fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f71b1ab7a5127da844d3de34669b1e4ea7ec03cf

4,069

py

Python

tests/components/smartthings/test_lock.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

1

2021-07-08T20:09:55.000Z

tests/components/smartthings/test_lock.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

47

2021-02-21T23:43:07.000Z

2022-03-31T06:07:10.000Z

tests/components/smartthings/test_lock.py

OpenPeerPower/core

f673dfac9f2d0c48fa30af37b0a99df9dd6640ee

[ "Apache-2.0" ]

null

""" Test for the SmartThings lock platform. The only mocking required is of the underlying SmartThings API object so real HTTP calls are not initiated during testing. """ from pysmartthings import Attribute, Capability from pysmartthings.device import Status from openpeerpower.components.lock import DOMAIN as LOCK_DOMAIN from openpeerpower.components.smartthings.const import DOMAIN, SIGNAL_SMARTTHINGS_UPDATE from openpeerpower.config_entries import ConfigEntryState from openpeerpower.const import STATE_UNAVAILABLE from openpeerpower.helpers import device_registry as dr, entity_registry as er from openpeerpower.helpers.dispatcher import async_dispatcher_send from .conftest import setup_platform async def test_entity_and_device_attributes(opp, device_factory): """Test the attributes of the entity are correct.""" # Arrange device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "unlocked"}) entity_registry = er.async_get(opp) device_registry = dr.async_get(opp) # Act await setup_platform(opp, LOCK_DOMAIN, devices=[device]) # Assert entry = entity_registry.async_get("lock.lock_1") assert entry assert entry.unique_id == device.device_id entry = device_registry.async_get_device({(DOMAIN, device.device_id)}) assert entry assert entry.name == device.label assert entry.model == device.device_type_name assert entry.manufacturer == "Unavailable" async def test_lock(opp, device_factory): """Test the lock locks successfully.""" # Arrange device = device_factory("Lock_1", [Capability.lock]) device.status.attributes[Attribute.lock] = Status( "unlocked", None, { "method": "Manual", "codeId": None, "codeName": "Code 1", "lockName": "Front Door", "usedCode": "Code 2", }, ) await setup_platform(opp, LOCK_DOMAIN, devices=[device]) # Act await opp.services.async_call( LOCK_DOMAIN, "lock", {"entity_id": "lock.lock_1"}, blocking=True ) # Assert state = opp.states.get("lock.lock_1") assert state is not None assert state.state == "locked" assert state.attributes["method"] == "Manual" assert state.attributes["lock_state"] == "locked" assert state.attributes["code_name"] == "Code 1" assert state.attributes["used_code"] == "Code 2" assert state.attributes["lock_name"] == "Front Door" assert "code_id" not in state.attributes async def test_unlock(opp, device_factory): """Test the lock unlocks successfully.""" # Arrange device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "locked"}) await setup_platform(opp, LOCK_DOMAIN, devices=[device]) # Act await opp.services.async_call( LOCK_DOMAIN, "unlock", {"entity_id": "lock.lock_1"}, blocking=True ) # Assert state = opp.states.get("lock.lock_1") assert state is not None assert state.state == "unlocked" async def test_update_from_signal(opp, device_factory): """Test the lock updates when receiving a signal.""" # Arrange device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "unlocked"}) await setup_platform(opp, LOCK_DOMAIN, devices=[device]) await device.lock(True) # Act async_dispatcher_send(opp, SIGNAL_SMARTTHINGS_UPDATE, [device.device_id]) # Assert await opp.async_block_till_done() state = opp.states.get("lock.lock_1") assert state is not None assert state.state == "locked" async def test_unload_config_entry(opp, device_factory): """Test the lock is removed when the config entry is unloaded.""" # Arrange device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "locked"}) config_entry = await setup_platform(opp, LOCK_DOMAIN, devices=[device]) config_entry.state = ConfigEntryState.LOADED # Act await opp.config_entries.async_forward_entry_unload(config_entry, "lock") # Assert assert opp.states.get("lock.lock_1").state == STATE_UNAVAILABLE

36.330357

88

0.706562

from pysmartthings import Attribute, Capability from pysmartthings.device import Status from openpeerpower.components.lock import DOMAIN as LOCK_DOMAIN from openpeerpower.components.smartthings.const import DOMAIN, SIGNAL_SMARTTHINGS_UPDATE from openpeerpower.config_entries import ConfigEntryState from openpeerpower.const import STATE_UNAVAILABLE from openpeerpower.helpers import device_registry as dr, entity_registry as er from openpeerpower.helpers.dispatcher import async_dispatcher_send from .conftest import setup_platform async def test_entity_and_device_attributes(opp, device_factory): device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "unlocked"}) entity_registry = er.async_get(opp) device_registry = dr.async_get(opp) await setup_platform(opp, LOCK_DOMAIN, devices=[device]) entry = entity_registry.async_get("lock.lock_1") assert entry assert entry.unique_id == device.device_id entry = device_registry.async_get_device({(DOMAIN, device.device_id)}) assert entry assert entry.name == device.label assert entry.model == device.device_type_name assert entry.manufacturer == "Unavailable" async def test_lock(opp, device_factory): device = device_factory("Lock_1", [Capability.lock]) device.status.attributes[Attribute.lock] = Status( "unlocked", None, { "method": "Manual", "codeId": None, "codeName": "Code 1", "lockName": "Front Door", "usedCode": "Code 2", }, ) await setup_platform(opp, LOCK_DOMAIN, devices=[device]) await opp.services.async_call( LOCK_DOMAIN, "lock", {"entity_id": "lock.lock_1"}, blocking=True ) state = opp.states.get("lock.lock_1") assert state is not None assert state.state == "locked" assert state.attributes["method"] == "Manual" assert state.attributes["lock_state"] == "locked" assert state.attributes["code_name"] == "Code 1" assert state.attributes["used_code"] == "Code 2" assert state.attributes["lock_name"] == "Front Door" assert "code_id" not in state.attributes async def test_unlock(opp, device_factory): device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "locked"}) await setup_platform(opp, LOCK_DOMAIN, devices=[device]) await opp.services.async_call( LOCK_DOMAIN, "unlock", {"entity_id": "lock.lock_1"}, blocking=True ) state = opp.states.get("lock.lock_1") assert state is not None assert state.state == "unlocked" async def test_update_from_signal(opp, device_factory): device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "unlocked"}) await setup_platform(opp, LOCK_DOMAIN, devices=[device]) await device.lock(True) async_dispatcher_send(opp, SIGNAL_SMARTTHINGS_UPDATE, [device.device_id]) await opp.async_block_till_done() state = opp.states.get("lock.lock_1") assert state is not None assert state.state == "locked" async def test_unload_config_entry(opp, device_factory): device = device_factory("Lock_1", [Capability.lock], {Attribute.lock: "locked"}) config_entry = await setup_platform(opp, LOCK_DOMAIN, devices=[device]) config_entry.state = ConfigEntryState.LOADED await opp.config_entries.async_forward_entry_unload(config_entry, "lock") assert opp.states.get("lock.lock_1").state == STATE_UNAVAILABLE

true

f71b1af96cc4062163b2c26f68ca4f8e5e16759c

21,877

py

Python

scripts/linters/general_purpose_linter.py

ParitoshKabra/oppia

a8945a5ff28fcbe4eaca1e22d99ed4d3e82f2dca

[ "Apache-2.0" ]

2

2022-02-24T14:06:42.000Z

2022-02-24T14:11:05.000Z

scripts/linters/general_purpose_linter.py

ParitoshKabra/oppia

a8945a5ff28fcbe4eaca1e22d99ed4d3e82f2dca

[ "Apache-2.0" ]

null

scripts/linters/general_purpose_linter.py

ParitoshKabra/oppia

a8945a5ff28fcbe4eaca1e22d99ed4d3e82f2dca

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2020 The Oppia Authors. 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. """Lint checks used by all the linters.""" from __future__ import annotations import os import re from . import js_ts_linter from . import warranted_angular_security_bypasses from .. import build from .. import common from .. import concurrent_task_utils EXCLUDED_PATHS = ( 'third_party/*', 'build/*', '.git/*', '*.pyc', 'CHANGELOG', 'integrations/*', 'integrations_dev/*', '*.svg', '*.gif', '*.png', '*.webp', '*.zip', '*.ico', '*.jpg', '*.min.js', 'backend_prod_files/*', 'assets/scripts/*', 'core/domain/proto/*.py', 'core/tests/data/*', 'core/tests/build_sources/*', '*.mp3', '*.mp4', 'node_modules/*', 'typings/*', 'local_compiled_js/*', 'webpack_bundles/*', 'core/tests/services_sources/*', 'core/tests/release_sources/tmp_unzip.zip', 'scripts/linters/test_files/*', 'proto_files/*', 'core/tests/release_sources/tmp_unzip.tar.gz', 'core/templates/combined-tests.spec.ts', 'core/templates/css/oppia-material.css', 'core/templates/google-analytics.initializer.ts', 'extensions/classifiers/proto/*', '*.rtl.css', '%s/*' % js_ts_linter.COMPILED_TYPESCRIPT_TMP_PATH) GENERATED_FILE_PATHS = ( 'core/templates/expressions/parser.js',) CONFIG_FILE_PATHS = ( 'core/tests/.browserstack.env.example', 'core/tests/protractor.conf.js', 'core/tests/karma.conf.ts', 'core/templates/mathjaxConfig.ts', 'assets/constants.ts', 'assets/rich_text_components_definitions.ts', 'webpack.config.ts', 'webpack.dev.config.ts', 'webpack.prod.config.ts') BAD_STRINGS_CONSTANTS = { '"DEV_MODE": false': { 'message': 'Please set the DEV_MODE variable in constants.ts ' 'to true before committing.', 'excluded_files': () }, '"EMULATOR_MODE": false': { 'message': 'Please set the EMULATOR_MODE variable in constants.ts ' 'to true before committing.', 'excluded_files': () } } BAD_PATTERNS = { '\t': { 'message': 'Please use spaces instead of tabs.', 'excluded_files': (), 'excluded_dirs': ( 'assets/i18n/', 'core/tests/build_sources/assets/')}, '\r': { 'message': 'Please make sure all files only have LF endings (no CRLF).', 'excluded_files': (), 'excluded_dirs': ()}, '<<<<<<<': { 'message': 'Please fully resolve existing merge conflicts.', 'excluded_files': (), 'excluded_dirs': ()}, '>>>>>>>': { 'message': 'Please fully resolve existing merge conflicts.', 'excluded_files': (), 'excluded_dirs': ()}, 'glyphicon': { 'message': 'Please use equivalent material-icons ' 'instead of glyphicons.', 'excluded_files': (), 'excluded_dirs': ()} } BAD_PATTERNS_REGEXP = [ { 'regexp': re.compile(r'TODO[^$]*[^$][^:]*[^A-Z]+[^\w]*$'), 'message': 'Please link TODO comments to an issue ' 'in the format TODO(#issuenum): XXX. ', 'excluded_files': (), 'excluded_dirs': () } ] MANDATORY_PATTERNS_REGEXP = [ { 'regexp': re.compile( r'Copyright \d{4} The Oppia Authors\. All Rights Reserved\.'), 'message': 'Please ensure this file should contain a proper ' 'copyright notice.', 'included_types': ('.py', '.js', '.sh', '.ts'), 'excluded_files': GENERATED_FILE_PATHS + CONFIG_FILE_PATHS + ( '__init__.py', ), 'excluded_dirs': EXCLUDED_PATHS }, { 'regexp': re.compile('from __future__ import annotations'), 'message': 'Please ensure this file should contain annotations ' 'future import.', 'included_types': ('.py'), 'excluded_files': GENERATED_FILE_PATHS + CONFIG_FILE_PATHS + ( '__init__.py',), 'excluded_dirs': EXCLUDED_PATHS } ] MANDATORY_PATTERNS_JS_REGEXP = [ { 'regexp': re.compile(r'^\s\*\s@fileoverview\s[a-zA-Z0-9_]+'), 'message': 'Please ensure this file should contain a file ' 'overview i.e. a short description of the file.', 'included_types': ('.js', '.ts'), 'excluded_files': GENERATED_FILE_PATHS + CONFIG_FILE_PATHS, 'excluded_dirs': EXCLUDED_PATHS } ] BAD_LINE_PATTERNS_HTML_REGEXP = [ { 'regexp': re.compile(r'text\/ng-template'), 'message': 'The directives must be directly referenced.', 'excluded_files': (), 'excluded_dirs': ( 'extensions/answer_summarizers/', 'extensions/classifiers/', 'extensions/objects/', 'extensions/value_generators/') }, { 'regexp': re.compile(r'[ \t]+$'), 'message': 'There should not be any trailing whitespaces.', 'excluded_files': (), 'excluded_dirs': () }, { 'regexp': re.compile(r'\$parent'), 'message': 'Please do not access parent properties ' + 'using $parent. Use the scope object ' + 'for this purpose.', 'excluded_files': (), 'excluded_dirs': () }, { 'regexp': re.compile(r'\s+style\s*=\s*'), 'message': 'Please do not use inline styling.', 'excluded_files': (), 'excluded_dirs': () } ] BAD_PATTERNS_PYTHON_REGEXP = [ { 'regexp': re.compile(r'__author__'), 'message': 'Please remove author tags from this file.', 'excluded_files': (), 'excluded_dirs': () }, { 'regexp': re.compile(r'ndb\.'), 'message': ( 'Please use datastore_services instead of ndb, for example:\n' '\n' 'datastore_services = models.Registry.import_datastore_services()\n' '\n' 'class SampleModel(datastore_services.Model):\n' ' ...\n'), 'excluded_files': (), 'excluded_dirs': ('core/platform',), }, { 'regexp': re.compile(r'\Wprint\('), 'message': 'Please do not use print statement.', 'excluded_files': ( 'core/tests/test_utils.py', 'core/tests/performance_framework/perf_domain.py', 'core/tests/test_utils_test.py'), 'excluded_dirs': ('scripts/',) }, { 'regexp': re.compile(r'# pylint:\s*disable=[A-Z][0-9]{4}'), 'message': 'Please remove pylint exclusion if it is unnecessary, or ' 'make it human readable with a sentence instead of an id. ' 'The id-to-message list can be seen ' 'here->http://pylint-messages.wikidot.com/all-codes', 'excluded_files': (), 'excluded_dirs': () }, ] BAD_PATTERNS_MAP = { '.html': BAD_LINE_PATTERNS_HTML_REGEXP, '.py': BAD_PATTERNS_PYTHON_REGEXP } def is_filepath_excluded_for_bad_patterns_check(pattern, filepath): """Checks if file is excluded from the bad patterns check. Args: pattern: str. The pattern to be checked against. filepath: str. Path of the file. Returns: bool. Whether to exclude the given file from this particular pattern check. """ return (any( filepath.startswith(bad_pattern) for bad_pattern in BAD_PATTERNS[pattern]['excluded_dirs']) or filepath in BAD_PATTERNS[pattern]['excluded_files']) def check_bad_pattern_in_file(filepath, file_content, pattern): """Detects whether the given pattern is present in the file. Args: filepath: str. Path of the file. file_content: str. Contents of the file. pattern: dict. (regexp(regex pattern) : Object containing details for the pattern to be checked. Pattern to match: message: str. Message to show if pattern matches. excluded_files: tuple(str). Files to be excluded from matching. excluded_dirs: tuple(str). Directories to be excluded from matching). Returns: tuple(bool, list(str)). A 2-tuple whose first element is a bool which set to True if there is bad pattern found else False, whose second element is a list of failed messages. """ error_messages = [] failed = False regexp = pattern['regexp'] if not (any( filepath.startswith(excluded_dir) for excluded_dir in pattern['excluded_dirs']) or any( filepath.endswith(excluded_file) for excluded_file in pattern['excluded_files'])): bad_pattern_count = 0 for line_num, line in enumerate(file_content, 1): if line.endswith('\n'): stripped_line = line[:-1] else: stripped_line = line if stripped_line.endswith('disable-bad-pattern-check'): continue if regexp.search(stripped_line): error_message = ('%s --> Line %s: %s' % ( filepath, line_num, pattern['message'])) error_messages.append(error_message) bad_pattern_count += 1 if bad_pattern_count: failed = True return failed, error_messages return failed, error_messages def check_file_type_specific_bad_pattern(filepath, content): """Check the file content based on the file's extension. Args: filepath: str. Path of the file. content: str. Contents of the file. Returns: bool. True if there is bad pattern else false. total_error_count: int. The number of errors. """ error_messages = [] failed = False _, extension = os.path.splitext(filepath) pattern = BAD_PATTERNS_MAP.get(extension) total_error_count = 0 if pattern: for regexp in pattern: failed, error_message = check_bad_pattern_in_file( filepath, content, regexp) error_messages.extend(error_message) if failed: total_error_count += 1 if total_error_count: failed = True return failed, total_error_count, error_messages class GeneralPurposeLinter: """Manages all the common linting functions. As an abstract base class, this is not intended to be used directly. """ def __init__(self, files_to_lint, file_cache): """Constructs a GeneralPurposeLinter object. Args: files_to_lint: list(str). A list of filepaths to lint. file_cache: object(FileCache). Provides thread-safe access to cached file content. """ # Set path for node. # The path for node is set explicitly, since otherwise the lint # tests fail on CircleCI due to the TypeScript files not being # compilable. os.environ['PATH'] = '%s/bin:' % common.NODE_PATH + os.environ['PATH'] self.files_to_lint = files_to_lint self.file_cache = file_cache @property def all_filepaths(self): """Returns all file paths.""" return self.files_to_lint def _check_for_mandatory_pattern_in_file( self, pattern_list, filepath, failed): """Checks for a given mandatory pattern in a file. Args: pattern_list: list(dict). The list of the mandatory patterns list to be checked for in the file. filepath: str. The path to the file to be linted. failed: bool. Status of failure of the check. Returns: bool. The failure status of the check. """ # This boolean list keeps track of the regex matches # found in the file. pattern_found_list = [] error_messages = [] try: file_content = self.file_cache.readlines(filepath) except Exception as e: raise Exception('%s %s' % (filepath, e)) from e for index, regexp_to_check in enumerate( pattern_list): if (any(filepath.endswith( allowed_type) for allowed_type in ( regexp_to_check['included_types'])) and ( not any( filepath.endswith( pattern) for pattern in ( regexp_to_check['excluded_files'] + regexp_to_check['excluded_dirs'])))): pattern_found_list.append(index) for line in file_content: if regexp_to_check['regexp'].search(line): pattern_found_list.pop() break if pattern_found_list: failed = True for pattern_found in pattern_found_list: error_message = ('%s --> %s' % ( filepath, pattern_list[pattern_found]['message'])) error_messages.append(error_message) return failed, error_messages def check_mandatory_patterns(self): """This function checks that all files contain the mandatory patterns. """ name = 'Mandatory pattern' error_messages = [] failed = False sets_of_patterns_to_match = [ MANDATORY_PATTERNS_REGEXP, MANDATORY_PATTERNS_JS_REGEXP] for filepath in self.all_filepaths: for pattern_list in sets_of_patterns_to_match: failed, mandatory_error_messages = ( self._check_for_mandatory_pattern_in_file( pattern_list, filepath, failed)) error_messages.extend(mandatory_error_messages) return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_bad_patterns(self): """This function is used for detecting bad patterns.""" name = 'Bad pattern' total_files_checked = 0 total_error_count = 0 error_messages = [] all_filepaths = [ filepath for filepath in self.all_filepaths if not ( filepath.endswith('general_purpose_linter.py') or ( filepath.endswith('general_purpose_linter_test.py')))] failed = False for filepath in all_filepaths: file_content = self.file_cache.readlines(filepath) total_files_checked += 1 for pattern, error in BAD_PATTERNS.items(): if is_filepath_excluded_for_bad_patterns_check( pattern, filepath): continue for line_num, line in enumerate(file_content): if pattern in line: failed = True error_message = ('%s --> Line %s: %s' % ( filepath, line_num + 1, error['message'])) error_messages.append(error_message) total_error_count += 1 for regexp in BAD_PATTERNS_REGEXP: bad_pattern_check_failed, bad_pattern_error_messages = ( check_bad_pattern_in_file( filepath, file_content, regexp)) if bad_pattern_check_failed: error_messages.extend(bad_pattern_error_messages) total_error_count += 1 ( file_type_specific_bad_pattern_failed, temp_count, bad_pattern_error_messages) = ( check_file_type_specific_bad_pattern( filepath, file_content)) failed = ( failed or file_type_specific_bad_pattern_failed or bad_pattern_check_failed) total_error_count += temp_count error_messages.extend(bad_pattern_error_messages) if filepath.endswith('constants.ts'): for pattern, constants in BAD_STRINGS_CONSTANTS.items(): for line in file_content: if pattern in line: failed = True error_message = ('%s --> %s' % ( filepath, constants['message'])) error_messages.append(error_message) total_error_count += 1 return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_newline_at_eof(self): """This function is used to detect newline at the end of file.""" name = 'Newline at EOF' error_messages = [] files_to_lint = self.all_filepaths failed = False for filepath in files_to_lint: file_content = self.file_cache.readlines(filepath) file_length = len(file_content) if ( file_length >= 1 and not re.search(r'[^\n]\n', file_content[-1])): error_message = ( '%s --> There should be a single newline at the ' 'end of file.' % filepath) error_messages.append(error_message) failed = True return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_disallowed_flags(self): """This function is used to disallow flags.""" name = 'Disallow flags' disallow_flag = ( 'eslint-disable-next-line oppia/no-bypass-security-phrase') error_messages = [] files_to_lint = self.all_filepaths failed = False excluded_files = ( warranted_angular_security_bypasses .EXCLUDED_BYPASS_SECURITY_TRUST_FILES) allowed_files = '' for filepath in files_to_lint: for excluded_file in excluded_files: if excluded_file in filepath: allowed_files = filepath if not filepath.endswith('.ts') or filepath == allowed_files: continue file_content = self.file_cache.read(filepath) if disallow_flag in file_content: error_message = ( '%s --> Please do not use "no-bypass-security-phrase" flag.' ' It is only expected to be used in files listed in' ' warranted_angular_security_bypasses.py' % filepath) error_messages.append(error_message) failed = True return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_extra_js_files(self): """Checks if the changes made include extra js files in core or extensions folder which are not specified in build.JS_FILEPATHS_NOT_TO_BUILD. Returns: TaskResult. A TaskResult object representing the result of the lint check. """ name = 'Extra JS files' error_messages = [] files_to_lint = self.all_filepaths failed = False for filepath in files_to_lint: if filepath.endswith( ('.js')) and filepath.startswith( ('core/templates', 'extensions')) and ( filepath not in build.JS_FILEPATHS_NOT_TO_BUILD ) and not filepath.endswith('protractor.js'): error_message = ( '%s --> Found extra .js file' % filepath) error_messages.append(error_message) failed = True if failed: err_msg = ( 'If you want the above files to be present as js files, ' 'add them to the list JS_FILEPATHS_NOT_TO_BUILD in ' 'build.py. Otherwise, rename them to .ts') error_messages.append(err_msg) return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def perform_all_lint_checks(self): """Perform all the lint checks and returns the messages returned by all the checks. Returns: list(TaskResult). A list of TaskResult objects representing the results of the lint checks. """ if not self.all_filepaths: return [ concurrent_task_utils.TaskResult( 'General purpose lint', False, [], ['There are no files to be checked.'])] task_results = [ self.check_mandatory_patterns(), self.check_bad_patterns(), self.check_newline_at_eof(), self.check_extra_js_files(), self.check_disallowed_flags()] return task_results def get_linters(files_to_lint, file_cache): """Creates GeneralPurposeLinter object and returns it. Args: files_to_lint: list(str). A list of filepaths to lint. file_cache: object(FileCache). Provides thread-safe access to cached file content. Returns: tuple(GeneralPurposeLinter, None). A 2-tuple of custom and third_party linter objects. """ custom_linter = GeneralPurposeLinter(files_to_lint, file_cache) return custom_linter, None

37.589347

80

0.581341

from __future__ import annotations import os import re from . import js_ts_linter from . import warranted_angular_security_bypasses from .. import build from .. import common from .. import concurrent_task_utils EXCLUDED_PATHS = ( 'third_party/*', 'build/*', '.git/*', '*.pyc', 'CHANGELOG', 'integrations/*', 'integrations_dev/*', '*.svg', '*.gif', '*.png', '*.webp', '*.zip', '*.ico', '*.jpg', '*.min.js', 'backend_prod_files/*', 'assets/scripts/*', 'core/domain/proto/*.py', 'core/tests/data/*', 'core/tests/build_sources/*', '*.mp3', '*.mp4', 'node_modules/*', 'typings/*', 'local_compiled_js/*', 'webpack_bundles/*', 'core/tests/services_sources/*', 'core/tests/release_sources/tmp_unzip.zip', 'scripts/linters/test_files/*', 'proto_files/*', 'core/tests/release_sources/tmp_unzip.tar.gz', 'core/templates/combined-tests.spec.ts', 'core/templates/css/oppia-material.css', 'core/templates/google-analytics.initializer.ts', 'extensions/classifiers/proto/*', '*.rtl.css', '%s/*' % js_ts_linter.COMPILED_TYPESCRIPT_TMP_PATH) GENERATED_FILE_PATHS = ( 'core/templates/expressions/parser.js',) CONFIG_FILE_PATHS = ( 'core/tests/.browserstack.env.example', 'core/tests/protractor.conf.js', 'core/tests/karma.conf.ts', 'core/templates/mathjaxConfig.ts', 'assets/constants.ts', 'assets/rich_text_components_definitions.ts', 'webpack.config.ts', 'webpack.dev.config.ts', 'webpack.prod.config.ts') BAD_STRINGS_CONSTANTS = { '"DEV_MODE": false': { 'message': 'Please set the DEV_MODE variable in constants.ts ' 'to true before committing.', 'excluded_files': () }, '"EMULATOR_MODE": false': { 'message': 'Please set the EMULATOR_MODE variable in constants.ts ' 'to true before committing.', 'excluded_files': () } } BAD_PATTERNS = { '\t': { 'message': 'Please use spaces instead of tabs.', 'excluded_files': (), 'excluded_dirs': ( 'assets/i18n/', 'core/tests/build_sources/assets/')}, '\r': { 'message': 'Please make sure all files only have LF endings (no CRLF).', 'excluded_files': (), 'excluded_dirs': ()}, '<<<<<<<': { 'message': 'Please fully resolve existing merge conflicts.', 'excluded_files': (), 'excluded_dirs': ()}, '>>>>>>>': { 'message': 'Please fully resolve existing merge conflicts.', 'excluded_files': (), 'excluded_dirs': ()}, 'glyphicon': { 'message': 'Please use equivalent material-icons ' 'instead of glyphicons.', 'excluded_files': (), 'excluded_dirs': ()} } BAD_PATTERNS_REGEXP = [ { 'regexp': re.compile(r'TODO[^$]*[^$][^:]*[^A-Z]+[^\w]*$'), 'message': 'Please link TODO comments to an issue ' 'in the format TODO(#issuenum): XXX. ', 'excluded_files': (), 'excluded_dirs': () } ] MANDATORY_PATTERNS_REGEXP = [ { 'regexp': re.compile( r'Copyright \d{4} The Oppia Authors\. All Rights Reserved\.'), 'message': 'Please ensure this file should contain a proper ' 'copyright notice.', 'included_types': ('.py', '.js', '.sh', '.ts'), 'excluded_files': GENERATED_FILE_PATHS + CONFIG_FILE_PATHS + ( '__init__.py', ), 'excluded_dirs': EXCLUDED_PATHS }, { 'regexp': re.compile('from __future__ import annotations'), 'message': 'Please ensure this file should contain annotations ' 'future import.', 'included_types': ('.py'), 'excluded_files': GENERATED_FILE_PATHS + CONFIG_FILE_PATHS + ( '__init__.py',), 'excluded_dirs': EXCLUDED_PATHS } ] MANDATORY_PATTERNS_JS_REGEXP = [ { 'regexp': re.compile(r'^\s\*\s@fileoverview\s[a-zA-Z0-9_]+'), 'message': 'Please ensure this file should contain a file ' 'overview i.e. a short description of the file.', 'included_types': ('.js', '.ts'), 'excluded_files': GENERATED_FILE_PATHS + CONFIG_FILE_PATHS, 'excluded_dirs': EXCLUDED_PATHS } ] BAD_LINE_PATTERNS_HTML_REGEXP = [ { 'regexp': re.compile(r'text\/ng-template'), 'message': 'The directives must be directly referenced.', 'excluded_files': (), 'excluded_dirs': ( 'extensions/answer_summarizers/', 'extensions/classifiers/', 'extensions/objects/', 'extensions/value_generators/') }, { 'regexp': re.compile(r'[ \t]+$'), 'message': 'There should not be any trailing whitespaces.', 'excluded_files': (), 'excluded_dirs': () }, { 'regexp': re.compile(r'\$parent'), 'message': 'Please do not access parent properties ' + 'using $parent. Use the scope object ' + 'for this purpose.', 'excluded_files': (), 'excluded_dirs': () }, { 'regexp': re.compile(r'\s+style\s*=\s*'), 'message': 'Please do not use inline styling.', 'excluded_files': (), 'excluded_dirs': () } ] BAD_PATTERNS_PYTHON_REGEXP = [ { 'regexp': re.compile(r'__author__'), 'message': 'Please remove author tags from this file.', 'excluded_files': (), 'excluded_dirs': () }, { 'regexp': re.compile(r'ndb\.'), 'message': ( 'Please use datastore_services instead of ndb, for example:\n' '\n' 'datastore_services = models.Registry.import_datastore_services()\n' '\n' 'class SampleModel(datastore_services.Model):\n' ' ...\n'), 'excluded_files': (), 'excluded_dirs': ('core/platform',), }, { 'regexp': re.compile(r'\Wprint\('), 'message': 'Please do not use print statement.', 'excluded_files': ( 'core/tests/test_utils.py', 'core/tests/performance_framework/perf_domain.py', 'core/tests/test_utils_test.py'), 'excluded_dirs': ('scripts/',) }, { 'regexp': re.compile(r'# pylint:\s*disable=[A-Z][0-9]{4}'), 'message': 'Please remove pylint exclusion if it is unnecessary, or ' 'make it human readable with a sentence instead of an id. ' 'The id-to-message list can be seen ' 'here->http://pylint-messages.wikidot.com/all-codes', 'excluded_files': (), 'excluded_dirs': () }, ] BAD_PATTERNS_MAP = { '.html': BAD_LINE_PATTERNS_HTML_REGEXP, '.py': BAD_PATTERNS_PYTHON_REGEXP } def is_filepath_excluded_for_bad_patterns_check(pattern, filepath): return (any( filepath.startswith(bad_pattern) for bad_pattern in BAD_PATTERNS[pattern]['excluded_dirs']) or filepath in BAD_PATTERNS[pattern]['excluded_files']) def check_bad_pattern_in_file(filepath, file_content, pattern): error_messages = [] failed = False regexp = pattern['regexp'] if not (any( filepath.startswith(excluded_dir) for excluded_dir in pattern['excluded_dirs']) or any( filepath.endswith(excluded_file) for excluded_file in pattern['excluded_files'])): bad_pattern_count = 0 for line_num, line in enumerate(file_content, 1): if line.endswith('\n'): stripped_line = line[:-1] else: stripped_line = line if stripped_line.endswith('disable-bad-pattern-check'): continue if regexp.search(stripped_line): error_message = ('%s --> Line %s: %s' % ( filepath, line_num, pattern['message'])) error_messages.append(error_message) bad_pattern_count += 1 if bad_pattern_count: failed = True return failed, error_messages return failed, error_messages def check_file_type_specific_bad_pattern(filepath, content): error_messages = [] failed = False _, extension = os.path.splitext(filepath) pattern = BAD_PATTERNS_MAP.get(extension) total_error_count = 0 if pattern: for regexp in pattern: failed, error_message = check_bad_pattern_in_file( filepath, content, regexp) error_messages.extend(error_message) if failed: total_error_count += 1 if total_error_count: failed = True return failed, total_error_count, error_messages class GeneralPurposeLinter: def __init__(self, files_to_lint, file_cache): os.environ['PATH'] = '%s/bin:' % common.NODE_PATH + os.environ['PATH'] self.files_to_lint = files_to_lint self.file_cache = file_cache @property def all_filepaths(self): return self.files_to_lint def _check_for_mandatory_pattern_in_file( self, pattern_list, filepath, failed): pattern_found_list = [] error_messages = [] try: file_content = self.file_cache.readlines(filepath) except Exception as e: raise Exception('%s %s' % (filepath, e)) from e for index, regexp_to_check in enumerate( pattern_list): if (any(filepath.endswith( allowed_type) for allowed_type in ( regexp_to_check['included_types'])) and ( not any( filepath.endswith( pattern) for pattern in ( regexp_to_check['excluded_files'] + regexp_to_check['excluded_dirs'])))): pattern_found_list.append(index) for line in file_content: if regexp_to_check['regexp'].search(line): pattern_found_list.pop() break if pattern_found_list: failed = True for pattern_found in pattern_found_list: error_message = ('%s --> %s' % ( filepath, pattern_list[pattern_found]['message'])) error_messages.append(error_message) return failed, error_messages def check_mandatory_patterns(self): name = 'Mandatory pattern' error_messages = [] failed = False sets_of_patterns_to_match = [ MANDATORY_PATTERNS_REGEXP, MANDATORY_PATTERNS_JS_REGEXP] for filepath in self.all_filepaths: for pattern_list in sets_of_patterns_to_match: failed, mandatory_error_messages = ( self._check_for_mandatory_pattern_in_file( pattern_list, filepath, failed)) error_messages.extend(mandatory_error_messages) return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_bad_patterns(self): name = 'Bad pattern' total_files_checked = 0 total_error_count = 0 error_messages = [] all_filepaths = [ filepath for filepath in self.all_filepaths if not ( filepath.endswith('general_purpose_linter.py') or ( filepath.endswith('general_purpose_linter_test.py')))] failed = False for filepath in all_filepaths: file_content = self.file_cache.readlines(filepath) total_files_checked += 1 for pattern, error in BAD_PATTERNS.items(): if is_filepath_excluded_for_bad_patterns_check( pattern, filepath): continue for line_num, line in enumerate(file_content): if pattern in line: failed = True error_message = ('%s --> Line %s: %s' % ( filepath, line_num + 1, error['message'])) error_messages.append(error_message) total_error_count += 1 for regexp in BAD_PATTERNS_REGEXP: bad_pattern_check_failed, bad_pattern_error_messages = ( check_bad_pattern_in_file( filepath, file_content, regexp)) if bad_pattern_check_failed: error_messages.extend(bad_pattern_error_messages) total_error_count += 1 ( file_type_specific_bad_pattern_failed, temp_count, bad_pattern_error_messages) = ( check_file_type_specific_bad_pattern( filepath, file_content)) failed = ( failed or file_type_specific_bad_pattern_failed or bad_pattern_check_failed) total_error_count += temp_count error_messages.extend(bad_pattern_error_messages) if filepath.endswith('constants.ts'): for pattern, constants in BAD_STRINGS_CONSTANTS.items(): for line in file_content: if pattern in line: failed = True error_message = ('%s --> %s' % ( filepath, constants['message'])) error_messages.append(error_message) total_error_count += 1 return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_newline_at_eof(self): name = 'Newline at EOF' error_messages = [] files_to_lint = self.all_filepaths failed = False for filepath in files_to_lint: file_content = self.file_cache.readlines(filepath) file_length = len(file_content) if ( file_length >= 1 and not re.search(r'[^\n]\n', file_content[-1])): error_message = ( '%s --> There should be a single newline at the ' 'end of file.' % filepath) error_messages.append(error_message) failed = True return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_disallowed_flags(self): name = 'Disallow flags' disallow_flag = ( 'eslint-disable-next-line oppia/no-bypass-security-phrase') error_messages = [] files_to_lint = self.all_filepaths failed = False excluded_files = ( warranted_angular_security_bypasses .EXCLUDED_BYPASS_SECURITY_TRUST_FILES) allowed_files = '' for filepath in files_to_lint: for excluded_file in excluded_files: if excluded_file in filepath: allowed_files = filepath if not filepath.endswith('.ts') or filepath == allowed_files: continue file_content = self.file_cache.read(filepath) if disallow_flag in file_content: error_message = ( '%s --> Please do not use "no-bypass-security-phrase" flag.' ' It is only expected to be used in files listed in' ' warranted_angular_security_bypasses.py' % filepath) error_messages.append(error_message) failed = True return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def check_extra_js_files(self): name = 'Extra JS files' error_messages = [] files_to_lint = self.all_filepaths failed = False for filepath in files_to_lint: if filepath.endswith( ('.js')) and filepath.startswith( ('core/templates', 'extensions')) and ( filepath not in build.JS_FILEPATHS_NOT_TO_BUILD ) and not filepath.endswith('protractor.js'): error_message = ( '%s --> Found extra .js file' % filepath) error_messages.append(error_message) failed = True if failed: err_msg = ( 'If you want the above files to be present as js files, ' 'add them to the list JS_FILEPATHS_NOT_TO_BUILD in ' 'build.py. Otherwise, rename them to .ts') error_messages.append(err_msg) return concurrent_task_utils.TaskResult( name, failed, error_messages, error_messages) def perform_all_lint_checks(self): if not self.all_filepaths: return [ concurrent_task_utils.TaskResult( 'General purpose lint', False, [], ['There are no files to be checked.'])] task_results = [ self.check_mandatory_patterns(), self.check_bad_patterns(), self.check_newline_at_eof(), self.check_extra_js_files(), self.check_disallowed_flags()] return task_results def get_linters(files_to_lint, file_cache): custom_linter = GeneralPurposeLinter(files_to_lint, file_cache) return custom_linter, None

true

f71b1b5dacb5a5c7e4853d46d9e70c5797445611

1,049

py

Python

prxgt/domain/attribute.py

praxigento/teq_test_db_schema_attrs

20ec030dc095c644d22631491e066697203d983d

[ "MIT" ]

null

prxgt/domain/attribute.py

praxigento/teq_test_db_schema_attrs

20ec030dc095c644d22631491e066697203d983d

[ "MIT" ]

null

prxgt/domain/attribute.py

praxigento/teq_test_db_schema_attrs

20ec030dc095c644d22631491e066697203d983d

[ "MIT" ]

null

__author__ = 'Alex Gusev <alex@flancer64.com>' import prxgt.const as const from prxgt.domain.meta.attribute import Attribute as AttributeBase class Attribute(AttributeBase): """ Attribute model contains data. """ def __init__(self, name=None, type_=None, value=None): super(Attribute, self).__init__(name, type_) self._value = value return @property def value(self): return self._value @value.setter def value(self, val): self._value = val @property def meta(self): """ META Attribute (name and type only) :return: """ return AttributeBase(self._name, self._type) def __repr__(self): result = super(Attribute, self).__repr__() if (self.value is not None) and (self.type == const.ATTR_TYPE_TXT): # [name@type='value'] result += "=" + repr(self.value[:4] + "...") else: # [name@text='valu...'] result += "=" + repr(self.value) return result

26.225

75

0.578646

__author__ = 'Alex Gusev <alex@flancer64.com>' import prxgt.const as const from prxgt.domain.meta.attribute import Attribute as AttributeBase class Attribute(AttributeBase): def __init__(self, name=None, type_=None, value=None): super(Attribute, self).__init__(name, type_) self._value = value return @property def value(self): return self._value @value.setter def value(self, val): self._value = val @property def meta(self): return AttributeBase(self._name, self._type) def __repr__(self): result = super(Attribute, self).__repr__() if (self.value is not None) and (self.type == const.ATTR_TYPE_TXT): result += "=" + repr(self.value[:4] + "...") else: result += "=" + repr(self.value) return result

true

f71b1c721a28949ee40ddf327761ea6fcd2fe45b

11,435

py

Python

config/settings/common.py

devermaslinfy/rejot

8c07a42a73be8422f16874684be3b46ab70b5c18

[ "BSD-3-Clause" ]

1

2020-07-23T16:21:44.000Z

config/settings/common.py

devermaslinfy/rejot

8c07a42a73be8422f16874684be3b46ab70b5c18

[ "BSD-3-Clause" ]

7

2020-02-12T01:21:01.000Z

2022-03-11T23:25:57.000Z

config/settings/common.py

devermaslinfy/rejot

8c07a42a73be8422f16874684be3b46ab70b5c18

[ "BSD-3-Clause" ]

1

2019-07-27T10:00:01.000Z

# -*- coding: utf-8 -*- """ Django settings for roojet project. For more information on this file, see https://docs.djangoproject.com/en/dev/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/dev/ref/settings/ """ from __future__ import absolute_import, unicode_literals import environ from decimal import Decimal ROOT_DIR = environ.Path(__file__) - 3 # (/a/b/myfile.py - 3 = /) APPS_DIR = ROOT_DIR.path('roojet') env = environ.Env() environ.Env.read_env(ROOT_DIR()+'/.env') # APP CONFIGURATION # ------------------------------------------------------------------------------ DJANGO_APPS = ( # Default Django apps: 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.messages', 'django.contrib.staticfiles', # Useful template tags: # 'django.contrib.humanize', # Admin 'django.contrib.admin', ) THIRD_PARTY_APPS = ( 'crispy_forms', # Form layouts 'allauth', # registration 'allauth.account', # registration 'allauth.socialaccount', # registration 'loginas', # loginas 'bootstrap3', 'djrill', 'plans', 'ordered_model', 'payments', 'django_cron', ) # Apps specific for this project go here. LOCAL_APPS = ( 'roojet.users', # custom users app # Your stuff: custom apps go here 'roojet.core', 'roojet.services', 'roojet.payment_roojet', 'roojet.mailerlite', ) # See: https://docs.djangoproject.com/en/dev/ref/settings/#installed-apps INSTALLED_APPS = DJANGO_APPS + THIRD_PARTY_APPS + LOCAL_APPS # MIDDLEWARE CONFIGURATION # ------------------------------------------------------------------------------ MIDDLEWARE_CLASSES = ( # Make sure djangosecure.middleware.SecurityMiddleware is listed first 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) # MIGRATIONS CONFIGURATION # ------------------------------------------------------------------------------ MIGRATION_MODULES = { 'sites': 'roojet.contrib.sites.migrations' } # DEBUG # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#debug DEBUG = env.bool("DJANGO_DEBUG", False) # FIXTURE CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#std:setting-FIXTURE_DIRS FIXTURE_DIRS = ( str(APPS_DIR.path('fixtures')), ) # EMAIL CONFIGURATION # ------------------------------------------------------------------------------ EMAIL_BACKEND = env('DJANGO_EMAIL_BACKEND', default='django.core.mail.backends.smtp.EmailBackend') # MANAGER CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#admins ADMINS = ( ("Nija",'nijap@techversantinfotech.com'), ('Scott','scott@roojet.com'), ) SERVER_EMAIL = 'dev@swapps.co' # See: https://docs.djangoproject.com/en/dev/ref/settings/#managers MANAGERS = ADMINS # DATABASE CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#databases # DATABASES = { # ## Raises ImproperlyConfigured exception if DATABASE_URL not in os.environ # 'default': env.db("DATABASE_URL", default="postgres:///roojet"), # } # DATABASES['default']['ATOMIC_REQUESTS'] = True #DO not harcode. Use environment variables or deployment will fail DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'postgres', 'USER': 'postgres', 'PASSWORD': '123456', 'HOST': '127.0.0.1', 'PORT': '5432', } } # GENERAL CONFIGURATION # ------------------------------------------------------------------------------ # Local time zone for this installation. Choices can be found here: # http://en.wikipedia.org/wiki/List_of_tz_zones_by_name # although not all choices may be available on all operating systems. # In a Windows environment this must be set to your system time zone. TIME_ZONE = 'UTC' # See: https://docs.djangoproject.com/en/dev/ref/settings/#language-code LANGUAGE_CODE = 'en-us' # See: https://docs.djangoproject.com/en/dev/ref/settings/#site-id SITE_ID = 1 # See: https://docs.djangoproject.com/en/dev/ref/settings/#use-i18n USE_I18N = True # See: https://docs.djangoproject.com/en/dev/ref/settings/#use-l10n USE_L10N = True # See: https://docs.djangoproject.com/en/dev/ref/settings/#use-tz USE_TZ = True # TEMPLATE CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#templates TEMPLATES = [ { # See: https://docs.djangoproject.com/en/dev/ref/settings/#std:setting-TEMPLATES-BACKEND 'BACKEND': 'django.template.backends.django.DjangoTemplates', # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-dirs 'DIRS': [ str(APPS_DIR.path('templates')), ], 'OPTIONS': { # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-debug 'debug': DEBUG, # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-loaders # https://docs.djangoproject.com/en/dev/ref/templates/api/#loader-types 'loaders': [ 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', ], # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-context-processors 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.contrib.messages.context_processors.messages', # Your stuff: custom template context processors go here ], }, }, ] # See: http://django-crispy-forms.readthedocs.org/en/latest/install.html#template-packs CRISPY_TEMPLATE_PACK = 'bootstrap3' # STATIC FILE CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#static-root STATIC_ROOT = str(ROOT_DIR('staticfiles')) # See: https://docs.djangoproject.com/en/dev/ref/settings/#static-url STATIC_URL = '/static/' # See: https://docs.djangoproject.com/en/dev/ref/contrib/staticfiles/#std:setting-STATICFILES_DIRS STATICFILES_DIRS = ( str(APPS_DIR.path('static')), ) # See: https://docs.djangoproject.com/en/dev/ref/contrib/staticfiles/#staticfiles-finders STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', ) # MEDIA CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#media-root MEDIA_ROOT = str(APPS_DIR('media')) # See: https://docs.djangoproject.com/en/dev/ref/settings/#media-url MEDIA_URL = '/media/' # URL Configuration # ------------------------------------------------------------------------------ ROOT_URLCONF = 'config.urls' # See: https://docs.djangoproject.com/en/dev/ref/settings/#wsgi-application WSGI_APPLICATION = 'config.wsgi.application' # AUTHENTICATION CONFIGURATION # ------------------------------------------------------------------------------ AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend', ) # Some really nice defaults ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = 'none' # Custom user app defaults # Select the correct user model AUTH_USER_MODEL = 'users.User' LOGIN_REDIRECT_URL = 'core:dashboard' LOGIN_URL = 'account_login' # SLUGLIFIER AUTOSLUG_SLUGIFY_FUNCTION = 'slugify.slugify' # LOGGING CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#logging # A sample logging configuration. The only tangible logging # performed by this configuration is to send an email to # the site admins on every HTTP 500 error when DEBUG=False. # See http://docs.djangoproject.com/en/dev/topics/logging for # more details on how to customize your logging configuration. LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'filters': { 'require_debug_false': { '()': 'django.utils.log.RequireDebugFalse' } }, 'handlers': { 'mail_admins': { 'level': 'ERROR', 'filters': ['require_debug_false'], 'class': 'django.utils.log.AdminEmailHandler' }, 'console': { 'level': 'DEBUG', 'class': 'logging.StreamHandler', }, }, 'loggers': { 'django.request': { 'handlers': ['mail_admins'], 'level': 'ERROR', 'propagate': True, }, } } # django-plans PLANS_CURRENCY = 'USD' PLANS_INVOICE_ISSUER = { "issuer_name": "Joe Doe Company", "issuer_street": "Django street, 34", "issuer_zipcode": "123-3444", "issuer_city": "Djangoko", "issuer_country": "US", # Must be a country code with 2 characters "issuer_tax_number": "1222233334444555", } PLANS_TAX = Decimal('0.0') PLANS_TAX_COUNTRY = 'US' SEND_PLANS_EMAILS = False # Payment settings PAYMENT_HOST = env.str("PAYMENT_HOST", default='') PAYMENT_USES_SSL = env.bool("PAYMENT_USES_SSL", default='') PAYMENT_MODEL = 'payment_roojet.Payment' # use sandbox PAYMENT_VARIANTS = { 'default': ('payments.stripe.StripeProvider', { 'secret_key': env('STRIPE_API_KEY', default=''), 'public_key': env('STRIPE_PUBLISHABLE_KEY', default=''), 'name': 'roojet', }) } # Location of root django.contrib.admin URL, use {% url 'admin:index' %} ADMIN_URL = r'^admin/' SHOPIFY_API_KEY = env('SHOPIFY_API_KEY', default='') SHOPIFY_SECRET = env('SHOPIFY_SECRET', default='') SHOPIFY_URL = 'myshopify.com' SHOPIFY_AUTHORIZE_SUFIX = '/admin/oauth/authorize' SHOPIFY_SCOPES = ['read_products', 'read_orders', 'write_products'] MOE_URL = env('MOE_URL', default='') CODESHIP_API_KEY = env("CODESHIP_API_KEY", default='') MANDRILL_API_URL = env("MANDRILL_API_URL", default='') MANDRILL_API_KEY = env('MANDRILL_API_KEY', default='') DEFAULT_FROM_EMAIL = env('DJANGO_DEFAULT_FROM_EMAIL', default='') ENOUGH_DATA = env.int('ENOUGH_DATA', default=5) ACCOUNT_ADAPTER = 'roojet.users.adapter.AccountAdapter' CRON_CLASSES = [ "roojet.core.cron.MyCronJob", ]

33.338192

98

0.61784

from __future__ import absolute_import, unicode_literals import environ from decimal import Decimal ROOT_DIR = environ.Path(__file__) - 3 APPS_DIR = ROOT_DIR.path('roojet') env = environ.Env() environ.Env.read_env(ROOT_DIR()+'/.env') DJANGO_APPS = ( 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.admin', ) THIRD_PARTY_APPS = ( 'crispy_forms', 'allauth', 'allauth.account', 'allauth.socialaccount', 'loginas', 'bootstrap3', 'djrill', 'plans', 'ordered_model', 'payments', 'django_cron', ) LOCAL_APPS = ( 'roojet.users', 'roojet.core', 'roojet.services', 'roojet.payment_roojet', 'roojet.mailerlite', ) = DJANGO_APPS + THIRD_PARTY_APPS + LOCAL_APPS MIDDLEWARE_CLASSES = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) MIGRATION_MODULES = { 'sites': 'roojet.contrib.sites.migrations' } = env.bool("DJANGO_DEBUG", False) (APPS_DIR.path('fixtures')), ) EMAIL_BACKEND = env('DJANGO_EMAIL_BACKEND', default='django.core.mail.backends.smtp.EmailBackend') = ( ("Nija",'nijap@techversantinfotech.com'), ('Scott','scott@roojet.com'), ) SERVER_EMAIL = 'dev@swapps.co' = ADMINS 'PASSWORD': '123456', 'HOST': '127.0.0.1', 'PORT': '5432', } } TIME_ZONE = 'UTC' = 'en-us' = 1 = True = True = True = [ { mplate.backends.django.DjangoTemplates', ': [ str(APPS_DIR.path('templates')), ], 'OPTIONS': { ebug': DEBUG, ders': [ 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', ], sors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.contrib.messages.context_processors.messages', ], }, }, ] E_PACK = 'bootstrap3' = str(ROOT_DIR('staticfiles')) = '/static/' (APPS_DIR.path('static')), ) = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', ) = str(APPS_DIR('media')) = '/media/' ROOT_URLCONF = 'config.urls' = 'config.wsgi.application' AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend', ) ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = 'none' AUTH_USER_MODEL = 'users.User' LOGIN_REDIRECT_URL = 'core:dashboard' LOGIN_URL = 'account_login' AUTOSLUG_SLUGIFY_FUNCTION = 'slugify.slugify' GGING = { 'version': 1, 'disable_existing_loggers': False, 'filters': { 'require_debug_false': { '()': 'django.utils.log.RequireDebugFalse' } }, 'handlers': { 'mail_admins': { 'level': 'ERROR', 'filters': ['require_debug_false'], 'class': 'django.utils.log.AdminEmailHandler' }, 'console': { 'level': 'DEBUG', 'class': 'logging.StreamHandler', }, }, 'loggers': { 'django.request': { 'handlers': ['mail_admins'], 'level': 'ERROR', 'propagate': True, }, } } PLANS_CURRENCY = 'USD' PLANS_INVOICE_ISSUER = { "issuer_name": "Joe Doe Company", "issuer_street": "Django street, 34", "issuer_zipcode": "123-3444", "issuer_city": "Djangoko", "issuer_country": "US", "issuer_tax_number": "1222233334444555", } PLANS_TAX = Decimal('0.0') PLANS_TAX_COUNTRY = 'US' SEND_PLANS_EMAILS = False PAYMENT_HOST = env.str("PAYMENT_HOST", default='') PAYMENT_USES_SSL = env.bool("PAYMENT_USES_SSL", default='') PAYMENT_MODEL = 'payment_roojet.Payment' PAYMENT_VARIANTS = { 'default': ('payments.stripe.StripeProvider', { 'secret_key': env('STRIPE_API_KEY', default=''), 'public_key': env('STRIPE_PUBLISHABLE_KEY', default=''), 'name': 'roojet', }) } ADMIN_URL = r'^admin/' SHOPIFY_API_KEY = env('SHOPIFY_API_KEY', default='') SHOPIFY_SECRET = env('SHOPIFY_SECRET', default='') SHOPIFY_URL = 'myshopify.com' SHOPIFY_AUTHORIZE_SUFIX = '/admin/oauth/authorize' SHOPIFY_SCOPES = ['read_products', 'read_orders', 'write_products'] MOE_URL = env('MOE_URL', default='') CODESHIP_API_KEY = env("CODESHIP_API_KEY", default='') MANDRILL_API_URL = env("MANDRILL_API_URL", default='') MANDRILL_API_KEY = env('MANDRILL_API_KEY', default='') DEFAULT_FROM_EMAIL = env('DJANGO_DEFAULT_FROM_EMAIL', default='') ENOUGH_DATA = env.int('ENOUGH_DATA', default=5) ACCOUNT_ADAPTER = 'roojet.users.adapter.AccountAdapter' CRON_CLASSES = [ "roojet.core.cron.MyCronJob", ]

true

f71b1dc503a1e22e10ad5b8a916812053338361e

3,578

py

Python

appStore/tests.py

Rubensrvsc/Store

ab3a3aea6518b76d9d5de9c1b9e90236def8954d

[ "MIT" ]

null

appStore/tests.py

Rubensrvsc/Store

ab3a3aea6518b76d9d5de9c1b9e90236def8954d

[ "MIT" ]

null

appStore/tests.py

Rubensrvsc/Store

ab3a3aea6518b76d9d5de9c1b9e90236def8954d

[ "MIT" ]

null

from django.test import TestCase from rest_framework.test import APIRequestFactory, APIClient from .models import * from rest_framework import status # Create your tests here. class ProductCategoryTest(TestCase): @classmethod def setUpTestData(cls): ProductCategory.objects.create(name_product_category="Infantil") ProductCategory.objects.create(name_product_category="Adulto") ProductCategory.objects.create(name_product_category="Idoso") ProductType.objects.create(name_product_type="camisa") ProductType.objects.create(name_product_type="calça") Size.objects.create(size="GG") Size.objects.create(size="G") size = Size.objects.get(id=1) category_product = ProductCategory.objects.get(id=1) type_product = ProductType.objects.get(id=1) size_two = Size.objects.get(id=2) category_product_two = ProductCategory.objects.get(id=2) type_product_two = ProductType.objects.get(id=2) Product.objects.create( name_product="camisa 1", price=100, color="red", product_category = category_product, product_size = size, product_type = type_product ) Product.objects.create( name_product="camisa 2", price=150, color="green", product_category = category_product_two, product_size = size_two, product_type = type_product_two ) Product.objects.create( name_product="camisa 3", price=190, color="yellow", product_category = category_product_two, product_size = size_two, product_type = type_product_two ) Product.objects.create( name_product="camisa 4", price=220, color="magento", product_category = category_product, product_size = size, product_type = type_product ) def test_create_product_category(self): factory = APIClient() product_category = {"name_product_category": "infantil"} request = factory.post('/createcategory',product_category) self.assertEquals(request.status_code,status.HTTP_201_CREATED) def test_list_product_category(self): factory = APIClient() products_categories = ProductCategory.objects.all().count() request = factory.get("/productcategories") self.assertEquals(products_categories,3) def test_create_product(self): factory = APIClient() size = Size.objects.get(id=1) category_product = ProductCategory.objects.get(id=1) type_product = ProductType.objects.get(id=1) product = {'name_product':"camisa 1", 'price':100, 'color':"red", 'product_category' : category_product.id, 'size': size.id, 'product_type': type_product.id } response = factory.post('/create_product',product) self.assertEquals(response.status_code,status.HTTP_301_MOVED_PERMANENTLY) def test_get_product(self): factory = APIClient() response = factory.get('/products') self.assertEquals(response.status_code,status.HTTP_200_OK) def test_search_price_products(self): factory = APIClient() response = factory.get('/searchpriceproduct/80/170') self.assertEquals(response.status_code,status.HTTP_200_OK)

31.663717

81

0.630799

from django.test import TestCase from rest_framework.test import APIRequestFactory, APIClient from .models import * from rest_framework import status class ProductCategoryTest(TestCase): @classmethod def setUpTestData(cls): ProductCategory.objects.create(name_product_category="Infantil") ProductCategory.objects.create(name_product_category="Adulto") ProductCategory.objects.create(name_product_category="Idoso") ProductType.objects.create(name_product_type="camisa") ProductType.objects.create(name_product_type="calça") Size.objects.create(size="GG") Size.objects.create(size="G") size = Size.objects.get(id=1) category_product = ProductCategory.objects.get(id=1) type_product = ProductType.objects.get(id=1) size_two = Size.objects.get(id=2) category_product_two = ProductCategory.objects.get(id=2) type_product_two = ProductType.objects.get(id=2) Product.objects.create( name_product="camisa 1", price=100, color="red", product_category = category_product, product_size = size, product_type = type_product ) Product.objects.create( name_product="camisa 2", price=150, color="green", product_category = category_product_two, product_size = size_two, product_type = type_product_two ) Product.objects.create( name_product="camisa 3", price=190, color="yellow", product_category = category_product_two, product_size = size_two, product_type = type_product_two ) Product.objects.create( name_product="camisa 4", price=220, color="magento", product_category = category_product, product_size = size, product_type = type_product ) def test_create_product_category(self): factory = APIClient() product_category = {"name_product_category": "infantil"} request = factory.post('/createcategory',product_category) self.assertEquals(request.status_code,status.HTTP_201_CREATED) def test_list_product_category(self): factory = APIClient() products_categories = ProductCategory.objects.all().count() request = factory.get("/productcategories") self.assertEquals(products_categories,3) def test_create_product(self): factory = APIClient() size = Size.objects.get(id=1) category_product = ProductCategory.objects.get(id=1) type_product = ProductType.objects.get(id=1) product = {'name_product':"camisa 1", 'price':100, 'color':"red", 'product_category' : category_product.id, 'size': size.id, 'product_type': type_product.id } response = factory.post('/create_product',product) self.assertEquals(response.status_code,status.HTTP_301_MOVED_PERMANENTLY) def test_get_product(self): factory = APIClient() response = factory.get('/products') self.assertEquals(response.status_code,status.HTTP_200_OK) def test_search_price_products(self): factory = APIClient() response = factory.get('/searchpriceproduct/80/170') self.assertEquals(response.status_code,status.HTTP_200_OK)

true

f71b1e2fd58d30c28062d37e9f1ae54392548686

6,411

py

Python

src/datadog_api_client/v1/model/synthetics_trigger_ci_tests_response.py

DataDog/datadog-api-client-python

de2fc57dbde9acf4b8c8eef94ac29911227a62a2

[ "Apache-2.0" ]

32

2021-01-07T15:09:56.000Z

2022-01-30T05:49:23.000Z

src/datadog_api_client/v1/model/synthetics_trigger_ci_tests_response.py

DataDog/datadog-api-client-python

de2fc57dbde9acf4b8c8eef94ac29911227a62a2

[ "Apache-2.0" ]

228

2020-09-03T14:03:54.000Z

2022-03-31T20:16:12.000Z

src/datadog_api_client/v1/model/synthetics_trigger_ci_tests_response.py

DataDog/datadog-api-client-python

de2fc57dbde9acf4b8c8eef94ac29911227a62a2

[ "Apache-2.0" ]

12

2020-09-15T21:36:03.000Z

2022-03-31T17:13:17.000Z

# Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License. # This product includes software developed at Datadog (https://www.datadoghq.com/). # Copyright 2019-Present Datadog, Inc. from datadog_api_client.v1.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, ) def lazy_import(): from datadog_api_client.v1.model.synthetics_trigger_ci_test_location import SyntheticsTriggerCITestLocation from datadog_api_client.v1.model.synthetics_trigger_ci_test_run_result import SyntheticsTriggerCITestRunResult globals()["SyntheticsTriggerCITestLocation"] = SyntheticsTriggerCITestLocation globals()["SyntheticsTriggerCITestRunResult"] = SyntheticsTriggerCITestRunResult class SyntheticsTriggerCITestsResponse(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = {} validations = {} additional_properties_type = None _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { "batch_id": (str,), # noqa: E501 "locations": ([SyntheticsTriggerCITestLocation],), # noqa: E501 "results": ([SyntheticsTriggerCITestRunResult],), # noqa: E501 "triggered_check_ids": ([str],), # noqa: E501 } discriminator = None attribute_map = { "batch_id": "batch_id", # noqa: E501 "locations": "locations", # noqa: E501 "results": "results", # noqa: E501 "triggered_check_ids": "triggered_check_ids", # noqa: E501 } read_only_vars = {} _composed_schemas = {} @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """SyntheticsTriggerCITestsResponse - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) batch_id (str): The public ID of the batch triggered.. [optional] # noqa: E501 locations ([SyntheticsTriggerCITestLocation]): List of Synthetics locations.. [optional] # noqa: E501 results ([SyntheticsTriggerCITestRunResult]): Information about the tests runs.. [optional] # noqa: E501 triggered_check_ids ([str]): The public IDs of the Synthetics test triggered.. [optional] # noqa: E501 """ super().__init__(kwargs) self._check_pos_args(args) @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """Helper creating a new instance from a response.""" self = super(SyntheticsTriggerCITestsResponse, cls)._from_openapi_data(kwargs) self._check_pos_args(args) return self

43.910959

117

0.61644

from datadog_api_client.v1.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, ) def lazy_import(): from datadog_api_client.v1.model.synthetics_trigger_ci_test_location import SyntheticsTriggerCITestLocation from datadog_api_client.v1.model.synthetics_trigger_ci_test_run_result import SyntheticsTriggerCITestRunResult globals()["SyntheticsTriggerCITestLocation"] = SyntheticsTriggerCITestLocation globals()["SyntheticsTriggerCITestRunResult"] = SyntheticsTriggerCITestRunResult class SyntheticsTriggerCITestsResponse(ModelNormal): allowed_values = {} validations = {} additional_properties_type = None _nullable = False @cached_property def openapi_types(): lazy_import() return { "batch_id": (str,), "locations": ([SyntheticsTriggerCITestLocation],), "results": ([SyntheticsTriggerCITestRunResult],), "triggered_check_ids": ([str],), } discriminator = None attribute_map = { "batch_id": "batch_id", "locations": "locations", "results": "results", "triggered_check_ids": "triggered_check_ids", } read_only_vars = {} _composed_schemas = {} @convert_js_args_to_python_args def __init__(self, *args, **kwargs): super().__init__(kwargs) self._check_pos_args(args) @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): self = super(SyntheticsTriggerCITestsResponse, cls)._from_openapi_data(kwargs) self._check_pos_args(args) return self

true

f71b1e406ddf2cb9954d1454378cd13f4b53e7f4

1,801

py

Python

nodes/wind.py

JavierRefuerzo/WeatherFlow

5ea3669edfd7449797cef3184689d6c540383cde

[ "MIT" ]

4

2018-08-26T02:40:09.000Z

2020-06-21T22:59:04.000Z

nodes/wind.py

JavierRefuerzo/WeatherFlow

5ea3669edfd7449797cef3184689d6c540383cde

[ "MIT" ]

9

2018-04-20T15:37:46.000Z

2020-07-15T20:22:31.000Z

nodes/wind.py

JavierRefuerzo/WeatherFlow

5ea3669edfd7449797cef3184689d6c540383cde

[ "MIT" ]

2

2018-09-16T23:13:39.000Z

2021-12-20T16:35:20.000Z

#!/usr/bin/env python3 """ Polyglot v2 node server for WeatherFlow Weather Station data. Copyright (c) 2018,2019 Robert Paauwe """ import polyinterface import sys import time import datetime import urllib3 import json import socket import math import threading LOGGER = polyinterface.LOGGER class WindNode(polyinterface.Node): id = 'wind' hint = [1,11,4,0] units = 'metric' drivers = [ {'driver': 'ST', 'value': 0, 'uom': 32}, # speed {'driver': 'GV0', 'value': 0, 'uom': 76}, # direction {'driver': 'GV1', 'value': 0, 'uom': 32}, # gust {'driver': 'GV2', 'value': 0, 'uom': 76}, # gust direction {'driver': 'GV3', 'value': 0, 'uom': 32} # lull ] def SetUnits(self, u): self.units = u if (u == 'kph'): self.drivers[0]['uom'] = 32 self.drivers[2]['uom'] = 32 self.drivers[4]['uom'] = 32 self.id = 'wind' if (u == 'ms'): self.drivers[0]['uom'] = 40 self.drivers[2]['uom'] = 40 self.drivers[4]['uom'] = 40 self.id = 'wind' elif (u == 'mph'): self.drivers[0]['uom'] = 48 self.drivers[2]['uom'] = 48 self.drivers[4]['uom'] = 48 self.id = 'windUS' def setDriver(self, driver, value): if (driver == 'ST' or driver == 'GV1' or driver == 'GV3'): if (self.units == 'mph'): value = round(value / 1.609344, 2) super(WindNode, self).setDriver(driver, value, report=True, force=True) def update(self, ws, wd, wg, wl): self.setDriver('ST', ws) self.setDriver('GV0', wd) self.setDriver('GV1', wg) self.setDriver('GV2', wd) self.setDriver('GV3', wl)

29.52459

79

0.513048

import polyinterface import sys import time import datetime import urllib3 import json import socket import math import threading LOGGER = polyinterface.LOGGER class WindNode(polyinterface.Node): id = 'wind' hint = [1,11,4,0] units = 'metric' drivers = [ {'driver': 'ST', 'value': 0, 'uom': 32}, {'driver': 'GV0', 'value': 0, 'uom': 76}, {'driver': 'GV1', 'value': 0, 'uom': 32}, {'driver': 'GV2', 'value': 0, 'uom': 76}, {'driver': 'GV3', 'value': 0, 'uom': 32} ] def SetUnits(self, u): self.units = u if (u == 'kph'): self.drivers[0]['uom'] = 32 self.drivers[2]['uom'] = 32 self.drivers[4]['uom'] = 32 self.id = 'wind' if (u == 'ms'): self.drivers[0]['uom'] = 40 self.drivers[2]['uom'] = 40 self.drivers[4]['uom'] = 40 self.id = 'wind' elif (u == 'mph'): self.drivers[0]['uom'] = 48 self.drivers[2]['uom'] = 48 self.drivers[4]['uom'] = 48 self.id = 'windUS' def setDriver(self, driver, value): if (driver == 'ST' or driver == 'GV1' or driver == 'GV3'): if (self.units == 'mph'): value = round(value / 1.609344, 2) super(WindNode, self).setDriver(driver, value, report=True, force=True) def update(self, ws, wd, wg, wl): self.setDriver('ST', ws) self.setDriver('GV0', wd) self.setDriver('GV1', wg) self.setDriver('GV2', wd) self.setDriver('GV3', wl)

true

f71b1e9aa887b542b88ff076fa75931ec6dc85c7

2,532

py

Python

cohesity_management_sdk/models/smb_active_file_path.py

nick6655/management-sdk-python

88e792cb83e5c24a22af495b220c145d0c45841d

[ "Apache-2.0" ]

18

2019-09-24T17:35:53.000Z

2022-03-25T08:08:47.000Z

cohesity_management_sdk/models/smb_active_file_path.py

nick6655/management-sdk-python

88e792cb83e5c24a22af495b220c145d0c45841d

[ "Apache-2.0" ]

18

2019-03-29T19:32:29.000Z

2022-01-03T23:16:45.000Z

cohesity_management_sdk/models/smb_active_file_path.py

nick6655/management-sdk-python

88e792cb83e5c24a22af495b220c145d0c45841d

[ "Apache-2.0" ]

16

2019-02-27T06:54:12.000Z

2021-11-16T18:10:24.000Z

# -*- coding: utf-8 -*- # Copyright 2021 Cohesity Inc. import cohesity_management_sdk.models.smb_active_session class SmbActiveFilePath(object): """Implementation of the 'SmbActiveFilePath' model. Specifies a file path in an SMB view that has active sessions and opens. Attributes: active_sessions (list of SmbActiveSession): Specifies the sessions where the file is open. file_path (string): Specifies the filepath in the view. view_id (long|int): Specifies the id of the View assigned by the Cohesity Cluster. Either viewName or viewId must be specified. view_name (string): Specifies the name of the View. """ # Create a mapping from Model property names to API property names _names = { "active_sessions":'activeSessions', "file_path":'filePath', "view_id":'viewId', "view_name":'viewName' } def __init__(self, active_sessions=None, file_path=None, view_id=None, view_name=None): """Constructor for the SmbActiveFilePath class""" # Initialize members of the class self.active_sessions = active_sessions self.file_path = file_path self.view_id = view_id self.view_name = view_name @classmethod def from_dictionary(cls, dictionary): """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class. """ if dictionary is None: return None # Extract variables from the dictionary active_sessions = None if dictionary.get('activeSessions') != None: active_sessions = list() for structure in dictionary.get('activeSessions'): active_sessions.append(cohesity_management_sdk.models.smb_active_session.SmbActiveSession.from_dictionary(structure)) file_path = dictionary.get('filePath') view_id = dictionary.get('viewId') view_name = dictionary.get('viewName') # Return an object of this model return cls(active_sessions, file_path, view_id, view_name)

32.461538

133

0.625197

import cohesity_management_sdk.models.smb_active_session class SmbActiveFilePath(object): _names = { "active_sessions":'activeSessions', "file_path":'filePath', "view_id":'viewId', "view_name":'viewName' } def __init__(self, active_sessions=None, file_path=None, view_id=None, view_name=None): self.active_sessions = active_sessions self.file_path = file_path self.view_id = view_id self.view_name = view_name @classmethod def from_dictionary(cls, dictionary): if dictionary is None: return None active_sessions = None if dictionary.get('activeSessions') != None: active_sessions = list() for structure in dictionary.get('activeSessions'): active_sessions.append(cohesity_management_sdk.models.smb_active_session.SmbActiveSession.from_dictionary(structure)) file_path = dictionary.get('filePath') view_id = dictionary.get('viewId') view_name = dictionary.get('viewName') return cls(active_sessions, file_path, view_id, view_name)

true

f71b206d98b3712aa33781c5090859c94fbf8680

373

py

Python

source/Objects/XTRA_Solvers_Class.py

afarahi/XTRA

6550b216264abaa3ed705835aca0981f2934e069

[ "MIT" ]

2

2018-11-01T12:38:56.000Z

2019-10-22T07:02:54.000Z

source/Objects/XTRA_Solvers_Class.py

afarahi/XTRA

6550b216264abaa3ed705835aca0981f2934e069

[ "MIT" ]

null

source/Objects/XTRA_Solvers_Class.py

afarahi/XTRA

6550b216264abaa3ed705835aca0981f2934e069

[ "MIT" ]

null

class Solvers_Class(): def __init__(self): pass def ProperDistanceTabulate(self, Input_Param, z_max): from Distance_Solver import Proper_Distance_Tabulate Proper_Distance_Tabulate(Input_Param, z_max) def LxTxSolver(self, Halos): from LxTx_Solver import LxTx_Solver solver = LxTx_Solver() solver.solve(Halos)

23.3125

60

0.691689

class Solvers_Class(): def __init__(self): pass def ProperDistanceTabulate(self, Input_Param, z_max): from Distance_Solver import Proper_Distance_Tabulate Proper_Distance_Tabulate(Input_Param, z_max) def LxTxSolver(self, Halos): from LxTx_Solver import LxTx_Solver solver = LxTx_Solver() solver.solve(Halos)

true

f71b20c6a58525d0ad6e5a5b0ad92dbbdf9f5849

1,599

py

Python

user/tests.py

Vr3n/django_react_cart_system

f6d2572b640f711ff9c7020641051e3f92c3dd59

[ "MIT" ]

null

user/tests.py

Vr3n/django_react_cart_system

f6d2572b640f711ff9c7020641051e3f92c3dd59

[ "MIT" ]

3

2021-06-18T15:13:46.000Z

2021-06-18T18:24:43.000Z

user/tests.py

Vr3n/django_react_cart_system

f6d2572b640f711ff9c7020641051e3f92c3dd59

[ "MIT" ]

null

from django.contrib.auth import get_user_model from django.test import TestCase # Create your tests here. class UserManagersTests(TestCase): def test_create_user(self): User = get_user_model() user = User.objects.create_user( email="normal@user.com", password="testing@123") self.assertEqual(user.email, 'normal@user.com') self.assertTrue(user.is_active) self.assertFalse(user.is_staff) self.assertFalse(user.is_superuser) try: self.assertIsNotNone(user.username) self.assertIsNotNone(user.email) except AttributeError: pass with self.assertRaises(TypeError): User.objects.create_user() with self.assertRaises(TypeError): User.objects.create_user(email='') with self.assertRaises(ValueError): User.objects.create_user(email='', password="testing@123") def test_create_superuser(self): User = get_user_model() admin = User.objects.create_superuser( email="admin@user.com", password="testing@123") self.assertEqual(admin.email, 'admin@user.com') self.assertTrue(admin.is_active) self.assertTrue(admin.is_staff) self.assertTrue(admin.is_superuser) try: self.assertIsNotNone(admin.username) self.assertIsNotNone(admin.email) except AttributeError: pass with self.assertRaises(ValueError): User.objects.create_user( email='', password="testing@123", is_superuser=False)

34.76087

70

0.642276

from django.contrib.auth import get_user_model from django.test import TestCase class UserManagersTests(TestCase): def test_create_user(self): User = get_user_model() user = User.objects.create_user( email="normal@user.com", password="testing@123") self.assertEqual(user.email, 'normal@user.com') self.assertTrue(user.is_active) self.assertFalse(user.is_staff) self.assertFalse(user.is_superuser) try: self.assertIsNotNone(user.username) self.assertIsNotNone(user.email) except AttributeError: pass with self.assertRaises(TypeError): User.objects.create_user() with self.assertRaises(TypeError): User.objects.create_user(email='') with self.assertRaises(ValueError): User.objects.create_user(email='', password="testing@123") def test_create_superuser(self): User = get_user_model() admin = User.objects.create_superuser( email="admin@user.com", password="testing@123") self.assertEqual(admin.email, 'admin@user.com') self.assertTrue(admin.is_active) self.assertTrue(admin.is_staff) self.assertTrue(admin.is_superuser) try: self.assertIsNotNone(admin.username) self.assertIsNotNone(admin.email) except AttributeError: pass with self.assertRaises(ValueError): User.objects.create_user( email='', password="testing@123", is_superuser=False)

true

f71b21a5dd538deb30896fbf0e23be55c42a7ec4

22

py

Python

tsu/__init__.py

KiraDank/tsu

7a2c6508daf6d797e8371acf3b473d0f92cb11c3

[ "MIT" ]

251

2019-08-18T17:19:19.000Z

2022-03-31T20:38:20.000Z

tsu/__init__.py

KiraDank/tsu

7a2c6508daf6d797e8371acf3b473d0f92cb11c3

[ "MIT" ]

37

2019-09-05T08:03:00.000Z

2022-01-27T12:49:50.000Z

tsu/__init__.py

KiraDank/tsu

7a2c6508daf6d797e8371acf3b473d0f92cb11c3

[ "MIT" ]

20

2019-09-01T15:29:56.000Z

2022-01-27T03:03:18.000Z

__version__ = '3.1.2'

11

21

0.636364

__version__ = '3.1.2'

true

f71b21c32e28c3f04700fe8235f89c2fac3de98b

3,420

py

Python

ays_api/app.py

Jumpscale/g8cockpit

5e9ede183f77fec4adff9cd038567173b68677f0

[ "Apache-2.0" ]

null

ays_api/app.py

Jumpscale/g8cockpit

5e9ede183f77fec4adff9cd038567173b68677f0

[ "Apache-2.0" ]

332

2016-05-24T10:51:45.000Z

2021-09-08T12:28:50.000Z

ays_api/app.py

Jumpscale/g8cockpit

5e9ede183f77fec4adff9cd038567173b68677f0

[ "Apache-2.0" ]

1

2016-08-02T07:52:49.000Z

from flask import Flask, send_from_directory, make_response, request, send_file, jsonify import werkzeug.exceptions from jose import jwt, exceptions import wtforms_json from .ays import ays_api from .oauth import oauth_api from .webhooks import webhooks_api from .cockpit import cockpit_api from JumpScale import j app = Flask(__name__) app.config["WTF_CSRF_ENABLED"] = False wtforms_json.init() logger = j.logger.get('j.cockpit.api') def init_blueprints(): if app.config.get('production',True): print('JWT middleware enable') ays_api.before_request(process_jwt_token) cockpit_api.before_request(process_jwt_token) app.register_blueprint(ays_api) app.register_blueprint(oauth_api) app.register_blueprint(webhooks_api) app.register_blueprint(cockpit_api) def process_jwt_token(): authorization = request.cookies.get( 'jwt', request.headers.get( 'Authorization', None )) if authorization is None: response = make_response('Not JWT token') response.status_code = 401 return response msg = "" ss = authorization.split(' ', 1) if len(ss) != 2: msg = "Unauthorized" else: type, token = ss[0], ss[1] if type.lower() == 'bearer': try: headers = jwt.get_unverified_header(token) payload = jwt.decode( token, app.config['oauth'].get('jwt_key'), algorithms=[headers['alg']], audience=app.config['oauth']['organization'], issuer='itsyouonline') # case JWT is for an organization if 'globalid' in payload and payload['globalid'] == app.config['oauth'].get('organization'): return # case JWT is for a user if 'scope' in payload and 'user:memberof:%s' % app.config[ 'oauth'].get('organization') in payload['scope']: return msg = 'Unauthorized' except exceptions.ExpiredSignatureError as e: msg = 'Your JWT has expired' except exceptions.JOSEError as e: msg = 'JWT Error: %s' % str(e) except Exception as e: msg = 'Unexpected error : %s' % str(e) else: msg = 'Your JWT is invalid' logger.error(msg) response = make_response(msg) response.status_code = 401 return response @app.route('/apidocs/<path:path>') def send_js(path): root = j.sal.fs.joinPaths(j.sal.fs.getParent(__file__), 'apidocs') return send_from_directory(root, path) @app.route('/', methods=['GET']) def home(): path = j.sal.fs.joinPaths(j.sal.fs.getParent(__file__), 'index.html') return send_file(path) @app.errorhandler(j.exceptions.NotFound) def handle_bad_request(e): return jsonify(error=e.msg), 404 @app.errorhandler(j.exceptions.AYSNotFound) def handle_bad_request(e): return jsonify(error=e.msg), 404 @app.errorhandler(j.exceptions.Timeout) def handle_bad_request(e): return jsonify(error=e.msg), 408 @app.errorhandler(j.exceptions.BaseJSException) def handle_bad_request(e): return jsonify(error=e.msg), 500 @app.errorhandler(werkzeug.exceptions.HTTPException) def handle_bad_request(e): return jsonify(error=e.msg), e.code

