name = matched_name.replace("/".join(name_pieces[2: 4]), "attention/context_projection_layer") elif name_pieces[2] == "ln_1": matched_name = matched_name.replace(name_pieces[2], "LayerNorm_mlp_ln0") elif name_pieces[2] == "ln_2": matched_name = matched_name.replace(name_pieces[2], "LayerNorm_mlp_ln1") elif name_pieces[2] == "mlp": if name_pieces[3] == "c_fc": matched_name = matched_name.replace("/".join(name_pieces[2: 4]), "intermediate") elif name_pieces[3] == "c_proj": matched_name = matched_name.replace("/".join(name_pieces[2: 4]), "output") else: continue value = vars_gold_refinded.get(matched_name) if value is None: continue assert value.shape == item.shape tf.keras.backend.set_value(item, value) # name_to_values.append((item, value)) # tf.keras.backend.batch_set_value(name_to_values)

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py

Python

office365/sharepoint/storagemetrics/storage_metrics.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/sharepoint/storagemetrics/storage_metrics.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/sharepoint/storagemetrics/storage_metrics.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

from office365.sharepoint.base_entity import BaseEntity class StorageMetrics(BaseEntity): """Specifies the storage-related metrics for list folders in the site""" pass

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Python

code/model/layer/__init__.py

muzishen/Huawei_Digix_Retrieval_Top4

39151e2f8493221138404e2942afbf03e3afbf08

[ "Apache-2.0" ]

4

2021-02-21T14:56:01.000Z

2021-08-17T16:22:44.000Z

code/model/layer/__init__.py

muzishen/Huawei_Digix_Retrieval_Top4

39151e2f8493221138404e2942afbf03e3afbf08

[ "Apache-2.0" ]

null

code/model/layer/__init__.py

muzishen/Huawei_Digix_Retrieval_Top4

39151e2f8493221138404e2942afbf03e3afbf08

[ "Apache-2.0" ]

null

from .non_local import Non_local

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680f86011b823829b50b7b70feff5c2e38613c24

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py

Python

azure-iot-device/azure/iot/device/provisioning/pipeline/__init__.py

danewalton/azure-iot-sdk-python

addc82a8c28478738602bd698acdaf1a16dc39b4

[ "MIT" ]

366

2016-12-02T20:38:05.000Z

2022-03-29T10:08:14.000Z

azure-iot-device/azure/iot/device/provisioning/pipeline/__init__.py

danewalton/azure-iot-sdk-python

addc82a8c28478738602bd698acdaf1a16dc39b4

[ "MIT" ]

640

2016-12-16T21:59:48.000Z

2022-03-30T20:17:52.000Z

azure-iot-device/azure/iot/device/provisioning/pipeline/__init__.py

danewalton/azure-iot-sdk-python

addc82a8c28478738602bd698acdaf1a16dc39b4

[ "MIT" ]

371

2016-11-16T16:06:04.000Z

2022-03-31T10:10:57.000Z

"""Azure Provisioning Device Communication Pipeline This package provides pipeline for use with the Azure Provisioning Device SDK. INTERNAL USAGE ONLY """ from .mqtt_pipeline import MQTTPipeline from .config import ProvisioningPipelineConfig

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py

Python

Server/app/docs/friend.py

Nerd-Bear/But

5213288568dd4442f6c7a4251131fd66889c727a

[ "MIT" ]

1

2019-07-15T07:36:54.000Z

Server/app/docs/friend.py

Nerd-Bear/But

5213288568dd4442f6c7a4251131fd66889c727a

[ "MIT" ]

null

Server/app/docs/friend.py

Nerd-Bear/But

5213288568dd4442f6c7a4251131fd66889c727a

[ "MIT" ]

null

FIND_FRIEND_POST = { 'tags': ['friend'], 'description': '벗 찾기', 'parameters': [ { 'name': 'Authorization', 'description': 'api를 호출한 사람의 uuid', 'in': 'header', 'type': 'string', 'required': True }, { 'name': 'region', 'description': '본인 지역과 같은 벗을 검색할 것인가', 'in': 'json', 'type': 'bool', 'required': True }, { 'name': 'count', 'description': '가져올 친구 수', 'in': 'json', 'type': 'int', 'required': True } ], 'responses': { '201': { 'description': '성공', 'example': [{ 'name': '이름', 'profile_image': '프로필 사진', 'id': 'uuid', 'region': '사는 곳', 'age': '나이' }, { 'name': '이름 ㅁㅁㅁ', 'profile_image': '프로필 사진 ㅁㅁㅁ', 'id': 'uuid ㅁㅁㅁ', 'region': '사는 곳 ㅁㅁㅁ', 'age': '나이 ㅁㅁㅁ' } ] }, '204': { 'description': '검색 결과 없음' }, '401': { 'description': 'api를 호출한 사람의 uuid 오류' } } } FRIEND_LIST_GET = { 'tags': ['friend'], 'description': '벗 리스트 조회', 'parameters': [ { 'name': 'Authorization', 'description': 'api를 호출한 사람의 uuid', 'in': 'header', 'type': 'string', 'required': True } ], 'responses': { '201': { 'description': '성공', 'example': [{ 'name': '이름', 'profile_image': '프로필 사진', 'id': 'uuid', 'region': '사는 곳', 'age': '나이' }, { 'name': '이름 ㅁㅁㅁ', 'profile_image': '프로필 사진 ㅁㅁㅁ', 'id': 'uuid ㅁㅁㅁ', 'region': '사는 곳 ㅁㅁㅁ', 'age': '나이 ㅁㅁㅁ' } ] }, '204': { 'description': '검색 결과 없음' }, '401': { 'description': 'api를 호출한 사람의 uuid 오류' } } }