28.264463

108

0.625146

from flask import Flask, send_from_directory, make_response, request, send_file, jsonify import werkzeug.exceptions from jose import jwt, exceptions import wtforms_json from .ays import ays_api from .oauth import oauth_api from .webhooks import webhooks_api from .cockpit import cockpit_api from JumpScale import j app = Flask(__name__) app.config["WTF_CSRF_ENABLED"] = False wtforms_json.init() logger = j.logger.get('j.cockpit.api') def init_blueprints(): if app.config.get('production',True): print('JWT middleware enable') ays_api.before_request(process_jwt_token) cockpit_api.before_request(process_jwt_token) app.register_blueprint(ays_api) app.register_blueprint(oauth_api) app.register_blueprint(webhooks_api) app.register_blueprint(cockpit_api) def process_jwt_token(): authorization = request.cookies.get( 'jwt', request.headers.get( 'Authorization', None )) if authorization is None: response = make_response('Not JWT token') response.status_code = 401 return response msg = "" ss = authorization.split(' ', 1) if len(ss) != 2: msg = "Unauthorized" else: type, token = ss[0], ss[1] if type.lower() == 'bearer': try: headers = jwt.get_unverified_header(token) payload = jwt.decode( token, app.config['oauth'].get('jwt_key'), algorithms=[headers['alg']], audience=app.config['oauth']['organization'], issuer='itsyouonline') if 'globalid' in payload and payload['globalid'] == app.config['oauth'].get('organization'): return if 'scope' in payload and 'user:memberof:%s' % app.config[ 'oauth'].get('organization') in payload['scope']: return msg = 'Unauthorized' except exceptions.ExpiredSignatureError as e: msg = 'Your JWT has expired' except exceptions.JOSEError as e: msg = 'JWT Error: %s' % str(e) except Exception as e: msg = 'Unexpected error : %s' % str(e) else: msg = 'Your JWT is invalid' logger.error(msg) response = make_response(msg) response.status_code = 401 return response @app.route('/apidocs/<path:path>') def send_js(path): root = j.sal.fs.joinPaths(j.sal.fs.getParent(__file__), 'apidocs') return send_from_directory(root, path) @app.route('/', methods=['GET']) def home(): path = j.sal.fs.joinPaths(j.sal.fs.getParent(__file__), 'index.html') return send_file(path) @app.errorhandler(j.exceptions.NotFound) def handle_bad_request(e): return jsonify(error=e.msg), 404 @app.errorhandler(j.exceptions.AYSNotFound) def handle_bad_request(e): return jsonify(error=e.msg), 404 @app.errorhandler(j.exceptions.Timeout) def handle_bad_request(e): return jsonify(error=e.msg), 408 @app.errorhandler(j.exceptions.BaseJSException) def handle_bad_request(e): return jsonify(error=e.msg), 500 @app.errorhandler(werkzeug.exceptions.HTTPException) def handle_bad_request(e): return jsonify(error=e.msg), e.code

true

f71b21ee28dbbf48f0569576807de49f2234149c

3,475

py

Python

include/Node.py

Yperidis/DAGFLOW

84ea30eea480e095dc8e24f80ca84a733170a365

[ "MIT" ]

null

include/Node.py

Yperidis/DAGFLOW

84ea30eea480e095dc8e24f80ca84a733170a365

[ "MIT" ]

null

include/Node.py

Yperidis/DAGFLOW

84ea30eea480e095dc8e24f80ca84a733170a365

[ "MIT" ]

null

import random import networkx as nx class Node: def __init__(self, ID=None, message=None, highercmnd=None, lev=None, parent=None, childNo=None, children_list=None, cmndlineup=None, cmndnodes=None, ancestor_list=None, descendant_list=None): self.ID = ID # an integer ascending from 0 to the number of the nodes minus 1) self.message = message # some integer number representing an initial message self.parent = parent # an integer if applicable (non applicable to the root node) self.highercmnd = highercmnd # an integer ascending from 1 to the height minus 1 indicating the value of knowing # what superiours know self.lev = lev # reflects the depth in a tree structure self.children_list = [] self.cmndlineup = [] # initialization for the full line of command including the node in question self.cmndnodes = [] self.ancestor_list = [] # a list of the node's direct ancestors self.descendant_list = [] # a list of the node's direct descendants def MakeChildren(self, child): ''' Instantiation of a child node. Needs the node in question. ''' self.children_list.append(child) def HasChildren(self): ''' Boolean determining whether the node in question has a child or not. ''' if len(self.children_list) != 0: return True else: return False def MakeDescendants(self, descendant): ''' Instantiation of a direct descendant node. Needs the node in question. ''' self.descendant_list.append(descendant) def children(self): return self.children_list def parent(self): return self.parent def CommandLineUp(self, l): ''' This function gathers all the ancestors of the node in question in a list up to l (min(l)=1). The list is returned. ''' temp = self.parent for i in range(l): # depth of message: for l=0 leaf sees only parent. For l=n-1 leaf sees all ancestors if temp is not None: self.cmndlineup.append(temp.ID) temp = temp.parent else: break # reached the root return self.cmndlineup # the final ancestry of the node (or its upstream communication) def CommandedNodes(self): # attaches to cmndnodes of the node its whole subtree ''' Attaches to cmndnodes of the node its whole subtree (the node in question is not included). ''' self.cmndnodes = self.children_list ncmndnodes = len(self.cmndnodes) ii = 0 # ATTENTION! CHANGE TO A FOR LOOP IF THE GRAPH IS NOT A TREE ANYMORE while ii < ncmndnodes: child = self.cmndnodes[ii] for childchild in child.children_list: self.cmndnodes.append(childchild) ncmndnodes = len(self.cmndnodes) ii += 1 def Efficiency(self, levin, levcur): ''' A function to compute the exponential drop in efficiency with level of a node substitution from another in its subtree. levin: Int>=0. The substitute's initial level. levcur: Int>=0. The level on which the candidate node is called to substitute. ''' self.q = 1/2.**(levin-levcur)

39.942529

122

0.606906

import random import networkx as nx class Node: def __init__(self, ID=None, message=None, highercmnd=None, lev=None, parent=None, childNo=None, children_list=None, cmndlineup=None, cmndnodes=None, ancestor_list=None, descendant_list=None): self.ID = ID self.message = message self.parent = parent self.highercmnd = highercmnd self.lev = lev self.children_list = [] self.cmndlineup = [] self.cmndnodes = [] self.ancestor_list = [] self.descendant_list = [] # a list of the node's direct descendants def MakeChildren(self, child): self.children_list.append(child) def HasChildren(self): if len(self.children_list) != 0: return True else: return False def MakeDescendants(self, descendant): self.descendant_list.append(descendant) def children(self): return self.children_list def parent(self): return self.parent def CommandLineUp(self, l): temp = self.parent for i in range(l): if temp is not None: self.cmndlineup.append(temp.ID) temp = temp.parent else: break return self.cmndlineup def CommandedNodes(self): self.cmndnodes = self.children_list ncmndnodes = len(self.cmndnodes) ii = 0 while ii < ncmndnodes: child = self.cmndnodes[ii] for childchild in child.children_list: self.cmndnodes.append(childchild) ncmndnodes = len(self.cmndnodes) ii += 1 def Efficiency(self, levin, levcur): self.q = 1/2.**(levin-levcur)

true

f71b22fcc5f5f5eba2d54b6f00ae8b7fb89c6a76

2,334

py

Python

cltk/utils/frequency.py

Akash-Pardasani/cltk

2a430e9407452b06f44847202ebce8446007d96b

[ "MIT" ]

null

cltk/utils/frequency.py

Akash-Pardasani/cltk

2a430e9407452b06f44847202ebce8446007d96b

[ "MIT" ]

null

cltk/utils/frequency.py

Akash-Pardasani/cltk

2a430e9407452b06f44847202ebce8446007d96b

[ "MIT" ]

1

2019-06-16T06:41:47.000Z

"""This module's main class reads a text corpus and assembles a list of n most common words.""" __author__ = 'Kyle P. Johnson <kyle@kyle-p-johnson.com>' __license__ = 'MIT License. See LICENSE.' from cltk.corpus.utils.formatter import assemble_tlg_author_filepaths from cltk.corpus.utils.formatter import assemble_phi5_author_filepaths from cltk.corpus.utils.formatter import tlg_plaintext_cleanup from cltk.corpus.utils.formatter import phi5_plaintext_cleanup from cltk.utils.cltk_logger import logger from collections import Counter from nltk.tokenize.punkt import PunktLanguageVars class Frequency: """Methods for making word frequency lists.""" def __init__(self): """Language taken as argument, necessary used when saving word frequencies to ``cltk_data/user_data``.""" self.punkt = PunktLanguageVars() self.punctuation = [',', '.', ';', ':', '"', "'", '?', '-', '!', '*', '[', ']', '{', '}'] def counter_from_str(self, string): """Build word frequency list from incoming string.""" string_list = [chars for chars in string if chars not in self.punctuation] string_joined = ''.join(string_list) tokens = self.punkt.word_tokenize(string_joined) return Counter(tokens) def counter_from_corpus(self, corpus): """Build word frequency list from one of several available corpora. TODO: Make this count iteratively, not all at once """ assert corpus in ['phi5', 'tlg'], \ "Corpus '{0}' not available. Choose from 'phi5' or 'tlg'.".format(corpus) all_strings = self._assemble_corpus_string(corpus=corpus) return self.counter_from_str(all_strings) def _assemble_corpus_string(self, corpus): """Takes a list of filepaths, returns a string containing contents of all files.""" if corpus == 'phi5': filepaths = assemble_phi5_author_filepaths() file_cleaner = phi5_plaintext_cleanup elif corpus == 'tlg': filepaths = assemble_tlg_author_filepaths() file_cleaner = tlg_plaintext_cleanup for filepath in filepaths: with open(filepath) as file_open: file_read = file_open.read().lower() file_clean = file_cleaner(file_read) yield file_clean

39.559322

97

0.670094

__author__ = 'Kyle P. Johnson <kyle@kyle-p-johnson.com>' __license__ = 'MIT License. See LICENSE.' from cltk.corpus.utils.formatter import assemble_tlg_author_filepaths from cltk.corpus.utils.formatter import assemble_phi5_author_filepaths from cltk.corpus.utils.formatter import tlg_plaintext_cleanup from cltk.corpus.utils.formatter import phi5_plaintext_cleanup from cltk.utils.cltk_logger import logger from collections import Counter from nltk.tokenize.punkt import PunktLanguageVars class Frequency: def __init__(self): self.punkt = PunktLanguageVars() self.punctuation = [',', '.', ';', ':', '"', "'", '?', '-', '!', '*', '[', ']', '{', '}'] def counter_from_str(self, string): string_list = [chars for chars in string if chars not in self.punctuation] string_joined = ''.join(string_list) tokens = self.punkt.word_tokenize(string_joined) return Counter(tokens) def counter_from_corpus(self, corpus): assert corpus in ['phi5', 'tlg'], \ "Corpus '{0}' not available. Choose from 'phi5' or 'tlg'.".format(corpus) all_strings = self._assemble_corpus_string(corpus=corpus) return self.counter_from_str(all_strings) def _assemble_corpus_string(self, corpus): if corpus == 'phi5': filepaths = assemble_phi5_author_filepaths() file_cleaner = phi5_plaintext_cleanup elif corpus == 'tlg': filepaths = assemble_tlg_author_filepaths() file_cleaner = tlg_plaintext_cleanup for filepath in filepaths: with open(filepath) as file_open: file_read = file_open.read().lower() file_clean = file_cleaner(file_read) yield file_clean

true

f71b22feb4672df4c2b53105a50c79c3c5c48548

3,225

py

Python

Midterm/server.py

eebbk-zhou/Web-Design-1

98703942dbb58e15c38a07de551a78398e2a7ab1

[ "Unlicense" ]

null

Midterm/server.py

eebbk-zhou/Web-Design-1

98703942dbb58e15c38a07de551a78398e2a7ab1

[ "Unlicense" ]

null

Midterm/server.py

eebbk-zhou/Web-Design-1

98703942dbb58e15c38a07de551a78398e2a7ab1

[ "Unlicense" ]

null

from bottle import route, get, post from bottle import run, debug from bottle import request, response, redirect, template from bottle import static_file import dataset import json from bottle import default_app #http://localhost:8090 @route("/") def get_midterm(): todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') items = todo_table.find() items = [ dict(x) for x in list(items) ] return template("Midterm", items=items) @route("/static/png/<filename:re:.*\.png") @route("/image/<filename:re:.*\.png") def get_picture(): return static_file(filename="the_boat.png", root="static", mimetype="image/png") @route("/static/<filename:path>") def get_static(filename): return static_file(filename=filename, root="static") @route("/delete/<id>") def get_delete(id): id = int(id) try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') print(f"We need to delete id# {id}...") todo_table.delete(id=id) except Exception as e: response.status="409 Bad Request:"+str(e) return return template("deleted", id=id) @get("/insert") def get_insert(): return template("insert") @post("/insert") def post_insert(): course_number = request.forms.get('course_number') print("course_number=", course_number) course_name = request.forms.get('course_name') try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') todo_table.insert({ 'course_number' : course_number.strip(), 'course_name' : course_name.strip(), 'done' : 1 }) except Exception as e: response.status="409 Bad Request:"+str(e) return return redirect('/') @get("/edit/<id>") def get_edit(id): try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') items = list(todo_table.find(id=id)) if len(items) != 1: response.status="404 Not Found:"+str(id) return items = [ dict(x) for x in items ] print(items) print(items[0]) except Exception as e: print(e) response.status="409 Bad Request:"+str(e) return return template("edit", item=items[0]) # put something here @post("/edit") def post_edit(): id = request.forms.get('id') id = int(id) course_number = request.forms.get('course_number') course_name = request.forms.get('course_name') print("course_number=", course_number) try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') todo_table.update({ 'id' : id, 'course_number' : course_number.strip(), 'course_name' : course_name.strip() }, ['id']) except Exception as e: response.status="409 Bad Request:"+str(e) return return redirect('/') if __name__ == "__main__": debug(True) run(host="localhost", port=8090) else: application = default_app()

26.434426

84

0.622636

from bottle import route, get, post from bottle import run, debug from bottle import request, response, redirect, template from bottle import static_file import dataset import json from bottle import default_app @route("/") def get_midterm(): todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') items = todo_table.find() items = [ dict(x) for x in list(items) ] return template("Midterm", items=items) @route("/static/png/<filename:re:.*\.png") @route("/image/<filename:re:.*\.png") def get_picture(): return static_file(filename="the_boat.png", root="static", mimetype="image/png") @route("/static/<filename:path>") def get_static(filename): return static_file(filename=filename, root="static") @route("/delete/<id>") def get_delete(id): id = int(id) try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') print(f"We need to delete id# {id}...") todo_table.delete(id=id) except Exception as e: response.status="409 Bad Request:"+str(e) return return template("deleted", id=id) @get("/insert") def get_insert(): return template("insert") @post("/insert") def post_insert(): course_number = request.forms.get('course_number') print("course_number=", course_number) course_name = request.forms.get('course_name') try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') todo_table.insert({ 'course_number' : course_number.strip(), 'course_name' : course_name.strip(), 'done' : 1 }) except Exception as e: response.status="409 Bad Request:"+str(e) return return redirect('/') @get("/edit/<id>") def get_edit(id): try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') items = list(todo_table.find(id=id)) if len(items) != 1: response.status="404 Not Found:"+str(id) return items = [ dict(x) for x in items ] print(items) print(items[0]) except Exception as e: print(e) response.status="409 Bad Request:"+str(e) return return template("edit", item=items[0]) @post("/edit") def post_edit(): id = request.forms.get('id') id = int(id) course_number = request.forms.get('course_number') course_name = request.forms.get('course_name') print("course_number=", course_number) try: todo_list_db = dataset.connect('sqlite:///todo_list.db') todo_table = todo_list_db.get_table('todo') todo_table.update({ 'id' : id, 'course_number' : course_number.strip(), 'course_name' : course_name.strip() }, ['id']) except Exception as e: response.status="409 Bad Request:"+str(e) return return redirect('/') if __name__ == "__main__": debug(True) run(host="localhost", port=8090) else: application = default_app()

true

f71b23bfc71f48b0e7ac2b5989a80a9a6d09b17b

4,803

py

Python

CA/ca.py

PIR3-Internet/server

181962e392be47a39848f3a88703163a140b3d3a

[ "MIT" ]

null

CA/ca.py

PIR3-Internet/server

181962e392be47a39848f3a88703163a140b3d3a

[ "MIT" ]

null

CA/ca.py

PIR3-Internet/server

181962e392be47a39848f3a88703163a140b3d3a

[ "MIT" ]

null

import ssl import socket import OpenSSL import sqlite3 import signal from functools import wraps from numpy.core.numeric import count_nonzero import requests from multiprocessing import Process, Value TIMEOUT = Value('i', 5) cMax = Value('i', 2) ca_num = Value('i', 0) class TimeoutException(Exception): pass def deadline(timeout, *args): def decorate(f): def handler(signum, frame): raise TimeoutException() #when the signal have been handle raise the exception @wraps(timeout, *args) def new_f(*args): signal.signal(signal.SIGALRM, handler) #link the SIGALARM signal to the handler signal.alarm(timeout) #create an alarm of timeout second res = f(*args) signal.alarm(0) #reinitiate the alarm return res return new_f return decorate @deadline(TIMEOUT.value) def get_certificate(host, port=443, timeout=10): context = ssl.create_default_context() context.set_ciphers('DEFAULT:@SECLEVEL=1') context.check_hostname = False context.verify_mode = ssl.CERT_NONE conn = socket.create_connection((host, port)) sock = context.wrap_socket(conn, server_hostname=host) sock.settimeout(timeout) try: der_cert = sock.getpeercert(True) finally: sock.close() return ssl.DER_cert_to_PEM_cert(der_cert) @deadline(60) def url_direct(user): user = 'http://' + user user = requests.get(user).url.split('/')[2] return user @deadline(60) def url_with_header(user): user = 'http://' + user user = requests.head(user).headers['location'].split('/')[2] return user def get_url(user, counter, error): try: user = url_direct(user) except TimeoutException: print(" Impossible to get url (TimeoutException) from ", user) cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], error)) counter = cMax.value-1 except: try: user = url_with_header(user) except TimeoutException: print(" Impossible to get url (TimeoutException) from ", user) cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], error)) counter = cMax.value-1 except: print(" Impossible to get url from ", user) cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], error)) counter = cMax.value-1 return user, counter def processus(user): counter = 0 ok = False while ok == False: try: certificate = get_certificate(user) x509 = OpenSSL.crypto.load_certificate(OpenSSL.crypto.FILETYPE_PEM, certificate) provider = x509.get_issuer().organizationName cur.execute("INSERT INTO ca VALUES (?, ?, ?)", (user, provider, ca_num.value)) print(user, ": ", provider) ok = True except TimeoutException as e: if (counter == cMax.value-1): if (TIMEOUT.value != 60): TIMEOUT.value = 60 counter -= counter else: cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], repr(e))) else: user, counter = get_url(user, counter, repr(e)) print(" ", repr(e), user) ok = False counter += 1 except Exception as e: if (counter == cMax.value-1): cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], repr(e))) else: user, counter = get_url(user, counter, repr(e)) print(" ", repr(e), user) ok = False counter += 1 finally: con.commit() ca_num.value += 1 if counter == cMax.value: ok = True con = sqlite3.connect('ca-providers.db') cur = con.cursor() try: cur.execute("CREATE TABLE ca (ca_user, ca_provider, ca_num)") except sqlite3.OperationalError: cur.execute("DELETE FROM ca") try: cur.execute("CREATE TABLE errors (user, extension, error)") except sqlite3.OperationalError: cur.execute("DELETE FROM errors") con.commit() debut = 0 with open("list1m2020.csv", "r") as f: for line in f: user = line.split()[0] p = Process(target=processus, args=(user,)) p.start() p.join() if (TIMEOUT.value != 5): TIMEOUT.value = 5 con.close()

29.466258

128

0.564022

import ssl import socket import OpenSSL import sqlite3 import signal from functools import wraps from numpy.core.numeric import count_nonzero import requests from multiprocessing import Process, Value TIMEOUT = Value('i', 5) cMax = Value('i', 2) ca_num = Value('i', 0) class TimeoutException(Exception): pass def deadline(timeout, *args): def decorate(f): def handler(signum, frame): raise TimeoutException() @wraps(timeout, *args) def new_f(*args): signal.signal(signal.SIGALRM, handler) signal.alarm(timeout) res = f(*args) signal.alarm(0) return res return new_f return decorate @deadline(TIMEOUT.value) def get_certificate(host, port=443, timeout=10): context = ssl.create_default_context() context.set_ciphers('DEFAULT:@SECLEVEL=1') context.check_hostname = False context.verify_mode = ssl.CERT_NONE conn = socket.create_connection((host, port)) sock = context.wrap_socket(conn, server_hostname=host) sock.settimeout(timeout) try: der_cert = sock.getpeercert(True) finally: sock.close() return ssl.DER_cert_to_PEM_cert(der_cert) @deadline(60) def url_direct(user): user = 'http://' + user user = requests.get(user).url.split('/')[2] return user @deadline(60) def url_with_header(user): user = 'http://' + user user = requests.head(user).headers['location'].split('/')[2] return user def get_url(user, counter, error): try: user = url_direct(user) except TimeoutException: print(" Impossible to get url (TimeoutException) from ", user) cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], error)) counter = cMax.value-1 except: try: user = url_with_header(user) except TimeoutException: print(" Impossible to get url (TimeoutException) from ", user) cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], error)) counter = cMax.value-1 except: print(" Impossible to get url from ", user) cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], error)) counter = cMax.value-1 return user, counter def processus(user): counter = 0 ok = False while ok == False: try: certificate = get_certificate(user) x509 = OpenSSL.crypto.load_certificate(OpenSSL.crypto.FILETYPE_PEM, certificate) provider = x509.get_issuer().organizationName cur.execute("INSERT INTO ca VALUES (?, ?, ?)", (user, provider, ca_num.value)) print(user, ": ", provider) ok = True except TimeoutException as e: if (counter == cMax.value-1): if (TIMEOUT.value != 60): TIMEOUT.value = 60 counter -= counter else: cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], repr(e))) else: user, counter = get_url(user, counter, repr(e)) print(" ", repr(e), user) ok = False counter += 1 except Exception as e: if (counter == cMax.value-1): cur.execute("INSERT INTO errors VALUES (?, ?, ?)", (user, user.split('.')[len(user.split('.'))-1], repr(e))) else: user, counter = get_url(user, counter, repr(e)) print(" ", repr(e), user) ok = False counter += 1 finally: con.commit() ca_num.value += 1 if counter == cMax.value: ok = True con = sqlite3.connect('ca-providers.db') cur = con.cursor() try: cur.execute("CREATE TABLE ca (ca_user, ca_provider, ca_num)") except sqlite3.OperationalError: cur.execute("DELETE FROM ca") try: cur.execute("CREATE TABLE errors (user, extension, error)") except sqlite3.OperationalError: cur.execute("DELETE FROM errors") con.commit() debut = 0 with open("list1m2020.csv", "r") as f: for line in f: user = line.split()[0] p = Process(target=processus, args=(user,)) p.start() p.join() if (TIMEOUT.value != 5): TIMEOUT.value = 5 con.close()

true

f71b2498aa5fdb11b49d58a54f14653f45df1cb1

10,087

py

Python

docs/sphinx/conf.py

sdss/lvmscraper

5b169487963fd06000c0a593993bb3c2c9418951

[ "BSD-3-Clause" ]

null

docs/sphinx/conf.py

sdss/lvmscraper

5b169487963fd06000c0a593993bb3c2c9418951

[ "BSD-3-Clause" ]

null

docs/sphinx/conf.py

sdss/lvmscraper

5b169487963fd06000c0a593993bb3c2c9418951

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # type: ignore import os from pkg_resources import parse_version try: from cluplus import __version__ except ModuleNotFoundError: from sdsstools import get_package_version __version__ = get_package_version(__file__, 'sdss-cluplus') or 'dev' # Are we building in RTD? on_rtd = os.environ.get('READTHEDOCS') == 'True' # Sphinx template selected in cookiecutter and whether to use releases sphinx_template = 'sphinx-bootstrap' use_releases = 'no' if sphinx_template == 'sphinx-bootstrap': import sphinx_bootstrap_theme # Importing matplotlib here with agg to prevent tkinter error in readthedocs # import matplotlib # matplotlib.use('agg') # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.autosummary', 'sphinx.ext.todo', 'sphinx.ext.viewcode', 'sphinx.ext.mathjax', 'sphinx.ext.intersphinx'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] # source_suffix = '.rst' # source_parsers = { # '.md': 'recommonmark.parser.CommonMarkParser', # } # The master toctree document. master_doc = 'index' # General information about the project. project = 'cluplus' copyright = '{0}, {1}'.format('2021', 'Florian Briegel') author = 'Florian Briegel' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # The short X.Y version. version = parse_version(__version__).base_version # The full version, including alpha/beta/rc tags. release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The reST default role (used for this markup: `text`) to use for all # documents. default_role = 'py:obj' # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # Intersphinx mappings intersphinx_mapping = {'python': ('https://docs.python.org/', None), 'astropy': ('http://docs.astropy.org/en/latest', None), 'numpy': ('http://docs.scipy.org/doc/numpy/', None)} autodoc_mock_imports = ['_tkinter'] autodoc_member_order = 'groupwise' napoleon_use_rtype = False napoleon_use_ivar = True rst_epilog = f""" .. |numpy_array| replace:: Numpy array .. |HDUList| replace:: :class:`~astropy.io.fits.HDUList` .. |cluplus_version| replace:: {__version__} """ # -- Options for HTML output ---------------------------------------------- html_css_files = [] # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. if sphinx_template == 'sphinx-bootstrap': html_theme = 'bootstrap' html_sidebars = {} # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { # Navigation bar title. (Default: ``project`` value) 'navbar_title': "SDSS: {0}".format(project), # Tab name for entire site. (Default: "Site") 'navbar_site_name': "Site", # A list of tuples containing pages or urls to link to. # Valid tuples should be in the following forms: # (name, page) # a link to a page # (name, "/aa/bb", 1) # a link to an arbitrary relative url # (name, "http://example.com", True) # arbitrary absolute url # Note the "1" or "True" value above as the third argument to indicate # an arbitrary url. 'navbar_links': [ ], # Render the next and previous page links in navbar. (Default: true) 'navbar_sidebarrel': False, # Render the current pages TOC in the navbar. (Default: true) 'navbar_pagenav': False, # Tab name for the current pages TOC. (Default: "Page") 'navbar_pagenav_name': "Page", # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. 'globaltoc_depth': 2, # Include hidden TOCs in Site navbar? # # Note: If this is "false", you cannot have mixed ``:hidden:`` and # non-hidden ``toctree`` directives in the same page, or else the build # will break. # # Values: "true" (default) or "false" 'globaltoc_includehidden': "true", # HTML navbar class (Default: "navbar") to attach to <div> element. # For black navbar, do "navbar navbar-inverse" 'navbar_class': "navbar", # Fix navigation bar to top of page? # Values: "true" (default) or "false" 'navbar_fixed_top': "true", # Location of link to source. # Options are "nav" (default), "footer" or anything else to exclude. 'source_link_position': "", # Bootswatch (http://bootswatch.com/) theme. # # Options are nothing (default) or the name of a valid theme # such as "amelia" or "cosmo". 'bootswatch_theme': "paper", # Choose Bootstrap version. # Values: "3" (default) or "2" (in quotes) 'bootstrap_version': "3", } # Add any paths that contain custom themes here, relative to this directory. html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() html_logo = '_static/sdssv_logo_small.png' html_css_files += ["custom_bootstrap.css"] html_sidebars = {'**': ['localtoc.html']} elif sphinx_template == 'alabaster': html_theme = 'alabaster' html_theme_options = { 'logo': 'sdssv_logo.png', 'github_user': 'sdss', 'github_repo': project, 'github_button': True, 'github_type': 'star', 'sidebar_collapse': True, 'page_width': '80%' } html_sidebars = { '**': [ 'about.html', 'navigation.html', 'relations.html', 'searchbox.html', ] } html_css_files += ["custom.css"] html_favicon = './_static/favicon_sdssv.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # See https://github.com/rtfd/readthedocs.org/issues/1776 for why we do this if on_rtd: html_static_path = [] else: html_static_path = ['_static'] # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = '{0}pdoc'.format('cluplus') # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, '{0}.tex'.format(project), u'{0} Documentation'.format(project), author, 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'cluplus', u'{0} Documentation'.format(project), [author], 1) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, project, u'{0} Documentation'.format(project), author, project, 'One line description of project.', 'Miscellaneous'), ] if use_releases == 'yes': extensions += ['sdsstools.releases'] releases_github_path = 'wasndas/cluplus' releases_document_name = ['CHANGELOG'] releases_unstable_prehistory = True

30.847095

84

0.652027

import os from pkg_resources import parse_version try: from cluplus import __version__ except ModuleNotFoundError: from sdsstools import get_package_version __version__ = get_package_version(__file__, 'sdss-cluplus') or 'dev' on_rtd = os.environ.get('READTHEDOCS') == 'True' sphinx_template = 'sphinx-bootstrap' use_releases = 'no' if sphinx_template == 'sphinx-bootstrap': import sphinx_bootstrap_theme extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.autosummary', 'sphinx.ext.todo', 'sphinx.ext.viewcode', 'sphinx.ext.mathjax', 'sphinx.ext.intersphinx'] templates_path = ['_templates'] source_suffix = ['.rst', '.md'] master_doc = 'index' project = 'cluplus' copyright = '{0}, {1}'.format('2021', 'Florian Briegel') author = 'Florian Briegel' # |version| and |release|, also used in various other places throughout the # built documents. # The short X.Y version. version = parse_version(__version__).base_version # The full version, including alpha/beta/rc tags. release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The reST default role (used for this markup: `text`) to use for all # documents. default_role = 'py:obj' # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # Intersphinx mappings intersphinx_mapping = {'python': ('https://docs.python.org/', None), 'astropy': ('http://docs.astropy.org/en/latest', None), 'numpy': ('http://docs.scipy.org/doc/numpy/', None)} autodoc_mock_imports = ['_tkinter'] autodoc_member_order = 'groupwise' napoleon_use_rtype = False napoleon_use_ivar = True rst_epilog = f""" .. |numpy_array| replace:: Numpy array .. |HDUList| replace:: :class:`~astropy.io.fits.HDUList` .. |cluplus_version| replace:: {__version__} """ # -- Options for HTML output ---------------------------------------------- html_css_files = [] # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. if sphinx_template == 'sphinx-bootstrap': html_theme = 'bootstrap' html_sidebars = {} # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { # Navigation bar title. (Default: ``project`` value) 'navbar_title': "SDSS: {0}".format(project), # Tab name for entire site. (Default: "Site") 'navbar_site_name': "Site", # A list of tuples containing pages or urls to link to. # Valid tuples should be in the following forms: # (name, page) # a link to a page # (name, "/aa/bb", 1) # a link to an arbitrary relative url # (name, "http://example.com", True) # arbitrary absolute url # Note the "1" or "True" value above as the third argument to indicate # an arbitrary url. 'navbar_links': [ ], # Render the next and previous page links in navbar. (Default: true) 'navbar_sidebarrel': False, # Render the current pages TOC in the navbar. (Default: true) 'navbar_pagenav': False, # Tab name for the current pages TOC. (Default: "Page") 'navbar_pagenav_name': "Page", # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. 'globaltoc_depth': 2, # Include hidden TOCs in Site navbar? # # Note: If this is "false", you cannot have mixed ``:hidden:`` and # non-hidden ``toctree`` directives in the same page, or else the build # will break. # # Values: "true" (default) or "false" 'globaltoc_includehidden': "true", # HTML navbar class (Default: "navbar") to attach to <div> element. # For black navbar, do "navbar navbar-inverse" 'navbar_class': "navbar", # Fix navigation bar to top of page? # Values: "true" (default) or "false" 'navbar_fixed_top': "true", # Location of link to source. # Options are "nav" (default), "footer" or anything else to exclude. 'source_link_position': "", # Bootswatch (http://bootswatch.com/) theme. # # Options are nothing (default) or the name of a valid theme # such as "amelia" or "cosmo". 'bootswatch_theme': "paper", # Choose Bootstrap version. # Values: "3" (default) or "2" (in quotes) 'bootstrap_version': "3", } # Add any paths that contain custom themes here, relative to this directory. html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() html_logo = '_static/sdssv_logo_small.png' html_css_files += ["custom_bootstrap.css"] html_sidebars = {'**': ['localtoc.html']} elif sphinx_template == 'alabaster': html_theme = 'alabaster' html_theme_options = { 'logo': 'sdssv_logo.png', 'github_user': 'sdss', 'github_repo': project, 'github_button': True, 'github_type': 'star', 'sidebar_collapse': True, 'page_width': '80%' } html_sidebars = { '**': [ 'about.html', 'navigation.html', 'relations.html', 'searchbox.html', ] } html_css_files += ["custom.css"] html_favicon = './_static/favicon_sdssv.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # See https://github.com/rtfd/readthedocs.org/issues/1776 for why we do this if on_rtd: html_static_path = [] else: html_static_path = ['_static'] # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = '{0}pdoc'.format('cluplus') # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, '{0}.tex'.format(project), u'{0} Documentation'.format(project), author, 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'cluplus', u'{0} Documentation'.format(project), [author], 1) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, project, u'{0} Documentation'.format(project), author, project, 'One line description of project.', 'Miscellaneous'), ] if use_releases == 'yes': extensions += ['sdsstools.releases'] releases_github_path = 'wasndas/cluplus' releases_document_name = ['CHANGELOG'] releases_unstable_prehistory = True

true

f71b252375625601941b43c73fbf023daa436c67

304

py

Python

src/pygame_setup.py

sheepy0125/some-platformer-game

5b623c7ffeb7d1e0ba8bf1d75bc37b1798f31379

[ "MIT" ]

null

src/pygame_setup.py

sheepy0125/some-platformer-game

5b623c7ffeb7d1e0ba8bf1d75bc37b1798f31379

[ "MIT" ]

null

src/pygame_setup.py

sheepy0125/some-platformer-game

5b623c7ffeb7d1e0ba8bf1d75bc37b1798f31379

[ "MIT" ]

null

""" Pygame setup for Some Platformer Game Created by sheepy0125 08/10/2021 """ import pygame SCREEN_SIZE = (500, 500) SCROLL_OFFSET = (SCREEN_SIZE[0] // 2, SCREEN_SIZE[1] // 2) screen = pygame.display.set_mode(SCREEN_SIZE) pygame.display.set_caption("Some Platformer Game") clock = pygame.time.Clock()

21.714286

58

0.746711

import pygame SCREEN_SIZE = (500, 500) SCROLL_OFFSET = (SCREEN_SIZE[0] // 2, SCREEN_SIZE[1] // 2) screen = pygame.display.set_mode(SCREEN_SIZE) pygame.display.set_caption("Some Platformer Game") clock = pygame.time.Clock()

true

f71b256176da366107fb45bcef939ee05c453375

13,883

py

Python

nexmo/__init__.py

cook-health/messaging

1a827b97d9af6e56d55c362b29dd79a6cb373f88

[ "MIT" ]

null

nexmo/__init__.py

cook-health/messaging

1a827b97d9af6e56d55c362b29dd79a6cb373f88

[ "MIT" ]

2

2018-03-14T10:42:37.000Z

2018-03-14T11:01:31.000Z

nexmo/__init__.py

Seliniux777/Nexmo-nexmo-python

d1d60e8068b1cb23f12507a6ec1cd500285890b5

[ "MIT" ]

1

2020-10-18T09:41:15.000Z

from platform import python_version import hashlib import hmac import jwt import os import requests import sys import time from uuid import uuid4 import warnings if sys.version_info[0] == 3: string_types = (str, bytes) else: string_types = (unicode, str) __version__ = '2.0.0' class Error(Exception): pass class ClientError(Error): pass class ServerError(Error): pass class AuthenticationError(ClientError): pass class Client(): def __init__(self, **kwargs): self.api_key = kwargs.get('key', None) or os.environ.get('NEXMO_API_KEY', None) self.api_secret = kwargs.get('secret', None) or os.environ.get('NEXMO_API_SECRET', None) self.signature_secret = kwargs.get('signature_secret', None) or os.environ.get('NEXMO_SIGNATURE_SECRET', None) self.signature_method = kwargs.get('signature_method', None) or os.environ.get('NEXMO_SIGNATURE_METHOD', None) if self.signature_method == 'md5': self.signature_method = hashlib.md5 elif self.signature_method == 'sha1': self.signature_method = hashlib.sha1 elif self.signature_method == 'sha256': self.signature_method = hashlib.sha256 elif self.signature_method == 'sha512': self.signature_method = hashlib.sha512 self.application_id = kwargs.get('application_id', None) self.private_key = kwargs.get('private_key', None) if isinstance(self.private_key, string_types) and '\n' not in self.private_key: with open(self.private_key, 'rb') as key_file: self.private_key = key_file.read() self.host = 'rest.nexmo.com' self.api_host = 'api.nexmo.com' user_agent = 'nexmo-python/{0}/{1}'.format(__version__, python_version()) if 'app_name' in kwargs and 'app_version' in kwargs: user_agent += '/{0}/{1}'.format(kwargs['app_name'], kwargs['app_version']) self.headers = {'User-Agent': user_agent} self.auth_params = {} def auth(self, params=None, **kwargs): self.auth_params = params or kwargs def send_message(self, params): return self.post(self.host, '/sms/json', params) def get_balance(self): return self.get(self.host, '/account/get-balance') def get_country_pricing(self, country_code): return self.get(self.host, '/account/get-pricing/outbound', {'country': country_code}) def get_prefix_pricing(self, prefix): return self.get(self.host, '/account/get-prefix-pricing/outbound', {'prefix': prefix}) def get_sms_pricing(self, number): return self.get(self.host, '/account/get-phone-pricing/outbound/sms', {'phone': number}) def get_voice_pricing(self, number): return self.get(self.host, '/account/get-phone-pricing/outbound/voice', {'phone': number}) def update_settings(self, params=None, **kwargs): return self.post(self.host, '/account/settings', params or kwargs) def topup(self, params=None, **kwargs): return self.post(self.host, '/account/top-up', params or kwargs) def get_account_numbers(self, params=None, **kwargs): return self.get(self.host, '/account/numbers', params or kwargs) def get_available_numbers(self, country_code, params=None, **kwargs): return self.get(self.host, '/number/search', dict(params or kwargs, country=country_code)) def buy_number(self, params=None, **kwargs): return self.post(self.host, '/number/buy', params or kwargs) def cancel_number(self, params=None, **kwargs): return self.post(self.host, '/number/cancel', params or kwargs) def update_number(self, params=None, **kwargs): return self.post(self.host, '/number/update', params or kwargs) def get_message(self, message_id): return self.get(self.host, '/search/message', {'id': message_id}) def get_message_rejections(self, params=None, **kwargs): return self.get(self.host, '/search/rejections', params or kwargs) def search_messages(self, params=None, **kwargs): return self.get(self.host, '/search/messages', params or kwargs) def send_ussd_push_message(self, params=None, **kwargs): return self.post(self.host, '/ussd/json', params or kwargs) def send_ussd_prompt_message(self, params=None, **kwargs): return self.post(self.host, '/ussd-prompt/json', params or kwargs) def send_2fa_message(self, params=None, **kwargs): return self.post(self.host, '/sc/us/2fa/json', params or kwargs) def send_event_alert_message(self, params=None, **kwargs): return self.post(self.host, '/sc/us/alert/json', params or kwargs) def send_marketing_message(self, params=None, **kwargs): return self.post(self.host, '/sc/us/marketing/json', params or kwargs) def get_event_alert_numbers(self): return self.get(self.host, '/sc/us/alert/opt-in/query/json') def resubscribe_event_alert_number(self, params=None, **kwargs): return self.post(self.host, '/sc/us/alert/opt-in/manage/json', params or kwargs) def initiate_call(self, params=None, **kwargs): return self.post(self.host, '/call/json', params or kwargs) def initiate_tts_call(self, params=None, **kwargs): return self.post(self.api_host, '/tts/json', params or kwargs) def initiate_tts_prompt_call(self, params=None, **kwargs): return self.post(self.api_host, '/tts-prompt/json', params or kwargs) def start_verification(self, params=None, **kwargs): return self.post(self.api_host, '/verify/json', params or kwargs) def send_verification_request(self, params=None, **kwargs): warnings.warn('nexmo.Client#send_verification_request is deprecated (use #start_verification instead)', DeprecationWarning, stacklevel=2) return self.post(self.api_host, '/verify/json', params or kwargs) def check_verification(self, request_id, params=None, **kwargs): return self.post(self.api_host, '/verify/check/json', dict(params or kwargs, request_id=request_id)) def check_verification_request(self, params=None, **kwargs): warnings.warn('nexmo.Client#check_verification_request is deprecated (use #check_verification instead)', DeprecationWarning, stacklevel=2) return self.post(self.api_host, '/verify/check/json', params or kwargs) def get_verification(self, request_id): return self.get(self.api_host, '/verify/search/json', {'request_id': request_id}) def get_verification_request(self, request_id): warnings.warn('nexmo.Client#get_verification_request is deprecated (use #get_verification instead)', DeprecationWarning, stacklevel=2) return self.get(self.api_host, '/verify/search/json', {'request_id': request_id}) def cancel_verification(self, request_id): return self.post(self.api_host, '/verify/control/json', {'request_id': request_id, 'cmd': 'cancel'}) def trigger_next_verification_event(self, request_id): return self.post(self.api_host, '/verify/control/json', {'request_id': request_id, 'cmd': 'trigger_next_event'}) def control_verification_request(self, params=None, **kwargs): warnings.warn('nexmo.Client#control_verification_request is deprecated', DeprecationWarning, stacklevel=2) return self.post(self.api_host, '/verify/control/json', params or kwargs) def get_basic_number_insight(self, params=None, **kwargs): return self.get(self.api_host, '/ni/basic/json', params or kwargs) def get_standard_number_insight(self, params=None, **kwargs): return self.get(self.api_host, '/ni/standard/json', params or kwargs) def get_number_insight(self, params=None, **kwargs): warnings.warn('nexmo.Client#get_number_insight is deprecated (use #get_standard_number_insight instead)', DeprecationWarning, stacklevel=2) return self.get(self.api_host, '/number/lookup/json', params or kwargs) def get_advanced_number_insight(self, params=None, **kwargs): return self.get(self.api_host, '/ni/advanced/json', params or kwargs) def request_number_insight(self, params=None, **kwargs): return self.post(self.host, '/ni/json', params or kwargs) def get_applications(self, params=None, **kwargs): return self.get(self.api_host, '/v1/applications', params or kwargs) def get_application(self, application_id): return self.get(self.api_host, '/v1/applications/' + application_id) def create_application(self, params=None, **kwargs): return self.post(self.api_host, '/v1/applications', params or kwargs) def update_application(self, application_id, params=None, **kwargs): return self.put(self.api_host, '/v1/applications/' + application_id, params or kwargs) def delete_application(self, application_id): return self.delete(self.api_host, '/v1/applications/' + application_id) def create_call(self, params=None, **kwargs): return self.__post('/v1/calls', params or kwargs) def get_calls(self, params=None, **kwargs): return self.__get('/v1/calls', params or kwargs) def get_call(self, uuid): return self.__get('/v1/calls/' + uuid) def update_call(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid, params or kwargs) def send_audio(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid + '/stream', params or kwargs) def stop_audio(self, uuid): return self.__delete('/v1/calls/' + uuid + '/stream') def send_speech(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid + '/talk', params or kwargs) def stop_speech(self, uuid): return self.__delete('/v1/calls/' + uuid + '/talk') def send_dtmf(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid + '/dtmf', params or kwargs) def check_signature(self, params): params = dict(params) signature = params.pop('sig', '').lower() return hmac.compare_digest(signature, self.signature(params)) def signature(self, params): if self.signature_method: hasher = hmac.new(self.signature_secret.encode(), digestmod=self.signature_method) else: hasher = hashlib.md5() # Add timestamp if not already present if not params.get("timestamp"): params["timestamp"] = int(time.time()) for key in sorted(params): value = params[key] if isinstance(value, str): value = value.replace('&', '_').replace('=', '_') hasher.update('&{0}={1}'.format(key, value).encode('utf-8')) if self.signature_method is None: hasher.update(self.signature_secret.encode()) return hasher.hexdigest() def get(self, host, request_uri, params=None): uri = 'https://' + host + request_uri params = dict(params or {}, api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.get(uri, params=params, headers=self.headers)) def post(self, host, request_uri, params): uri = 'https://' + host + request_uri params = dict(params, api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.post(uri, data=params, headers=self.headers)) def put(self, host, request_uri, params): uri = 'https://' + host + request_uri params = dict(params, api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.put(uri, json=params, headers=self.headers)) def delete(self, host, request_uri): uri = 'https://' + host + request_uri params = dict(api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.delete(uri, params=params, headers=self.headers)) def parse(self, host, response): if response.status_code == 401: raise AuthenticationError elif response.status_code == 204: return None elif 200 <= response.status_code < 300: return response.json() elif 400 <= response.status_code < 500: message = "{code} response from {host}".format(code=response.status_code, host=host) raise ClientError(message) elif 500 <= response.status_code < 600: message = "{code} response from {host}".format(code=response.status_code, host=host) raise ServerError(message) def __get(self, request_uri, params=None): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.get(uri, params=params or {}, headers=self.__headers())) def __post(self, request_uri, params): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.post(uri, json=params, headers=self.__headers())) def __put(self, request_uri, params): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.put(uri, json=params, headers=self.__headers())) def __delete(self, request_uri): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.delete(uri, headers=self.__headers())) def __headers(self): iat = int(time.time()) payload = dict(self.auth_params) payload.setdefault('application_id', self.application_id) payload.setdefault('iat', iat) payload.setdefault('exp', iat + 60) payload.setdefault('jti', str(uuid4())) token = jwt.encode(payload, self.private_key, algorithm='RS256') return dict(self.headers, Authorization=b'Bearer ' + token)

38.457064

120

0.666859

from platform import python_version import hashlib import hmac import jwt import os import requests import sys import time from uuid import uuid4 import warnings if sys.version_info[0] == 3: string_types = (str, bytes) else: string_types = (unicode, str) __version__ = '2.0.0' class Error(Exception): pass class ClientError(Error): pass class ServerError(Error): pass class AuthenticationError(ClientError): pass class Client(): def __init__(self, **kwargs): self.api_key = kwargs.get('key', None) or os.environ.get('NEXMO_API_KEY', None) self.api_secret = kwargs.get('secret', None) or os.environ.get('NEXMO_API_SECRET', None) self.signature_secret = kwargs.get('signature_secret', None) or os.environ.get('NEXMO_SIGNATURE_SECRET', None) self.signature_method = kwargs.get('signature_method', None) or os.environ.get('NEXMO_SIGNATURE_METHOD', None) if self.signature_method == 'md5': self.signature_method = hashlib.md5 elif self.signature_method == 'sha1': self.signature_method = hashlib.sha1 elif self.signature_method == 'sha256': self.signature_method = hashlib.sha256 elif self.signature_method == 'sha512': self.signature_method = hashlib.sha512 self.application_id = kwargs.get('application_id', None) self.private_key = kwargs.get('private_key', None) if isinstance(self.private_key, string_types) and '\n' not in self.private_key: with open(self.private_key, 'rb') as key_file: self.private_key = key_file.read() self.host = 'rest.nexmo.com' self.api_host = 'api.nexmo.com' user_agent = 'nexmo-python/{0}/{1}'.format(__version__, python_version()) if 'app_name' in kwargs and 'app_version' in kwargs: user_agent += '/{0}/{1}'.format(kwargs['app_name'], kwargs['app_version']) self.headers = {'User-Agent': user_agent} self.auth_params = {} def auth(self, params=None, **kwargs): self.auth_params = params or kwargs def send_message(self, params): return self.post(self.host, '/sms/json', params) def get_balance(self): return self.get(self.host, '/account/get-balance') def get_country_pricing(self, country_code): return self.get(self.host, '/account/get-pricing/outbound', {'country': country_code}) def get_prefix_pricing(self, prefix): return self.get(self.host, '/account/get-prefix-pricing/outbound', {'prefix': prefix}) def get_sms_pricing(self, number): return self.get(self.host, '/account/get-phone-pricing/outbound/sms', {'phone': number}) def get_voice_pricing(self, number): return self.get(self.host, '/account/get-phone-pricing/outbound/voice', {'phone': number}) def update_settings(self, params=None, **kwargs): return self.post(self.host, '/account/settings', params or kwargs) def topup(self, params=None, **kwargs): return self.post(self.host, '/account/top-up', params or kwargs) def get_account_numbers(self, params=None, **kwargs): return self.get(self.host, '/account/numbers', params or kwargs) def get_available_numbers(self, country_code, params=None, **kwargs): return self.get(self.host, '/number/search', dict(params or kwargs, country=country_code)) def buy_number(self, params=None, **kwargs): return self.post(self.host, '/number/buy', params or kwargs) def cancel_number(self, params=None, **kwargs): return self.post(self.host, '/number/cancel', params or kwargs) def update_number(self, params=None, **kwargs): return self.post(self.host, '/number/update', params or kwargs) def get_message(self, message_id): return self.get(self.host, '/search/message', {'id': message_id}) def get_message_rejections(self, params=None, **kwargs): return self.get(self.host, '/search/rejections', params or kwargs) def search_messages(self, params=None, **kwargs): return self.get(self.host, '/search/messages', params or kwargs) def send_ussd_push_message(self, params=None, **kwargs): return self.post(self.host, '/ussd/json', params or kwargs) def send_ussd_prompt_message(self, params=None, **kwargs): return self.post(self.host, '/ussd-prompt/json', params or kwargs) def send_2fa_message(self, params=None, **kwargs): return self.post(self.host, '/sc/us/2fa/json', params or kwargs) def send_event_alert_message(self, params=None, **kwargs): return self.post(self.host, '/sc/us/alert/json', params or kwargs) def send_marketing_message(self, params=None, **kwargs): return self.post(self.host, '/sc/us/marketing/json', params or kwargs) def get_event_alert_numbers(self): return self.get(self.host, '/sc/us/alert/opt-in/query/json') def resubscribe_event_alert_number(self, params=None, **kwargs): return self.post(self.host, '/sc/us/alert/opt-in/manage/json', params or kwargs) def initiate_call(self, params=None, **kwargs): return self.post(self.host, '/call/json', params or kwargs) def initiate_tts_call(self, params=None, **kwargs): return self.post(self.api_host, '/tts/json', params or kwargs) def initiate_tts_prompt_call(self, params=None, **kwargs): return self.post(self.api_host, '/tts-prompt/json', params or kwargs) def start_verification(self, params=None, **kwargs): return self.post(self.api_host, '/verify/json', params or kwargs) def send_verification_request(self, params=None, **kwargs): warnings.warn('nexmo.Client#send_verification_request is deprecated (use #start_verification instead)', DeprecationWarning, stacklevel=2) return self.post(self.api_host, '/verify/json', params or kwargs) def check_verification(self, request_id, params=None, **kwargs): return self.post(self.api_host, '/verify/check/json', dict(params or kwargs, request_id=request_id)) def check_verification_request(self, params=None, **kwargs): warnings.warn('nexmo.Client#check_verification_request is deprecated (use #check_verification instead)', DeprecationWarning, stacklevel=2) return self.post(self.api_host, '/verify/check/json', params or kwargs) def get_verification(self, request_id): return self.get(self.api_host, '/verify/search/json', {'request_id': request_id}) def get_verification_request(self, request_id): warnings.warn('nexmo.Client#get_verification_request is deprecated (use #get_verification instead)', DeprecationWarning, stacklevel=2) return self.get(self.api_host, '/verify/search/json', {'request_id': request_id}) def cancel_verification(self, request_id): return self.post(self.api_host, '/verify/control/json', {'request_id': request_id, 'cmd': 'cancel'}) def trigger_next_verification_event(self, request_id): return self.post(self.api_host, '/verify/control/json', {'request_id': request_id, 'cmd': 'trigger_next_event'}) def control_verification_request(self, params=None, **kwargs): warnings.warn('nexmo.Client#control_verification_request is deprecated', DeprecationWarning, stacklevel=2) return self.post(self.api_host, '/verify/control/json', params or kwargs) def get_basic_number_insight(self, params=None, **kwargs): return self.get(self.api_host, '/ni/basic/json', params or kwargs) def get_standard_number_insight(self, params=None, **kwargs): return self.get(self.api_host, '/ni/standard/json', params or kwargs) def get_number_insight(self, params=None, **kwargs): warnings.warn('nexmo.Client#get_number_insight is deprecated (use #get_standard_number_insight instead)', DeprecationWarning, stacklevel=2) return self.get(self.api_host, '/number/lookup/json', params or kwargs) def get_advanced_number_insight(self, params=None, **kwargs): return self.get(self.api_host, '/ni/advanced/json', params or kwargs) def request_number_insight(self, params=None, **kwargs): return self.post(self.host, '/ni/json', params or kwargs) def get_applications(self, params=None, **kwargs): return self.get(self.api_host, '/v1/applications', params or kwargs) def get_application(self, application_id): return self.get(self.api_host, '/v1/applications/' + application_id) def create_application(self, params=None, **kwargs): return self.post(self.api_host, '/v1/applications', params or kwargs) def update_application(self, application_id, params=None, **kwargs): return self.put(self.api_host, '/v1/applications/' + application_id, params or kwargs) def delete_application(self, application_id): return self.delete(self.api_host, '/v1/applications/' + application_id) def create_call(self, params=None, **kwargs): return self.__post('/v1/calls', params or kwargs) def get_calls(self, params=None, **kwargs): return self.__get('/v1/calls', params or kwargs) def get_call(self, uuid): return self.__get('/v1/calls/' + uuid) def update_call(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid, params or kwargs) def send_audio(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid + '/stream', params or kwargs) def stop_audio(self, uuid): return self.__delete('/v1/calls/' + uuid + '/stream') def send_speech(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid + '/talk', params or kwargs) def stop_speech(self, uuid): return self.__delete('/v1/calls/' + uuid + '/talk') def send_dtmf(self, uuid, params=None, **kwargs): return self.__put('/v1/calls/' + uuid + '/dtmf', params or kwargs) def check_signature(self, params): params = dict(params) signature = params.pop('sig', '').lower() return hmac.compare_digest(signature, self.signature(params)) def signature(self, params): if self.signature_method: hasher = hmac.new(self.signature_secret.encode(), digestmod=self.signature_method) else: hasher = hashlib.md5() if not params.get("timestamp"): params["timestamp"] = int(time.time()) for key in sorted(params): value = params[key] if isinstance(value, str): value = value.replace('&', '_').replace('=', '_') hasher.update('&{0}={1}'.format(key, value).encode('utf-8')) if self.signature_method is None: hasher.update(self.signature_secret.encode()) return hasher.hexdigest() def get(self, host, request_uri, params=None): uri = 'https://' + host + request_uri params = dict(params or {}, api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.get(uri, params=params, headers=self.headers)) def post(self, host, request_uri, params): uri = 'https://' + host + request_uri params = dict(params, api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.post(uri, data=params, headers=self.headers)) def put(self, host, request_uri, params): uri = 'https://' + host + request_uri params = dict(params, api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.put(uri, json=params, headers=self.headers)) def delete(self, host, request_uri): uri = 'https://' + host + request_uri params = dict(api_key=self.api_key, api_secret=self.api_secret) return self.parse(host, requests.delete(uri, params=params, headers=self.headers)) def parse(self, host, response): if response.status_code == 401: raise AuthenticationError elif response.status_code == 204: return None elif 200 <= response.status_code < 300: return response.json() elif 400 <= response.status_code < 500: message = "{code} response from {host}".format(code=response.status_code, host=host) raise ClientError(message) elif 500 <= response.status_code < 600: message = "{code} response from {host}".format(code=response.status_code, host=host) raise ServerError(message) def __get(self, request_uri, params=None): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.get(uri, params=params or {}, headers=self.__headers())) def __post(self, request_uri, params): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.post(uri, json=params, headers=self.__headers())) def __put(self, request_uri, params): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.put(uri, json=params, headers=self.__headers())) def __delete(self, request_uri): uri = 'https://' + self.api_host + request_uri return self.parse(self.api_host, requests.delete(uri, headers=self.__headers())) def __headers(self): iat = int(time.time()) payload = dict(self.auth_params) payload.setdefault('application_id', self.application_id) payload.setdefault('iat', iat) payload.setdefault('exp', iat + 60) payload.setdefault('jti', str(uuid4())) token = jwt.encode(payload, self.private_key, algorithm='RS256') return dict(self.headers, Authorization=b'Bearer ' + token)

true

f71b26b2f58e18b2342f9b9601e14fbabdb77fb3

2,930

py

Python

src/python/pants/util/process_handler.py

revl/pants

8ad83e4ca80c095d44efceafd8b41e575da39c65

[ "Apache-2.0" ]

1

2021-05-05T18:58:28.000Z

src/python/pants/util/process_handler.py

revl/pants

8ad83e4ca80c095d44efceafd8b41e575da39c65

[ "Apache-2.0" ]

null

src/python/pants/util/process_handler.py

revl/pants

8ad83e4ca80c095d44efceafd8b41e575da39c65

[ "Apache-2.0" ]

3

2020-06-30T08:28:13.000Z

2021-07-28T09:35:57.000Z

# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). import io import multiprocessing import subprocess import sys from abc import ABC, abstractmethod from typing import Optional class ProcessHandler(ABC): """An abstraction of process handling calls using the same interface as subprocess.Popen. See SubprocessProcessHandler below for an example. """ @abstractmethod def wait(self, timeout: Optional[float] = None) -> int: """Wait for the underlying process to terminate. :param timeout: The time to wait for the process to terminate in fractional seconds. Wait forever by default. :returns: The process exit code is it has terminated. :raises: :class:`subprocess.TimeoutExpired` """ @abstractmethod def kill(self) -> None: pass @abstractmethod def terminate(self) -> None: pass @abstractmethod def poll(self) -> int: pass class SubprocessProcessHandler(ProcessHandler): """A `ProcessHandler` that delegates directly to a subprocess.Popen object.""" def __init__(self, process: subprocess.Popen) -> None: self._process = process def wait(self, timeout: Optional[float] = None) -> int: return self._process.wait(timeout=timeout) def kill(self) -> None: self._process.kill() def terminate(self) -> None: self._process.terminate() def poll(self) -> int: return self._process.poll() def communicate_teeing_stdout_and_stderr(self, stdin=None): """Just like subprocess.communicate, but tees stdout and stderr to both sys.std{out,err} and a buffer. Only operates on stdout/stderr if the Popen call send them to subprocess.PIPE. :param stdin: A string to send to the stdin of the subprocess. :return: (stdout, stderr) as strings. """ if stdin is not None and self._process.stdin is not None: self._process.stdin.write(stdin) def fork_tee(infile, outfile): if infile is None: return lambda: None queue = multiprocessing.Queue() process = multiprocessing.Process(target=_tee, args=(infile, outfile, queue.put)) process.start() def join_and_get_output(): process.join() return queue.get() return join_and_get_output stdout = fork_tee(self._process.stdout, sys.stdout) stderr = fork_tee(self._process.stderr, sys.stderr) self._process.wait() return stdout(), stderr() def _tee(infile, outfile, return_function): accumulator = io.BytesIO() for line in iter(infile.readline, b""): accumulator.write(line) outfile.buffer.write(line) infile.close() return_function(accumulator.getvalue())

29.897959

100

0.650853

import io import multiprocessing import subprocess import sys from abc import ABC, abstractmethod from typing import Optional class ProcessHandler(ABC): @abstractmethod def wait(self, timeout: Optional[float] = None) -> int: @abstractmethod def kill(self) -> None: pass @abstractmethod def terminate(self) -> None: pass @abstractmethod def poll(self) -> int: pass class SubprocessProcessHandler(ProcessHandler): def __init__(self, process: subprocess.Popen) -> None: self._process = process def wait(self, timeout: Optional[float] = None) -> int: return self._process.wait(timeout=timeout) def kill(self) -> None: self._process.kill() def terminate(self) -> None: self._process.terminate() def poll(self) -> int: return self._process.poll() def communicate_teeing_stdout_and_stderr(self, stdin=None): if stdin is not None and self._process.stdin is not None: self._process.stdin.write(stdin) def fork_tee(infile, outfile): if infile is None: return lambda: None queue = multiprocessing.Queue() process = multiprocessing.Process(target=_tee, args=(infile, outfile, queue.put)) process.start() def join_and_get_output(): process.join() return queue.get() return join_and_get_output stdout = fork_tee(self._process.stdout, sys.stdout) stderr = fork_tee(self._process.stderr, sys.stderr) self._process.wait() return stdout(), stderr() def _tee(infile, outfile, return_function): accumulator = io.BytesIO() for line in iter(infile.readline, b""): accumulator.write(line) outfile.buffer.write(line) infile.close() return_function(accumulator.getvalue())

true

f71b2735ba192a6cc12f568c018d29bbdfde9f83

3,035

py

Python

code/evaluate_DSTC2.py

avinashsai/GCN-SeA

26968d8a71269581f7400293064314b5a18b5748

[ "Apache-2.0" ]

12

2019-09-06T03:48:36.000Z

2021-04-12T08:36:45.000Z

code/evaluate_DSTC2.py

avinashsai/GCN-SeA

26968d8a71269581f7400293064314b5a18b5748

[ "Apache-2.0" ]

null

code/evaluate_DSTC2.py

avinashsai/GCN-SeA

26968d8a71269581f7400293064314b5a18b5748

[ "Apache-2.0" ]

9

2019-06-30T07:23:01.000Z

2020-10-16T10:05:37.000Z

from metrics import bleu, rouge import argparse def get_args(): ''' Parse input arguments: preds_path: The directory in which labels and predictions files are dumped after inference config_id: The config id mentioned in the labels and predictions filenames ''' parser = argparse.ArgumentParser() parser.add_argument("--preds_path") parser.add_argument("--kb_path") parser.add_argument("--config_id") args = parser.parse_args() return args def read_results(path,num): with open(path+"/labels"+str(num)+".txt","r") as fp: l=fp.readlines() with open(path+"/predictions"+str(num)+".txt","r") as fp: p=fp.readlines() return p,l def exact_match(p,l): c=0 for i1,i in enumerate(l): if p[i1]==l[i1]: c+=1 print("Per-Resp Acc: ",c/len(l)) def moses_bl_rouge(p,l): bl = bleu.moses_multi_bleu(p,l) x = rouge.rouge(p,l) print('BLEU: %f\nROUGE1-F: %f\nROUGE1-P: %f\nROUGE1-R: %f\nROUGE2-F: %f\nROUGE2-P: %f\nROUGE2-R: %f\nROUGEL-F: %f\nROUGEL-P: %f\nROUGEL-R: %f'%(bl,x['rouge_1/f_score'],x['rouge_1/p_score'],x['rouge_1/r_score'],x['rouge_2/f_score'], x['rouge_2/p_score'],x['rouge_2/r_score'],x['rouge_l/f_score'],x['rouge_l/p_score'],x['rouge_l/r_score'])) def micro_compute_prf(gold, pred, global_entity_list): TP, FP, FN = 0, 0, 0 if len(gold)!= 0: count = 1 for g in gold: if g in pred: TP += 1 else: FN += 1 for p in set(pred): if p in global_entity_list: if p not in gold: FP += 1 else: count = 0 return TP,FP,FN,count def ent_f1(preds,labels,kb_path): with open(kb_path,'r') as fp: kb=fp.readlines() ent=[] for i in kb: triples = i.split(' ') ent.append(triples[1].strip()) ent.append(triples[3].strip()) ent = set(ent) ent_list = sorted(ent) mic_pred=0 les=[] all_TP=0 all_FP=0 all_FN=0 for i in range(len(labels)): l = labels[i].strip().split() le=[] for j in l: if j in ent_list: le.append(j) les.append(le) p = preds[i].strip().split() tp,fp,fn,c = micro_compute_prf(le,p,ent_list) all_TP+=tp all_FP+=fp all_FN+=fn mic_pred+=c mic_prec = all_TP/float(all_TP+all_FP) mic_rec = all_TP/float(all_TP + all_FN) mic_f1=2 * mic_prec * mic_rec / float(mic_prec + mic_rec) print("Entity-F1:",mic_f1) if __name__=='__main__': args = get_args() result_path = args.preds_path kb_path = args.kb_path config_id = args.config_id print(config_id,"\n") preds,labels = read_results(result_path,config_id) exact_match(preds,labels) moses_bl_rouge(preds,labels) ent_f1(preds,labels,kb_path)

28.632075

235

0.559143

from metrics import bleu, rouge import argparse def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--preds_path") parser.add_argument("--kb_path") parser.add_argument("--config_id") args = parser.parse_args() return args def read_results(path,num): with open(path+"/labels"+str(num)+".txt","r") as fp: l=fp.readlines() with open(path+"/predictions"+str(num)+".txt","r") as fp: p=fp.readlines() return p,l def exact_match(p,l): c=0 for i1,i in enumerate(l): if p[i1]==l[i1]: c+=1 print("Per-Resp Acc: ",c/len(l)) def moses_bl_rouge(p,l): bl = bleu.moses_multi_bleu(p,l) x = rouge.rouge(p,l) print('BLEU: %f\nROUGE1-F: %f\nROUGE1-P: %f\nROUGE1-R: %f\nROUGE2-F: %f\nROUGE2-P: %f\nROUGE2-R: %f\nROUGEL-F: %f\nROUGEL-P: %f\nROUGEL-R: %f'%(bl,x['rouge_1/f_score'],x['rouge_1/p_score'],x['rouge_1/r_score'],x['rouge_2/f_score'], x['rouge_2/p_score'],x['rouge_2/r_score'],x['rouge_l/f_score'],x['rouge_l/p_score'],x['rouge_l/r_score'])) def micro_compute_prf(gold, pred, global_entity_list): TP, FP, FN = 0, 0, 0 if len(gold)!= 0: count = 1 for g in gold: if g in pred: TP += 1 else: FN += 1 for p in set(pred): if p in global_entity_list: if p not in gold: FP += 1 else: count = 0 return TP,FP,FN,count def ent_f1(preds,labels,kb_path): with open(kb_path,'r') as fp: kb=fp.readlines() ent=[] for i in kb: triples = i.split(' ') ent.append(triples[1].strip()) ent.append(triples[3].strip()) ent = set(ent) ent_list = sorted(ent) mic_pred=0 les=[] all_TP=0 all_FP=0 all_FN=0 for i in range(len(labels)): l = labels[i].strip().split() le=[] for j in l: if j in ent_list: le.append(j) les.append(le) p = preds[i].strip().split() tp,fp,fn,c = micro_compute_prf(le,p,ent_list) all_TP+=tp all_FP+=fp all_FN+=fn mic_pred+=c mic_prec = all_TP/float(all_TP+all_FP) mic_rec = all_TP/float(all_TP + all_FN) mic_f1=2 * mic_prec * mic_rec / float(mic_prec + mic_rec) print("Entity-F1:",mic_f1) if __name__=='__main__': args = get_args() result_path = args.preds_path kb_path = args.kb_path config_id = args.config_id print(config_id,"\n") preds,labels = read_results(result_path,config_id) exact_match(preds,labels) moses_bl_rouge(preds,labels) ent_f1(preds,labels,kb_path)

true

f71b27f6c3b0d480c4f0a1707e1a22e41cb11bbd

7,941

py

Python

venv/lib/python3.8/site-packages/django/middleware/cache.py

Joshua-Barawa/My-Photos

adcaea48149c6b31e9559b045709d538d0b749bc

[ "PostgreSQL", "Unlicense" ]

1

2022-03-16T14:43:26.000Z

venv/lib/python3.8/site-packages/django/middleware/cache.py

Joshua-Barawa/My-Photos

adcaea48149c6b31e9559b045709d538d0b749bc

[ "PostgreSQL", "Unlicense" ]

null

venv/lib/python3.8/site-packages/django/middleware/cache.py

Joshua-Barawa/My-Photos

adcaea48149c6b31e9559b045709d538d0b749bc

[ "PostgreSQL", "Unlicense" ]

4

2022-03-12T10:17:00.000Z

2022-03-26T08:40:43.000Z

""" Cache middleware. If enabled, each Django-powered page will be cached based on URL. The canonical way to enable cache middleware is to set ``UpdateCacheMiddleware`` as your first piece of middleware, and ``FetchFromCacheMiddleware`` as the last:: MIDDLEWARE = [ 'django.middleware.cache.UpdateCacheMiddleware', ... 'django.middleware.cache.FetchFromCacheMiddleware' ] This is counter-intuitive, but correct: ``UpdateCacheMiddleware`` needs to run last during the response phase, which processes middleware bottom-up; ``FetchFromCacheMiddleware`` needs to run last during the request phase, which processes middleware top-down. The single-class ``CacheMiddleware`` can be used for some simple sites. However, if any other piece of middleware needs to affect the cache key, you'll need to use the two-part ``UpdateCacheMiddleware`` and ``FetchFromCacheMiddleware``. This'll most often happen when you're using Django's ``LocaleMiddleware``. More details about how the caching works: * Only GET or HEAD-requests with status code 200 are cached. * The number of seconds each page is stored for is set by the "max-age" section of the response's "Cache-Control" header, falling back to the CACHE_MIDDLEWARE_SECONDS setting if the section was not found. * This middleware expects that a HEAD request is answered with the same response headers exactly like the corresponding GET request. * When a hit occurs, a shallow copy of the original response object is returned from process_request. * Pages will be cached based on the contents of the request headers listed in the response's "Vary" header. * This middleware also sets ETag, Last-Modified, Expires and Cache-Control headers on the response object. """ from django.conf import settings from django.core.cache import DEFAULT_CACHE_ALIAS, caches from django.utils.cache import ( get_cache_key, get_max_age, has_vary_header, learn_cache_key, patch_response_headers, ) from django.utils.deprecation import MiddlewareMixin class UpdateCacheMiddleware(MiddlewareMixin): """ Response-phase cache middleware that updates the cache if the response is cacheable. Must be used as part of the two-part update/fetch cache middleware. UpdateCacheMiddleware must be the first piece of middleware in MIDDLEWARE so that it'll get called last during the response phase. """ def __init__(self, get_response): super().__init__(get_response) self.cache_timeout = settings.CACHE_MIDDLEWARE_SECONDS self.page_timeout = None self.key_prefix = settings.CACHE_MIDDLEWARE_KEY_PREFIX self.cache_alias = settings.CACHE_MIDDLEWARE_ALIAS self.cache = caches[self.cache_alias] def _should_update_cache(self, request, response): return hasattr(request, "_cache_update_cache") and request._cache_update_cache def process_response(self, request, response): """Set the cache, if needed.""" if not self._should_update_cache(request, response): # We don't need to update the cache, just return. return response if response.streaming or response.status_code not in (200, 304): return response # Don't cache responses that set a user-specific (and maybe security # sensitive) cookie in response to a cookie-less request. if ( not request.COOKIES and response.cookies and has_vary_header(response, "Cookie") ): return response # Don't cache a response with 'Cache-Control: private' if "private" in response.get("Cache-Control", ()): return response # Page timeout takes precedence over the "max-age" and the default # cache timeout. timeout = self.page_timeout if timeout is None: # The timeout from the "max-age" section of the "Cache-Control" # header takes precedence over the default cache timeout. timeout = get_max_age(response) if timeout is None: timeout = self.cache_timeout elif timeout == 0: # max-age was set to 0, don't cache. return response patch_response_headers(response, timeout) if timeout and response.status_code == 200: cache_key = learn_cache_key( request, response, timeout, self.key_prefix, cache=self.cache ) if hasattr(response, "render") and callable(response.render): response.add_post_render_callback( lambda r: self.cache.set(cache_key, r, timeout) ) else: self.cache.set(cache_key, response, timeout) return response class FetchFromCacheMiddleware(MiddlewareMixin): """ Request-phase cache middleware that fetches a page from the cache. Must be used as part of the two-part update/fetch cache middleware. FetchFromCacheMiddleware must be the last piece of middleware in MIDDLEWARE so that it'll get called last during the request phase. """ def __init__(self, get_response): super().__init__(get_response) self.key_prefix = settings.CACHE_MIDDLEWARE_KEY_PREFIX self.cache_alias = settings.CACHE_MIDDLEWARE_ALIAS self.cache = caches[self.cache_alias] def process_request(self, request): """ Check whether the page is already cached and return the cached version if available. """ if request.method not in ("GET", "HEAD"): request._cache_update_cache = False return None # Don't bother checking the cache. # try and get the cached GET response cache_key = get_cache_key(request, self.key_prefix, "GET", cache=self.cache) if cache_key is None: request._cache_update_cache = True return None # No cache information available, need to rebuild. response = self.cache.get(cache_key) # if it wasn't found and we are looking for a HEAD, try looking just for that if response is None and request.method == "HEAD": cache_key = get_cache_key( request, self.key_prefix, "HEAD", cache=self.cache ) response = self.cache.get(cache_key) if response is None: request._cache_update_cache = True return None # No cache information available, need to rebuild. # hit, return cached response request._cache_update_cache = False return response class CacheMiddleware(UpdateCacheMiddleware, FetchFromCacheMiddleware): """ Cache middleware that provides basic behavior for many simple sites. Also used as the hook point for the cache decorator, which is generated using the decorator-from-middleware utility. """ def __init__(self, get_response, cache_timeout=None, page_timeout=None, **kwargs): super().__init__(get_response) # We need to differentiate between "provided, but using default value", # and "not provided". If the value is provided using a default, then # we fall back to system defaults. If it is not provided at all, # we need to use middleware defaults. try: key_prefix = kwargs["key_prefix"] if key_prefix is None: key_prefix = "" self.key_prefix = key_prefix except KeyError: pass try: cache_alias = kwargs["cache_alias"] if cache_alias is None: cache_alias = DEFAULT_CACHE_ALIAS self.cache_alias = cache_alias self.cache = caches[self.cache_alias] except KeyError: pass if cache_timeout is not None: self.cache_timeout = cache_timeout self.page_timeout = page_timeout

38.362319

86

0.673467

from django.conf import settings from django.core.cache import DEFAULT_CACHE_ALIAS, caches from django.utils.cache import ( get_cache_key, get_max_age, has_vary_header, learn_cache_key, patch_response_headers, ) from django.utils.deprecation import MiddlewareMixin class UpdateCacheMiddleware(MiddlewareMixin): def __init__(self, get_response): super().__init__(get_response) self.cache_timeout = settings.CACHE_MIDDLEWARE_SECONDS self.page_timeout = None self.key_prefix = settings.CACHE_MIDDLEWARE_KEY_PREFIX self.cache_alias = settings.CACHE_MIDDLEWARE_ALIAS self.cache = caches[self.cache_alias] def _should_update_cache(self, request, response): return hasattr(request, "_cache_update_cache") and request._cache_update_cache def process_response(self, request, response): if not self._should_update_cache(request, response): return response if response.streaming or response.status_code not in (200, 304): return response # Don't cache responses that set a user-specific (and maybe security if ( not request.COOKIES and response.cookies and has_vary_header(response, "Cookie") ): return response if "private" in response.get("Cache-Control", ()): return response # Page timeout takes precedence over the "max-age" and the default # cache timeout. timeout = self.page_timeout if timeout is None: # The timeout from the "max-age" section of the "Cache-Control" # header takes precedence over the default cache timeout. timeout = get_max_age(response) if timeout is None: timeout = self.cache_timeout elif timeout == 0: # max-age was set to 0, don't cache. return response patch_response_headers(response, timeout) if timeout and response.status_code == 200: cache_key = learn_cache_key( request, response, timeout, self.key_prefix, cache=self.cache ) if hasattr(response, "render") and callable(response.render): response.add_post_render_callback( lambda r: self.cache.set(cache_key, r, timeout) ) else: self.cache.set(cache_key, response, timeout) return response class FetchFromCacheMiddleware(MiddlewareMixin): def __init__(self, get_response): super().__init__(get_response) self.key_prefix = settings.CACHE_MIDDLEWARE_KEY_PREFIX self.cache_alias = settings.CACHE_MIDDLEWARE_ALIAS self.cache = caches[self.cache_alias] def process_request(self, request): if request.method not in ("GET", "HEAD"): request._cache_update_cache = False return None # try and get the cached GET response cache_key = get_cache_key(request, self.key_prefix, "GET", cache=self.cache) if cache_key is None: request._cache_update_cache = True return None # No cache information available, need to rebuild. response = self.cache.get(cache_key) # if it wasn't found and we are looking for a HEAD, try looking just for that if response is None and request.method == "HEAD": cache_key = get_cache_key( request, self.key_prefix, "HEAD", cache=self.cache ) response = self.cache.get(cache_key) if response is None: request._cache_update_cache = True return None request._cache_update_cache = False return response class CacheMiddleware(UpdateCacheMiddleware, FetchFromCacheMiddleware): def __init__(self, get_response, cache_timeout=None, page_timeout=None, **kwargs): super().__init__(get_response) try: key_prefix = kwargs["key_prefix"] if key_prefix is None: key_prefix = "" self.key_prefix = key_prefix except KeyError: pass try: cache_alias = kwargs["cache_alias"] if cache_alias is None: cache_alias = DEFAULT_CACHE_ALIAS self.cache_alias = cache_alias self.cache = caches[self.cache_alias] except KeyError: pass if cache_timeout is not None: self.cache_timeout = cache_timeout self.page_timeout = page_timeout

true

f71b286c1207f4d6dae4a96c65379f266e26d4b1

7,686

py

Python

newrelic/hooks/framework_grpc.py

odidev/newrelic-python-agent

e6c4ddc158ab694dd7ff6bd75e54077d736674f1

[ "Apache-2.0" ]

null

newrelic/hooks/framework_grpc.py

odidev/newrelic-python-agent

e6c4ddc158ab694dd7ff6bd75e54077d736674f1

[ "Apache-2.0" ]

null

newrelic/hooks/framework_grpc.py

odidev/newrelic-python-agent

e6c4ddc158ab694dd7ff6bd75e54077d736674f1

[ "Apache-2.0" ]

null

# Copyright 2010 New Relic, 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. import random import time from newrelic.api.external_trace import ExternalTrace from newrelic.api.web_transaction import WebTransactionWrapper from newrelic.api.transaction import current_transaction from newrelic.api.time_trace import record_exception from newrelic.common.object_wrapper import wrap_function_wrapper from newrelic.common.object_names import callable_name def _get_uri_method(instance, *args, **kwargs): target = instance._channel.target().decode('utf-8') method = instance._method.decode('utf-8').lstrip('/') uri = 'grpc://%s/%s' % (target, method) return (uri, method) def _prepare_request( transaction, guid, request, timeout=None, metadata=None, *args, **kwargs): metadata = metadata and list(metadata) or [] dt_metadata = transaction._create_distributed_trace_data_with_guid(guid) metadata.extend( transaction._generate_distributed_trace_headers(dt_metadata) ) args = (request, timeout, metadata) + args return args, kwargs def _prepare_request_stream( transaction, guid, request_iterator, *args, **kwargs): return _prepare_request( transaction, guid, request_iterator, *args, **kwargs) def wrap_call(module, object_path, prepare): def _call_wrapper(wrapped, instance, args, kwargs): transaction = current_transaction() if transaction is None: return wrapped(*args, **kwargs) uri, method = _get_uri_method(instance) with ExternalTrace('gRPC', uri, method): args, kwargs = prepare(transaction, None, *args, **kwargs) return wrapped(*args, **kwargs) wrap_function_wrapper(module, object_path, _call_wrapper) def wrap_future(module, object_path, prepare): def _future_wrapper(wrapped, instance, args, kwargs): transaction = current_transaction() if transaction is None: return wrapped(*args, **kwargs) guid = '%016x' % random.getrandbits(64) uri, method = _get_uri_method(instance) args, kwargs = prepare(transaction, guid, *args, **kwargs) future = wrapped(*args, **kwargs) future._nr_guid = guid future._nr_args = ('gRPC', uri, method) future._nr_start_time = time.time() # In non-streaming responses, result is typically called instead of # using the iterator. In streaming calls, the iterator is typically # used. return future wrap_function_wrapper(module, object_path, _future_wrapper) def wrap_next(_wrapped, _instance, _args, _kwargs): _nr_args = getattr(_instance, '_nr_args', None) if not _nr_args: return _wrapped(*_args, **_kwargs) try: return _wrapped(*_args, **_kwargs) except Exception: delattr(_instance, '_nr_args') _nr_start_time = getattr(_instance, '_nr_start_time', 0.0) _nr_guid = getattr(_instance, '_nr_guid', None) with ExternalTrace(*_nr_args) as t: t.start_time = _nr_start_time or t.start_time t.guid = _nr_guid or t.guid raise def wrap_result(_wrapped, _instance, _args, _kwargs): _nr_args = getattr(_instance, '_nr_args', None) if not _nr_args: return _wrapped(*_args, **_kwargs) delattr(_instance, '_nr_args') _nr_start_time = getattr(_instance, '_nr_start_time', 0.0) _nr_guid = getattr(_instance, '_nr_guid', None) try: result = _wrapped(*_args, **_kwargs) except Exception: with ExternalTrace(*_nr_args) as t: t.start_time = _nr_start_time or t.start_time t.guid = _nr_guid or t.guid raise else: with ExternalTrace(*_nr_args) as t: t.start_time = _nr_start_time or t.start_time t.guid = _nr_guid or t.guid return result def _bind_transaction_args(rpc_event, state, behavior, *args, **kwargs): return rpc_event, behavior def grpc_web_transaction(wrapped, instance, args, kwargs): rpc_event, behavior = _bind_transaction_args(*args, **kwargs) behavior_name = callable_name(behavior) call_details = ( getattr(rpc_event, 'call_details', None) or getattr(rpc_event, 'request_call_details', None)) metadata = ( getattr(rpc_event, 'invocation_metadata', None) or getattr(rpc_event, 'request_metadata', None)) host = port = None if call_details: try: host, port = call_details.host.split(b':', 1) except Exception: pass request_path = call_details.method return WebTransactionWrapper( wrapped, name=behavior_name, request_path=request_path, host=host, port=port, headers=metadata)(*args, **kwargs) def _trailing_metadata(state, *args, **kwargs): return state.trailing_metadata def _nr_wrap_status_code(wrapped, instance, args, kwargs): status_code = wrapped(*args, **kwargs) response_headers = _trailing_metadata(*args, **kwargs) transaction = current_transaction() if transaction: transaction.process_response(status_code, response_headers) return status_code def _nr_wrap_abort(wrapped, instance, args, kwargs): record_exception() return wrapped(*args, **kwargs) def instrument_grpc__channel(module): wrap_call(module, '_UnaryUnaryMultiCallable.__call__', _prepare_request) wrap_call(module, '_UnaryUnaryMultiCallable.with_call', _prepare_request) wrap_future(module, '_UnaryUnaryMultiCallable.future', _prepare_request) wrap_future(module, '_UnaryStreamMultiCallable.__call__', _prepare_request) wrap_call(module, '_StreamUnaryMultiCallable.__call__', _prepare_request_stream) wrap_call(module, '_StreamUnaryMultiCallable.with_call', _prepare_request_stream) wrap_future(module, '_StreamUnaryMultiCallable.future', _prepare_request_stream) wrap_future(module, '_StreamStreamMultiCallable.__call__', _prepare_request_stream) if hasattr(module, '_MultiThreadedRendezvous'): wrap_function_wrapper(module, '_MultiThreadedRendezvous.result', wrap_result) wrap_function_wrapper(module, '_MultiThreadedRendezvous._next', wrap_next) else: wrap_function_wrapper(module, '_Rendezvous.result', wrap_result) wrap_function_wrapper(module, '_Rendezvous._next', wrap_next) wrap_function_wrapper(module, '_Rendezvous.cancel', wrap_result) def instrument_grpc_server(module): wrap_function_wrapper(module, '_unary_response_in_pool', grpc_web_transaction) wrap_function_wrapper(module, '_stream_response_in_pool', grpc_web_transaction) wrap_function_wrapper(module, '_completion_code', _nr_wrap_status_code) wrap_function_wrapper(module, '_abortion_code', _nr_wrap_status_code) wrap_function_wrapper(module, '_abort', _nr_wrap_abort)

33.710526

76

0.683971

import random import time from newrelic.api.external_trace import ExternalTrace from newrelic.api.web_transaction import WebTransactionWrapper from newrelic.api.transaction import current_transaction from newrelic.api.time_trace import record_exception from newrelic.common.object_wrapper import wrap_function_wrapper from newrelic.common.object_names import callable_name def _get_uri_method(instance, *args, **kwargs): target = instance._channel.target().decode('utf-8') method = instance._method.decode('utf-8').lstrip('/') uri = 'grpc://%s/%s' % (target, method) return (uri, method) def _prepare_request( transaction, guid, request, timeout=None, metadata=None, *args, **kwargs): metadata = metadata and list(metadata) or [] dt_metadata = transaction._create_distributed_trace_data_with_guid(guid) metadata.extend( transaction._generate_distributed_trace_headers(dt_metadata) ) args = (request, timeout, metadata) + args return args, kwargs def _prepare_request_stream( transaction, guid, request_iterator, *args, **kwargs): return _prepare_request( transaction, guid, request_iterator, *args, **kwargs) def wrap_call(module, object_path, prepare): def _call_wrapper(wrapped, instance, args, kwargs): transaction = current_transaction() if transaction is None: return wrapped(*args, **kwargs) uri, method = _get_uri_method(instance) with ExternalTrace('gRPC', uri, method): args, kwargs = prepare(transaction, None, *args, **kwargs) return wrapped(*args, **kwargs) wrap_function_wrapper(module, object_path, _call_wrapper) def wrap_future(module, object_path, prepare): def _future_wrapper(wrapped, instance, args, kwargs): transaction = current_transaction() if transaction is None: return wrapped(*args, **kwargs) guid = '%016x' % random.getrandbits(64) uri, method = _get_uri_method(instance) args, kwargs = prepare(transaction, guid, *args, **kwargs) future = wrapped(*args, **kwargs) future._nr_guid = guid future._nr_args = ('gRPC', uri, method) future._nr_start_time = time.time() return future wrap_function_wrapper(module, object_path, _future_wrapper) def wrap_next(_wrapped, _instance, _args, _kwargs): _nr_args = getattr(_instance, '_nr_args', None) if not _nr_args: return _wrapped(*_args, **_kwargs) try: return _wrapped(*_args, **_kwargs) except Exception: delattr(_instance, '_nr_args') _nr_start_time = getattr(_instance, '_nr_start_time', 0.0) _nr_guid = getattr(_instance, '_nr_guid', None) with ExternalTrace(*_nr_args) as t: t.start_time = _nr_start_time or t.start_time t.guid = _nr_guid or t.guid raise def wrap_result(_wrapped, _instance, _args, _kwargs): _nr_args = getattr(_instance, '_nr_args', None) if not _nr_args: return _wrapped(*_args, **_kwargs) delattr(_instance, '_nr_args') _nr_start_time = getattr(_instance, '_nr_start_time', 0.0) _nr_guid = getattr(_instance, '_nr_guid', None) try: result = _wrapped(*_args, **_kwargs) except Exception: with ExternalTrace(*_nr_args) as t: t.start_time = _nr_start_time or t.start_time t.guid = _nr_guid or t.guid raise else: with ExternalTrace(*_nr_args) as t: t.start_time = _nr_start_time or t.start_time t.guid = _nr_guid or t.guid return result def _bind_transaction_args(rpc_event, state, behavior, *args, **kwargs): return rpc_event, behavior def grpc_web_transaction(wrapped, instance, args, kwargs): rpc_event, behavior = _bind_transaction_args(*args, **kwargs) behavior_name = callable_name(behavior) call_details = ( getattr(rpc_event, 'call_details', None) or getattr(rpc_event, 'request_call_details', None)) metadata = ( getattr(rpc_event, 'invocation_metadata', None) or getattr(rpc_event, 'request_metadata', None)) host = port = None if call_details: try: host, port = call_details.host.split(b':', 1) except Exception: pass request_path = call_details.method return WebTransactionWrapper( wrapped, name=behavior_name, request_path=request_path, host=host, port=port, headers=metadata)(*args, **kwargs) def _trailing_metadata(state, *args, **kwargs): return state.trailing_metadata def _nr_wrap_status_code(wrapped, instance, args, kwargs): status_code = wrapped(*args, **kwargs) response_headers = _trailing_metadata(*args, **kwargs) transaction = current_transaction() if transaction: transaction.process_response(status_code, response_headers) return status_code def _nr_wrap_abort(wrapped, instance, args, kwargs): record_exception() return wrapped(*args, **kwargs) def instrument_grpc__channel(module): wrap_call(module, '_UnaryUnaryMultiCallable.__call__', _prepare_request) wrap_call(module, '_UnaryUnaryMultiCallable.with_call', _prepare_request) wrap_future(module, '_UnaryUnaryMultiCallable.future', _prepare_request) wrap_future(module, '_UnaryStreamMultiCallable.__call__', _prepare_request) wrap_call(module, '_StreamUnaryMultiCallable.__call__', _prepare_request_stream) wrap_call(module, '_StreamUnaryMultiCallable.with_call', _prepare_request_stream) wrap_future(module, '_StreamUnaryMultiCallable.future', _prepare_request_stream) wrap_future(module, '_StreamStreamMultiCallable.__call__', _prepare_request_stream) if hasattr(module, '_MultiThreadedRendezvous'): wrap_function_wrapper(module, '_MultiThreadedRendezvous.result', wrap_result) wrap_function_wrapper(module, '_MultiThreadedRendezvous._next', wrap_next) else: wrap_function_wrapper(module, '_Rendezvous.result', wrap_result) wrap_function_wrapper(module, '_Rendezvous._next', wrap_next) wrap_function_wrapper(module, '_Rendezvous.cancel', wrap_result) def instrument_grpc_server(module): wrap_function_wrapper(module, '_unary_response_in_pool', grpc_web_transaction) wrap_function_wrapper(module, '_stream_response_in_pool', grpc_web_transaction) wrap_function_wrapper(module, '_completion_code', _nr_wrap_status_code) wrap_function_wrapper(module, '_abortion_code', _nr_wrap_status_code) wrap_function_wrapper(module, '_abort', _nr_wrap_abort)

true

f71b28ebefb77cb9a3e1c49a1442eb967f6d40ea

2,038

py

Python

pytorch_lightning/plugins/training_type/sharded_spawn.py

peblair/pytorch-lightning

e676ff96b16224331297dbd0e5ecd5cf364965b8

[ "Apache-2.0" ]

1

2021-02-12T04:15:31.000Z

pytorch_lightning/plugins/training_type/sharded_spawn.py

peblair/pytorch-lightning

e676ff96b16224331297dbd0e5ecd5cf364965b8

[ "Apache-2.0" ]

null

pytorch_lightning/plugins/training_type/sharded_spawn.py

peblair/pytorch-lightning

e676ff96b16224331297dbd0e5ecd5cf364965b8

[ "Apache-2.0" ]

null

from typing import Optional from pytorch_lightning.core.optimizer import is_lightning_optimizer from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE, rank_zero_only if _FAIRSCALE_AVAILABLE: from fairscale.optim import OSS from pytorch_lightning.overrides.fairscale import LightningShardedDataParallel class DDPSpawnShardedPlugin(DDPSpawnPlugin): def configure_ddp(self): self._wrap_optimizers() self._model = LightningShardedDataParallel( self.model, sharded_optimizer=self.lightning_module.trainer.optimizers ) def _reinit_optimizers_with_oss(self): optimizers = self.lightning_module.trainer.optimizers for x, optimizer in enumerate(optimizers): if is_lightning_optimizer(optimizer): optimizer = optimizer._optimizer if not isinstance(optimizer, OSS): optim_class = type(optimizer) zero_optimizer = OSS(params=optimizer.param_groups, optim=optim_class, **optimizer.defaults) optimizers[x] = zero_optimizer del optimizer trainer = self.lightning_module.trainer trainer.optimizers = trainer.convert_to_lightning_optimizers(optimizers) def _wrap_optimizers(self): trainer = self.model.trainer if trainer.testing: return self._reinit_optimizers_with_oss() def optimizer_state(self, optimizer: 'OSS') -> Optional[dict]: if is_lightning_optimizer(optimizer): optimizer = optimizer._optimizer if isinstance(optimizer, OSS): optimizer.consolidate_state_dict() return self._optim_state_dict(optimizer) @rank_zero_only def _optim_state_dict(self, optimizer): """ Retrieves state dict only on rank 0, which contains the entire optimizer state after calling :meth:`consolidate_state_dict`. """ return optimizer.state_dict()

37.054545

108

0.707066

from typing import Optional from pytorch_lightning.core.optimizer import is_lightning_optimizer from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE, rank_zero_only if _FAIRSCALE_AVAILABLE: from fairscale.optim import OSS from pytorch_lightning.overrides.fairscale import LightningShardedDataParallel class DDPSpawnShardedPlugin(DDPSpawnPlugin): def configure_ddp(self): self._wrap_optimizers() self._model = LightningShardedDataParallel( self.model, sharded_optimizer=self.lightning_module.trainer.optimizers ) def _reinit_optimizers_with_oss(self): optimizers = self.lightning_module.trainer.optimizers for x, optimizer in enumerate(optimizers): if is_lightning_optimizer(optimizer): optimizer = optimizer._optimizer if not isinstance(optimizer, OSS): optim_class = type(optimizer) zero_optimizer = OSS(params=optimizer.param_groups, optim=optim_class, **optimizer.defaults) optimizers[x] = zero_optimizer del optimizer trainer = self.lightning_module.trainer trainer.optimizers = trainer.convert_to_lightning_optimizers(optimizers) def _wrap_optimizers(self): trainer = self.model.trainer if trainer.testing: return self._reinit_optimizers_with_oss() def optimizer_state(self, optimizer: 'OSS') -> Optional[dict]: if is_lightning_optimizer(optimizer): optimizer = optimizer._optimizer if isinstance(optimizer, OSS): optimizer.consolidate_state_dict() return self._optim_state_dict(optimizer) @rank_zero_only def _optim_state_dict(self, optimizer): return optimizer.state_dict()

true

f71b296b1a35ac64d40aa0c9ca07717a2e1e1b1b

17,693

py

Python

models/pages.py

tobiassernhede/multi_user_blog

c657c5dacdab7b04cf226f75a085e8ac5a1d54a2

[ "MIT" ]

null

models/pages.py

tobiassernhede/multi_user_blog

c657c5dacdab7b04cf226f75a085e8ac5a1d54a2

[ "MIT" ]

null

models/pages.py

tobiassernhede/multi_user_blog

c657c5dacdab7b04cf226f75a085e8ac5a1d54a2

[ "MIT" ]

null

# Split up the pages functionality in separate file to make the code # easier to read import os import re import webapp2 import jinja2 import json from google.appengine.ext import ndb from google.appengine.api import images # Importing local .py files from models.users import User, users_key, make_secure_val, check_secure_val from models.posts import Post, blog_key from models.comments import Comment, comment_key from models.likes import Likes template_dir = os.path.join(os.path.dirname(__file__), '..', 'templates') jinja_env = jinja2.Environment(loader=jinja2.FileSystemLoader(template_dir), autoescape=True) class Handler(webapp2.RequestHandler): def write(self, *a, **kw): self.response.out.write(*a, **kw) def render_str(self, template, **params): params['user'] = self.user t = jinja_env.get_template(template) return t.render(params) def render(self, template, **kw): self.write(self.render_str(template, **kw)) def set_secure_cookie(self, name, val): cookie_val = make_secure_val(val) self.response.headers.add_header( 'Set-Cookie', '%s=%s; Path=/' % (name, cookie_val)) def read_secure_cookie(self, name): cookie_val = self.request.cookies.get(name) return cookie_val and check_secure_val(cookie_val) def login(self, user): self.set_secure_cookie('user_id', str(user.key.id())) def logout(self): self.response.headers.add_header('Set-Cookie', 'user_id=; Path=/') def initialize(self, *a, **kw): webapp2.RequestHandler.initialize(self, *a, **kw) uid = self.read_secure_cookie('user_id') self.user = uid and User.by_id(int(uid)) class MainPage(Handler): def get(self): # Running a post query for frontpage sorted by created date posts = Post.query() posts = posts.order(-Post.created) posts = posts.fetch() self.render('index.html', posts=posts) #### User Pages #### USER_RE = re.compile(r"^[a-zA-Z0-9_-]{3,20}$") def valid_username(username): return username and USER_RE.match(username) PASS_RE = re.compile(r"^.{3,20}$") def valid_password(password): return password and PASS_RE.match(password) EMAIL_RE = re.compile(r'^[\S]+@[\S]+\.[\S]+$') def valid_email(email): return not email or EMAIL_RE.match(email) class RegisterPage(Handler): def get(self): if self.user: self.redirect("/profile/" + self.user.name) else: self.render("register.html") def post(self): have_error = False self.username = self.request.get('username') self.password = self.request.get('password') self.verify = self.request.get('verify') self.email = self.request.get('email') self.description = self.request.get('description') self.profile_img = self.request.get('profile_img') self.error_msg = [] # setting up a list of potential error messages self.params = dict(username=self.username, email=self.email, description=self.description) if not valid_username(self.username): self.error_msg.extend(["That's not a valid username."]) have_error = True if not valid_password(self.password): self.error_msg.extend(["That wasn't a valid password."]) have_error = True elif self.password != self.verify: self.error_msg.extend(["Your passwords didn't match."]) have_error = True if not valid_email(self.email): self.serror_msg.extend(["That's not a valid email."]) have_error = True if have_error: # add error_msg to params dict if have error self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: self.done() def done(self): # make sure the user doesn't already exist u = User.by_name(self.username) if u: self.error_msg.extend(["That user already exists."]) self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: u = User.register( self.username, self.password, self.email, self.description, self.profile_img) u.put() self.login(u) self.redirect('/profile/' + u.name) class ProfilePage(Handler): def get(self, user_profile): # fetching the username from the uri # get the profile page of the user by name current_user_profile = User.by_name(user_profile) if not current_user_profile: self.response.set_status(404) self.render("404.html") return # run a query of all the posts this user has made posts = Post.query() posts = posts.filter(Post.user_id == current_user_profile.key.id()) posts = posts.order(-Post.created) posts = posts.fetch() self.render( 'profile.html', user_profile=current_user_profile, posts=posts) class EditProfilePage(Handler): def get(self): # Can only edit your own profile and you must be logged in so just # checks if user is logged in if self.user: user = User.by_id(int(self.user.key.id())) self.render("edit-profile.html", user=user) else: self.redirect("/login") def post(self): if not self.user: self.redirect("/login") return have_error = False self.username = self.request.get('username') self.password = self.request.get('password') self.verify = self.request.get('verify') self.email = self.request.get('email') self.description = self.request.get('description') self.profile_img = self.request.get('profile_img') self.delete_profile_img = self.request.get('delete_profile_img') self.user_id = self.user.key.id() self.error_msg = [] self.params = dict(username=self.username, email=self.email, description=self.description) if not valid_username(self.username): self.error_msg.extend(["That's not a valid username."]) have_error = True if self.password: if not valid_password(self.password): self.error_msg.extend(["That wasn't a valid password."]) have_error = True elif self.password != self.verify: self.error_msg.extend(["Your passwords didn't match."]) have_error = True if not valid_email(self.email): self.serror_msg.extend(["That's not a valid email."]) have_error = True if have_error: self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: self.done() def done(self): # make sure the user doesn't already exist u = User.by_name(self.username) if u and not self.username == self.user.name: self.error_msg.extend(["That user already exists."]) self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: user_update = User.update(self.username, self.password, self.email, self.description, self.profile_img, self.delete_profile_img, self.user_id) self.redirect('/profile/' + self.user.name) class LoginPage(Handler): def get(self): self.render('login.html') def post(self): username = self.request.get('username') password = self.request.get('password') u = User.login(username, password) if u: self.login(u) self.redirect('/blog') else: # simplified the error message handling as there is only one error # message possible msg = 'Invalid login' self.render('login.html', error=msg) class Logout(Handler): def get(self): self.logout() # Call logout function of parent class Handler self.redirect('/') # Redirect to frontpage on logout #### Blog Pages #### class BlogPage(Handler): def get(self): # if you try to reach /blog then redirect to frontpage. self.redirect('/') class PostPage(Handler): def get(self, post_id): # get the post_id from the uri key = ndb.Key('Post', int(post_id), parent=blog_key()) post = key.get() # used for comments if there is an error in the comment form # See CommentPost class comment_error = self.request.get('comment_error') if not post: # If there is no post then show a 404 as users can delete their own # posts self.response.set_status(404) self.render("404.html") return # Collect the comments that belongs to this post comments = Comment.by_post_id(int(post_id)) # For smarter rendering in the template I put the comments in a list # with a dict inside comment_output = [] # loop through each comment and create the dict for each comment for comment in comments: user_name = User.username_by_id(int(comment.user_id)) if not user_name: user_name = "Deleted User" comment = dict(content=comment.content, created=comment.created, user_name=user_name, comment_id=comment.key.id()) comment_output.append(comment) author = User.by_id(post.user_id) self.render( "post.html", post=post, author=author, comment_output=comment_output, comment_error=comment_error) class CreatePostPage(Handler): def get(self): if self.user: self.render('create-post.html') else: self.redirect('/login') def post(self): if not self.user: self.redirect('/login') subject = self.request.get('subject') content = self.request.get('content') user_id = self.user.key.id() featured_img = self.request.get('featured_img') # error handling is done inside the Post.create class found in posts.py post = Post.create(subject, content, featured_img, user_id) if post.has_error: params = dict( subject=subject, content=content, error_msg=post.error_msg) self.render('create-post.html', **params) else: self.redirect('/blog/%s' % str(post.p.key.id())) class EditPost(Handler): def get(self, post_id): if not self.user: self.redirect('/login') return post = Post.by_id(int(post_id)) if post.user_id == self.user.key.id(): self.render("edit-post.html", post=post) else: self.redirect('/blog/' + post_id) def post(self, post_id): if not self.user: self.redirect('/login') return subject = self.request.get('subject') content = self.request.get('content') # Possible to delete featured image so added one extra var # for edit post delete_featured_img = self.request.get('delete_featured_img') featured_img = self.request.get('featured_img') # error handling is done inside the Post.update class found in posts.py post = Post.update( int(post_id), subject, content, featured_img, delete_featured_img) if post.has_error: # If errors show the form again with the error messages params = dict( subject=subject, content=content, error_msg=post.error_msg) self.render('edit-post.html', **params) else: # Else redirect to the updated post self.redirect('/blog/%s' % str(post.p.key.id())) class DeletePost(Handler): def get(self, post_id): if not self.user: # check if user is logged in self.redirect('/login') return post = Post.by_id(int(post_id)) # check if user the same as the author if post.user_id == self.user.key.id(): post.key.delete() self.redirect('/profile/' + self.user.name) else: self.redirect('/blog/' + post_id) class DeleteComment(Handler): def get(self, comment_id, post_id): if not self.user: # check if user is logged in self.redirect('/login') return self.write(comment_id) comment = Comment.get_by_id(int(comment_id), parent=comment_key()) if not comment: self.redirect('/blog/' + post_id + '#comments-list') return # check if user is the same as the author if comment.user_id == self.user.key.id(): comment.key.delete() self.redirect('/blog/' + post_id + '#comments-list') else: self.redirect('/blog/' + post_id + '#comments-list') class CommentPost(Handler): def post(self, post_id): if not self.user: self.rediret('/login') return content = self.request.get('comment') user_id = self.user.key.id() comment = Comment.create(content, post_id, user_id) if comment.has_error: self.redirect( "/blog/" + post_id + "?comment_error=true#commentform") # redirect to PostPage class where the error messsage is handled else: self.redirect('/blog/%s#%s' % (str(post_id), "comments-list")) class LikePost(Handler): def get(self, post_id): key = ndb.Key('Post', int(post_id), parent=blog_key()) post = key.get() error = dict() response = None logged_in = False if post: author_id = post.user_id if self.user: logged_in = True if author_id == self.user.key.id(): error['has_error'] = True error['error_msg'] = "Can't like your own post" else: add_like = Likes.add_like(int(post_id), self.user.key.id()) response = add_like.response else: error['has_error'] = True error['error_msg'] = "No post found" self.write( json.dumps(({'logged_in': logged_in, 'response': response, 'error': error}))) class MissingPage(Handler): def get(self): # If a user tries to write a url that doesn't exist fallback is a 404 # template self.response.set_status(404) self.render("404.html") return class RouteProfile(Handler): def get(self): # If a user tries to visit just /profile rediret to frontpage self.redirect('/') class Image(Handler): """ Class for image handling. There are two types of images; Featured Image (featured_img) for posts and Profile Images (profile_img) for profile. This is used to create a unique url for each image and make it possible to serve images. """ def get(self): img_id = self.request.get('id') img_type = self.request.get('type') if img_id.isdigit(): if img_type == 'featured_img': img_key = ndb.Key('Post', int(img_id), parent=blog_key()) elif img_type == "profile_img": img_key = ndb.Key('User', int(img_id), parent=users_key()) if img_key: img = img_key.get() if img_type == "featured_img": if img.featured_img: self.response.headers['Content-Type'] = 'image/png' self.response.out.write(img.featured_img) return elif img_type == "profile_img": if img.profile_img: self.response.headers['Content-Type'] = 'image/png' self.response.out.write(img.profile_img) return self.response.set_status(404) self.render("404.html") appLoader = webapp2.WSGIApplication([('/', MainPage), ('/register', RegisterPage), ('/login', LoginPage), ('/logout', Logout), ('/profile', RouteProfile), ('/profile/(\w+)', ProfilePage), ('/edit-profile', EditProfilePage), ('/create-post', CreatePostPage), ('/blog/([0-9]+)/edit', EditPost), ('/blog/([0-9]+)/delete', DeletePost), ('/comment/([0-9]+)', CommentPost), ('/blog/([0-9]+)/like', LikePost), ('/comment/([0-9]+)/([0-9]+)/delete', DeleteComment), ('/blog', BlogPage), ('/blog/([0-9]+)', PostPage), ('/img', Image), ('/.*', MissingPage) ], debug=True)

33.50947

92

0.562652

import os import re import webapp2 import jinja2 import json from google.appengine.ext import ndb from google.appengine.api import images from models.users import User, users_key, make_secure_val, check_secure_val from models.posts import Post, blog_key from models.comments import Comment, comment_key from models.likes import Likes template_dir = os.path.join(os.path.dirname(__file__), '..', 'templates') jinja_env = jinja2.Environment(loader=jinja2.FileSystemLoader(template_dir), autoescape=True) class Handler(webapp2.RequestHandler): def write(self, *a, **kw): self.response.out.write(*a, **kw) def render_str(self, template, **params): params['user'] = self.user t = jinja_env.get_template(template) return t.render(params) def render(self, template, **kw): self.write(self.render_str(template, **kw)) def set_secure_cookie(self, name, val): cookie_val = make_secure_val(val) self.response.headers.add_header( 'Set-Cookie', '%s=%s; Path=/' % (name, cookie_val)) def read_secure_cookie(self, name): cookie_val = self.request.cookies.get(name) return cookie_val and check_secure_val(cookie_val) def login(self, user): self.set_secure_cookie('user_id', str(user.key.id())) def logout(self): self.response.headers.add_header('Set-Cookie', 'user_id=; Path=/') def initialize(self, *a, **kw): webapp2.RequestHandler.initialize(self, *a, **kw) uid = self.read_secure_cookie('user_id') self.user = uid and User.by_id(int(uid)) class MainPage(Handler): def get(self): posts = Post.query() posts = posts.order(-Post.created) posts = posts.fetch() self.render('index.html', posts=posts) rname(username): return username and USER_RE.match(username) PASS_RE = re.compile(r"^.{3,20}$") def valid_password(password): return password and PASS_RE.match(password) EMAIL_RE = re.compile(r'^[\S]+@[\S]+\.[\S]+$') def valid_email(email): return not email or EMAIL_RE.match(email) class RegisterPage(Handler): def get(self): if self.user: self.redirect("/profile/" + self.user.name) else: self.render("register.html") def post(self): have_error = False self.username = self.request.get('username') self.password = self.request.get('password') self.verify = self.request.get('verify') self.email = self.request.get('email') self.description = self.request.get('description') self.profile_img = self.request.get('profile_img') self.error_msg = [] self.params = dict(username=self.username, email=self.email, description=self.description) if not valid_username(self.username): self.error_msg.extend(["That's not a valid username."]) have_error = True if not valid_password(self.password): self.error_msg.extend(["That wasn't a valid password."]) have_error = True elif self.password != self.verify: self.error_msg.extend(["Your passwords didn't match."]) have_error = True if not valid_email(self.email): self.serror_msg.extend(["That's not a valid email."]) have_error = True if have_error: self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: self.done() def done(self): u = User.by_name(self.username) if u: self.error_msg.extend(["That user already exists."]) self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: u = User.register( self.username, self.password, self.email, self.description, self.profile_img) u.put() self.login(u) self.redirect('/profile/' + u.name) class ProfilePage(Handler): def get(self, user_profile): # fetching the username from the uri # get the profile page of the user by name current_user_profile = User.by_name(user_profile) if not current_user_profile: self.response.set_status(404) self.render("404.html") return # run a query of all the posts this user has made posts = Post.query() posts = posts.filter(Post.user_id == current_user_profile.key.id()) posts = posts.order(-Post.created) posts = posts.fetch() self.render( 'profile.html', user_profile=current_user_profile, posts=posts) class EditProfilePage(Handler): def get(self): # Can only edit your own profile and you must be logged in so just # checks if user is logged in if self.user: user = User.by_id(int(self.user.key.id())) self.render("edit-profile.html", user=user) else: self.redirect("/login") def post(self): if not self.user: self.redirect("/login") return have_error = False self.username = self.request.get('username') self.password = self.request.get('password') self.verify = self.request.get('verify') self.email = self.request.get('email') self.description = self.request.get('description') self.profile_img = self.request.get('profile_img') self.delete_profile_img = self.request.get('delete_profile_img') self.user_id = self.user.key.id() self.error_msg = [] self.params = dict(username=self.username, email=self.email, description=self.description) if not valid_username(self.username): self.error_msg.extend(["That's not a valid username."]) have_error = True if self.password: if not valid_password(self.password): self.error_msg.extend(["That wasn't a valid password."]) have_error = True elif self.password != self.verify: self.error_msg.extend(["Your passwords didn't match."]) have_error = True if not valid_email(self.email): self.serror_msg.extend(["That's not a valid email."]) have_error = True if have_error: self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: self.done() def done(self): # make sure the user doesn't already exist u = User.by_name(self.username) if u and not self.username == self.user.name: self.error_msg.extend(["That user already exists."]) self.params['error_msg'] = self.error_msg self.render('register.html', **self.params) else: user_update = User.update(self.username, self.password, self.email, self.description, self.profile_img, self.delete_profile_img, self.user_id) self.redirect('/profile/' + self.user.name) class LoginPage(Handler): def get(self): self.render('login.html') def post(self): username = self.request.get('username') password = self.request.get('password') u = User.login(username, password) if u: self.login(u) self.redirect('/blog') else: msg = 'Invalid login' self.render('login.html', error=msg) class Logout(Handler): def get(self): self.logout() self.redirect('/') self.redirect('/') class PostPage(Handler): def get(self, post_id): key = ndb.Key('Post', int(post_id), parent=blog_key()) post = key.get() comment_error = self.request.get('comment_error') if not post: self.response.set_status(404) self.render("404.html") return comments = Comment.by_post_id(int(post_id)) comment_output = [] for comment in comments: user_name = User.username_by_id(int(comment.user_id)) if not user_name: user_name = "Deleted User" comment = dict(content=comment.content, created=comment.created, user_name=user_name, comment_id=comment.key.id()) comment_output.append(comment) author = User.by_id(post.user_id) self.render( "post.html", post=post, author=author, comment_output=comment_output, comment_error=comment_error) class CreatePostPage(Handler): def get(self): if self.user: self.render('create-post.html') else: self.redirect('/login') def post(self): if not self.user: self.redirect('/login') subject = self.request.get('subject') content = self.request.get('content') user_id = self.user.key.id() featured_img = self.request.get('featured_img') post = Post.create(subject, content, featured_img, user_id) if post.has_error: params = dict( subject=subject, content=content, error_msg=post.error_msg) self.render('create-post.html', **params) else: self.redirect('/blog/%s' % str(post.p.key.id())) class EditPost(Handler): def get(self, post_id): if not self.user: self.redirect('/login') return post = Post.by_id(int(post_id)) if post.user_id == self.user.key.id(): self.render("edit-post.html", post=post) else: self.redirect('/blog/' + post_id) def post(self, post_id): if not self.user: self.redirect('/login') return subject = self.request.get('subject') content = self.request.get('content') delete_featured_img = self.request.get('delete_featured_img') featured_img = self.request.get('featured_img') post = Post.update( int(post_id), subject, content, featured_img, delete_featured_img) if post.has_error: params = dict( subject=subject, content=content, error_msg=post.error_msg) self.render('edit-post.html', **params) else: self.redirect('/blog/%s' % str(post.p.key.id())) class DeletePost(Handler): def get(self, post_id): if not self.user: self.redirect('/login') return post = Post.by_id(int(post_id)) if post.user_id == self.user.key.id(): post.key.delete() self.redirect('/profile/' + self.user.name) else: self.redirect('/blog/' + post_id) class DeleteComment(Handler): def get(self, comment_id, post_id): if not self.user: self.redirect('/login') return self.write(comment_id) comment = Comment.get_by_id(int(comment_id), parent=comment_key()) if not comment: self.redirect('/blog/' + post_id + '#comments-list') return if comment.user_id == self.user.key.id(): comment.key.delete() self.redirect('/blog/' + post_id + '#comments-list') else: self.redirect('/blog/' + post_id + '#comments-list') class CommentPost(Handler): def post(self, post_id): if not self.user: self.rediret('/login') return content = self.request.get('comment') user_id = self.user.key.id() comment = Comment.create(content, post_id, user_id) if comment.has_error: self.redirect( "/blog/" + post_id + "?comment_error=true#commentform") else: self.redirect('/blog/%s#%s' % (str(post_id), "comments-list")) class LikePost(Handler): def get(self, post_id): key = ndb.Key('Post', int(post_id), parent=blog_key()) post = key.get() error = dict() response = None logged_in = False if post: author_id = post.user_id if self.user: logged_in = True if author_id == self.user.key.id(): error['has_error'] = True error['error_msg'] = "Can't like your own post" else: add_like = Likes.add_like(int(post_id), self.user.key.id()) response = add_like.response else: error['has_error'] = True error['error_msg'] = "No post found" self.write( json.dumps(({'logged_in': logged_in, 'response': response, 'error': error}))) class MissingPage(Handler): def get(self): # If a user tries to write a url that doesn't exist fallback is a 404 self.response.set_status(404) self.render("404.html") return class RouteProfile(Handler): def get(self): self.redirect('/') class Image(Handler): def get(self): img_id = self.request.get('id') img_type = self.request.get('type') if img_id.isdigit(): if img_type == 'featured_img': img_key = ndb.Key('Post', int(img_id), parent=blog_key()) elif img_type == "profile_img": img_key = ndb.Key('User', int(img_id), parent=users_key()) if img_key: img = img_key.get() if img_type == "featured_img": if img.featured_img: self.response.headers['Content-Type'] = 'image/png' self.response.out.write(img.featured_img) return elif img_type == "profile_img": if img.profile_img: self.response.headers['Content-Type'] = 'image/png' self.response.out.write(img.profile_img) return self.response.set_status(404) self.render("404.html") appLoader = webapp2.WSGIApplication([('/', MainPage), ('/register', RegisterPage), ('/login', LoginPage), ('/logout', Logout), ('/profile', RouteProfile), ('/profile/(\w+)', ProfilePage), ('/edit-profile', EditProfilePage), ('/create-post', CreatePostPage), ('/blog/([0-9]+)/edit', EditPost), ('/blog/([0-9]+)/delete', DeletePost), ('/comment/([0-9]+)', CommentPost), ('/blog/([0-9]+)/like', LikePost), ('/comment/([0-9]+)/([0-9]+)/delete', DeleteComment), ('/blog', BlogPage), ('/blog/([0-9]+)', PostPage), ('/img', Image), ('/.*', MissingPage) ], debug=True)

true

f71b2aad75e30594e61025ad33be2a2c17932235

2,792

py

Python

reinvent_models/link_invent/networks/encoder_decoder.py

GT4SD/-reinvent_models

e1cf00d1b24fe5f39354e34829adc25460da84e2

[ "MIT" ]

null

reinvent_models/link_invent/networks/encoder_decoder.py

GT4SD/-reinvent_models

e1cf00d1b24fe5f39354e34829adc25460da84e2

[ "MIT" ]

1

2022-03-07T12:18:00.000Z

reinvent_models/link_invent/networks/encoder_decoder.py

GT4SD/reinvent_models

e1cf00d1b24fe5f39354e34829adc25460da84e2

[ "MIT" ]

null

""" Implementation of a network using an Encoder-Decoder architecture. """ import torch.nn as tnn from torch import Tensor from reinvent_models.link_invent.networks.decoder import Decoder from reinvent_models.link_invent.networks.encoder import Encoder class EncoderDecoder(tnn.Module): """ An encoder-decoder that combines input with generated targets. """ def __init__(self, encoder_params: dict, decoder_params: dict): super(EncoderDecoder, self).__init__() self._encoder = Encoder(**encoder_params) self._decoder = Decoder(**decoder_params) def forward(self, encoder_seqs: Tensor, encoder_seq_lengths: Tensor, decoder_seqs: Tensor, decoder_seq_lengths: Tensor): """ Performs the forward pass. :param encoder_seqs: A tensor with the output sequences (batch, seq_d, dim). :param encoder_seq_lengths: A list with the length of each input sequence. :param decoder_seqs: A tensor with the encoded input input sequences (batch, seq_e, dim). :param decoder_seq_lengths: The lengths of the decoder sequences. :return : The output logits as a tensor (batch, seq_d, dim). """ encoder_padded_seqs, hidden_states = self.forward_encoder(encoder_seqs, encoder_seq_lengths) logits, _, _ = self.forward_decoder(decoder_seqs, decoder_seq_lengths, encoder_padded_seqs, hidden_states) return logits def forward_encoder(self, padded_seqs: Tensor, seq_lengths: Tensor): """ Does a forward pass only of the encoder. :param padded_seqs: The data to feed the encoder. :param seq_lengths: The length of each sequence in the batch. :return : Returns a tuple with (encoded_seqs, hidden_states) """ return self._encoder(padded_seqs, seq_lengths) def forward_decoder(self, padded_seqs: Tensor, seq_lengths: Tensor, encoder_padded_seqs: Tensor, hidden_states: Tensor): """ Does a forward pass only of the decoder. :param hidden_states: The hidden states from the encoder. :param padded_seqs: The data to feed to the decoder. :param seq_lengths: The length of each sequence in the batch. :return : Returns the logits and the hidden state for each element of the sequence passed. """ return self._decoder(padded_seqs, seq_lengths, encoder_padded_seqs, hidden_states) def get_params(self): """ Obtains the params for the network. :return : A dict with the params. """ return { "encoder_params": self._encoder.get_params(), "decoder_params": self._decoder.get_params() }

42.30303

115

0.666189

import torch.nn as tnn from torch import Tensor from reinvent_models.link_invent.networks.decoder import Decoder from reinvent_models.link_invent.networks.encoder import Encoder class EncoderDecoder(tnn.Module): def __init__(self, encoder_params: dict, decoder_params: dict): super(EncoderDecoder, self).__init__() self._encoder = Encoder(**encoder_params) self._decoder = Decoder(**decoder_params) def forward(self, encoder_seqs: Tensor, encoder_seq_lengths: Tensor, decoder_seqs: Tensor, decoder_seq_lengths: Tensor): encoder_padded_seqs, hidden_states = self.forward_encoder(encoder_seqs, encoder_seq_lengths) logits, _, _ = self.forward_decoder(decoder_seqs, decoder_seq_lengths, encoder_padded_seqs, hidden_states) return logits def forward_encoder(self, padded_seqs: Tensor, seq_lengths: Tensor): return self._encoder(padded_seqs, seq_lengths) def forward_decoder(self, padded_seqs: Tensor, seq_lengths: Tensor, encoder_padded_seqs: Tensor, hidden_states: Tensor): return self._decoder(padded_seqs, seq_lengths, encoder_padded_seqs, hidden_states) def get_params(self): return { "encoder_params": self._encoder.get_params(), "decoder_params": self._decoder.get_params() }

true

f71b2accb33c9e4fb30a401746d3041c3b953b26

11,528

py

Python

second/mayank_scripts/infer_ros_melodic_pretained_same_frame.py

mayanks888/second.pytorch

02d37885a543ee46516648dcab7db8f5d677a179

[ "MIT" ]

null

second/mayank_scripts/infer_ros_melodic_pretained_same_frame.py

mayanks888/second.pytorch

02d37885a543ee46516648dcab7db8f5d677a179

[ "MIT" ]

null

second/mayank_scripts/infer_ros_melodic_pretained_same_frame.py

mayanks888/second.pytorch

02d37885a543ee46516648dcab7db8f5d677a179

[ "MIT" ]

null

#!/usr/bin/env python # ROS node libs import time import numpy as np import rospy import torch # from geometry_msgs.msg import Quaternion, Pose, Point, Vector3 from pyquaternion import Quaternion from google.protobuf import text_format from sensor_msgs.msg import PointCloud2 from std_msgs.msg import Header, ColorRGBA # from cv_bridge import CvBridge, CvBridgeError from visualization_msgs.msg import Marker, MarkerArray from second.protos import pipeline_pb2 # from second.utils import simplevis from second.pytorch.train import build_network from second.utils import config_tool from std_msgs.msg import Int16, Float32MultiArray from jsk_recognition_msgs.msg import BoundingBox, BoundingBoxArray # import ros_numpy # GPU settings: Select GPUs to use. Coment it to let the system decide # os.environ["CUDA_VISIBLE_DEVICES"]="0" class ros_tensorflow_obj(): def __init__(self): # ## Initial msg rospy.loginfo(' ## Starting ROS interface ##') # ## Load a (frozen) Tensorflow model into memory. print("ready to process----------------------------------------------------------") ####################################################################################333 # config_path = "../configs/nuscenes/all.pp.largea.config" # config_path = "/home/mayank_sati/codebase/python/lidar/second.pytorch/second/configs/pointpillars/car/xyres_28.config" config_path = "/home/mayank_sati/codebase/python/lidar/second.pytorch/second/configs/pointpillars/car/xyres_24.config" config = pipeline_pb2.TrainEvalPipelineConfig() with open(config_path, "r") as f: proto_str = f.read() text_format.Merge(proto_str, config) input_cfg = config.eval_input_reader model_cfg = config.model.second # config_tool.change_detection_range(model_cfg, [-50, -50, 50, 50]) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # ckpt_path = "../checkpoint/voxelnet-140670.tckpt" ckpt_path="/home/mayank_sati/Downloads/pretrained_models_v1.5/pp_model_for_nuscenes_pretrain/voxelnet-296960.tckpt" net = build_network(model_cfg).to(device).eval() net.load_state_dict(torch.load(ckpt_path)) target_assigner = net.target_assigner self.voxel_generator = net.voxel_generator class_names = target_assigner.classes grid_size = self.voxel_generator.grid_size feature_map_size = grid_size[:2] // config_tool.get_downsample_factor(model_cfg) feature_map_size = [*feature_map_size, 1][::-1] anchors = target_assigner.generate_anchors(feature_map_size)["anchors"] anchors = torch.tensor(anchors, dtype=torch.float32, device=device) anchors = anchors.view(1, -1, 7) # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ feature_map_size = [1, 50, 50] ret = target_assigner.generate_anchors(feature_map_size) class_names = target_assigner.classes anchors_dict = target_assigner.generate_anchors_dict(feature_map_size) anchors_list = [] for k, v in anchors_dict.items(): anchors_list.append(v["anchors"]) # anchors = ret["anchors"] anchors = np.concatenate(anchors_list, axis=0) anchors = anchors.reshape([-1, target_assigner.box_ndim]) assert np.allclose(anchors, ret["anchors"].reshape(-1, target_assigner.box_ndim)) matched_thresholds = ret["matched_thresholds"] unmatched_thresholds = ret["unmatched_thresholds"] # anchors_bv = box_np_ops.rbbox2d_to_near_bbox(anchors[:, [0, 1, 3, 4, 6]]) anchors_bv = 2 anchor_cache = { "anchors": anchors, "anchors_bv": anchors_bv, "matched_thresholds": matched_thresholds, "unmatched_thresholds": unmatched_thresholds, "anchors_dict": anchors_dict, } anchors = torch.tensor(anchors, dtype=torch.float32, device=device) self.anchors = anchors.view(1, -1, 7) self.net = net self.device = device ########################################################################################## # self.marker_publisher = rospy.Publisher('visualization_marker', MarkerArray, queue_size=5) self.pcl_publisher = rospy.Publisher('result_pcl', PointCloud2, queue_size=1) ############ # [print(n.name) for n in tf.get_default_graph().as_graph_def().node] # ROS environment setup # ## Define subscribers self.subscribers_def() # ## Define publishers self.publishers_def() self.now = rospy.Time.now() # Define subscribers def subscribers_def(self): # subs_topic = '/kitti/velo/pointcloud' #subs_topic = '/apollo/sensor/velodyne64/compensator/PointCloud2' # subs_topic = '/velodyne64_points' # subs_topic = '/apollo/sensor/velodyne64/PointCloud2' # subs_topic = '/points_raw' # subs_topic = '/livox/lidar' # subs_topic = '/apollo/sensor/velodyne32C/compensator/PointCloud2' subs_topic = '/lidar_top' self._sub = rospy.Subscriber(subs_topic, PointCloud2, self.lidar_callback, queue_size=10, buff_size=2 ** 24) # mydata = rospy.Subscriber( subs_topic , PointCloud2, self.lidar_callback, queue_size=1, buff_size=2**24) # print(mydata) # self._sub = rospy.Subscriber( subs_topic , Image, self.lidar_callback, queue_size=1, buff_size=100) # Define publishers def publishers_def(self): self._pub = rospy.Publisher('pc_bbox_topic', Float32MultiArray, queue_size=1) self.pub_arr_bbox = rospy.Publisher("Detections", BoundingBoxArray, queue_size=1) # Camera image callback def lidar_callback(self, point_cl_msg): arr_bbox = BoundingBoxArray() ############################################################################3 # lidar = np.fromstring(point_cl_msg.data, dtype=np.float32) # points = lidar.reshape(-1, 4) # print('gotit"') # pc = ros_numpy.numpify(point_cl_msg) # points = np.zeros((pc.shape[0], 4)) # points[:, 0] = pc['x'] # points[:, 1] = pc['y'] # points[:, 2] = pc['z'] # points[:, 3] = pc['intensity'] # points[:, 3] /= 255 #########################################################333 lidar = np.fromstring(point_cl_msg.data, dtype=np.float32) points = lidar.reshape(-1, 4) points[:, 3] /= 255 ####################################################################### res = self.voxel_generator.generate(points, max_voxels=30000) voxels = res["voxels"] coords = res["coordinates"] num_points = res["num_points_per_voxel"] num_voxels = np.array([voxels.shape[0]], dtype=np.int64) # print("voxel_generator_time",(time.time() - t)*1000) ############################################################### # print(voxels.shape) # add batch idx to coords coords = np.pad(coords, ((0, 0), (1, 0)), mode='constant', constant_values=0) voxels = torch.tensor(voxels, dtype=torch.float32, device=self.device) coords = torch.tensor(coords, dtype=torch.int32, device=self.device) num_points = torch.tensor(num_points, dtype=torch.int32, device=self.device) # print("conversion time",(time.time() - t)*1000) example = {"anchors": self.anchors, "voxels": voxels, "num_points": num_points, "coordinates": coords, } t2 = time.time() pred = self.net(example)[0] # print(pred) # print("prediction",(time.time() - t2)*1000) # print("total_time",(time.time() - t)*1000) boxes_lidar = pred["box3d_lidar"].detach().cpu().numpy() scores_lidar = pred["scores"].detach().cpu().numpy() labels_lidar = pred["label_preds"].detach().cpu().numpy() ##############################3333 threshold = 0.2 keep = np.where((scores_lidar >= threshold))[0] scores_lidar = scores_lidar[keep] print(scores_lidar) boxes_lidar = boxes_lidar[keep] labels_lidar = labels_lidar[keep] # sco # print(scores_lidar) ################################################################################ # self.show_text_in_rviz_mullti_cube(boxes_lidar,point_cl_msg) # self.show_text_in_rviz_mullti_sphere(boxes_lidar,point_cl_msg) ################################################################################## # apollo integration # numboxes = np.squeeze(scores_lidar) numboxes = len(scores_lidar) tl_bbox = Float32MultiArray() iLen = boxes_lidar.shape[0] lidar_bbox = Float32MultiArray() print('Processing no of object:', iLen) if (numboxes) >= 1: tmp = -np.ones(10 * (numboxes) + 1) for i in range(0, int(numboxes)): try: score = float((scores_lidar)[i]) if (boxes_lidar.shape[0]) == 1: bboxes = [float(v) for v in (boxes_lidar)[i]] else: bboxes = [float(v) for v in np.squeeze(boxes_lidar)[i]] tmp[0] = numboxes tmp[10 * i + 1] = score tmp[10 * i + 2] = bboxes[0] tmp[10 * i + 3] = bboxes[1] tmp[10 * i + 4] = bboxes[2] tmp[10 * i + 5] = bboxes[3] tmp[10 * i + 6] = bboxes[4] tmp[10 * i + 7] = bboxes[5] tmp[10 * i + 8] = bboxes[6] tmp[10 * i + 9] = 0 tmp[10 * i + 10] = 0 bbox = BoundingBox() # bbox.header.frame_id = point_cl_msg.header.frame_id # bbox.header.frame_id = 'livox_frame' bbox.header.frame_id = 'lidar_top' q = Quaternion(axis=(0, 0, 1), radians=-1.0 * float(boxes_lidar[i][6])) bbox.pose.orientation.x = q.x bbox.pose.orientation.y = q.y bbox.pose.orientation.z = q.z bbox.pose.orientation.w = q.w bbox.pose.position.x = float(boxes_lidar[i][0]) bbox.pose.position.y = float(boxes_lidar[i][1]) bbox.pose.position.z = float(boxes_lidar[i][2]) bbox.dimensions.x = float(boxes_lidar[i][3]) bbox.dimensions.y = float(boxes_lidar[i][4]) bbox.dimensions.z = float(boxes_lidar[i][5]) arr_bbox.boxes.append(bbox) except: print("I am here") # here data for publishing tl_bbox.data = tmp self._pub.publish(tl_bbox) arr_bbox.header.frame_id = point_cl_msg.header.frame_id self.pub_arr_bbox.publish(arr_bbox) point_cl_msg.header.frame_id = point_cl_msg.header.frame_id self.pcl_publisher.publish(point_cl_msg) arr_bbox.boxes.clear() def spin(self): rospy.spin() def main(): rospy.init_node('LIDAR_NODE', anonymous=True) tf_ob = ros_tensorflow_obj() # tf_ob.subscribers_def try: rospy.spin() except KeyboardInterrupt: print("Shutting down") if __name__ == '__main__': main()

45.207843

128

0.567661

import time import numpy as np import rospy import torch from pyquaternion import Quaternion from google.protobuf import text_format from sensor_msgs.msg import PointCloud2 from std_msgs.msg import Header, ColorRGBA from visualization_msgs.msg import Marker, MarkerArray from second.protos import pipeline_pb2 from second.pytorch.train import build_network from second.utils import config_tool from std_msgs.msg import Int16, Float32MultiArray from jsk_recognition_msgs.msg import BoundingBox, BoundingBoxArray class ros_tensorflow_obj(): def __init__(self): # Starting ROS interface ##')

true

f71b2b5262d128663739d9f88003925845a959b1

27,166

py

Python

colour/corresponding/datasets/breneman1987.py

MaxSchambach/colour

3f3685d616fda4be58cec20bc1e16194805d7e2d

[ "BSD-3-Clause" ]

null

colour/corresponding/datasets/breneman1987.py

MaxSchambach/colour

3f3685d616fda4be58cec20bc1e16194805d7e2d

[ "BSD-3-Clause" ]

null

colour/corresponding/datasets/breneman1987.py

MaxSchambach/colour

3f3685d616fda4be58cec20bc1e16194805d7e2d

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Breneman Corresponding Chromaticities Dataset ============================================= Defines *Breneman (1987)* results for corresponding chromaticities experiments. See Also -------- `Corresponding Chromaticities Prediction Jupyter Notebook <http://nbviewer.jupyter.org/github/colour-science/colour-notebooks/\ blob/master/notebooks/corresponding/prediction.ipynb>`_ References ---------- - :cite:`Breneman1987b` : Breneman, E. J. (1987). Corresponding chromaticities for different states of adaptation to complex visual fields. Journal of the Optical Society of America A, 4(6), 1115. doi:10.1364/JOSAA.4.001115 """ from __future__ import division, unicode_literals import numpy as np from collections import namedtuple from colour.utilities.documentation import DocstringDict __author__ = 'Colour Developers' __copyright__ = 'Copyright (C) 2013-2019 - Colour Developers' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'Colour Developers' __email__ = 'colour-developers@colour-science.org' __status__ = 'Production' __all__ = [ 'BrenemanExperimentResult', 'PrimariesChromaticityCoordinates', 'BRENEMAN_EXPERIMENT_1_RESULTS', 'BRENEMAN_EXPERIMENT_2_RESULTS', 'BRENEMAN_EXPERIMENT_3_RESULTS', 'BRENEMAN_EXPERIMENT_4_RESULTS', 'BRENEMAN_EXPERIMENT_5_RESULTS', 'BRENEMAN_EXPERIMENT_6_RESULTS', 'BRENEMAN_EXPERIMENT_7_RESULTS', 'BRENEMAN_EXPERIMENT_10_RESULTS', 'BRENEMAN_EXPERIMENT_8_RESULTS', 'BRENEMAN_EXPERIMENT_9_RESULTS', 'BRENEMAN_EXPERIMENT_11_RESULTS', 'BRENEMAN_EXPERIMENT_12_RESULTS', 'BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES', 'BRENEMAN_EXPERIMENTS' ] class BrenemanExperimentResult( namedtuple('BrenemanExperimentResult', ('name', 'uv_t', 'uv_m', 's_uv', 'd_uv_i', 'd_uv_g'))): """ Experiment result. Parameters ---------- name : unicode Test colour name. uv_t : numeric Chromaticity coordinates :math:`uv_t^p` of test colour. uv_m : array_like, (2,) Chromaticity coordinates :math:`uv_m^p` of matching colour. s_uv : array_like, (2,), optional Interobserver variation (:math:`x10^3`) :math:`\\sigma_uv^p`. d_uv_i : array_like, (2,), optional Deviation of individual linear transformation (:math:`x10^3`) :math:`\\delta_uv_i^p`. d_uv_g : array_like, (2,), optional Deviation of individual linear transformation (:math:`x10^3`) :math:`\\delta_uv_g^p`. """ def __new__(cls, name, uv_t, uv_m, s_uv=None, d_uv_i=None, d_uv_g=None): """ Returns a new instance of the :class:`colour.corresponding.datasets.corresponding_chromaticities.\ BrenemanExperimentResult` class. """ return super(BrenemanExperimentResult, cls).__new__( cls, name, np.array(uv_t), np.array(uv_m), np.array(s_uv), np.array(d_uv_i), np.array(d_uv_g)) class PrimariesChromaticityCoordinates( namedtuple( 'PrimariesChromaticityCoordinates', ('experiment', 'illuminants', 'Y', 'P_uvp', 'D_uvp', 'T_uvp'))): """ Chromaticity coordinates of primaries. Parameters ---------- experiment : integer Experiment. illuminants : array_like, (2,) Chromaticity coordinates :math:`uv_t^p` of test colour. Y : numeric White luminance :math:`Y` in :math:`cd/m^2`. P_uvp : numeric Chromaticity coordinates :math:`uv^p` of primary :math:`P`. D_uvp : numeric Chromaticity coordinates :math:`uv^p` of primary :math:`D`. T_uvp : numeric Chromaticity coordinates :math:`uv^p` of primary :math:`T`. """ def __new__(cls, experiment, illuminants, Y, P_uvp=None, D_uvp=None, T_uvp=None): """ Returns a new instance of the :class:`colour.corresponding.datasets.corresponding_chromaticities.\ PrimariesChromaticityCoordinates` class. """ return super(PrimariesChromaticityCoordinates, cls).__new__( cls, experiment, np.array(illuminants), np.array(Y), np.array(P_uvp), np.array(D_uvp), np.array(T_uvp)) # yapf: disable BRENEMAN_EXPERIMENT_1_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.259, 0.526), (0.200, 0.475)), BrenemanExperimentResult( 'Gray', (0.259, 0.524), (0.199, 0.487), (4, 4), (2, 3), (0, 0)), BrenemanExperimentResult( 'Red', (0.459, 0.522), (0.420, 0.509), (19, 4), (-10, -7), (-19, -3)), BrenemanExperimentResult( 'Skin', (0.307, 0.526), (0.249, 0.497), (7, 4), (-1, 1), (-6, -1)), BrenemanExperimentResult( 'Orange', (0.360, 0.544), (0.302, 0.548), (12, 1), (1, -2), (-7, -6)), BrenemanExperimentResult( 'Brown', (0.350, 0.541), (0.290, 0.537), (11, 4), (3, 0), (-5, -3)), BrenemanExperimentResult( 'Yellow', (0.318, 0.550), (0.257, 0.554), (8, 2), (0, 2), (-5, -5)), BrenemanExperimentResult( 'Foliage', (0.258, 0.542), (0.192, 0.529), (4, 6), (3, 2), (3, -6)), BrenemanExperimentResult( 'Green', (0.193, 0.542), (0.129, 0.521), (7, 5), (3, 2), (9, -7)), BrenemanExperimentResult( 'Blue-green', (0.180, 0.516), (0.133, 0.469), (4, 6), (-3, -2), (2, -5)), BrenemanExperimentResult( 'Blue', (0.186, 0.445), (0.158, 0.340), (13, 33), (2, 7), (1, 13)), BrenemanExperimentResult( 'Sky', (0.226, 0.491), (0.178, 0.426), (3, 14), (1, -3), (0, -1)), BrenemanExperimentResult( 'Purple', (0.278, 0.456), (0.231, 0.365), (4, 25), (0, 2), (-5, 7))) # yapf: enable """ *Breneman (1987)* experiment 1 results. BRENEMAN_EXPERIMENT_1_RESULTS : tuple Notes ----- - Illuminants : *A*, *D65* - White Luminance : 1500 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENT_2_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.222, 0.521), (0.204, 0.479)), BrenemanExperimentResult( 'Gray', (0.227, 0.517), (0.207, 0.486), (2, 5), (-1, 0), (0, 0)), BrenemanExperimentResult( 'Red', (0.464, 0.520), (0.449, 0.511), (22, 3), (-8, -8), (-7, -2)), BrenemanExperimentResult( 'Skin', (0.286, 0.526), (0.263, 0.505), (7, 2), (0, -1), (0, -1)), BrenemanExperimentResult( 'Orange', (0.348, 0.546), (0.322, 0.545), (13, 3), (3, -1), (3, -2)), BrenemanExperimentResult( 'Brown', (0.340, 0.543), (0.316, 0.537), (11, 3), (1, 1), (0, 0)), BrenemanExperimentResult( 'Yellow', (0.288, 0.554), (0.265, 0.553), (5, 2), (-2, 2), (-1, -2)), BrenemanExperimentResult( 'Foliage', (0.244, 0.547), (0.221, 0.538), (4, 3), (-2, 1), (0, -3)), BrenemanExperimentResult( 'Green', (0.156, 0.548), (0.135, 0.532), (4, 3), (-1, 3), (3, -4)), BrenemanExperimentResult( 'Blue-green', (0.159, 0.511), (0.145, 0.472), (9, 7), (-1, 2), (2, 1)), BrenemanExperimentResult( 'Blue', (0.160, 0.406), (0.163, 0.331), (23, 31), (2, -3), (-1, 3)), BrenemanExperimentResult( 'Sky', (0.190, 0.481), (0.176, 0.431), (5, 24), (2, -2), (2, 0)), BrenemanExperimentResult( 'Purple', (0.258, 0.431), (0.244, 0.349), (4, 19), (-3, 13), (-4, 19))) # yapf: enable """ *Breneman (1987)* experiment 2 results. BRENEMAN_EXPERIMENT_2_RESULTS : tuple Notes ----- - Illuminants : *Projector*, *D55* - White Luminance : 1500 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENT_3_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.223, 0.521), (0.206, 0.478)), BrenemanExperimentResult( 'Gray', (0.228, 0.517), (0.211, 0.494), (1, 3), (0, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.462, 0.519), (0.448, 0.505), (11, 4), (-3, 6), (-4, 6)), BrenemanExperimentResult( 'Skin', (0.285, 0.524), (0.267, 0.507), (6, 3), (-1, 1), (-2, 1)), BrenemanExperimentResult( 'Orange', (0.346, 0.546), (0.325, 0.541), (11, 3), (1, -2), (2, 3)), BrenemanExperimentResult( 'Brown', (0.338, 0.543), (0.321, 0.532), (9, 6), (-3, 2), (-3, 7)), BrenemanExperimentResult( 'Yellow', (0.287, 0.554), (0.267, 0.548), (4, 5), (1, -2), (0, 5)), BrenemanExperimentResult( 'Foliage', (0.244, 0.547), (0.226, 0.531), (3, 6), (-1, 3), (-2, 8)), BrenemanExperimentResult( 'Green', (0.157, 0.548), (0.141, 0.528), (9, 6), (2, 2), (0, 6)), BrenemanExperimentResult( 'Blue-green', (0.160, 0.510), (0.151, 0.486), (8, 5), (-2, -1), (-2, -5)), BrenemanExperimentResult( 'Blue', (0.162, 0.407), (0.158, 0.375), (6, 7), (1, -6), (4, -23)), BrenemanExperimentResult( 'Sky', (0.191, 0.482), (0.179, 0.452), (4, 5), (0, 1), (1, -7)), BrenemanExperimentResult( 'Purple', (0.258, 0.432), (0.238, 0.396), (4, 8), (5, 3), (4, -11))) # yapf: enable """ *Breneman (1987)* experiment 3 results. BRENEMAN_EXPERIMENT_3_RESULTS : tuple Notes ----- - Illuminants : *Projector*, *D55* - White Luminance : 75 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENT_4_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.258, 0.523), (0.199, 0.467)), BrenemanExperimentResult( 'Gray', (0.257, 0.524), (0.205, 0.495), (2, 2), (0, 4), (0, 0)), BrenemanExperimentResult( 'Red', (0.460, 0.521), (0.416, 0.501), (11, 6), (-6, 4), (-6, 9)), BrenemanExperimentResult( 'Skin', (0.308, 0.526), (0.253, 0.503), (7, 3), (-1, 1), (-1, 0)), BrenemanExperimentResult( 'Orange', (0.360, 0.544), (0.303, 0.541), (14, 5), (1, -4), (1, 2)), BrenemanExperimentResult( 'Brown', (0.350, 0.541), (0.296, 0.527), (11, 7), (-2, 4), (-3, 9)), BrenemanExperimentResult( 'Yellow', (0.317, 0.550), (0.260, 0.547), (9, 5), (1, -3), (0, 3)), BrenemanExperimentResult( 'Foliage', (0.258, 0.543), (0.203, 0.520), (4, 6), (0, 8), (0, 9)), BrenemanExperimentResult( 'Green', (0.193, 0.543), (0.142, 0.516), (6, 9), (3, 8), (2, 6)), BrenemanExperimentResult( 'Blue-green', (0.180, 0.516), (0.140, 0.484), (9, 5), (-2, -1), (-1, -9)), BrenemanExperimentResult( 'Blue', (0.185, 0.445), (0.151, 0.394), (8, 10), (2, -8), (8, -24)), BrenemanExperimentResult( 'Sky', (0.225, 0.490), (0.180, 0.448), (4, 8), (1, -1), (3, -11)), BrenemanExperimentResult( 'Purple', (0.278, 0.455), (0.229, 0.388), (6, 14), (1, 12), (3, 0))) # yapf: enable """ *Breneman (1987)* experiment 4 results. BRENEMAN_EXPERIMENT_4_RESULTS : tuple Notes ----- - Illuminants : *A*, *D65* - White Luminance : 75 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENT_5_RESULTS = ( BrenemanExperimentResult( 'Gray', (0.028, 0.480), (0.212, 0.491), (2, 2)), BrenemanExperimentResult( 'Red', (0.449, 0.512), (0.408, 0.514), (11, 5)), BrenemanExperimentResult( 'Skin', (0.269, 0.505), (0.262, 0.511), (4, 2)), BrenemanExperimentResult( 'Orange', (0.331, 0.548), (0.303, 0.545), (4, 3)), BrenemanExperimentResult( 'Brown', (0.322, 0.541), (0.303, 0.538), (4, 4)), BrenemanExperimentResult( 'Yellow', (0.268, 0.555), (0.264, 0.550), (3, 2)), BrenemanExperimentResult( 'Foliage', (0.224, 0.538), (0.227, 0.535), (3, 3)), BrenemanExperimentResult( 'Green', (0.134, 0.531), (0.159, 0.530), (9, 3)), BrenemanExperimentResult( 'Blue-green', (0.145, 0.474), (0.165, 0.490), (8, 3)), BrenemanExperimentResult( 'Blue', (0.163, 0.329), (0.173, 0.378), (7, 12)), BrenemanExperimentResult( 'Sky', (0.179, 0.438), (0.189, 0.462), (5, 4)), BrenemanExperimentResult( 'Purple', (0.245, 0.364), (0.239, 0.401), (4, 16))) # yapf: enable """ *Breneman (1987)* experiment 5 results. BRENEMAN_EXPERIMENT_5_RESULTS : tuple Notes ----- - Effective White Levels : 130 and 2120 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENT_6_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.257, 0.525), (0.201, 0.482)), BrenemanExperimentResult( 'Gray', (0.267, 0.521), (0.207, 0.485), (5, 3), (-1, 0), (0, 0)), BrenemanExperimentResult( 'Red', (0.457, 0.521), (0.398, 0.516), (9, 4), (-2, -5), (1, -9)), BrenemanExperimentResult( 'Skin', (0.316, 0.526), (0.253, 0.503), (5, 3), (-3, -2), (-1, -3)), BrenemanExperimentResult( 'Orange', (0.358, 0.545), (0.287, 0.550), (7, 3), (3, 0), (7, -6)), BrenemanExperimentResult( 'Brown', (0.350, 0.541), (0.282, 0.540), (6, 3), (-1, 0), (2, -5)), BrenemanExperimentResult( 'Yellow', (0.318, 0.551), (0.249, 0.556), (7, 2), (-1, 1), (2, -5)), BrenemanExperimentResult( 'Foliage', (0.256, 0.547), (0.188, 0.537), (5, 4), (3, 1), (4, -2)), BrenemanExperimentResult( 'Green', (0.193, 0.542), (0.133, 0.520), (13, 3), (5, -2), (5, -4)), BrenemanExperimentResult( 'Blue-green', (0.180, 0.516), (0.137, 0.466), (12, 10), (0, 0), (-2, 2)), BrenemanExperimentResult( 'Blue', (0.186, 0.445), (0.156, 0.353), (12, 45), (6, 1), (2, 6)), BrenemanExperimentResult( 'Sky', (0.225, 0.492), (0.178, 0.428), (6, 14), (1, -1), (-1, 3)), BrenemanExperimentResult( 'Purple', (0.276, 0.456), (0.227, 0.369), (6, 27), (-2, 4), (-3, 9))) # yapf: enable """ *Breneman (1987)* experiment 6 results. BRENEMAN_EXPERIMENT_6_RESULTS : tuple Notes ----- - Illuminants : *A*, *D55* - White Luminance : 11100 :math:`cd/m^2` - Observers Count : 8 """ # yapf: disable BRENEMAN_EXPERIMENT_7_RESULTS = ( BrenemanExperimentResult( 'Gray', (0.208, 0.481), (0.211, 0.486), (2, 3)), BrenemanExperimentResult( 'Red', (0.448, 0.512), (0.409, 0.516), (9, 2)), BrenemanExperimentResult( 'Skin', (0.269, 0.505), (0.256, 0.506), (4, 3)), BrenemanExperimentResult( 'Orange', (0.331, 0.549), (0.305, 0.547), (5, 4)), BrenemanExperimentResult( 'Brown', (0.322, 0.541), (0.301, 0.539), (5, 2)), BrenemanExperimentResult( 'Yellow', (0.268, 0.555), (0.257, 0.552), (3, 4)), BrenemanExperimentResult( 'Foliage', (0.225, 0.538), (0.222, 0.536), (3, 2)), BrenemanExperimentResult( 'Green', (0.135, 0.531), (0.153, 0.529), (8, 2)), BrenemanExperimentResult( 'Blue-green', (0.145, 0.475), (0.160, 0.484), (3, 5)), BrenemanExperimentResult( 'Blue', (0.163, 0.331), (0.171, 0.379), (4, 11)), BrenemanExperimentResult( 'Sky', (0.179, 0.438), (0.187, 0.452), (4, 7)), BrenemanExperimentResult( 'Purple', (0.245, 0.365), (0.240, 0.398), (4, 10))) # yapf: enable """ *Breneman (1987)* experiment 7 results. BRENEMAN_EXPERIMENT_7_RESULTS : tuple Notes ----- - Effective White Levels : 850 and 11100 :math:`cd/m^2` - Observers Count : 8 """ # yapf: disable BRENEMAN_EXPERIMENT_8_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.258, 0.524), (0.195, 0.469)), BrenemanExperimentResult( 'Gray', (0.257, 0.525), (0.200, 0.494), (2, 3), (1, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.458, 0.522), (0.410, 0.508), (12, 4), (-3, 5), (-7, 2)), BrenemanExperimentResult( 'Skin', (0.308, 0.526), (0.249, 0.502), (6, 2), (-1, 1), (-3, -1)), BrenemanExperimentResult( 'Orange', (0.359, 0.545), (0.299, 0.545), (12, 4), (0, -2), (-3, 0)), BrenemanExperimentResult( 'Brown', (0.349, 0.540), (0.289, 0.532), (10, 4), (0, 1), (-2, 2)), BrenemanExperimentResult( 'Yellow', (0.317, 0.550), (0.256, 0.549), (9, 5), (0, -3), (-3, 1)), BrenemanExperimentResult( 'Foliage', (0.260, 0.545), (0.198, 0.529), (5, 5), (3, 1), (0, 3)), BrenemanExperimentResult( 'Green', (0.193, 0.543), (0.137, 0.520), (9, 5), (3, 0), (2, 1)), BrenemanExperimentResult( 'Blue-green', (0.182, 0.516), (0.139, 0.477), (9, 4), (-3, 0), (-2, -4)), BrenemanExperimentResult( 'Blue', (0.184, 0.444), (0.150, 0.387), (5, 11), (3, -10), (6, -22)), BrenemanExperimentResult( 'Sky', (0.224, 0.489), (0.177, 0.439), (5, 6), (1, 1), (1, -7)), BrenemanExperimentResult( 'Purple', (0.277, 0.454), (0.226, 0.389), (4, 10), (1, 4), (1, -8))) # yapf: enable """ *Breneman (1987)* experiment 8 results. BRENEMAN_EXPERIMENT_8_RESULTS : tuple Notes ----- - Illuminants : *A*, *D65* - White Luminance : 350 :math:`cd/m^2` - Observers Count : 8 """ # yapf: disable BRENEMAN_EXPERIMENT_9_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.254, 0.525), (0.195, 0.465)), BrenemanExperimentResult( 'Gray', (0.256, 0.524), (0.207, 0.496), (4, 6), (3, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.459, 0.521), (0.415, 0.489), (20, 14), (2, 12), (-2, 21)), BrenemanExperimentResult( 'Skin', (0.307, 0.525), (0.261, 0.500), (7, 7), (0, 1), (-5, 2)), BrenemanExperimentResult( 'Orange', (0.359, 0.545), (0.313, 0.532), (7, 5), (-2, -3), (-6, 13)), BrenemanExperimentResult( 'Brown', (0.349, 0.540), (0.302, 0.510), (11, 15), (0, 12), (-5, 24)), BrenemanExperimentResult( 'Yellow', (0.317, 0.550), (0.268, 0.538), (7, 10), (1, -4), (-4, 12)), BrenemanExperimentResult( 'Foliage', (0.259, 0.544), (0.212, 0.510), (10, 11), (0, 14), (-4, 22)), BrenemanExperimentResult( 'Green', (0.193, 0.542), (0.150, 0.506), (6, 10), (-1, 13), (-2, 15)), BrenemanExperimentResult( 'Blue-green', (0.181, 0.517), (0.144, 0.487), (9, 6), (-3, 0), (-1, -9)), BrenemanExperimentResult( 'Blue', (0.184, 0.444), (0.155, 0.407), (4, 11), (-2, -6), (6, -36)), BrenemanExperimentResult( 'Sky', (0.225, 0.490), (0.183, 0.458), (5, 8), (1, -3), (2, -19)), BrenemanExperimentResult( 'Purple', (0.276, 0.454), (0.233, 0.404), (7, 12), (2, 9), (0, -16)), BrenemanExperimentResult( '(Gray)h', (0.256, 0.525), (0.208, 0.498)), BrenemanExperimentResult( '(Red)h', (0.456, 0.521), (0.416, 0.501), (15, 7), None, (-6, -9)), BrenemanExperimentResult( '(Brown)h', (0.349, 0.539), (0.306, 0.526), (11, 8), None, (-8, 7)), BrenemanExperimentResult( '(Foliage)h', (0.260, 0.545), (0.213, 0.528), (7, 9), None, (-4, 5)), BrenemanExperimentResult( '(Green)h', (0.193, 0.543), (0.149, 0.525), (10, 8), None, (-1, -1)), BrenemanExperimentResult( '(Blue)h', (0.184, 0.444), (0.156, 0.419), (7, 8), None, (4, -45)), BrenemanExperimentResult( '(Purple)h', (0.277, 0.456), (0.236, 0.422), (6, 11), None, (-2, -29))) # yapf: enable """ *Breneman (1987)* experiment 9 results. BRENEMAN_EXPERIMENT_9_RESULTS : tuple Notes ----- - Illuminants : *A*, *D65* - White Luminance : 15 :math:`cd/m^2` - Observers Count : 8 - The colors indicated by (.)h are the darker colors presented at the higher luminescence level of the lighter colors. """ # yapf: disable BRENEMAN_EXPERIMENT_10_RESULTS = ( BrenemanExperimentResult( 'Gray', (0.208, 0.482), (0.213, 0.494), (3, 3)), BrenemanExperimentResult( 'Red', (0.447, 0.512), (0.411, 0.506), (15, 7)), BrenemanExperimentResult( 'Skin', (0.269, 0.505), (0.269, 0.511), (4, 3)), BrenemanExperimentResult( 'Orange', (0.331, 0.549), (0.315, 0.536), (7, 8)), BrenemanExperimentResult( 'Brown', (0.323, 0.542), (0.310, 0.526), (6, 8)), BrenemanExperimentResult( 'Yellow', (0.268, 0.556), (0.268, 0.541), (3, 6)), BrenemanExperimentResult( 'Foliage', (0.226, 0.538), (0.230, 0.525), (4, 8)), BrenemanExperimentResult( 'Green', (0.135, 0.531), (0.158, 0.524), (6, 3)), BrenemanExperimentResult( 'Blue-green', (0.145, 0.476), (0.161, 0.491), (4, 4)), BrenemanExperimentResult( 'Blue', (0.163, 0.330), (0.171, 0.377), (6, 19)), BrenemanExperimentResult( 'Sky', (0.179, 0.439), (0.187, 0.465), (5, 5)), BrenemanExperimentResult( 'Purple', (0.245, 0.366), (0.240, 0.402), (3, 12))) # yapf: enable """ *Breneman (1987)* experiment 10 results. BRENEMAN_EXPERIMENT_10_RESULTS : tuple Notes ----- - Effective White Levels : 15 and 270 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENT_11_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.208, 0.482), (0.174, 0.520)), BrenemanExperimentResult( 'Gray', (0.209, 0.483), (0.176, 0.513), (3, 4), (2, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.450, 0.512), (0.419, 0.524), (10, 2), (3, 2), (8, -1)), BrenemanExperimentResult( 'Skin', (0.268, 0.506), (0.240, 0.528), (6, 2), (-4, 0), (-3, 0)), BrenemanExperimentResult( 'Orange', (0.331, 0.547), (0.293, 0.553), (6, 2), (3, -1), (5, 1)), BrenemanExperimentResult( 'Brown', (0.323, 0.542), (0.290, 0.552), (5, 2), (-1, -3), (0, -1)), BrenemanExperimentResult( 'Yellow', (0.266, 0.549), (0.236, 0.557), (4, 2), (-3, -2), (-4, 2)), BrenemanExperimentResult( 'Foliage', (0.227, 0.538), (0.194, 0.552), (4, 2), (2, -3), (-1, 1)), BrenemanExperimentResult( 'Green', (0.146, 0.534), (0.118, 0.551), (8, 3), (4, -2), (-6, 3)), BrenemanExperimentResult( 'Blue-green', (0.160, 0.475), (0.130, 0.513), (9, 4), (1, -1), (-4, -3)), BrenemanExperimentResult( 'Blue', (0.177, 0.340), (0.133, 0.427), (6, 14), (4, -17), (11, -29)), BrenemanExperimentResult( 'Sky', (0.179, 0.438), (0.146, 0.482), (6, 10), (1, 4), (0, -1)), BrenemanExperimentResult( 'Purple', (0.245, 0.366), (0.216, 0.419), (4, 13), (-3, 8), (4, -2))) # yapf: enable """ *Breneman (1987)* experiment 1 results. BRENEMAN_EXPERIMENT_11_RESULTS : tuple Notes ----- - Illuminants : *green*, *D65* - White Luminance : 1560 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENT_12_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.205, 0.482), (0.174, 0.519)), BrenemanExperimentResult( 'Gray', (0.208, 0.482), (0.181, 0.507), (4, 3), (0, 1), (0, 0)), BrenemanExperimentResult( 'Red', (0.451, 0.512), (0.422, 0.526), (20, 3), (0, -5), (10, -5)), BrenemanExperimentResult( 'Skin', (0.268, 0.506), (0.244, 0.525), (5, 2), (-6, 0), (-2, -1)), BrenemanExperimentResult( 'Orange', (0.331, 0.548), (0.292, 0.553), (10, 2), (5, 2), (11, 1)), BrenemanExperimentResult( 'Brown', (0.324, 0.542), (0.286, 0.554), (8, 1), (5, -3), (10, -4)), BrenemanExperimentResult( 'Yellow', (0.266, 0.548), (0.238, 0.558), (6, 2), (-3, -1), (-1, -2)), BrenemanExperimentResult( 'Foliage', (0.227, 0.538), (0.196, 0.555), (6, 3), (3, -4), (2, -5)), BrenemanExperimentResult( 'Green', (0.145, 0.534), (0.124, 0.551), (8, 6), (1, -1), (-8, -1)), BrenemanExperimentResult( 'Blue-green', (0.160, 0.474), (0.135, 0.505), (5, 2), (1, -1), (-4, -3)), BrenemanExperimentResult( 'Blue', (0.178, 0.339), (0.149, 0.392), (4, 20), (-1, -5), (3, -7)), BrenemanExperimentResult( 'Sky', (0.179, 0.440), (0.150, 0.473), (4, 8), (3, 2), (2, 0)), BrenemanExperimentResult( 'Purple', (0.246, 0.366), (0.222, 0.404), (5, 15), (-4, 2), (4, 2))) # yapf: enable """ *Breneman (1987)* experiment 12 results. BRENEMAN_EXPERIMENT_12_RESULTS : tuple Notes ----- - Illuminants : *D55*, *green* - White Luminance : 75 :math:`cd/m^2` - Observers Count : 7 """ # yapf: disable BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES = DocstringDict({ 1: PrimariesChromaticityCoordinates( 1, ('A', 'D65'), 1500, (0.671, 0.519), (-0.586, 0.627), (0.253, 0.016)), 2: PrimariesChromaticityCoordinates( 2, ('Projector', 'D55'), 1500, (0.675, 0.523), (-0.466, 0.617), (0.255, 0.018)), 3: PrimariesChromaticityCoordinates( 3, ('Projector', 'D55'), 75, (0.664, 0.510), (-0.256, 0.729), (0.244, 0.003)), 4: PrimariesChromaticityCoordinates( 4, ('A', 'D65'), 75, (0.674, 0.524), (-0.172, 0.628), (0.218, -0.026)), 6: PrimariesChromaticityCoordinates( 6, ('A', 'D55'), 11100, (0.659, 0.506), (-0.141, 0.615), (0.249, 0.009)), 8: PrimariesChromaticityCoordinates( 8, ('A', 'D65'), 350, (0.659, 0.505), (-0.246, 0.672), (0.235, -0.006)), 9: PrimariesChromaticityCoordinates( 9, ('A', 'D65'), 15, (0.693, 0.546), (-0.446, 0.773), (0.221, -0.023)), 11: PrimariesChromaticityCoordinates( 11, ('D55', 'green'), 1560, (0.680, 0.529), (0.018, 0.576), (0.307, 0.080)), 12: PrimariesChromaticityCoordinates( 12, ('D55', 'green'), 75, (0.661, 0.505), (0.039, 0.598), (0.345, 0.127))}) # yapf: enable BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.__doc__ = """ *Breneman (1987)* experiments primaries chromaticities. References ---------- :cite:`Breneman1987b` BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES : dict """ BRENEMAN_EXPERIMENTS = DocstringDict({ 1: BRENEMAN_EXPERIMENT_1_RESULTS, 2: BRENEMAN_EXPERIMENT_2_RESULTS, 3: BRENEMAN_EXPERIMENT_3_RESULTS, 4: BRENEMAN_EXPERIMENT_4_RESULTS, 5: BRENEMAN_EXPERIMENT_5_RESULTS, 6: BRENEMAN_EXPERIMENT_6_RESULTS, 7: BRENEMAN_EXPERIMENT_7_RESULTS, 8: BRENEMAN_EXPERIMENT_8_RESULTS, 9: BRENEMAN_EXPERIMENT_9_RESULTS, 10: BRENEMAN_EXPERIMENT_10_RESULTS, 11: BRENEMAN_EXPERIMENT_11_RESULTS, 12: BRENEMAN_EXPERIMENT_12_RESULTS }) BRENEMAN_EXPERIMENTS.__doc__ = """ *Breneman (1987)* experiments. References ---------- :cite:`Breneman1987b` BRENEMAN_EXPERIMENTS : dict """