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Python

addons14/base_technical_features/models/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-06-10T14:59:13.000Z

addons14/base_technical_features/models/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

null

addons14/base_technical_features/models/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-04-09T09:44:44.000Z

from . import base from . import ir_ui_menu from . import res_users

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6

a8a8d176e1649ddbb795ee0bac0588b9789fbb69

43

py

Python

tervis/db/meta.py

robopsi/sentry-health

276fdd1fd33ae9602c8ab650954ea46dc5ec5e88

[ "BSD-3-Clause" ]

3

2016-11-06T19:51:29.000Z

2017-10-31T11:31:46.000Z

tervis/db/meta.py

robopsi/sentry-health

276fdd1fd33ae9602c8ab650954ea46dc5ec5e88

[ "BSD-3-Clause" ]

1

2016-12-19T16:42:25.000Z

2016-12-19T17:11:34.000Z

tervis/db/meta.py

getsentry/sentry-health

276fdd1fd33ae9602c8ab650954ea46dc5ec5e88

[ "BSD-3-Clause" ]

4

2018-06-05T02:49:06.000Z

2021-03-04T10:18:35.000Z

from sqlalchemy import * # noqa: F403,401

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null

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6

a8b87ef9e609d73610b0b7fc5c812a87c7a399ad

96

py

Python

nmt/my_module/__init__.py

awesome-archive/RAdam

56d2847bce23f8ec551ca3b2ff4a3aaeb96b0ebf

[ "Apache-2.0" ]

1

2019-08-16T07:36:33.000Z

nmt/my_module/__init__.py

awesome-archive/RAdam

56d2847bce23f8ec551ca3b2ff4a3aaeb96b0ebf

[ "Apache-2.0" ]

null

nmt/my_module/__init__.py

awesome-archive/RAdam

56d2847bce23f8ec551ca3b2ff4a3aaeb96b0ebf

[ "Apache-2.0" ]

1

2019-08-29T14:36:18.000Z

from .ada2 import * from .adam2 import * from .adadelta import * from .linear_schedule import *

19.2

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1

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6

a8bb45de7062b70640fb9dfef6110871089ffed6

21

py

Python

cride/rides/serializers/__init__.py

stalinchiguano98/Advanced_Django

642576deaf569663d5dbc0d5820cfbc49c17fd2e

[ "MIT" ]

9

2020-05-10T05:56:40.000Z

2022-01-24T08:49:27.000Z

cride/rides/serializers/__init__.py

stalinchiguano98/Advanced_Django

642576deaf569663d5dbc0d5820cfbc49c17fd2e

[ "MIT" ]

7

2020-06-05T19:54:39.000Z

2022-03-11T23:41:06.000Z

cride/rides/serializers/__init__.py

stalinchiguano98/Advanced_Django

642576deaf569663d5dbc0d5820cfbc49c17fd2e

[ "MIT" ]

5

2020-04-24T11:38:25.000Z

2021-01-02T09:41:04.000Z

from .rides import *

10.5

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6

4f23cbb23a5abe71f808e1867ace33d4935ac8d1

220

py

Python

atlas/foundations_contrib/src/test/job_bundling/__init__.py

DeepLearnI/atlas

8aca652d7e647b4e88530b93e265b536de7055ed

[ "Apache-2.0" ]

296

2020-03-16T19:55:00.000Z

2022-01-10T19:46:05.000Z

atlas/foundations_contrib/src/test/job_bundling/__init__.py

DeepLearnI/atlas

8aca652d7e647b4e88530b93e265b536de7055ed

[ "Apache-2.0" ]

57

2020-03-17T11:15:57.000Z

2021-07-10T14:42:27.000Z

atlas/foundations_contrib/src/test/job_bundling/__init__.py

DeepLearnI/atlas

8aca652d7e647b4e88530b93e265b536de7055ed

[ "Apache-2.0" ]

38

2020-03-17T21:06:05.000Z

2022-02-08T03:19:34.000Z

from test.job_bundling.test_script_environment import TestScriptEnvironment from test.job_bundling.test_folder_job_source_bundle import TestFolderJobSourceBundle from test.job_bundling.test_empty_job import TestEmptyJob

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6

4f5aa990deda947fd44a1d87c7208fa5b8eb9286

124

py

Python

auto_struct/data_types/base/__init__.py

Valmarelox/auto_struct

ec06fc426d468d4d01f300add3081df9eda87f41

[ "MIT" ]

7

2020-09-03T20:54:13.000Z

2022-03-09T01:21:07.000Z

auto_struct/data_types/base/__init__.py

Valmarelox/auto_struct

ec06fc426d468d4d01f300add3081df9eda87f41

[ "MIT" ]

null

auto_struct/data_types/base/__init__.py

Valmarelox/auto_struct

ec06fc426d468d4d01f300add3081df9eda87f41

[ "MIT" ]

null

from .base_type import BaseType from .base_single_value_type import BaseSingleValueType from .base_struct import BaseStruct

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6

4f860590578a7cdca92c24ec847999f941adee5c

131

py

Python

lib_rovpp/attacks/__init__.py

iReynaldo/lib_rovpp

eb201adc948e9375123c2e2301ee524392dd7b0d

[ "BSD-3-Clause" ]

1

2021-12-05T07:42:35.000Z

lib_rovpp/attacks/__init__.py

iReynaldo/lib_rovpp

eb201adc948e9375123c2e2301ee524392dd7b0d

[ "BSD-3-Clause" ]

null

lib_rovpp/attacks/__init__.py

iReynaldo/lib_rovpp

eb201adc948e9375123c2e2301ee524392dd7b0d

[ "BSD-3-Clause" ]

null

from .attacks import ROVPPPrefixHijack from .attacks import ROVPPSubprefixHijack from .attacks import ROVPPUnannouncedPrefixHijack

32.75

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6

4fa6798144ee6753a08a6c6d0591b57336ccce3a

35,319

py

Python

test/unit_testing/grid/element_linear_dx_data/test_element_linearB/element/geom_element_SYM.py

nwukie/ChiDG

d096548ba3bd0a338a29f522fb00a669f0e33e9b

[ "BSD-3-Clause" ]

36

2016-10-05T15:12:22.000Z

2022-03-17T02:08:23.000Z

test/unit_testing/grid/element_linear_dx_data/test_element_linearC/element/geom_element_SYM.py

nwukie/ChiDG

d096548ba3bd0a338a29f522fb00a669f0e33e9b

[ "BSD-3-Clause" ]

17

2016-05-17T02:21:05.000Z

2017-08-10T16:33:07.000Z

test/unit_testing/grid/element_linear_dx_data/test_element_linearB/element/geom_element_SYM.py

nwukie/ChiDG

d096548ba3bd0a338a29f522fb00a669f0e33e9b

[ "BSD-3-Clause" ]