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0.536406

from __future__ import division, unicode_literals import numpy as np from collections import namedtuple from colour.utilities.documentation import DocstringDict __author__ = 'Colour Developers' __copyright__ = 'Copyright (C) 2013-2019 - Colour Developers' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'Colour Developers' __email__ = 'colour-developers@colour-science.org' __status__ = 'Production' __all__ = [ 'BrenemanExperimentResult', 'PrimariesChromaticityCoordinates', 'BRENEMAN_EXPERIMENT_1_RESULTS', 'BRENEMAN_EXPERIMENT_2_RESULTS', 'BRENEMAN_EXPERIMENT_3_RESULTS', 'BRENEMAN_EXPERIMENT_4_RESULTS', 'BRENEMAN_EXPERIMENT_5_RESULTS', 'BRENEMAN_EXPERIMENT_6_RESULTS', 'BRENEMAN_EXPERIMENT_7_RESULTS', 'BRENEMAN_EXPERIMENT_10_RESULTS', 'BRENEMAN_EXPERIMENT_8_RESULTS', 'BRENEMAN_EXPERIMENT_9_RESULTS', 'BRENEMAN_EXPERIMENT_11_RESULTS', 'BRENEMAN_EXPERIMENT_12_RESULTS', 'BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES', 'BRENEMAN_EXPERIMENTS' ] class BrenemanExperimentResult( namedtuple('BrenemanExperimentResult', ('name', 'uv_t', 'uv_m', 's_uv', 'd_uv_i', 'd_uv_g'))): def __new__(cls, name, uv_t, uv_m, s_uv=None, d_uv_i=None, d_uv_g=None): return super(BrenemanExperimentResult, cls).__new__( cls, name, np.array(uv_t), np.array(uv_m), np.array(s_uv), np.array(d_uv_i), np.array(d_uv_g)) class PrimariesChromaticityCoordinates( namedtuple( 'PrimariesChromaticityCoordinates', ('experiment', 'illuminants', 'Y', 'P_uvp', 'D_uvp', 'T_uvp'))): def __new__(cls, experiment, illuminants, Y, P_uvp=None, D_uvp=None, T_uvp=None): return super(PrimariesChromaticityCoordinates, cls).__new__( cls, experiment, np.array(illuminants), np.array(Y), np.array(P_uvp), np.array(D_uvp), np.array(T_uvp)) BRENEMAN_EXPERIMENT_1_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.259, 0.526), (0.200, 0.475)), BrenemanExperimentResult( 'Gray', (0.259, 0.524), (0.199, 0.487), (4, 4), (2, 3), (0, 0)), BrenemanExperimentResult( 'Red', (0.459, 0.522), (0.420, 0.509), (19, 4), (-10, -7), (-19, -3)), BrenemanExperimentResult( 'Skin', (0.307, 0.526), (0.249, 0.497), (7, 4), (-1, 1), (-6, -1)), BrenemanExperimentResult( 'Orange', (0.360, 0.544), (0.302, 0.548), (12, 1), (1, -2), (-7, -6)), BrenemanExperimentResult( 'Brown', (0.350, 0.541), (0.290, 0.537), (11, 4), (3, 0), (-5, -3)), BrenemanExperimentResult( 'Yellow', (0.318, 0.550), (0.257, 0.554), (8, 2), (0, 2), (-5, -5)), BrenemanExperimentResult( 'Foliage', (0.258, 0.542), (0.192, 0.529), (4, 6), (3, 2), (3, -6)), BrenemanExperimentResult( 'Green', (0.193, 0.542), (0.129, 0.521), (7, 5), (3, 2), (9, -7)), BrenemanExperimentResult( 'Blue-green', (0.180, 0.516), (0.133, 0.469), (4, 6), (-3, -2), (2, -5)), BrenemanExperimentResult( 'Blue', (0.186, 0.445), (0.158, 0.340), (13, 33), (2, 7), (1, 13)), BrenemanExperimentResult( 'Sky', (0.226, 0.491), (0.178, 0.426), (3, 14), (1, -3), (0, -1)), BrenemanExperimentResult( 'Purple', (0.278, 0.456), (0.231, 0.365), (4, 25), (0, 2), (-5, 7))) BRENEMAN_EXPERIMENT_2_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.222, 0.521), (0.204, 0.479)), BrenemanExperimentResult( 'Gray', (0.227, 0.517), (0.207, 0.486), (2, 5), (-1, 0), (0, 0)), BrenemanExperimentResult( 'Red', (0.464, 0.520), (0.449, 0.511), (22, 3), (-8, -8), (-7, -2)), BrenemanExperimentResult( 'Skin', (0.286, 0.526), (0.263, 0.505), (7, 2), (0, -1), (0, -1)), BrenemanExperimentResult( 'Orange', (0.348, 0.546), (0.322, 0.545), (13, 3), (3, -1), (3, -2)), BrenemanExperimentResult( 'Brown', (0.340, 0.543), (0.316, 0.537), (11, 3), (1, 1), (0, 0)), BrenemanExperimentResult( 'Yellow', (0.288, 0.554), (0.265, 0.553), (5, 2), (-2, 2), (-1, -2)), BrenemanExperimentResult( 'Foliage', (0.244, 0.547), (0.221, 0.538), (4, 3), (-2, 1), (0, -3)), BrenemanExperimentResult( 'Green', (0.156, 0.548), (0.135, 0.532), (4, 3), (-1, 3), (3, -4)), BrenemanExperimentResult( 'Blue-green', (0.159, 0.511), (0.145, 0.472), (9, 7), (-1, 2), (2, 1)), BrenemanExperimentResult( 'Blue', (0.160, 0.406), (0.163, 0.331), (23, 31), (2, -3), (-1, 3)), BrenemanExperimentResult( 'Sky', (0.190, 0.481), (0.176, 0.431), (5, 24), (2, -2), (2, 0)), BrenemanExperimentResult( 'Purple', (0.258, 0.431), (0.244, 0.349), (4, 19), (-3, 13), (-4, 19))) BRENEMAN_EXPERIMENT_3_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.223, 0.521), (0.206, 0.478)), BrenemanExperimentResult( 'Gray', (0.228, 0.517), (0.211, 0.494), (1, 3), (0, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.462, 0.519), (0.448, 0.505), (11, 4), (-3, 6), (-4, 6)), BrenemanExperimentResult( 'Skin', (0.285, 0.524), (0.267, 0.507), (6, 3), (-1, 1), (-2, 1)), BrenemanExperimentResult( 'Orange', (0.346, 0.546), (0.325, 0.541), (11, 3), (1, -2), (2, 3)), BrenemanExperimentResult( 'Brown', (0.338, 0.543), (0.321, 0.532), (9, 6), (-3, 2), (-3, 7)), BrenemanExperimentResult( 'Yellow', (0.287, 0.554), (0.267, 0.548), (4, 5), (1, -2), (0, 5)), BrenemanExperimentResult( 'Foliage', (0.244, 0.547), (0.226, 0.531), (3, 6), (-1, 3), (-2, 8)), BrenemanExperimentResult( 'Green', (0.157, 0.548), (0.141, 0.528), (9, 6), (2, 2), (0, 6)), BrenemanExperimentResult( 'Blue-green', (0.160, 0.510), (0.151, 0.486), (8, 5), (-2, -1), (-2, -5)), BrenemanExperimentResult( 'Blue', (0.162, 0.407), (0.158, 0.375), (6, 7), (1, -6), (4, -23)), BrenemanExperimentResult( 'Sky', (0.191, 0.482), (0.179, 0.452), (4, 5), (0, 1), (1, -7)), BrenemanExperimentResult( 'Purple', (0.258, 0.432), (0.238, 0.396), (4, 8), (5, 3), (4, -11))) BRENEMAN_EXPERIMENT_4_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.258, 0.523), (0.199, 0.467)), BrenemanExperimentResult( 'Gray', (0.257, 0.524), (0.205, 0.495), (2, 2), (0, 4), (0, 0)), BrenemanExperimentResult( 'Red', (0.460, 0.521), (0.416, 0.501), (11, 6), (-6, 4), (-6, 9)), BrenemanExperimentResult( 'Skin', (0.308, 0.526), (0.253, 0.503), (7, 3), (-1, 1), (-1, 0)), BrenemanExperimentResult( 'Orange', (0.360, 0.544), (0.303, 0.541), (14, 5), (1, -4), (1, 2)), BrenemanExperimentResult( 'Brown', (0.350, 0.541), (0.296, 0.527), (11, 7), (-2, 4), (-3, 9)), BrenemanExperimentResult( 'Yellow', (0.317, 0.550), (0.260, 0.547), (9, 5), (1, -3), (0, 3)), BrenemanExperimentResult( 'Foliage', (0.258, 0.543), (0.203, 0.520), (4, 6), (0, 8), (0, 9)), BrenemanExperimentResult( 'Green', (0.193, 0.543), (0.142, 0.516), (6, 9), (3, 8), (2, 6)), BrenemanExperimentResult( 'Blue-green', (0.180, 0.516), (0.140, 0.484), (9, 5), (-2, -1), (-1, -9)), BrenemanExperimentResult( 'Blue', (0.185, 0.445), (0.151, 0.394), (8, 10), (2, -8), (8, -24)), BrenemanExperimentResult( 'Sky', (0.225, 0.490), (0.180, 0.448), (4, 8), (1, -1), (3, -11)), BrenemanExperimentResult( 'Purple', (0.278, 0.455), (0.229, 0.388), (6, 14), (1, 12), (3, 0))) BRENEMAN_EXPERIMENT_5_RESULTS = ( BrenemanExperimentResult( 'Gray', (0.028, 0.480), (0.212, 0.491), (2, 2)), BrenemanExperimentResult( 'Red', (0.449, 0.512), (0.408, 0.514), (11, 5)), BrenemanExperimentResult( 'Skin', (0.269, 0.505), (0.262, 0.511), (4, 2)), BrenemanExperimentResult( 'Orange', (0.331, 0.548), (0.303, 0.545), (4, 3)), BrenemanExperimentResult( 'Brown', (0.322, 0.541), (0.303, 0.538), (4, 4)), BrenemanExperimentResult( 'Yellow', (0.268, 0.555), (0.264, 0.550), (3, 2)), BrenemanExperimentResult( 'Foliage', (0.224, 0.538), (0.227, 0.535), (3, 3)), BrenemanExperimentResult( 'Green', (0.134, 0.531), (0.159, 0.530), (9, 3)), BrenemanExperimentResult( 'Blue-green', (0.145, 0.474), (0.165, 0.490), (8, 3)), BrenemanExperimentResult( 'Blue', (0.163, 0.329), (0.173, 0.378), (7, 12)), BrenemanExperimentResult( 'Sky', (0.179, 0.438), (0.189, 0.462), (5, 4)), BrenemanExperimentResult( 'Purple', (0.245, 0.364), (0.239, 0.401), (4, 16))) BRENEMAN_EXPERIMENT_6_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.257, 0.525), (0.201, 0.482)), BrenemanExperimentResult( 'Gray', (0.267, 0.521), (0.207, 0.485), (5, 3), (-1, 0), (0, 0)), BrenemanExperimentResult( 'Red', (0.457, 0.521), (0.398, 0.516), (9, 4), (-2, -5), (1, -9)), BrenemanExperimentResult( 'Skin', (0.316, 0.526), (0.253, 0.503), (5, 3), (-3, -2), (-1, -3)), BrenemanExperimentResult( 'Orange', (0.358, 0.545), (0.287, 0.550), (7, 3), (3, 0), (7, -6)), BrenemanExperimentResult( 'Brown', (0.350, 0.541), (0.282, 0.540), (6, 3), (-1, 0), (2, -5)), BrenemanExperimentResult( 'Yellow', (0.318, 0.551), (0.249, 0.556), (7, 2), (-1, 1), (2, -5)), BrenemanExperimentResult( 'Foliage', (0.256, 0.547), (0.188, 0.537), (5, 4), (3, 1), (4, -2)), BrenemanExperimentResult( 'Green', (0.193, 0.542), (0.133, 0.520), (13, 3), (5, -2), (5, -4)), BrenemanExperimentResult( 'Blue-green', (0.180, 0.516), (0.137, 0.466), (12, 10), (0, 0), (-2, 2)), BrenemanExperimentResult( 'Blue', (0.186, 0.445), (0.156, 0.353), (12, 45), (6, 1), (2, 6)), BrenemanExperimentResult( 'Sky', (0.225, 0.492), (0.178, 0.428), (6, 14), (1, -1), (-1, 3)), BrenemanExperimentResult( 'Purple', (0.276, 0.456), (0.227, 0.369), (6, 27), (-2, 4), (-3, 9))) BRENEMAN_EXPERIMENT_7_RESULTS = ( BrenemanExperimentResult( 'Gray', (0.208, 0.481), (0.211, 0.486), (2, 3)), BrenemanExperimentResult( 'Red', (0.448, 0.512), (0.409, 0.516), (9, 2)), BrenemanExperimentResult( 'Skin', (0.269, 0.505), (0.256, 0.506), (4, 3)), BrenemanExperimentResult( 'Orange', (0.331, 0.549), (0.305, 0.547), (5, 4)), BrenemanExperimentResult( 'Brown', (0.322, 0.541), (0.301, 0.539), (5, 2)), BrenemanExperimentResult( 'Yellow', (0.268, 0.555), (0.257, 0.552), (3, 4)), BrenemanExperimentResult( 'Foliage', (0.225, 0.538), (0.222, 0.536), (3, 2)), BrenemanExperimentResult( 'Green', (0.135, 0.531), (0.153, 0.529), (8, 2)), BrenemanExperimentResult( 'Blue-green', (0.145, 0.475), (0.160, 0.484), (3, 5)), BrenemanExperimentResult( 'Blue', (0.163, 0.331), (0.171, 0.379), (4, 11)), BrenemanExperimentResult( 'Sky', (0.179, 0.438), (0.187, 0.452), (4, 7)), BrenemanExperimentResult( 'Purple', (0.245, 0.365), (0.240, 0.398), (4, 10))) BRENEMAN_EXPERIMENT_8_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.258, 0.524), (0.195, 0.469)), BrenemanExperimentResult( 'Gray', (0.257, 0.525), (0.200, 0.494), (2, 3), (1, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.458, 0.522), (0.410, 0.508), (12, 4), (-3, 5), (-7, 2)), BrenemanExperimentResult( 'Skin', (0.308, 0.526), (0.249, 0.502), (6, 2), (-1, 1), (-3, -1)), BrenemanExperimentResult( 'Orange', (0.359, 0.545), (0.299, 0.545), (12, 4), (0, -2), (-3, 0)), BrenemanExperimentResult( 'Brown', (0.349, 0.540), (0.289, 0.532), (10, 4), (0, 1), (-2, 2)), BrenemanExperimentResult( 'Yellow', (0.317, 0.550), (0.256, 0.549), (9, 5), (0, -3), (-3, 1)), BrenemanExperimentResult( 'Foliage', (0.260, 0.545), (0.198, 0.529), (5, 5), (3, 1), (0, 3)), BrenemanExperimentResult( 'Green', (0.193, 0.543), (0.137, 0.520), (9, 5), (3, 0), (2, 1)), BrenemanExperimentResult( 'Blue-green', (0.182, 0.516), (0.139, 0.477), (9, 4), (-3, 0), (-2, -4)), BrenemanExperimentResult( 'Blue', (0.184, 0.444), (0.150, 0.387), (5, 11), (3, -10), (6, -22)), BrenemanExperimentResult( 'Sky', (0.224, 0.489), (0.177, 0.439), (5, 6), (1, 1), (1, -7)), BrenemanExperimentResult( 'Purple', (0.277, 0.454), (0.226, 0.389), (4, 10), (1, 4), (1, -8))) BRENEMAN_EXPERIMENT_9_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.254, 0.525), (0.195, 0.465)), BrenemanExperimentResult( 'Gray', (0.256, 0.524), (0.207, 0.496), (4, 6), (3, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.459, 0.521), (0.415, 0.489), (20, 14), (2, 12), (-2, 21)), BrenemanExperimentResult( 'Skin', (0.307, 0.525), (0.261, 0.500), (7, 7), (0, 1), (-5, 2)), BrenemanExperimentResult( 'Orange', (0.359, 0.545), (0.313, 0.532), (7, 5), (-2, -3), (-6, 13)), BrenemanExperimentResult( 'Brown', (0.349, 0.540), (0.302, 0.510), (11, 15), (0, 12), (-5, 24)), BrenemanExperimentResult( 'Yellow', (0.317, 0.550), (0.268, 0.538), (7, 10), (1, -4), (-4, 12)), BrenemanExperimentResult( 'Foliage', (0.259, 0.544), (0.212, 0.510), (10, 11), (0, 14), (-4, 22)), BrenemanExperimentResult( 'Green', (0.193, 0.542), (0.150, 0.506), (6, 10), (-1, 13), (-2, 15)), BrenemanExperimentResult( 'Blue-green', (0.181, 0.517), (0.144, 0.487), (9, 6), (-3, 0), (-1, -9)), BrenemanExperimentResult( 'Blue', (0.184, 0.444), (0.155, 0.407), (4, 11), (-2, -6), (6, -36)), BrenemanExperimentResult( 'Sky', (0.225, 0.490), (0.183, 0.458), (5, 8), (1, -3), (2, -19)), BrenemanExperimentResult( 'Purple', (0.276, 0.454), (0.233, 0.404), (7, 12), (2, 9), (0, -16)), BrenemanExperimentResult( '(Gray)h', (0.256, 0.525), (0.208, 0.498)), BrenemanExperimentResult( '(Red)h', (0.456, 0.521), (0.416, 0.501), (15, 7), None, (-6, -9)), BrenemanExperimentResult( '(Brown)h', (0.349, 0.539), (0.306, 0.526), (11, 8), None, (-8, 7)), BrenemanExperimentResult( '(Foliage)h', (0.260, 0.545), (0.213, 0.528), (7, 9), None, (-4, 5)), BrenemanExperimentResult( '(Green)h', (0.193, 0.543), (0.149, 0.525), (10, 8), None, (-1, -1)), BrenemanExperimentResult( '(Blue)h', (0.184, 0.444), (0.156, 0.419), (7, 8), None, (4, -45)), BrenemanExperimentResult( '(Purple)h', (0.277, 0.456), (0.236, 0.422), (6, 11), None, (-2, -29))) BRENEMAN_EXPERIMENT_10_RESULTS = ( BrenemanExperimentResult( 'Gray', (0.208, 0.482), (0.213, 0.494), (3, 3)), BrenemanExperimentResult( 'Red', (0.447, 0.512), (0.411, 0.506), (15, 7)), BrenemanExperimentResult( 'Skin', (0.269, 0.505), (0.269, 0.511), (4, 3)), BrenemanExperimentResult( 'Orange', (0.331, 0.549), (0.315, 0.536), (7, 8)), BrenemanExperimentResult( 'Brown', (0.323, 0.542), (0.310, 0.526), (6, 8)), BrenemanExperimentResult( 'Yellow', (0.268, 0.556), (0.268, 0.541), (3, 6)), BrenemanExperimentResult( 'Foliage', (0.226, 0.538), (0.230, 0.525), (4, 8)), BrenemanExperimentResult( 'Green', (0.135, 0.531), (0.158, 0.524), (6, 3)), BrenemanExperimentResult( 'Blue-green', (0.145, 0.476), (0.161, 0.491), (4, 4)), BrenemanExperimentResult( 'Blue', (0.163, 0.330), (0.171, 0.377), (6, 19)), BrenemanExperimentResult( 'Sky', (0.179, 0.439), (0.187, 0.465), (5, 5)), BrenemanExperimentResult( 'Purple', (0.245, 0.366), (0.240, 0.402), (3, 12))) BRENEMAN_EXPERIMENT_11_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.208, 0.482), (0.174, 0.520)), BrenemanExperimentResult( 'Gray', (0.209, 0.483), (0.176, 0.513), (3, 4), (2, 2), (0, 0)), BrenemanExperimentResult( 'Red', (0.450, 0.512), (0.419, 0.524), (10, 2), (3, 2), (8, -1)), BrenemanExperimentResult( 'Skin', (0.268, 0.506), (0.240, 0.528), (6, 2), (-4, 0), (-3, 0)), BrenemanExperimentResult( 'Orange', (0.331, 0.547), (0.293, 0.553), (6, 2), (3, -1), (5, 1)), BrenemanExperimentResult( 'Brown', (0.323, 0.542), (0.290, 0.552), (5, 2), (-1, -3), (0, -1)), BrenemanExperimentResult( 'Yellow', (0.266, 0.549), (0.236, 0.557), (4, 2), (-3, -2), (-4, 2)), BrenemanExperimentResult( 'Foliage', (0.227, 0.538), (0.194, 0.552), (4, 2), (2, -3), (-1, 1)), BrenemanExperimentResult( 'Green', (0.146, 0.534), (0.118, 0.551), (8, 3), (4, -2), (-6, 3)), BrenemanExperimentResult( 'Blue-green', (0.160, 0.475), (0.130, 0.513), (9, 4), (1, -1), (-4, -3)), BrenemanExperimentResult( 'Blue', (0.177, 0.340), (0.133, 0.427), (6, 14), (4, -17), (11, -29)), BrenemanExperimentResult( 'Sky', (0.179, 0.438), (0.146, 0.482), (6, 10), (1, 4), (0, -1)), BrenemanExperimentResult( 'Purple', (0.245, 0.366), (0.216, 0.419), (4, 13), (-3, 8), (4, -2))) BRENEMAN_EXPERIMENT_12_RESULTS = ( BrenemanExperimentResult( 'Illuminant', (0.205, 0.482), (0.174, 0.519)), BrenemanExperimentResult( 'Gray', (0.208, 0.482), (0.181, 0.507), (4, 3), (0, 1), (0, 0)), BrenemanExperimentResult( 'Red', (0.451, 0.512), (0.422, 0.526), (20, 3), (0, -5), (10, -5)), BrenemanExperimentResult( 'Skin', (0.268, 0.506), (0.244, 0.525), (5, 2), (-6, 0), (-2, -1)), BrenemanExperimentResult( 'Orange', (0.331, 0.548), (0.292, 0.553), (10, 2), (5, 2), (11, 1)), BrenemanExperimentResult( 'Brown', (0.324, 0.542), (0.286, 0.554), (8, 1), (5, -3), (10, -4)), BrenemanExperimentResult( 'Yellow', (0.266, 0.548), (0.238, 0.558), (6, 2), (-3, -1), (-1, -2)), BrenemanExperimentResult( 'Foliage', (0.227, 0.538), (0.196, 0.555), (6, 3), (3, -4), (2, -5)), BrenemanExperimentResult( 'Green', (0.145, 0.534), (0.124, 0.551), (8, 6), (1, -1), (-8, -1)), BrenemanExperimentResult( 'Blue-green', (0.160, 0.474), (0.135, 0.505), (5, 2), (1, -1), (-4, -3)), BrenemanExperimentResult( 'Blue', (0.178, 0.339), (0.149, 0.392), (4, 20), (-1, -5), (3, -7)), BrenemanExperimentResult( 'Sky', (0.179, 0.440), (0.150, 0.473), (4, 8), (3, 2), (2, 0)), BrenemanExperimentResult( 'Purple', (0.246, 0.366), (0.222, 0.404), (5, 15), (-4, 2), (4, 2))) BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES = DocstringDict({ 1: PrimariesChromaticityCoordinates( 1, ('A', 'D65'), 1500, (0.671, 0.519), (-0.586, 0.627), (0.253, 0.016)), 2: PrimariesChromaticityCoordinates( 2, ('Projector', 'D55'), 1500, (0.675, 0.523), (-0.466, 0.617), (0.255, 0.018)), 3: PrimariesChromaticityCoordinates( 3, ('Projector', 'D55'), 75, (0.664, 0.510), (-0.256, 0.729), (0.244, 0.003)), 4: PrimariesChromaticityCoordinates( 4, ('A', 'D65'), 75, (0.674, 0.524), (-0.172, 0.628), (0.218, -0.026)), 6: PrimariesChromaticityCoordinates( 6, ('A', 'D55'), 11100, (0.659, 0.506), (-0.141, 0.615), (0.249, 0.009)), 8: PrimariesChromaticityCoordinates( 8, ('A', 'D65'), 350, (0.659, 0.505), (-0.246, 0.672), (0.235, -0.006)), 9: PrimariesChromaticityCoordinates( 9, ('A', 'D65'), 15, (0.693, 0.546), (-0.446, 0.773), (0.221, -0.023)), 11: PrimariesChromaticityCoordinates( 11, ('D55', 'green'), 1560, (0.680, 0.529), (0.018, 0.576), (0.307, 0.080)), 12: PrimariesChromaticityCoordinates( 12, ('D55', 'green'), 75, (0.661, 0.505), (0.039, 0.598), (0.345, 0.127))}) BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.__doc__ = """ *Breneman (1987)* experiments primaries chromaticities. References ---------- :cite:`Breneman1987b` BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES : dict """ BRENEMAN_EXPERIMENTS = DocstringDict({ 1: BRENEMAN_EXPERIMENT_1_RESULTS, 2: BRENEMAN_EXPERIMENT_2_RESULTS, 3: BRENEMAN_EXPERIMENT_3_RESULTS, 4: BRENEMAN_EXPERIMENT_4_RESULTS, 5: BRENEMAN_EXPERIMENT_5_RESULTS, 6: BRENEMAN_EXPERIMENT_6_RESULTS, 7: BRENEMAN_EXPERIMENT_7_RESULTS, 8: BRENEMAN_EXPERIMENT_8_RESULTS, 9: BRENEMAN_EXPERIMENT_9_RESULTS, 10: BRENEMAN_EXPERIMENT_10_RESULTS, 11: BRENEMAN_EXPERIMENT_11_RESULTS, 12: BRENEMAN_EXPERIMENT_12_RESULTS }) BRENEMAN_EXPERIMENTS.__doc__ = """ *Breneman (1987)* experiments. References ---------- :cite:`Breneman1987b` BRENEMAN_EXPERIMENTS : dict """

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Python

src/Application/PythonScriptModule/proto/state_2.py

antont/tundra

5c9b0a3957071f08ab425dff701cdbb34f9e1868

[ "Apache-2.0" ]

1

2018-04-02T15:38:10.000Z

src/Application/PythonScriptModule/proto/state_2.py

antont/tundra

5c9b0a3957071f08ab425dff701cdbb34f9e1868

[ "Apache-2.0" ]

null

src/Application/PythonScriptModule/proto/state_2.py

antont/tundra

5c9b0a3957071f08ab425dff701cdbb34f9e1868

[ "Apache-2.0" ]

1

2021-09-04T12:37:34.000Z

import state def change(): state.x = 2

10.75

15

0.627907

import state def change(): state.x = 2

true

f71b2c8b5ead15b27ff28d5dc1c80528e9c46c18

2,755

py

Python

profiler/torchmodules/torchlogger/activation_gradient_logger.py

NestLakerJasonLIN/pipedream

f50827f2e28cbdbd82a4ea686c0498272b1460d6

[ "MIT" ]

273

2019-08-31T14:12:11.000Z

2022-03-05T13:34:25.000Z

profiler/torchmodules/torchlogger/activation_gradient_logger.py

albertsh10/pipedream

cad624f79a71f44ba79099f0c38321347b13e5c2

[ "MIT" ]

67

2019-09-19T15:36:59.000Z

2022-01-13T09:11:54.000Z

profiler/torchmodules/torchlogger/activation_gradient_logger.py

albertsh10/pipedream

cad624f79a71f44ba79099f0c38321347b13e5c2

[ "MIT" ]

100

2019-09-16T20:59:14.000Z

2022-03-23T12:56:56.000Z

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import os import pickle import torch class ActivationAndGradientLogger: def __init__(self, directory): self.directory = directory try: os.mkdir(self.directory) except: pass self.iteration = 0 self.forward_counter = 0 self.backward_counter = 0 def reset_counters(self): self.forward_counter = 0 self.backward_counter = 0 def hook_modules(self, module, iteration): self.iteration = iteration sub_directory = os.path.join(self.directory, str(iteration)) try: os.mkdir(sub_directory) except: pass self.hook_modules_helper(module, sub_directory) def hook_modules_helper(self, module, sub_directory): sub_modules = module.__dict__['_modules'] for name, sub_module in sub_modules.items(): if sub_module is None or isinstance(sub_module, torch.nn.Module) is False: break sub_sub_modules = sub_module.__dict__['_modules'] if len(sub_sub_modules) > 0: # Recursively visit this module's descendants. self.hook_modules_helper(sub_module, sub_directory) else: def forward_hook(*args): activation = args[2] filename = os.path.join(sub_directory, 'activations.%d.pkl' % self.forward_counter) with open(filename, 'wb') as f: torch.save(activation, f) self.forward_counter += 1 def backward_hook(*args): gradient = args[2] filename = os.path.join(sub_directory, 'gradients.%d.pkl' % self.backward_counter) with open(filename, 'wb') as f: torch.save(gradient, f) self.backward_counter += 1 sub_module.register_forward_hook(forward_hook) sub_module.register_backward_hook(backward_hook) def unhook_modules(self, module): self.unhook_modules_helper(module) self.reset_counters() def unhook_modules_helper(self, module): sub_modules = module.__dict__['_modules'] for name, sub_module in sub_modules.items(): if sub_module is None or isinstance(sub_module, torch.nn.Module) is False: break sub_sub_modules = sub_module.__dict__['_modules'] if len(sub_sub_modules) > 0: # Recursively visit this module's descendants. self.unhook_modules_helper(sub_module) else: sub_module.reset_hooks()

34.873418

103

0.591652

import os import pickle import torch class ActivationAndGradientLogger: def __init__(self, directory): self.directory = directory try: os.mkdir(self.directory) except: pass self.iteration = 0 self.forward_counter = 0 self.backward_counter = 0 def reset_counters(self): self.forward_counter = 0 self.backward_counter = 0 def hook_modules(self, module, iteration): self.iteration = iteration sub_directory = os.path.join(self.directory, str(iteration)) try: os.mkdir(sub_directory) except: pass self.hook_modules_helper(module, sub_directory) def hook_modules_helper(self, module, sub_directory): sub_modules = module.__dict__['_modules'] for name, sub_module in sub_modules.items(): if sub_module is None or isinstance(sub_module, torch.nn.Module) is False: break sub_sub_modules = sub_module.__dict__['_modules'] if len(sub_sub_modules) > 0: self.hook_modules_helper(sub_module, sub_directory) else: def forward_hook(*args): activation = args[2] filename = os.path.join(sub_directory, 'activations.%d.pkl' % self.forward_counter) with open(filename, 'wb') as f: torch.save(activation, f) self.forward_counter += 1 def backward_hook(*args): gradient = args[2] filename = os.path.join(sub_directory, 'gradients.%d.pkl' % self.backward_counter) with open(filename, 'wb') as f: torch.save(gradient, f) self.backward_counter += 1 sub_module.register_forward_hook(forward_hook) sub_module.register_backward_hook(backward_hook) def unhook_modules(self, module): self.unhook_modules_helper(module) self.reset_counters() def unhook_modules_helper(self, module): sub_modules = module.__dict__['_modules'] for name, sub_module in sub_modules.items(): if sub_module is None or isinstance(sub_module, torch.nn.Module) is False: break sub_sub_modules = sub_module.__dict__['_modules'] if len(sub_sub_modules) > 0: # Recursively visit this module's descendants. self.unhook_modules_helper(sub_module) else: sub_module.reset_hooks()

true

f71b2c90cb12b8290d45257d9a8169e55982187d

4,207

py

Python

twine/commands/check.py

chadwhawkins/twine

bd1d8b0f3ffdae9b91672d075d58cf635aa0e0f6

[ "Apache-2.0" ]

null

twine/commands/check.py

chadwhawkins/twine

bd1d8b0f3ffdae9b91672d075d58cf635aa0e0f6

[ "Apache-2.0" ]

null

twine/commands/check.py

chadwhawkins/twine

bd1d8b0f3ffdae9b91672d075d58cf635aa0e0f6

[ "Apache-2.0" ]

null

# Copyright 2018 Dustin Ingram # # 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. from __future__ import absolute_import, division, print_function from __future__ import unicode_literals import argparse import cgi import re import sys try: from StringIO import StringIO except ImportError: from _io import StringIO import readme_renderer.markdown import readme_renderer.rst import readme_renderer.txt from twine.commands import _find_dists from twine.package import PackageFile _RENDERERS = { None: readme_renderer.rst, # Default if description_content_type is None "text/plain": readme_renderer.txt, "text/x-rst": readme_renderer.rst, "text/markdown": readme_renderer.markdown, } # Regular expression used to capture and reformat doctuils warnings into # something that a human can understand. This is loosely borrowed from # Sphinx: https://github.com/sphinx-doc/sphinx/blob # /c35eb6fade7a3b4a6de4183d1dd4196f04a5edaf/sphinx/util/docutils.py#L199 _REPORT_RE = re.compile( r"^<string>:(?P<line>(?:\d+)?): " r"$(?P<level>DEBUG|INFO|WARNING|ERROR|SEVERE)/(\d+)?$ " r"(?P<message>.*)", re.DOTALL | re.MULTILINE, ) class _WarningStream(object): def __init__(self): self.output = StringIO() def write(self, text): matched = _REPORT_RE.search(text) if not matched: self.output.write(text) return self.output.write( "line {line}: {level_text}: {message}\n".format( level_text=matched.group("level").capitalize(), line=matched.group("line"), message=matched.group("message").rstrip("\r\n"), ) ) def __str__(self): return self.output.getvalue() def check(dists, output_stream=sys.stdout): uploads = [i for i in _find_dists(dists) if not i.endswith(".asc")] stream = _WarningStream() failure = False for filename in uploads: output_stream.write("Checking distribution %s: " % filename) package = PackageFile.from_filename(filename, comment=None) metadata = package.metadata_dictionary() description = metadata["description"] description_content_type = metadata["description_content_type"] if description_content_type is None: output_stream.write( 'warning: `long_description_content_type` missing. ' 'defaulting to `text/x-rst`.\n' ) description_content_type = 'text/x-rst' content_type, params = cgi.parse_header(description_content_type) renderer = _RENDERERS.get(content_type, _RENDERERS[None]) if description in {None, 'UNKNOWN\n\n\n'}: output_stream.write('warning: `long_description` missing.\n') output_stream.write("Passed\n") else: if renderer.render(description, stream=stream, **params) is None: failure = True output_stream.write("Failed\n") output_stream.write( "The project's long_description has invalid markup which " "will not be rendered on PyPI. The following syntax " "errors were detected:\n%s" % stream ) else: output_stream.write("Passed\n") return failure def main(args): parser = argparse.ArgumentParser(prog="twine check") parser.add_argument( "dists", nargs="+", metavar="dist", help="The distribution files to check, usually dist/*", ) args = parser.parse_args(args) # Call the check function with the arguments from the command line return check(args.dists)

32.114504

78

0.659853

from __future__ import absolute_import, division, print_function from __future__ import unicode_literals import argparse import cgi import re import sys try: from StringIO import StringIO except ImportError: from _io import StringIO import readme_renderer.markdown import readme_renderer.rst import readme_renderer.txt from twine.commands import _find_dists from twine.package import PackageFile _RENDERERS = { None: readme_renderer.rst, "text/plain": readme_renderer.txt, "text/x-rst": readme_renderer.rst, "text/markdown": readme_renderer.markdown, } ORT_RE = re.compile( r"^<string>:(?P<line>(?:\d+)?): " r"$(?P<level>DEBUG|INFO|WARNING|ERROR|SEVERE)/(\d+)?$ " r"(?P<message>.*)", re.DOTALL | re.MULTILINE, ) class _WarningStream(object): def __init__(self): self.output = StringIO() def write(self, text): matched = _REPORT_RE.search(text) if not matched: self.output.write(text) return self.output.write( "line {line}: {level_text}: {message}\n".format( level_text=matched.group("level").capitalize(), line=matched.group("line"), message=matched.group("message").rstrip("\r\n"), ) ) def __str__(self): return self.output.getvalue() def check(dists, output_stream=sys.stdout): uploads = [i for i in _find_dists(dists) if not i.endswith(".asc")] stream = _WarningStream() failure = False for filename in uploads: output_stream.write("Checking distribution %s: " % filename) package = PackageFile.from_filename(filename, comment=None) metadata = package.metadata_dictionary() description = metadata["description"] description_content_type = metadata["description_content_type"] if description_content_type is None: output_stream.write( 'warning: `long_description_content_type` missing. ' 'defaulting to `text/x-rst`.\n' ) description_content_type = 'text/x-rst' content_type, params = cgi.parse_header(description_content_type) renderer = _RENDERERS.get(content_type, _RENDERERS[None]) if description in {None, 'UNKNOWN\n\n\n'}: output_stream.write('warning: `long_description` missing.\n') output_stream.write("Passed\n") else: if renderer.render(description, stream=stream, **params) is None: failure = True output_stream.write("Failed\n") output_stream.write( "The project's long_description has invalid markup which " "will not be rendered on PyPI. The following syntax " "errors were detected:\n%s" % stream ) else: output_stream.write("Passed\n") return failure def main(args): parser = argparse.ArgumentParser(prog="twine check") parser.add_argument( "dists", nargs="+", metavar="dist", help="The distribution files to check, usually dist/*", ) args = parser.parse_args(args) # Call the check function with the arguments from the command line return check(args.dists)

true

f71b2d261e9d06c923fdc731f3a69eb747347726

11,627

py

Python

bioactive_lab.py

PriyamvadaKumar/AWS_BioActive_Classification

b6a4413618586712ca4dc196f2dfaa3ceca804fb

[ "MIT" ]

1

2021-06-04T02:46:37.000Z

bioactive_lab.py

PriyamvadaKumar/AWS_BioActive_classification

b6a4413618586712ca4dc196f2dfaa3ceca804fb

[ "MIT" ]

null

bioactive_lab.py

PriyamvadaKumar/AWS_BioActive_classification

b6a4413618586712ca4dc196f2dfaa3ceca804fb

[ "MIT" ]

null

import os, sys dirpath = os.getcwd() sys.path.insert(0, dirpath + '/goal_tether_functions') sys.path.insert(0, dirpath + '/predictive_modelers') sys.path.insert(0, dirpath + '/predictive_modelers/assessment_resources') sys.path.insert(0, dirpath + '/active_learners') sys.path.insert(0, dirpath + '/data_acquisition') sys.path.insert(0, dirpath + '/diagnostics') from createCampaign_battleship import main as createCampaign # from createImageCampaign_Bria import main as createCampaign from runCampaign2 import main as runCampaign from database import * import outputManager import time import boto3 import matplotlib.pyplot as plt import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder from sklearn.cluster import KMeans # Part 1 Plotting Function def plot_simulation_accuracy(acc, title, mul_accuracy=False): fig, ax = plt.subplots() ax.set_ylabel("Accuracy (%)") ax.set_xlabel("Iterations") ax.set_title(title) if mul_accuracy: ax.plot(np.arange(len(acc[0])), acc[0], label="Full Space") ax.plot(np.arange(len(acc[1])), acc[1], label="Forward Modeling") ax.plot(np.arange(len(acc[2])), acc[2], label="Prediction Only") else: ax.plot(np.arange(len(acc)), acc) ax.legend() plt.show() def average_arrays(mat): array = [] for i in range(25): avg = 0 for m in range(len(mat)): if len(mat[m]) < i: continue avg += mat[m][i] avg = avg/len(mat) array.append(avg) return array wd =os.getcwd() print("Current Working Directory: ", wd) print() if path.exists("data/data.csv") is False: print("Retrieving Data from S3") # read data from S3 s3 = boto3.resource('s3') s3.Bucket('whatyouknowaboutmybucket').download_file('data.csv', wd + '/data/data.csv') if path.exists("data/data.csv") is False: print("Retrieving Data from S3") time.sleep(5) data = pd.read_csv("data/data.csv").dropna().to_numpy() features = data[:, 4:] labels = data[:, 2] l = LabelEncoder() labels = l.fit_transform(labels) print(l.classes_) s = KMeans(n_clusters=5) # s.decision_function(features[:1000]) s.fit_transform(features[:1500]) print(s.score(features[1500:])) d = np.zeros((20,20)) # create groundTruth for i in range(len(data)): if data[i][0] - 1 >= len(d) or data[i][1] >= len(d[0]): continue d[data[i][0]-1][data[i][1]-1] = s.predict(features[i].reshape(1,-1)) print(d) np.savetxt('data_acquisition/project.txt', d) print(labels) # exit() ''' campaign = createCampaign() runCampaign(campaign) acc = [np.array(campaign.accuracy_full), np.array(campaign.accuracy_forwardModeling), np.array(campaign.accuracy_onlyPredictions)] plot_simulation_accuracy(acc, "Model Accuracies for a Single Simulation", mul_accuracy=True) ''' # Part 2 of Assignment - 2 independent variables (0-20) and 1 dependent variable (0-10) for 20 simulations acc = [] for i in range(1): campaign = createCampaign() campaign.randoseed = 2 # campaign.ESS.iVars = [('int', 0, 9), ('int', 0, 9)] # campaign.ESS.dVars = [('int', 0, 2)] campaign.groundtruthData = 'data_acquisition/project.txt' campaign.simsFlag = True runCampaign(campaign) acc = [campaign.accuracy_full, campaign.accuracy_forwardModeling, campaign.accuracy_onlyPredictions] # acc = average_arrays(acc) plot_simulation_accuracy(acc, "Three Accuracies for the Experimental Space", mul_accuracy=True) # Part 3 of Assignment - # acc1, acc2, acc3, acc4 = [], [], [], [] # for i in range(5): # campaign = createCampaign() # campaign.ESS.high_homogeneity = True # campaign.ESS.h_num = 2 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 2)] # campaign.ESS.dimarr = [20,20] # runCampaign(campaign) # acc = campaign.accuracy_onlyPredictions # acc1.append(acc) # # for i in range(5): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.h_num = 2 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 2)] # runCampaign(campaign) # acc = campaign.accuracy_onlyPredictions # acc2.append(acc) # # for i in range(5): # campaign = createCampaign() # campaign.ESS.high_homogeneity = True # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20,20] # runCampaign(campaign) # acc = campaign.accuracy_onlyPredictions # acc3.append(acc) # # for i in range(5): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20,20] # runCampaign(campaign) # acc = campaign.accuracy_onlyPredictions # acc4.append(acc) # # acc1, acc2, acc3, acc4 = average_arrays(acc1), average_arrays(acc2), average_arrays(acc3), average_arrays(acc4) # # plt.plot([i+1 for i in range(len(acc1))], acc1, label="H-2", color="blue") # plt.plot([i+1 for i in range(len(acc2))], acc2, label="L-2", color="green") # plt.plot([i+1 for i in range(len(acc3))], acc3, label="H-10", color="red") # plt.plot([i+1 for i in range(len(acc4))], acc4, label="L-10", color="black") # plt.ylabel("Accuracy (%)") # plt.xlabel("Iterations") # plt.title("Different Homogeneity within Experimental Spaces") # plt.legend() # plt.show() # Part 4 of Assignment - # acc1, acc2, acc3, acc4 = [], [], [], [] # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0 # campaign.randoseed= 45 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc = campaign.accuracy_onlyPredictions # acc1.append(acc) # # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.randoseed = 1 # campaign.ESS.error = 0.1 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc = campaign.accuracy_onlyPredictions # acc2.append(acc) # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0.5 # campaign.randoseed = 2 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc = campaign.accuracy_onlyPredictions # acc3.append(acc) # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 1.0 # campaign.randoseed=3 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc = campaign.accuracy_onlyPredictions # acc4.append(acc) # # acc1, acc2, acc3, acc4 = average_arrays(acc1), average_arrays(acc2), average_arrays(acc3), average_arrays(acc4) # # plt.plot([i+1 for i in range(len(acc1))], acc1, label="0.0", color="blue") # plt.plot([i+1 for i in range(len(acc2))], acc2, label="0.1", color="green") # plt.plot([i+1 for i in range(len(acc3))], acc3, label="0.5", color="red") # plt.plot([i+1 for i in range(len(acc4))], acc4, label="1.0", color="black") # plt.ylabel("Accuracy (%)") # plt.xlabel("Iterations") # plt.title("Different Error Rates within Experimental Spaces") # plt.legend() # plt.show() # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0 # campaign.randoseed = 53 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc1 = campaign.accuracy_onlyPredictions # # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0 # campaign.randoseed = 39 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc2 = campaign.accuracy_onlyPredictions # # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0.1 # campaign.randoseed = 32 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc3 = campaign.accuracy_onlyPredictions # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0.1 # campaign.randoseed = 17 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc4 = campaign.accuracy_onlyPredictions # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0.5 # campaign.randoseed = 3 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc5 = campaign.accuracy_onlyPredictions # # for i in range(1): # campaign = createCampaign() # campaign.ESS.low_homogeneity = True # campaign.ESS.error = True # campaign.ESS.error = 0.5 # campaign.randoseed = 15 # campaign.ESS.h_num = 10 # campaign.ESS.iVars = [('int', 0, 19), ('int', 0, 19)] # campaign.ESS.dVars = [('int', 0, 9)] # campaign.ESS.dimarr = [20, 20] # runCampaign(campaign) # print(campaign.groundTruth) # acc6 = campaign.accuracy_onlyPredictions # # # plt.plot([i+1 for i in range(len(acc1))], acc1, label="0.0 - B", color="blue") # plt.plot([i+1 for i in range(len(acc2))], acc2, label="0.0 - N", color="green") # plt.plot([i+1 for i in range(len(acc3))], acc3, label="0.1 - B", color="red") # plt.plot([i+1 for i in range(len(acc4))], acc4, label="0.1 - N", color="black") # plt.plot([i+1 for i in range(len(acc5))], acc5, label="0.5 - B", color="yellow") # plt.plot([i+1 for i in range(len(acc6))], acc6, label="0.5 - N", color="cyan") # plt.ylabel("Accuracy (%)") # plt.xlabel("Iterations") # plt.title("Different Categorical Models within Experimental Spaces") # plt.legend() # plt.show()

32.387187

113

0.637224

import os, sys dirpath = os.getcwd() sys.path.insert(0, dirpath + '/goal_tether_functions') sys.path.insert(0, dirpath + '/predictive_modelers') sys.path.insert(0, dirpath + '/predictive_modelers/assessment_resources') sys.path.insert(0, dirpath + '/active_learners') sys.path.insert(0, dirpath + '/data_acquisition') sys.path.insert(0, dirpath + '/diagnostics') from createCampaign_battleship import main as createCampaign from runCampaign2 import main as runCampaign from database import * import outputManager import time import boto3 import matplotlib.pyplot as plt import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder from sklearn.cluster import KMeans def plot_simulation_accuracy(acc, title, mul_accuracy=False): fig, ax = plt.subplots() ax.set_ylabel("Accuracy (%)") ax.set_xlabel("Iterations") ax.set_title(title) if mul_accuracy: ax.plot(np.arange(len(acc[0])), acc[0], label="Full Space") ax.plot(np.arange(len(acc[1])), acc[1], label="Forward Modeling") ax.plot(np.arange(len(acc[2])), acc[2], label="Prediction Only") else: ax.plot(np.arange(len(acc)), acc) ax.legend() plt.show() def average_arrays(mat): array = [] for i in range(25): avg = 0 for m in range(len(mat)): if len(mat[m]) < i: continue avg += mat[m][i] avg = avg/len(mat) array.append(avg) return array wd =os.getcwd() print("Current Working Directory: ", wd) print() if path.exists("data/data.csv") is False: print("Retrieving Data from S3") s3 = boto3.resource('s3') s3.Bucket('whatyouknowaboutmybucket').download_file('data.csv', wd + '/data/data.csv') if path.exists("data/data.csv") is False: print("Retrieving Data from S3") time.sleep(5) data = pd.read_csv("data/data.csv").dropna().to_numpy() features = data[:, 4:] labels = data[:, 2] l = LabelEncoder() labels = l.fit_transform(labels) print(l.classes_) s = KMeans(n_clusters=5) s.fit_transform(features[:1500]) print(s.score(features[1500:])) d = np.zeros((20,20)) for i in range(len(data)): if data[i][0] - 1 >= len(d) or data[i][1] >= len(d[0]): continue d[data[i][0]-1][data[i][1]-1] = s.predict(features[i].reshape(1,-1)) print(d) np.savetxt('data_acquisition/project.txt', d) print(labels) acc = [] for i in range(1): campaign = createCampaign() campaign.randoseed = 2 campaign.groundtruthData = 'data_acquisition/project.txt' campaign.simsFlag = True runCampaign(campaign) acc = [campaign.accuracy_full, campaign.accuracy_forwardModeling, campaign.accuracy_onlyPredictions] plot_simulation_accuracy(acc, "Three Accuracies for the Experimental Space", mul_accuracy=True)

true

f71b2dd96ed4ebc42bacf10ebdd08e56bd022192

1,747

py

Python

s3-bucket-cleaner/clean_s3_bucket.py

artsy/opstools

889b08c6b741dfeac0c32b0a4d96d0f9f3bbc0e7

[ "MIT" ]

null

s3-bucket-cleaner/clean_s3_bucket.py

artsy/opstools

889b08c6b741dfeac0c32b0a4d96d0f9f3bbc0e7

[ "MIT" ]

null

s3-bucket-cleaner/clean_s3_bucket.py

artsy/opstools

889b08c6b741dfeac0c32b0a4d96d0f9f3bbc0e7

[ "MIT" ]

null

#!/usr/bin/env python # Sourced from https://gist.github.com/seventhskye/0cc7b2804252975d36dca047ab7729e9 with some modifications import os import boto3 def main(): client = boto3.client('s3') Bucket = os.environ.get('S3_BUCKET') Prefix = os.environ.get('S3_PREFIX', '') # leave blank to delete the entire contents IsTruncated = True MaxKeys = 1000 KeyMarker = None if Bucket is None: print("Environment variable S3_BUCKET must be set!") return while IsTruncated == True: if not KeyMarker: version_list = client.list_object_versions( Bucket=Bucket, MaxKeys=MaxKeys, Prefix=Prefix) else: version_list = client.list_object_versions( Bucket=Bucket, MaxKeys=MaxKeys, Prefix=Prefix, KeyMarker=KeyMarker) try: objects = [] versions = version_list['Versions'] for v in versions: objects.append({'VersionId':v['VersionId'],'Key': v['Key']}) response = client.delete_objects(Bucket=Bucket,Delete={'Objects':objects}) for item in response['Deleted']: print("Deleted %s" % item['Key']) except: pass try: objects = [] delete_markers = version_list['DeleteMarkers'] for d in delete_markers: objects.append({'VersionId':d['VersionId'],'Key': d['Key']}) response = client.delete_objects(Bucket=Bucket,Delete={'Objects':objects}) for item in response['Deleted']: print("Deleted %s" % item['Key']) except: pass IsTruncated = version_list['IsTruncated'] if 'NextKeyMarker' in version_list: KeyMarker = version_list['NextKeyMarker'] if __name__ == '__main__': main()

28.639344

107

0.627934

import os import boto3 def main(): client = boto3.client('s3') Bucket = os.environ.get('S3_BUCKET') Prefix = os.environ.get('S3_PREFIX', '') IsTruncated = True MaxKeys = 1000 KeyMarker = None if Bucket is None: print("Environment variable S3_BUCKET must be set!") return while IsTruncated == True: if not KeyMarker: version_list = client.list_object_versions( Bucket=Bucket, MaxKeys=MaxKeys, Prefix=Prefix) else: version_list = client.list_object_versions( Bucket=Bucket, MaxKeys=MaxKeys, Prefix=Prefix, KeyMarker=KeyMarker) try: objects = [] versions = version_list['Versions'] for v in versions: objects.append({'VersionId':v['VersionId'],'Key': v['Key']}) response = client.delete_objects(Bucket=Bucket,Delete={'Objects':objects}) for item in response['Deleted']: print("Deleted %s" % item['Key']) except: pass try: objects = [] delete_markers = version_list['DeleteMarkers'] for d in delete_markers: objects.append({'VersionId':d['VersionId'],'Key': d['Key']}) response = client.delete_objects(Bucket=Bucket,Delete={'Objects':objects}) for item in response['Deleted']: print("Deleted %s" % item['Key']) except: pass IsTruncated = version_list['IsTruncated'] if 'NextKeyMarker' in version_list: KeyMarker = version_list['NextKeyMarker'] if __name__ == '__main__': main()

true

f71b2e2da61209cbca5bf4e3278259b97ab94ddd

135

py

Python

scripts/migrate_piwik/settings/local-dist.py

DanielSBrown/osf.io

98dda2ac237377197acacce78274bc0a4ce8f303

[ "Apache-2.0" ]

null

scripts/migrate_piwik/settings/local-dist.py

DanielSBrown/osf.io

98dda2ac237377197acacce78274bc0a4ce8f303

[ "Apache-2.0" ]

null

scripts/migrate_piwik/settings/local-dist.py

DanielSBrown/osf.io

98dda2ac237377197acacce78274bc0a4ce8f303

[ "Apache-2.0" ]

null

PIWIK_DB_HOST = 'localhost' PIWIK_DB_PORT = 3336 PIWIK_DB_USER = 'root' PIWIK_DB_PASSWORD = 'changeme' PIWIK_DB_NAME = 'piwik_staging'

22.5

31

0.792593

PIWIK_DB_HOST = 'localhost' PIWIK_DB_PORT = 3336 PIWIK_DB_USER = 'root' PIWIK_DB_PASSWORD = 'changeme' PIWIK_DB_NAME = 'piwik_staging'

true

f71b2e305d3d87fbaf19ce272d3f1ec378c3ef49

1,017

py

Python

tenants/monkey/db/migrations/recorder.py

bugkiwi/django-tenants-mysql

bc008bde01f5dbbd0e85bcacfa48db2ee8347e50

[ "MIT" ]

2

2017-08-01T10:29:00.000Z

2022-03-05T12:51:43.000Z

tenants/monkey/db/migrations/recorder.py

bugkiwi/django-tenants-mysql

bc008bde01f5dbbd0e85bcacfa48db2ee8347e50

[ "MIT" ]

1

2018-08-07T13:40:38.000Z

tenants/monkey/db/migrations/recorder.py

bugkiwi/django-tenants-mysql

bc008bde01f5dbbd0e85bcacfa48db2ee8347e50

[ "MIT" ]

2

2019-11-27T09:34:44.000Z

2022-03-05T12:59:12.000Z

#!/usr/bin/env python #coding:utf-8 __author__ = 'gkiwi' from django.db.utils import DatabaseError from django.db.migrations.exceptions import MigrationSchemaMissing __all__ = ['MigrationRecorder'] class MigrationRecorder(object): def ensure_schema(self): """ Ensures the table exists and has the correct schema. """ # If the table's there, that's fine - we've never changed its schema # in the codebase. # gkiwi #TOPATCH from django.db import connection db_table = connection.get_schemaed_db_table(self.Migration._meta.db_table) # end if db_table in self.connection.introspection.table_names(self.connection.cursor()): return # Make the table try: with self.connection.schema_editor() as editor: editor.create_model(self.Migration) except DatabaseError as exc: raise MigrationSchemaMissing("Unable to create the django_migrations table (%s)" % exc)

31.78125

99

0.665683

__author__ = 'gkiwi' from django.db.utils import DatabaseError from django.db.migrations.exceptions import MigrationSchemaMissing __all__ = ['MigrationRecorder'] class MigrationRecorder(object): def ensure_schema(self): # in the codebase. # gkiwi #TOPATCH from django.db import connection db_table = connection.get_schemaed_db_table(self.Migration._meta.db_table) # end if db_table in self.connection.introspection.table_names(self.connection.cursor()): return # Make the table try: with self.connection.schema_editor() as editor: editor.create_model(self.Migration) except DatabaseError as exc: raise MigrationSchemaMissing("Unable to create the django_migrations table (%s)" % exc)

true

f71b2e92c0252192242ac618201c720c01142e52

46,863

py

Python

pyAudioAnalysis/audioSegmentation.py

polewczakp/pyAudioAnalysis

7dc2d8e18da1ca2f2485a402bb7399b43bbb2b24

[ "Apache-2.0" ]

null

pyAudioAnalysis/audioSegmentation.py

polewczakp/pyAudioAnalysis

7dc2d8e18da1ca2f2485a402bb7399b43bbb2b24

[ "Apache-2.0" ]

null

pyAudioAnalysis/audioSegmentation.py

polewczakp/pyAudioAnalysis

7dc2d8e18da1ca2f2485a402bb7399b43bbb2b24

[ "Apache-2.0" ]

null

from __future__ import print_function import os import csv import glob import scipy import sklearn import numpy as np import hmmlearn.hmm import sklearn.cluster import pickle as cpickle import matplotlib.pyplot as plt from scipy.spatial import distance import sklearn.discriminant_analysis from pyAudioAnalysis import audioBasicIO from pyAudioAnalysis import audioTrainTest as at from pyAudioAnalysis import MidTermFeatures as mtf from pyAudioAnalysis import ShortTermFeatures as stf """ General utility functions """ def smooth_moving_avg(signal, window=11): window = int(window) if signal.ndim != 1: raise ValueError("") if signal.size < window: raise ValueError("Input vector needs to be bigger than window size.") if window < 3: return signal s = np.r_[2 * signal[0] - signal[window - 1::-1], signal, 2 * signal[-1] - signal[-1:-window:-1]] w = np.ones(window, 'd') y = np.convolve(w/w.sum(), s, mode='same') return y[window:-window + 1] def self_similarity_matrix(feature_vectors): """ This function computes the self-similarity matrix for a sequence of feature vectors. ARGUMENTS: - feature_vectors: a np matrix (nDims x nVectors) whose i-th column corresponds to the i-th feature vector RETURNS: - sim_matrix: the self-similarity matrix (nVectors x nVectors) """ norm_feature_vectors, mean, std = at.normalize_features([feature_vectors.T]) norm_feature_vectors = norm_feature_vectors[0].T sim_matrix = 1.0 - distance.squareform( distance.pdist(norm_feature_vectors.T, 'cosine')) return sim_matrix def labels_to_segments(labels, window): """ ARGUMENTS: - labels: a sequence of class labels (per time window) - window: window duration (in seconds) RETURNS: - segments: a sequence of segment's limits: segs[i, 0] is start and segs[i, 1] are start and end point of segment i - classes: a sequence of class flags: class[i] is the class ID of the i-th segment """ if len(labels)==1: segs = [0, window] classes = labels return segs, classes num_segs = 0 index = 0 classes = [] segment_list = [] cur_label = labels[index] while index < len(labels) - 1: previous_value = cur_label while True: index += 1 compare_flag = labels[index] if (compare_flag != cur_label) | (index == len(labels) - 1): num_segs += 1 cur_label = labels[index] segment_list.append((index * window)) classes.append(previous_value) break segments = np.zeros((len(segment_list), 2)) for i in range(len(segment_list)): if i > 0: segments[i, 0] = segment_list[i-1] segments[i, 1] = segment_list[i] return segments, classes def segments_to_labels(start_times, end_times, labels, window): """ This function converts segment endpoints and respective segment labels to fix-sized class labels. ARGUMENTS: - start_times: segment start points (in seconds) - end_times: segment endpoints (in seconds) - labels: segment labels - window: fix-sized window (in seconds) RETURNS: - flags: np array of class indices - class_names: list of classnames (strings) """ flags = [] class_names = list(set(labels)) index = window / 2.0 while index < end_times[-1]: for i in range(len(start_times)): if start_times[i] < index <= end_times[i]: break flags.append(class_names.index(labels[i])) index += window return np.array(flags), class_names def compute_metrics(confusion_matrix, class_names): """ This function computes the precision, recall and f1 measures, given a confusion matrix """ f1 = [] recall = [] precision = [] n_classes = confusion_matrix.shape[0] if len(class_names) != n_classes: print("Error in computePreRec! Confusion matrix and class_names " "list must be of the same size!") else: for i, c in enumerate(class_names): precision.append(confusion_matrix[i, i] / np.sum(confusion_matrix[:, i])) recall.append(confusion_matrix[i, i] / np.sum(confusion_matrix[i, :])) f1.append(2 * precision[-1] * recall[-1] / (precision[-1] + recall[-1])) return recall, precision, f1 def read_segmentation_gt(gt_file): """ This function reads a segmentation ground truth file, following a simple CSV format with the following columns: <segment start>,<segment end>,<class label> ARGUMENTS: - gt_file: the path of the CSV segment file RETURNS: - seg_start: a np array of segments' start positions - seg_end: a np array of segments' ending positions - seg_label: a list of respective class labels (strings) """ with open(gt_file, 'rt') as f_handle: reader = csv.reader(f_handle, delimiter='\t') start_times = [] end_times = [] labels = [] for row in reader: if len(row) == 3: start_times.append(float(row[0])) end_times.append(float(row[1])) labels.append((row[2])) return np.array(start_times), np.array(end_times), labels def plot_segmentation_results(flags_ind, flags_ind_gt, class_names, mt_step, evaluate_only=False): """ This function plots statistics on the classification-segmentation results produced either by the fix-sized supervised method or the HMM method. It also computes the overall accuracy achieved by the respective method if ground-truth is available. """ flags = [class_names[int(f)] for f in flags_ind] segments, classes = labels_to_segments(flags, mt_step) min_len = min(flags_ind.shape[0], flags_ind_gt.shape[0]) if min_len > 0: accuracy = np.sum(flags_ind[0:min_len] == flags_ind_gt[0:min_len]) / float(min_len) else: accuracy = -1 if not evaluate_only: duration = segments[-1, 1] s_percentages = np.zeros((len(class_names), )) percentages = np.zeros((len(class_names), )) av_durations = np.zeros((len(class_names), )) for i_seg in range(segments.shape[0]): s_percentages[class_names.index(classes[i_seg])] += \ (segments[i_seg, 1]-segments[i_seg, 0]) for i in range(s_percentages.shape[0]): percentages[i] = 100.0 * s_percentages[i] / duration class_sum = sum(1 for c in classes if c == class_names[i]) if class_sum > 0: av_durations[i] = s_percentages[i] / class_sum else: av_durations[i] = 0.0 for i in range(percentages.shape[0]): print(class_names[i], percentages[i], av_durations[i]) font = {'size': 10} plt.rc('font', **font) fig = plt.figure() ax1 = fig.add_subplot(211) ax1.set_yticks(np.array(range(len(class_names)))) ax1.axis((0, duration, -1, len(class_names))) ax1.set_yticklabels(class_names) ax1.plot(np.array(range(len(flags_ind))) * mt_step + mt_step / 2.0, flags_ind) if flags_ind_gt.shape[0] > 0: ax1.plot(np.array(range(len(flags_ind_gt))) * mt_step + mt_step / 2.0, flags_ind_gt + 0.05, '--r') plt.xlabel("time (seconds)") if accuracy >= 0: plt.title('Accuracy = {0:.1f}%'.format(100.0 * accuracy)) ax2 = fig.add_subplot(223) plt.title("Classes percentage durations") ax2.axis((0, len(class_names) + 1, 0, 100)) ax2.set_xticks(np.array(range(len(class_names) + 1))) ax2.set_xticklabels([" "] + class_names) print(np.array(range(len(class_names))), percentages) ax2.bar(np.array(range(len(class_names))) + 0.5, percentages) ax3 = fig.add_subplot(224) plt.title("Segment average duration per class") ax3.axis((0, len(class_names)+1, 0, av_durations.max())) ax3.set_xticks(np.array(range(len(class_names) + 1))) ax3.set_xticklabels([" "] + class_names) ax3.bar(np.array(range(len(class_names))) + 0.5, av_durations) fig.tight_layout() plt.show() return accuracy def evaluate_speaker_diarization(labels, labels_gt): min_len = min(labels.shape[0], labels_gt.shape[0]) labels = labels[0:min_len] labels_gt = labels_gt[0:min_len] unique_flags = np.unique(labels) unique_flags_gt = np.unique(labels_gt) # compute contigency table: contigency_matrix = np.zeros((unique_flags.shape[0], unique_flags_gt.shape[0])) for i in range(min_len): contigency_matrix[int(np.nonzero(unique_flags == labels[i])[0]), int(np.nonzero(unique_flags_gt == labels_gt[i])[0])] += 1.0 columns, rows = contigency_matrix.shape row_sum = np.sum(contigency_matrix, axis=0) column_sum = np.sum(contigency_matrix, axis=1) matrix_sum = np.sum(contigency_matrix) purity_clust = np.zeros((columns, )) purity_speak = np.zeros((rows, )) # compute cluster purity: for i in range(columns): purity_clust[i] = np.max((contigency_matrix[i, :])) / (column_sum[i]) for j in range(rows): purity_speak[j] = np.max((contigency_matrix[:, j])) / (row_sum[j]) purity_cluster_m = np.sum(purity_clust * column_sum) / matrix_sum purity_speaker_m = np.sum(purity_speak * row_sum) / matrix_sum return purity_cluster_m, purity_speaker_m def train_hmm_compute_statistics(features, labels): """ This function computes the statistics used to train an HMM joint segmentation-classification model using a sequence of sequential features and respective labels ARGUMENTS: - features: a np matrix of feature vectors (numOfDimensions x n_wins) - labels: a np array of class indices (n_wins x 1) RETURNS: - class_priors: matrix of prior class probabilities (n_classes x 1) - transmutation_matrix: transition matrix (n_classes x n_classes) - means: means matrix (numOfDimensions x 1) - cov: deviation matrix (numOfDimensions x 1) """ unique_labels = np.unique(labels) n_comps = len(unique_labels) n_feats = features.shape[0] if features.shape[1] < labels.shape[0]: print("trainHMM warning: number of short-term feature vectors " "must be greater or equal to the labels length!") labels = labels[0:features.shape[1]] # compute prior probabilities: class_priors = np.zeros((n_comps,)) for i, u_label in enumerate(unique_labels): class_priors[i] = np.count_nonzero(labels == u_label) # normalize prior probabilities class_priors = class_priors / class_priors.sum() # compute transition matrix: transmutation_matrix = np.zeros((n_comps, n_comps)) for i in range(labels.shape[0]-1): transmutation_matrix[int(labels[i]), int(labels[i + 1])] += 1 # normalize rows of transition matrix: for i in range(n_comps): transmutation_matrix[i, :] /= transmutation_matrix[i, :].sum() means = np.zeros((n_comps, n_feats)) for i in range(n_comps): means[i, :] = \ np.array(features[:, np.nonzero(labels == unique_labels[i])[0]].mean(axis=1)) cov = np.zeros((n_comps, n_feats)) for i in range(n_comps): """ cov[i, :, :] = np.cov(features[:, np.nonzero(labels == u_labels[i])[0]]) """ # use line above if HMM using full gaussian distributions are to be used cov[i, :] = np.std(features[:, np.nonzero(labels == unique_labels[i])[0]], axis=1) return class_priors, transmutation_matrix, means, cov def train_hmm_from_file(wav_file, gt_file, hmm_model_name, mid_window, mid_step): """ This function trains a HMM model for segmentation-classification using a single annotated audio file ARGUMENTS: - wav_file: the path of the audio filename - gt_file: the path of the ground truth filename (a csv file of the form <segment start in seconds>, <segment end in seconds>,<segment label> in each row - hmm_model_name: the name of the HMM model to be stored - mt_win: mid-term window size - mt_step: mid-term window step RETURNS: - hmm: an object to the resulting HMM - class_names: a list of class_names After training, hmm, class_names, along with the mt_win and mt_step values are stored in the hmm_model_name file """ seg_start, seg_end, seg_labs = read_segmentation_gt(gt_file) flags, class_names = segments_to_labels(seg_start, seg_end, seg_labs, mid_step) sampling_rate, signal = audioBasicIO.read_audio_file(wav_file) features, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * 0.050), round(sampling_rate * 0.050)) class_priors, transumation_matrix, means, cov = \ train_hmm_compute_statistics(features, flags) hmm = hmmlearn.hmm.GaussianHMM(class_priors.shape[0], "diag") hmm.covars_ = cov hmm.means_ = means hmm.startprob_ = class_priors hmm.transmat_ = transumation_matrix save_hmm(hmm_model_name, hmm, class_names, mid_window, mid_step) return hmm, class_names def train_hmm_from_directory(folder_path, hmm_model_name, mid_window, mid_step): """ This function trains a HMM model for segmentation-classification using a where WAV files and .segment (ground-truth files) are stored ARGUMENTS: - folder_path: the path of the data diretory - hmm_model_name: the name of the HMM model to be stored - mt_win: mid-term window size - mt_step: mid-term window step RETURNS: - hmm: an object to the resulting HMM - class_names: a list of class_names After training, hmm, class_names, along with the mt_win and mt_step values are stored in the hmm_model_name file """ flags_all = np.array([]) class_names_all = [] for i, f in enumerate(glob.glob(folder_path + os.sep + '*.wav')): # for each WAV file wav_file = f gt_file = f.replace('.wav', '.segments') if os.path.isfile(gt_file): seg_start, seg_end, seg_labs = read_segmentation_gt(gt_file) flags, class_names = \ segments_to_labels(seg_start, seg_end, seg_labs, mid_step) for c in class_names: # update class names: if c not in class_names_all: class_names_all.append(c) sampling_rate, signal = audioBasicIO.read_audio_file(wav_file) feature_vector, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * 0.050), round(sampling_rate * 0.050)) flag_len = len(flags) feat_cols = feature_vector.shape[1] min_sm = min(feat_cols, flag_len) feature_vector = feature_vector[:, 0:min_sm] flags = flags[0:min_sm] flags_new = [] # append features and labels for j, fl in enumerate(flags): flags_new.append(class_names_all.index(class_names_all[flags[j]])) flags_all = np.append(flags_all, np.array(flags_new)) if i == 0: f_all = feature_vector else: f_all = np.concatenate((f_all, feature_vector), axis=1) # compute HMM statistics class_priors, transmutation_matrix, means, cov = \ train_hmm_compute_statistics(f_all, flags_all) # train the HMM hmm = hmmlearn.hmm.GaussianHMM(class_priors.shape[0], "diag") hmm.covars_ = cov hmm.means_ = means hmm.startprob_ = class_priors hmm.transmat_ = transmutation_matrix save_hmm(hmm_model_name, hmm, class_names_all, mid_window, mid_step) return hmm, class_names_all def save_hmm(hmm_model_name, model, classes, mid_window, mid_step): """Save HMM model""" with open(hmm_model_name, "wb") as f_handle: cpickle.dump(model, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) cpickle.dump(classes, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) cpickle.dump(mid_window, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) cpickle.dump(mid_step, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) def hmm_segmentation(audio_file, hmm_model_name, plot_results=False, gt_file=""): sampling_rate, signal = audioBasicIO.read_audio_file(audio_file) with open(hmm_model_name, "rb") as f_handle: hmm = cpickle.load(f_handle) class_names = cpickle.load(f_handle) mid_window = cpickle.load(f_handle) mid_step = cpickle.load(f_handle) features, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * 0.050), round(sampling_rate * 0.050)) # apply model labels = hmm.predict(features.T) labels_gt, class_names_gt, accuracy, cm = \ load_ground_truth(gt_file, labels, class_names, mid_step, plot_results) return labels, class_names, accuracy, cm def load_ground_truth_segments(gt_file, mt_step): seg_start, seg_end, seg_labels = read_segmentation_gt(gt_file) labels, class_names = segments_to_labels(seg_start, seg_end, seg_labels, mt_step) labels_temp = [] for index, label in enumerate(labels): # "align" labels with GT if class_names[labels[index]] in class_names: labels_temp.append(class_names.index(class_names[ labels[index]])) else: labels_temp.append(-1) labels = np.array(labels_temp) return labels, class_names def calculate_confusion_matrix(predictions, ground_truth, classes): cm = np.zeros((len(classes), len(classes))) for index in range(min(predictions.shape[0], ground_truth.shape[0])): cm[int(ground_truth[index]), int(predictions[index])] += 1 return cm def mid_term_file_classification(input_file, model_name, model_type, plot_results=False, gt_file=""): """ This function performs mid-term classification of an audio stream. Towards this end, supervised knowledge is used, i.e. a pre-trained classifier. ARGUMENTS: - input_file: path of the input WAV file - model_name: name of the classification model - model_type: svm or knn depending on the classifier type - plot_results: True if results are to be plotted using matplotlib along with a set of statistics RETURNS: - segs: a sequence of segment's endpoints: segs[i] is the endpoint of the i-th segment (in seconds) - classes: a sequence of class flags: class[i] is the class ID of the i-th segment """ labels = [] accuracy = 0.0 class_names = [] cm = np.array([]) if not os.path.isfile(model_name): print("mtFileClassificationError: input model_type not found!") return labels, class_names, accuracy, cm # Load classifier: if model_type == "knn": classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model_knn(model_name) else: classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model(model_name) if compute_beat: print("Model " + model_name + " contains long-term music features " "(beat etc) and cannot be used in " "segmentation") return labels, class_names, accuracy, cm # load input file sampling_rate, signal = audioBasicIO.read_audio_file(input_file) # could not read file if sampling_rate == 0: return labels, class_names, accuracy, cm # convert stereo (if) to mono signal = audioBasicIO.stereo_to_mono(signal) # mid-term feature extraction: mt_feats, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mt_win * sampling_rate, mid_step * sampling_rate, round(sampling_rate * st_win), round(sampling_rate * st_step)) posterior_matrix = [] # for each feature vector (i.e. for each fix-sized segment): for col_index in range(mt_feats.shape[1]): # normalize current feature v feature_vector = (mt_feats[:, col_index] - mean) / std # classify vector: label_predicted, posterior = \ at.classifier_wrapper(classifier, model_type, feature_vector) labels.append(label_predicted) # update probability matrix posterior_matrix.append(np.max(posterior)) labels = np.array(labels) # convert fix-sized flags to segments and classes segs, classes = labels_to_segments(labels, mid_step) segs[-1] = len(signal) / float(sampling_rate) # Load grount-truth: labels_gt, class_names_gt, accuracy, cm = \ load_ground_truth(gt_file, labels, class_names, mid_step, plot_results) return labels, class_names, accuracy, cm def load_ground_truth(gt_file, labels, class_names, mid_step, plot_results): accuracy = 0 cm = np.array([]) labels_gt = np.array([]) if os.path.isfile(gt_file): # load ground truth and class names labels_gt, class_names_gt = load_ground_truth_segments(gt_file, mid_step) # map predicted labels to ground truth class names # Note: if a predicted label does not belong to the ground truth # classes --> -1 labels_new = [] for il, l in enumerate(labels): if class_names[int(l)] in class_names_gt: labels_new.append(class_names_gt.index(class_names[int(l)])) else: labels_new.append(-1) labels_new = np.array(labels_new) cm = calculate_confusion_matrix(labels_new, labels_gt, class_names_gt) accuracy = plot_segmentation_results(labels_new, labels_gt, class_names, mid_step, not plot_results) if accuracy >= 0: print("Overall Accuracy: {0:.2f}".format(accuracy)) return labels_gt, class_names, accuracy, cm def evaluate_segmentation_classification_dir(dir_name, model_name, method_name): accuracies = [] class_names = [] cm_total = np.array([]) for index, wav_file in enumerate(glob.glob(dir_name + os.sep + '*.wav')): print(wav_file) gt_file = wav_file.replace('.wav', '.segments') if method_name.lower() in ["svm", "svm_rbf", "knn", "randomforest", "gradientboosting", "extratrees"]: flags_ind, class_names, accuracy, cm_temp = \ mid_term_file_classification(wav_file, model_name, method_name, False, gt_file) else: flags_ind, class_names, accuracy, cm_temp = \ hmm_segmentation(wav_file, model_name, False, gt_file) if accuracy > 0: if not index: cm_total = np.copy(cm_temp) else: cm_total = cm_total + cm_temp accuracies.append(accuracy) print(cm_temp, class_names) print(cm_total) if len(cm_total.shape) > 1: cm_total = cm_total / np.sum(cm_total) rec, pre, f1 = compute_metrics(cm_total, class_names) print(" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ") print("Average Accuracy: {0:.1f}". format(100.0*np.array(accuracies).mean())) print("Average recall: {0:.1f}".format(100.0*np.array(rec).mean())) print("Average precision: {0:.1f}".format(100.0*np.array(pre).mean())) print("Average f1: {0:.1f}".format(100.0*np.array(f1).mean())) print("Median Accuracy: {0:.1f}". format(100.0*np.median(np.array(accuracies)))) print("Min Accuracy: {0:.1f}".format(100.0*np.array(accuracies).min())) print("Max Accuracy: {0:.1f}".format(100.0*np.array(accuracies).max())) else: print("Confusion matrix was empty, accuracy for every file was 0") def silence_removal(signal, sampling_rate, st_win, st_step, smooth_window=0.5, weight=0.5, plot=False): """ Event Detection (silence removal) ARGUMENTS: - signal: the input audio signal - sampling_rate: sampling freq - st_win, st_step: window size and step in seconds - smoothWindow: (optinal) smooth window (in seconds) - weight: (optinal) weight factor (0 < weight < 1) the higher, the more strict - plot: (optinal) True if results are to be plotted RETURNS: - seg_limits: list of segment limits in seconds (e.g [[0.1, 0.9], [1.4, 3.0]] means that the resulting segments are (0.1 - 0.9) seconds and (1.4, 3.0) seconds """ if weight >= 1: weight = 0.99 if weight <= 0: weight = 0.01 # Step 1: feature extraction signal = audioBasicIO.stereo_to_mono(signal) st_feats, _ = stf.feature_extraction(signal, sampling_rate, st_win * sampling_rate, st_step * sampling_rate) # Step 2: train binary svm classifier of low vs high energy frames # keep only the energy short-term sequence (2nd feature) st_energy = st_feats[1, :] en = np.sort(st_energy) # number of 10% of the total short-term windows st_windows_fraction = int(len(en) / 10) # compute "lower" 10% energy threshold low_threshold = np.mean(en[0:st_windows_fraction]) + 1e-15 # compute "higher" 10% energy threshold high_threshold = np.mean(en[-st_windows_fraction:-1]) + 1e-15 # get all features that correspond to low energy low_energy = st_feats[:, np.where(st_energy <= low_threshold)[0]] # get all features that correspond to high energy high_energy = st_feats[:, np.where(st_energy >= high_threshold)[0]] # form the binary classification task and ... features = [low_energy.T, high_energy.T] # normalize and train the respective svm probabilistic model # (ONSET vs SILENCE) features_norm, mean, std = at.normalize_features(features) svm = at.train_svm(features_norm, 1.0) # Step 3: compute onset probability based on the trained svm prob_on_set = [] for index in range(st_feats.shape[1]): # for each frame cur_fv = (st_feats[:, index] - mean) / std # get svm probability (that it belongs to the ONSET class) prob_on_set.append(svm.predict_proba(cur_fv.reshape(1, -1))[0][1]) prob_on_set = np.array(prob_on_set) # smooth probability: prob_on_set = smooth_moving_avg(prob_on_set, smooth_window / st_step) # Step 4A: detect onset frame indices: prog_on_set_sort = np.sort(prob_on_set) # find probability Threshold as a weighted average # of top 10% and lower 10% of the values nt = int(prog_on_set_sort.shape[0] / 10) threshold = (np.mean((1 - weight) * prog_on_set_sort[0:nt]) + weight * np.mean(prog_on_set_sort[-nt::])) max_indices = np.where(prob_on_set > threshold)[0] # get the indices of the frames that satisfy the thresholding index = 0 seg_limits = [] time_clusters = [] # Step 4B: group frame indices to onset segments while index < len(max_indices): # for each of the detected onset indices cur_cluster = [max_indices[index]] if index == len(max_indices)-1: break while max_indices[index+1] - cur_cluster[-1] <= 2: cur_cluster.append(max_indices[index+1]) index += 1 if index == len(max_indices)-1: break index += 1 time_clusters.append(cur_cluster) seg_limits.append([cur_cluster[0] * st_step, cur_cluster[-1] * st_step]) # Step 5: Post process: remove very small segments: min_duration = 0.2 seg_limits_2 = [] for s_lim in seg_limits: if s_lim[1] - s_lim[0] > min_duration: seg_limits_2.append(s_lim) seg_limits = seg_limits_2 if plot: time_x = np.arange(0, signal.shape[0] / float(sampling_rate), 1.0 / sampling_rate) plt.subplot(2, 1, 1) plt.plot(time_x, signal) for s_lim in seg_limits: plt.axvline(x=s_lim[0], color='red') plt.axvline(x=s_lim[1], color='red') plt.subplot(2, 1, 2) plt.plot(np.arange(0, prob_on_set.shape[0] * st_step, st_step), prob_on_set) plt.title('Signal') for s_lim in seg_limits: plt.axvline(x=s_lim[0], color='red') plt.axvline(x=s_lim[1], color='red') plt.title('svm Probability') plt.show() return seg_limits def speaker_diarization(filename, n_speakers, mid_window=2.0, mid_step=0.2, short_window=0.05, lda_dim=35, plot_res=False): """ ARGUMENTS: - filename: the name of the WAV file to be analyzed - n_speakers the number of speakers (clusters) in the recording (<=0 for unknown) - mid_window (opt) mid-term window size - mid_step (opt) mid-term window step - short_window (opt) short-term window size - lda_dim (opt LDA dimension (0 for no LDA) - plot_res (opt) 0 for not plotting the results 1 for plotting """ sampling_rate, signal = audioBasicIO.read_audio_file(filename) signal = audioBasicIO.stereo_to_mono(signal) duration = len(signal) / sampling_rate base_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data/models") classifier_all, mean_all, std_all, class_names_all, _, _, _, _, _ = \ at.load_model_knn(os.path.join(base_dir, "knn_speaker_10")) classifier_fm, mean_fm, std_fm, class_names_fm, _, _, _, _, _ = \ at.load_model_knn(os.path.join(base_dir, "knn_speaker_male_female")) mid_feats, st_feats, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * short_window), round(sampling_rate * short_window * 0.5)) mid_term_features = np.zeros((mid_feats.shape[0] + len(class_names_all) + len(class_names_fm), mid_feats.shape[1])) for index in range(mid_feats.shape[1]): feature_norm_all = (mid_feats[:, index] - mean_all) / std_all feature_norm_fm = (mid_feats[:, index] - mean_fm) / std_fm _, p1 = at.classifier_wrapper(classifier_all, "knn", feature_norm_all) _, p2 = at.classifier_wrapper(classifier_fm, "knn", feature_norm_fm) start = mid_feats.shape[0] end = mid_feats.shape[0] + len(class_names_all) mid_term_features[0:mid_feats.shape[0], index] = mid_feats[:, index] mid_term_features[start:end, index] = p1 + 1e-4 mid_term_features[end::, index] = p2 + 1e-4 mid_feats = mid_term_features # TODO feature_selected = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53] mid_feats = mid_feats[feature_selected, :] mid_feats_norm, mean, std = at.normalize_features([mid_feats.T]) mid_feats_norm = mid_feats_norm[0].T n_wins = mid_feats.shape[1] # remove outliers: dist_all = np.sum(distance.squareform(distance.pdist(mid_feats_norm.T)), axis=0) m_dist_all = np.mean(dist_all) i_non_outliers = np.nonzero(dist_all < 1.2 * m_dist_all)[0] # TODO: Combine energy threshold for outlier removal: # EnergyMin = np.min(mt_feats[1,:]) # EnergyMean = np.mean(mt_feats[1,:]) # Thres = (1.5*EnergyMin + 0.5*EnergyMean) / 2.0 # i_non_outliers = np.nonzero(mt_feats[1,:] > Thres)[0] # print i_non_outliers mt_feats_norm_or = mid_feats_norm mid_feats_norm = mid_feats_norm[:, i_non_outliers] # LDA dimensionality reduction: if lda_dim > 0: # extract mid-term features with minimum step: window_ratio = int(round(mid_window / short_window)) step_ratio = int(round(short_window / short_window)) mt_feats_to_red = [] num_of_features = len(st_feats) num_of_stats = 2 for index in range(num_of_stats * num_of_features): mt_feats_to_red.append([]) # for each of the short-term features: for index in range(num_of_features): cur_pos = 0 feat_len = len(st_feats[index]) while cur_pos < feat_len: n1 = cur_pos n2 = cur_pos + window_ratio if n2 > feat_len: n2 = feat_len short_features = st_feats[index][n1:n2] mt_feats_to_red[index].append(np.mean(short_features)) mt_feats_to_red[index + num_of_features].\ append(np.std(short_features)) cur_pos += step_ratio mt_feats_to_red = np.array(mt_feats_to_red) mt_feats_to_red_2 = np.zeros((mt_feats_to_red.shape[0] + len(class_names_all) + len(class_names_fm), mt_feats_to_red.shape[1])) limit = mt_feats_to_red.shape[0] + len(class_names_all) for index in range(mt_feats_to_red.shape[1]): feature_norm_all = (mt_feats_to_red[:, index] - mean_all) / std_all feature_norm_fm = (mt_feats_to_red[:, index] - mean_fm) / std_fm _, p1 = at.classifier_wrapper(classifier_all, "knn", feature_norm_all) _, p2 = at.classifier_wrapper(classifier_fm, "knn", feature_norm_fm) mt_feats_to_red_2[0:mt_feats_to_red.shape[0], index] = \ mt_feats_to_red[:, index] mt_feats_to_red_2[mt_feats_to_red.shape[0]:limit, index] = p1 + 1e-4 mt_feats_to_red_2[limit::, index] = p2 + 1e-4 mt_feats_to_red = mt_feats_to_red_2 mt_feats_to_red = mt_feats_to_red[feature_selected, :] mt_feats_to_red, mean, std = at.normalize_features([mt_feats_to_red.T]) mt_feats_to_red = mt_feats_to_red[0].T labels = np.zeros((mt_feats_to_red.shape[1], )) lda_step = 1.0 lda_step_ratio = lda_step / short_window for index in range(labels.shape[0]): labels[index] = int(index * short_window / lda_step_ratio) clf = sklearn.discriminant_analysis.\ LinearDiscriminantAnalysis(n_components=lda_dim) clf.fit(mt_feats_to_red.T, labels) mid_feats_norm = (clf.transform(mid_feats_norm.T)).T if n_speakers <= 0: s_range = range(2, 10) else: s_range = [n_speakers] cluster_labels = [] sil_all = [] cluster_centers = [] for speakers in s_range: k_means = sklearn.cluster.KMeans(n_clusters=speakers) k_means.fit(mid_feats_norm.T) cls = k_means.labels_ means = k_means.cluster_centers_ cluster_labels.append(cls) cluster_centers.append(means) sil_1, sil_2 = [], [] for c in range(speakers): # for each speaker (i.e. for each extracted cluster) clust_per_cent = np.nonzero(cls == c)[0].shape[0] / float(len(cls)) if clust_per_cent < 0.020: sil_1.append(0.0) sil_2.append(0.0) else: # get subset of feature vectors mt_feats_norm_temp = mid_feats_norm[:, cls == c] # compute average distance between samples # that belong to the cluster (a values) dist = distance.pdist(mt_feats_norm_temp.T) sil_1.append(np.mean(dist)*clust_per_cent) sil_temp = [] for c2 in range(speakers): # compute distances from samples of other clusters if c2 != c: clust_per_cent_2 = np.nonzero(cls == c2)[0].shape[0] /\ float(len(cls)) mid_features_temp = mid_feats_norm[:, cls == c2] dist = distance.cdist(mt_feats_norm_temp.T, mid_features_temp.T) sil_temp.append(np.mean(dist)*(clust_per_cent + clust_per_cent_2)/2.0) sil_temp = np.array(sil_temp) # ... and keep the minimum value (i.e. # the distance from the "nearest" cluster) sil_2.append(min(sil_temp)) sil_1 = np.array(sil_1) sil_2 = np.array(sil_2) sil = [] for c in range(speakers): # for each cluster (speaker) compute silhouette sil.append((sil_2[c] - sil_1[c]) / (max(sil_2[c], sil_1[c]) + 1e-5)) # keep the AVERAGE SILLOUETTE sil_all.append(np.mean(sil)) imax = int(np.argmax(sil_all)) # optimal number of clusters num_speakers = s_range[imax] # generate the final set of cluster labels # (important: need to retrieve the outlier windows: # this is achieved by giving them the value of their # nearest non-outlier window) cls = np.zeros((n_wins,)) for index in range(n_wins): j = np.argmin(np.abs(index-i_non_outliers)) cls[index] = cluster_labels[imax][j] # Post-process method 1: hmm smoothing for index in range(1): # hmm training start_prob, transmat, means, cov = \ train_hmm_compute_statistics(mt_feats_norm_or, cls) hmm = hmmlearn.hmm.GaussianHMM(start_prob.shape[0], "diag") hmm.startprob_ = start_prob hmm.transmat_ = transmat hmm.means_ = means hmm.covars_ = cov cls = hmm.predict(mt_feats_norm_or.T) # Post-process method 2: median filtering: cls = scipy.signal.medfilt(cls, 13) cls = scipy.signal.medfilt(cls, 11) class_names = ["speaker{0:d}".format(c) for c in range(num_speakers)] # load ground-truth if available gt_file = filename.replace('.wav', '.segments') # if groundtruth exists if os.path.isfile(gt_file): seg_start, seg_end, seg_labs = read_segmentation_gt(gt_file) flags_gt, class_names_gt = segments_to_labels(seg_start, seg_end, seg_labs, mid_step) if plot_res: fig = plt.figure() if n_speakers > 0: ax1 = fig.add_subplot(111) else: ax1 = fig.add_subplot(211) ax1.set_yticks(np.array(range(len(class_names)))) ax1.axis((0, duration, -1, len(class_names))) ax1.set_yticklabels(class_names) ax1.plot(np.array(range(len(cls))) * mid_step + mid_step / 2.0, cls) if os.path.isfile(gt_file): if plot_res: ax1.plot(np.array(range(len(flags_gt))) * mid_step + mid_step / 2.0, flags_gt, 'r') purity_cluster_m, purity_speaker_m = \ evaluate_speaker_diarization(cls, flags_gt) print("{0:.1f}\t{1:.1f}".format(100 * purity_cluster_m, 100 * purity_speaker_m)) if plot_res: plt.title("Cluster purity: {0:.1f}% - " "Speaker purity: {1:.1f}%".format(100 * purity_cluster_m, 100 * purity_speaker_m)) if plot_res: plt.xlabel("time (seconds)") if n_speakers <= 0: plt.subplot(212) plt.plot(s_range, sil_all) plt.xlabel("number of clusters") plt.ylabel("average clustering's sillouette") plt.show() return cls def speaker_diarization_evaluation(folder_name, lda_dimensions): """ This function prints the cluster purity and speaker purity for each WAV file stored in a provided directory (.SEGMENT files are needed as ground-truth) ARGUMENTS: - folder_name: the full path of the folder where the WAV and segment (ground-truth) files are stored - lda_dimensions: a list of LDA dimensions (0 for no LDA) """ types = ('*.wav', ) wav_files = [] for files in types: wav_files.extend(glob.glob(os.path.join(folder_name, files))) wav_files = sorted(wav_files) # get number of unique speakers per file (from ground-truth) num_speakers = [] for wav_file in wav_files: gt_file = wav_file.replace('.wav', '.segments') if os.path.isfile(gt_file): _, _, seg_labs = read_segmentation_gt(gt_file) num_speakers.append(len(list(set(seg_labs)))) else: num_speakers.append(-1) for dim in lda_dimensions: print("LDA = {0:d}".format(dim)) for i, wav_file in enumerate(wav_files): speaker_diarization(wav_file, num_speakers[i], 2.0, 0.2, 0.05, dim, plot_res=False) def music_thumbnailing(signal, sampling_rate, short_window=1.0, short_step=0.5, thumb_size=10.0, limit_1=0, limit_2=1): """ This function detects instances of the most representative part of a music recording, also called "music thumbnails". A technique similar to the one proposed in [1], however a wider set of audio features is used instead of chroma features. In particular the following steps are followed: - Extract short-term audio features. Typical short-term window size: 1 second - Compute the self-similarity matrix, i.e. all pairwise similarities between feature vectors - Apply a diagonal mask is as a moving average filter on the values of the self-similarty matrix. The size of the mask is equal to the desirable thumbnail length. - Find the position of the maximum value of the new (filtered) self-similarity matrix. The audio segments that correspond to the diagonial around that position are the selected thumbnails ARGUMENTS: - signal: input signal - sampling_rate: sampling frequency - short_window: window size (in seconds) - short_step: window step (in seconds) - thumb_size: desider thumbnail size (in seconds) RETURNS: - A1: beginning of 1st thumbnail (in seconds) - A2: ending of 1st thumbnail (in seconds) - B1: beginning of 2nd thumbnail (in seconds) - B2: ending of 2nd thumbnail (in seconds) USAGE EXAMPLE: import audioFeatureExtraction as aF [fs, x] = basicIO.readAudioFile(input_file) [A1, A2, B1, B2] = musicThumbnailing(x, fs) [1] Bartsch, M. A., & Wakefield, G. H. (2005). Audio thumbnailing of popular music using chroma-based representations. Multimedia, IEEE Transactions on, 7(1), 96-104. """ signal = audioBasicIO.stereo_to_mono(signal) # feature extraction: st_feats, _ = stf.feature_extraction(signal, sampling_rate, sampling_rate * short_window, sampling_rate * short_step) # self-similarity matrix sim_matrix = self_similarity_matrix(st_feats) # moving filter: m_filter = int(round(thumb_size / short_step)) diagonal = np.eye(m_filter, m_filter) sim_matrix = scipy.signal.convolve2d(sim_matrix, diagonal, 'valid') # post-processing (remove main diagonal elements) min_sm = np.min(sim_matrix) for i in range(sim_matrix.shape[0]): for j in range(sim_matrix.shape[1]): if abs(i-j) < 5.0 / short_step or i > j: sim_matrix[i, j] = min_sm # find max position: sim_matrix[0:int(limit_1 * sim_matrix.shape[0]), :] = min_sm sim_matrix[:, 0:int(limit_1 * sim_matrix.shape[0])] = min_sm sim_matrix[int(limit_2 * sim_matrix.shape[0])::, :] = min_sm sim_matrix[:, int(limit_2 * sim_matrix.shape[0])::] = min_sm rows, cols = np.unravel_index(sim_matrix.argmax(), sim_matrix.shape) i1 = rows i2 = rows j1 = cols j2 = cols while i2-i1 < m_filter: if i1 <= 0 or j1 <= 0 or i2 >= sim_matrix.shape[0]-2 or \ j2 >= sim_matrix.shape[1]-2: break if sim_matrix[i1-1, j1-1] > sim_matrix[i2 + 1, j2 + 1]: i1 -= 1 j1 -= 1 else: i2 += 1 j2 += 1 return short_step * i1, short_step * i2, short_step * j1, short_step * j2, \ sim_matrix