20

2016-07-18T16:20:47.000Z

2020-11-27T19:26:12.000Z

from __future__ import division import sys import os import time import numpy import pickle from sympy import * from sympy.tensor.array import MutableSparseNDimArray def update_progress(job_title, progress): length = 20 # modify this to change the length block = int(round(length*progress)) msg = "\r{0}: [{1}] {2}%".format(job_title, "#"*block + "-"*(length-block), round(progress*100, 2)) if progress >= 1: msg += " DONE\r\n" sys.stdout.write(msg) sys.stdout.flush() def cls(): os.system('cls' if os.name=='nt' else 'clear') cls() print "WARNING: This script is very slow, it might run for hours. It is strongly recommended to watch Netflix in the meanwhile." ################################################################################################################ # Define symbols for each coordinate for support node x1,y1,z1 = symbols('x1 y1 z1') x2,y2,z2 = symbols('x2 y2 z2') x3,y3,z3 = symbols('x3 y3 z3') x4,y4,z4 = symbols('x4 y4 z4') x5,y5,z5 = symbols('x5 y5 z5') x6,y6,z6 = symbols('x6 y6 z6') x7,y7,z7 = symbols('x7 y7 z7') x8,y8,z8 = symbols('x8 y8 z8') coords_ = Matrix( [[x1,y1,z1], [x2,y2,z2], [x3,y3,z3], [x4,y4,z4], [x5,y5,z5], [x6,y6,z6], [x7,y7,z7], [x8,y8,z8], ] ) nnodes_r = coords_.shape[0] nnodes_ie = 8 nnodes_if = 4 nterms_s = 8 ndirs = 3 # Define coordinate values at support nodes coords = Matrix( [[0.0,0.0,0.0], [5.0,0.0,0.0], [0.0,1.0,0.0], [5.0,1.0,0.0], [0.0,0.0,1.0], [5.0,0.0,1.0], [0.0,1.0,1.0], [5.0,1.0,1.0], ] ) # Define matrix of polynomial basis terms at support nodes val_r = Matrix( [[ 1.0,-1.0,-1.0,-1.0, 1.0, 1.0, 1.0,-1.0], [ 1.0,-1.0,-1.0, 1.0,-1.0,-1.0, 1.0, 1.0], [ 1.0, 1.0,-1.0,-1.0,-1.0, 1.0,-1.0, 1.0], [ 1.0, 1.0,-1.0, 1.0, 1.0,-1.0,-1.0,-1.0], [ 1.0,-1.0, 1.0,-1.0, 1.0,-1.0,-1.0, 1.0], [ 1.0,-1.0, 1.0, 1.0,-1.0, 1.0,-1.0,-1.0], [ 1.0, 1.0, 1.0,-1.0,-1.0,-1.0, 1.0,-1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ] ) # Define matrices at interpolation nodes (quadrature, level = 1) val_i = Matrix( [[ 1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0, 1.0/3.0,-1.0/3.0*sqrt(1.0/3.0)], [ 1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0, 1.0/3.0, 1.0/3.0*sqrt(1.0/3.0)], [ 1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0,-1.0/3.0, 1.0/3.0*sqrt(1.0/3.0)], [ 1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0,-1.0/3.0,-1.0/3.0*sqrt(1.0/3.0)], [ 1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0,-1.0/3.0, 1.0/3.0*sqrt(1.0/3.0)], [ 1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0,-1.0/3.0,-1.0/3.0*sqrt(1.0/3.0)], [ 1.0, sqrt(1.0/3.0), sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0, 1.0/3.0,-1.0/3.0*sqrt(1.0/3.0)], [ 1.0, sqrt(1.0/3.0), sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0, 1.0/3.0, 1.0/3.0*sqrt(1.0/3.0)], ] ) ddxi_i = Matrix( [[ 0.0,0.0,0.0,1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0),0.0, 1.0/3.0], [ 0.0,0.0,0.0,1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0),0.0, 1.0/3.0], [ 0.0,0.0,0.0,1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0, sqrt(1.0/3.0), sqrt(1.0/3.0),0.0, 1.0/3.0], [ 0.0,0.0,0.0,1.0, sqrt(1.0/3.0), sqrt(1.0/3.0),0.0, 1.0/3.0], ] ) ddeta_i = Matrix( [[ 0.0,1.0,0.0,0.0,-sqrt(1.0/3.0),0.0,-sqrt(1.0/3.0), 1.0/3.0], [ 0.0,1.0,0.0,0.0, sqrt(1.0/3.0),0.0,-sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0,-sqrt(1.0/3.0),0.0,-sqrt(1.0/3.0), 1.0/3.0], [ 0.0,1.0,0.0,0.0, sqrt(1.0/3.0),0.0,-sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0,-sqrt(1.0/3.0),0.0, sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0, sqrt(1.0/3.0),0.0, sqrt(1.0/3.0), 1.0/3.0], [ 0.0,1.0,0.0,0.0,-sqrt(1.0/3.0),0.0, sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0, sqrt(1.0/3.0),0.0, sqrt(1.0/3.0), 1.0/3.0], ] ) ddzeta_i= Matrix( [[ 0.0,0.0,1.0,0.0,0.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0], ] ) # Define element interpolation nodes weights for linear element weights_e = Matrix( [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0] ) # Define val_f for each face # Face 1, XI_MIN val_1 = Matrix( [[ 1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0, sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0], [ 1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0], [ 1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0], [ 1.0, sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0], ] ) # Face 2, XI_MAX val_2 = Matrix( [[ 1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0),1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0], [ 1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0], [ 1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0], [ 1.0, sqrt(1.0/3.0), sqrt(1.0/3.0),1.0, sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0], ] ) # Face 3, ETA_MIN val_3 = Matrix( [[ 1.0,-1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0, sqrt(1.0/3.0),-1.0/3.0], [ 1.0,-1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0, sqrt(1.0/3.0), 1.0/3.0], [ 1.0,-1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0,-sqrt(1.0/3.0), 1.0/3.0], [ 1.0,-1.0, sqrt(1.0/3.0), sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0,-sqrt(1.0/3.0),-1.0/3.0], ] ) # Face 4, ETA_MAX val_4 = Matrix( [[ 1.0,1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0,-sqrt(1.0/3.0), 1.0/3.0], [ 1.0,1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0,-sqrt(1.0/3.0),-1.0/3.0], [ 1.0,1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0, sqrt(1.0/3.0),-1.0/3.0], [ 1.0,1.0, sqrt(1.0/3.0), sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0, sqrt(1.0/3.0), 1.0/3.0], ] ) # Face 5, ZETA_MIN val_5 = Matrix( [[ 1.0,-sqrt(1.0/3.0),-1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0, sqrt(1.0/3.0),-1.0/3.0], [ 1.0,-sqrt(1.0/3.0),-1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0, sqrt(1.0/3.0), 1.0/3.0], [ 1.0, sqrt(1.0/3.0),-1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0,-sqrt(1.0/3.0), 1.0/3.0], [ 1.0, sqrt(1.0/3.0),-1.0, sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0,-sqrt(1.0/3.0),-1.0/3.0], ] ) # Face 6, ZETA_MAX val_6 = Matrix( [[ 1.0,-sqrt(1.0/3.0),1.0,-sqrt(1.0/3.0), sqrt(1.0/3.0),-1.0/3.0,-sqrt(1.0/3.0), 1.0/3.0], [ 1.0,-sqrt(1.0/3.0),1.0, sqrt(1.0/3.0),-sqrt(1.0/3.0), 1.0/3.0,-sqrt(1.0/3.0),-1.0/3.0], [ 1.0, sqrt(1.0/3.0),1.0,-sqrt(1.0/3.0),-sqrt(1.0/3.0),-1.0/3.0, sqrt(1.0/3.0),-1.0/3.0], [ 1.0, sqrt(1.0/3.0),1.0, sqrt(1.0/3.0), sqrt(1.0/3.0), 1.0/3.0, sqrt(1.0/3.0), 1.0/3.0], ] ) #-------------------------------------------------------------------- # Matrix modes_to_nodes val_r_inv = val_r**(-1) # Computes coordiantes modes coords_modes_ = val_r_inv * coords_ coords_modes = lambdify(coords_,coords_modes_,"numpy") # Initialized coordiantes interp_coords_ = MutableSparseNDimArray.zeros(nnodes_ie,3) for inode in range(0,nnodes_ie): for idir in range(0,3): interp_coords_[inode,idir] = val_i[inode,:] * coords_modes_[:,idir] # Initialized jacobian jacobian_ = MutableSparseNDimArray.zeros(3, 3, nnodes_ie) for inode in range(0,nnodes_ie): jacobian_[0,0,inode] = ddxi_i[inode,:] * coords_modes_[:,0] jacobian_[0,1,inode] = ddeta_i[inode,:] * coords_modes_[:,0] jacobian_[0,2,inode] = ddzeta_i[inode,:] * coords_modes_[:,0] jacobian_[1,0,inode] = ddxi_i[inode,:] * coords_modes_[:,1] jacobian_[1,1,inode] = ddeta_i[inode,:] * coords_modes_[:,1] jacobian_[1,2,inode] = ddzeta_i[inode,:] * coords_modes_[:,1] jacobian_[2,0,inode] = ddxi_i[inode,:] * coords_modes_[:,2] jacobian_[2,1,inode] = ddeta_i[inode,:] * coords_modes_[:,2] jacobian_[2,2,inode] = ddzeta_i[inode,:] * coords_modes_[:,2] update_progress("Computing Jacobian ", inode/(nnodes_ie-1)) # Matrics and Determinant metrics_ = MutableSparseNDimArray.zeros(3, 3, nnodes_ie) jinv_ = zeros(nnodes_ie) for inode in range(0,nnodes_ie): ijacobian = zeros(3,3) for irow in range(0,3): for icol in range(0,3): ijacobian[irow,icol] = jacobian_[irow,icol,inode] # Compute jacobian for the ith node update_progress("Computing Jinv and Metric ", inode/(nnodes_ie-1)) jinv_[inode] = ijacobian.det() imetric = ijacobian**(-1) for irow in range(0,3): for icol in range(0,3): metrics_[irow,icol,inode] = imetric[irow,icol] # Compute inverse Mass matrix invmass_ = zeros(nterms_s,nterms_s) mass_ = zeros(nterms_s,nterms_s) i = 1 val_tmp = val_i for iterm in range(0,nterms_s): for inode in range(0,nnodes_ie): val_tmp[inode,iterm] = val_tmp[inode,iterm] * weights_e[inode] * jinv_[inode] update_progress("Computing invmass ", i/(nterms_s*nnodes_ie)) i += 1 mass_ = transpose(val_tmp)*val_i invmass_ = (mass_)**(-1) # Compute BR2_VOL for each face br2_vol_face1_ = zeros(nnodes_ie,nnodes_if) br2_vol_face2_ = zeros(nnodes_ie,nnodes_if) br2_vol_face3_ = zeros(nnodes_ie,nnodes_if) br2_vol_face4_ = zeros(nnodes_ie,nnodes_if) br2_vol_face5_ = zeros(nnodes_ie,nnodes_if) br2_vol_face6_ = zeros(nnodes_ie,nnodes_if) br2_vol_face1_ = val_i*(invmass_*transpose(val_1)) br2_vol_face2_ = val_i*(invmass_*transpose(val_2)) br2_vol_face3_ = val_i*(invmass_*transpose(val_3)) br2_vol_face4_ = val_i*(invmass_*transpose(val_4)) br2_vol_face5_ = val_i*(invmass_*transpose(val_5)) br2_vol_face6_ = val_i*(invmass_*transpose(val_6)) update_progress("Computing br2_vol ", 1) # Compute BR2_FACE for each face br2_face_face1_ = zeros(nnodes_if,nnodes_if) br2_face_face2_ = zeros(nnodes_if,nnodes_if) br2_face_face3_ = zeros(nnodes_if,nnodes_if) br2_face_face4_ = zeros(nnodes_if,nnodes_if) br2_face_face5_ = zeros(nnodes_if,nnodes_if) br2_face_face6_ = zeros(nnodes_if,nnodes_if) br2_face_face1_ = val_1*(invmass_*transpose(val_1)) br2_face_face2_ = val_2*(invmass_*transpose(val_2)) br2_face_face3_ = val_3*(invmass_*transpose(val_3)) br2_face_face4_ = val_4*(invmass_*transpose(val_4)) br2_face_face5_ = val_5*(invmass_*transpose(val_5)) br2_face_face6_ = val_6*(invmass_*transpose(val_6)) update_progress("Computing br2_face ", 1) ## Grad1, Grad2, and Grad3 #grad1_ = zeros(nnodes_ie,nterms_s) #grad2_ = zeros(nnodes_ie,nterms_s) #grad3_ = zeros(nnodes_ie,nterms_s) #i = 1 #for iterm in range(0,nterms_s): # for inode in range(0,nnodes_ie): # grad1_[inode,iterm] = metrics_[0,0,inode] * ddxi_i[inode,iterm] + metrics_[1,0,inode] * ddeta_i[inode,iterm] + metrics_[2,0,inode] * ddzeta_i[inode,iterm] # grad2_[inode,iterm] = metrics_[0,1,inode] * ddxi_i[inode,iterm] + metrics_[1,1,inode] * ddeta_i[inode,iterm] + metrics_[2,1,inode] * ddzeta_i[inode,iterm] # grad3_[inode,iterm] = metrics_[0,2,inode] * ddxi_i[inode,iterm] + metrics_[1,2,inode] * ddeta_i[inode,iterm] + metrics_[2,2,inode] * ddzeta_i[inode,iterm] # update_progress("Computing grad1, grad2, grad3 ", i/(nnodes_ie*nterms_s)) # i += 1 # Differentiate coordinates at interpolation points interp_coords_dx_ = MutableSparseNDimArray.zeros(nnodes_ie, 3, nnodes_r, ndirs) i = 1 for inode in range(0,nnodes_ie): for direct in range(0,3): for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): interp_coords_dx_[inode,direct,inode_diff,idir] = interp_coords_[inode,direct].diff(coords_[inode_diff,idir]) update_progress("Computing interp_coords_dx ", i/(nnodes_ie*nnodes_r*ndirs*3)) i += 1 # Differentiate determinant djinv_dx_ = MutableSparseNDimArray.zeros(nnodes_ie, nnodes_r, ndirs) i = 1 for inode in range(0,nnodes_ie): for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): djinv_dx_[inode,inode_diff,idir] = jinv_[inode].diff(coords_[inode_diff,idir]) update_progress("Computing djinv_dx ", i/(nnodes_ie*nnodes_r*ndirs)) i += 1 # Differentiate metrics dmetric_dx_ = MutableSparseNDimArray.zeros(3,3,nnodes_ie,nnodes_r,ndirs) i = 1 for inode in range(0,nnodes_ie): for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): for irow in range(0,3): for icol in range(0,3): dmetric_dx_[irow,icol,inode,inode_diff,idir] = metrics_[irow,icol,inode].diff(coords_[inode_diff,idir]) update_progress("Computing dmetric_dx ", i/(nnodes_ie*nnodes_r*ndirs*9)) i += 1 # Differentiate invmass dinvmass_dx_ = MutableSparseNDimArray.zeros(nterms_s,nterms_s,nnodes_r,ndirs) i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): for irow in range(0,nterms_s): for icol in range(0,nterms_s): dinvmass_dx_[irow,icol,inode_diff,idir] = invmass_[irow,icol].diff(coords_[inode_diff,idir]) update_progress("Computing dinvmass_dx ", i/(nnodes_r*ndirs*nterms_s*nterms_s)) i += 1 # Differentiate BR2_vol