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from __future__ import print_function import os import csv import glob import scipy import sklearn import numpy as np import hmmlearn.hmm import sklearn.cluster import pickle as cpickle import matplotlib.pyplot as plt from scipy.spatial import distance import sklearn.discriminant_analysis from pyAudioAnalysis import audioBasicIO from pyAudioAnalysis import audioTrainTest as at from pyAudioAnalysis import MidTermFeatures as mtf from pyAudioAnalysis import ShortTermFeatures as stf def smooth_moving_avg(signal, window=11): window = int(window) if signal.ndim != 1: raise ValueError("") if signal.size < window: raise ValueError("Input vector needs to be bigger than window size.") if window < 3: return signal s = np.r_[2 * signal[0] - signal[window - 1::-1], signal, 2 * signal[-1] - signal[-1:-window:-1]] w = np.ones(window, 'd') y = np.convolve(w/w.sum(), s, mode='same') return y[window:-window + 1] def self_similarity_matrix(feature_vectors): norm_feature_vectors, mean, std = at.normalize_features([feature_vectors.T]) norm_feature_vectors = norm_feature_vectors[0].T sim_matrix = 1.0 - distance.squareform( distance.pdist(norm_feature_vectors.T, 'cosine')) return sim_matrix def labels_to_segments(labels, window): if len(labels)==1: segs = [0, window] classes = labels return segs, classes num_segs = 0 index = 0 classes = [] segment_list = [] cur_label = labels[index] while index < len(labels) - 1: previous_value = cur_label while True: index += 1 compare_flag = labels[index] if (compare_flag != cur_label) | (index == len(labels) - 1): num_segs += 1 cur_label = labels[index] segment_list.append((index * window)) classes.append(previous_value) break segments = np.zeros((len(segment_list), 2)) for i in range(len(segment_list)): if i > 0: segments[i, 0] = segment_list[i-1] segments[i, 1] = segment_list[i] return segments, classes def segments_to_labels(start_times, end_times, labels, window): flags = [] class_names = list(set(labels)) index = window / 2.0 while index < end_times[-1]: for i in range(len(start_times)): if start_times[i] < index <= end_times[i]: break flags.append(class_names.index(labels[i])) index += window return np.array(flags), class_names def compute_metrics(confusion_matrix, class_names): f1 = [] recall = [] precision = [] n_classes = confusion_matrix.shape[0] if len(class_names) != n_classes: print("Error in computePreRec! Confusion matrix and class_names " "list must be of the same size!") else: for i, c in enumerate(class_names): precision.append(confusion_matrix[i, i] / np.sum(confusion_matrix[:, i])) recall.append(confusion_matrix[i, i] / np.sum(confusion_matrix[i, :])) f1.append(2 * precision[-1] * recall[-1] / (precision[-1] + recall[-1])) return recall, precision, f1 def read_segmentation_gt(gt_file): with open(gt_file, 'rt') as f_handle: reader = csv.reader(f_handle, delimiter='\t') start_times = [] end_times = [] labels = [] for row in reader: if len(row) == 3: start_times.append(float(row[0])) end_times.append(float(row[1])) labels.append((row[2])) return np.array(start_times), np.array(end_times), labels def plot_segmentation_results(flags_ind, flags_ind_gt, class_names, mt_step, evaluate_only=False): flags = [class_names[int(f)] for f in flags_ind] segments, classes = labels_to_segments(flags, mt_step) min_len = min(flags_ind.shape[0], flags_ind_gt.shape[0]) if min_len > 0: accuracy = np.sum(flags_ind[0:min_len] == flags_ind_gt[0:min_len]) / float(min_len) else: accuracy = -1 if not evaluate_only: duration = segments[-1, 1] s_percentages = np.zeros((len(class_names), )) percentages = np.zeros((len(class_names), )) av_durations = np.zeros((len(class_names), )) for i_seg in range(segments.shape[0]): s_percentages[class_names.index(classes[i_seg])] += \ (segments[i_seg, 1]-segments[i_seg, 0]) for i in range(s_percentages.shape[0]): percentages[i] = 100.0 * s_percentages[i] / duration class_sum = sum(1 for c in classes if c == class_names[i]) if class_sum > 0: av_durations[i] = s_percentages[i] / class_sum else: av_durations[i] = 0.0 for i in range(percentages.shape[0]): print(class_names[i], percentages[i], av_durations[i]) font = {'size': 10} plt.rc('font', **font) fig = plt.figure() ax1 = fig.add_subplot(211) ax1.set_yticks(np.array(range(len(class_names)))) ax1.axis((0, duration, -1, len(class_names))) ax1.set_yticklabels(class_names) ax1.plot(np.array(range(len(flags_ind))) * mt_step + mt_step / 2.0, flags_ind) if flags_ind_gt.shape[0] > 0: ax1.plot(np.array(range(len(flags_ind_gt))) * mt_step + mt_step / 2.0, flags_ind_gt + 0.05, '--r') plt.xlabel("time (seconds)") if accuracy >= 0: plt.title('Accuracy = {0:.1f}%'.format(100.0 * accuracy)) ax2 = fig.add_subplot(223) plt.title("Classes percentage durations") ax2.axis((0, len(class_names) + 1, 0, 100)) ax2.set_xticks(np.array(range(len(class_names) + 1))) ax2.set_xticklabels([" "] + class_names) print(np.array(range(len(class_names))), percentages) ax2.bar(np.array(range(len(class_names))) + 0.5, percentages) ax3 = fig.add_subplot(224) plt.title("Segment average duration per class") ax3.axis((0, len(class_names)+1, 0, av_durations.max())) ax3.set_xticks(np.array(range(len(class_names) + 1))) ax3.set_xticklabels([" "] + class_names) ax3.bar(np.array(range(len(class_names))) + 0.5, av_durations) fig.tight_layout() plt.show() return accuracy def evaluate_speaker_diarization(labels, labels_gt): min_len = min(labels.shape[0], labels_gt.shape[0]) labels = labels[0:min_len] labels_gt = labels_gt[0:min_len] unique_flags = np.unique(labels) unique_flags_gt = np.unique(labels_gt) contigency_matrix = np.zeros((unique_flags.shape[0], unique_flags_gt.shape[0])) for i in range(min_len): contigency_matrix[int(np.nonzero(unique_flags == labels[i])[0]), int(np.nonzero(unique_flags_gt == labels_gt[i])[0])] += 1.0 columns, rows = contigency_matrix.shape row_sum = np.sum(contigency_matrix, axis=0) column_sum = np.sum(contigency_matrix, axis=1) matrix_sum = np.sum(contigency_matrix) purity_clust = np.zeros((columns, )) purity_speak = np.zeros((rows, )) for i in range(columns): purity_clust[i] = np.max((contigency_matrix[i, :])) / (column_sum[i]) for j in range(rows): purity_speak[j] = np.max((contigency_matrix[:, j])) / (row_sum[j]) purity_cluster_m = np.sum(purity_clust * column_sum) / matrix_sum purity_speaker_m = np.sum(purity_speak * row_sum) / matrix_sum return purity_cluster_m, purity_speaker_m def train_hmm_compute_statistics(features, labels): unique_labels = np.unique(labels) n_comps = len(unique_labels) n_feats = features.shape[0] if features.shape[1] < labels.shape[0]: print("trainHMM warning: number of short-term feature vectors " "must be greater or equal to the labels length!") labels = labels[0:features.shape[1]] class_priors = np.zeros((n_comps,)) for i, u_label in enumerate(unique_labels): class_priors[i] = np.count_nonzero(labels == u_label) class_priors = class_priors / class_priors.sum() transmutation_matrix = np.zeros((n_comps, n_comps)) for i in range(labels.shape[0]-1): transmutation_matrix[int(labels[i]), int(labels[i + 1])] += 1 for i in range(n_comps): transmutation_matrix[i, :] /= transmutation_matrix[i, :].sum() means = np.zeros((n_comps, n_feats)) for i in range(n_comps): means[i, :] = \ np.array(features[:, np.nonzero(labels == unique_labels[i])[0]].mean(axis=1)) cov = np.zeros((n_comps, n_feats)) for i in range(n_comps): cov[i, :] = np.std(features[:, np.nonzero(labels == unique_labels[i])[0]], axis=1) return class_priors, transmutation_matrix, means, cov def train_hmm_from_file(wav_file, gt_file, hmm_model_name, mid_window, mid_step): seg_start, seg_end, seg_labs = read_segmentation_gt(gt_file) flags, class_names = segments_to_labels(seg_start, seg_end, seg_labs, mid_step) sampling_rate, signal = audioBasicIO.read_audio_file(wav_file) features, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * 0.050), round(sampling_rate * 0.050)) class_priors, transumation_matrix, means, cov = \ train_hmm_compute_statistics(features, flags) hmm = hmmlearn.hmm.GaussianHMM(class_priors.shape[0], "diag") hmm.covars_ = cov hmm.means_ = means hmm.startprob_ = class_priors hmm.transmat_ = transumation_matrix save_hmm(hmm_model_name, hmm, class_names, mid_window, mid_step) return hmm, class_names def train_hmm_from_directory(folder_path, hmm_model_name, mid_window, mid_step): flags_all = np.array([]) class_names_all = [] for i, f in enumerate(glob.glob(folder_path + os.sep + '*.wav')): wav_file = f gt_file = f.replace('.wav', '.segments') if os.path.isfile(gt_file): seg_start, seg_end, seg_labs = read_segmentation_gt(gt_file) flags, class_names = \ segments_to_labels(seg_start, seg_end, seg_labs, mid_step) for c in class_names: if c not in class_names_all: class_names_all.append(c) sampling_rate, signal = audioBasicIO.read_audio_file(wav_file) feature_vector, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * 0.050), round(sampling_rate * 0.050)) flag_len = len(flags) feat_cols = feature_vector.shape[1] min_sm = min(feat_cols, flag_len) feature_vector = feature_vector[:, 0:min_sm] flags = flags[0:min_sm] flags_new = [] for j, fl in enumerate(flags): flags_new.append(class_names_all.index(class_names_all[flags[j]])) flags_all = np.append(flags_all, np.array(flags_new)) if i == 0: f_all = feature_vector else: f_all = np.concatenate((f_all, feature_vector), axis=1) class_priors, transmutation_matrix, means, cov = \ train_hmm_compute_statistics(f_all, flags_all) hmm = hmmlearn.hmm.GaussianHMM(class_priors.shape[0], "diag") hmm.covars_ = cov hmm.means_ = means hmm.startprob_ = class_priors hmm.transmat_ = transmutation_matrix save_hmm(hmm_model_name, hmm, class_names_all, mid_window, mid_step) return hmm, class_names_all def save_hmm(hmm_model_name, model, classes, mid_window, mid_step): with open(hmm_model_name, "wb") as f_handle: cpickle.dump(model, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) cpickle.dump(classes, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) cpickle.dump(mid_window, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) cpickle.dump(mid_step, f_handle, protocol=cpickle.HIGHEST_PROTOCOL) def hmm_segmentation(audio_file, hmm_model_name, plot_results=False, gt_file=""): sampling_rate, signal = audioBasicIO.read_audio_file(audio_file) with open(hmm_model_name, "rb") as f_handle: hmm = cpickle.load(f_handle) class_names = cpickle.load(f_handle) mid_window = cpickle.load(f_handle) mid_step = cpickle.load(f_handle) features, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * 0.050), round(sampling_rate * 0.050)) labels = hmm.predict(features.T) labels_gt, class_names_gt, accuracy, cm = \ load_ground_truth(gt_file, labels, class_names, mid_step, plot_results) return labels, class_names, accuracy, cm def load_ground_truth_segments(gt_file, mt_step): seg_start, seg_end, seg_labels = read_segmentation_gt(gt_file) labels, class_names = segments_to_labels(seg_start, seg_end, seg_labels, mt_step) labels_temp = [] for index, label in enumerate(labels): if class_names[labels[index]] in class_names: labels_temp.append(class_names.index(class_names[ labels[index]])) else: labels_temp.append(-1) labels = np.array(labels_temp) return labels, class_names def calculate_confusion_matrix(predictions, ground_truth, classes): cm = np.zeros((len(classes), len(classes))) for index in range(min(predictions.shape[0], ground_truth.shape[0])): cm[int(ground_truth[index]), int(predictions[index])] += 1 return cm def mid_term_file_classification(input_file, model_name, model_type, plot_results=False, gt_file=""): labels = [] accuracy = 0.0 class_names = [] cm = np.array([]) if not os.path.isfile(model_name): print("mtFileClassificationError: input model_type not found!") return labels, class_names, accuracy, cm if model_type == "knn": classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model_knn(model_name) else: classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model(model_name) if compute_beat: print("Model " + model_name + " contains long-term music features " "(beat etc) and cannot be used in " "segmentation") return labels, class_names, accuracy, cm sampling_rate, signal = audioBasicIO.read_audio_file(input_file) if sampling_rate == 0: return labels, class_names, accuracy, cm signal = audioBasicIO.stereo_to_mono(signal) mt_feats, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mt_win * sampling_rate, mid_step * sampling_rate, round(sampling_rate * st_win), round(sampling_rate * st_step)) posterior_matrix = [] for col_index in range(mt_feats.shape[1]): feature_vector = (mt_feats[:, col_index] - mean) / std label_predicted, posterior = \ at.classifier_wrapper(classifier, model_type, feature_vector) labels.append(label_predicted) posterior_matrix.append(np.max(posterior)) labels = np.array(labels) segs, classes = labels_to_segments(labels, mid_step) segs[-1] = len(signal) / float(sampling_rate) labels_gt, class_names_gt, accuracy, cm = \ load_ground_truth(gt_file, labels, class_names, mid_step, plot_results) return labels, class_names, accuracy, cm def load_ground_truth(gt_file, labels, class_names, mid_step, plot_results): accuracy = 0 cm = np.array([]) labels_gt = np.array([]) if os.path.isfile(gt_file): labels_gt, class_names_gt = load_ground_truth_segments(gt_file, mid_step) labels_new = [] for il, l in enumerate(labels): if class_names[int(l)] in class_names_gt: labels_new.append(class_names_gt.index(class_names[int(l)])) else: labels_new.append(-1) labels_new = np.array(labels_new) cm = calculate_confusion_matrix(labels_new, labels_gt, class_names_gt) accuracy = plot_segmentation_results(labels_new, labels_gt, class_names, mid_step, not plot_results) if accuracy >= 0: print("Overall Accuracy: {0:.2f}".format(accuracy)) return labels_gt, class_names, accuracy, cm def evaluate_segmentation_classification_dir(dir_name, model_name, method_name): accuracies = [] class_names = [] cm_total = np.array([]) for index, wav_file in enumerate(glob.glob(dir_name + os.sep + '*.wav')): print(wav_file) gt_file = wav_file.replace('.wav', '.segments') if method_name.lower() in ["svm", "svm_rbf", "knn", "randomforest", "gradientboosting", "extratrees"]: flags_ind, class_names, accuracy, cm_temp = \ mid_term_file_classification(wav_file, model_name, method_name, False, gt_file) else: flags_ind, class_names, accuracy, cm_temp = \ hmm_segmentation(wav_file, model_name, False, gt_file) if accuracy > 0: if not index: cm_total = np.copy(cm_temp) else: cm_total = cm_total + cm_temp accuracies.append(accuracy) print(cm_temp, class_names) print(cm_total) if len(cm_total.shape) > 1: cm_total = cm_total / np.sum(cm_total) rec, pre, f1 = compute_metrics(cm_total, class_names) print(" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ") print("Average Accuracy: {0:.1f}". format(100.0*np.array(accuracies).mean())) print("Average recall: {0:.1f}".format(100.0*np.array(rec).mean())) print("Average precision: {0:.1f}".format(100.0*np.array(pre).mean())) print("Average f1: {0:.1f}".format(100.0*np.array(f1).mean())) print("Median Accuracy: {0:.1f}". format(100.0*np.median(np.array(accuracies)))) print("Min Accuracy: {0:.1f}".format(100.0*np.array(accuracies).min())) print("Max Accuracy: {0:.1f}".format(100.0*np.array(accuracies).max())) else: print("Confusion matrix was empty, accuracy for every file was 0") def silence_removal(signal, sampling_rate, st_win, st_step, smooth_window=0.5, weight=0.5, plot=False): if weight >= 1: weight = 0.99 if weight <= 0: weight = 0.01 signal = audioBasicIO.stereo_to_mono(signal) st_feats, _ = stf.feature_extraction(signal, sampling_rate, st_win * sampling_rate, st_step * sampling_rate) st_energy = st_feats[1, :] en = np.sort(st_energy) st_windows_fraction = int(len(en) / 10) low_threshold = np.mean(en[0:st_windows_fraction]) + 1e-15 high_threshold = np.mean(en[-st_windows_fraction:-1]) + 1e-15 low_energy = st_feats[:, np.where(st_energy <= low_threshold)[0]] high_energy = st_feats[:, np.where(st_energy >= high_threshold)[0]] features = [low_energy.T, high_energy.T] features_norm, mean, std = at.normalize_features(features) svm = at.train_svm(features_norm, 1.0) prob_on_set = [] for index in range(st_feats.shape[1]): cur_fv = (st_feats[:, index] - mean) / std prob_on_set.append(svm.predict_proba(cur_fv.reshape(1, -1))[0][1]) prob_on_set = np.array(prob_on_set) prob_on_set = smooth_moving_avg(prob_on_set, smooth_window / st_step) prog_on_set_sort = np.sort(prob_on_set) nt = int(prog_on_set_sort.shape[0] / 10) threshold = (np.mean((1 - weight) * prog_on_set_sort[0:nt]) + weight * np.mean(prog_on_set_sort[-nt::])) max_indices = np.where(prob_on_set > threshold)[0] index = 0 seg_limits = [] time_clusters = [] while index < len(max_indices): cur_cluster = [max_indices[index]] if index == len(max_indices)-1: break while max_indices[index+1] - cur_cluster[-1] <= 2: cur_cluster.append(max_indices[index+1]) index += 1 if index == len(max_indices)-1: break index += 1 time_clusters.append(cur_cluster) seg_limits.append([cur_cluster[0] * st_step, cur_cluster[-1] * st_step]) min_duration = 0.2 seg_limits_2 = [] for s_lim in seg_limits: if s_lim[1] - s_lim[0] > min_duration: seg_limits_2.append(s_lim) seg_limits = seg_limits_2 if plot: time_x = np.arange(0, signal.shape[0] / float(sampling_rate), 1.0 / sampling_rate) plt.subplot(2, 1, 1) plt.plot(time_x, signal) for s_lim in seg_limits: plt.axvline(x=s_lim[0], color='red') plt.axvline(x=s_lim[1], color='red') plt.subplot(2, 1, 2) plt.plot(np.arange(0, prob_on_set.shape[0] * st_step, st_step), prob_on_set) plt.title('Signal') for s_lim in seg_limits: plt.axvline(x=s_lim[0], color='red') plt.axvline(x=s_lim[1], color='red') plt.title('svm Probability') plt.show() return seg_limits def speaker_diarization(filename, n_speakers, mid_window=2.0, mid_step=0.2, short_window=0.05, lda_dim=35, plot_res=False): sampling_rate, signal = audioBasicIO.read_audio_file(filename) signal = audioBasicIO.stereo_to_mono(signal) duration = len(signal) / sampling_rate base_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data/models") classifier_all, mean_all, std_all, class_names_all, _, _, _, _, _ = \ at.load_model_knn(os.path.join(base_dir, "knn_speaker_10")) classifier_fm, mean_fm, std_fm, class_names_fm, _, _, _, _, _ = \ at.load_model_knn(os.path.join(base_dir, "knn_speaker_male_female")) mid_feats, st_feats, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mid_window * sampling_rate, mid_step * sampling_rate, round(sampling_rate * short_window), round(sampling_rate * short_window * 0.5)) mid_term_features = np.zeros((mid_feats.shape[0] + len(class_names_all) + len(class_names_fm), mid_feats.shape[1])) for index in range(mid_feats.shape[1]): feature_norm_all = (mid_feats[:, index] - mean_all) / std_all feature_norm_fm = (mid_feats[:, index] - mean_fm) / std_fm _, p1 = at.classifier_wrapper(classifier_all, "knn", feature_norm_all) _, p2 = at.classifier_wrapper(classifier_fm, "knn", feature_norm_fm) start = mid_feats.shape[0] end = mid_feats.shape[0] + len(class_names_all) mid_term_features[0:mid_feats.shape[0], index] = mid_feats[:, index] mid_term_features[start:end, index] = p1 + 1e-4 mid_term_features[end::, index] = p2 + 1e-4 mid_feats = mid_term_features feature_selected = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53] mid_feats = mid_feats[feature_selected, :] mid_feats_norm, mean, std = at.normalize_features([mid_feats.T]) mid_feats_norm = mid_feats_norm[0].T n_wins = mid_feats.shape[1] dist_all = np.sum(distance.squareform(distance.pdist(mid_feats_norm.T)), axis=0) m_dist_all = np.mean(dist_all) i_non_outliers = np.nonzero(dist_all < 1.2 * m_dist_all)[0] mt_feats_norm_or = mid_feats_norm mid_feats_norm = mid_feats_norm[:, i_non_outliers] if lda_dim > 0: window_ratio = int(round(mid_window / short_window)) step_ratio = int(round(short_window / short_window)) mt_feats_to_red = [] num_of_features = len(st_feats) num_of_stats = 2 for index in range(num_of_stats * num_of_features): mt_feats_to_red.append([]) for index in range(num_of_features): cur_pos = 0 feat_len = len(st_feats[index]) while cur_pos < feat_len: n1 = cur_pos n2 = cur_pos + window_ratio if n2 > feat_len: n2 = feat_len short_features = st_feats[index][n1:n2] mt_feats_to_red[index].append(np.mean(short_features)) mt_feats_to_red[index + num_of_features].\ append(np.std(short_features)) cur_pos += step_ratio mt_feats_to_red = np.array(mt_feats_to_red) mt_feats_to_red_2 = np.zeros((mt_feats_to_red.shape[0] + len(class_names_all) + len(class_names_fm), mt_feats_to_red.shape[1])) limit = mt_feats_to_red.shape[0] + len(class_names_all) for index in range(mt_feats_to_red.shape[1]): feature_norm_all = (mt_feats_to_red[:, index] - mean_all) / std_all feature_norm_fm = (mt_feats_to_red[:, index] - mean_fm) / std_fm _, p1 = at.classifier_wrapper(classifier_all, "knn", feature_norm_all) _, p2 = at.classifier_wrapper(classifier_fm, "knn", feature_norm_fm) mt_feats_to_red_2[0:mt_feats_to_red.shape[0], index] = \ mt_feats_to_red[:, index] mt_feats_to_red_2[mt_feats_to_red.shape[0]:limit, index] = p1 + 1e-4 mt_feats_to_red_2[limit::, index] = p2 + 1e-4 mt_feats_to_red = mt_feats_to_red_2 mt_feats_to_red = mt_feats_to_red[feature_selected, :] mt_feats_to_red, mean, std = at.normalize_features([mt_feats_to_red.T]) mt_feats_to_red = mt_feats_to_red[0].T labels = np.zeros((mt_feats_to_red.shape[1], )) lda_step = 1.0 lda_step_ratio = lda_step / short_window for index in range(labels.shape[0]): labels[index] = int(index * short_window / lda_step_ratio) clf = sklearn.discriminant_analysis.\ LinearDiscriminantAnalysis(n_components=lda_dim) clf.fit(mt_feats_to_red.T, labels) mid_feats_norm = (clf.transform(mid_feats_norm.T)).T if n_speakers <= 0: s_range = range(2, 10) else: s_range = [n_speakers] cluster_labels = [] sil_all = [] cluster_centers = [] for speakers in s_range: k_means = sklearn.cluster.KMeans(n_clusters=speakers) k_means.fit(mid_feats_norm.T) cls = k_means.labels_ means = k_means.cluster_centers_ cluster_labels.append(cls) cluster_centers.append(means) sil_1, sil_2 = [], [] for c in range(speakers): clust_per_cent = np.nonzero(cls == c)[0].shape[0] / float(len(cls)) if clust_per_cent < 0.020: sil_1.append(0.0) sil_2.append(0.0) else: mt_feats_norm_temp = mid_feats_norm[:, cls == c] dist = distance.pdist(mt_feats_norm_temp.T) sil_1.append(np.mean(dist)*clust_per_cent) sil_temp = [] for c2 in range(speakers): if c2 != c: clust_per_cent_2 = np.nonzero(cls == c2)[0].shape[0] /\ float(len(cls)) mid_features_temp = mid_feats_norm[:, cls == c2] dist = distance.cdist(mt_feats_norm_temp.T, mid_features_temp.T) sil_temp.append(np.mean(dist)*(clust_per_cent + clust_per_cent_2)/2.0) sil_temp = np.array(sil_temp) sil_2.append(min(sil_temp)) sil_1 = np.array(sil_1) sil_2 = np.array(sil_2) sil = [] for c in range(speakers): sil.append((sil_2[c] - sil_1[c]) / (max(sil_2[c], sil_1[c]) + 1e-5)) sil_all.append(np.mean(sil)) imax = int(np.argmax(sil_all)) num_speakers = s_range[imax] cls = np.zeros((n_wins,)) for index in range(n_wins): j = np.argmin(np.abs(index-i_non_outliers)) cls[index] = cluster_labels[imax][j] for index in range(1): start_prob, transmat, means, cov = \ train_hmm_compute_statistics(mt_feats_norm_or, cls) hmm = hmmlearn.hmm.GaussianHMM(start_prob.shape[0], "diag") hmm.startprob_ = start_prob hmm.transmat_ = transmat hmm.means_ = means hmm.covars_ = cov cls = hmm.predict(mt_feats_norm_or.T) cls = scipy.signal.medfilt(cls, 13) cls = scipy.signal.medfilt(cls, 11) class_names = ["speaker{0:d}".format(c) for c in range(num_speakers)] gt_file = filename.replace('.wav', '.segments') if os.path.isfile(gt_file): seg_start, seg_end, seg_labs = read_segmentation_gt(gt_file) flags_gt, class_names_gt = segments_to_labels(seg_start, seg_end, seg_labs, mid_step) if plot_res: fig = plt.figure() if n_speakers > 0: ax1 = fig.add_subplot(111) else: ax1 = fig.add_subplot(211) ax1.set_yticks(np.array(range(len(class_names)))) ax1.axis((0, duration, -1, len(class_names))) ax1.set_yticklabels(class_names) ax1.plot(np.array(range(len(cls))) * mid_step + mid_step / 2.0, cls) if os.path.isfile(gt_file): if plot_res: ax1.plot(np.array(range(len(flags_gt))) * mid_step + mid_step / 2.0, flags_gt, 'r') purity_cluster_m, purity_speaker_m = \ evaluate_speaker_diarization(cls, flags_gt) print("{0:.1f}\t{1:.1f}".format(100 * purity_cluster_m, 100 * purity_speaker_m)) if plot_res: plt.title("Cluster purity: {0:.1f}% - " "Speaker purity: {1:.1f}%".format(100 * purity_cluster_m, 100 * purity_speaker_m)) if plot_res: plt.xlabel("time (seconds)") if n_speakers <= 0: plt.subplot(212) plt.plot(s_range, sil_all) plt.xlabel("number of clusters") plt.ylabel("average clustering's sillouette") plt.show() return cls def speaker_diarization_evaluation(folder_name, lda_dimensions): types = ('*.wav', ) wav_files = [] for files in types: wav_files.extend(glob.glob(os.path.join(folder_name, files))) wav_files = sorted(wav_files) # get number of unique speakers per file (from ground-truth) num_speakers = [] for wav_file in wav_files: gt_file = wav_file.replace('.wav', '.segments') if os.path.isfile(gt_file): _, _, seg_labs = read_segmentation_gt(gt_file) num_speakers.append(len(list(set(seg_labs)))) else: num_speakers.append(-1) for dim in lda_dimensions: print("LDA = {0:d}".format(dim)) for i, wav_file in enumerate(wav_files): speaker_diarization(wav_file, num_speakers[i], 2.0, 0.2, 0.05, dim, plot_res=False) def music_thumbnailing(signal, sampling_rate, short_window=1.0, short_step=0.5, thumb_size=10.0, limit_1=0, limit_2=1): signal = audioBasicIO.stereo_to_mono(signal) # feature extraction: st_feats, _ = stf.feature_extraction(signal, sampling_rate, sampling_rate * short_window, sampling_rate * short_step) # self-similarity matrix sim_matrix = self_similarity_matrix(st_feats) # moving filter: m_filter = int(round(thumb_size / short_step)) diagonal = np.eye(m_filter, m_filter) sim_matrix = scipy.signal.convolve2d(sim_matrix, diagonal, 'valid') # post-processing (remove main diagonal elements) min_sm = np.min(sim_matrix) for i in range(sim_matrix.shape[0]): for j in range(sim_matrix.shape[1]): if abs(i-j) < 5.0 / short_step or i > j: sim_matrix[i, j] = min_sm # find max position: sim_matrix[0:int(limit_1 * sim_matrix.shape[0]), :] = min_sm sim_matrix[:, 0:int(limit_1 * sim_matrix.shape[0])] = min_sm sim_matrix[int(limit_2 * sim_matrix.shape[0])::, :] = min_sm sim_matrix[:, int(limit_2 * sim_matrix.shape[0])::] = min_sm rows, cols = np.unravel_index(sim_matrix.argmax(), sim_matrix.shape) i1 = rows i2 = rows j1 = cols j2 = cols while i2-i1 < m_filter: if i1 <= 0 or j1 <= 0 or i2 >= sim_matrix.shape[0]-2 or \ j2 >= sim_matrix.shape[1]-2: break if sim_matrix[i1-1, j1-1] > sim_matrix[i2 + 1, j2 + 1]: i1 -= 1 j1 -= 1 else: i2 += 1 j2 += 1 return short_step * i1, short_step * i2, short_step * j1, short_step * j2, \ sim_matrix

true

f71b2ed9253b60e916abe7efa50cc6715f2d213c

2,044

py

Python

test/crawler/testICrawler.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

[ "Apache-2.0" ]

3

2020-02-12T01:24:46.000Z

2020-02-13T00:50:46.000Z

test/crawler/testICrawler.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

[ "Apache-2.0" ]

32

2020-02-20T10:20:56.000Z

2022-02-10T01:42:46.000Z

test/crawler/testICrawler.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

[ "Apache-2.0" ]

1

2020-02-22T02:47:19.000Z

#!/usr/bin/env python3 import unittest from src.crawler.iCrawler import iCrawler, UndefinedDatabaseException from src.data.MetaDataItem import MetaDataItem from test.mock.MockDataAccessor import MockDataAccessor class MockCrawler(iCrawler): def __init__(self): super().__init__() def next_downloadable(self): return MetaDataItem( title="title", url="fake url 1", download_src="youtube") class TestICrawler(unittest.TestCase): def setUp(self): self.crawler = MockCrawler() self.database = MockDataAccessor() def test_compiles(self): self.assertEqual(True, True) def test_no_database(self): metadata = self.crawler.next_downloadable() try: self.crawler.check_new_url(metadata.url) self.assertTrue(False) except UndefinedDatabaseException: # Expected error pass def test_check_new_url(self): self.crawler.set_database(self.database) metadata = self.crawler.next_downloadable() self.assertTrue(self.crawler.check_new_url(metadata.url)) self.database.publish_new_metadata(metadata) self.assertFalse(self.crawler.check_new_url(metadata.url)) def test_run(self): self.crawler.set_database(self.database) metadata = self.crawler.run({}) self.database.publish_new_metadata(metadata) id_list = self.database.fetch_video_id_list() self.assertTrue(len(id_list) == 1) metadata = self.database.fetch_metadata(id_list[0]) # Get exact copy of the metadata item that was published copy_metadata = self.crawler.next_downloadable() # need to do this cause the times can be off copy_metadata.date_created = metadata.date_created copy_metadata.id = metadata.id #need to do this because otherwise cant compare self.assertEqual(metadata.to_json(), copy_metadata.to_json()) if __name__ == '__main__': unittest.main()

29.623188

86

0.675147

import unittest from src.crawler.iCrawler import iCrawler, UndefinedDatabaseException from src.data.MetaDataItem import MetaDataItem from test.mock.MockDataAccessor import MockDataAccessor class MockCrawler(iCrawler): def __init__(self): super().__init__() def next_downloadable(self): return MetaDataItem( title="title", url="fake url 1", download_src="youtube") class TestICrawler(unittest.TestCase): def setUp(self): self.crawler = MockCrawler() self.database = MockDataAccessor() def test_compiles(self): self.assertEqual(True, True) def test_no_database(self): metadata = self.crawler.next_downloadable() try: self.crawler.check_new_url(metadata.url) self.assertTrue(False) except UndefinedDatabaseException: pass def test_check_new_url(self): self.crawler.set_database(self.database) metadata = self.crawler.next_downloadable() self.assertTrue(self.crawler.check_new_url(metadata.url)) self.database.publish_new_metadata(metadata) self.assertFalse(self.crawler.check_new_url(metadata.url)) def test_run(self): self.crawler.set_database(self.database) metadata = self.crawler.run({}) self.database.publish_new_metadata(metadata) id_list = self.database.fetch_video_id_list() self.assertTrue(len(id_list) == 1) metadata = self.database.fetch_metadata(id_list[0]) copy_metadata = self.crawler.next_downloadable() copy_metadata.date_created = metadata.date_created copy_metadata.id = metadata.id self.assertEqual(metadata.to_json(), copy_metadata.to_json()) if __name__ == '__main__': unittest.main()

true

f71b2f0c6d371df241e52fd406f5828a49387ea4

4,395

py

Python

prepare.py

binmahone/Raven

40b7e24f14a72af978341c311250f15795be1eb0

[ "Apache-2.0" ]

1

2021-12-23T02:45:06.000Z

prepare.py

Mukvin/Raven

40b7e24f14a72af978341c311250f15795be1eb0

[ "Apache-2.0" ]

null

prepare.py

Mukvin/Raven

40b7e24f14a72af978341c311250f15795be1eb0

[ "Apache-2.0" ]

2

2021-09-16T10:18:01.000Z

2021-09-17T08:40:47.000Z

import time import boto3 from lib.Logger import Logger from lib.popen import subprocess_popen def prepare(): # 0. Initialize boto3 clients emr = boto3.client('emr') ec2 = boto3.client('ec2') # 1. Create an EMR cluster on AWS logger.info("Creating the EMR cluster...") with open("./cloud/cluster.sh", 'r') as f: cmd = f.read() res = subprocess_popen(cmd) cid = res[1][res[1].find("j-"):len(res[1])-2] logger.info("Cluster created! Cluster ID is " + cid + ".") # 2. Check if all EC2 instances are ready logger.info("Creating EC2 instances for the cluster...") found_flag = False while found_flag is False: time.sleep(15) masters = [] slaves = [] masters_to_find = 1 slaves_to_find = 2 reservations = ec2.describe_instances() for reservation in reservations['Reservations']: for instance in reservation['Instances']: is_instance = False try: for tag in instance['Tags']: if tag['Key'] == 'aws:elasticmapreduce:job-flow-id': if tag['Value'] == cid: is_instance = True if is_instance: for tag in instance['Tags']: if tag['Key'] == 'aws:elasticmapreduce:instance-group-role': if tag['Value'] == 'MASTER': masters.append(instance) else: slaves.append(instance) except KeyError: pass if len(masters) == masters_to_find and len(slaves) == slaves_to_find: with open("./cloud/instances", 'w') as f: for instance in masters: print(str(instance['ImageId'] + ', ' + instance['InstanceId'] + ', ' + instance['InstanceType'] + ', ' + instance['KeyName'] + ', ' + instance['PublicDnsName'] + ', ' + instance['PrivateDnsName']), file=f) for instance in slaves: print(str(instance['ImageId'] + ', ' + instance['InstanceId'] + ', ' + instance['InstanceType'] + ', ' + instance['KeyName'] + ', ' + instance['PublicDnsName'] + ', ' + instance['PrivateDnsName']), file=f) print("Commands:", file=f) for instance in masters: print("ssh -i \"./cloud/" + str(instance['KeyName']) + ".pem\" -o StrictHostKeyCHecking=no hadoop@" + str(instance['PublicDnsName']), file=f) for instance in slaves: print("ssh -i \"./cloud/" + str(instance['KeyName']) + ".pem\" -o StrictHostKeyCHecking=no hadoop@" + str(instance['PublicDnsName']), file=f) print("Tunnels:", file=f) for instance in masters: print("ssh -i \"./cloud/" + str(instance['KeyName']) + ".pem\" -N hadoop@" + str(instance['PublicDnsName']) + " -L PORT:localhost:PORT", file=f) found_flag = True else: logger.info("MASTERs to create: " + str(masters_to_find - len(masters)) + ", " + "SLAVEs to create: " + str(slaves_to_find - len(slaves)) + ".") logger.info("All instances are created! Starting cluster...") logger.info("It may take up to 10 minutes to start a cluster.") started_flag = False while started_flag is False: time.sleep(55) clusters = emr.list_clusters() for cluster in clusters['Clusters']: if cluster['Id'] == cid: if cluster['Status']['State'] == 'WAITING': started_flag = True else: logger.info("Cluster starting, please wait...") break logger.info("Cluster started!") logger.info("Please connect to servers in Shell consoles. IPs to be connected is in ./cloud/instances.csv.") logger.info("Remember to edit the configuration of your engine regarding internal network (if needed).") return if __name__ == '__main__': logger = Logger('./log/benchmark.log', 'preparer') prepare()

46.755319

119

0.513311

import time import boto3 from lib.Logger import Logger from lib.popen import subprocess_popen def prepare(): emr = boto3.client('emr') ec2 = boto3.client('ec2') logger.info("Creating the EMR cluster...") with open("./cloud/cluster.sh", 'r') as f: cmd = f.read() res = subprocess_popen(cmd) cid = res[1][res[1].find("j-"):len(res[1])-2] logger.info("Cluster created! Cluster ID is " + cid + ".") logger.info("Creating EC2 instances for the cluster...") found_flag = False while found_flag is False: time.sleep(15) masters = [] slaves = [] masters_to_find = 1 slaves_to_find = 2 reservations = ec2.describe_instances() for reservation in reservations['Reservations']: for instance in reservation['Instances']: is_instance = False try: for tag in instance['Tags']: if tag['Key'] == 'aws:elasticmapreduce:job-flow-id': if tag['Value'] == cid: is_instance = True if is_instance: for tag in instance['Tags']: if tag['Key'] == 'aws:elasticmapreduce:instance-group-role': if tag['Value'] == 'MASTER': masters.append(instance) else: slaves.append(instance) except KeyError: pass if len(masters) == masters_to_find and len(slaves) == slaves_to_find: with open("./cloud/instances", 'w') as f: for instance in masters: print(str(instance['ImageId'] + ', ' + instance['InstanceId'] + ', ' + instance['InstanceType'] + ', ' + instance['KeyName'] + ', ' + instance['PublicDnsName'] + ', ' + instance['PrivateDnsName']), file=f) for instance in slaves: print(str(instance['ImageId'] + ', ' + instance['InstanceId'] + ', ' + instance['InstanceType'] + ', ' + instance['KeyName'] + ', ' + instance['PublicDnsName'] + ', ' + instance['PrivateDnsName']), file=f) print("Commands:", file=f) for instance in masters: print("ssh -i \"./cloud/" + str(instance['KeyName']) + ".pem\" -o StrictHostKeyCHecking=no hadoop@" + str(instance['PublicDnsName']), file=f) for instance in slaves: print("ssh -i \"./cloud/" + str(instance['KeyName']) + ".pem\" -o StrictHostKeyCHecking=no hadoop@" + str(instance['PublicDnsName']), file=f) print("Tunnels:", file=f) for instance in masters: print("ssh -i \"./cloud/" + str(instance['KeyName']) + ".pem\" -N hadoop@" + str(instance['PublicDnsName']) + " -L PORT:localhost:PORT", file=f) found_flag = True else: logger.info("MASTERs to create: " + str(masters_to_find - len(masters)) + ", " + "SLAVEs to create: " + str(slaves_to_find - len(slaves)) + ".") logger.info("All instances are created! Starting cluster...") logger.info("It may take up to 10 minutes to start a cluster.") started_flag = False while started_flag is False: time.sleep(55) clusters = emr.list_clusters() for cluster in clusters['Clusters']: if cluster['Id'] == cid: if cluster['Status']['State'] == 'WAITING': started_flag = True else: logger.info("Cluster starting, please wait...") break logger.info("Cluster started!") logger.info("Please connect to servers in Shell consoles. IPs to be connected is in ./cloud/instances.csv.") logger.info("Remember to edit the configuration of your engine regarding internal network (if needed).") return if __name__ == '__main__': logger = Logger('./log/benchmark.log', 'preparer') prepare()

true

f71b2fb9a2d9df9315262d217475ffce3958a2f8

9,376

py

Python

src/snowflake/connector/auth_okta.py

groodt/snowflake-connector-python

26d0a36cb9a65a728e745f077bd11ab536d386f8

[ "Apache-2.0" ]

3

2021-03-05T22:01:00.000Z

2021-04-02T17:48:33.000Z

src/snowflake/connector/auth_okta.py

groodt/snowflake-connector-python

26d0a36cb9a65a728e745f077bd11ab536d386f8

[ "Apache-2.0" ]

26

2021-06-01T09:43:42.000Z

2022-03-16T15:11:52.000Z

src/snowflake/connector/auth_okta.py

groodt/snowflake-connector-python

26d0a36cb9a65a728e745f077bd11ab536d386f8

[ "Apache-2.0" ]

1

2021-03-05T22:08:46.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (c) 2012-2020 Snowflake Computing Inc. All right reserved. # import json import logging from .auth import Auth from .auth_by_plugin import AuthByPlugin from .compat import unescape, urlencode, urlsplit from .constants import HTTP_HEADER_ACCEPT, HTTP_HEADER_CONTENT_TYPE, HTTP_HEADER_SERVICE_NAME, HTTP_HEADER_USER_AGENT from .errorcode import ER_IDP_CONNECTION_ERROR, ER_INCORRECT_DESTINATION from .errors import DatabaseError, Error from .network import CONTENT_TYPE_APPLICATION_JSON, PYTHON_CONNECTOR_USER_AGENT from .sqlstate import SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED logger = logging.getLogger(__name__) def _is_prefix_equal(url1, url2): """Checks if URL prefixes are identical. The scheme, hostname and port number are compared. If the port number is not specified and the scheme is https, the port number is assumed to be 443. """ parsed_url1 = urlsplit(url1) parsed_url2 = urlsplit(url2) port1 = parsed_url1.port if not port1 and parsed_url1.scheme == 'https': port1 = '443' port2 = parsed_url1.port if not port2 and parsed_url2.scheme == 'https': port2 = '443' return parsed_url1.hostname == parsed_url2.hostname and \ port1 == port2 and \ parsed_url1.scheme == parsed_url2.scheme def _get_post_back_url_from_html(html): """Gets the post back URL. Since the HTML is not well formed, minidom cannot be used to convert to DOM. The first discovered form is assumed to be the form to post back and the URL is taken from action attributes. """ logger.debug(html) idx = html.find('<form') start_idx = html.find('action="', idx) end_idx = html.find('"', start_idx + 8) return unescape(html[start_idx + 8:end_idx]) class AuthByOkta(AuthByPlugin): """Authenticate user by OKTA.""" def __init__(self, rest, application): self._rest = rest self._saml_response = None self._application = application @property def assertion_content(self): return self._saml_response def update_body(self, body): body['data']['RAW_SAML_RESPONSE'] = self._saml_response def authenticate( self, authenticator, service_name, account, user, password): """SAML Authentication. Steps are: 1. query GS to obtain IDP token and SSO url 2. IMPORTANT Client side validation: validate both token url and sso url contains same prefix (protocol + host + port) as the given authenticator url. Explanation: This provides a way for the user to 'authenticate' the IDP it is sending his/her credentials to. Without such a check, the user could be coerced to provide credentials to an IDP impersonator. 3. query IDP token url to authenticate and retrieve access token 4. given access token, query IDP URL snowflake app to get SAML response 5. IMPORTANT Client side validation: validate the post back url come back with the SAML response contains the same prefix as the Snowflake's server url, which is the intended destination url to Snowflake. Explanation: This emulates the behavior of IDP initiated login flow in the user browser where the IDP instructs the browser to POST the SAML assertion to the specific SP endpoint. This is critical in preventing a SAML assertion issued to one SP from being sent to another SP. """ logger.debug('authenticating by SAML') headers, sso_url, token_url = self._step1( authenticator, service_name, account, user) self._step2(authenticator, sso_url, token_url) one_time_token = self._step3(headers, token_url, user, password) response_html = self._step4(one_time_token, sso_url) self._step5(response_html) def _step1(self, authenticator, service_name, account, user): logger.debug('step 1: query GS to obtain IDP token and SSO url') headers = { HTTP_HEADER_CONTENT_TYPE: CONTENT_TYPE_APPLICATION_JSON, HTTP_HEADER_ACCEPT: CONTENT_TYPE_APPLICATION_JSON, HTTP_HEADER_USER_AGENT: PYTHON_CONNECTOR_USER_AGENT, } if service_name: headers[HTTP_HEADER_SERVICE_NAME] = service_name url = "/session/authenticator-request" body = Auth.base_auth_data( user, account, self._rest._connection.application, self._rest._connection._internal_application_name, self._rest._connection._internal_application_version, self._rest._connection._ocsp_mode(), self._rest._connection._login_timeout, self._rest._connection._network_timeout, ) body["data"]["AUTHENTICATOR"] = authenticator logger.debug( 'account=%s, authenticator=%s', account, authenticator, ) ret = self._rest._post_request( url, headers, json.dumps(body), timeout=self._rest._connection.login_timeout, socket_timeout=self._rest._connection.login_timeout) if not ret['success']: self.handle_failure(ret) data = ret['data'] token_url = data['tokenUrl'] sso_url = data['ssoUrl'] return headers, sso_url, token_url def _step2(self, authenticator, sso_url, token_url): logger.debug('step 2: validate Token and SSO URL has the same prefix ' 'as authenticator') if not _is_prefix_equal(authenticator, token_url) or \ not _is_prefix_equal(authenticator, sso_url): Error.errorhandler_wrapper( self._rest._connection, None, DatabaseError, { 'msg': ("The specified authenticator is not supported: " "{authenticator}, token_url: {token_url}, " "sso_url: {sso_url}".format( authenticator=authenticator, token_url=token_url, sso_url=sso_url, )), 'errno': ER_IDP_CONNECTION_ERROR, 'sqlstate': SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED } ) def _step3(self, headers, token_url, user, password): logger.debug('step 3: query IDP token url to authenticate and ' 'retrieve access token') data = { 'username': user, 'password': password, } ret = self._rest.fetch( 'post', token_url, headers, data=json.dumps(data), timeout=self._rest._connection.login_timeout, socket_timeout=self._rest._connection.login_timeout, catch_okta_unauthorized_error=True) one_time_token = ret.get('cookieToken') if not one_time_token: Error.errorhandler_wrapper( self._rest._connection, None, DatabaseError, { 'msg': ("The authentication failed for {user} " "by {token_url}.".format( token_url=token_url, user=user, )), 'errno': ER_IDP_CONNECTION_ERROR, 'sqlstate': SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED } ) return one_time_token def _step4(self, one_time_token, sso_url): logger.debug('step 4: query IDP URL snowflake app to get SAML ' 'response') url_parameters = { 'RelayState': "/some/deep/link", 'onetimetoken': one_time_token, } sso_url = sso_url + '?' + urlencode(url_parameters) headers = { HTTP_HEADER_ACCEPT: '*/*', } response_html = self._rest.fetch( 'get', sso_url, headers, timeout=self._rest._connection.login_timeout, socket_timeout=self._rest._connection.login_timeout, is_raw_text=True) return response_html def _step5(self, response_html): logger.debug('step 5: validate post_back_url matches Snowflake URL') post_back_url = _get_post_back_url_from_html(response_html) full_url = '{protocol}://{host}:{port}'.format( protocol=self._rest._protocol, host=self._rest._host, port=self._rest._port, ) if not _is_prefix_equal(post_back_url, full_url): Error.errorhandler_wrapper( self._rest._connection, None, DatabaseError, { 'msg': ("The specified authenticator and destination " "URL in the SAML assertion do not match: " "expected: {url}, " "post back: {post_back_url}".format( url=full_url, post_back_url=post_back_url, )), 'errno': ER_INCORRECT_DESTINATION, 'sqlstate': SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED } ) self._saml_response = response_html

39.561181

117

0.611455

import json import logging from .auth import Auth from .auth_by_plugin import AuthByPlugin from .compat import unescape, urlencode, urlsplit from .constants import HTTP_HEADER_ACCEPT, HTTP_HEADER_CONTENT_TYPE, HTTP_HEADER_SERVICE_NAME, HTTP_HEADER_USER_AGENT from .errorcode import ER_IDP_CONNECTION_ERROR, ER_INCORRECT_DESTINATION from .errors import DatabaseError, Error from .network import CONTENT_TYPE_APPLICATION_JSON, PYTHON_CONNECTOR_USER_AGENT from .sqlstate import SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED logger = logging.getLogger(__name__) def _is_prefix_equal(url1, url2): parsed_url1 = urlsplit(url1) parsed_url2 = urlsplit(url2) port1 = parsed_url1.port if not port1 and parsed_url1.scheme == 'https': port1 = '443' port2 = parsed_url1.port if not port2 and parsed_url2.scheme == 'https': port2 = '443' return parsed_url1.hostname == parsed_url2.hostname and \ port1 == port2 and \ parsed_url1.scheme == parsed_url2.scheme def _get_post_back_url_from_html(html): logger.debug(html) idx = html.find('<form') start_idx = html.find('action="', idx) end_idx = html.find('"', start_idx + 8) return unescape(html[start_idx + 8:end_idx]) class AuthByOkta(AuthByPlugin): def __init__(self, rest, application): self._rest = rest self._saml_response = None self._application = application @property def assertion_content(self): return self._saml_response def update_body(self, body): body['data']['RAW_SAML_RESPONSE'] = self._saml_response def authenticate( self, authenticator, service_name, account, user, password): logger.debug('authenticating by SAML') headers, sso_url, token_url = self._step1( authenticator, service_name, account, user) self._step2(authenticator, sso_url, token_url) one_time_token = self._step3(headers, token_url, user, password) response_html = self._step4(one_time_token, sso_url) self._step5(response_html) def _step1(self, authenticator, service_name, account, user): logger.debug('step 1: query GS to obtain IDP token and SSO url') headers = { HTTP_HEADER_CONTENT_TYPE: CONTENT_TYPE_APPLICATION_JSON, HTTP_HEADER_ACCEPT: CONTENT_TYPE_APPLICATION_JSON, HTTP_HEADER_USER_AGENT: PYTHON_CONNECTOR_USER_AGENT, } if service_name: headers[HTTP_HEADER_SERVICE_NAME] = service_name url = "/session/authenticator-request" body = Auth.base_auth_data( user, account, self._rest._connection.application, self._rest._connection._internal_application_name, self._rest._connection._internal_application_version, self._rest._connection._ocsp_mode(), self._rest._connection._login_timeout, self._rest._connection._network_timeout, ) body["data"]["AUTHENTICATOR"] = authenticator logger.debug( 'account=%s, authenticator=%s', account, authenticator, ) ret = self._rest._post_request( url, headers, json.dumps(body), timeout=self._rest._connection.login_timeout, socket_timeout=self._rest._connection.login_timeout) if not ret['success']: self.handle_failure(ret) data = ret['data'] token_url = data['tokenUrl'] sso_url = data['ssoUrl'] return headers, sso_url, token_url def _step2(self, authenticator, sso_url, token_url): logger.debug('step 2: validate Token and SSO URL has the same prefix ' 'as authenticator') if not _is_prefix_equal(authenticator, token_url) or \ not _is_prefix_equal(authenticator, sso_url): Error.errorhandler_wrapper( self._rest._connection, None, DatabaseError, { 'msg': ("The specified authenticator is not supported: " "{authenticator}, token_url: {token_url}, " "sso_url: {sso_url}".format( authenticator=authenticator, token_url=token_url, sso_url=sso_url, )), 'errno': ER_IDP_CONNECTION_ERROR, 'sqlstate': SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED } ) def _step3(self, headers, token_url, user, password): logger.debug('step 3: query IDP token url to authenticate and ' 'retrieve access token') data = { 'username': user, 'password': password, } ret = self._rest.fetch( 'post', token_url, headers, data=json.dumps(data), timeout=self._rest._connection.login_timeout, socket_timeout=self._rest._connection.login_timeout, catch_okta_unauthorized_error=True) one_time_token = ret.get('cookieToken') if not one_time_token: Error.errorhandler_wrapper( self._rest._connection, None, DatabaseError, { 'msg': ("The authentication failed for {user} " "by {token_url}.".format( token_url=token_url, user=user, )), 'errno': ER_IDP_CONNECTION_ERROR, 'sqlstate': SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED } ) return one_time_token def _step4(self, one_time_token, sso_url): logger.debug('step 4: query IDP URL snowflake app to get SAML ' 'response') url_parameters = { 'RelayState': "/some/deep/link", 'onetimetoken': one_time_token, } sso_url = sso_url + '?' + urlencode(url_parameters) headers = { HTTP_HEADER_ACCEPT: '*/*', } response_html = self._rest.fetch( 'get', sso_url, headers, timeout=self._rest._connection.login_timeout, socket_timeout=self._rest._connection.login_timeout, is_raw_text=True) return response_html def _step5(self, response_html): logger.debug('step 5: validate post_back_url matches Snowflake URL') post_back_url = _get_post_back_url_from_html(response_html) full_url = '{protocol}://{host}:{port}'.format( protocol=self._rest._protocol, host=self._rest._host, port=self._rest._port, ) if not _is_prefix_equal(post_back_url, full_url): Error.errorhandler_wrapper( self._rest._connection, None, DatabaseError, { 'msg': ("The specified authenticator and destination " "URL in the SAML assertion do not match: " "expected: {url}, " "post back: {post_back_url}".format( url=full_url, post_back_url=post_back_url, )), 'errno': ER_INCORRECT_DESTINATION, 'sqlstate': SQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED } ) self._saml_response = response_html

true

f71b2fcfe4e1bf00db7f8c9aa8bc9eac22fc3f1c

2,248

py

Python

mobile/db/prompts.py

TRIP-Lab/itinerum-mobile-api

a3b31b411d9d46434c54be1d21415024dec86ae7

[ "MIT" ]

4

2019-02-04T15:34:49.000Z

2021-04-30T17:33:05.000Z

mobile/db/prompts.py

TRIP-Lab/itinerum-mobile-api

a3b31b411d9d46434c54be1d21415024dec86ae7

[ "MIT" ]

1

2021-03-19T22:26:11.000Z

mobile/db/prompts.py

TRIP-Lab/itinerum-mobile-api

a3b31b411d9d46434c54be1d21415024dec86ae7

[ "MIT" ]

5

2018-03-13T20:24:55.000Z

2021-01-18T14:44:35.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Kyle Fitzsimmons, 2017 from datetime import datetime import pytz from models import db, PromptResponse class MobilePromptsActions: def get(self, prompts_uuids): prompts_filters = PromptResponse.prompt_uuid.in_(prompts_uuids) return db.session.query(PromptResponse).filter(prompts_filters) # formats a prompts query to a lookup dictionary # [p0, p1, p2] --> {p0_uuid: {p0_prompt_num: p0}, ...} def create_lookup(self, prompts): prompts_lookup = {} for p in prompts: prompts_lookup.setdefault(p.prompt_uuid, {}) prompts_lookup[p.prompt_uuid][p.prompt_num] = p return prompts_lookup def upsert(self, user, prompts): prompts_uuids = {p['uuid'] for p in prompts} existing_prompts = self.get(prompts_uuids) existing_lookup = self.create_lookup(existing_prompts) responses = [] for prompt in prompts: # gracefully handle change of 'timestamp' -> 'displayed_at' if 'timestamp' in prompt: prompt['displayed_at'] = prompt.pop('timestamp') uuid = prompt['uuid'] prompt_num = int(prompt['prompt_num']) if uuid in existing_lookup: response = existing_lookup[uuid][prompt_num] response.response = prompt['answer'] response.recorded_at = prompt['recorded_at'] response.latitude = prompt['latitude'] response.longitude = prompt['longitude'] response.edited_at = datetime.now(pytz.utc) else: response = PromptResponse( survey_id=user.survey_id, mobile_id=user.id, prompt_uuid=uuid, prompt_num=prompt_num, response=prompt['answer'], displayed_at=prompt['displayed_at'], recorded_at=prompt['recorded_at'], latitude=prompt['latitude'], longitude=prompt['longitude']) responses.append(response) db.session.bulk_save_objects(responses) db.session.commit() return responses

36.852459

71

0.589858

from datetime import datetime import pytz from models import db, PromptResponse class MobilePromptsActions: def get(self, prompts_uuids): prompts_filters = PromptResponse.prompt_uuid.in_(prompts_uuids) return db.session.query(PromptResponse).filter(prompts_filters) def create_lookup(self, prompts): prompts_lookup = {} for p in prompts: prompts_lookup.setdefault(p.prompt_uuid, {}) prompts_lookup[p.prompt_uuid][p.prompt_num] = p return prompts_lookup def upsert(self, user, prompts): prompts_uuids = {p['uuid'] for p in prompts} existing_prompts = self.get(prompts_uuids) existing_lookup = self.create_lookup(existing_prompts) responses = [] for prompt in prompts: if 'timestamp' in prompt: prompt['displayed_at'] = prompt.pop('timestamp') uuid = prompt['uuid'] prompt_num = int(prompt['prompt_num']) if uuid in existing_lookup: response = existing_lookup[uuid][prompt_num] response.response = prompt['answer'] response.recorded_at = prompt['recorded_at'] response.latitude = prompt['latitude'] response.longitude = prompt['longitude'] response.edited_at = datetime.now(pytz.utc) else: response = PromptResponse( survey_id=user.survey_id, mobile_id=user.id, prompt_uuid=uuid, prompt_num=prompt_num, response=prompt['answer'], displayed_at=prompt['displayed_at'], recorded_at=prompt['recorded_at'], latitude=prompt['latitude'], longitude=prompt['longitude']) responses.append(response) db.session.bulk_save_objects(responses) db.session.commit() return responses

true

f71b2fd793a7e41ee094ac47d7408dbb3c13f221

804

py

Python

garf_data.py

doesnotsitproperly/garfbot

04e2a8409bd171ca29b6cdb5e864fe2fa1c13b6d

[ "Unlicense" ]

null

garf_data.py

doesnotsitproperly/garfbot

04e2a8409bd171ca29b6cdb5e864fe2fa1c13b6d

[ "Unlicense" ]

null

garf_data.py

doesnotsitproperly/garfbot

04e2a8409bd171ca29b6cdb5e864fe2fa1c13b6d

[ "Unlicense" ]

null

import json, os class GarfData: file = os.path.join(os.getcwd(), "garf_data.json") token: str path_to_ffmpeg: str jokes: list trigger_words: list def __init__(self): with open(self.file, "r") as f: json_dict = json.loads(f.read()) self.token = json_dict["token"] self.path_to_ffmpeg = json_dict["path_to_ffmpeg"] self.jokes = json_dict["jokes"] self.trigger_words = json_dict["trigger_words"] def overwrite(self): json_dict = { "token": self.token, "path_to_ffmpeg": self.path_to_ffmpeg, "jokes": self.jokes, "trigger_words": self.trigger_words } with open(self.file, "w") as f: f.write(json.dumps(json_dict, indent = 4) + os.linesep)

27.724138

67

0.584577

import json, os class GarfData: file = os.path.join(os.getcwd(), "garf_data.json") token: str path_to_ffmpeg: str jokes: list trigger_words: list def __init__(self): with open(self.file, "r") as f: json_dict = json.loads(f.read()) self.token = json_dict["token"] self.path_to_ffmpeg = json_dict["path_to_ffmpeg"] self.jokes = json_dict["jokes"] self.trigger_words = json_dict["trigger_words"] def overwrite(self): json_dict = { "token": self.token, "path_to_ffmpeg": self.path_to_ffmpeg, "jokes": self.jokes, "trigger_words": self.trigger_words } with open(self.file, "w") as f: f.write(json.dumps(json_dict, indent = 4) + os.linesep)

true

f71b2fde98fb40c654242c9096ab88064334974c

33,682

py

Python

models/adaptation_model_stage1.py

BwCai/DCAA-UDA

359c2122060aebfbe4384c918768c261fe2dc9c7

[ "Apache-2.0" ]

2

2022-01-28T10:35:53.000Z

2022-03-09T14:38:59.000Z

models/adaptation_model_stage1.py

BwCai/DCAA-UDA

359c2122060aebfbe4384c918768c261fe2dc9c7

[ "Apache-2.0" ]