dbr2_vol_face1_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nnodes_if,nnodes_r,ndirs) dbr2_vol_face2_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nnodes_if,nnodes_r,ndirs) dbr2_vol_face3_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nnodes_if,nnodes_r,ndirs) dbr2_vol_face4_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nnodes_if,nnodes_r,ndirs) dbr2_vol_face5_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nnodes_if,nnodes_r,ndirs) dbr2_vol_face6_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nnodes_if,nnodes_r,ndirs) i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): dbr2_vol_face1_dx_[irow,icol,inode_diff,idir] = br2_vol_face1_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_vol_face2_dx_[irow,icol,inode_diff,idir] = br2_vol_face2_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_vol_face3_dx_[irow,icol,inode_diff,idir] = br2_vol_face3_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_vol_face4_dx_[irow,icol,inode_diff,idir] = br2_vol_face4_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_vol_face5_dx_[irow,icol,inode_diff,idir] = br2_vol_face5_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_vol_face6_dx_[irow,icol,inode_diff,idir] = br2_vol_face6_[irow,icol].diff(coords_[inode_diff,idir]) update_progress("Computing dbr2_vol_faces_dx ", i/(nnodes_r*ndirs*nnodes_ie*nnodes_if)) i += 1 # Differentiate BR2_face dbr2_face_face1_dx_ = MutableSparseNDimArray.zeros(nnodes_if,nnodes_if,nnodes_r,ndirs) dbr2_face_face2_dx_ = MutableSparseNDimArray.zeros(nnodes_if,nnodes_if,nnodes_r,ndirs) dbr2_face_face3_dx_ = MutableSparseNDimArray.zeros(nnodes_if,nnodes_if,nnodes_r,ndirs) dbr2_face_face4_dx_ = MutableSparseNDimArray.zeros(nnodes_if,nnodes_if,nnodes_r,ndirs) dbr2_face_face5_dx_ = MutableSparseNDimArray.zeros(nnodes_if,nnodes_if,nnodes_r,ndirs) dbr2_face_face6_dx_ = MutableSparseNDimArray.zeros(nnodes_if,nnodes_if,nnodes_r,ndirs) i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): dbr2_face_face1_dx_[irow,icol,inode_diff,idir] = br2_face_face1_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_face_face2_dx_[irow,icol,inode_diff,idir] = br2_face_face2_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_face_face3_dx_[irow,icol,inode_diff,idir] = br2_face_face3_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_face_face4_dx_[irow,icol,inode_diff,idir] = br2_face_face4_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_face_face5_dx_[irow,icol,inode_diff,idir] = br2_face_face5_[irow,icol].diff(coords_[inode_diff,idir]) dbr2_face_face6_dx_[irow,icol,inode_diff,idir] = br2_face_face6_[irow,icol].diff(coords_[inode_diff,idir]) update_progress("Computing dbr2_face_faces_dx ", i/(nnodes_r*ndirs*nnodes_if*nnodes_if)) i += 1 ## Differentaite Gradients #dgrad1_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nterms_s,nnodes_r,ndirs) #dgrad2_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nterms_s,nnodes_r,ndirs) #dgrad3_dx_ = MutableSparseNDimArray.zeros(nnodes_ie,nterms_s,nnodes_r,ndirs) #i = 1 #for inode in range(0,nnodes_ie): # for inode_diff in range(0,nnodes_r): # for idir in range(0,ndirs): # for inode in range(0,nnodes_ie): # for iterm in range(0,nterms_s): # dgrad1_dx_[inode,iterm,inode_diff,idir] = grad1_[inode,iterm].diff(coords_[inode_diff,idir]) # dgrad2_dx_[inode,iterm,inode_diff,idir] = grad2_[inode,iterm].diff(coords_[inode_diff,idir]) # dgrad3_dx_[inode,iterm,inode_diff,idir] = grad3_[inode,iterm].diff(coords_[inode_diff,idir]) # update_progress("Computing dgrad1_dx, dgrad2_dx, .. ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_ie*nterms_s)) # i += 1 #WRITE_____________________ ## ## Metrics ## #f = open("metrics.txt","w") #i = 1 #for inode in range (0,nnodes_ie): # f.write("Metric interpolation node %d \n" % (inode+1)) # array = numpy.zeros([3, 3]) # for irow in range(0,3): # for icol in range(0,3): # data_sym = lambdify(coords_,metrics_[irow,icol,inode],"numpy") # data_val = data_sym(*flatten(coords)) # array[irow,icol] = data_val # update_progress("Writing metrics to file ", i/(nnodes_ie*9)) # i += 1 # numpy.savetxt(f,array) #f.close() # ## ## jinv ## #f = open("jinv.txt","w") #array = numpy.zeros([1]) #i = 1 #for inode in range (0,nnodes_ie): # f.write("Jinv interpolation node %d \n" % (inode+1)) # data_sym = lambdify(coords_,jinv_[inode],"numpy") # data_val = data_sym(*flatten(coords)) # array[0] = data_val # numpy.savetxt(f,array) # update_progress("Writing jinv to file ", i/(nnodes_ie)) # i += 1 #f.close() ## ## Grad1 ## #f = open("grad1.txt","w") #f.write("Grad1 \n") #array = numpy.zeros([nnodes_ie,nterms_s]) #i = 1 #for inode in range (0,nnodes_ie): # for iterm in range(0,nterms_s): # data_sym = lambdify(coords_,grad1_[inode,iterm],"numpy") # data_val = (data_sym(*flatten(coords))) # array[inode,iterm] = data_val # update_progress("Writing grad1 to file ", i/(nnodes_ie*nterms_s)) # i += 1 #numpy.savetxt(f,array) #f.close() # ## ## Grad2 ## #f = open("grad2.txt","w") #f.write("Grad2\n") #array = numpy.zeros([nnodes_ie,nterms_s]) #i = 1 #for inode in range (0,nnodes_ie): # for iterm in range(0,nterms_s): # data_sym = lambdify(coords_,grad2_[inode,iterm],"numpy") # data_val = (data_sym(*flatten(coords))) # array[inode,iterm] = data_val # update_progress("Writing grad2 to file ", i/(nnodes_ie*nterms_s)) # i += 1 #numpy.savetxt(f,array) #f.close() # ## ## Grad3 ## #f = open("grad3.txt","w") #f.write("Grad3\n") #array = numpy.zeros([nnodes_ie,nterms_s]) #i = 1 #for inode in range (0,nnodes_ie): # for iterm in range(0,nterms_s): # data_sym = lambdify(coords_,grad3_[inode,iterm],"numpy") # data_val = (data_sym(*flatten(coords))) # array[inode,iterm] = data_val # update_progress("Writing grad3 to file ", i/(nnodes_ie*nterms_s)) # i += 1 #numpy.savetxt(f,array) #f.close() ## ## dmetric_dx ## #f = open("dmetric_dx.txt","w") #i = 1 #for inode in range (0,nnodes_ie): # for inode_diff in range(0,nnodes_r): # for idir in range(0,ndirs): # array = numpy.zeros([3,3]) # f.write("dmetric_dx interpolation node %s, diff_node %s, diff_dir %s \n" % (inode+1,inode_diff+1,idir+1)) # for irow in range(0,3): # for icol in range(0,3): # data_sym = lambdify(coords_,dmetric_dx_[irow,icol,inode,inode_diff,idir],"numpy") # data_val = data_sym(*flatten(coords)) # array[irow,icol] = data_val # update_progress("Writing dmetric_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*3*3)) # i += 1 # numpy.savetxt(f,array) #f.close() # # interp_coords_dx # f = open("interp_coords_dx.txt","w") i = 1 for inode in range (0,nnodes_ie): for direct in range (0,3): array = numpy.zeros([nnodes_r,ndirs]) f.write("coord interpolation node %s, coord %s, row=inode_diff, col=dir \n" % (inode+1,direct+1)) for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): data_sym = lambdify(coords_,interp_coords_dx_[inode,direct,inode_diff,idir],"numpy") data_val = data_sym(*flatten(coords)) array[inode_diff,idir] = data_val update_progress("Writing interp_coords_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*3)) i += 1 numpy.savetxt(f,array) f.close() ## ## djinv_dx ## #f = open("djinv_dx.txt","w") #i = 1 #for inode in range (0,nnodes_ie): # array = numpy.zeros([nnodes_r,ndirs]) # f.write("djinv_dx interpolation node %s, row=inode_diff, col=dir \n" % (inode+1)) # for inode_diff in range(0,nnodes_r): # for idir in range(0,ndirs): # data_sym = lambdify(coords_,djinv_dx_[inode,inode_diff,idir],"numpy") # data_val = data_sym(*flatten(coords)) # array[inode_diff,idir] = data_val # update_progress("Writing djinv_dx to file ", i/(nnodes_ie*nnodes_r*ndirs)) # i += 1 # numpy.savetxt(f,array) #f.close() # # dinvmass_dx # f = open("dinvmass_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dinvmass_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nterms_s,nterms_s]) for irow in range(0,nterms_s): for icol in range(0,nterms_s): data_sym = lambdify(coords_,dinvmass_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dinvmass_dx to file ", i/(nterms_s*nnodes_r*ndirs*nterms_s)) i += 1 numpy.savetxt(f,array) f.close() # # dbr2_vol_dx # f = open("dbr2_vol_face1_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face1_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_vol_face1_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_vol_face1_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_vol_face2_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face2_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_vol_face2_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_vol_face2_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_vol_face3_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face3_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_vol_face3_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_vol_face3_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_vol_face4_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face4_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_vol_face4_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_vol_face4_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_vol_face5_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face5_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_vol_face5_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_vol_face5_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_vol_face6_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face6_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_vol_face6_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_vol_face6_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() # # dbr2_face_dx # f = open("dbr2_face_face1_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face1_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_face_face1_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_face_face1_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_face_face2_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face2_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_face_face2_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_face_face2_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_face_face3_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face3_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_face_face3_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_face_face3_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_face_face4_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face4_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_face_face4_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_face_face4_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_face_face5_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face5_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_face_face5_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_face_face5_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() f = open("dbr2_face_face6_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face6_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = numpy.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data_sym = lambdify(coords_,dbr2_face_face6_dx_[irow,icol,inode_diff,idir],"numpy") data_val = (data_sym(*flatten(coords))) array[irow,icol] = data_val update_progress("Writing dbr2_face_face6_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) i += 1 numpy.savetxt(f,array) f.close() ## ## dgrad1_dx ## #f = open("dgrad1_dx.txt","w") #i = 1 #for inode_diff in range(0,nnodes_r): # for idir in range(0,ndirs): # f.write("dgrad1_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) # array = numpy.zeros([nnodes_ie,nterms_s]) # for irow in range(0,nnodes_ie): # for icol in range(0,nterms_s): # data_sym = lambdify(coords_,dgrad1_dx_[irow,icol,inode_diff,idir],"numpy") # data_val = (data_sym(*flatten(coords))) # array[irow,icol] = data_val # update_progress("Writing dgrad1_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nterms_s)) # i += 1 # numpy.savetxt(f,array) #f.close() # ## ## dgrad2_dx ## #f = open("dgrad2_dx.txt","w") #i = 1 #for inode_diff in range(0,nnodes_r): # for idir in range(0,ndirs): # f.write("dgrad2_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) # array = numpy.zeros([nnodes_ie,nterms_s]) # for irow in range(0,nnodes_ie): # for icol in range(0,nterms_s): # data_sym = lambdify(coords_,dgrad2_dx_[irow,icol,inode_diff,idir],"numpy") # data_val = (data_sym(*flatten(coords))) # array[irow,icol] = data_val # update_progress("Writing dgrad2_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nterms_s)) # i += 1 # numpy.savetxt(f,array) #f.close() # ## ## dgrad3_dx ## #f = open("dgrad3_dx.txt","w") #i = 1 #for inode_diff in range(0,nnodes_r): # for idir in range(0,ndirs): # f.write("dgrad3_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) # array = numpy.zeros([nnodes_ie,nterms_s]) # for irow in range(0,nnodes_ie): # for icol in range(0,nterms_s): # data_sym = lambdify(coords_,dgrad3_dx_[irow,icol,inode_diff,idir],"numpy") # data_val = (data_sym(*flatten(coords))) # array[irow,icol] = data_val # update_progress("Writing dgrad3_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nterms_s)) # i += 1 # numpy.savetxt(f,array) #f.close()