1

2022-03-07T10:48:11.000Z

models/adaptation_model_stage1.py

BwCai/DCAA-UDA

359c2122060aebfbe4384c918768c261fe2dc9c7

[ "Apache-2.0" ]

null

from models.base_model import BaseModel import torch.nn as nn import torch.nn.functional as F import os, sys import torch import numpy as np import itertools from torch.autograd import Variable from optimizers import get_optimizer from schedulers import get_scheduler from models.sync_batchnorm import SynchronizedBatchNorm2d, DataParallelWithCallback from models.deeplab_multimodal import DeepLab from models.decoder import Decoder from models.aspp import ASPP from models.discriminator import FCDiscriminator, FCDiscriminator_low, FCDiscriminator_out, FCDiscriminator_class from loss import get_loss_function from .utils import freeze_bn, GradReverse, normalisation_pooling from metrics import runningScore import pdb def multimodal_merger(multi_modal_data, is_upsample=False, up_size=None): """ [Func Handler] multimodal_merger: @Input Params: multi_modal_data: dict. examples: { "feat_cls": feat_cls, "output": output, } @Reture: merge_out: dict. examples: { "feat_cls": feat_cls, "output_comb": output_comb, "output": output, } """ feat_cls = multi_modal_data['feat_cls'] # merge class features feat_cls_cat = torch.cat(feat_cls, 1) # concat # merge output pred output = multi_modal_data['output'] output_comb = 0 for _i in range(len(output)): if is_upsample: output[_i] = F.interpolate(output[_i], size=up_size, mode='bilinear', align_corners=True) output_comb += output[_i] merge_out = { 'feat_cls': feat_cls, 'feat_cls_cat': feat_cls_cat, 'output_comb': output_comb, 'output': output, } return merge_out class CustomMetricsMultimodalMerger(): """ [Func Handler] objective_vectors_multimodal_merger: @Input Params: multi_modal_data: dict. examples: { "class_threshold_group": [model.class_threshold_group[modal_idx][i], ...] "objective_vectors_group": [model.objective_vectors_group[modal_idx][i], ...], } cate_idx: int. 0 ~ 18 modal_ids: list. examples: [0, 1] or [0,] @Reture: merge_out: dict. examples: { "class_threshold": class_threshold, "objective_vectors": objective_vectors, } """ def __init__(self, modal_num, category_num, model): self.modal_num = modal_num self.category_num = category_num self._model = model def initialize_model(model): self._model = model def merge_class_threshold(self, modal_ids=[]): assert self._model is not None, "[ERROR] Deeplab Model not initialize before using!" _class_threshold_group = self._model.class_threshold_group[modal_ids] return torch.mean(_class_threshold_group, dim=0) # modal_num x 19 --> 19 def merge_clu_threshold(self, clu_threshold, modal_ids=[]): _clu_threshold_group = clu_threshold[modal_ids] return torch.mean(_clu_threshold_group, dim=0) def merge_objective_vectors(self, modal_ids=[]): assert self._model is not None, "[ERROR] Deeplab Model not initialize before using!" _modal_num, _cate_num, _feat_dim = self._model.objective_vectors_group.size() _objective_vectors = self._model.objective_vectors_group[modal_ids] # modal_num x 19 x 256 --> 19 x modal_num x 256 --> 19 x (modal_num x 256) assert _objective_vectors.dim() == 4, "objective_vector dimension != 4" _objective_vectors = _objective_vectors.permute(1, 0, 2).contiguous() return _objective_vectors.view(_cate_num, -1) class CustomMetrics(): def __init__(self, numbers=19, modal_num=3, model=None): self.class_numbers = numbers self.classes_recall_thr = np.zeros([19, 3]) self.classes_recall_thr_num = np.zeros([19]) self.classes_recall_clu = np.zeros([19, 3]) self.classes_recall_clu_num = np.zeros([19]) self.running_metrics_val_threshold = runningScore(self.class_numbers) self.running_metrics_val_clusters = runningScore(self.class_numbers) self.clu_threshold = torch.full((modal_num + 1, 19), 2.5).cuda() self.multimodal_merger = CustomMetricsMultimodalMerger( modal_num=modal_num + 1, category_num=numbers, model=model ) def update(self, feat_cls, outputs, labels, modal_ids=[0,]): '''calculate accuracy. caring about recall but not IoU''' batch, width, height = labels.shape labels = labels.reshape([batch, 1, width, height]).float() labels = F.interpolate(labels, size=feat_cls.size()[2:], mode='nearest') outputs_threshold = outputs.clone() outputs_threshold = F.softmax(outputs_threshold, dim=1) #self.running_metrics_val_threshold.update(labels.cpu().numpy(), outputs_threshold.argmax(1).cpu().numpy()) self.running_metrics_val_threshold.update(labels, outputs_threshold.argmax(1)) _class_threshold_set = self.multimodal_merger.merge_class_threshold(modal_ids=modal_idx) for i in range(19): outputs_threshold[:, i, :, :] = torch.where(outputs_threshold[:, i, :, :] > _class_threshold_set[i], torch.Tensor([1]).cuda(), torch.Tensor([0]).cuda()) _batch, _channel, _w, _h = outputs_threshold.shape _tmp = torch.full([_batch, 1, _w, _h], 0.2,).cuda() _tmp = torch.cat((outputs_threshold, _tmp), 1) threshold_arg = _tmp.argmax(1, keepdim=True) threshold_arg[threshold_arg == 19] = 250 #ignore index truth, pred_all, truth_all = self.calc_recall(labels.cpu().int().numpy(), threshold_arg.cpu().int().numpy()) self.classes_recall_thr[:, 0] += truth self.classes_recall_thr[:, 2] += pred_all self.classes_recall_thr[:, 1] += truth_all outputs_cluster = outputs.clone() _objective_vectors_set = self.multimodal_merger.merge_objective_vectors(modal_ids=modal_idx) for i in range(19): outputs_cluster[:, i, :, :] = torch.norm( _objective_vectors_set[i].reshape(-1,1,1).expand(-1,128,256) - feat_cls, 2, dim=1,) outputs_cluster_min, outputs_cluster_arg = outputs_cluster.min(dim=1, keepdim=True) outputs_cluster_second = outputs_cluster.scatter_(1, outputs_cluster_arg, 100) if torch.unique(outputs_cluster_second.argmax(1) - outputs_cluster_arg.squeeze()).squeeze().item() != 0: raise NotImplementedError('wrong when computing L2 norm!!') outputs_cluster_secondmin, outputs_cluster_secondarg = outputs_cluster_second.min(dim=1, keepdim=True) #self.running_metrics_val_clusters.update(labels.cpu().numpy(), outputs_cluster_arg.cpu().numpy()) self.running_metrics_val_clusters.update(labels, outputs_cluster_arg) tmp_arg = outputs_cluster_arg.clone() pdb.set_trace() _clu_thresholds = self.multimodal_merger.merge_clu_threshold(self.clu_threshold, modal_ids=modal_ids) outputs_cluster_arg[(outputs_cluster_secondmin - outputs_cluster_min) < _clu_thresholds] = 250 truth, pred_all, truth_all = self.calc_recall(labels.cpu().int().numpy(), outputs_cluster_arg.cpu().int().numpy()) self.classes_recall_clu[:, 0] += truth self.classes_recall_clu[:, 2] += pred_all self.classes_recall_clu[:, 1] += truth_all return threshold_arg, outputs_cluster_arg def calc_recall(self, gt, argmax): truth = np.zeros([self.class_numbers]) pred_all = np.zeros([self.class_numbers]) truth_all = np.zeros([self.class_numbers]) for i in range(self.class_numbers): truth[i] = (gt == i)[argmax == i].sum() pred_all[i] = (argmax == i).sum() truth_all[i] = (gt == i).sum() pass return truth, pred_all, truth_all def calc_mean_Clu_recall(self, ): return np.mean(self.classes_recall_clu[:, 0] / self.classes_recall_clu[:, 1]) def calc_mean_Thr_recall(self, ): return np.mean(self.classes_recall_thr[:, 0] / self.classes_recall_thr[:, 1]) def reset(self, ): self.running_metrics_val_clusters.reset() self.running_metrics_val_threshold.reset() self.classes_recall_clu = np.zeros([19, 3]) self.classes_recall_thr = np.zeros([19, 3]) class CustomModel(): def __init__(self, cfg, writer, logger, use_pseudo_label=False, modal_num=3): self.cfg = cfg self.writer = writer self.class_numbers = 19 self.logger = logger cfg_model = cfg['model'] self.cfg_model = cfg_model self.best_iou = -100 self.iter = 0 self.nets = [] self.split_gpu = 0 self.default_gpu = cfg['model']['default_gpu'] self.PredNet_Dir = None self.valid_classes = cfg['training']['valid_classes'] self.G_train = True self.cls_feature_weight = cfg['training']['cls_feature_weight'] self.use_pseudo_label = use_pseudo_label self.modal_num = modal_num # cluster vectors & cuda initialization self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda() self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda() self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda() self.class_threshold_group = torch.full([self.modal_num + 1, 19], 0.95).cuda() #self.metrics = CustomMetrics(self.class_numbers) self.metrics = CustomMetrics(self.class_numbers, modal_num=self.modal_num, model=self) bn = cfg_model['bn'] if bn == 'sync_bn': BatchNorm = SynchronizedBatchNorm2d elif bn == 'bn': BatchNorm = nn.BatchNorm2d elif bn == 'gn': BatchNorm = nn.GroupNorm else: raise NotImplementedError('batch norm choice {} is not implemented'.format(bn)) if use_pseudo_label: self.PredNet = DeepLab( num_classes=19, backbone=cfg_model['basenet']['version'], output_stride=16, bn=cfg_model['bn'], freeze_bn=True, modal_num=self.modal_num ).cuda() self.load_PredNet(cfg, writer, logger, dir=None, net=self.PredNet) self.PredNet_DP = self.init_device(self.PredNet, gpu_id=self.default_gpu, whether_DP=True) self.PredNet.eval() self.PredNet_num = 0 self.BaseNet = DeepLab( num_classes=19, backbone=cfg_model['basenet']['version'], output_stride=16, bn=cfg_model['bn'], freeze_bn=True, modal_num=self.modal_num ) logger.info('the backbone is {}'.format(cfg_model['basenet']['version'])) self.BaseNet_DP = self.init_device(self.BaseNet, gpu_id=self.default_gpu, whether_DP=True) self.nets.extend([self.BaseNet]) self.nets_DP = [self.BaseNet_DP] # Discriminator self.SOURCE_LABEL = 0 self.TARGET_LABEL = 1 self.DNets = [] self.DNets_DP = [] for _ in range(self.modal_num+1): _net_d = FCDiscriminator(inplanes=19) self.DNets.append(_net_d) _net_d_DP = self.init_device(_net_d, gpu_id=self.default_gpu, whether_DP=True) self.DNets_DP.append(_net_d_DP) self.nets.extend(self.DNets) self.nets_DP.extend(self.DNets_DP) self.optimizers = [] self.schedulers = [] optimizer_cls = torch.optim.SGD optimizer_params = {k:v for k, v in cfg['training']['optimizer'].items() if k != 'name'} optimizer_cls_D = torch.optim.Adam optimizer_params_D = {k:v for k, v in cfg['training']['optimizer_D'].items() if k != 'name'} if self.use_pseudo_label: self.BaseOpti = optimizer_cls(self.BaseNet.parameters(), **optimizer_params) else: self.BaseOpti = optimizer_cls(self.BaseNet.optim_parameters(cfg['training']['optimizer']['lr']), **optimizer_params) self.optimizers.extend([self.BaseOpti]) self.DiscOptis = [] for _d_net in self.DNets: self.DiscOptis.append( optimizer_cls_D(_d_net.parameters(), **optimizer_params_D) ) self.optimizers.extend(self.DiscOptis) self.schedulers = [] if self.use_pseudo_label: self.BaseSchedule = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule']) self.schedulers.extend([self.BaseSchedule]) else: """BaseSchedule detail see FUNC: scheduler_step()""" self.learning_rate = cfg['training']['optimizer']['lr'] self.gamma = cfg['training']['lr_schedule']['gamma'] self.num_steps = cfg['training']['lr_schedule']['max_iter'] self._BaseSchedule_nouse = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule']) self.schedulers.extend([self._BaseSchedule_nouse]) self.DiscSchedules = [] for _disc_opt in self.DiscOptis: self.DiscSchedules.append( get_scheduler(_disc_opt, cfg['training']['lr_schedule']) ) self.schedulers.extend(self.DiscSchedules) self.setup(cfg, writer, logger) self.adv_source_label = 0 self.adv_target_label = 1 self.bceloss = nn.BCEWithLogitsLoss(reduce=False) self.loss_fn = get_loss_function(cfg) self.mseloss = nn.MSELoss() self.l1loss = nn.L1Loss() self.smoothloss = nn.SmoothL1Loss() self.triplet_loss = nn.TripletMarginLoss() def create_PredNet(self,): ss = DeepLab( num_classes=19, backbone=self.cfg_model['basenet']['version'], output_stride=16, bn=self.cfg_model['bn'], freeze_bn=True, modal_num=self.modal_num, ).cuda() ss.eval() return ss def setup(self, cfg, writer, logger): ''' set optimizer and load pretrained model ''' for net in self.nets: # name = net.__class__.__name__ self.init_weights(cfg['model']['init'], logger, net) print("Initializition completed") if hasattr(net, '_load_pretrained_model') and cfg['model']['pretrained']: print("loading pretrained model for {}".format(net.__class__.__name__)) net._load_pretrained_model() '''load pretrained model ''' if cfg['training']['resume_flag']: self.load_nets(cfg, writer, logger) pass def lr_poly(self): return self.learning_rate * ((1 - float(self.iter) / self.num_steps) ** (self.gamma)) def adjust_basenet_learning_rate(self): lr = self.lr_poly() self.BaseOpti.param_groups[0]['lr'] = lr if len(self.BaseOpti.param_groups) > 1: self.BaseOpti.param_groups[1]['lr'] = lr * 10 def forward(self, input): feat, feat_low, att_mask, feat_cls, output = self.BaseNet_DP(input) return feat, feat_low, feat_cls, output def forward_Up(self, input): feat, feat_low, feat_cls, outputs = self.forward(input) output = F.interpolate(outputs[-1], size=input.size()[2:], mode='bilinear', align_corners=True) return feat, feat_low, feat_cls, output def PredNet_Forward(self, input): with torch.no_grad(): _, _, att_mask, feat_cls, output_result = self.PredNet_DP(input) return _, _, feat_cls, output_result def calculate_mean_vector(self, feat_cls, outputs, labels, ): outputs_softmax = F.softmax(outputs, dim=1) outputs_argmax = outputs_softmax.argmax(dim=1, keepdim=True) outputs_argmax = self.process_label(outputs_argmax.float()) labels_expanded = self.process_label(labels) outputs_pred = labels_expanded * outputs_argmax scale_factor = F.adaptive_avg_pool2d(outputs_pred, 1) vectors = [] ids = [] for n in range(feat_cls.size()[0]): for t in range(self.class_numbers): if scale_factor[n][t].item()==0: continue if (outputs_pred[n][t] > 0).sum() < 10: continue s = feat_cls[n] * outputs_pred[n][t] scale = torch.sum(outputs_pred[n][t]) / labels.shape[2] / labels.shape[3] * 2 s = normalisation_pooling()(s, scale) s = F.adaptive_avg_pool2d(s, 1) / scale_factor[n][t] vectors.append(s) ids.append(t) return vectors, ids def step(self, source_x, source_label, source_modal_ids, target_x, target_label, target_modal_ids, use_pseudo_loss=False): assert len(source_modal_ids) == source_x.size(0), "modal_ids' batchsize != source_x's batchsize" _, _, source_feat_cls, source_output = self.forward(input=source_x) """source_output: [B x 19 x W x H, ...] select modal-branch output in each batchsize Specific-modal output """ source_output_modal_k = torch.stack( [ source_output[_modal_i][_batch_i] for _batch_i, _modal_i in enumerate(source_modal_ids) ], dim=0, ) # attention output & specific-modal output source_output_comb = torch.cat([source_output_modal_k, source_output[-1]], dim=0) source_label_comb = torch.cat([source_label, source_label.clone()], dim=0) source_outputUp = F.interpolate(source_output_comb, size=source_x.size()[-2:], mode='bilinear', align_corners=True) loss_GTA = self.loss_fn(input=source_outputUp, target=source_label_comb) #self.PredNet.eval() # adversarial loss # ----------------------------- """Generator (segmentation)""" # ----------------------------- # On Source Domain loss_adv = torch.Tensor([0]).cuda() _batch_size = 0 _, _, _, target_output = self.forward(target_x) target_modal_ids_tensor = torch.Tensor(target_modal_ids).cuda() for t_out, _d_net_DP, _d_net, modal_idx in zip(target_output, self.DNets_DP, self.DNets, range(len(target_output))): # set grad false self.set_requires_grad(self.logger, _d_net, requires_grad = False) # true/false discriminator t_D_out = _d_net_DP(F.softmax(t_out)) #source_modal_ids loss_temp = torch.mean(self.bceloss( t_D_out, torch.FloatTensor(t_D_out.data.size()).fill_(1.0).cuda() ), [1,2,3]) if modal_idx >= self.modal_num: loss_adv += torch.mean(loss_temp) elif torch.mean(torch.as_tensor((modal_idx==target_modal_ids_tensor), dtype=torch.float32)) == 0: loss_adv += 0.0 else: loss_adv += torch.mean(torch.masked_select(loss_temp, target_modal_ids_tensor==modal_idx)) _batch_size += t_out.size(0) #loss_adv /= _batch_size loss_adv *= self.cfg['training']['loss_adv_lambda'] loss_G = torch.Tensor([0]).cuda() loss_G = loss_G + loss_GTA + loss_adv self.BaseOpti.zero_grad() if loss_G.item() != 0: loss_G.backward() self.BaseOpti.step() # ----------------------------- """Discriminator """ # ----------------------------- _batch_size = 0 loss_D_comb = torch.Tensor([0]).cuda() source_modal_ids_tensor = torch.Tensor(source_modal_ids).cuda() for s_out, t_out, _d_net_DP, _d_net, _disc_opt, modal_idx in zip(source_output, target_output, self.DNets_DP, self.DNets, self.DiscOptis, range(len(source_output))): self.set_requires_grad(self.logger, _d_net, requires_grad = True) _batch_size = 0 loss_D = torch.Tensor([0]).cuda() # source domain s_D_out = _d_net_DP(F.softmax(s_out.detach())) loss_temp_s = torch.mean(self.bceloss( s_D_out, torch.FloatTensor(s_D_out.data.size()).fill_(1.0).cuda() ), [1,2,3]) if modal_idx >= self.modal_num: loss_D += torch.mean(loss_temp_s) elif torch.mean(torch.as_tensor((modal_idx==source_modal_ids_tensor), dtype=torch.float32)) == 0: loss_D += 0.0 else: loss_D += torch.mean(torch.masked_select(loss_temp_s, source_modal_ids_tensor==modal_idx)) # target domain _batch_size += (s_out.size(0) + t_out.size(0)) t_D_out = _d_net_DP(F.softmax(t_out.detach())) loss_temp_t = torch.mean(self.bceloss( t_D_out, torch.FloatTensor(t_D_out.data.size()).fill_(0.0).cuda() ), [1,2,3]) if modal_idx >= self.modal_num: loss_D += torch.mean(loss_temp_t) elif torch.mean(torch.as_tensor((modal_idx==target_modal_ids_tensor), dtype=torch.float32)) == 0: loss_D += 0.0 else: loss_D += torch.mean(torch.masked_select(loss_temp_t, target_modal_ids_tensor==modal_idx)) loss_D *= self.cfg['training']['loss_adv_lambda']*0.5 loss_D_comb += loss_D _disc_opt.zero_grad() if loss_D_comb.item() != 0: loss_D_comb.backward() _disc_opt.step() return loss_GTA, loss_adv, loss_D_comb def process_label(self, label): batch, channel, w, h = label.size() pred1 = torch.zeros(batch, 20, w, h).cuda() id = torch.where(label < 19, label, torch.Tensor([19]).cuda()) pred1 = pred1.scatter_(1, id.long(), 1) return pred1 def class_vectors_alignment(self, ids, vectors, modal_ids=[0,]): #loss = torch.Tensor([0]).cuda(self.default_gpu) loss = torch.Tensor([0]).cuda() """construct category objective vectors""" # objective_vectors_group 2 x 19 x 256 --> 19 x 512 _objective_vectors_set = self.metrics.multimodal_merger.merge_objective_vectors(modal_ids=modal_idx) for i in range(len(ids)): if ids[i] not in self.valid_classes: continue new_loss = self.smoothloss(vectors[i].squeeze().cuda(), _objective_vectors[ids[i]]) while (new_loss.item() > 5): new_loss = new_loss / 10 loss = loss + new_loss loss = loss / len(ids) * 10 return loss def freeze_bn_apply(self): for net in self.nets: net.apply(freeze_bn) for net in self.nets_DP: net.apply(freeze_bn) def scheduler_step(self): if self.use_pseudo_label: for scheduler in self.schedulers: scheduler.step() else: """skipped _BaseScheduler_nouse""" for scheduler in self.schedulers[1:]: scheduler.step() # baseNet scheduler self.adjust_basenet_learning_rate() def optimizer_zerograd(self): for optimizer in self.optimizers: optimizer.zero_grad() def optimizer_step(self): for opt in self.optimizers: opt.step() def init_device(self, net, gpu_id=None, whether_DP=False): gpu_id = gpu_id or self.default_gpu device = torch.device("cuda:{}".format(gpu_id) if torch.cuda.is_available() else 'cpu') net = net.to(device) # if torch.cuda.is_available(): if whether_DP: net = DataParallelWithCallback(net, device_ids=range(torch.cuda.device_count())) return net def eval(self, net=None, logger=None): """Make specific models eval mode during test time""" if net == None: for net in self.nets: net.eval() for net in self.nets_DP: net.eval() if logger!=None: logger.info("Successfully set the model eval mode") else: net.eval() if logger!=None: logger("Successfully set {} eval mode".format(net.__class__.__name__)) return def train(self, net=None, logger=None): if net==None: for net in self.nets: net.train() for net in self.nets_DP: net.train() else: net.train() return def set_requires_grad(self, logger, net, requires_grad = False): """Set requires_grad=Fasle for all the networks to avoid unnecessary computations Parameters: net (BaseModel) -- the network which will be operated on requires_grad (bool) -- whether the networks require gradients or not """ for parameter in net.parameters(): parameter.requires_grad = requires_grad def set_requires_grad_layer(self, logger, net, layer_type='batchnorm', requires_grad=False): ''' set specific type of layers whether needing grad ''' # print('Warning: all the BatchNorm params are fixed!') # logger.info('Warning: all the BatchNorm params are fixed!') for net in self.nets: for _i in net.modules(): if _i.__class__.__name__.lower().find(layer_type.lower()) != -1: _i.weight.requires_grad = requires_grad return def init_weights(self, cfg, logger, net, init_type='normal', init_gain=0.02): """Initialize network weights. Parameters: net (network) -- network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal init_gain (float) -- scaling factor for normal, xavier and orthogonal. We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might work better for some applications. Feel free to try yourself. """ init_type = cfg.get('init_type', init_type) init_gain = cfg.get('init_gain', init_gain) def init_func(m): # define the initialization function classname = m.__class__.__name__ if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): if init_type == 'normal': nn.init.normal_(m.weight.data, 0.0, init_gain) elif init_type == 'xavier': nn.init.xavier_normal_(m.weight.data, gain=init_gain) elif init_type == 'kaiming': nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': nn.init.orthogonal_(m.weight.data, gain=init_gain) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) elif isinstance(m, SynchronizedBatchNorm2d) or classname.find('BatchNorm2d') != -1 \ or isinstance(m, nn.GroupNorm): m.weight.data.fill_(1) m.bias.data.zero_() # BatchNorm Layer's weight is not a matrix; only normal distribution applies. print('initialize {} with {}'.format(init_type, net.__class__.__name__)) logger.info('initialize {} with {}'.format(init_type, net.__class__.__name__)) net.apply(init_func) # apply the initialization function <init_func> pass def adaptive_load_nets(self, net, model_weight): model_dict = net.state_dict() pretrained_dict = {k : v for k, v in model_weight.items() if k in model_dict} # print("[INFO] Pretrained dict:", pretrained_dict.keys()) model_dict.update(pretrained_dict) net.load_state_dict(model_dict) def load_nets(self, cfg, writer, logger): # load pretrained weights on the net if os.path.isfile(cfg['training']['resume']): logger.info( "Loading model and optimizer from checkpoint '{}'".format(cfg['training']['resume']) ) checkpoint = torch.load(cfg['training']['resume']) _k = -1 net_state_no = {} for net in self.nets: name = net.__class__.__name__ if name not in net_state_no: net_state_no[name] = 0 else: net_state_no[name] += 1 _k += 1 if checkpoint.get(name) == None: continue if name.find('FCDiscriminator') != -1 and cfg['training']['gan_resume'] == False: continue if isinstance(checkpoint[name], list): self.adaptive_load_nets(net, checkpoint[name][net_state_no[name]]["model_state"]) else: print("*****************************************") print("[WARNING] Using depreciated load version! Model {}".format(name)) print("*****************************************") self.adaptive_load_nets(net, checkpoint[name]["model_state"]) if cfg['training']['optimizer_resume']: if isinstance(checkpoint[name], list): self.adaptive_load_nets(self.optimizers[_k], checkpoint[name][net_state_no[name]]["optimizer_state"]) self.adaptive_load_nets(self.schedulers[_k], checkpoint[name][net_state_no[name]]["scheduler_state"]) else: self.adaptive_load_nets(self.optimizers[_k], checkpoint[name]["optimizer_state"]) self.adaptive_load_nets(self.schedulers[_k], checkpoint[name]["scheduler_state"]) self.iter = checkpoint["iter"] #self.best_iou = checkpoint['best_iou'] logger.info( "Loaded checkpoint '{}' (iter {})".format( cfg['training']['resume'], checkpoint["iter"] ) ) else: raise Exception("No checkpoint found at '{}'".format(cfg['training']['resume'])) def load_PredNet(self, cfg, writer, logger, dir=None, net=None): # load pretrained weights on the net dir = dir or cfg['training']['Pred_resume'] best_iou = 0 if os.path.isfile(dir): logger.info( "Loading model and optimizer from checkpoint '{}'".format(dir) ) checkpoint = torch.load(dir) name = net.__class__.__name__ if checkpoint.get(name) == None: return if name.find('FCDiscriminator') != -1 and cfg['training']['gan_resume'] == False: return if isinstance(checkpoint[name], list): self.adaptive_load_nets(net, checkpoint[name][0]["model_state"]) else: self.adaptive_load_nets(net, checkpoint[name]["model_state"]) iter = checkpoint["iter"] best_iou = checkpoint['best_iou'] logger.info( "Loaded checkpoint '{}' (iter {}) (best iou {}) for PredNet".format( dir, checkpoint["iter"], best_iou ) ) else: raise Exception("No checkpoint found at '{}'".format(dir)) if hasattr(net, 'best_iou'): #net.best_iou = best_iou pass return best_iou def set_optimizer(self, optimizer): #set optimizer to all nets pass def reset_objective_SingleVector(self,): self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda() self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda() self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda() def update_objective_SingleVector(self, vectors, vectors_num, name='moving_average'): #vector = vector.squeeze().detach() if torch.sum(vectors) == 0: return if name == 'moving_average': self.objective_vectors_group = self.objective_vectors_group * 0.9999 + 0.0001 * vectors self.objective_vectors_num_group += vectors_num self.objective_vectors_num_group = min(self.objective_vectors_num_group, 3000) elif name == 'mean': self.objective_vectors_group = self.objective_vectors_group * self.objective_vectors_num_group + vectors self.objective_vectors_num_group += vectors_num self.objective_vectors_group = self.objective_vectors_group / self.objective_vectors_num_group self.objective_vectors_num_group = min(self.objective_vectors_num_group, 3000) else: raise NotImplementedError('no such updating way of objective vectors {}'.format(name)) def grad_reverse(x): return GradReverse()(x)

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from models.base_model import BaseModel import torch.nn as nn import torch.nn.functional as F import os, sys import torch import numpy as np import itertools from torch.autograd import Variable from optimizers import get_optimizer from schedulers import get_scheduler from models.sync_batchnorm import SynchronizedBatchNorm2d, DataParallelWithCallback from models.deeplab_multimodal import DeepLab from models.decoder import Decoder from models.aspp import ASPP from models.discriminator import FCDiscriminator, FCDiscriminator_low, FCDiscriminator_out, FCDiscriminator_class from loss import get_loss_function from .utils import freeze_bn, GradReverse, normalisation_pooling from metrics import runningScore import pdb def multimodal_merger(multi_modal_data, is_upsample=False, up_size=None): feat_cls = multi_modal_data['feat_cls'] feat_cls_cat = torch.cat(feat_cls, 1) output = multi_modal_data['output'] output_comb = 0 for _i in range(len(output)): if is_upsample: output[_i] = F.interpolate(output[_i], size=up_size, mode='bilinear', align_corners=True) output_comb += output[_i] merge_out = { 'feat_cls': feat_cls, 'feat_cls_cat': feat_cls_cat, 'output_comb': output_comb, 'output': output, } return merge_out class CustomMetricsMultimodalMerger(): def __init__(self, modal_num, category_num, model): self.modal_num = modal_num self.category_num = category_num self._model = model def initialize_model(model): self._model = model def merge_class_threshold(self, modal_ids=[]): assert self._model is not None, "[ERROR] Deeplab Model not initialize before using!" _class_threshold_group = self._model.class_threshold_group[modal_ids] return torch.mean(_class_threshold_group, dim=0) def merge_clu_threshold(self, clu_threshold, modal_ids=[]): _clu_threshold_group = clu_threshold[modal_ids] return torch.mean(_clu_threshold_group, dim=0) def merge_objective_vectors(self, modal_ids=[]): assert self._model is not None, "[ERROR] Deeplab Model not initialize before using!" _modal_num, _cate_num, _feat_dim = self._model.objective_vectors_group.size() _objective_vectors = self._model.objective_vectors_group[modal_ids] assert _objective_vectors.dim() == 4, "objective_vector dimension != 4" _objective_vectors = _objective_vectors.permute(1, 0, 2).contiguous() return _objective_vectors.view(_cate_num, -1) class CustomMetrics(): def __init__(self, numbers=19, modal_num=3, model=None): self.class_numbers = numbers self.classes_recall_thr = np.zeros([19, 3]) self.classes_recall_thr_num = np.zeros([19]) self.classes_recall_clu = np.zeros([19, 3]) self.classes_recall_clu_num = np.zeros([19]) self.running_metrics_val_threshold = runningScore(self.class_numbers) self.running_metrics_val_clusters = runningScore(self.class_numbers) self.clu_threshold = torch.full((modal_num + 1, 19), 2.5).cuda() self.multimodal_merger = CustomMetricsMultimodalMerger( modal_num=modal_num + 1, category_num=numbers, model=model ) def update(self, feat_cls, outputs, labels, modal_ids=[0,]): batch, width, height = labels.shape labels = labels.reshape([batch, 1, width, height]).float() labels = F.interpolate(labels, size=feat_cls.size()[2:], mode='nearest') outputs_threshold = outputs.clone() outputs_threshold = F.softmax(outputs_threshold, dim=1) self.running_metrics_val_threshold.update(labels, outputs_threshold.argmax(1)) _class_threshold_set = self.multimodal_merger.merge_class_threshold(modal_ids=modal_idx) for i in range(19): outputs_threshold[:, i, :, :] = torch.where(outputs_threshold[:, i, :, :] > _class_threshold_set[i], torch.Tensor([1]).cuda(), torch.Tensor([0]).cuda()) _batch, _channel, _w, _h = outputs_threshold.shape _tmp = torch.full([_batch, 1, _w, _h], 0.2,).cuda() _tmp = torch.cat((outputs_threshold, _tmp), 1) threshold_arg = _tmp.argmax(1, keepdim=True) threshold_arg[threshold_arg == 19] = 250 truth, pred_all, truth_all = self.calc_recall(labels.cpu().int().numpy(), threshold_arg.cpu().int().numpy()) self.classes_recall_thr[:, 0] += truth self.classes_recall_thr[:, 2] += pred_all self.classes_recall_thr[:, 1] += truth_all outputs_cluster = outputs.clone() _objective_vectors_set = self.multimodal_merger.merge_objective_vectors(modal_ids=modal_idx) for i in range(19): outputs_cluster[:, i, :, :] = torch.norm( _objective_vectors_set[i].reshape(-1,1,1).expand(-1,128,256) - feat_cls, 2, dim=1,) outputs_cluster_min, outputs_cluster_arg = outputs_cluster.min(dim=1, keepdim=True) outputs_cluster_second = outputs_cluster.scatter_(1, outputs_cluster_arg, 100) if torch.unique(outputs_cluster_second.argmax(1) - outputs_cluster_arg.squeeze()).squeeze().item() != 0: raise NotImplementedError('wrong when computing L2 norm!!') outputs_cluster_secondmin, outputs_cluster_secondarg = outputs_cluster_second.min(dim=1, keepdim=True) self.running_metrics_val_clusters.update(labels, outputs_cluster_arg) tmp_arg = outputs_cluster_arg.clone() pdb.set_trace() _clu_thresholds = self.multimodal_merger.merge_clu_threshold(self.clu_threshold, modal_ids=modal_ids) outputs_cluster_arg[(outputs_cluster_secondmin - outputs_cluster_min) < _clu_thresholds] = 250 truth, pred_all, truth_all = self.calc_recall(labels.cpu().int().numpy(), outputs_cluster_arg.cpu().int().numpy()) self.classes_recall_clu[:, 0] += truth self.classes_recall_clu[:, 2] += pred_all self.classes_recall_clu[:, 1] += truth_all return threshold_arg, outputs_cluster_arg def calc_recall(self, gt, argmax): truth = np.zeros([self.class_numbers]) pred_all = np.zeros([self.class_numbers]) truth_all = np.zeros([self.class_numbers]) for i in range(self.class_numbers): truth[i] = (gt == i)[argmax == i].sum() pred_all[i] = (argmax == i).sum() truth_all[i] = (gt == i).sum() pass return truth, pred_all, truth_all def calc_mean_Clu_recall(self, ): return np.mean(self.classes_recall_clu[:, 0] / self.classes_recall_clu[:, 1]) def calc_mean_Thr_recall(self, ): return np.mean(self.classes_recall_thr[:, 0] / self.classes_recall_thr[:, 1]) def reset(self, ): self.running_metrics_val_clusters.reset() self.running_metrics_val_threshold.reset() self.classes_recall_clu = np.zeros([19, 3]) self.classes_recall_thr = np.zeros([19, 3]) class CustomModel(): def __init__(self, cfg, writer, logger, use_pseudo_label=False, modal_num=3): self.cfg = cfg self.writer = writer self.class_numbers = 19 self.logger = logger cfg_model = cfg['model'] self.cfg_model = cfg_model self.best_iou = -100 self.iter = 0 self.nets = [] self.split_gpu = 0 self.default_gpu = cfg['model']['default_gpu'] self.PredNet_Dir = None self.valid_classes = cfg['training']['valid_classes'] self.G_train = True self.cls_feature_weight = cfg['training']['cls_feature_weight'] self.use_pseudo_label = use_pseudo_label self.modal_num = modal_num self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda() self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda() self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda() self.class_threshold_group = torch.full([self.modal_num + 1, 19], 0.95).cuda() self.metrics = CustomMetrics(self.class_numbers, modal_num=self.modal_num, model=self) bn = cfg_model['bn'] if bn == 'sync_bn': BatchNorm = SynchronizedBatchNorm2d elif bn == 'bn': BatchNorm = nn.BatchNorm2d elif bn == 'gn': BatchNorm = nn.GroupNorm else: raise NotImplementedError('batch norm choice {} is not implemented'.format(bn)) if use_pseudo_label: self.PredNet = DeepLab( num_classes=19, backbone=cfg_model['basenet']['version'], output_stride=16, bn=cfg_model['bn'], freeze_bn=True, modal_num=self.modal_num ).cuda() self.load_PredNet(cfg, writer, logger, dir=None, net=self.PredNet) self.PredNet_DP = self.init_device(self.PredNet, gpu_id=self.default_gpu, whether_DP=True) self.PredNet.eval() self.PredNet_num = 0 self.BaseNet = DeepLab( num_classes=19, backbone=cfg_model['basenet']['version'], output_stride=16, bn=cfg_model['bn'], freeze_bn=True, modal_num=self.modal_num ) logger.info('the backbone is {}'.format(cfg_model['basenet']['version'])) self.BaseNet_DP = self.init_device(self.BaseNet, gpu_id=self.default_gpu, whether_DP=True) self.nets.extend([self.BaseNet]) self.nets_DP = [self.BaseNet_DP] self.SOURCE_LABEL = 0 self.TARGET_LABEL = 1 self.DNets = [] self.DNets_DP = [] for _ in range(self.modal_num+1): _net_d = FCDiscriminator(inplanes=19) self.DNets.append(_net_d) _net_d_DP = self.init_device(_net_d, gpu_id=self.default_gpu, whether_DP=True) self.DNets_DP.append(_net_d_DP) self.nets.extend(self.DNets) self.nets_DP.extend(self.DNets_DP) self.optimizers = [] self.schedulers = [] optimizer_cls = torch.optim.SGD optimizer_params = {k:v for k, v in cfg['training']['optimizer'].items() if k != 'name'} optimizer_cls_D = torch.optim.Adam optimizer_params_D = {k:v for k, v in cfg['training']['optimizer_D'].items() if k != 'name'} if self.use_pseudo_label: self.BaseOpti = optimizer_cls(self.BaseNet.parameters(), **optimizer_params) else: self.BaseOpti = optimizer_cls(self.BaseNet.optim_parameters(cfg['training']['optimizer']['lr']), **optimizer_params) self.optimizers.extend([self.BaseOpti]) self.DiscOptis = [] for _d_net in self.DNets: self.DiscOptis.append( optimizer_cls_D(_d_net.parameters(), **optimizer_params_D) ) self.optimizers.extend(self.DiscOptis) self.schedulers = [] if self.use_pseudo_label: self.BaseSchedule = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule']) self.schedulers.extend([self.BaseSchedule]) else: """BaseSchedule detail see FUNC: scheduler_step()""" self.learning_rate = cfg['training']['optimizer']['lr'] self.gamma = cfg['training']['lr_schedule']['gamma'] self.num_steps = cfg['training']['lr_schedule']['max_iter'] self._BaseSchedule_nouse = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule']) self.schedulers.extend([self._BaseSchedule_nouse]) self.DiscSchedules = [] for _disc_opt in self.DiscOptis: self.DiscSchedules.append( get_scheduler(_disc_opt, cfg['training']['lr_schedule']) ) self.schedulers.extend(self.DiscSchedules) self.setup(cfg, writer, logger) self.adv_source_label = 0 self.adv_target_label = 1 self.bceloss = nn.BCEWithLogitsLoss(reduce=False) self.loss_fn = get_loss_function(cfg) self.mseloss = nn.MSELoss() self.l1loss = nn.L1Loss() self.smoothloss = nn.SmoothL1Loss() self.triplet_loss = nn.TripletMarginLoss() def create_PredNet(self,): ss = DeepLab( num_classes=19, backbone=self.cfg_model['basenet']['version'], output_stride=16, bn=self.cfg_model['bn'], freeze_bn=True, modal_num=self.modal_num, ).cuda() ss.eval() return ss def setup(self, cfg, writer, logger): for net in self.nets: self.init_weights(cfg['model']['init'], logger, net) print("Initializition completed") if hasattr(net, '_load_pretrained_model') and cfg['model']['pretrained']: print("loading pretrained model for {}".format(net.__class__.__name__)) net._load_pretrained_model() if cfg['training']['resume_flag']: self.load_nets(cfg, writer, logger) pass def lr_poly(self): return self.learning_rate * ((1 - float(self.iter) / self.num_steps) ** (self.gamma)) def adjust_basenet_learning_rate(self): lr = self.lr_poly() self.BaseOpti.param_groups[0]['lr'] = lr if len(self.BaseOpti.param_groups) > 1: self.BaseOpti.param_groups[1]['lr'] = lr * 10 def forward(self, input): feat, feat_low, att_mask, feat_cls, output = self.BaseNet_DP(input) return feat, feat_low, feat_cls, output def forward_Up(self, input): feat, feat_low, feat_cls, outputs = self.forward(input) output = F.interpolate(outputs[-1], size=input.size()[2:], mode='bilinear', align_corners=True) return feat, feat_low, feat_cls, output def PredNet_Forward(self, input): with torch.no_grad(): _, _, att_mask, feat_cls, output_result = self.PredNet_DP(input) return _, _, feat_cls, output_result def calculate_mean_vector(self, feat_cls, outputs, labels, ): outputs_softmax = F.softmax(outputs, dim=1) outputs_argmax = outputs_softmax.argmax(dim=1, keepdim=True) outputs_argmax = self.process_label(outputs_argmax.float()) labels_expanded = self.process_label(labels) outputs_pred = labels_expanded * outputs_argmax scale_factor = F.adaptive_avg_pool2d(outputs_pred, 1) vectors = [] ids = [] for n in range(feat_cls.size()[0]): for t in range(self.class_numbers): if scale_factor[n][t].item()==0: continue if (outputs_pred[n][t] > 0).sum() < 10: continue s = feat_cls[n] * outputs_pred[n][t] scale = torch.sum(outputs_pred[n][t]) / labels.shape[2] / labels.shape[3] * 2 s = normalisation_pooling()(s, scale) s = F.adaptive_avg_pool2d(s, 1) / scale_factor[n][t] vectors.append(s) ids.append(t) return vectors, ids def step(self, source_x, source_label, source_modal_ids, target_x, target_label, target_modal_ids, use_pseudo_loss=False): assert len(source_modal_ids) == source_x.size(0), "modal_ids' batchsize != source_x's batchsize" _, _, source_feat_cls, source_output = self.forward(input=source_x) source_output_modal_k = torch.stack( [ source_output[_modal_i][_batch_i] for _batch_i, _modal_i in enumerate(source_modal_ids) ], dim=0, ) source_output_comb = torch.cat([source_output_modal_k, source_output[-1]], dim=0) source_label_comb = torch.cat([source_label, source_label.clone()], dim=0) source_outputUp = F.interpolate(source_output_comb, size=source_x.size()[-2:], mode='bilinear', align_corners=True) loss_GTA = self.loss_fn(input=source_outputUp, target=source_label_comb) loss_adv = torch.Tensor([0]).cuda() _batch_size = 0 _, _, _, target_output = self.forward(target_x) target_modal_ids_tensor = torch.Tensor(target_modal_ids).cuda() for t_out, _d_net_DP, _d_net, modal_idx in zip(target_output, self.DNets_DP, self.DNets, range(len(target_output))): self.set_requires_grad(self.logger, _d_net, requires_grad = False) t_D_out = _d_net_DP(F.softmax(t_out)) loss_temp = torch.mean(self.bceloss( t_D_out, torch.FloatTensor(t_D_out.data.size()).fill_(1.0).cuda() ), [1,2,3]) if modal_idx >= self.modal_num: loss_adv += torch.mean(loss_temp) elif torch.mean(torch.as_tensor((modal_idx==target_modal_ids_tensor), dtype=torch.float32)) == 0: loss_adv += 0.0 else: loss_adv += torch.mean(torch.masked_select(loss_temp, target_modal_ids_tensor==modal_idx)) _batch_size += t_out.size(0) loss_adv *= self.cfg['training']['loss_adv_lambda'] loss_G = torch.Tensor([0]).cuda() loss_G = loss_G + loss_GTA + loss_adv self.BaseOpti.zero_grad() if loss_G.item() != 0: loss_G.backward() self.BaseOpti.step() _batch_size = 0 loss_D_comb = torch.Tensor([0]).cuda() source_modal_ids_tensor = torch.Tensor(source_modal_ids).cuda() for s_out, t_out, _d_net_DP, _d_net, _disc_opt, modal_idx in zip(source_output, target_output, self.DNets_DP, self.DNets, self.DiscOptis, range(len(source_output))): self.set_requires_grad(self.logger, _d_net, requires_grad = True) _batch_size = 0 loss_D = torch.Tensor([0]).cuda() s_D_out = _d_net_DP(F.softmax(s_out.detach())) loss_temp_s = torch.mean(self.bceloss( s_D_out, torch.FloatTensor(s_D_out.data.size()).fill_(1.0).cuda() ), [1,2,3]) if modal_idx >= self.modal_num: loss_D += torch.mean(loss_temp_s) elif torch.mean(torch.as_tensor((modal_idx==source_modal_ids_tensor), dtype=torch.float32)) == 0: loss_D += 0.0 else: loss_D += torch.mean(torch.masked_select(loss_temp_s, source_modal_ids_tensor==modal_idx)) _batch_size += (s_out.size(0) + t_out.size(0)) t_D_out = _d_net_DP(F.softmax(t_out.detach())) loss_temp_t = torch.mean(self.bceloss( t_D_out, torch.FloatTensor(t_D_out.data.size()).fill_(0.0).cuda() ), [1,2,3]) if modal_idx >= self.modal_num: loss_D += torch.mean(loss_temp_t) elif torch.mean(torch.as_tensor((modal_idx==target_modal_ids_tensor), dtype=torch.float32)) == 0: loss_D += 0.0 else: loss_D += torch.mean(torch.masked_select(loss_temp_t, target_modal_ids_tensor==modal_idx)) loss_D *= self.cfg['training']['loss_adv_lambda']*0.5 loss_D_comb += loss_D _disc_opt.zero_grad() if loss_D_comb.item() != 0: loss_D_comb.backward() _disc_opt.step() return loss_GTA, loss_adv, loss_D_comb def process_label(self, label): batch, channel, w, h = label.size() pred1 = torch.zeros(batch, 20, w, h).cuda() id = torch.where(label < 19, label, torch.Tensor([19]).cuda()) pred1 = pred1.scatter_(1, id.long(), 1) return pred1 def class_vectors_alignment(self, ids, vectors, modal_ids=[0,]): loss = torch.Tensor([0]).cuda() _objective_vectors_set = self.metrics.multimodal_merger.merge_objective_vectors(modal_ids=modal_idx) for i in range(len(ids)): if ids[i] not in self.valid_classes: continue new_loss = self.smoothloss(vectors[i].squeeze().cuda(), _objective_vectors[ids[i]]) while (new_loss.item() > 5): new_loss = new_loss / 10 loss = loss + new_loss loss = loss / len(ids) * 10 return loss def freeze_bn_apply(self): for net in self.nets: net.apply(freeze_bn) for net in self.nets_DP: net.apply(freeze_bn) def scheduler_step(self): if self.use_pseudo_label: for scheduler in self.schedulers: scheduler.step() else: """skipped _BaseScheduler_nouse""" for scheduler in self.schedulers[1:]: scheduler.step() self.adjust_basenet_learning_rate() def optimizer_zerograd(self): for optimizer in self.optimizers: optimizer.zero_grad() def optimizer_step(self): for opt in self.optimizers: opt.step() def init_device(self, net, gpu_id=None, whether_DP=False): gpu_id = gpu_id or self.default_gpu device = torch.device("cuda:{}".format(gpu_id) if torch.cuda.is_available() else 'cpu') net = net.to(device) if whether_DP: net = DataParallelWithCallback(net, device_ids=range(torch.cuda.device_count())) return net def eval(self, net=None, logger=None): if net == None: for net in self.nets: net.eval() for net in self.nets_DP: net.eval() if logger!=None: logger.info("Successfully set the model eval mode") else: net.eval() if logger!=None: logger("Successfully set {} eval mode".format(net.__class__.__name__)) return def train(self, net=None, logger=None): if net==None: for net in self.nets: net.train() for net in self.nets_DP: net.train() else: net.train() return def set_requires_grad(self, logger, net, requires_grad = False): for parameter in net.parameters(): parameter.requires_grad = requires_grad def set_requires_grad_layer(self, logger, net, layer_type='batchnorm', requires_grad=False): for net in self.nets: for _i in net.modules(): if _i.__class__.__name__.lower().find(layer_type.lower()) != -1: _i.weight.requires_grad = requires_grad return def init_weights(self, cfg, logger, net, init_type='normal', init_gain=0.02): init_type = cfg.get('init_type', init_type) init_gain = cfg.get('init_gain', init_gain) def init_func(m): classname = m.__class__.__name__ if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): if init_type == 'normal': nn.init.normal_(m.weight.data, 0.0, init_gain) elif init_type == 'xavier': nn.init.xavier_normal_(m.weight.data, gain=init_gain) elif init_type == 'kaiming': nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': nn.init.orthogonal_(m.weight.data, gain=init_gain) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) elif isinstance(m, SynchronizedBatchNorm2d) or classname.find('BatchNorm2d') != -1 \ or isinstance(m, nn.GroupNorm): m.weight.data.fill_(1) m.bias.data.zero_() print('initialize {} with {}'.format(init_type, net.__class__.__name__)) logger.info('initialize {} with {}'.format(init_type, net.__class__.__name__)) net.apply(init_func) # apply the initialization function <init_func> pass def adaptive_load_nets(self, net, model_weight): model_dict = net.state_dict() pretrained_dict = {k : v for k, v in model_weight.items() if k in model_dict} # print("[INFO] Pretrained dict:", pretrained_dict.keys()) model_dict.update(pretrained_dict) net.load_state_dict(model_dict) def load_nets(self, cfg, writer, logger): # load pretrained weights on the net if os.path.isfile(cfg['training']['resume']): logger.info( "Loading model and optimizer from checkpoint '{}'".format(cfg['training']['resume']) ) checkpoint = torch.load(cfg['training']['resume']) _k = -1 net_state_no = {} for net in self.nets: name = net.__class__.__name__ if name not in net_state_no: net_state_no[name] = 0 else: net_state_no[name] += 1 _k += 1 if checkpoint.get(name) == None: continue if name.find('FCDiscriminator') != -1 and cfg['training']['gan_resume'] == False: continue if isinstance(checkpoint[name], list): self.adaptive_load_nets(net, checkpoint[name][net_state_no[name]]["model_state"]) else: print("*****************************************") print("[WARNING] Using depreciated load version! Model {}".format(name)) print("*****************************************") self.adaptive_load_nets(net, checkpoint[name]["model_state"]) if cfg['training']['optimizer_resume']: if isinstance(checkpoint[name], list): self.adaptive_load_nets(self.optimizers[_k], checkpoint[name][net_state_no[name]]["optimizer_state"]) self.adaptive_load_nets(self.schedulers[_k], checkpoint[name][net_state_no[name]]["scheduler_state"]) else: self.adaptive_load_nets(self.optimizers[_k], checkpoint[name]["optimizer_state"]) self.adaptive_load_nets(self.schedulers[_k], checkpoint[name]["scheduler_state"]) self.iter = checkpoint["iter"] #self.best_iou = checkpoint['best_iou'] logger.info( "Loaded checkpoint '{}' (iter {})".format( cfg['training']['resume'], checkpoint["iter"] ) ) else: raise Exception("No checkpoint found at '{}'".format(cfg['training']['resume'])) def load_PredNet(self, cfg, writer, logger, dir=None, net=None): # load pretrained weights on the net dir = dir or cfg['training']['Pred_resume'] best_iou = 0 if os.path.isfile(dir): logger.info( "Loading model and optimizer from checkpoint '{}'".format(dir) ) checkpoint = torch.load(dir) name = net.__class__.__name__ if checkpoint.get(name) == None: return if name.find('FCDiscriminator') != -1 and cfg['training']['gan_resume'] == False: return if isinstance(checkpoint[name], list): self.adaptive_load_nets(net, checkpoint[name][0]["model_state"]) else: self.adaptive_load_nets(net, checkpoint[name]["model_state"]) iter = checkpoint["iter"] best_iou = checkpoint['best_iou'] logger.info( "Loaded checkpoint '{}' (iter {}) (best iou {}) for PredNet".format( dir, checkpoint["iter"], best_iou ) ) else: raise Exception("No checkpoint found at '{}'".format(dir)) if hasattr(net, 'best_iou'): #net.best_iou = best_iou pass return best_iou def set_optimizer(self, optimizer): #set optimizer to all nets pass def reset_objective_SingleVector(self,): self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda() self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda() self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda() def update_objective_SingleVector(self, vectors, vectors_num, name='moving_average'): #vector = vector.squeeze().detach() if torch.sum(vectors) == 0: return if name == 'moving_average': self.objective_vectors_group = self.objective_vectors_group * 0.9999 + 0.0001 * vectors self.objective_vectors_num_group += vectors_num self.objective_vectors_num_group = min(self.objective_vectors_num_group, 3000) elif name == 'mean': self.objective_vectors_group = self.objective_vectors_group * self.objective_vectors_num_group + vectors self.objective_vectors_num_group += vectors_num self.objective_vectors_group = self.objective_vectors_group / self.objective_vectors_num_group self.objective_vectors_num_group = min(self.objective_vectors_num_group, 3000) else: raise NotImplementedError('no such updating way of objective vectors {}'.format(name)) def grad_reverse(x): return GradReverse()(x)

true

f71b30ea10a42f00072f8fb902d4bdeca0fdac2c

4,663

py

Python

pose_sync_pytorch/generate_basic.py

lilly9117/Cross-Cutting

d534e8b5d4bf071883b7cb5f1832bba74b9a52d0

[ "Apache-2.0" ]

null

pose_sync_pytorch/generate_basic.py

lilly9117/Cross-Cutting

d534e8b5d4bf071883b7cb5f1832bba74b9a52d0

[ "Apache-2.0" ]

null

pose_sync_pytorch/generate_basic.py

lilly9117/Cross-Cutting

d534e8b5d4bf071883b7cb5f1832bba74b9a52d0

[ "Apache-2.0" ]

null

import os from moviepy.editor import VideoFileClip, concatenate_videoclips import random import numpy as np import time # from video_facial_landmarks_minmax import calculate_distance from video_pose_landmarks import calculate_pose_distance TEST = True TEST_TIME = 20 INIT_NUM = float("Inf") WINDOW_TIME = 10 PADDED_TIME = 4 # 얼굴이 클로즈업 된게 있으면 계속 클로즈업 된 부분만 찾으므로 3초정도 띄어준다. # def distance(reference_clip, clip): # min_diff, min_idx, additional_info = calculate_distance(reference_clip, clip) # return min_diff, min_idx def pose_distance(reference_clip, clip): min_diff, min_idx, additional_info = calculate_pose_distance(reference_clip, clip) return min_diff, min_idx def crosscut(videos_path="./video", option="random"): min_time = 1000.0 min_idx = 0 audioclip = None extracted_clips_array = [] video_num = len(os.listdir(videos_path)) start_times = [0] * video_num # VIDEO ALIGNMENT -> SLICE START TIME # VIDEO ALIGNMENT -> SLICE START TIME for i in range(len(os.listdir(videos_path))): video_path = os.path.join(videos_path, sorted(os.listdir(videos_path))[i]) clip = VideoFileClip(video_path) clip = clip.subclip(start_times[i], clip.duration) # 그냥 전체 영상을 시작점 맞게 자르기 print(video_path, clip.fps, clip.duration) if min_time > clip.duration: # ?? 제일 작은거 기준으로 자르려는건가?? 근데 그러면 그 앞에건 이미 크지않나?? audioclip = clip.audio min_time = clip.duration min_idx = i print(video_path, clip.fps, clip.duration) extracted_clips_array.append(clip) print(len(extracted_clips_array)) if TEST: # test하면 일부분만 생성해서 빠르게 확인하기 min_time = TEST_TIME audioclip = audioclip.set_duration(TEST_TIME) # GENERATE STAGEMIX # CONCAT SUBCLIP 0~ MIN DURATION CLIP TIME con_clips = [] t = 3 # 초반 3초 INIT current_idx = 0 # INIT con_clips.append(extracted_clips_array[current_idx].subclip(0, min(t, int(min_time)))) while t < min_time: # 10 sec. cur_t = t next_t = min(t+WINDOW_TIME, min_time) # 마지막은 window초보다 작은초일수도 있으니 # RANDOM BASED METHOD if option=="random" or min(min_time,t + PADDED_TIME)==min_time: random_video_idx = random.randint(0, len(extracted_clips_array)-1) clip = extracted_clips_array[random_video_idx].subclip(cur_t, next_t) t = next_t con_clips.append(clip) else: # 지금 현재 영상! reference_clip = extracted_clips_array[current_idx].subclip(cur_t, next_t) d = INIT_NUM cur_clip = None # inf가 있을때는 이 idx로 설정됨! min_idx = (current_idx+1)%len(extracted_clips_array) for video_idx in range(len(extracted_clips_array)): if video_idx == current_idx: continue # 10초간 영상 확인 clip = extracted_clips_array[video_idx].subclip(cur_t, next_t) # 이미 확인한 앞부분은 무시해야 함!!(! 첫번째 영상은 3초는 무조건 안겹치는 문제 있음) # !! ㅜㅜ 제일 좋은 얼굴 부분 놓칠수도 있을듯! # CALCULATE DISTANCE cur_d, plus_frame = pose_distance(reference_clip, clip) print(current_idx, video_idx, cur_d, cur_t + plus_frame) if d > cur_d: d = cur_d min_idx = video_idx next_t = cur_t + plus_frame # 바로 옮길 frame cur_clip = reference_clip.subclip(0, plus_frame) # next_clip = clip.subclip(0, plus_frame) # 그 바꿀 부분만 자르는 클립! # 이번 clip : 10초 내에서 자르거나 10초 full append if cur_clip: # 계산이 가능하면 clip = cur_clip # 현재 클립(바꾸면 가장 좋은 부분까지 잘린 현재 클립) else: clip = reference_clip # 현재 클립 10초 모두 t = next_t con_clips.append(clip) # 다음 clip : padding 길이는 반드시 append # 뒤에 padding 데이터 더하기 current_idx = min_idx # 바로 다음에 이어지면 가까운 거리로 연결되는 데이터 print("idx : {}".format(current_idx)) pad_clip = extracted_clips_array[current_idx].subclip(t, min(min_time,t+PADDED_TIME)) # min_time을 넘어가면 안됨! t = min(min_time,t + PADDED_TIME) # padding 된 시간 더하기 con_clips.append(pad_clip) final_clip = concatenate_videoclips(con_clips) if audioclip !=None: print("Not None") final_clip.audio = audioclip final_clip.write_videofile("crosscut_fiesta.mp4") return final_clip start_time = time.time() crosscut(videos_path="./video", option="norandom") end_time = time.time() print(end_time - start_time)

36.716535

118

0.619558

import os from moviepy.editor import VideoFileClip, concatenate_videoclips import random import numpy as np import time from video_pose_landmarks import calculate_pose_distance TEST = True TEST_TIME = 20 INIT_NUM = float("Inf") WINDOW_TIME = 10 PADDED_TIME = 4 def pose_distance(reference_clip, clip): min_diff, min_idx, additional_info = calculate_pose_distance(reference_clip, clip) return min_diff, min_idx def crosscut(videos_path="./video", option="random"): min_time = 1000.0 min_idx = 0 audioclip = None extracted_clips_array = [] video_num = len(os.listdir(videos_path)) start_times = [0] * video_num for i in range(len(os.listdir(videos_path))): video_path = os.path.join(videos_path, sorted(os.listdir(videos_path))[i]) clip = VideoFileClip(video_path) clip = clip.subclip(start_times[i], clip.duration) print(video_path, clip.fps, clip.duration) if min_time > clip.duration: audioclip = clip.audio min_time = clip.duration min_idx = i print(video_path, clip.fps, clip.duration) extracted_clips_array.append(clip) print(len(extracted_clips_array)) if TEST: min_time = TEST_TIME audioclip = audioclip.set_duration(TEST_TIME) con_clips = [] t = 3 current_idx = 0 con_clips.append(extracted_clips_array[current_idx].subclip(0, min(t, int(min_time)))) while t < min_time: cur_t = t next_t = min(t+WINDOW_TIME, min_time) if option=="random" or min(min_time,t + PADDED_TIME)==min_time: random_video_idx = random.randint(0, len(extracted_clips_array)-1) clip = extracted_clips_array[random_video_idx].subclip(cur_t, next_t) t = next_t con_clips.append(clip) else: reference_clip = extracted_clips_array[current_idx].subclip(cur_t, next_t) d = INIT_NUM cur_clip = None min_idx = (current_idx+1)%len(extracted_clips_array) for video_idx in range(len(extracted_clips_array)): if video_idx == current_idx: continue clip = extracted_clips_array[video_idx].subclip(cur_t, next_t) cur_d, plus_frame = pose_distance(reference_clip, clip) print(current_idx, video_idx, cur_d, cur_t + plus_frame) if d > cur_d: d = cur_d min_idx = video_idx next_t = cur_t + plus_frame cur_clip = reference_clip.subclip(0, plus_frame) if cur_clip: clip = cur_clip else: clip = reference_clip t = next_t con_clips.append(clip) current_idx = min_idx print("idx : {}".format(current_idx)) pad_clip = extracted_clips_array[current_idx].subclip(t, min(min_time,t+PADDED_TIME)) t = min(min_time,t + PADDED_TIME) con_clips.append(pad_clip) final_clip = concatenate_videoclips(con_clips) if audioclip !=None: print("Not None") final_clip.audio = audioclip final_clip.write_videofile("crosscut_fiesta.mp4") return final_clip start_time = time.time() crosscut(videos_path="./video", option="norandom") end_time = time.time() print(end_time - start_time)

true

f71b30f5180889ca3dd9df9574f878409863dfe6

1,003

py

Python

Tests/test_TimeAverager.py

chipgarner/yourair

22415389256cfa283e817970d6c79c187cbded4c

[ "MIT" ]

null

Tests/test_TimeAverager.py

chipgarner/yourair

22415389256cfa283e817970d6c79c187cbded4c

[ "MIT" ]

null

Tests/test_TimeAverager.py

chipgarner/yourair

22415389256cfa283e817970d6c79c187cbded4c

[ "MIT" ]

null

import Averager as Avg class Average: def __init__(self): self.answer = 0 self.delta_time = 0 def average(self, avg, delta_t): print("Average: " + str(avg) + " Timespan: " + str(delta_t)) self.answer = avg self.delta_time = delta_t def test_averager(): avg = Average() averager = Avg.TimeAverager(1, avg.average) averager.update_average(12.345) assert avg.answer == 12.345 assert avg.delta_time > 0 def test_averager_averages(): avg = Average() averager = Avg.TimeAverager(10, avg.average) averager.update_average(1) assert avg.answer == 0 assert avg.delta_time == 0 for i in range(2, 10): averager.update_average(i) assert avg.answer == 0 assert avg.delta_time == 0 averager.update_average(10) assert avg.answer == 5.5 assert avg.delta_time > 0 def test_averager_function_none(): averager = Avg.TimeAverager(1, None) averager.update_average(12.345)

20.469388

68

0.639083

import Averager as Avg class Average: def __init__(self): self.answer = 0 self.delta_time = 0 def average(self, avg, delta_t): print("Average: " + str(avg) + " Timespan: " + str(delta_t)) self.answer = avg self.delta_time = delta_t def test_averager(): avg = Average() averager = Avg.TimeAverager(1, avg.average) averager.update_average(12.345) assert avg.answer == 12.345 assert avg.delta_time > 0 def test_averager_averages(): avg = Average() averager = Avg.TimeAverager(10, avg.average) averager.update_average(1) assert avg.answer == 0 assert avg.delta_time == 0 for i in range(2, 10): averager.update_average(i) assert avg.answer == 0 assert avg.delta_time == 0 averager.update_average(10) assert avg.answer == 5.5 assert avg.delta_time > 0 def test_averager_function_none(): averager = Avg.TimeAverager(1, None) averager.update_average(12.345)

true

f71b315d6312d73a4f7581bd22785f23c8cb7785

5,935

py

Python

sprokit/tests/bindings/python/sprokit/pipeline/test-scheduler_registry.py

dstoup/kwiver

a3a36317b446baf0feb6274235ab1ac6b4329ead

[ "BSD-3-Clause" ]

1

2017-07-31T07:07:32.000Z

sprokit/tests/bindings/python/sprokit/pipeline/test-scheduler_registry.py

dstoup/kwiver

a3a36317b446baf0feb6274235ab1ac6b4329ead

[ "BSD-3-Clause" ]

3

2021-03-19T15:39:43.000Z

2021-09-08T02:47:15.000Z

sprokit/tests/bindings/python/sprokit/pipeline/test-scheduler_registry.py

acidburn0zzz/kwiver

6e4205f1c46df04759c57c040f01cc804b27e00d

[ "BSD-3-Clause" ]

null

#!@PYTHON_EXECUTABLE@ #ckwg +28 # Copyright 2011-2013 by Kitware, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither name of Kitware, Inc. nor the names of any contributors may be used # to endorse or promote products derived from this software without specific # prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE LIABLE FOR # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. def test_import(): try: from sprokit.pipeline import config import sprokit.pipeline.scheduler_factory except: test_error("Failed to import the scheduler_factory module") def test_create(): from sprokit.pipeline import config from sprokit.pipeline import scheduler_factory scheduler_factory.SchedulerType() ## scheduler_factory.SchedulerTypes() scheduler_factory.SchedulerDescription() scheduler_factory.SchedulerModule() def test_api_calls(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import scheduler_factory modules.load_known_modules() sched_type = 'thread_per_process' c = config.empty_config() p = pipeline.Pipeline() scheduler_factory.create_scheduler(sched_type, p) scheduler_factory.create_scheduler(sched_type, p, c) scheduler_factory.types() scheduler_factory.description(sched_type) scheduler_factory.default_type def example_scheduler(check_init): from sprokit.pipeline import scheduler class PythonExample(scheduler.PythonScheduler): def __init__(self, pipe, conf): scheduler.PythonScheduler.__init__(self, pipe, conf) self.ran_start = check_init self.ran_wait = check_init self.ran_stop = check_init self.ran_pause = check_init self.ran_resume = check_init def _start(self): self.ran_start = True def _wait(self): self.ran_wait = True def _stop(self): self.ran_stop = True def _pause(self): self.ran_pause = True def _resume(self): self.ran_resume = True def __del__(self): if not self.ran_start: test_error("start override was not called") if not self.ran_wait: test_error("wait override was not called") if not self.ran_stop: test_error("stop override was not called") if not self.ran_pause: test_error("pause override was not called") if not self.ran_resume: test_error("resume override was not called") return PythonExample def test_register(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import scheduler_factory modules.load_known_modules() sched_type = 'python_example' sched_desc = 'simple description' scheduler_factory.add_scheduler(sched_type, sched_desc, example_scheduler(True)) if not sched_desc == scheduler_factory.description(sched_type): test_error("Description was not preserved when registering") p = pipeline.Pipeline() try: s = scheduler_factory.create_scheduler(sched_type, p) if s is None: raise Exception() except: test_error("Could not create newly registered scheduler type") def test_wrapper_api(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import process_factory from sprokit.pipeline import scheduler_factory sched_type = 'python_example' sched_desc = 'simple description' modules.load_known_modules() scheduler_factory.add_scheduler(sched_type, sched_desc, example_scheduler(False)) p = pipeline.Pipeline() proc_type = 'orphan' proc_name = 'orphan' proc = process_factory.create_process(proc_type, proc_name) p.add_process(proc) def check_scheduler(s): if s is None: test_error("Got a 'None' scheduler") return s.start() s.pause() s.resume() s.stop() s.start() s.wait() del s p.reset() p.setup_pipeline() s = scheduler_factory.create_scheduler(sched_type, p) check_scheduler(s) if __name__ == '__main__': import os import sys if not len(sys.argv) == 4: test_error("Expected three arguments") sys.exit(1) testname = sys.argv[1] os.chdir(sys.argv[2]) sys.path.append(sys.argv[3]) from sprokit.test.test import * run_test(testname, find_tests(locals()))

29.824121

85

0.694356

def test_import(): try: from sprokit.pipeline import config import sprokit.pipeline.scheduler_factory except: test_error("Failed to import the scheduler_factory module") def test_create(): from sprokit.pipeline import config from sprokit.pipeline import scheduler_factory scheduler_factory.SchedulerType() ription() scheduler_factory.SchedulerModule() def test_api_calls(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import scheduler_factory modules.load_known_modules() sched_type = 'thread_per_process' c = config.empty_config() p = pipeline.Pipeline() scheduler_factory.create_scheduler(sched_type, p) scheduler_factory.create_scheduler(sched_type, p, c) scheduler_factory.types() scheduler_factory.description(sched_type) scheduler_factory.default_type def example_scheduler(check_init): from sprokit.pipeline import scheduler class PythonExample(scheduler.PythonScheduler): def __init__(self, pipe, conf): scheduler.PythonScheduler.__init__(self, pipe, conf) self.ran_start = check_init self.ran_wait = check_init self.ran_stop = check_init self.ran_pause = check_init self.ran_resume = check_init def _start(self): self.ran_start = True def _wait(self): self.ran_wait = True def _stop(self): self.ran_stop = True def _pause(self): self.ran_pause = True def _resume(self): self.ran_resume = True def __del__(self): if not self.ran_start: test_error("start override was not called") if not self.ran_wait: test_error("wait override was not called") if not self.ran_stop: test_error("stop override was not called") if not self.ran_pause: test_error("pause override was not called") if not self.ran_resume: test_error("resume override was not called") return PythonExample def test_register(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import scheduler_factory modules.load_known_modules() sched_type = 'python_example' sched_desc = 'simple description' scheduler_factory.add_scheduler(sched_type, sched_desc, example_scheduler(True)) if not sched_desc == scheduler_factory.description(sched_type): test_error("Description was not preserved when registering") p = pipeline.Pipeline() try: s = scheduler_factory.create_scheduler(sched_type, p) if s is None: raise Exception() except: test_error("Could not create newly registered scheduler type") def test_wrapper_api(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import process_factory from sprokit.pipeline import scheduler_factory sched_type = 'python_example' sched_desc = 'simple description' modules.load_known_modules() scheduler_factory.add_scheduler(sched_type, sched_desc, example_scheduler(False)) p = pipeline.Pipeline() proc_type = 'orphan' proc_name = 'orphan' proc = process_factory.create_process(proc_type, proc_name) p.add_process(proc) def check_scheduler(s): if s is None: test_error("Got a 'None' scheduler") return s.start() s.pause() s.resume() s.stop() s.start() s.wait() del s p.reset() p.setup_pipeline() s = scheduler_factory.create_scheduler(sched_type, p) check_scheduler(s) if __name__ == '__main__': import os import sys if not len(sys.argv) == 4: test_error("Expected three arguments") sys.exit(1) testname = sys.argv[1] os.chdir(sys.argv[2]) sys.path.append(sys.argv[3]) from sprokit.test.test import * run_test(testname, find_tests(locals()))

true

f71b32a53e1eb2f384ead41803a3f5892542c5b5

6,308

py

Python

dbms/tests/queries/0_stateless/helpers/uexpect.py

sunadm/ClickHouse

55903fbe23ef6dff8fc7ec25ae68e04919bc9b7f

[ "Apache-2.0" ]

8

2019-06-04T02:50:13.000Z

2022-02-10T06:46:51.000Z

dbms/tests/queries/0_stateless/helpers/uexpect.py

sunadm/ClickHouse

55903fbe23ef6dff8fc7ec25ae68e04919bc9b7f

[ "Apache-2.0" ]

16

2021-06-07T21:32:30.000Z

2022-03-31T21:08:29.000Z

dbms/tests/queries/0_stateless/helpers/uexpect.py

sunadm/ClickHouse

55903fbe23ef6dff8fc7ec25ae68e04919bc9b7f

[ "Apache-2.0" ]

3

2020-02-24T12:57:54.000Z

2021-10-04T13:29:00.000Z

# Copyright (c) 2019 Vitaliy Zakaznikov # # 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 os import pty import time import sys import re from threading import Thread, Event from subprocess import Popen from Queue import Queue, Empty class TimeoutError(Exception): def __init__(self, timeout): self.timeout = timeout def __str__(self): return 'Timeout %.3fs' % float(self.timeout) class ExpectTimeoutError(Exception): def __init__(self, pattern, timeout, buffer): self.pattern = pattern self.timeout = timeout self.buffer = buffer def __str__(self): s = 'Timeout %.3fs ' % float(self.timeout) if self.pattern: s += 'for %s ' % repr(self.pattern.pattern) if self.buffer: s += 'buffer %s ' % repr(self.buffer[:]) s += 'or \'%s\'' % ','.join(['%x' % ord(c) for c in self.buffer[:]]) return s class IO(object): class EOF(object): pass class Timeout(object): pass EOF = EOF TIMEOUT = Timeout class Logger(object): def __init__(self, logger, prefix=''): self._logger = logger self._prefix = prefix def write(self, data): self._logger.write(('\n' + data).replace('\n','\n' + self._prefix)) def flush(self): self._logger.flush() def __init__(self, process, master, queue, reader): self.process = process self.master = master self.queue = queue self.buffer = None self.before = None self.after = None self.match = None self.pattern = None self.reader = reader self._timeout = None self._logger = None self._eol = '' def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def logger(self, logger=None, prefix=''): if logger: self._logger = self.Logger(logger, prefix=prefix) return self._logger def timeout(self, timeout=None): if timeout: self._timeout = timeout return self._timeout def eol(self, eol=None): if eol: self._eol = eol return self._eol def close(self, force=True): self.reader['kill_event'].set() os.system('pkill -TERM -P %d' % self.process.pid) if force: self.process.kill() else: self.process.terminate() os.close(self.master) if self._logger: self._logger.write('\n') self._logger.flush() def send(self, data, eol=None): if eol is None: eol = self._eol return self.write(data + eol) def write(self, data): return os.write(self.master, data) def expect(self, pattern, timeout=None, escape=False): self.match = None self.before = None self.after = None if escape: pattern = re.escape(pattern) pattern = re.compile(pattern) if timeout is None: timeout = self._timeout timeleft = timeout while True: start_time = time.time() if self.buffer is not None: self.match = pattern.search(self.buffer, 0) if self.match is not None: self.after = self.buffer[self.match.start():self.match.end()] self.before = self.buffer[:self.match.start()] self.buffer = self.buffer[self.match.end():] break if timeleft < 0: break try: data = self.read(timeout=timeleft, raise_exception=True) except TimeoutError: if self._logger: self._logger.write((self.buffer or '') + '\n') self._logger.flush() exception = ExpectTimeoutError(pattern, timeout, self.buffer) self.buffer = None raise exception timeleft -= (time.time() - start_time) if data: self.buffer = (self.buffer + data) if self.buffer else data if self._logger: self._logger.write((self.before or '') + (self.after or '')) self._logger.flush() if self.match is None: exception = ExpectTimeoutError(pattern, timeout, self.buffer) self.buffer = None raise exception return self.match def read(self, timeout=0, raise_exception=False): data = '' timeleft = timeout try: while timeleft >= 0 : start_time = time.time() data += self.queue.get(timeout=timeleft) if data: break timeleft -= (time.time() - start_time) except Empty: if data: return data if raise_exception: raise TimeoutError(timeout) pass if not data and raise_exception: raise TimeoutError(timeout) return data def spawn(command): master, slave = pty.openpty() process = Popen(command, preexec_fn=os.setsid, stdout=slave, stdin=slave, stderr=slave, bufsize=1) os.close(slave) queue = Queue() reader_kill_event = Event() thread = Thread(target=reader, args=(process, master, queue, reader_kill_event)) thread.daemon = True thread.start() return IO(process, master, queue, reader={'thread':thread, 'kill_event':reader_kill_event}) def reader(process, out, queue, kill_event): while True: try: data = os.read(out, 65536) queue.put(data) except: if kill_event.is_set(): break raise

30.47343

102

0.566265

import os import pty import time import sys import re from threading import Thread, Event from subprocess import Popen from Queue import Queue, Empty class TimeoutError(Exception): def __init__(self, timeout): self.timeout = timeout def __str__(self): return 'Timeout %.3fs' % float(self.timeout) class ExpectTimeoutError(Exception): def __init__(self, pattern, timeout, buffer): self.pattern = pattern self.timeout = timeout self.buffer = buffer def __str__(self): s = 'Timeout %.3fs ' % float(self.timeout) if self.pattern: s += 'for %s ' % repr(self.pattern.pattern) if self.buffer: s += 'buffer %s ' % repr(self.buffer[:]) s += 'or \'%s\'' % ','.join(['%x' % ord(c) for c in self.buffer[:]]) return s class IO(object): class EOF(object): pass class Timeout(object): pass EOF = EOF TIMEOUT = Timeout class Logger(object): def __init__(self, logger, prefix=''): self._logger = logger self._prefix = prefix def write(self, data): self._logger.write(('\n' + data).replace('\n','\n' + self._prefix)) def flush(self): self._logger.flush() def __init__(self, process, master, queue, reader): self.process = process self.master = master self.queue = queue self.buffer = None self.before = None self.after = None self.match = None self.pattern = None self.reader = reader self._timeout = None self._logger = None self._eol = '' def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def logger(self, logger=None, prefix=''): if logger: self._logger = self.Logger(logger, prefix=prefix) return self._logger def timeout(self, timeout=None): if timeout: self._timeout = timeout return self._timeout def eol(self, eol=None): if eol: self._eol = eol return self._eol def close(self, force=True): self.reader['kill_event'].set() os.system('pkill -TERM -P %d' % self.process.pid) if force: self.process.kill() else: self.process.terminate() os.close(self.master) if self._logger: self._logger.write('\n') self._logger.flush() def send(self, data, eol=None): if eol is None: eol = self._eol return self.write(data + eol) def write(self, data): return os.write(self.master, data) def expect(self, pattern, timeout=None, escape=False): self.match = None self.before = None self.after = None if escape: pattern = re.escape(pattern) pattern = re.compile(pattern) if timeout is None: timeout = self._timeout timeleft = timeout while True: start_time = time.time() if self.buffer is not None: self.match = pattern.search(self.buffer, 0) if self.match is not None: self.after = self.buffer[self.match.start():self.match.end()] self.before = self.buffer[:self.match.start()] self.buffer = self.buffer[self.match.end():] break if timeleft < 0: break try: data = self.read(timeout=timeleft, raise_exception=True) except TimeoutError: if self._logger: self._logger.write((self.buffer or '') + '\n') self._logger.flush() exception = ExpectTimeoutError(pattern, timeout, self.buffer) self.buffer = None raise exception timeleft -= (time.time() - start_time) if data: self.buffer = (self.buffer + data) if self.buffer else data if self._logger: self._logger.write((self.before or '') + (self.after or '')) self._logger.flush() if self.match is None: exception = ExpectTimeoutError(pattern, timeout, self.buffer) self.buffer = None raise exception return self.match def read(self, timeout=0, raise_exception=False): data = '' timeleft = timeout try: while timeleft >= 0 : start_time = time.time() data += self.queue.get(timeout=timeleft) if data: break timeleft -= (time.time() - start_time) except Empty: if data: return data if raise_exception: raise TimeoutError(timeout) pass if not data and raise_exception: raise TimeoutError(timeout) return data def spawn(command): master, slave = pty.openpty() process = Popen(command, preexec_fn=os.setsid, stdout=slave, stdin=slave, stderr=slave, bufsize=1) os.close(slave) queue = Queue() reader_kill_event = Event() thread = Thread(target=reader, args=(process, master, queue, reader_kill_event)) thread.daemon = True thread.start() return IO(process, master, queue, reader={'thread':thread, 'kill_event':reader_kill_event}) def reader(process, out, queue, kill_event): while True: try: data = os.read(out, 65536) queue.put(data) except: if kill_event.is_set(): break raise

true

f71b32db7390644ae31d4b98d84b70883c847091

1,777

py

Python

generate/uk.py

mczub/because-moe

d57164399832e32f505a081d7196e0f3828a6e35

[ "Unlicense", "MIT" ]

78

2015-09-09T00:48:19.000Z

2022-02-25T14:18:46.000Z

generate/uk.py

mczub/because-moe

d57164399832e32f505a081d7196e0f3828a6e35

[ "Unlicense", "MIT" ]

15

2015-09-09T03:56:29.000Z

2020-01-03T07:18:50.000Z

generate/uk.py

mczub/because-moe

d57164399832e32f505a081d7196e0f3828a6e35

[ "Unlicense", "MIT" ]

22

2015-09-09T03:05:37.000Z

2021-07-24T07:35:59.000Z

import sys sys.path.append("site-packages") import json import string from unidecode import unidecode from urllib import parse from azure.storage.blob import BlockBlobService from datetime import datetime import animesources indexedShows = {} shows = [] with open('title-map.json') as titlemap_file: titlemap = json.load(titlemap_file) with open('multi-season.json') as multiseason_file: multiseason = json.load(multiseason_file) with open('azure.json') as azure_file: azure_storage = json.load(azure_file) azure_blob = BlockBlobService(account_name=azure_storage['account'], account_key=azure_storage['key']) with open('proxies.json') as proxies_file: proxy_data = json.load(proxies_file) proxy = proxy_data['uk'] sources = [ animesources.Crunchyroll(titlemap, multiseason, 'uk', proxy), animesources.Funimation(titlemap, multiseason, 'gb', proxy), animesources.Netflix(titlemap, multiseason, 'uk', proxy), animesources.HiDive(titlemap, multiseason, 'uk', proxy), animesources.AmazonPrime(titlemap, multiseason, 'uk', proxy) ] for source in sources: source.UpdateShowList(indexedShows) print(source.GetName() + ': ' + str(len(indexedShows))) shows = indexedShows.values() with open('alternates.json') as alternates_file: alternates = json.load(alternates_file) for alternate in alternates: match_index = next((i for i, x in enumerate(shows) if animesources.compare(x['name'], alternate)), False) if (match_index): shows[match_index]['alt'] = alternates[alternate] shows = sorted(shows, key = lambda show: show['name'].lower()) blob = {"lastUpdated": datetime.utcnow().isoformat(), "shows": shows} out_file = open('uk.json', 'w') json.dump(blob, out_file) out_file.close() azure_blob.create_blob_from_path( 'assets', 'uk.json', 'uk.json' ) print('done')

34.843137

106

0.758582

import sys sys.path.append("site-packages") import json import string from unidecode import unidecode from urllib import parse from azure.storage.blob import BlockBlobService from datetime import datetime import animesources indexedShows = {} shows = [] with open('title-map.json') as titlemap_file: titlemap = json.load(titlemap_file) with open('multi-season.json') as multiseason_file: multiseason = json.load(multiseason_file) with open('azure.json') as azure_file: azure_storage = json.load(azure_file) azure_blob = BlockBlobService(account_name=azure_storage['account'], account_key=azure_storage['key']) with open('proxies.json') as proxies_file: proxy_data = json.load(proxies_file) proxy = proxy_data['uk'] sources = [ animesources.Crunchyroll(titlemap, multiseason, 'uk', proxy), animesources.Funimation(titlemap, multiseason, 'gb', proxy), animesources.Netflix(titlemap, multiseason, 'uk', proxy), animesources.HiDive(titlemap, multiseason, 'uk', proxy), animesources.AmazonPrime(titlemap, multiseason, 'uk', proxy) ] for source in sources: source.UpdateShowList(indexedShows) print(source.GetName() + ': ' + str(len(indexedShows))) shows = indexedShows.values() with open('alternates.json') as alternates_file: alternates = json.load(alternates_file) for alternate in alternates: match_index = next((i for i, x in enumerate(shows) if animesources.compare(x['name'], alternate)), False) if (match_index): shows[match_index]['alt'] = alternates[alternate] shows = sorted(shows, key = lambda show: show['name'].lower()) blob = {"lastUpdated": datetime.utcnow().isoformat(), "shows": shows} out_file = open('uk.json', 'w') json.dump(blob, out_file) out_file.close() azure_blob.create_blob_from_path( 'assets', 'uk.json', 'uk.json' ) print('done')

true

f71b33566c8e0a884e4d1704ac06c8583ef46398

7,939

py

Python

tests/test_storage.py

gregdan3/limits

f2c693b9009afe27c9ecbb94492455ad470127f1

[ "MIT" ]

null

tests/test_storage.py

gregdan3/limits

f2c693b9009afe27c9ecbb94492455ad470127f1

[ "MIT" ]

null

tests/test_storage.py

gregdan3/limits

f2c693b9009afe27c9ecbb94492455ad470127f1

[ "MIT" ]

null

import time import pytest from limits.errors import ConfigurationError from limits.storage import ( MemcachedStorage, MemoryStorage, MongoDBStorage, RedisClusterStorage, RedisSentinelStorage, RedisStorage, Storage, storage_from_string, ) from limits.strategies import MovingWindowRateLimiter class TestBaseStorage: @pytest.mark.parametrize( "uri, args, expected_instance, fixture", [ ("memory://", {}, MemoryStorage, None), pytest.param( "redis://localhost:7379", {}, RedisStorage, pytest.lazy_fixture("redis_basic"), marks=pytest.mark.redis, ), pytest.param( "redis+unix:///tmp/limits.redis.sock", {}, RedisStorage, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "redis+unix://:password/tmp/limits.redis.sock", {}, RedisStorage, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "memcached://localhost:22122", {}, MemcachedStorage, pytest.lazy_fixture("memcached"), marks=pytest.mark.memcached, ), pytest.param( "memcached://localhost:22122,localhost:22123", {}, MemcachedStorage, pytest.lazy_fixture("memcached_cluster"), marks=pytest.mark.memcached, ), pytest.param( "memcached:///tmp/limits.memcached.sock", {}, MemcachedStorage, pytest.lazy_fixture("memcached_uds"), marks=pytest.mark.memcached, ), pytest.param( "redis+sentinel://localhost:26379", {"service_name": "localhost-redis-sentinel"}, RedisSentinelStorage, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://localhost:26379/localhost-redis-sentinel", {}, RedisSentinelStorage, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://:sekret@localhost:26379/localhost-redis-sentinel", {}, RedisSentinelStorage, pytest.lazy_fixture("redis_sentinel_auth"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+cluster://localhost:7001/", {}, RedisClusterStorage, pytest.lazy_fixture("redis_cluster"), marks=pytest.mark.redis_cluster, ), pytest.param( "mongodb://localhost:37017/", {}, MongoDBStorage, pytest.lazy_fixture("mongodb"), marks=pytest.mark.mongodb, ), ], ) def test_storage_string(self, uri, args, expected_instance, fixture): assert isinstance(storage_from_string(uri, **args), expected_instance) @pytest.mark.parametrize( "uri, args", [("blah://", {}), ("redis+sentinel://localhost:26379", {})] ) def test_invalid_storage_string(self, uri, args): with pytest.raises(ConfigurationError): storage_from_string(uri, **args) @pytest.mark.parametrize( "uri, args, fixture", [ ("memory://", {}, None), pytest.param( "redis://localhost:7379", {}, pytest.lazy_fixture("redis_basic"), marks=pytest.mark.redis, ), pytest.param( "redis+unix:///tmp/limits.redis.sock", {}, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "redis+unix://:password/tmp/limits.redis.sock", {}, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "memcached://localhost:22122", {}, pytest.lazy_fixture("memcached"), marks=pytest.mark.memcached, ), pytest.param( "memcached://localhost:22122,localhost:22123", {}, pytest.lazy_fixture("memcached_cluster"), marks=pytest.mark.memcached, ), pytest.param( "memcached:///tmp/limits.memcached.sock", {}, pytest.lazy_fixture("memcached_uds"), marks=pytest.mark.memcached, ), pytest.param( "redis+sentinel://localhost:26379", {"service_name": "localhost-redis-sentinel"}, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://localhost:26379/localhost-redis-sentinel", {}, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://:sekret@localhost:36379/localhost-redis-sentinel", {}, pytest.lazy_fixture("redis_sentinel_auth"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+cluster://localhost:7001/", {}, pytest.lazy_fixture("redis_cluster"), marks=pytest.mark.redis_cluster, ), pytest.param( "mongodb://localhost:37017/", {}, pytest.lazy_fixture("mongodb"), marks=pytest.mark.mongodb, ), ], ) def test_storage_check(self, uri, args, fixture): assert storage_from_string(uri, **args).check() def test_pluggable_storage_no_moving_window(self): class MyStorage(Storage): STORAGE_SCHEME = ["mystorage"] def incr(self, key, expiry, elastic_expiry=False): return def get(self, key): return 0 def get_expiry(self, key): return time.time() def reset(self): return def check(self): return def clear(self): return storage = storage_from_string("mystorage://") assert isinstance(storage, MyStorage) with pytest.raises(NotImplementedError): MovingWindowRateLimiter(storage) def test_pluggable_storage_moving_window(self): class MyStorage(Storage): STORAGE_SCHEME = ["mystorage"] def incr(self, key, expiry, elastic_expiry=False): return def get(self, key): return 0 def get_expiry(self, key): return time.time() def reset(self): return def check(self): return def clear(self): return def acquire_entry(self, *a, **k): return True def get_moving_window(self, *a, **k): return (time.time(), 1) storage = storage_from_string("mystorage://") assert isinstance(storage, MyStorage) MovingWindowRateLimiter(storage)

32.272358

84

0.493513

import time import pytest from limits.errors import ConfigurationError from limits.storage import ( MemcachedStorage, MemoryStorage, MongoDBStorage, RedisClusterStorage, RedisSentinelStorage, RedisStorage, Storage, storage_from_string, ) from limits.strategies import MovingWindowRateLimiter class TestBaseStorage: @pytest.mark.parametrize( "uri, args, expected_instance, fixture", [ ("memory://", {}, MemoryStorage, None), pytest.param( "redis://localhost:7379", {}, RedisStorage, pytest.lazy_fixture("redis_basic"), marks=pytest.mark.redis, ), pytest.param( "redis+unix:///tmp/limits.redis.sock", {}, RedisStorage, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "redis+unix://:password/tmp/limits.redis.sock", {}, RedisStorage, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "memcached://localhost:22122", {}, MemcachedStorage, pytest.lazy_fixture("memcached"), marks=pytest.mark.memcached, ), pytest.param( "memcached://localhost:22122,localhost:22123", {}, MemcachedStorage, pytest.lazy_fixture("memcached_cluster"), marks=pytest.mark.memcached, ), pytest.param( "memcached:///tmp/limits.memcached.sock", {}, MemcachedStorage, pytest.lazy_fixture("memcached_uds"), marks=pytest.mark.memcached, ), pytest.param( "redis+sentinel://localhost:26379", {"service_name": "localhost-redis-sentinel"}, RedisSentinelStorage, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://localhost:26379/localhost-redis-sentinel", {}, RedisSentinelStorage, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://:sekret@localhost:26379/localhost-redis-sentinel", {}, RedisSentinelStorage, pytest.lazy_fixture("redis_sentinel_auth"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+cluster://localhost:7001/", {}, RedisClusterStorage, pytest.lazy_fixture("redis_cluster"), marks=pytest.mark.redis_cluster, ), pytest.param( "mongodb://localhost:37017/", {}, MongoDBStorage, pytest.lazy_fixture("mongodb"), marks=pytest.mark.mongodb, ), ], ) def test_storage_string(self, uri, args, expected_instance, fixture): assert isinstance(storage_from_string(uri, **args), expected_instance) @pytest.mark.parametrize( "uri, args", [("blah://", {}), ("redis+sentinel://localhost:26379", {})] ) def test_invalid_storage_string(self, uri, args): with pytest.raises(ConfigurationError): storage_from_string(uri, **args) @pytest.mark.parametrize( "uri, args, fixture", [ ("memory://", {}, None), pytest.param( "redis://localhost:7379", {}, pytest.lazy_fixture("redis_basic"), marks=pytest.mark.redis, ), pytest.param( "redis+unix:///tmp/limits.redis.sock", {}, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "redis+unix://:password/tmp/limits.redis.sock", {}, pytest.lazy_fixture("redis_uds"), marks=pytest.mark.redis, ), pytest.param( "memcached://localhost:22122", {}, pytest.lazy_fixture("memcached"), marks=pytest.mark.memcached, ), pytest.param( "memcached://localhost:22122,localhost:22123", {}, pytest.lazy_fixture("memcached_cluster"), marks=pytest.mark.memcached, ), pytest.param( "memcached:///tmp/limits.memcached.sock", {}, pytest.lazy_fixture("memcached_uds"), marks=pytest.mark.memcached, ), pytest.param( "redis+sentinel://localhost:26379", {"service_name": "localhost-redis-sentinel"}, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://localhost:26379/localhost-redis-sentinel", {}, pytest.lazy_fixture("redis_sentinel"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+sentinel://:sekret@localhost:36379/localhost-redis-sentinel", {}, pytest.lazy_fixture("redis_sentinel_auth"), marks=pytest.mark.redis_sentinel, ), pytest.param( "redis+cluster://localhost:7001/", {}, pytest.lazy_fixture("redis_cluster"), marks=pytest.mark.redis_cluster, ), pytest.param( "mongodb://localhost:37017/", {}, pytest.lazy_fixture("mongodb"), marks=pytest.mark.mongodb, ), ], ) def test_storage_check(self, uri, args, fixture): assert storage_from_string(uri, **args).check() def test_pluggable_storage_no_moving_window(self): class MyStorage(Storage): STORAGE_SCHEME = ["mystorage"] def incr(self, key, expiry, elastic_expiry=False): return def get(self, key): return 0 def get_expiry(self, key): return time.time() def reset(self): return def check(self): return def clear(self): return storage = storage_from_string("mystorage://") assert isinstance(storage, MyStorage) with pytest.raises(NotImplementedError): MovingWindowRateLimiter(storage) def test_pluggable_storage_moving_window(self): class MyStorage(Storage): STORAGE_SCHEME = ["mystorage"] def incr(self, key, expiry, elastic_expiry=False): return def get(self, key): return 0 def get_expiry(self, key): return time.time() def reset(self): return def check(self): return def clear(self): return def acquire_entry(self, *a, **k): return True def get_moving_window(self, *a, **k): return (time.time(), 1) storage = storage_from_string("mystorage://") assert isinstance(storage, MyStorage) MovingWindowRateLimiter(storage)

true

f71b336c74bf785c71596fc3f4e1c0603495a240

37,268

py

Python

desktop/libs/indexer/src/indexer/indexers/sql_tests.py

taklwu/hue

db661408f8fd206557b3d98670cf5edc4d52f869

[ "Apache-2.0" ]

1

2020-06-22T10:20:52.000Z

desktop/libs/indexer/src/indexer/indexers/sql_tests.py

taklwu/hue

db661408f8fd206557b3d98670cf5edc4d52f869

[ "Apache-2.0" ]

null

desktop/libs/indexer/src/indexer/indexers/sql_tests.py

taklwu/hue

db661408f8fd206557b3d98670cf5edc4d52f869

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Licensed to Cloudera, Inc. under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. Cloudera, Inc. licenses this file # to you 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. from builtins import object import json from mock import patch, Mock, MagicMock from nose.tools import assert_equal, assert_true from desktop.lib.django_test_util import make_logged_in_client from useradmin.models import User from indexer.indexers.sql import SQLIndexer class MockRequest(object): def __init__(self, fs=None, user=None): self.fs = fs if fs is not None else MockFs() if user is None: self.c = make_logged_in_client(username='test_importer', is_superuser=False) self.user = User.objects.get(username='test_importer') else: self.user = user class MockFs(object): def __init__(self, path=None): self.path = {'isDir': False, 'split': ('/A', 'a'), 'listdir': ['/A'], 'parent_path': '/A'} if path is None else path def isdir(self, path): return self.path['isDir'] def split(self, path): return self.path['split'] def listdir(self, path): return self.path['listdir'] def parent_path(self, path): return self.path['parent_path'] def stats(self, path): return {"mode": 0o0777} def test_generate_create_text_table_with_data_partition(): source = {u'sourceType': 'hive', u'sampleCols': [{u'operations': [], u'comment': u'', u'name': u'customers.id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.name', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.email_preferences', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.addresses', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.orders', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'name': u'', u'inputFormat': u'file', u'format': {u'status': 0, u'fieldSeparator': u',', u'hasHeader': True, u'quoteChar': u'"', u'recordSeparator': u'\\n', u'type': u'csv'}, u'defaultName': u'default.customer_stats', u'show': True, u'tableName': u'', u'sample': [], u'apiHelperType': u'hive', u'inputFormatsAll': [{u'name': u'File', u'value': u'file'}, {u'name': u'Manually', u'value': u'manual'}, {u'name': u'SQL Query', u'value': u'query'}, {u'name': u'Table', u'value': u'table'}], u'query': u'', u'databaseName': u'default', u'table': u'', u'inputFormats': [{u'name': u'File', u'value': u'file'}, {u'name': u'Manually', u'value': u'manual'}, {u'name': u'SQL Query', u'value': u'query'}, {u'name': u'Table', u'value': u'table'}], u'path': u'/user/romain/customer_stats.csv', u'draggedQuery': u'', u'inputFormatsManual': [{u'name': u'Manually', u'value': u'manual'}], u'isObjectStore': False} destination = {u'isTransactional': False, u'isInsertOnly': False, u'sourceType': 'hive', u'KUDU_DEFAULT_PARTITION_COLUMN': {u'int_val': 16, u'name': u'HASH', u'columns': [], u'range_partitions': [{u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}]}, u'isTargetChecking': False, u'tableName': u'customer_stats', u'outputFormatsList': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr index', u'value': u'index'}, {u'name': u'File', u'value': u'file'}, {u'name': u'Database', u'value': u'database'}], u'customRegexp': u'', u'isTargetExisting': False, u'partitionColumns': [{u'operations': [], u'comment': u'', u'name': u'new_field_1', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': True, u'length': 100, u'partitionValue': u'AAA', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'useCustomDelimiters': False, u'apiHelperType': u'hive', u'kuduPartitionColumns': [], u'outputFormats': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr index', u'value': u'index'}], u'customMapDelimiter': u'\\003', u'showProperties': False, u'useDefaultLocation': True, u'description': u'', u'primaryKeyObjects': [], u'customFieldDelimiter': u',', u'existingTargetUrl': u'', u'importData': True, u'databaseName': u'default', u'KUDU_DEFAULT_RANGE_PARTITION_COLUMN': {u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}, u'primaryKeys': [], u'outputFormat': u'table', u'nonDefaultLocation': u'/user/romain/customer_stats.csv', u'name': u'default.customer_stats', u'tableFormat': u'text', 'ouputFormat': u'table', u'bulkColumnNames': u'customers.id,customers.name,customers.email_preferences,customers.addresses,customers.orders', u'columns': [{u'operations': [], u'comment': u'', u'name': u'customers.id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.name', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.email_preferences', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.addresses', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.orders', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'hasHeader': True, u'tableFormats': [{u'name': u'Text', u'value': u'text'}, {u'name': u'Parquet', u'value': u'parquet'}, {u'name': u'Kudu', u'value': u'kudu'}, {u'name': u'Csv', u'value': u'csv'}, {u'name': u'Avro', u'value': u'avro'}, {u'name': u'Json', u'value': u'json'}, {u'name': u'Regexp', u'value': u'regexp'}, {u'name': u'ORC', u'value': u'orc'}], u'customCollectionDelimiter': u'\\002'} request = MockRequest(fs=MockFs()) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''USE default;''' in sql, sql) assert_true('''CREATE TABLE `default`.`customer_stats` ( `customers.id` bigint , `customers.name` string , `customers.email_preferences` string , `customers.addresses` string , `customers.orders` string ) PARTITIONED BY ( `new_field_1` string ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY '\\002' MAP KEYS TERMINATED BY '\\003' STORED AS TextFile TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1") ;''' in sql, sql) assert_true('''LOAD DATA INPATH '/user/romain/customer_stats.csv' INTO TABLE `default`.`customer_stats` PARTITION (new_field_1='AAA');''' in sql, sql) def test_generate_create_kudu_table_with_data(): source = {u'sourceType': 'impala', u'apiHelperType': 'hive', u'sampleCols': [], u'name': u'', u'inputFormat': u'file', u'format': {u'quoteChar': u'"', u'recordSeparator': u'\\n', u'type': u'csv', u'hasHeader': True, u'fieldSeparator': u','}, u'show': True, u'tableName': u'', u'sample': [], u'defaultName': u'index_data', u'query': u'', u'databaseName': u'default', u'table': u'', u'inputFormats': [{u'name': u'File', u'value': u'file'}, {u'name': u'Manually', u'value': u'manual'}], u'path': u'/user/admin/index_data.csv', u'draggedQuery': u'', u'isObjectStore': False} destination = {u'isTransactional': False, u'isInsertOnly': False, u'sourceType': 'impala', u'KUDU_DEFAULT_PARTITION_COLUMN': {u'int_val': 16, u'name': u'HASH', u'columns': [], u'range_partitions': [{u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}]}, u'tableName': u'index_data', u'outputFormatsList': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr+index', u'value': u'index'}, {u'name': u'File', u'value': u'file'}, {u'name': u'Database', u'value': u'database'}], u'customRegexp': u'', u'isTargetExisting': False, u'partitionColumns': [], u'useCustomDelimiters': True, u'kuduPartitionColumns': [{u'int_val': 16, u'name': u'HASH', u'columns': [u'id'], u'range_partitions': [{u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}]}], u'outputFormats': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr+index', u'value': u'index'}], u'customMapDelimiter': None, u'showProperties': False, u'useDefaultLocation': True, u'description': u'Big Data', u'primaryKeyObjects': [{u'operations': [], u'comment': u'', u'name': u'id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'customFieldDelimiter': u',', u'existingTargetUrl': u'', u'importData': True, u'databaseName': u'default', u'KUDU_DEFAULT_RANGE_PARTITION_COLUMN': {u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}, u'primaryKeys': [u'id'], u'outputFormat': u'table', u'nonDefaultLocation': u'/user/admin/index_data.csv', u'name': u'index_data', u'tableFormat': u'kudu', u'bulkColumnNames': u'business_id,cool,date,funny,id,stars,text,type,useful,user_id,name,full_address,latitude,longitude,neighborhoods,open,review_count,state', u'columns': [{u'operations': [], u'comment': u'', u'name': u'business_id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'cool', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': False}, {u'operations': [], u'comment': u'', u'name': u'date', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'funny', u'level': 0, u'scale':4, u'precision':10, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'decimal', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'stars', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'text', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'type', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'useful', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'user_id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'name', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'full_address', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'latitude', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'double', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'longitude', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'double', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'neighborhoods', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'open', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'review_count', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'state', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'hasHeader': True, u'tableFormats': [{u'name': u'Text', u'value': u'text'}, {u'name': u'Parquet', u'value': u'parquet'}, {u'name': u'Json', u'value': u'json'}, {u'name': u'Kudu', u'value': u'kudu'}, {u'name': u'Avro', u'value': u'avro'}, {u'name': u'Regexp', u'value': u'regexp'}, {u'name': u'RCFile', u'value': u'rcfile'}, {u'name': u'ORC', u'value': u'orc'}, {u'name': u'SequenceFile', u'value': u'sequencefile'}], u'customCollectionDelimiter': None} request = MockRequest(fs=MockFs()) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''DROP TABLE IF EXISTS `default`.`hue__tmp_index_data`;''' in sql, sql) assert_true('''CREATE EXTERNAL TABLE `default`.`hue__tmp_index_data` ( `business_id` string , `cool` bigint , `date` string , `funny` decimal(10, 4) , `id` string , `stars` bigint , `text` string , `type` string , `useful` bigint , `user_id` string , `name` string , `full_address` string , `latitude` double , `longitude` double , `neighborhoods` string , `open` string , `review_count` bigint , `state` string ) COMMENT "Big Data" ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TextFile LOCATION '/A' TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1")''' in sql, sql) assert_true('''CREATE TABLE `default`.`index_data` COMMENT "Big Data" PRIMARY KEY (id) PARTITION BY HASH PARTITIONS 16 STORED AS kudu TBLPROPERTIES( 'kudu.num_tablet_replicas' = '1' ) AS SELECT `id`, `business_id`, `date`, `funny`, `stars`, `text`, `type`, `useful`, `user_id`, `name`, `full_address`, `latitude`, `longitude`, `neighborhoods`, `open`, `review_count`, `state` FROM `default`.`hue__tmp_index_data`;''' in sql, sql) def test_generate_create_parquet_table(): source = json.loads('''{"sourceType": "hive", "name":"","sample":[["Bank Of America","3000000.0","US","Miami","37.6801986694","-121.92150116"],["Citi Bank","2800000.0","US","Richmond","37.5242004395","-77.4932022095"],["Deutsche Bank","2600000.0","US","Corpus Christi","40.7807998657","-73.9772033691"],["Thomson Reuters","2400000.0","US","Albany","35.7976989746","-78.6252975464"],["OpenX","2200000.0","US","Des Moines","40.5411987305","-119.586898804"]],"sampleCols":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"inputFormat":"file","inputFormatsAll":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"inputFormatsManual":[{"value":"manual","name":"Manually"}],"inputFormats":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"path":"/user/hue/data/query-hive-360.csv","isObjectStore":false,"table":"","tableName":"","databaseName":"default","apiHelperType":"hive","query":"","draggedQuery":"","format":{"type":"csv","fieldSeparator":",","recordSeparator":"\\n","quoteChar":"\\"","hasHeader":true,"status":0},"show":true,"defaultName":"default.query-hive-360"}''') destination = json.loads('''{"isTransactional": false, "isInsertOnly": false, "sourceType": "hive", "name":"default.parquet_table","apiHelperType":"hive","description":"","outputFormat":"table","outputFormatsList":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"},{"name":"File","value":"file"},{"name":"Database","value":"database"}],"outputFormats":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"}],"columns":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"bulkColumnNames":"acct_client,tran_amount,tran_country_cd,vrfcn_city,vrfcn_city_lat,vrfcn_city_lon","showProperties":false,"isTargetExisting":false,"isTargetChecking":false,"existingTargetUrl":"","tableName":"parquet_table","databaseName":"default","tableFormat":"parquet","KUDU_DEFAULT_RANGE_PARTITION_COLUMN":{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="},"KUDU_DEFAULT_PARTITION_COLUMN":{"columns":[],"range_partitions":[{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="}],"name":"HASH","int_val":16},"tableFormats":[{"value":"text","name":"Text"},{"value":"parquet","name":"Parquet"},{"value":"kudu","name":"Kudu"},{"value":"csv","name":"Csv"},{"value":"avro","name":"Avro"},{"value":"json","name":"Json"},{"value":"regexp","name":"Regexp"},{"value":"orc","name":"ORC"}],"partitionColumns":[],"kuduPartitionColumns":[],"primaryKeys":[],"primaryKeyObjects":[],"importData":true,"useDefaultLocation":true,"nonDefaultLocation":"/user/hue/data/query-hive-360.csv","hasHeader":true,"useCustomDelimiters":false,"customFieldDelimiter":",","customCollectionDelimiter":"\\\\002","customMapDelimiter":"\\\\003","customRegexp":""}''') path = {'isDir': False, 'split': ('/user/hue/data', 'query-hive-360.csv'), 'listdir': ['/user/hue/data']} request = MockRequest(fs=MockFs(path=path)) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''USE default;''' in sql, sql) assert_true('''CREATE EXTERNAL TABLE `default`.`hue__tmp_parquet_table` ( `acct_client` string , `tran_amount` double , `tran_country_cd` string , `vrfcn_city` string , `vrfcn_city_lat` double , `vrfcn_city_lon` double ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY '\\002' MAP KEYS TERMINATED BY '\\003' STORED AS TextFile LOCATION '/user/hue/data' TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1") ;''' in sql, sql) assert_true('''CREATE TABLE `default`.`parquet_table` STORED AS parquet AS SELECT * FROM `default`.`hue__tmp_parquet_table`; ''' in sql, sql) assert_true('''DROP TABLE IF EXISTS `default`.`hue__tmp_parquet_table`; ''' in sql, sql) def test_generate_create_orc_table_transactional(): source = json.loads('''{"sourceType": "hive", "name":"","sample":[["Bank Of America","3000000.0","US","Miami","37.6801986694","-121.92150116"],["Citi Bank","2800000.0","US","Richmond","37.5242004395","-77.4932022095"],["Deutsche Bank","2600000.0","US","Corpus Christi","40.7807998657","-73.9772033691"],["Thomson Reuters","2400000.0","US","Albany","35.7976989746","-78.6252975464"],["OpenX","2200000.0","US","Des Moines","40.5411987305","-119.586898804"]],"sampleCols":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"inputFormat":"file","inputFormatsAll":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"inputFormatsManual":[{"value":"manual","name":"Manually"}],"inputFormats":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"path":"/user/hue/data/query-hive-360.csv","isObjectStore":false,"table":"","tableName":"","databaseName":"default","apiHelperType":"hive","query":"","draggedQuery":"","format":{"type":"csv","fieldSeparator":",","recordSeparator":"\\n","quoteChar":"\\"","hasHeader":true,"status":0},"show":true,"defaultName":"default.query-hive-360"}''') destination = json.loads('''{"isTransactional": true, "isInsertOnly": true, "sourceType": "hive", "name":"default.parquet_table","apiHelperType":"hive","description":"","outputFormat":"table","outputFormatsList":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"},{"name":"File","value":"file"},{"name":"Database","value":"database"}],"outputFormats":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"}],"columns":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"bulkColumnNames":"acct_client,tran_amount,tran_country_cd,vrfcn_city,vrfcn_city_lat,vrfcn_city_lon","showProperties":false,"isTargetExisting":false,"isTargetChecking":false,"existingTargetUrl":"","tableName":"parquet_table","databaseName":"default","tableFormat":"orc","KUDU_DEFAULT_RANGE_PARTITION_COLUMN":{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="},"KUDU_DEFAULT_PARTITION_COLUMN":{"columns":[],"range_partitions":[{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="}],"name":"HASH","int_val":16},"tableFormats":[{"value":"text","name":"Text"},{"value":"parquet","name":"Parquet"},{"value":"kudu","name":"Kudu"},{"value":"csv","name":"Csv"},{"value":"avro","name":"Avro"},{"value":"json","name":"Json"},{"value":"regexp","name":"Regexp"},{"value":"orc","name":"ORC"}],"partitionColumns":[],"kuduPartitionColumns":[],"primaryKeys":[],"primaryKeyObjects":[],"importData":true,"useDefaultLocation":true,"nonDefaultLocation":"/user/hue/data/query-hive-360.csv","hasHeader":true,"useCustomDelimiters":false,"customFieldDelimiter":",","customCollectionDelimiter":"\\\\002","customMapDelimiter":"\\\\003","customRegexp":""}''') path = {'isDir': False, 'split': ('/user/hue/data', 'query-hive-360.csv'), 'listdir': ['/user/hue/data']} request = MockRequest(fs=MockFs(path=path)) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''USE default;''' in sql, sql) assert_true('''CREATE EXTERNAL TABLE `default`.`hue__tmp_parquet_table` ( `acct_client` string , `tran_amount` double , `tran_country_cd` string , `vrfcn_city` string , `vrfcn_city_lat` double , `vrfcn_city_lon` double ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY '\\002' MAP KEYS TERMINATED BY '\\003' STORED AS TextFile LOCATION '/user/hue/data' TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1") ;''' in sql, sql) assert_true('''CREATE TABLE `default`.`parquet_table` STORED AS orc TBLPROPERTIES("transactional"="true", "transactional_properties"="insert_only") AS SELECT * FROM `default`.`hue__tmp_parquet_table`; ''' in sql, sql) assert_true('''DROP TABLE IF EXISTS `default`.`hue__tmp_parquet_table`; ''' in sql, sql) def test_generate_create_empty_kudu_table(): source = json.loads('''{"sourceType": "impala", "apiHelperType": "impala", "path": "", "inputFormat": "manual"}''') destination = json.loads('''{"isTransactional": false, "isInsertOnly": false, "sourceType": "impala", "name":"default.manual_empty_kudu","apiHelperType":"impala","description":"","outputFormat":"table","columns":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"bulkColumnNames":"acct_client,tran_amount,tran_country_cd,vrfcn_city,vrfcn_city_lat,vrfcn_city_lon","showProperties":false,"isTargetExisting":false,"isTargetChecking":false,"existingTargetUrl":"","tableName":"manual_kudu_table","databaseName":"default","tableFormat":"kudu","KUDU_DEFAULT_RANGE_PARTITION_COLUMN":{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="},"KUDU_DEFAULT_PARTITION_COLUMN":{"columns":[],"range_partitions":[{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="}],"name":"HASH","int_val":16},"tableFormats":[{"value":"text","name":"Text"},{"value":"parquet","name":"Parquet"},{"value":"kudu","name":"Kudu"},{"value":"csv","name":"Csv"},{"value":"avro","name":"Avro"},{"value":"json","name":"Json"},{"value":"regexp","name":"Regexp"},{"value":"orc","name":"ORC"}],"partitionColumns":[],"kuduPartitionColumns":[],"primaryKeys": ["acct_client"],"primaryKeyObjects":[],"importData":false,"useDefaultLocation":true,"nonDefaultLocation":"/user/hue/data/query-hive-360.csv","hasHeader":false,"useCustomDelimiters":false,"customFieldDelimiter":",","customCollectionDelimiter":"\\\\002","customMapDelimiter":"\\\\003","customRegexp":""}''') path = {'isDir': False, 'split': ('/user/hue/data', 'query-hive-360.csv'), 'listdir': ['/user/hue/data']} request = MockRequest(fs=MockFs(path=path)) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''CREATE TABLE `default`.`manual_empty_kudu` ( `acct_client` string , `tran_amount` double , `tran_country_cd` string , `vrfcn_city` string , `vrfcn_city_lat` double , `vrfcn_city_lon` double , PRIMARY KEY (acct_client) ) STORED AS kudu TBLPROPERTIES("transactional" = "false") ;''' in sql, sql)

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from builtins import object import json from mock import patch, Mock, MagicMock from nose.tools import assert_equal, assert_true from desktop.lib.django_test_util import make_logged_in_client from useradmin.models import User from indexer.indexers.sql import SQLIndexer class MockRequest(object): def __init__(self, fs=None, user=None): self.fs = fs if fs is not None else MockFs() if user is None: self.c = make_logged_in_client(username='test_importer', is_superuser=False) self.user = User.objects.get(username='test_importer') else: self.user = user class MockFs(object): def __init__(self, path=None): self.path = {'isDir': False, 'split': ('/A', 'a'), 'listdir': ['/A'], 'parent_path': '/A'} if path is None else path def isdir(self, path): return self.path['isDir'] def split(self, path): return self.path['split'] def listdir(self, path): return self.path['listdir'] def parent_path(self, path): return self.path['parent_path'] def stats(self, path): return {"mode": 0o0777} def test_generate_create_text_table_with_data_partition(): source = {u'sourceType': 'hive', u'sampleCols': [{u'operations': [], u'comment': u'', u'name': u'customers.id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.name', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.email_preferences', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.addresses', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.orders', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'name': u'', u'inputFormat': u'file', u'format': {u'status': 0, u'fieldSeparator': u',', u'hasHeader': True, u'quoteChar': u'"', u'recordSeparator': u'\\n', u'type': u'csv'}, u'defaultName': u'default.customer_stats', u'show': True, u'tableName': u'', u'sample': [], u'apiHelperType': u'hive', u'inputFormatsAll': [{u'name': u'File', u'value': u'file'}, {u'name': u'Manually', u'value': u'manual'}, {u'name': u'SQL Query', u'value': u'query'}, {u'name': u'Table', u'value': u'table'}], u'query': u'', u'databaseName': u'default', u'table': u'', u'inputFormats': [{u'name': u'File', u'value': u'file'}, {u'name': u'Manually', u'value': u'manual'}, {u'name': u'SQL Query', u'value': u'query'}, {u'name': u'Table', u'value': u'table'}], u'path': u'/user/romain/customer_stats.csv', u'draggedQuery': u'', u'inputFormatsManual': [{u'name': u'Manually', u'value': u'manual'}], u'isObjectStore': False} destination = {u'isTransactional': False, u'isInsertOnly': False, u'sourceType': 'hive', u'KUDU_DEFAULT_PARTITION_COLUMN': {u'int_val': 16, u'name': u'HASH', u'columns': [], u'range_partitions': [{u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}]}, u'isTargetChecking': False, u'tableName': u'customer_stats', u'outputFormatsList': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr index', u'value': u'index'}, {u'name': u'File', u'value': u'file'}, {u'name': u'Database', u'value': u'database'}], u'customRegexp': u'', u'isTargetExisting': False, u'partitionColumns': [{u'operations': [], u'comment': u'', u'name': u'new_field_1', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': True, u'length': 100, u'partitionValue': u'AAA', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'useCustomDelimiters': False, u'apiHelperType': u'hive', u'kuduPartitionColumns': [], u'outputFormats': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr index', u'value': u'index'}], u'customMapDelimiter': u'\\003', u'showProperties': False, u'useDefaultLocation': True, u'description': u'', u'primaryKeyObjects': [], u'customFieldDelimiter': u',', u'existingTargetUrl': u'', u'importData': True, u'databaseName': u'default', u'KUDU_DEFAULT_RANGE_PARTITION_COLUMN': {u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}, u'primaryKeys': [], u'outputFormat': u'table', u'nonDefaultLocation': u'/user/romain/customer_stats.csv', u'name': u'default.customer_stats', u'tableFormat': u'text', 'ouputFormat': u'table', u'bulkColumnNames': u'customers.id,customers.name,customers.email_preferences,customers.addresses,customers.orders', u'columns': [{u'operations': [], u'comment': u'', u'name': u'customers.id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.name', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.email_preferences', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.addresses', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'customers.orders', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'partitionValue': u'', u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'hasHeader': True, u'tableFormats': [{u'name': u'Text', u'value': u'text'}, {u'name': u'Parquet', u'value': u'parquet'}, {u'name': u'Kudu', u'value': u'kudu'}, {u'name': u'Csv', u'value': u'csv'}, {u'name': u'Avro', u'value': u'avro'}, {u'name': u'Json', u'value': u'json'}, {u'name': u'Regexp', u'value': u'regexp'}, {u'name': u'ORC', u'value': u'orc'}], u'customCollectionDelimiter': u'\\002'} request = MockRequest(fs=MockFs()) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''USE default;''' in sql, sql) assert_true('''CREATE TABLE `default`.`customer_stats` ( `customers.id` bigint , `customers.name` string , `customers.email_preferences` string , `customers.addresses` string , `customers.orders` string ) PARTITIONED BY ( `new_field_1` string ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY '\\002' MAP KEYS TERMINATED BY '\\003' STORED AS TextFile TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1") ;''' in sql, sql) assert_true('''LOAD DATA INPATH '/user/romain/customer_stats.csv' INTO TABLE `default`.`customer_stats` PARTITION (new_field_1='AAA');''' in sql, sql) def test_generate_create_kudu_table_with_data(): source = {u'sourceType': 'impala', u'apiHelperType': 'hive', u'sampleCols': [], u'name': u'', u'inputFormat': u'file', u'format': {u'quoteChar': u'"', u'recordSeparator': u'\\n', u'type': u'csv', u'hasHeader': True, u'fieldSeparator': u','}, u'show': True, u'tableName': u'', u'sample': [], u'defaultName': u'index_data', u'query': u'', u'databaseName': u'default', u'table': u'', u'inputFormats': [{u'name': u'File', u'value': u'file'}, {u'name': u'Manually', u'value': u'manual'}], u'path': u'/user/admin/index_data.csv', u'draggedQuery': u'', u'isObjectStore': False} destination = {u'isTransactional': False, u'isInsertOnly': False, u'sourceType': 'impala', u'KUDU_DEFAULT_PARTITION_COLUMN': {u'int_val': 16, u'name': u'HASH', u'columns': [], u'range_partitions': [{u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}]}, u'tableName': u'index_data', u'outputFormatsList': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr+index', u'value': u'index'}, {u'name': u'File', u'value': u'file'}, {u'name': u'Database', u'value': u'database'}], u'customRegexp': u'', u'isTargetExisting': False, u'partitionColumns': [], u'useCustomDelimiters': True, u'kuduPartitionColumns': [{u'int_val': 16, u'name': u'HASH', u'columns': [u'id'], u'range_partitions': [{u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}]}], u'outputFormats': [{u'name': u'Table', u'value': u'table'}, {u'name': u'Solr+index', u'value': u'index'}], u'customMapDelimiter': None, u'showProperties': False, u'useDefaultLocation': True, u'description': u'Big Data', u'primaryKeyObjects': [{u'operations': [], u'comment': u'', u'name': u'id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'customFieldDelimiter': u',', u'existingTargetUrl': u'', u'importData': True, u'databaseName': u'default', u'KUDU_DEFAULT_RANGE_PARTITION_COLUMN': {u'include_upper_val': u'<=', u'upper_val': 1, u'name': u'VALUES', u'include_lower_val': u'<=', u'lower_val': 0, u'values': [{u'value': u''}]}, u'primaryKeys': [u'id'], u'outputFormat': u'table', u'nonDefaultLocation': u'/user/admin/index_data.csv', u'name': u'index_data', u'tableFormat': u'kudu', u'bulkColumnNames': u'business_id,cool,date,funny,id,stars,text,type,useful,user_id,name,full_address,latitude,longitude,neighborhoods,open,review_count,state', u'columns': [{u'operations': [], u'comment': u'', u'name': u'business_id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'cool', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': False}, {u'operations': [], u'comment': u'', u'name': u'date', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'funny', u'level': 0, u'scale':4, u'precision':10, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'decimal', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'stars', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'text', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'type', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'useful', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'user_id', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'name', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'full_address', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'latitude', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'double', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'longitude', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'double', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'neighborhoods', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'open', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'review_count', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'bigint', u'showProperties': False, u'keep': True}, {u'operations': [], u'comment': u'', u'name': u'state', u'level': 0, u'keyType': u'string', u'required': False, u'nested': [], u'isPartition': False, u'length': 100, u'multiValued': False, u'unique': False, u'type': u'string', u'showProperties': False, u'keep': True}], u'hasHeader': True, u'tableFormats': [{u'name': u'Text', u'value': u'text'}, {u'name': u'Parquet', u'value': u'parquet'}, {u'name': u'Json', u'value': u'json'}, {u'name': u'Kudu', u'value': u'kudu'}, {u'name': u'Avro', u'value': u'avro'}, {u'name': u'Regexp', u'value': u'regexp'}, {u'name': u'RCFile', u'value': u'rcfile'}, {u'name': u'ORC', u'value': u'orc'}, {u'name': u'SequenceFile', u'value': u'sequencefile'}], u'customCollectionDelimiter': None} request = MockRequest(fs=MockFs()) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''DROP TABLE IF EXISTS `default`.`hue__tmp_index_data`;''' in sql, sql) assert_true('''CREATE EXTERNAL TABLE `default`.`hue__tmp_index_data` ( `business_id` string , `cool` bigint , `date` string , `funny` decimal(10, 4) , `id` string , `stars` bigint , `text` string , `type` string , `useful` bigint , `user_id` string , `name` string , `full_address` string , `latitude` double , `longitude` double , `neighborhoods` string , `open` string , `review_count` bigint , `state` string ) COMMENT "Big Data" ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TextFile LOCATION '/A' TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1")''' in sql, sql) assert_true('''CREATE TABLE `default`.`index_data` COMMENT "Big Data" PRIMARY KEY (id) PARTITION BY HASH PARTITIONS 16 STORED AS kudu TBLPROPERTIES( 'kudu.num_tablet_replicas' = '1' ) AS SELECT `id`, `business_id`, `date`, `funny`, `stars`, `text`, `type`, `useful`, `user_id`, `name`, `full_address`, `latitude`, `longitude`, `neighborhoods`, `open`, `review_count`, `state` FROM `default`.`hue__tmp_index_data`;''' in sql, sql) def test_generate_create_parquet_table(): source = json.loads('''{"sourceType": "hive", "name":"","sample":[["Bank Of America","3000000.0","US","Miami","37.6801986694","-121.92150116"],["Citi Bank","2800000.0","US","Richmond","37.5242004395","-77.4932022095"],["Deutsche Bank","2600000.0","US","Corpus Christi","40.7807998657","-73.9772033691"],["Thomson Reuters","2400000.0","US","Albany","35.7976989746","-78.6252975464"],["OpenX","2200000.0","US","Des Moines","40.5411987305","-119.586898804"]],"sampleCols":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"inputFormat":"file","inputFormatsAll":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"inputFormatsManual":[{"value":"manual","name":"Manually"}],"inputFormats":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"path":"/user/hue/data/query-hive-360.csv","isObjectStore":false,"table":"","tableName":"","databaseName":"default","apiHelperType":"hive","query":"","draggedQuery":"","format":{"type":"csv","fieldSeparator":",","recordSeparator":"\\n","quoteChar":"\\"","hasHeader":true,"status":0},"show":true,"defaultName":"default.query-hive-360"}''') destination = json.loads('''{"isTransactional": false, "isInsertOnly": false, "sourceType": "hive", "name":"default.parquet_table","apiHelperType":"hive","description":"","outputFormat":"table","outputFormatsList":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"},{"name":"File","value":"file"},{"name":"Database","value":"database"}],"outputFormats":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"}],"columns":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"bulkColumnNames":"acct_client,tran_amount,tran_country_cd,vrfcn_city,vrfcn_city_lat,vrfcn_city_lon","showProperties":false,"isTargetExisting":false,"isTargetChecking":false,"existingTargetUrl":"","tableName":"parquet_table","databaseName":"default","tableFormat":"parquet","KUDU_DEFAULT_RANGE_PARTITION_COLUMN":{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="},"KUDU_DEFAULT_PARTITION_COLUMN":{"columns":[],"range_partitions":[{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="}],"name":"HASH","int_val":16},"tableFormats":[{"value":"text","name":"Text"},{"value":"parquet","name":"Parquet"},{"value":"kudu","name":"Kudu"},{"value":"csv","name":"Csv"},{"value":"avro","name":"Avro"},{"value":"json","name":"Json"},{"value":"regexp","name":"Regexp"},{"value":"orc","name":"ORC"}],"partitionColumns":[],"kuduPartitionColumns":[],"primaryKeys":[],"primaryKeyObjects":[],"importData":true,"useDefaultLocation":true,"nonDefaultLocation":"/user/hue/data/query-hive-360.csv","hasHeader":true,"useCustomDelimiters":false,"customFieldDelimiter":",","customCollectionDelimiter":"\\\\002","customMapDelimiter":"\\\\003","customRegexp":""}''') path = {'isDir': False, 'split': ('/user/hue/data', 'query-hive-360.csv'), 'listdir': ['/user/hue/data']} request = MockRequest(fs=MockFs(path=path)) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''USE default;''' in sql, sql) assert_true('''CREATE EXTERNAL TABLE `default`.`hue__tmp_parquet_table` ( `acct_client` string , `tran_amount` double , `tran_country_cd` string , `vrfcn_city` string , `vrfcn_city_lat` double , `vrfcn_city_lon` double ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY '\\002' MAP KEYS TERMINATED BY '\\003' STORED AS TextFile LOCATION '/user/hue/data' TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1") ;''' in sql, sql) assert_true('''CREATE TABLE `default`.`parquet_table` STORED AS parquet AS SELECT * FROM `default`.`hue__tmp_parquet_table`; ''' in sql, sql) assert_true('''DROP TABLE IF EXISTS `default`.`hue__tmp_parquet_table`; ''' in sql, sql) def test_generate_create_orc_table_transactional(): source = json.loads('''{"sourceType": "hive", "name":"","sample":[["Bank Of America","3000000.0","US","Miami","37.6801986694","-121.92150116"],["Citi Bank","2800000.0","US","Richmond","37.5242004395","-77.4932022095"],["Deutsche Bank","2600000.0","US","Corpus Christi","40.7807998657","-73.9772033691"],["Thomson Reuters","2400000.0","US","Albany","35.7976989746","-78.6252975464"],["OpenX","2200000.0","US","Des Moines","40.5411987305","-119.586898804"]],"sampleCols":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"inputFormat":"file","inputFormatsAll":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"inputFormatsManual":[{"value":"manual","name":"Manually"}],"inputFormats":[{"value":"file","name":"File"},{"value":"manual","name":"Manually"},{"value":"query","name":"SQL Query"},{"value":"table","name":"Table"}],"path":"/user/hue/data/query-hive-360.csv","isObjectStore":false,"table":"","tableName":"","databaseName":"default","apiHelperType":"hive","query":"","draggedQuery":"","format":{"type":"csv","fieldSeparator":",","recordSeparator":"\\n","quoteChar":"\\"","hasHeader":true,"status":0},"show":true,"defaultName":"default.query-hive-360"}''') destination = json.loads('''{"isTransactional": true, "isInsertOnly": true, "sourceType": "hive", "name":"default.parquet_table","apiHelperType":"hive","description":"","outputFormat":"table","outputFormatsList":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"},{"name":"File","value":"file"},{"name":"Database","value":"database"}],"outputFormats":[{"name":"Table","value":"table"},{"name":"Solr index","value":"index"}],"columns":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"bulkColumnNames":"acct_client,tran_amount,tran_country_cd,vrfcn_city,vrfcn_city_lat,vrfcn_city_lon","showProperties":false,"isTargetExisting":false,"isTargetChecking":false,"existingTargetUrl":"","tableName":"parquet_table","databaseName":"default","tableFormat":"orc","KUDU_DEFAULT_RANGE_PARTITION_COLUMN":{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="},"KUDU_DEFAULT_PARTITION_COLUMN":{"columns":[],"range_partitions":[{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="}],"name":"HASH","int_val":16},"tableFormats":[{"value":"text","name":"Text"},{"value":"parquet","name":"Parquet"},{"value":"kudu","name":"Kudu"},{"value":"csv","name":"Csv"},{"value":"avro","name":"Avro"},{"value":"json","name":"Json"},{"value":"regexp","name":"Regexp"},{"value":"orc","name":"ORC"}],"partitionColumns":[],"kuduPartitionColumns":[],"primaryKeys":[],"primaryKeyObjects":[],"importData":true,"useDefaultLocation":true,"nonDefaultLocation":"/user/hue/data/query-hive-360.csv","hasHeader":true,"useCustomDelimiters":false,"customFieldDelimiter":",","customCollectionDelimiter":"\\\\002","customMapDelimiter":"\\\\003","customRegexp":""}''') path = {'isDir': False, 'split': ('/user/hue/data', 'query-hive-360.csv'), 'listdir': ['/user/hue/data']} request = MockRequest(fs=MockFs(path=path)) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''USE default;''' in sql, sql) assert_true('''CREATE EXTERNAL TABLE `default`.`hue__tmp_parquet_table` ( `acct_client` string , `tran_amount` double , `tran_country_cd` string , `vrfcn_city` string , `vrfcn_city_lat` double , `vrfcn_city_lon` double ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY '\\002' MAP KEYS TERMINATED BY '\\003' STORED AS TextFile LOCATION '/user/hue/data' TBLPROPERTIES("transactional" = "false", "skip.header.line.count" = "1") ;''' in sql, sql) assert_true('''CREATE TABLE `default`.`parquet_table` STORED AS orc TBLPROPERTIES("transactional"="true", "transactional_properties"="insert_only") AS SELECT * FROM `default`.`hue__tmp_parquet_table`; ''' in sql, sql) assert_true('''DROP TABLE IF EXISTS `default`.`hue__tmp_parquet_table`; ''' in sql, sql) def test_generate_create_empty_kudu_table(): source = json.loads('''{"sourceType": "impala", "apiHelperType": "impala", "path": "", "inputFormat": "manual"}''') destination = json.loads('''{"isTransactional": false, "isInsertOnly": false, "sourceType": "impala", "name":"default.manual_empty_kudu","apiHelperType":"impala","description":"","outputFormat":"table","columns":[{"operations":[],"comment":"","nested":[],"name":"acct_client","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_amount","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"tran_country_cd","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"string","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lat","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0},{"operations":[],"comment":"","nested":[],"name":"vrfcn_city_lon","level":0,"keyType":"string","required":false,"precision":10,"keep":true,"isPartition":false,"length":100,"partitionValue":"","multiValued":false,"unique":false,"type":"double","showProperties":false,"scale":0}],"bulkColumnNames":"acct_client,tran_amount,tran_country_cd,vrfcn_city,vrfcn_city_lat,vrfcn_city_lon","showProperties":false,"isTargetExisting":false,"isTargetChecking":false,"existingTargetUrl":"","tableName":"manual_kudu_table","databaseName":"default","tableFormat":"kudu","KUDU_DEFAULT_RANGE_PARTITION_COLUMN":{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="},"KUDU_DEFAULT_PARTITION_COLUMN":{"columns":[],"range_partitions":[{"values":[{"value":""}],"name":"VALUES","lower_val":0,"include_lower_val":"<=","upper_val":1,"include_upper_val":"<="}],"name":"HASH","int_val":16},"tableFormats":[{"value":"text","name":"Text"},{"value":"parquet","name":"Parquet"},{"value":"kudu","name":"Kudu"},{"value":"csv","name":"Csv"},{"value":"avro","name":"Avro"},{"value":"json","name":"Json"},{"value":"regexp","name":"Regexp"},{"value":"orc","name":"ORC"}],"partitionColumns":[],"kuduPartitionColumns":[],"primaryKeys": ["acct_client"],"primaryKeyObjects":[],"importData":false,"useDefaultLocation":true,"nonDefaultLocation":"/user/hue/data/query-hive-360.csv","hasHeader":false,"useCustomDelimiters":false,"customFieldDelimiter":",","customCollectionDelimiter":"\\\\002","customMapDelimiter":"\\\\003","customRegexp":""}''') path = {'isDir': False, 'split': ('/user/hue/data', 'query-hive-360.csv'), 'listdir': ['/user/hue/data']} request = MockRequest(fs=MockFs(path=path)) sql = SQLIndexer(user=request.user, fs=request.fs).create_table_from_a_file(source, destination).get_str() assert_true('''CREATE TABLE `default`.`manual_empty_kudu` ( `acct_client` string , `tran_amount` double , `tran_country_cd` string , `vrfcn_city` string , `vrfcn_city_lat` double , `vrfcn_city_lon` double , PRIMARY KEY (acct_client) ) STORED AS kudu TBLPROPERTIES("transactional" = "false") ;''' in sql, sql)

true

f71b3428812e3f4af3ba5ec76b4fec00628e68ec

1,009

py

Python

web/tex_cnn_rest.py

wbj0110/cnn-text-classification-tf-chinese

42e47d34c300e9d571231e43c189ee292b595559

[ "Apache-2.0" ]

null

web/tex_cnn_rest.py

wbj0110/cnn-text-classification-tf-chinese

42e47d34c300e9d571231e43c189ee292b595559

[ "Apache-2.0" ]

null

web/tex_cnn_rest.py

wbj0110/cnn-text-classification-tf-chinese

42e47d34c300e9d571231e43c189ee292b595559

[ "Apache-2.0" ]

null

#! /usr/bin/env python from tornado import httpserver from tornado import gen from tornado.ioloop import IOLoop import tornado.web import json import single_eval as sev class IndexHandler(tornado.web.RequestHandler): def get(self): self.write("Hello,This is TextCNN") class ClassifyHandler(tornado.web.RequestHandler): def get(self): data = self.get_argument('q', 'Hello') predict_result = sev.classify(data) self.write("this is Classfication for text,get method and result:{}".format(predict_result)) def post(self): self.write("this is classfication for text ,post method") class Application(tornado.web.Application): def __init__(self): handlers = [ (r"/?",IndexHandler), (r"/classify/?",ClassifyHandler) ] tornado.web.Application.__init__(self,handlers=handlers) def main(): app = Application() app.listen(80) IOLoop.instance().start() if __name__ == '__main__': main()

25.871795

100

0.667988

from tornado import httpserver from tornado import gen from tornado.ioloop import IOLoop import tornado.web import json import single_eval as sev class IndexHandler(tornado.web.RequestHandler): def get(self): self.write("Hello,This is TextCNN") class ClassifyHandler(tornado.web.RequestHandler): def get(self): data = self.get_argument('q', 'Hello') predict_result = sev.classify(data) self.write("this is Classfication for text,get method and result:{}".format(predict_result)) def post(self): self.write("this is classfication for text ,post method") class Application(tornado.web.Application): def __init__(self): handlers = [ (r"/?",IndexHandler), (r"/classify/?",ClassifyHandler) ] tornado.web.Application.__init__(self,handlers=handlers) def main(): app = Application() app.listen(80) IOLoop.instance().start() if __name__ == '__main__': main()

true

f71b345986d63817e8ebf1e91022534a55821bb0

1,010

py

Python

setup.py

jiad-dev/flask-req-parser

f46a6c002381d7b74e9c80c6b3ae536dcda908f9

[ "BSD-2-Clause" ]

2

2020-10-17T04:46:08.000Z

2020-10-17T04:46:10.000Z

setup.py

jiad-dev/flask-req-parser

f46a6c002381d7b74e9c80c6b3ae536dcda908f9

[ "BSD-2-Clause" ]

null

setup.py

jiad-dev/flask-req-parser

f46a6c002381d7b74e9c80c6b3ae536dcda908f9

[ "BSD-2-Clause" ]

2

2020-10-07T03:33:19.000Z

2020-10-07T04:07:16.000Z

from setuptools import setup setup( name='flask_req_parser', version='0.1.4', url='https://github.com/Rhyanz46/flask-req-parser', license='BSD', author='Arian Saputra', author_email='rianariansaputra@gmail.com', description='Simple Request parser for flask', long_description=__doc__, # packages=find_packages(), # py_modules=['flask_req_parser'], # if you would be using a package instead use packages instead # of py_modules: packages=['flask_req_parser'], zip_safe=False, include_package_data=True, platforms='any', install_requires=[ 'Flask' ], classifiers=[ 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', 'Topic :: Software Development :: Libraries :: Python Modules' ] )

30.606061

70

0.643564

from setuptools import setup setup( name='flask_req_parser', version='0.1.4', url='https://github.com/Rhyanz46/flask-req-parser', license='BSD', author='Arian Saputra', author_email='rianariansaputra@gmail.com', description='Simple Request parser for flask', long_description=__doc__, packages=['flask_req_parser'], zip_safe=False, include_package_data=True, platforms='any', install_requires=[ 'Flask' ], classifiers=[ 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', 'Topic :: Software Development :: Libraries :: Python Modules' ] )

true

f71b36d9600e32803a5a8daae7b744b0672e4fd7

24,960

py

Python

src/data/database.py

aFoxPod/torrent-bot

29ffaea5edab81bfa3aa0d944a96a766d06770e5

[ "MIT" ]

null

src/data/database.py

aFoxPod/torrent-bot

29ffaea5edab81bfa3aa0d944a96a766d06770e5

[ "MIT" ]

null

src/data/database.py

aFoxPod/torrent-bot

29ffaea5edab81bfa3aa0d944a96a766d06770e5

[ "MIT" ]

null

import sqlite3 class TorrentState: SEARCHING = "SEARCHING" # Still being searched DOWNLOADING = "DOWNLOADING" # Currently being downloading SEEDING = "SEEDING" # Currently uploading COMPLETED = "COMPLETED" # Removed from seeding DELETING = "DELETING" # Torrent marked for deletion PAUSED = "PAUSED" # Download stopped @staticmethod def get_states() -> list: return [ TorrentState.SEARCHING, TorrentState.DOWNLOADING, TorrentState.SEEDING, TorrentState.COMPLETED, TorrentState.DELETING, TorrentState.PAUSED ] class TBDatabase: """ Database Handler Attributes ---------- db_file_path : str the database file path (sqlite) """ def __init__(self, db_file_path: str) -> None: self.db_file_path = db_file_path self.connection = sqlite3.connect(self.db_file_path) self.connection.execute("PRAGMA foreign_keys = ON") self.connection.row_factory = dict_factory self.states = TorrentState() def create_schema(self) -> None: """Initializes the database by creating the necessary schema. Args: db_file (str): the file to were the database state will be stored """ cur = self.connection.cursor() # Create Movies table sql = f"""CREATE TABLE IF NOT EXISTS movies ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "name" TEXT UNIQUE NOT NULL, "max_size_mb" INTEGER NOT NULL, "resolution_profile" TEXT NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "imdbid" INTEGER UNIQUE NOT NULL, "cover_url" TEXT, "hash" TEXT) """ cur.execute(sql) # Create TV Shows Table sql = f"""CREATE TABLE IF NOT EXISTS tv_shows ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "name" TEXT UNIQUE NOT NULL, "max_episode_size_mb" INTEGER NOT NULL, "resolution_profile" TEXT NOT NULL, "imdbid" INTEGER UNIQUE NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "cover_url" TEXT ) """ cur.execute(sql) # Create TV Show seasons sql = f"""CREATE TABLE IF NOT EXISTS tv_show_seasons ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "show_id" INTEGER, "season_number" INTEGER NOT NULL, "season_number_episodes" INTEGER NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "hash" TEXT, FOREIGN KEY(show_id) REFERENCES tv_shows(id) ON DELETE CASCADE, UNIQUE(show_id, season_number)) """ cur.execute(sql) # Create TV Show season episodes sql = f"""CREATE TABLE IF NOT EXISTS tv_show_season_episodes ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "season_id" INTEGER, "name" TEXT NOT NULL, "episode_number" INTEGER NOT NULL, "air_date" TEXT NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "hash" TEXT, FOREIGN KEY(season_id) REFERENCES tv_show_seasons(id) ON DELETE CASCADE, UNIQUE(season_id, episode_number)) """ cur.execute(sql) # Create tv shows with seasons view sql = f"""CREATE VIEW IF NOT EXISTS tv_shows_with_seasons_view AS SELECT tv_shows.id as show_id, tv_shows.name as show_name, tv_shows.state as show_state, tv_shows.resolution_profile as resolution_profile, tv_shows.name as show_name, tv_shows.max_episode_size_mb as max_episode_size_mb, tv_shows.imdbid as show_imdbid, tv_show_seasons.id as season_id, tv_show_seasons.season_number as season_number, tv_show_seasons.season_number_episodes as season_number_episodes, tv_show_seasons.state as season_state, tv_show_seasons.hash as season_hash FROM tv_shows INNER JOIN tv_show_seasons on tv_shows.id = tv_show_seasons.show_id; """ cur.execute(sql) # Create seaons with episodes view sql = f"""CREATE VIEW IF NOT EXISTS tv_show_seasons_with_episodes_view AS SELECT tv_show_seasons.id as season_id, tv_show_seasons.hash as season_hash, tv_show_seasons.state as season_state, tv_show_seasons.season_number as season_number, tv_show_season_episodes.id as episode_id, tv_show_season_episodes.name as episode_name, tv_show_season_episodes.air_date as episode_air_date, tv_show_season_episodes.episode_number as episode_number, tv_show_season_episodes.state as episode_state, tv_show_season_episodes.hash as episode_hash FROM tv_show_seasons INNER JOIN tv_show_season_episodes on tv_show_seasons.id = tv_show_season_episodes.season_id; """ cur.execute(sql) # commit changes and close the connection self.connection.commit() def get_all_movies(self) -> list: """Retrieves all movies Returns: list: the the list of movies """ cur = self.connection.cursor() cur.execute("SELECT * FROM movies") return cur.fetchall() def get_all_tv_shows(self) -> list: """Retrieves all tv shows Returns: list: the list of tv shows """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows;") return cur.fetchall() def get_all_seasons(self) -> list: """Retrieves all tv show seasons Returns: list: the list of seasons """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_seasons;") return cur.fetchall() def get_all_episodes(self) -> list: """Retrieves all episodes Returns: list: the list of episdoes """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_season_episodes;") return cur.fetchall() def get_all_tv_shows_with_seasons(self) -> list: """Retrieves all tv shows and seasons Returns: list: the list of tv shows and seaons """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows_with_seasons_view;") return cur.fetchall() def get_all_tv_shows_season_episodes(self) -> list: """Retrieves all tv shows seasons and episodes Returns: list: the list of tv shows season episodes """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows_season_with_episodes_view;") return cur.fetchall() def get_movies_by_state(self, state: str) -> list: """Retrieves all movies stored in the database with the specified state Args: state (str): the state (must match a valid state) Returns: list: the list of movies """ if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute("SELECT * FROM movies WHERE state=?", (state,)) return cur.fetchall() def get_tv_shows_by_state(self, state: str) -> list: """Retrieves all tv shows with the specified state Args: state (str): the state (must match a valid state) Raises: Exception: If the state is not valid Returns: list: the list of tv shows """ if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows WHERE state=?", (state,)) return cur.fetchall() def get_tv_show_with_seasons_by_state(self, state: str) -> list: """Retrieves all tv show seaons with the specified state Args: state (str): the state (must match a valid state) Raises: Exception: If the state is not valid Returns: list: the list of tv show seasons """ if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute( "SELECT * FROM tv_shows_with_seasons_view WHERE season_state=?", (state,)) return cur.fetchall() def get_tv_show_seasons_with_episodes_by_state(self, state: str) -> list: """Retrieves all tv show season episodes with the specified state Args: state (str): the state (must match a valid state) Raises: Exception: If the state is not valid Returns: list: the list of tv show season episodes """ if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute( "SELECT * FROM tv_show_seasons_with_episodes_view WHERE season_state=?", (state,)) return cur.fetchall() def get_movie(self, id: int) -> dict: """Retrieves a movie Args: id (int): the id of the movie to retrieve Returns: dict: the movie """ cur = self.connection.cursor() cur.execute("SELECT * FROM movies WHERE id=?", (id,)) return cur.fetchone() def get_tv_show(self, id: int) -> dict: """Retrieves a tv show Args: id (int): the id of the tv show to retrieve Returns: dict: the tv show """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows WHERE id=?", (id,)) return cur.fetchone() def get_tv_show_season(self, id: str) -> dict: """Retrieves a tv show season Args: id (str): the id of the tv show season to retrieve Returns: dict: the tv show season """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_seasons WHERE id=?", (id,)) return cur.fetchone() def get_tv_show_with_seasons(self, id: str) -> list: """Retrieves all seasons for the tv show with the sepecified id Args: id (str): the tv show id Returns: list: the list of seasons """ cur = self.connection.cursor() cur.execute("SELECT * from tv_shows_with_seasons_view WHERE show_id=?", (id,)) return cur.fetchall() def get_tv_show_season_with_episodes(self, id: str) -> list: """Retrieves all seasons for the tv show with the sepecified id Args: id (str): the tv show id Returns: list: the list of seasons """ cur = self.connection.cursor() cur.execute("SELECT * from tv_show_seasons_with_episodes_view WHERE season_id=?", (id,)) return cur.fetchall() def get_season_episodes(self, season_id: str) -> list: """Retrieves all episodes for the specified season id Args: season_id (str): the season id Returns: list: the list of episodes """ cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_season_episodes WHERE season_id=?", (season_id,)) result = cur.fetchall() return result def delete_movie(self, id: int) -> None: """Delete a movie Args: id (int): the id of the movie to delete """ cur = self.connection.cursor() cur.execute("DELETE FROM movies WHERE id=?", (id,)) self.connection.commit() def delete_tv_show(self, id: int) -> None: """Delete a tv show Args: id (int): the id of the tv show to delete """ cur = self.connection.cursor() cur.execute("DELETE FROM tv_shows WHERE id=?", (id,)) self.connection.commit() def delete_season(self, id: int): """Delete a season Args: id (int): the season id """ cur = self.connection.cursor() cur.execute("DELETE FROM tv_show_seasons WHERE id=?", (id,)) self.connection.commit() def delete_episode(self, id: int): """Delete an epidose Args: id (int): the episode id """ cur = self.connection.cursor() cur.execute("DELETE FROM tv_show_season_episodes WHERE id=?", (id,)) self.connection.commit() def add_movie(self, name: str, max_size_mb: int, resolution_profile: str, imdbid: str, cover_url: str) -> int: """Adds a movie to the database Args: name (str): the movie name max_size_mb (int): the movie max size in megabytes resolution_profile (str): the desired resolutions imdbid (str): the imdbid cover_url (str): the cover image url Returns: int: the id of the inserted movie """ cur = self.connection.cursor() cur.execute( """ INSERT INTO movies(name,max_size_mb,resolution_profile, imdbid, cover_url) VALUES(?,?,?,?,?) """, (name, max_size_mb, resolution_profile, imdbid,cover_url), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def add_tv_show(self, name: str, max_episode_size_mb: int, resolution_profile: str, imdbid: str, cover_url: str) -> int: """Adds a tv show to the database Args: name (str): the tv show name max_episode_size_mb (int): the max size of an episode resolution_profile (str): the desired resolutions imdbid (str): the imdb id cover_url (str): the cover image url Returns: int: the id of the inserted tv show """ cur = self.connection.cursor() cur.execute( """ INSERT INTO tv_shows(name,max_episode_size_mb,resolution_profile, imdbid, cover_url) VALUES(?,?,?,?, ?) """, (name, max_episode_size_mb, resolution_profile, imdbid, cover_url), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def add_tv_show_season(self, show_id: int, season_number: str, season_number_episodes: int) -> int: """Add tv show season Args: show_id (int): the tv show id season_number (str): the season number Returns: int: the id of the inserted tv show """ cur = self.connection.cursor() cur.execute( """ INSERT INTO tv_show_seasons(show_id,season_number, season_number_episodes) VALUES(?,?,?) """, (show_id, season_number, season_number_episodes), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def add_season_episode(self, season_id: int, episode_name: str, episode_number: int, air_date: str) -> int: """Add a new episode Args: season_id (int): the season id episode_name (str): the episode name episode_number (int): the episode number air_date (str): air_date Returns: int: the id of the inserted tv show """ cur = self.connection.cursor() cur.execute( """ INSERT INTO tv_show_season_episodes(season_id, name, episode_number, air_date) VALUES(?,?,?,?) """, (season_id, episode_name, episode_number, air_date), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def get_season_id(self, show_id: int, season_number: int) -> int: """Retrieves the season id from the show_id and season_number Args: show_id (int): the show id season_number (int): the season number Returns: int: the season id """ cur = self.connection.cursor() row = cur.execute( """ SELECT id FROM tv_show_seasons WHERE show_id=? AND season_number=? """, (show_id, season_number), ).fetchone() return row['id'] def update_movie(self, id: int, **kwargs: dict) -> None: """Update a movie Args: id (int): The movie identifier Raises: Exception: if the kwargs is empty or none or if the key arguments don't correspond to a database column """ movie_table_columns = ["name", "max_size_mb", "resolution_profile", "state", "hash", "imdbid", "cover_url"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in movie_table_columns: raise Exception( f"The key argument must be one of the following: {movie_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE movies SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def update_tv_show(self, id: int, **kwargs: dict) -> None: """Update a tv show Args: id (int): The tv show id Raises: Exception: if the kwargs is empty or none or if the key arguments don't correspond to a database column """ tv_shows_table_columns = ["name", "max_episode_size_mb", "resolution_profile", "state", "imdbid", "cover_url"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in tv_shows_table_columns: raise Exception( f"The key argument must be one of the following: {tv_shows_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE tv_shows SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def update_show_season(self, id: int, **kwargs: dict) -> None: """Update a tv show season Args: id (int): The tv show season id Raises: Exception: if the kwargs is empty or none or if the key arguments don't correspond to a database column """ tv_show_season_table_columns = ["season_number", "season_number_episodes", "state", "hash"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in tv_show_season_table_columns: raise Exception( f"The key argument must be one of the following: {tv_show_season_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE tv_show_seasons SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def update_tv_show_season_episode(self, id: int, **kwargs: dict) -> None: """Update a tv show season episode Args: id (int): The tv show season episode id Raises: Exception: if the kwargs is empty or none or if the key arguments don't correspond to a database column """ tv_show_season_episode_table_columns = [ "season_id", "episode_number", "air_date", "state", "hash"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in tv_show_season_episode_table_columns: raise Exception( f"The key argument must be one of the following: {tv_show_season_episode_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE tv_show_season_episodes SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def get_season_states(self, show_id: int) -> set: """Retrieves a set of all current season states for the specified show Args: show_id (int): the show id Returns: set: the set of season states """ cur = self.connection.cursor() cur.execute( "SELECT season_state FROM tv_shows_with_seasons_view WHERE show_id=?", (show_id,)) result = cur.fetchall() state_set = set() for row in result: state_set.add(row['season_state']) return state_set def get_season_episodes_states(self, season_id) -> set: """Retrieves a set of all current season states for the specified show Args: show_id (int): the show id Returns: set: the set of season states """ cur = self.connection.cursor() cur.execute( "SELECT state FROM tv_show_season_episodes WHERE season_id=?", (season_id,)) result = cur.fetchall() state_set = set() for row in result: state_set.add(row['state']) return state_set def get_tv_show_season_numbers(self, show_id: int) -> set: """Get all seasons numbers of the specified tv show Args: show_id (int): the show id Returns: set: the tv show season numbers """ cur = self.connection.cursor() cur.execute("SELECT season_number FROM tv_show_seasons WHERE show_id=?", (show_id,)) result = cur.fetchall() seasons = set() for row in result: seasons.add(row['season_number']) return seasons def get_tv_show_season_episode_numbers(self, season_id: int) -> set: """Get all episode numbers for the specified season_id Args: show_id (int): the show id Returns: set: the episode numbers """ cur = self.connection.cursor() cur.execute( "SELECT episode_number FROM tv_show_season_episodes WHERE season_id=?", (season_id,)) result = cur.fetchall() episodes = set() for row in result: episodes.add(row['episode_number']) return episodes def close(self) -> None: """Close database connection""" self.connection.close() def dict_factory(cursor, row) -> dict: """Transform tuple rows into a dictionary with column names and values Args: cursor: database cursor row: row Returns: dict: a dictionary containing the column names as keys and the respective values """ output = {} for idx, col in enumerate(cursor.description): output[col[0]] = row[idx] return output if __name__ == "__main__": import sys if len(sys.argv) < 2: print("Usage: python database.py <db_path>", file=sys.stderr) exit(0) db = TBDatabase(sys.argv[1]) db.create_schema() print(db.get_all_movies()) db.close()

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import sqlite3 class TorrentState: SEARCHING = "SEARCHING" DOWNLOADING = "DOWNLOADING" SEEDING = "SEEDING" COMPLETED = "COMPLETED" DELETING = "DELETING" PAUSED = "PAUSED" @staticmethod def get_states() -> list: return [ TorrentState.SEARCHING, TorrentState.DOWNLOADING, TorrentState.SEEDING, TorrentState.COMPLETED, TorrentState.DELETING, TorrentState.PAUSED ] class TBDatabase: def __init__(self, db_file_path: str) -> None: self.db_file_path = db_file_path self.connection = sqlite3.connect(self.db_file_path) self.connection.execute("PRAGMA foreign_keys = ON") self.connection.row_factory = dict_factory self.states = TorrentState() def create_schema(self) -> None: cur = self.connection.cursor() sql = f"""CREATE TABLE IF NOT EXISTS movies ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "name" TEXT UNIQUE NOT NULL, "max_size_mb" INTEGER NOT NULL, "resolution_profile" TEXT NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "imdbid" INTEGER UNIQUE NOT NULL, "cover_url" TEXT, "hash" TEXT) """ cur.execute(sql) sql = f"""CREATE TABLE IF NOT EXISTS tv_shows ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "name" TEXT UNIQUE NOT NULL, "max_episode_size_mb" INTEGER NOT NULL, "resolution_profile" TEXT NOT NULL, "imdbid" INTEGER UNIQUE NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "cover_url" TEXT ) """ cur.execute(sql) sql = f"""CREATE TABLE IF NOT EXISTS tv_show_seasons ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "show_id" INTEGER, "season_number" INTEGER NOT NULL, "season_number_episodes" INTEGER NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "hash" TEXT, FOREIGN KEY(show_id) REFERENCES tv_shows(id) ON DELETE CASCADE, UNIQUE(show_id, season_number)) """ cur.execute(sql) sql = f"""CREATE TABLE IF NOT EXISTS tv_show_season_episodes ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "season_id" INTEGER, "name" TEXT NOT NULL, "episode_number" INTEGER NOT NULL, "air_date" TEXT NOT NULL, "state" TEXT NOT NULL DEFAULT '{self.states.SEARCHING}', "hash" TEXT, FOREIGN KEY(season_id) REFERENCES tv_show_seasons(id) ON DELETE CASCADE, UNIQUE(season_id, episode_number)) """ cur.execute(sql) sql = f"""CREATE VIEW IF NOT EXISTS tv_shows_with_seasons_view AS SELECT tv_shows.id as show_id, tv_shows.name as show_name, tv_shows.state as show_state, tv_shows.resolution_profile as resolution_profile, tv_shows.name as show_name, tv_shows.max_episode_size_mb as max_episode_size_mb, tv_shows.imdbid as show_imdbid, tv_show_seasons.id as season_id, tv_show_seasons.season_number as season_number, tv_show_seasons.season_number_episodes as season_number_episodes, tv_show_seasons.state as season_state, tv_show_seasons.hash as season_hash FROM tv_shows INNER JOIN tv_show_seasons on tv_shows.id = tv_show_seasons.show_id; """ cur.execute(sql) sql = f"""CREATE VIEW IF NOT EXISTS tv_show_seasons_with_episodes_view AS SELECT tv_show_seasons.id as season_id, tv_show_seasons.hash as season_hash, tv_show_seasons.state as season_state, tv_show_seasons.season_number as season_number, tv_show_season_episodes.id as episode_id, tv_show_season_episodes.name as episode_name, tv_show_season_episodes.air_date as episode_air_date, tv_show_season_episodes.episode_number as episode_number, tv_show_season_episodes.state as episode_state, tv_show_season_episodes.hash as episode_hash FROM tv_show_seasons INNER JOIN tv_show_season_episodes on tv_show_seasons.id = tv_show_season_episodes.season_id; """ cur.execute(sql) self.connection.commit() def get_all_movies(self) -> list: cur = self.connection.cursor() cur.execute("SELECT * FROM movies") return cur.fetchall() def get_all_tv_shows(self) -> list: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows;") return cur.fetchall() def get_all_seasons(self) -> list: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_seasons;") return cur.fetchall() def get_all_episodes(self) -> list: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_season_episodes;") return cur.fetchall() def get_all_tv_shows_with_seasons(self) -> list: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows_with_seasons_view;") return cur.fetchall() def get_all_tv_shows_season_episodes(self) -> list: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows_season_with_episodes_view;") return cur.fetchall() def get_movies_by_state(self, state: str) -> list: if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute("SELECT * FROM movies WHERE state=?", (state,)) return cur.fetchall() def get_tv_shows_by_state(self, state: str) -> list: if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows WHERE state=?", (state,)) return cur.fetchall() def get_tv_show_with_seasons_by_state(self, state: str) -> list: if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute( "SELECT * FROM tv_shows_with_seasons_view WHERE season_state=?", (state,)) return cur.fetchall() def get_tv_show_seasons_with_episodes_by_state(self, state: str) -> list: if state not in self.states.get_states(): raise Exception(f"Non allowed state={state}!") cur = self.connection.cursor() cur.execute( "SELECT * FROM tv_show_seasons_with_episodes_view WHERE season_state=?", (state,)) return cur.fetchall() def get_movie(self, id: int) -> dict: cur = self.connection.cursor() cur.execute("SELECT * FROM movies WHERE id=?", (id,)) return cur.fetchone() def get_tv_show(self, id: int) -> dict: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_shows WHERE id=?", (id,)) return cur.fetchone() def get_tv_show_season(self, id: str) -> dict: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_seasons WHERE id=?", (id,)) return cur.fetchone() def get_tv_show_with_seasons(self, id: str) -> list: cur = self.connection.cursor() cur.execute("SELECT * from tv_shows_with_seasons_view WHERE show_id=?", (id,)) return cur.fetchall() def get_tv_show_season_with_episodes(self, id: str) -> list: cur = self.connection.cursor() cur.execute("SELECT * from tv_show_seasons_with_episodes_view WHERE season_id=?", (id,)) return cur.fetchall() def get_season_episodes(self, season_id: str) -> list: cur = self.connection.cursor() cur.execute("SELECT * FROM tv_show_season_episodes WHERE season_id=?", (season_id,)) result = cur.fetchall() return result def delete_movie(self, id: int) -> None: cur = self.connection.cursor() cur.execute("DELETE FROM movies WHERE id=?", (id,)) self.connection.commit() def delete_tv_show(self, id: int) -> None: cur = self.connection.cursor() cur.execute("DELETE FROM tv_shows WHERE id=?", (id,)) self.connection.commit() def delete_season(self, id: int): cur = self.connection.cursor() cur.execute("DELETE FROM tv_show_seasons WHERE id=?", (id,)) self.connection.commit() def delete_episode(self, id: int): cur = self.connection.cursor() cur.execute("DELETE FROM tv_show_season_episodes WHERE id=?", (id,)) self.connection.commit() def add_movie(self, name: str, max_size_mb: int, resolution_profile: str, imdbid: str, cover_url: str) -> int: cur = self.connection.cursor() cur.execute( """ INSERT INTO movies(name,max_size_mb,resolution_profile, imdbid, cover_url) VALUES(?,?,?,?,?) """, (name, max_size_mb, resolution_profile, imdbid,cover_url), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def add_tv_show(self, name: str, max_episode_size_mb: int, resolution_profile: str, imdbid: str, cover_url: str) -> int: cur = self.connection.cursor() cur.execute( """ INSERT INTO tv_shows(name,max_episode_size_mb,resolution_profile, imdbid, cover_url) VALUES(?,?,?,?, ?) """, (name, max_episode_size_mb, resolution_profile, imdbid, cover_url), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def add_tv_show_season(self, show_id: int, season_number: str, season_number_episodes: int) -> int: cur = self.connection.cursor() cur.execute( """ INSERT INTO tv_show_seasons(show_id,season_number, season_number_episodes) VALUES(?,?,?) """, (show_id, season_number, season_number_episodes), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def add_season_episode(self, season_id: int, episode_name: str, episode_number: int, air_date: str) -> int: cur = self.connection.cursor() cur.execute( """ INSERT INTO tv_show_season_episodes(season_id, name, episode_number, air_date) VALUES(?,?,?,?) """, (season_id, episode_name, episode_number, air_date), ) self.connection.commit() return cur.execute('SELECT last_insert_rowid() as id').fetchone()['id'] def get_season_id(self, show_id: int, season_number: int) -> int: cur = self.connection.cursor() row = cur.execute( """ SELECT id FROM tv_show_seasons WHERE show_id=? AND season_number=? """, (show_id, season_number), ).fetchone() return row['id'] def update_movie(self, id: int, **kwargs: dict) -> None: movie_table_columns = ["name", "max_size_mb", "resolution_profile", "state", "hash", "imdbid", "cover_url"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in movie_table_columns: raise Exception( f"The key argument must be one of the following: {movie_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE movies SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def update_tv_show(self, id: int, **kwargs: dict) -> None: tv_shows_table_columns = ["name", "max_episode_size_mb", "resolution_profile", "state", "imdbid", "cover_url"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in tv_shows_table_columns: raise Exception( f"The key argument must be one of the following: {tv_shows_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE tv_shows SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def update_show_season(self, id: int, **kwargs: dict) -> None: tv_show_season_table_columns = ["season_number", "season_number_episodes", "state", "hash"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in tv_show_season_table_columns: raise Exception( f"The key argument must be one of the following: {tv_show_season_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE tv_show_seasons SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def update_tv_show_season_episode(self, id: int, **kwargs: dict) -> None: tv_show_season_episode_table_columns = [ "season_id", "episode_number", "air_date", "state", "hash"] self.connection.row_factory = dict_factory cur = self.connection.cursor() columns_to_update = "" values = () if not kwargs: raise Exception("At least one argument must be specified") for key, value in kwargs.items(): if key not in tv_show_season_episode_table_columns: raise Exception( f"The key argument must be one of the following: {tv_show_season_episode_table_columns}" ) columns_to_update += f"{key}=?, " values += (value,) values += (id,) columns_to_update = columns_to_update[:-2] cur.execute( f"UPDATE tv_show_season_episodes SET {columns_to_update} WHERE id=?", values, ) self.connection.commit() def get_season_states(self, show_id: int) -> set: cur = self.connection.cursor() cur.execute( "SELECT season_state FROM tv_shows_with_seasons_view WHERE show_id=?", (show_id,)) result = cur.fetchall() state_set = set() for row in result: state_set.add(row['season_state']) return state_set def get_season_episodes_states(self, season_id) -> set: cur = self.connection.cursor() cur.execute( "SELECT state FROM tv_show_season_episodes WHERE season_id=?", (season_id,)) result = cur.fetchall() state_set = set() for row in result: state_set.add(row['state']) return state_set def get_tv_show_season_numbers(self, show_id: int) -> set: cur = self.connection.cursor() cur.execute("SELECT season_number FROM tv_show_seasons WHERE show_id=?", (show_id,)) result = cur.fetchall() seasons = set() for row in result: seasons.add(row['season_number']) return seasons def get_tv_show_season_episode_numbers(self, season_id: int) -> set: cur = self.connection.cursor() cur.execute( "SELECT episode_number FROM tv_show_season_episodes WHERE season_id=?", (season_id,)) result = cur.fetchall() episodes = set() for row in result: episodes.add(row['episode_number']) return episodes def close(self) -> None: self.connection.close() def dict_factory(cursor, row) -> dict: output = {} for idx, col in enumerate(cursor.description): output[col[0]] = row[idx] return output if __name__ == "__main__": import sys if len(sys.argv) < 2: print("Usage: python database.py <db_path>", file=sys.stderr) exit(0) db = TBDatabase(sys.argv[1]) db.create_schema() print(db.get_all_movies()) db.close()

true

f71b389bb26a910057e54795c0cf91314386c30f

11,436

py

Python

sparseConv/multitask/semseg/models/res16unet.py

ShengyuH/Scene-Recognition-in-3D

8fb869e1f8e8ff48c6f1082bb75f60a562875fc5

[ "MIT" ]

48

2020-03-02T23:05:59.000Z

2022-02-22T11:23:17.000Z

sparseConv/multitask/semseg/models/res16unet.py

HenrryBryant/Scene-Recognition-in-3D

8fb869e1f8e8ff48c6f1082bb75f60a562875fc5

[ "MIT" ]

5

2020-10-29T14:19:04.000Z

2022-01-25T05:33:59.000Z

sparseConv/multitask/semseg/models/res16unet.py

HenrryBryant/Scene-Recognition-in-3D

8fb869e1f8e8ff48c6f1082bb75f60a562875fc5

[ "MIT" ]

7

2020-06-18T05:23:01.000Z

2021-05-13T01:26:32.000Z

from models.resnet import ResNetBase, get_norm from models.modules.common import ConvType, NormType, conv, conv_tr from models.modules.resnet_block import BasicBlock, Bottleneck, BasicBlockIN, BottleneckIN, BasicBlockLN from MinkowskiEngine import MinkowskiReLU import MinkowskiEngine.MinkowskiOps as me class Res16UNetBase(ResNetBase): BLOCK = None PLANES = (32, 64, 128, 256, 256, 256, 256, 256) DILATIONS = (1, 1, 1, 1, 1, 1, 1, 1) LAYERS = (2, 2, 2, 2, 2, 2, 2, 2) INIT_DIM = 32 OUT_PIXEL_DIST = 1 NORM_TYPE = NormType.BATCH_NORM NON_BLOCK_CONV_TYPE = ConvType.SPATIAL_HYPERCUBE CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS # To use the model, must call initialize_coords before forward pass. # Once data is processed, call clear to reset the model before calling initialize_coords def __init__(self, in_channels, out_channels, config, D=3, **kwargs): super(Res16UNetBase, self).__init__(in_channels, out_channels, config, D) def network_initialization(self, in_channels, out_channels, config, D): # Setup net_metadata dilations = self.DILATIONS bn_momentum = config['bn_momentum'] def space_n_time_m(n, m): return n if D == 3 else [n, n, n, m] if D == 4: self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1) # Output of the first conv concated to conv6 self.inplanes = self.INIT_DIM self.conv0p1s1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config['conv1_kernel_size'], 1), stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn0 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.conv1p1s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block1 = self._make_layer( self.BLOCK, self.PLANES[0], self.LAYERS[0], dilation=dilations[0], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv2p2s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn2 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block2 = self._make_layer( self.BLOCK, self.PLANES[1], self.LAYERS[1], dilation=dilations[1], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv3p4s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn3 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block3 = self._make_layer( self.BLOCK, self.PLANES[2], self.LAYERS[2], dilation=dilations[2], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv4p8s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn4 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block4 = self._make_layer( self.BLOCK, self.PLANES[3], self.LAYERS[3], dilation=dilations[3], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr4p16s2 = conv_tr( self.inplanes, self.PLANES[4], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr4 = get_norm(self.NORM_TYPE, self.PLANES[4], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion self.block5 = self._make_layer( self.BLOCK, self.PLANES[4], self.LAYERS[4], dilation=dilations[4], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr5p8s2 = conv_tr( self.inplanes, self.PLANES[5], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr5 = get_norm(self.NORM_TYPE, self.PLANES[5], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion self.block6 = self._make_layer( self.BLOCK, self.PLANES[5], self.LAYERS[5], dilation=dilations[5], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr6p4s2 = conv_tr( self.inplanes, self.PLANES[6], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr6 = get_norm(self.NORM_TYPE, self.PLANES[6], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion self.block7 = self._make_layer( self.BLOCK, self.PLANES[6], self.LAYERS[6], dilation=dilations[6], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr7p2s2 = conv_tr( self.inplanes, self.PLANES[7], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr7 = get_norm(self.NORM_TYPE, self.PLANES[7], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[7] + self.INIT_DIM self.block8 = self._make_layer( self.BLOCK, self.PLANES[7], self.LAYERS[7], dilation=dilations[7], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.final = conv(self.PLANES[7], out_channels, kernel_size=1, stride=1, bias=True, D=D) self.relu = MinkowskiReLU(inplace=True) def forward(self, x): out = self.conv0p1s1(x) out = self.bn0(out) out_p1 = self.relu(out) out = self.conv1p1s2(out_p1) out = self.bn1(out) out = self.relu(out) out_b1p2 = self.block1(out) out = self.conv2p2s2(out_b1p2) out = self.bn2(out) out = self.relu(out) out_b2p4 = self.block2(out) out = self.conv3p4s2(out_b2p4) out = self.bn3(out) out = self.relu(out) out_b3p8 = self.block3(out) # pixel_dist=16 out = self.conv4p8s2(out_b3p8) out = self.bn4(out) out = self.relu(out) out = self.block4(out) # pixel_dist=8 out = self.convtr4p16s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p8) out = self.block5(out) # pixel_dist=4 out = self.convtr5p8s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p4) out = self.block6(out) # pixel_dist=2 out = self.convtr6p4s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p2) out = self.block7(out) # pixel_dist=1 out = self.convtr7p2s2(out) out = self.bntr7(out) out = self.relu(out) out = me.cat(out, out_p1) out = self.block8(out) return self.final(out) class Res16UNet14(Res16UNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1, 1, 1, 1, 1) class Res16UNet18(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2, 2, 2, 2, 2) class Res16UNet34(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet50(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet101(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 23, 2, 2, 2, 2) class Res16UNet14A(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet14A2(Res16UNet14A): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet14B2(Res16UNet14B): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B3(Res16UNet14B): LAYERS = (2, 2, 2, 2, 1, 1, 1, 1) class Res16UNet14C(Res16UNet14): PLANES = (32, 64, 128, 256, 192, 192, 128, 128) class Res16UNet14D(Res16UNet14): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet18A(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet18B(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet18D(Res16UNet18): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet34A(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 64) class Res16UNet34B(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 32) class Res16UNet34C(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 96, 96) # Experimentally, worse than others class Res16UNetLN14(Res16UNet14): NORM_TYPE = NormType.SPARSE_LAYER_NORM BLOCK = BasicBlockLN class Res16UNetTemporalBase(Res16UNetBase): """ Res16UNet that can take 4D independently. No temporal convolution. """ CONV_TYPE = ConvType.SPATIAL_HYPERCUBE def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(Res16UNetTemporalBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class Res16UNetTemporal14(Res16UNet14, Res16UNetTemporalBase): pass class Res16UNetTemporal18(Res16UNet18, Res16UNetTemporalBase): pass class Res16UNetTemporal34(Res16UNet34, Res16UNetTemporalBase): pass class Res16UNetTemporal50(Res16UNet50, Res16UNetTemporalBase): pass class Res16UNetTemporal101(Res16UNet101, Res16UNetTemporalBase): pass class Res16UNetTemporalIN14(Res16UNetTemporal14): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BasicBlockIN class Res16UNetTemporalIN18(Res16UNetTemporal18): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BasicBlockIN class Res16UNetTemporalIN34(Res16UNetTemporal34): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BasicBlockIN class Res16UNetTemporalIN50(Res16UNetTemporal50): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BottleneckIN class Res16UNetTemporalIN101(Res16UNetTemporal101): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BottleneckIN class STRes16UNetBase(Res16UNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STRes16UNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STRes16UNet14(STRes16UNetBase, Res16UNet14): pass class STRes16UNet18(STRes16UNetBase, Res16UNet18): pass class STRes16UNet34(STRes16UNetBase, Res16UNet34): pass class STRes16UNet50(STRes16UNetBase, Res16UNet50): pass class STRes16UNet101(STRes16UNetBase, Res16UNet101): pass class STRes16UNet18A(STRes16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class STResTesseract16UNetBase(STRes16UNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseract16UNet18A(STRes16UNet18A, STResTesseract16UNetBase): pass