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0.057699

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null

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0.016701

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null

0

1

0

1

0

null

0

1

0

6

96fcf3db76ed42c278bc9f7e35c302521a261bcc

48

py

Python

clickapptest/lib/common/common.py

AtsushiSakai/clickapptest

5fd38e3056d9d63b962ab531950d5f124003fc3a

[ "MIT" ]

null

clickapptest/lib/common/common.py

AtsushiSakai/clickapptest

5fd38e3056d9d63b962ab531950d5f124003fc3a

[ "MIT" ]

null

clickapptest/lib/common/common.py

AtsushiSakai/clickapptest

5fd38e3056d9d63b962ab531950d5f124003fc3a

[ "MIT" ]

null

def print_common(): print("this is common")

16

27

0.666667

7

48

4.428571

0.714286

0

0.1875

48

3

27

16

0.794872

0

0.291667

0

1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

6

8c07af07a184ddf00713f94bf206e65b652e34cb

36

py

Python

goamazondownloader/sbandradar/__init__.py

AdrianoPereira/goamazondownloader

05671ee59c0a5a4c20edd5e4825d5ebf589d1327

[ "MIT" ]

1

2021-05-07T16:04:29.000Z

goamazondownloader/sbandradar/__init__.py

AdrianoPereira/goamazondownloader

05671ee59c0a5a4c20edd5e4825d5ebf589d1327

[ "MIT" ]

1

2020-08-14T16:34:32.000Z

goamazondownloader/sbandradar/__init__.py

AdrianoPereira/goamazondownloader

05671ee59c0a5a4c20edd5e4825d5ebf589d1327

[ "MIT" ]

null

from ._sbandradar import SBandRadar

18

35

0.861111

4

36

7.5

0.75

0

0.111111

36

1

36

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

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null

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1

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1

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6