26.411085

104

0.669902

from models.resnet import ResNetBase, get_norm from models.modules.common import ConvType, NormType, conv, conv_tr from models.modules.resnet_block import BasicBlock, Bottleneck, BasicBlockIN, BottleneckIN, BasicBlockLN from MinkowskiEngine import MinkowskiReLU import MinkowskiEngine.MinkowskiOps as me class Res16UNetBase(ResNetBase): BLOCK = None PLANES = (32, 64, 128, 256, 256, 256, 256, 256) DILATIONS = (1, 1, 1, 1, 1, 1, 1, 1) LAYERS = (2, 2, 2, 2, 2, 2, 2, 2) INIT_DIM = 32 OUT_PIXEL_DIST = 1 NORM_TYPE = NormType.BATCH_NORM NON_BLOCK_CONV_TYPE = ConvType.SPATIAL_HYPERCUBE CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=3, **kwargs): super(Res16UNetBase, self).__init__(in_channels, out_channels, config, D) def network_initialization(self, in_channels, out_channels, config, D): dilations = self.DILATIONS bn_momentum = config['bn_momentum'] def space_n_time_m(n, m): return n if D == 3 else [n, n, n, m] if D == 4: self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1) self.inplanes = self.INIT_DIM self.conv0p1s1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config['conv1_kernel_size'], 1), stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn0 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.conv1p1s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block1 = self._make_layer( self.BLOCK, self.PLANES[0], self.LAYERS[0], dilation=dilations[0], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv2p2s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn2 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block2 = self._make_layer( self.BLOCK, self.PLANES[1], self.LAYERS[1], dilation=dilations[1], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv3p4s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn3 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block3 = self._make_layer( self.BLOCK, self.PLANES[2], self.LAYERS[2], dilation=dilations[2], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv4p8s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn4 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block4 = self._make_layer( self.BLOCK, self.PLANES[3], self.LAYERS[3], dilation=dilations[3], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr4p16s2 = conv_tr( self.inplanes, self.PLANES[4], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr4 = get_norm(self.NORM_TYPE, self.PLANES[4], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion self.block5 = self._make_layer( self.BLOCK, self.PLANES[4], self.LAYERS[4], dilation=dilations[4], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr5p8s2 = conv_tr( self.inplanes, self.PLANES[5], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr5 = get_norm(self.NORM_TYPE, self.PLANES[5], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion self.block6 = self._make_layer( self.BLOCK, self.PLANES[5], self.LAYERS[5], dilation=dilations[5], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr6p4s2 = conv_tr( self.inplanes, self.PLANES[6], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr6 = get_norm(self.NORM_TYPE, self.PLANES[6], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion self.block7 = self._make_layer( self.BLOCK, self.PLANES[6], self.LAYERS[6], dilation=dilations[6], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr7p2s2 = conv_tr( self.inplanes, self.PLANES[7], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr7 = get_norm(self.NORM_TYPE, self.PLANES[7], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[7] + self.INIT_DIM self.block8 = self._make_layer( self.BLOCK, self.PLANES[7], self.LAYERS[7], dilation=dilations[7], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.final = conv(self.PLANES[7], out_channels, kernel_size=1, stride=1, bias=True, D=D) self.relu = MinkowskiReLU(inplace=True) def forward(self, x): out = self.conv0p1s1(x) out = self.bn0(out) out_p1 = self.relu(out) out = self.conv1p1s2(out_p1) out = self.bn1(out) out = self.relu(out) out_b1p2 = self.block1(out) out = self.conv2p2s2(out_b1p2) out = self.bn2(out) out = self.relu(out) out_b2p4 = self.block2(out) out = self.conv3p4s2(out_b2p4) out = self.bn3(out) out = self.relu(out) out_b3p8 = self.block3(out) out = self.conv4p8s2(out_b3p8) out = self.bn4(out) out = self.relu(out) out = self.block4(out) out = self.convtr4p16s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p8) out = self.block5(out) out = self.convtr5p8s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p4) out = self.block6(out) out = self.convtr6p4s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p2) out = self.block7(out) out = self.convtr7p2s2(out) out = self.bntr7(out) out = self.relu(out) out = me.cat(out, out_p1) out = self.block8(out) return self.final(out) class Res16UNet14(Res16UNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1, 1, 1, 1, 1) class Res16UNet18(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2, 2, 2, 2, 2) class Res16UNet34(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet50(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet101(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 23, 2, 2, 2, 2) class Res16UNet14A(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet14A2(Res16UNet14A): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet14B2(Res16UNet14B): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B3(Res16UNet14B): LAYERS = (2, 2, 2, 2, 1, 1, 1, 1) class Res16UNet14C(Res16UNet14): PLANES = (32, 64, 128, 256, 192, 192, 128, 128) class Res16UNet14D(Res16UNet14): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet18A(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet18B(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet18D(Res16UNet18): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet34A(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 64) class Res16UNet34B(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 32) class Res16UNet34C(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 96, 96) class Res16UNetLN14(Res16UNet14): NORM_TYPE = NormType.SPARSE_LAYER_NORM BLOCK = BasicBlockLN class Res16UNetTemporalBase(Res16UNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(Res16UNetTemporalBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class Res16UNetTemporal14(Res16UNet14, Res16UNetTemporalBase): pass class Res16UNetTemporal18(Res16UNet18, Res16UNetTemporalBase): pass class Res16UNetTemporal34(Res16UNet34, Res16UNetTemporalBase): pass class Res16UNetTemporal50(Res16UNet50, Res16UNetTemporalBase): pass class Res16UNetTemporal101(Res16UNet101, Res16UNetTemporalBase): pass class Res16UNetTemporalIN14(Res16UNetTemporal14): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BasicBlockIN class Res16UNetTemporalIN18(Res16UNetTemporal18): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BasicBlockIN class Res16UNetTemporalIN34(Res16UNetTemporal34): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BasicBlockIN class Res16UNetTemporalIN50(Res16UNetTemporal50): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BottleneckIN class Res16UNetTemporalIN101(Res16UNetTemporal101): NORM_TYPE = NormType.SPARSE_INSTANCE_NORM BLOCK = BottleneckIN class STRes16UNetBase(Res16UNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STRes16UNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STRes16UNet14(STRes16UNetBase, Res16UNet14): pass class STRes16UNet18(STRes16UNetBase, Res16UNet18): pass class STRes16UNet34(STRes16UNetBase, Res16UNet34): pass class STRes16UNet50(STRes16UNetBase, Res16UNet50): pass class STRes16UNet101(STRes16UNetBase, Res16UNet101): pass class STRes16UNet18A(STRes16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class STResTesseract16UNetBase(STRes16UNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseract16UNet18A(STRes16UNet18A, STResTesseract16UNetBase): pass

true

f71b38a82f278340c42a84945a9e9d87c7755673

5,609

py

Python

pinyinsplit.py

throput/pinyinsplit

e500da5b4b37e4d7762790825e1efd1e6e0f4765

[ "MIT" ]

5

2018-11-12T19:33:37.000Z

2021-05-26T05:03:48.000Z

pinyinsplit.py

throput/pinyinsplit

e500da5b4b37e4d7762790825e1efd1e6e0f4765

[ "MIT" ]

null

pinyinsplit.py

throput/pinyinsplit

e500da5b4b37e4d7762790825e1efd1e6e0f4765

[ "MIT" ]

1

2021-08-10T07:15:18.000Z

from pygtrie import CharTrie import copy """ Split a Chinese Pinyin phrase into a list of possible permutations of Pinyin words.This is the "example" module. For example, >>> from pinyinsplit import PinyinSplit >>> pys = PinyinSplit() >>> pys.split('XiangGangDaXue') [['Xiang', 'Gang', 'Da', 'Xue'], ['Xiang', 'Gang', 'Da', 'Xu', 'e'], ['Xi', 'ang', 'Gang', 'Da', 'Xue'], ['Xi', 'ang', 'Gang', 'Da', 'Xu', 'e']] """ class PinyinSplit: """Split a Chinese Pinyin phrase into a list of possible permutations of Pinyin words. It returns a list of all possible permutations of valid Pinyin words. If the Pinyin phrase cannot be exhaustively split into valid Pinyin words, an empty list will be returned. >>> from pinyinsplit import PinyinSplit >>> pys = PinyinSplit() >>> pys.split('shediaoyingxiongchuan') [['she', 'diao', 'ying', 'xiong', 'chuan'], ['she', 'diao', 'ying', 'xiong', 'chu', 'an'], ['she', 'di', 'ao', 'ying', 'xiong', 'chuan'], ['she', 'di', 'ao', 'ying', 'xiong', 'chu', 'an']] >>> pys.split('shediaoyingxiongchuanxyz') [] """ pylist = [ 'a', 'ai', 'an', 'ang', 'ao', 'ba', 'bai', 'ban', 'bang', 'bao', 'bei', 'ben', 'beng', 'bi', 'bian', 'biang', 'biao', 'bie', 'bin', 'bing', 'bo', 'bu', 'ca', 'cai', 'can', 'cang', 'cao', 'ce', 'cen', 'ceng', 'cha', 'chai', 'chan', 'chang', 'chao', 'che', 'chen', 'cheng', 'chi', 'chong', 'chou', 'chu', 'chua', 'chuai', 'chuan', 'chuang', 'chui', 'chun', 'chuo', 'ci', 'cong', 'cou', 'cu', 'cuan', 'cui', 'cun', 'cuo', 'da', 'dai', 'dan', 'dang', 'dao', 'de', 'dei', 'den', 'deng', 'di', 'dia', 'dian', 'diang', 'diao', 'die', 'ding', 'diu', 'dong', 'dou', 'du', 'duan', 'dui', 'dun', 'duo', 'e', 'ei', 'en', 'eng', 'er', 'fa', 'fan', 'fang', 'fei', 'fen', 'feng', 'fiao', 'fo', 'fou', 'fu', 'ga', 'gai', 'gan', 'gang', 'gao', 'ge', 'gei', 'gen', 'geng', 'gong', 'gou', 'gu', 'gua', 'guai', 'guan', 'guang', 'gui', 'gun', 'guo', 'ha', 'hai', 'han', 'hang', 'hao', 'he', 'hei', 'hen', 'heng', 'hong', 'hou', 'hu', 'hua', 'huai', 'huan', 'huang', 'hui', 'hun', 'huo', 'ji', 'jia', 'jian', 'jiang', 'jiao', 'jie', 'jin', 'jing', 'jiong', 'jiu', 'ju', 'juan', 'jue', 'jun', 'ka', 'kai', 'kan', 'kang', 'kao', 'ke', 'kei', 'ken', 'keng', 'kong', 'kou', 'ku', 'kua', 'kuai', 'kuan', 'kuang', 'kui', 'kun', 'kuo', 'la', 'lai', 'lan', 'lang', 'lao', 'le', 'lei', 'leng', 'li', 'lia', 'lian', 'liang', 'liao', 'lie', 'lin', 'ling', 'liu', 'long', 'lou', 'lu', 'luan', 'lue', 'lun', 'luo', 'lv', 'lve', 'lvn', 'lü', 'lüe', 'lün', 'ma', 'mai', 'man', 'mang', 'mao', 'me', 'mei', 'men', 'meng', 'mi', 'mian', 'miao', 'mie', 'min', 'ming', 'miu', 'mo', 'mou', 'mu', 'na', 'nai', 'nan', 'nang', 'nao', 'ne', 'nei', 'nen', 'neng', 'ni', 'nia', 'nian', 'niang', 'niao', 'nie', 'nin', 'ning', 'niu', 'nong', 'nou', 'nu', 'nuan', 'nue', 'nun', 'nuo', 'nv', 'nve', 'nü', 'nüe', 'ou', 'pa', 'pai', 'pan', 'pang', 'pao', 'pei', 'pen', 'peng', 'pi', 'pian', 'piao', 'pie', 'pin', 'ping', 'po', 'pou', 'pu', 'qi', 'qia', 'qian', 'qiang', 'qiao', 'qie', 'qin', 'qing', 'qiong', 'qiu', 'qu', 'quan', 'que', 'qun', 'ran', 'rang', 'rao', 're', 'ren', 'reng', 'ri', 'rong', 'rou', 'ru', 'rua', 'ruan', 'rui', 'run', 'ruo', 'sa', 'sai', 'san', 'sang', 'sao', 'se', 'sei', 'sen', 'seng', 'sha', 'shai', 'shan', 'shang', 'shao', 'she', 'shei', 'shen', 'sheng', 'shi', 'shong', 'shou', 'shu', 'shua', 'shuai', 'shuan', 'shuang', 'shui', 'shun', 'shuo', 'si', 'song', 'sou', 'su', 'suan', 'sui', 'sun', 'suo', 'ta', 'tai', 'tan', 'tang', 'tao', 'te', 'tei', 'teng', 'ti', 'tian', 'tiao', 'tie', 'ting', 'tong', 'tou', 'tu', 'tuan', 'tui', 'tun', 'tuo', 'wa', 'wai', 'wan', 'wang', 'wei', 'wen', 'weng', 'wo', 'wu', 'xi', 'xia', 'xian', 'xiang', 'xiao', 'xie', 'xin', 'xing', 'xiong', 'xiu', 'xu', 'xuan', 'xue', 'xun', 'ya', 'yai', 'yan', 'yang', 'yao', 'ye', 'yi', 'yin', 'ying', 'yo', 'yong', 'you', 'yu', 'yuan', 'yue', 'yun', 'za', 'zai', 'zan', 'zang', 'zao', 'ze', 'zei', 'zen', 'zeng', 'zha', 'zhai', 'zhan', 'zhang', 'zhao', 'zhe', 'zhei', 'zhen', 'zheng', 'zhi', 'zhong', 'zhou', 'zhu', 'zhua', 'zhuai', 'zhuan', 'zhuang', 'zhui', 'zhun', 'zhuo', 'zi', 'zong', 'zou', 'zu', 'zuan', 'zui', 'zun', 'zuo', 'ê' ] def __init__(self): self.trie = CharTrie() for py in self.pylist: self.trie[py] = len(py) def split(self, phrase): phrase_lc = phrase.lower() split_list = [] results = [] if phrase: split_list.append((phrase, phrase_lc, [])) while split_list: pair = split_list.pop() phrase = pair[0] phrase_lc = pair[1] words = pair[2] matches = self.trie.prefixes(phrase_lc) for match in matches: n = match[1] word = phrase[:n] tail = phrase[n:] tail_lc = phrase_lc[n:] words_copy = copy.deepcopy(words) words_copy.append(word) if tail: split_list.append((tail, tail_lc, words_copy)) else: results.append(words_copy) return results if __name__ == "__main__": import doctest doctest.testmod()

50.080357

192

0.456409

from pygtrie import CharTrie import copy class PinyinSplit: pylist = [ 'a', 'ai', 'an', 'ang', 'ao', 'ba', 'bai', 'ban', 'bang', 'bao', 'bei', 'ben', 'beng', 'bi', 'bian', 'biang', 'biao', 'bie', 'bin', 'bing', 'bo', 'bu', 'ca', 'cai', 'can', 'cang', 'cao', 'ce', 'cen', 'ceng', 'cha', 'chai', 'chan', 'chang', 'chao', 'che', 'chen', 'cheng', 'chi', 'chong', 'chou', 'chu', 'chua', 'chuai', 'chuan', 'chuang', 'chui', 'chun', 'chuo', 'ci', 'cong', 'cou', 'cu', 'cuan', 'cui', 'cun', 'cuo', 'da', 'dai', 'dan', 'dang', 'dao', 'de', 'dei', 'den', 'deng', 'di', 'dia', 'dian', 'diang', 'diao', 'die', 'ding', 'diu', 'dong', 'dou', 'du', 'duan', 'dui', 'dun', 'duo', 'e', 'ei', 'en', 'eng', 'er', 'fa', 'fan', 'fang', 'fei', 'fen', 'feng', 'fiao', 'fo', 'fou', 'fu', 'ga', 'gai', 'gan', 'gang', 'gao', 'ge', 'gei', 'gen', 'geng', 'gong', 'gou', 'gu', 'gua', 'guai', 'guan', 'guang', 'gui', 'gun', 'guo', 'ha', 'hai', 'han', 'hang', 'hao', 'he', 'hei', 'hen', 'heng', 'hong', 'hou', 'hu', 'hua', 'huai', 'huan', 'huang', 'hui', 'hun', 'huo', 'ji', 'jia', 'jian', 'jiang', 'jiao', 'jie', 'jin', 'jing', 'jiong', 'jiu', 'ju', 'juan', 'jue', 'jun', 'ka', 'kai', 'kan', 'kang', 'kao', 'ke', 'kei', 'ken', 'keng', 'kong', 'kou', 'ku', 'kua', 'kuai', 'kuan', 'kuang', 'kui', 'kun', 'kuo', 'la', 'lai', 'lan', 'lang', 'lao', 'le', 'lei', 'leng', 'li', 'lia', 'lian', 'liang', 'liao', 'lie', 'lin', 'ling', 'liu', 'long', 'lou', 'lu', 'luan', 'lue', 'lun', 'luo', 'lv', 'lve', 'lvn', 'lü', 'lüe', 'lün', 'ma', 'mai', 'man', 'mang', 'mao', 'me', 'mei', 'men', 'meng', 'mi', 'mian', 'miao', 'mie', 'min', 'ming', 'miu', 'mo', 'mou', 'mu', 'na', 'nai', 'nan', 'nang', 'nao', 'ne', 'nei', 'nen', 'neng', 'ni', 'nia', 'nian', 'niang', 'niao', 'nie', 'nin', 'ning', 'niu', 'nong', 'nou', 'nu', 'nuan', 'nue', 'nun', 'nuo', 'nv', 'nve', 'nü', 'nüe', 'ou', 'pa', 'pai', 'pan', 'pang', 'pao', 'pei', 'pen', 'peng', 'pi', 'pian', 'piao', 'pie', 'pin', 'ping', 'po', 'pou', 'pu', 'qi', 'qia', 'qian', 'qiang', 'qiao', 'qie', 'qin', 'qing', 'qiong', 'qiu', 'qu', 'quan', 'que', 'qun', 'ran', 'rang', 'rao', 're', 'ren', 'reng', 'ri', 'rong', 'rou', 'ru', 'rua', 'ruan', 'rui', 'run', 'ruo', 'sa', 'sai', 'san', 'sang', 'sao', 'se', 'sei', 'sen', 'seng', 'sha', 'shai', 'shan', 'shang', 'shao', 'she', 'shei', 'shen', 'sheng', 'shi', 'shong', 'shou', 'shu', 'shua', 'shuai', 'shuan', 'shuang', 'shui', 'shun', 'shuo', 'si', 'song', 'sou', 'su', 'suan', 'sui', 'sun', 'suo', 'ta', 'tai', 'tan', 'tang', 'tao', 'te', 'tei', 'teng', 'ti', 'tian', 'tiao', 'tie', 'ting', 'tong', 'tou', 'tu', 'tuan', 'tui', 'tun', 'tuo', 'wa', 'wai', 'wan', 'wang', 'wei', 'wen', 'weng', 'wo', 'wu', 'xi', 'xia', 'xian', 'xiang', 'xiao', 'xie', 'xin', 'xing', 'xiong', 'xiu', 'xu', 'xuan', 'xue', 'xun', 'ya', 'yai', 'yan', 'yang', 'yao', 'ye', 'yi', 'yin', 'ying', 'yo', 'yong', 'you', 'yu', 'yuan', 'yue', 'yun', 'za', 'zai', 'zan', 'zang', 'zao', 'ze', 'zei', 'zen', 'zeng', 'zha', 'zhai', 'zhan', 'zhang', 'zhao', 'zhe', 'zhei', 'zhen', 'zheng', 'zhi', 'zhong', 'zhou', 'zhu', 'zhua', 'zhuai', 'zhuan', 'zhuang', 'zhui', 'zhun', 'zhuo', 'zi', 'zong', 'zou', 'zu', 'zuan', 'zui', 'zun', 'zuo', 'ê' ] def __init__(self): self.trie = CharTrie() for py in self.pylist: self.trie[py] = len(py) def split(self, phrase): phrase_lc = phrase.lower() split_list = [] results = [] if phrase: split_list.append((phrase, phrase_lc, [])) while split_list: pair = split_list.pop() phrase = pair[0] phrase_lc = pair[1] words = pair[2] matches = self.trie.prefixes(phrase_lc) for match in matches: n = match[1] word = phrase[:n] tail = phrase[n:] tail_lc = phrase_lc[n:] words_copy = copy.deepcopy(words) words_copy.append(word) if tail: split_list.append((tail, tail_lc, words_copy)) else: results.append(words_copy) return results if __name__ == "__main__": import doctest doctest.testmod()

true

f71b39087446ecc9cc6e057576d78b80e52404ee

340

py

Python

src/chenv/__init__.py

jonathan-shemer/chenv

e2b86b7a53031a35def1be21ece87a05d74d2919

[ "MIT" ]

3

2020-10-15T07:46:48.000Z

2021-09-06T20:49:05.000Z

src/chenv/__init__.py

jonathan-shemer/chenv

e2b86b7a53031a35def1be21ece87a05d74d2919

[ "MIT" ]

5

2021-01-27T11:47:12.000Z

2021-08-30T08:49:37.000Z

src/chenv/__init__.py

jonathan-shemer/chenv

e2b86b7a53031a35def1be21ece87a05d74d2919

[ "MIT" ]

1

2022-03-15T09:29:19.000Z

"""chenv.""" try: from importlib.metadata import version, PackageNotFoundError # type: ignore except ImportError: # pragma: no cover from importlib_metadata import version, PackageNotFoundError # type: ignore try: __version__ = version(__name__) except PackageNotFoundError: # pragma: no cover __version__ = "unknown"

28.333333

80

0.744118

try: from importlib.metadata import version, PackageNotFoundError except ImportError: from importlib_metadata import version, PackageNotFoundError try: __version__ = version(__name__) except PackageNotFoundError: __version__ = "unknown"

true

f71b39d53d82554ce904392600c340709e0534bb

2,018

py

Python

pandas/tests/indexing/multiindex/test_chaining_and_caching.py

oricou/pandas

9405e58d9268041f5416711c051cf5429a19bf49

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

2

2021-05-07T04:58:36.000Z

2021-05-07T04:58:59.000Z

pandas/tests/indexing/multiindex/test_chaining_and_caching.py

oricou/pandas

9405e58d9268041f5416711c051cf5429a19bf49

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

pandas/tests/indexing/multiindex/test_chaining_and_caching.py

oricou/pandas

9405e58d9268041f5416711c051cf5429a19bf49

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

2

2021-06-16T07:19:12.000Z

2021-12-16T10:24:44.000Z

import numpy as np import pytest from pandas import ( DataFrame, MultiIndex, Series, ) import pandas._testing as tm import pandas.core.common as com def test_detect_chained_assignment(): # Inplace ops, originally from: # https://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug a = [12, 23] b = [123, None] c = [1234, 2345] d = [12345, 23456] tuples = [("eyes", "left"), ("eyes", "right"), ("ears", "left"), ("ears", "right")] events = { ("eyes", "left"): a, ("eyes", "right"): b, ("ears", "left"): c, ("ears", "right"): d, } multiind = MultiIndex.from_tuples(tuples, names=["part", "side"]) zed = DataFrame(events, index=["a", "b"], columns=multiind) msg = "A value is trying to be set on a copy of a slice from a DataFrame" with pytest.raises(com.SettingWithCopyError, match=msg): zed["eyes"]["right"].fillna(value=555, inplace=True) def test_cache_updating(): # 5216 # make sure that we don't try to set a dead cache a = np.random.rand(10, 3) df = DataFrame(a, columns=["x", "y", "z"]) tuples = [(i, j) for i in range(5) for j in range(2)] index = MultiIndex.from_tuples(tuples) df.index = index # setting via chained assignment # but actually works, since everything is a view df.loc[0]["z"].iloc[0] = 1.0 result = df.loc[(0, 0), "z"] assert result == 1 # correct setting df.loc[(0, 0), "z"] = 2 result = df.loc[(0, 0), "z"] assert result == 2 @pytest.mark.arm_slow def test_indexer_caching(): # GH5727 # make sure that indexers are in the _internal_names_set n = 1000001 arrays = (range(n), range(n)) index = MultiIndex.from_tuples(zip(*arrays)) s = Series(np.zeros(n), index=index) str(s) # setitem expected = Series(np.ones(n), index=index) s = Series(np.zeros(n), index=index) s[s == 0] = 1 tm.assert_series_equal(s, expected)

28.422535

118

0.606541

import numpy as np import pytest from pandas import ( DataFrame, MultiIndex, Series, ) import pandas._testing as tm import pandas.core.common as com def test_detect_chained_assignment(): a = [12, 23] b = [123, None] c = [1234, 2345] d = [12345, 23456] tuples = [("eyes", "left"), ("eyes", "right"), ("ears", "left"), ("ears", "right")] events = { ("eyes", "left"): a, ("eyes", "right"): b, ("ears", "left"): c, ("ears", "right"): d, } multiind = MultiIndex.from_tuples(tuples, names=["part", "side"]) zed = DataFrame(events, index=["a", "b"], columns=multiind) msg = "A value is trying to be set on a copy of a slice from a DataFrame" with pytest.raises(com.SettingWithCopyError, match=msg): zed["eyes"]["right"].fillna(value=555, inplace=True) def test_cache_updating(): a = np.random.rand(10, 3) df = DataFrame(a, columns=["x", "y", "z"]) tuples = [(i, j) for i in range(5) for j in range(2)] index = MultiIndex.from_tuples(tuples) df.index = index # setting via chained assignment # but actually works, since everything is a view df.loc[0]["z"].iloc[0] = 1.0 result = df.loc[(0, 0), "z"] assert result == 1 # correct setting df.loc[(0, 0), "z"] = 2 result = df.loc[(0, 0), "z"] assert result == 2 @pytest.mark.arm_slow def test_indexer_caching(): # GH5727 # make sure that indexers are in the _internal_names_set n = 1000001 arrays = (range(n), range(n)) index = MultiIndex.from_tuples(zip(*arrays)) s = Series(np.zeros(n), index=index) str(s) # setitem expected = Series(np.ones(n), index=index) s = Series(np.zeros(n), index=index) s[s == 0] = 1 tm.assert_series_equal(s, expected)

true

f71b3ac0d45395a6a7e2ff0955877634c8665bca

3,872

py

Python

gym-kinova-gripper/plotting_code/other_plots.py

OSUrobotics/KinovaGrasping

f22af60d3683fdc4ffecf49ccff179fbc6750748

[ "Linux-OpenIB" ]

16

2020-05-16T00:40:31.000Z

2022-02-22T11:59:03.000Z

gym-kinova-gripper/plotting_code/other_plots.py

OSUrobotics/KinovaGrasping

f22af60d3683fdc4ffecf49ccff179fbc6750748

[ "Linux-OpenIB" ]

9

2020-08-10T08:33:55.000Z

2021-08-17T02:10:50.000Z

gym-kinova-gripper/plotting_code/other_plots.py

OSUrobotics/KinovaGrasping

f22af60d3683fdc4ffecf49ccff179fbc6750748

[ "Linux-OpenIB" ]

7

2020-07-27T09:45:05.000Z

2021-06-21T21:42:50.000Z

import matplotlib.pyplot as plt import numpy as np ## Extra plotting functions that can be called for quick analysis def plot_timestep_distribution(success_timesteps=None, fail_timesteps=None, all_timesteps=None, expert_saving_dir=None): """ Plot the distribution of time steps over successful and failed episodes """ if all_timesteps is None: success_timesteps = np.load(expert_saving_dir + "/success_timesteps.npy") fail_timesteps = np.load(expert_saving_dir + "/fail_timesteps.npy") all_timesteps = np.load(expert_saving_dir + "/all_timesteps.npy") n_bins = 40 # We can set the number of bins with the `bins` kwarg plt.hist(all_timesteps, bins=n_bins, color="g") plt.title("Total time steps distribution for all episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.xlim(0, 800) plt.savefig(expert_saving_dir + "/total_timestep_distribution") plt.clf() plt.hist(success_timesteps, bins=n_bins, color="b") plt.title("Time steps distribution for Successful episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/success_timestep_distribution") plt.clf() plt.hist(fail_timesteps, bins=n_bins, color="r") plt.title("Time steps distribution for Failed episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/fail_timestep_distribution") plt.clf() ''' # Plot the average velocity over an episode def plot_average_velocity(replay_buffer,num_timesteps): """ Plot the average velocity over a certain number of episodes """ velocity_dir = "./expert_average_velocity" if not os.path.isdir(velocity_dir): os.mkdir(velocity_dir) #num_episodes = len(f1_vels) #plt.plot(np.arrange(len(f1_vels)), f1_vels) max_timesteps = 30 timestep_vel_count = np.zeros(max_timesteps) wrist_avg_vels = np.zeros(max_timesteps) f1_avg_vels = np.zeros(max_timesteps) f2_avg_vels = np.zeros(max_timesteps) f3_avg_vels = np.zeros(max_timesteps) for episode_actions in replay_buffer.action: for timestep_idx in range(len(episode_actions)): timestep_vel_count[timestep_idx] += 1 wrist_avg_vels[timestep_idx] = (wrist_avg_vels[timestep_idx] + episode_actions[timestep_idx][0]) / timestep_vel_count[timestep_idx] f1_avg_vels[timestep_idx] = (f1_avg_vels[timestep_idx] + episode_actions[timestep_idx][1]) / \ timestep_vel_count[timestep_idx] f2_avg_vels[timestep_idx] = (f2_avg_vels[timestep_idx] + episode_actions[timestep_idx][2]) / \ timestep_vel_count[timestep_idx] f3_avg_vels[timestep_idx] = (f3_avg_vels[timestep_idx] + episode_actions[timestep_idx][3]) / \ timestep_vel_count[timestep_idx] num_episodes = len(replay_buffer.action) print("replay_buffer.action: ",replay_buffer.action) print("f1_avg_vels: ",f1_avg_vels) plt.plot(np.arange(num_timesteps), f1_avg_vels, color="r", label="Finger1") plt.plot(np.arange(num_timesteps), f2_avg_vels, color="b", label="Finger2") plt.plot(np.arange(num_timesteps), f3_avg_vels, color="g", label="Finger3") plt.plot(np.arange(num_timesteps), wrist_avg_vels, color="y", label="Wrist") plt.legend() plt.title("Average velocity over "+str(num_episodes)+" episodes", weight='bold') plt.xlabel('Timestep within an episode') plt.ylabel('Average Velocity at Timestep') #plt.savefig(velocity_dir + "/velocity_plot") #plt.clf() plt.show() '''

46.650602

143

0.698089

import matplotlib.pyplot as plt import numpy as np mesteps=None, all_timesteps=None, expert_saving_dir=None): if all_timesteps is None: success_timesteps = np.load(expert_saving_dir + "/success_timesteps.npy") fail_timesteps = np.load(expert_saving_dir + "/fail_timesteps.npy") all_timesteps = np.load(expert_saving_dir + "/all_timesteps.npy") n_bins = 40 plt.hist(all_timesteps, bins=n_bins, color="g") plt.title("Total time steps distribution for all episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.xlim(0, 800) plt.savefig(expert_saving_dir + "/total_timestep_distribution") plt.clf() plt.hist(success_timesteps, bins=n_bins, color="b") plt.title("Time steps distribution for Successful episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/success_timestep_distribution") plt.clf() plt.hist(fail_timesteps, bins=n_bins, color="r") plt.title("Time steps distribution for Failed episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/fail_timestep_distribution") plt.clf()

true

f71b3b475439ea9ed08d69fbc7b9ab409bb33d5a

2,223

py

Python

src/utils.py

wenyuC94/LogConcComp

b17d6ba6a102ba83a8415774b0e6da27a362bd5d

[ "MIT" ]

null

src/utils.py

wenyuC94/LogConcComp

b17d6ba6a102ba83a8415774b0e6da27a362bd5d

[ "MIT" ]

null

src/utils.py

wenyuC94/LogConcComp

b17d6ba6a102ba83a8415774b0e6da27a362bd5d

[ "MIT" ]

null

import os import numpy as np import numba as nb def create_folder(storage_path): if not os.path.isdir(storage_path): os.makedirs(storage_path,exist_ok=True) lsdir = os.listdir(storage_path) for item in ["info","hist","soln","figs"]: if item not in lsdir: os.makedirs(storage_path+item+"/",exist_ok=True) if item == "figs": lsdir_figs = os.listdir(storage_path+item+"/") for item1 in ["crop","raw"]: if item1 not in lsdir_figs: os.makedirs(storage_path+item+"/"+item1+"/",exist_ok=True) def time_to_string(runtime): seconds = runtime%60 runmins = (runtime-seconds)/60 mins = int(runmins%60) runhrs = (runmins-mins)/60 hrs = int(runhrs) return "%.2d:%.2d:%05.2f"%(hrs,mins,seconds) def multivariate_laplace(n,d,rng=None, random_state=None): rng = rng if rng is not None else np.random.RandomState(random_state) X = rng.randn(n,d) Z = rng.exponential(size=(n,1)) return X*np.sqrt(Z) @nb.njit(cache=True) def np_apply_along_axis(func1d, axis, arr): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: result = np.empty(arr.shape[1]) for i in range(len(result)): result[i] = func1d(arr[:, i]) else: result = np.empty(arr.shape[0]) for i in range(len(result)): result[i] = func1d(arr[i, :]) return result @nb.njit(cache=True) def np_apply_along_axis_kd(funckd, axis, arr, k = -1): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: k = k if k > 0 else arr.shape[0] result = np.empty((k,arr.shape[1])) for i in range(arr.shape[1]): result[:, i] = funckd(arr[:, i]) else: k = k if k > 0 else arr.shape[1] result = np.empty((arr.shape[0],k)) for i in range(arr.shape[0]): result[i, :] = funckd(arr[i, :]) return result @nb.njit(cache=True) def split(n, B): sep = n//B rem = n%B indices = [] last = 0 cur = 0 for i in range(B): cur = last + sep + (i < rem) indices.append(cur) last = cur return indices

28.5

78

0.559154

import os import numpy as np import numba as nb def create_folder(storage_path): if not os.path.isdir(storage_path): os.makedirs(storage_path,exist_ok=True) lsdir = os.listdir(storage_path) for item in ["info","hist","soln","figs"]: if item not in lsdir: os.makedirs(storage_path+item+"/",exist_ok=True) if item == "figs": lsdir_figs = os.listdir(storage_path+item+"/") for item1 in ["crop","raw"]: if item1 not in lsdir_figs: os.makedirs(storage_path+item+"/"+item1+"/",exist_ok=True) def time_to_string(runtime): seconds = runtime%60 runmins = (runtime-seconds)/60 mins = int(runmins%60) runhrs = (runmins-mins)/60 hrs = int(runhrs) return "%.2d:%.2d:%05.2f"%(hrs,mins,seconds) def multivariate_laplace(n,d,rng=None, random_state=None): rng = rng if rng is not None else np.random.RandomState(random_state) X = rng.randn(n,d) Z = rng.exponential(size=(n,1)) return X*np.sqrt(Z) @nb.njit(cache=True) def np_apply_along_axis(func1d, axis, arr): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: result = np.empty(arr.shape[1]) for i in range(len(result)): result[i] = func1d(arr[:, i]) else: result = np.empty(arr.shape[0]) for i in range(len(result)): result[i] = func1d(arr[i, :]) return result @nb.njit(cache=True) def np_apply_along_axis_kd(funckd, axis, arr, k = -1): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: k = k if k > 0 else arr.shape[0] result = np.empty((k,arr.shape[1])) for i in range(arr.shape[1]): result[:, i] = funckd(arr[:, i]) else: k = k if k > 0 else arr.shape[1] result = np.empty((arr.shape[0],k)) for i in range(arr.shape[0]): result[i, :] = funckd(arr[i, :]) return result @nb.njit(cache=True) def split(n, B): sep = n//B rem = n%B indices = [] last = 0 cur = 0 for i in range(B): cur = last + sep + (i < rem) indices.append(cur) last = cur return indices

true

f71b3b5ffd9a0d39ef3e2a5f01e2965d34e8b74e

1,448

py

Python

make_string_alphabetic.py

Maffey/FunPythonScripts

7682ab1f8dc7924eb4b3fb19d58ebbabe3901a22

[ "Apache-2.0" ]

1

2022-01-29T21:07:38.000Z

make_string_alphabetic.py

Maffey/fun-python-scripts

7682ab1f8dc7924eb4b3fb19d58ebbabe3901a22

[ "Apache-2.0" ]

2

2019-05-15T13:48:58.000Z

2019-05-29T18:33:34.000Z

make_string_alphabetic.py

Maffey/FunPythonScripts

7682ab1f8dc7924eb4b3fb19d58ebbabe3901a22

[ "Apache-2.0" ]

1

2020-08-04T11:09:17.000Z

#! python # Removes letter from word to make characters go alphabetically. # It doesn't work all the time, but is efficient. import unittest class TestRemoveLettersAlphabet(unittest.TestCase): def test_object1(self): self.assertEqual(letters_to_remove('mateusz'), 3) def test_object2(self): self.assertEqual(letters_to_remove('cba'), 2) def test_object3(self): self.assertEqual(letters_to_remove('dirt'), 0) def test_object4(self): self.assertEqual(letters_to_remove('jablko'), 2) def test_repeating_letters1(self): self.assertEqual(letters_to_remove('gabriela'), 5) def test_repeating_letters2(self): self.assertEqual(letters_to_remove('banana'), 3) def test_repeating_letters3(self): self.assertEqual(letters_to_remove('apple'), 2) def letters_to_remove(string: str) -> int: string = list(string) sorted_string = sorted(string) letters_removed = 0 remaining_string = "" for character in sorted_string: index = string.index(character) to_remove = string[:index] letters_removed += len(to_remove) for letter in to_remove: string.remove(letter) sorted_string.remove(letter) remaining_string += character string.remove(character) print(f"[+] Remaining string: {remaining_string}") return letters_removed if __name__ == "__main__": unittest.main()

27.320755

64

0.685083

import unittest class TestRemoveLettersAlphabet(unittest.TestCase): def test_object1(self): self.assertEqual(letters_to_remove('mateusz'), 3) def test_object2(self): self.assertEqual(letters_to_remove('cba'), 2) def test_object3(self): self.assertEqual(letters_to_remove('dirt'), 0) def test_object4(self): self.assertEqual(letters_to_remove('jablko'), 2) def test_repeating_letters1(self): self.assertEqual(letters_to_remove('gabriela'), 5) def test_repeating_letters2(self): self.assertEqual(letters_to_remove('banana'), 3) def test_repeating_letters3(self): self.assertEqual(letters_to_remove('apple'), 2) def letters_to_remove(string: str) -> int: string = list(string) sorted_string = sorted(string) letters_removed = 0 remaining_string = "" for character in sorted_string: index = string.index(character) to_remove = string[:index] letters_removed += len(to_remove) for letter in to_remove: string.remove(letter) sorted_string.remove(letter) remaining_string += character string.remove(character) print(f"[+] Remaining string: {remaining_string}") return letters_removed if __name__ == "__main__": unittest.main()

true

f71b3ba93636d762ed00bb7c81089b7edadd08c2

1,407

py

Python

verifiers/statusCode.py

CalConnect/caldavtester

a17683554df8a9b80fceab91085de99945fefe48

[ "Apache-2.0" ]

2

2019-04-23T04:06:08.000Z

2020-08-26T17:30:45.000Z

verifiers/statusCode.py

CalConnect/caldavtester

a17683554df8a9b80fceab91085de99945fefe48

[ "Apache-2.0" ]

3

2017-09-28T13:23:39.000Z

2017-10-03T15:59:36.000Z

verifiers/statusCode.py

CalConnect/caldavtester

a17683554df8a9b80fceab91085de99945fefe48

[ "Apache-2.0" ]

2

2017-02-08T04:47:31.000Z

2021-08-05T20:09:10.000Z

## # Copyright (c) 2006-2016 Apple Inc. 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. ## """ Verifier that chec ks the response status code for a specific value. """ class Verifier(object): def verify(self, manager, uri, response, respdata, args): # @UnusedVariable # If no status verification requested, then assume all 2xx codes are OK teststatus = args.get("status", ["2xx"]) for test in teststatus: if test[1:3] == "xx": test = int(test[0]) else: test = int(test) if test < 100: result = ((response.status / 100) == test) else: result = (response.status == test) if result: return True, "" return False, " HTTP Status Code Wrong: %d expected one of %s" % (response.status, ", ".join(teststatus))

34.317073

120

0.628998

class Verifier(object): def verify(self, manager, uri, response, respdata, args): teststatus = args.get("status", ["2xx"]) for test in teststatus: if test[1:3] == "xx": test = int(test[0]) else: test = int(test) if test < 100: result = ((response.status / 100) == test) else: result = (response.status == test) if result: return True, "" return False, " HTTP Status Code Wrong: %d expected one of %s" % (response.status, ", ".join(teststatus))

true

f71b3d10ad093122f20e0962b3f6645057d8279b

613

py

Python

classifier_stgcn_real_only/utils/temp.py

1suancaiyu/STEP

54195112990feaee137f5137775c736d07c2d26f

[ "MIT" ]

32

2020-02-21T16:12:13.000Z

2022-03-11T09:00:47.000Z

classifier_stgcn_real_only/utils/temp.py

1suancaiyu/STEP

54195112990feaee137f5137775c736d07c2d26f

[ "MIT" ]

12

2020-06-23T08:11:25.000Z

2022-03-26T11:34:42.000Z

classifier_stgcn_real_only/utils/temp.py

1suancaiyu/STEP

54195112990feaee137f5137775c736d07c2d26f

[ "MIT" ]

13

2020-04-01T16:51:50.000Z

2022-03-03T10:15:10.000Z

import h5py import os import numpy as np base_path = os.path.dirname(os.path.realpath(__file__)) feature_file = '/media/uttaran/FCE1-7BF3/Gamma/Gait/classifier_stgcn/model_classifier_stgcn/featuresCombineddeep_features.txt' f = np.loadtxt(feature_file) fCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/featuresCombined.h5', 'r') fkeys = fCombined.keys() dfCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/deepFeaturesCombined.h5', 'w') for i, fkey in enumerate(fkeys): fname = [fkey][0] feature = f[i, :] dfCombined.create_dataset(fname, data=feature) dfCombined.close()

38.3125

126

0.76509

import h5py import os import numpy as np base_path = os.path.dirname(os.path.realpath(__file__)) feature_file = '/media/uttaran/FCE1-7BF3/Gamma/Gait/classifier_stgcn/model_classifier_stgcn/featuresCombineddeep_features.txt' f = np.loadtxt(feature_file) fCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/featuresCombined.h5', 'r') fkeys = fCombined.keys() dfCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/deepFeaturesCombined.h5', 'w') for i, fkey in enumerate(fkeys): fname = [fkey][0] feature = f[i, :] dfCombined.create_dataset(fname, data=feature) dfCombined.close()

true

f71b3da82b76452958e6ff8037b2e5c369373cfd

2,253

py

Python

python/hostconfig/machines/alexf.py

stu-l/Chaste

8efa8b440660553af66804067639f237c855f557

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

null

python/hostconfig/machines/alexf.py

stu-l/Chaste

8efa8b440660553af66804067639f237c855f557

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

null

python/hostconfig/machines/alexf.py

stu-l/Chaste

8efa8b440660553af66804067639f237c855f557

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

null

# Configuration for Finarfin """Copyright (c) 2005-2022, University of Oxford. All rights reserved. University of Oxford means the Chancellor, Masters and Scholars of the University of Oxford, having an administrative office at Wellington Square, Oxford OX1 2JD, UK. This file is part of Chaste. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the University of Oxford nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ petsc_2_2_path = None petsc_2_3_path = '/home/alex/petsc-2.3.2-p4/' petsc_build_name = 'linux-gnu' petsc_build_name_optimized = 'linux-gnu-opt' dealii_path = None metis_path = None intel_path = None #'/opt/intel_cc_80' #icpc = 'icpc' other_includepaths = [] other_libpaths = ['/home/alex/hdf5/lib'] blas_lapack = ['lapack', 'blas'] other_libraries = ['boost_serialization', 'boost_filesystem', 'xerces-c'] tools = {'mpirun': '/home/alex/mpi/bin/mpirun', 'mpicxx': '/home/alex/mpi/bin/mpicxx'}

44.176471

79

0.782956

petsc_2_2_path = None petsc_2_3_path = '/home/alex/petsc-2.3.2-p4/' petsc_build_name = 'linux-gnu' petsc_build_name_optimized = 'linux-gnu-opt' dealii_path = None metis_path = None intel_path = None other_includepaths = [] other_libpaths = ['/home/alex/hdf5/lib'] blas_lapack = ['lapack', 'blas'] other_libraries = ['boost_serialization', 'boost_filesystem', 'xerces-c'] tools = {'mpirun': '/home/alex/mpi/bin/mpirun', 'mpicxx': '/home/alex/mpi/bin/mpicxx'}

true

f71b400d9193c88784e05204664ee4a910ee628d

1,831

py

Python

awss3upload-capi/s3upload.py

krlex/aws-python-examples

1310e82c748e3d0898809bbe909870fe4c7cb37b

[ "MIT" ]

21

2021-02-13T04:11:01.000Z

2022-03-28T09:13:53.000Z

awss3upload-capi/s3upload.py

krlex/aws-python-examples

1310e82c748e3d0898809bbe909870fe4c7cb37b

[ "MIT" ]

null

awss3upload-capi/s3upload.py

krlex/aws-python-examples

1310e82c748e3d0898809bbe909870fe4c7cb37b

[ "MIT" ]

17

2019-07-26T06:02:27.000Z

2022-03-23T00:06:12.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # s3upload.py # It is an example that handles S3 buckets on AWS. # It uses Client API (low-level) of Boto3. # Upload a local file to a S3 bucket. # You must provide 1 parameter: # BUCKET_NAME = Name of the bucket # OBJECT_NAME = Object file name in the bucket # LOCAL_FILE_NAME = Local file name import sys import os import boto3 import botocore def main(): # Make a list of command line arguments, omitting the [0] element # which is the script itself. args = sys.argv[1:] if len(args) < 3: print('Not enough parameters.\n'\ 'Proper Usage is: python s3upload.py '\ '<BUCKET_NAME> <OBJECT_NAME> <LOCAL_FILE_NAME>') sys.exit(1) bucket_name = args[0] key_name = args[1] local_file_name = args[2] print('Bucket: ' + bucket_name) print('Object/Key: ' + key_name) print('Local file: ' + local_file_name) # Create an S3 Client s3_client = boto3.client('s3') if not os.path.isfile(local_file_name): print("Error: File Not Found!!") sys.exit(1) # Upload object try: print('Uploading object ...') s3_client.upload_file(local_file_name, bucket_name, key_name) print('Uploaded') except botocore.exceptions.ClientError as e: if e.response['Error']['Code'] == "NoSuchBucket": print("Error: Bucket does not exist!!") elif e.response['Error']['Code'] == "InvalidBucketName": print("Error: Invalid Bucket name!!") elif e.response['Error']['Code'] == "AllAccessDisabled": print("Error: You do not have access to the Bucket!!") else: raise return # This is the standard boilerplate that calls the main() function. if __name__ == '__main__': main()

28.609375

69

0.621518

import sys import os import boto3 import botocore def main(): args = sys.argv[1:] if len(args) < 3: print('Not enough parameters.\n'\ 'Proper Usage is: python s3upload.py '\ '<BUCKET_NAME> <OBJECT_NAME> <LOCAL_FILE_NAME>') sys.exit(1) bucket_name = args[0] key_name = args[1] local_file_name = args[2] print('Bucket: ' + bucket_name) print('Object/Key: ' + key_name) print('Local file: ' + local_file_name) s3_client = boto3.client('s3') if not os.path.isfile(local_file_name): print("Error: File Not Found!!") sys.exit(1) try: print('Uploading object ...') s3_client.upload_file(local_file_name, bucket_name, key_name) print('Uploaded') except botocore.exceptions.ClientError as e: if e.response['Error']['Code'] == "NoSuchBucket": print("Error: Bucket does not exist!!") elif e.response['Error']['Code'] == "InvalidBucketName": print("Error: Invalid Bucket name!!") elif e.response['Error']['Code'] == "AllAccessDisabled": print("Error: You do not have access to the Bucket!!") else: raise return if __name__ == '__main__': main()

true

f71b4058fcf0bfe5202371ab731ffe619ab85852

827

py

Python

src/lambda/face-detector-function/main/image_ops/resizer.py

gai6948/video-analytics-for-ppe-compliance

bb41ac010f5917bd7e85adfed689489f24830617

[ "Apache-2.0" ]

null

src/lambda/face-detector-function/main/image_ops/resizer.py

gai6948/video-analytics-for-ppe-compliance

bb41ac010f5917bd7e85adfed689489f24830617

[ "Apache-2.0" ]

null

src/lambda/face-detector-function/main/image_ops/resizer.py

gai6948/video-analytics-for-ppe-compliance

bb41ac010f5917bd7e85adfed689489f24830617

[ "Apache-2.0" ]

null

import numpy as np import timeit import cv2 from PIL import Image from aws_lambda_powertools.logging import Logger from aws_lambda_powertools.tracing import Tracer logger = Logger(service='face-detector', child=True) tracer = Tracer(service='face-detector') @tracer.capture_method(capture_response=False) def resize_image(frame: np.ndarray, target_image_width: int, target_image_height: int) -> np.ndarray: """ Resize the drawn image and save to /tmp directory before uploading to S3 :param `frame`: frame data in numpy array """ start_time = timeit.default_timer() new_frame: np.ndarray = cv2.resize(frame, dsize=( target_image_width, target_image_height), interpolation=cv2.INTER_LINEAR) logger.info(f'Resized frame after: {timeit.default_timer() - start_time}') return new_frame

33.08

101

0.759371

import numpy as np import timeit import cv2 from PIL import Image from aws_lambda_powertools.logging import Logger from aws_lambda_powertools.tracing import Tracer logger = Logger(service='face-detector', child=True) tracer = Tracer(service='face-detector') @tracer.capture_method(capture_response=False) def resize_image(frame: np.ndarray, target_image_width: int, target_image_height: int) -> np.ndarray: start_time = timeit.default_timer() new_frame: np.ndarray = cv2.resize(frame, dsize=( target_image_width, target_image_height), interpolation=cv2.INTER_LINEAR) logger.info(f'Resized frame after: {timeit.default_timer() - start_time}') return new_frame

true

f71b4158a84075698aa6f4b4d391c6b10747b9c5

2,190

py

Python

config/settings/local.py

suyash143/Base_todo

3284f24f8b5c611088af6189a2f264a280fbbbd6

[ "MIT" ]

1

2022-03-16T10:22:34.000Z

config/settings/local.py

suyash143/Base_todo

3284f24f8b5c611088af6189a2f264a280fbbbd6

[ "MIT" ]

1

2022-03-30T21:29:43.000Z

config/settings/local.py

suyash143/Base_todo

3284f24f8b5c611088af6189a2f264a280fbbbd6

[ "MIT" ]

null

from .base import * # noqa from .base import env # GENERAL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#debug DEBUG = True # https://docs.djangoproject.com/en/dev/ref/settings/#secret-key SECRET_KEY = env( "DJANGO_SECRET_KEY", default="00nep4XZom6FM9dVyJO6Y7kqt5JV8TN5GTNmcDnDhH0jTq3cDYEGLsyOsUYnOAsM", ) # https://docs.djangoproject.com/en/dev/ref/settings/#allowed-hosts ALLOWED_HOSTS = ["localhost", "0.0.0.0", "127.0.0.1"] # CACHES # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#caches CACHES = { "default": { "BACKEND": "django.core.cache.backends.locmem.LocMemCache", "LOCATION": "", } } # EMAIL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#email-backend EMAIL_BACKEND = env( "DJANGO_EMAIL_BACKEND", default="django.core.mail.backends.console.EmailBackend" ) # django-debug-toolbar # ------------------------------------------------------------------------------ # https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#prerequisites INSTALLED_APPS += ["debug_toolbar"] # noqa F405 # https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#middleware MIDDLEWARE += ["debug_toolbar.middleware.DebugToolbarMiddleware"] # noqa F405 # https://django-debug-toolbar.readthedocs.io/en/latest/configuration.html#debug-toolbar-config DEBUG_TOOLBAR_CONFIG = { "DISABLE_PANELS": ["debug_toolbar.panels.redirects.RedirectsPanel"], "SHOW_TEMPLATE_CONTEXT": True, } # https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#internal-ips INTERNAL_IPS = ["127.0.0.1", "10.0.2.2"] # django-extensions # ------------------------------------------------------------------------------ # https://django-extensions.readthedocs.io/en/latest/installation_instructions.html#configuration INSTALLED_APPS += ["django_extensions"] # noqa F405 # Your stuff... # ------------------------------------------------------------------------------

39.818182

97

0.583105

from .base import * from .base import env = True = env( "DJANGO_SECRET_KEY", default="00nep4XZom6FM9dVyJO6Y7kqt5JV8TN5GTNmcDnDhH0jTq3cDYEGLsyOsUYnOAsM", ) = ["localhost", "0.0.0.0", "127.0.0.1"] = { "default": { "BACKEND": "django.core.cache.backends.locmem.LocMemCache", "LOCATION": "", } } = env( "DJANGO_EMAIL_BACKEND", default="django.core.mail.backends.console.EmailBackend" ) S += ["debug_toolbar"] += ["debug_toolbar.middleware.DebugToolbarMiddleware"] = { "DISABLE_PANELS": ["debug_toolbar.panels.redirects.RedirectsPanel"], "SHOW_TEMPLATE_CONTEXT": True, } = ["127.0.0.1", "10.0.2.2"] S += ["django_extensions"]

true

f71b41b1f6968010d18f796949243c59c3f77265

2,847

py

Python

wagtail/hooks.py

stevedya/wagtail

52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c

[ "BSD-3-Clause" ]

1

2022-02-09T05:25:30.000Z

wagtail/hooks.py

stevedya/wagtail

52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c

[ "BSD-3-Clause" ]

null

wagtail/hooks.py

stevedya/wagtail

52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c

[ "BSD-3-Clause" ]

null

from contextlib import ContextDecorator from operator import itemgetter from wagtail.utils.apps import get_app_submodules _hooks = {} def register(hook_name, fn=None, order=0): """ Register hook for ``hook_name``. Can be used as a decorator:: @register('hook_name') def my_hook(...): pass or as a function call:: def my_hook(...): pass register('hook_name', my_hook) """ # Pretend to be a decorator if fn is not supplied if fn is None: def decorator(fn): register(hook_name, fn, order=order) return fn return decorator if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, order)) class TemporaryHook(ContextDecorator): def __init__(self, hooks, order): self.hooks = hooks self.order = order def __enter__(self): for hook_name, fn in self.hooks: if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, self.order)) def __exit__(self, exc_type, exc_value, traceback): for hook_name, fn in self.hooks: _hooks[hook_name].remove((fn, self.order)) def register_temporarily(hook_name_or_hooks, fn=None, *, order=0): """ Register hook for ``hook_name`` temporarily. This is useful for testing hooks. Can be used as a decorator:: def my_hook(...): pass class TestMyHook(Testcase): @hooks.register_temporarily('hook_name', my_hook) def test_my_hook(self): pass or as a context manager:: def my_hook(...): pass with hooks.register_temporarily('hook_name', my_hook): # Hook is registered here # Hook is unregistered here To register multiple hooks at the same time, pass in a list of 2-tuples: def my_hook(...): pass def my_other_hook(...): pass with hooks.register_temporarily([ ('hook_name', my_hook), ('hook_name', my_other_hook), ]): # Hooks are registered here """ if not isinstance(hook_name_or_hooks, list) and fn is not None: hooks = [(hook_name_or_hooks, fn)] else: hooks = hook_name_or_hooks return TemporaryHook(hooks, order) _searched_for_hooks = False def search_for_hooks(): global _searched_for_hooks if not _searched_for_hooks: list(get_app_submodules("wagtail_hooks")) _searched_for_hooks = True def get_hooks(hook_name): """Return the hooks function sorted by their order.""" search_for_hooks() hooks = _hooks.get(hook_name, []) hooks = sorted(hooks, key=itemgetter(1)) return [hook[0] for hook in hooks]

24.543103

82

0.60555

from contextlib import ContextDecorator from operator import itemgetter from wagtail.utils.apps import get_app_submodules _hooks = {} def register(hook_name, fn=None, order=0): if fn is None: def decorator(fn): register(hook_name, fn, order=order) return fn return decorator if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, order)) class TemporaryHook(ContextDecorator): def __init__(self, hooks, order): self.hooks = hooks self.order = order def __enter__(self): for hook_name, fn in self.hooks: if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, self.order)) def __exit__(self, exc_type, exc_value, traceback): for hook_name, fn in self.hooks: _hooks[hook_name].remove((fn, self.order)) def register_temporarily(hook_name_or_hooks, fn=None, *, order=0): if not isinstance(hook_name_or_hooks, list) and fn is not None: hooks = [(hook_name_or_hooks, fn)] else: hooks = hook_name_or_hooks return TemporaryHook(hooks, order) _searched_for_hooks = False def search_for_hooks(): global _searched_for_hooks if not _searched_for_hooks: list(get_app_submodules("wagtail_hooks")) _searched_for_hooks = True def get_hooks(hook_name): search_for_hooks() hooks = _hooks.get(hook_name, []) hooks = sorted(hooks, key=itemgetter(1)) return [hook[0] for hook in hooks]

true

f71b41c7e4894d5f578583e52382342d197a0a53

373

py

Python

tests/schema/mutation/snapshots/snap_test_maps_delete.py

TaiSakuma/acondbs

990ab44ce4081cc0e04148a8375f7ce7081c2dee

[ "MIT" ]

null

tests/schema/mutation/snapshots/snap_test_maps_delete.py

TaiSakuma/acondbs

990ab44ce4081cc0e04148a8375f7ce7081c2dee

[ "MIT" ]

null

tests/schema/mutation/snapshots/snap_test_maps_delete.py

TaiSakuma/acondbs

990ab44ce4081cc0e04148a8375f7ce7081c2dee

[ "MIT" ]

null

# -*- coding: utf-8 -*- # snapshottest: v1 - https://goo.gl/zC4yUc from __future__ import unicode_literals from snapshottest import Snapshot snapshots = Snapshot() snapshots['test_schema[deleteMap] 1'] = { 'data': { 'deleteMap': { 'ok': True } } } snapshots['test_schema[deleteMap] 2'] = { 'data': { 'map': None } }

16.217391

42

0.571046

from __future__ import unicode_literals from snapshottest import Snapshot snapshots = Snapshot() snapshots['test_schema[deleteMap] 1'] = { 'data': { 'deleteMap': { 'ok': True } } } snapshots['test_schema[deleteMap] 2'] = { 'data': { 'map': None } }

true

f71b4212c0a7a5b644b48292c8eaeacc6cbdda01

1,035

py

Python

src/products/mixins.py

bopopescu/django-estore

c092ffa965b8ef68e71d27d34a17fde1beacd90e

[ "MIT" ]

null

src/products/mixins.py

bopopescu/django-estore

c092ffa965b8ef68e71d27d34a17fde1beacd90e

[ "MIT" ]

null

src/products/mixins.py

bopopescu/django-estore

c092ffa965b8ef68e71d27d34a17fde1beacd90e

[ "MIT" ]

2

2019-04-29T14:16:10.000Z

2020-07-23T12:04:17.000Z

from django.contrib.admin.views.decorators import staff_member_required from django.contrib.auth.decorators import login_required from django.utils.decorators import method_decorator from django.http import Http404 class StaffRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(StaffRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): if request.user.is_staff: return super(StaffRequiredMixin, self).dispatch(request, *args, **kwargs) else: raise Http404 class LoginRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(LoginRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): return super(LoginRequiredMixin, self).dispatch(request, *args, **kwargs)

36.964286

85

0.713043

from django.contrib.admin.views.decorators import staff_member_required from django.contrib.auth.decorators import login_required from django.utils.decorators import method_decorator from django.http import Http404 class StaffRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(StaffRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): if request.user.is_staff: return super(StaffRequiredMixin, self).dispatch(request, *args, **kwargs) else: raise Http404 class LoginRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(LoginRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): return super(LoginRequiredMixin, self).dispatch(request, *args, **kwargs)

true

f71b4286b66f22ca7786f177364f1945a65ac3fc

3,123

py

Python

app/app/settings.py

M0narc/recipe-api

3b6c204ca76d98310d26fcbeaa4537646a93e023

[ "MIT" ]

1

2022-03-11T20:38:42.000Z

app/app/settings.py

M0narc/recipe-api

3b6c204ca76d98310d26fcbeaa4537646a93e023

[ "MIT" ]

null

app/app/settings.py

M0narc/recipe-api

3b6c204ca76d98310d26fcbeaa4537646a93e023

[ "MIT" ]

null

""" Django settings for app project. Generated by 'django-admin startproject' using Django 2.1.15. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'p2-y-!@#t0dny#e+nx-txbsphwp(yt(9t939=o_*sf%&3z2_p%' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'core', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'app.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'app.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' AUTH_USER_MODEL = 'core.User'

25.185484

91

0.693244

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'p2-y-!@#t0dny#e+nx-txbsphwp(yt(9t939=o_*sf%&3z2_p%' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'core', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'app.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'app.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' AUTH_USER_MODEL = 'core.User'

true

f71b43813e699367c1eaeee665cc3b3fd3d5c5d0

4,573

py

Python

hgext/fsmonitor/state.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

hgext/fsmonitor/state.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

hgext/fsmonitor/state.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

# state.py - fsmonitor persistent state # # Copyright 2013-2016 Facebook, Inc. # # This software may be used and distributed according to the terms of the # GNU General Public License version 2 or any later version. from __future__ import absolute_import import errno import os import socket import struct from mercurial.i18n import _ from mercurial import ( pathutil, util, ) _version = 4 _versionformat = ">I" class state(object): def __init__(self, repo): self._vfs = repo.vfs self._ui = repo.ui self._rootdir = pathutil.normasprefix(repo.root) self._lastclock = None self._identity = util.filestat(None) self.mode = self._ui.config('fsmonitor', 'mode', default='on') self.walk_on_invalidate = self._ui.configbool( 'fsmonitor', 'walk_on_invalidate', False) self.timeout = float(self._ui.config( 'fsmonitor', 'timeout', default='2')) def get(self): try: file = self._vfs('fsmonitor.state', 'rb') except IOError as inst: self._identity = util.filestat(None) if inst.errno != errno.ENOENT: raise return None, None, None self._identity = util.filestat.fromfp(file) versionbytes = file.read(4) if len(versionbytes) < 4: self._ui.log( 'fsmonitor', 'fsmonitor: state file only has %d bytes, ' 'nuking state\n' % len(versionbytes)) self.invalidate() return None, None, None try: diskversion = struct.unpack(_versionformat, versionbytes)[0] if diskversion != _version: # different version, nuke state and start over self._ui.log( 'fsmonitor', 'fsmonitor: version switch from %d to ' '%d, nuking state\n' % (diskversion, _version)) self.invalidate() return None, None, None state = file.read().split('\0') # state = hostname\0clock\0ignorehash\0 + list of files, each # followed by a \0 if len(state) < 3: self._ui.log( 'fsmonitor', 'fsmonitor: state file truncated (expected ' '3 chunks, found %d), nuking state\n', len(state)) self.invalidate() return None, None, None diskhostname = state[0] hostname = socket.gethostname() if diskhostname != hostname: # file got moved to a different host self._ui.log('fsmonitor', 'fsmonitor: stored hostname "%s" ' 'different from current "%s", nuking state\n' % (diskhostname, hostname)) self.invalidate() return None, None, None clock = state[1] ignorehash = state[2] # discard the value after the last \0 notefiles = state[3:-1] finally: file.close() return clock, ignorehash, notefiles def set(self, clock, ignorehash, notefiles): if clock is None: self.invalidate() return # Read the identity from the file on disk rather than from the open file # pointer below, because the latter is actually a brand new file. identity = util.filestat.frompath(self._vfs.join('fsmonitor.state')) if identity != self._identity: self._ui.debug('skip updating fsmonitor.state: identity mismatch\n') return try: file = self._vfs('fsmonitor.state', 'wb', atomictemp=True, checkambig=True) except (IOError, OSError): self._ui.warn(_("warning: unable to write out fsmonitor state\n")) return with file: file.write(struct.pack(_versionformat, _version)) file.write(socket.gethostname() + '\0') file.write(clock + '\0') file.write(ignorehash + '\0') if notefiles: file.write('\0'.join(notefiles)) file.write('\0') def invalidate(self): try: os.unlink(os.path.join(self._rootdir, '.hg', 'fsmonitor.state')) except OSError as inst: if inst.errno != errno.ENOENT: raise self._identity = util.filestat(None) def setlastclock(self, clock): self._lastclock = clock def getlastclock(self): return self._lastclock

33.625

80

0.557839

from __future__ import absolute_import import errno import os import socket import struct from mercurial.i18n import _ from mercurial import ( pathutil, util, ) _version = 4 _versionformat = ">I" class state(object): def __init__(self, repo): self._vfs = repo.vfs self._ui = repo.ui self._rootdir = pathutil.normasprefix(repo.root) self._lastclock = None self._identity = util.filestat(None) self.mode = self._ui.config('fsmonitor', 'mode', default='on') self.walk_on_invalidate = self._ui.configbool( 'fsmonitor', 'walk_on_invalidate', False) self.timeout = float(self._ui.config( 'fsmonitor', 'timeout', default='2')) def get(self): try: file = self._vfs('fsmonitor.state', 'rb') except IOError as inst: self._identity = util.filestat(None) if inst.errno != errno.ENOENT: raise return None, None, None self._identity = util.filestat.fromfp(file) versionbytes = file.read(4) if len(versionbytes) < 4: self._ui.log( 'fsmonitor', 'fsmonitor: state file only has %d bytes, ' 'nuking state\n' % len(versionbytes)) self.invalidate() return None, None, None try: diskversion = struct.unpack(_versionformat, versionbytes)[0] if diskversion != _version: self._ui.log( 'fsmonitor', 'fsmonitor: version switch from %d to ' '%d, nuking state\n' % (diskversion, _version)) self.invalidate() return None, None, None state = file.read().split('\0') if len(state) < 3: self._ui.log( 'fsmonitor', 'fsmonitor: state file truncated (expected ' '3 chunks, found %d), nuking state\n', len(state)) self.invalidate() return None, None, None diskhostname = state[0] hostname = socket.gethostname() if diskhostname != hostname: self._ui.log('fsmonitor', 'fsmonitor: stored hostname "%s" ' 'different from current "%s", nuking state\n' % (diskhostname, hostname)) self.invalidate() return None, None, None clock = state[1] ignorehash = state[2] notefiles = state[3:-1] finally: file.close() return clock, ignorehash, notefiles def set(self, clock, ignorehash, notefiles): if clock is None: self.invalidate() return identity = util.filestat.frompath(self._vfs.join('fsmonitor.state')) if identity != self._identity: self._ui.debug('skip updating fsmonitor.state: identity mismatch\n') return try: file = self._vfs('fsmonitor.state', 'wb', atomictemp=True, checkambig=True) except (IOError, OSError): self._ui.warn(_("warning: unable to write out fsmonitor state\n")) return with file: file.write(struct.pack(_versionformat, _version)) file.write(socket.gethostname() + '\0') file.write(clock + '\0') file.write(ignorehash + '\0') if notefiles: file.write('\0'.join(notefiles)) file.write('\0') def invalidate(self): try: os.unlink(os.path.join(self._rootdir, '.hg', 'fsmonitor.state')) except OSError as inst: if inst.errno != errno.ENOENT: raise self._identity = util.filestat(None) def setlastclock(self, clock): self._lastclock = clock def getlastclock(self): return self._lastclock

true

f71b45acabf22bbb1840898ea73829472d8e7060

731

py

Python

tests/test_python3_dbb.py

bopopescu/NovalIDE

590c2adb69d54fa4a6c9dad5459198be057b1329

[ "MulanPSL-1.0" ]

null

tests/test_python3_dbb.py

bopopescu/NovalIDE

590c2adb69d54fa4a6c9dad5459198be057b1329

[ "MulanPSL-1.0" ]

null

tests/test_python3_dbb.py

bopopescu/NovalIDE

590c2adb69d54fa4a6c9dad5459198be057b1329

[ "MulanPSL-1.0" ]

null

import inspect class _D: def _m(self): pass class _C: def _m(self): pass _x = _C() _x2 = _D() a=121111 r = input('hahah') print(r) raise AttributeError('Provider test already registered') print (type(_C),_x.__class__,dir(_x),"------------") import types ###print (dir(types)) print (type(inspect)) ###print type(inspect) is types.InstanceType,"=============" print (type(_x),type(type)) print (inspect.isclass(_x)) print (inspect.isclass(_x2)) print (inspect.isclass(_D)) print (inspect.isclass(_C)) print (inspect.ismodule(_C)) print (isinstance(inspect,object),"------------") print (1) ###print (g) print (2) print (3) print (4) print (6) print (7) print (8) print (9) print (10) print ("11111111111111")

17.404762

60

0.642955

import inspect class _D: def _m(self): pass class _C: def _m(self): pass _x = _C() _x2 = _D() a=121111 r = input('hahah') print(r) raise AttributeError('Provider test already registered') print (type(_C),_x.__class__,dir(_x),"------------") import types lass(_C)) print (inspect.ismodule(_C)) print (isinstance(inspect,object),"------------") print (1) print (4) print (6) print (7) print (8) print (9) print (10) print ("11111111111111")

true

f71b45baa1e78f59775296f091528acf3ccf2008

718

py

Python

meus_projetos/projetos_python/lista_de_tarefas/minhas_funcoes.py

SabinoEduardo/Python

f46e47f166150afdf0f4c4358b5848d52667a764

[ "MIT" ]

null

meus_projetos/projetos_python/lista_de_tarefas/minhas_funcoes.py

SabinoEduardo/Python

f46e47f166150afdf0f4c4358b5848d52667a764

[ "MIT" ]

null

meus_projetos/projetos_python/lista_de_tarefas/minhas_funcoes.py

SabinoEduardo/Python

f46e47f166150afdf0f4c4358b5848d52667a764

[ "MIT" ]

null

def adicionar_tarefa(lista_tarefas, tarefas): lista_tarefas.append(tarefas) def deletar_tarefa(lista_tarefas, tarefas_deletadas): """ Esta função serve para deletar a ultima tarefa da lista e guarda esta tarefa em outra lista. """ if not lista_tarefas: print("Nada a deletar") return tarefas_deletadas.append(lista_tarefas[-1]) lista_tarefas.pop() def repor_tarefa(lista_tarefas, tarefas_deletadas): """ Esta função serve para repor ultima tarefa deletada da lista de tarefas. """ if not tarefas_deletadas: print("Nada a repor") return else: lista_tarefas.append(tarefas_deletadas[-1]) tarefas_deletadas.pop()

27.615385

100

0.682451

def adicionar_tarefa(lista_tarefas, tarefas): lista_tarefas.append(tarefas) def deletar_tarefa(lista_tarefas, tarefas_deletadas): if not lista_tarefas: print("Nada a deletar") return tarefas_deletadas.append(lista_tarefas[-1]) lista_tarefas.pop() def repor_tarefa(lista_tarefas, tarefas_deletadas): if not tarefas_deletadas: print("Nada a repor") return else: lista_tarefas.append(tarefas_deletadas[-1]) tarefas_deletadas.pop()

true

f71b47bfd1af85c0318ff27db55a1a089a9d0ee9

4,549

py

Python

pyuvm/s09_phasing.py

ktbarrett/pyuvm

725e6e4b8088aa085a5ce16861b46db49ce46672

[ "Apache-2.0" ]

4

2021-11-07T13:22:27.000Z

2022-02-17T08:51:24.000Z

pyuvm/s09_phasing.py

ktbarrett/pyuvm

725e6e4b8088aa085a5ce16861b46db49ce46672

[ "Apache-2.0" ]

null

pyuvm/s09_phasing.py

ktbarrett/pyuvm

725e6e4b8088aa085a5ce16861b46db49ce46672

[ "Apache-2.0" ]

null

from pyuvm.s05_base_classes import uvm_object import pyuvm.error_classes as error_classes import cocotb # 9.1 # # This is a dramatically simplified version of UVM phasing. We don't have # to deal with simulation time and we are not going to deal with a generalized # phasing system. # # So this system simply traverses the common phases, calling the appropriate # method in each component. # # Much of the work in the SV phasing code has to do with handling the passage # of time. There is no timewheel in Python, so all of that code can go # away. # # Also, the generalized phasing system is rarely used and so that # is left as an exercise for future developers. Instead we have a simple # topdown and bottom up traversal of calling methods in component # classes based on the phase name. # # We're not doing schedules or domains. We're just creating a list of classes # and traversing them in order. The order it dependent upon whether they # are topdown or bottom up phases. # 9.3.1.2 Class declaration class uvm_phase(uvm_object): # Strips the "uvm_" from this class's name and uses the remainder # to get a function call out of the component and execute it. # 'uvm_run_phase' becomes 'run_phase' and is called as 'run_phase()' @classmethod def execute(cls, comp): """ :param comp: The component whose turn it is to execute """ method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") method() def __str__(self): return self.__name__[4:] class uvm_topdown_phase(uvm_phase): """ Runs phases from the top down. """ @classmethod def traverse(cls, comp): """ Given a component, we traverse the component tree top to bottom calling the phase functions as we go :param comp: The component whose hierarchy will be traversed """ cls.execute(comp) # first we execute this node then its children for child in comp.get_children(): cls.traverse(child) class uvm_bottomup_phase(uvm_phase): """ Runs the phases from bottom up. """ @classmethod def traverse(cls, comp): for child in comp.get_children(): cls.traverse(child) cls.execute(comp) class uvm_threaded_execute_phase(uvm_phase): """ This phase launches the phase function in a thread and returns the thread to the caller. The caller can then join all the threads. """ @classmethod def execute(cls, comp): phase_name = cls.__name__ assert phase_name.startswith("uvm_"), \ "We only support phases whose names start with uvm_" method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") cocotb.start_soon(method()) # 9.8 Predefined Phases # 9.8.1 Common Phases # The common phases are described in the order of their execution. # 9.8.1.1 class uvm_build_phase(uvm_topdown_phase): ... # 9.8.1.2 class uvm_connect_phase(uvm_bottomup_phase): ... # 9.8.1.3 class uvm_end_of_elaboration_phase(uvm_topdown_phase): ... # 9.8.1.4 class uvm_start_of_simulation_phase(uvm_topdown_phase): ... # 9.8.1.5 class uvm_run_phase(uvm_threaded_execute_phase, uvm_bottomup_phase): ... # 9.8.1.6 class uvm_extract_phase(uvm_topdown_phase): ... # 9.8.1.7 class uvm_check_phase(uvm_topdown_phase): ... # 9.8.1.8 class uvm_report_phase(uvm_topdown_phase): ... # 9.8.1.9 class uvm_final_phase(uvm_topdown_phase): ... # 9.8.2 # UVM run-time phases are left as an exercise for an enterprising soul # I cannot imagine why anyone would implement this. # One could add phases by simply extending uvm_topdown_phase # or uvm_bottom_up phase with a new phase named 'uvm_my_phase' and adding # the my_phase() method to a uvm component with setattr. uvm_common_phases = [uvm_build_phase, uvm_connect_phase, uvm_end_of_elaboration_phase, uvm_start_of_simulation_phase, uvm_run_phase, uvm_extract_phase, uvm_check_phase, uvm_report_phase, uvm_final_phase]

27.737805

78

0.667619

from pyuvm.s05_base_classes import uvm_object import pyuvm.error_classes as error_classes import cocotb # to deal with simulation time and we are not going to deal with a generalized # phasing system. # # So this system simply traverses the common phases, calling the appropriate # method in each component. # # Much of the work in the SV phasing code has to do with handling the passage # of time. There is no timewheel in Python, so all of that code can go # away. # # Also, the generalized phasing system is rarely used and so that # is left as an exercise for future developers. Instead we have a simple # topdown and bottom up traversal of calling methods in component # classes based on the phase name. # # We're not doing schedules or domains. We're just creating a list of classes # and traversing them in order. The order it dependent upon whether they # are topdown or bottom up phases. # 9.3.1.2 Class declaration class uvm_phase(uvm_object): # Strips the "uvm_" from this class's name and uses the remainder @classmethod def execute(cls, comp): method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") method() def __str__(self): return self.__name__[4:] class uvm_topdown_phase(uvm_phase): @classmethod def traverse(cls, comp): cls.execute(comp) for child in comp.get_children(): cls.traverse(child) class uvm_bottomup_phase(uvm_phase): @classmethod def traverse(cls, comp): for child in comp.get_children(): cls.traverse(child) cls.execute(comp) class uvm_threaded_execute_phase(uvm_phase): @classmethod def execute(cls, comp): phase_name = cls.__name__ assert phase_name.startswith("uvm_"), \ "We only support phases whose names start with uvm_" method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") cocotb.start_soon(method()) class uvm_build_phase(uvm_topdown_phase): ... class uvm_connect_phase(uvm_bottomup_phase): ... class uvm_end_of_elaboration_phase(uvm_topdown_phase): ... class uvm_start_of_simulation_phase(uvm_topdown_phase): ... class uvm_run_phase(uvm_threaded_execute_phase, uvm_bottomup_phase): ... class uvm_extract_phase(uvm_topdown_phase): ... class uvm_check_phase(uvm_topdown_phase): ... class uvm_report_phase(uvm_topdown_phase): ... class uvm_final_phase(uvm_topdown_phase): ... uvm_common_phases = [uvm_build_phase, uvm_connect_phase, uvm_end_of_elaboration_phase, uvm_start_of_simulation_phase, uvm_run_phase, uvm_extract_phase, uvm_check_phase, uvm_report_phase, uvm_final_phase]

true

f71b486fd1687af447da51c84625b2c67f3d5401

3,199

py

Python

homeassistant/components/harmony/subscriber.py

PiotrMachowski/core

b9d7d0cae2ccd2d88e90e49cc09e154a27ed809b

[ "Apache-2.0" ]

3

2020-11-27T06:26:27.000Z

2020-12-09T14:55:16.000Z

homeassistant/components/harmony/subscriber.py

PiotrMachowski/core

b9d7d0cae2ccd2d88e90e49cc09e154a27ed809b

[ "Apache-2.0" ]

277

2021-10-04T06:39:33.000Z

2021-12-28T22:04:17.000Z

homeassistant/components/harmony/subscriber.py

PiotrMachowski/core

b9d7d0cae2ccd2d88e90e49cc09e154a27ed809b

[ "Apache-2.0" ]

3

2022-01-02T18:49:54.000Z

2022-01-25T02:03:54.000Z

"""Mixin class for handling harmony callback subscriptions.""" import asyncio import logging # pylint: disable-next=deprecated-typing-alias # Issue with Python 3.9.0 and 3.9.1 with collections.abc.Callable # https://bugs.python.org/issue42965 from typing import Any, Callable, NamedTuple, Optional from homeassistant.core import callback _LOGGER = logging.getLogger(__name__) NoParamCallback = Optional[Callable[[object], Any]] ActivityCallback = Optional[Callable[[object, tuple], Any]] class HarmonyCallback(NamedTuple): """Callback type for Harmony Hub notifications.""" connected: NoParamCallback disconnected: NoParamCallback config_updated: NoParamCallback activity_starting: ActivityCallback activity_started: ActivityCallback class HarmonySubscriberMixin: """Base implementation for a subscriber.""" def __init__(self, hass): """Initialize an subscriber.""" super().__init__() self._hass = hass self._subscriptions = [] self._activity_lock = asyncio.Lock() async def async_lock_start_activity(self): """Acquire the lock.""" await self._activity_lock.acquire() @callback def async_unlock_start_activity(self): """Release the lock.""" if self._activity_lock.locked(): self._activity_lock.release() @callback def async_subscribe(self, update_callbacks: HarmonyCallback) -> Callable: """Add a callback subscriber.""" self._subscriptions.append(update_callbacks) def _unsubscribe(): self.async_unsubscribe(update_callbacks) return _unsubscribe @callback def async_unsubscribe(self, update_callback: HarmonyCallback): """Remove a callback subscriber.""" self._subscriptions.remove(update_callback) def _config_updated(self, _=None) -> None: _LOGGER.debug("config_updated") self._call_callbacks("config_updated") def _connected(self, _=None) -> None: _LOGGER.debug("connected") self.async_unlock_start_activity() self._available = True self._call_callbacks("connected") def _disconnected(self, _=None) -> None: _LOGGER.debug("disconnected") self.async_unlock_start_activity() self._available = False self._call_callbacks("disconnected") def _activity_starting(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s starting", activity_info) self._call_callbacks("activity_starting", activity_info) def _activity_started(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s started", activity_info) self.async_unlock_start_activity() self._call_callbacks("activity_started", activity_info) def _call_callbacks(self, callback_func_name: str, argument: tuple = None): for subscription in self._subscriptions: current_callback = getattr(subscription, callback_func_name) if current_callback: if argument: self._hass.async_run_job(current_callback, argument) else: self._hass.async_run_job(current_callback)

32.979381

79

0.689278

import asyncio import logging from typing import Any, Callable, NamedTuple, Optional from homeassistant.core import callback _LOGGER = logging.getLogger(__name__) NoParamCallback = Optional[Callable[[object], Any]] ActivityCallback = Optional[Callable[[object, tuple], Any]] class HarmonyCallback(NamedTuple): connected: NoParamCallback disconnected: NoParamCallback config_updated: NoParamCallback activity_starting: ActivityCallback activity_started: ActivityCallback class HarmonySubscriberMixin: def __init__(self, hass): super().__init__() self._hass = hass self._subscriptions = [] self._activity_lock = asyncio.Lock() async def async_lock_start_activity(self): await self._activity_lock.acquire() @callback def async_unlock_start_activity(self): if self._activity_lock.locked(): self._activity_lock.release() @callback def async_subscribe(self, update_callbacks: HarmonyCallback) -> Callable: self._subscriptions.append(update_callbacks) def _unsubscribe(): self.async_unsubscribe(update_callbacks) return _unsubscribe @callback def async_unsubscribe(self, update_callback: HarmonyCallback): self._subscriptions.remove(update_callback) def _config_updated(self, _=None) -> None: _LOGGER.debug("config_updated") self._call_callbacks("config_updated") def _connected(self, _=None) -> None: _LOGGER.debug("connected") self.async_unlock_start_activity() self._available = True self._call_callbacks("connected") def _disconnected(self, _=None) -> None: _LOGGER.debug("disconnected") self.async_unlock_start_activity() self._available = False self._call_callbacks("disconnected") def _activity_starting(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s starting", activity_info) self._call_callbacks("activity_starting", activity_info) def _activity_started(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s started", activity_info) self.async_unlock_start_activity() self._call_callbacks("activity_started", activity_info) def _call_callbacks(self, callback_func_name: str, argument: tuple = None): for subscription in self._subscriptions: current_callback = getattr(subscription, callback_func_name) if current_callback: if argument: self._hass.async_run_job(current_callback, argument) else: self._hass.async_run_job(current_callback)

true

f71b492ed7fe05c2dd8f787b6d743c15e42b6651

23,428

py

Python

test/test_framework/util.py

vpubchain/Phore

7819f046e629ccb5a00fa4f89a7399a7732b4113

[ "MIT" ]

null

test/test_framework/util.py

vpubchain/Phore

7819f046e629ccb5a00fa4f89a7399a7732b4113

[ "MIT" ]

null

test/test_framework/util.py

vpubchain/Phore

7819f046e629ccb5a00fa4f89a7399a7732b4113

[ "MIT" ]

1

2019-08-10T08:20:56.000Z

#!/usr/bin/env python3 # Copyright (c) 2014-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # Helpful routines for regression testing # import os import sys from binascii import hexlify, unhexlify from base64 import b64encode from decimal import Decimal, ROUND_DOWN import json import http.client import random import shutil import subprocess import time import re import errno from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException COVERAGE_DIR = None # The maximum number of nodes a single test can spawn MAX_NODES = 8 # Don't assign rpc or p2p ports lower than this PORT_MIN = 11000 # The number of ports to "reserve" for p2p and rpc, each PORT_RANGE = 5000 VPUBD_PROC_WAIT_TIMEOUT = 60 class PortSeed: # Must be initialized with a unique integer for each process n = None #Set Mocktime default to OFF. #MOCKTIME is only needed for scripts that use the #cached version of the blockchain. If the cached #version of the blockchain is used without MOCKTIME #then the mempools will not sync due to IBD. MOCKTIME = 0 def enable_mocktime(): #For backwared compatibility of the python scripts #with previous versions of the cache, set MOCKTIME #to Jan 1, 2014 + (201 * 10 * 60) global MOCKTIME MOCKTIME = 1388534400 + (201 * 10 * 60) def disable_mocktime(): global MOCKTIME MOCKTIME = 0 def get_mocktime(): return MOCKTIME def enable_coverage(dirname): """Maintain a log of which RPC calls are made during testing.""" global COVERAGE_DIR COVERAGE_DIR = dirname def get_rpc_proxy(url, node_number, timeout=None): """ Args: url (str): URL of the RPC server to call node_number (int): the node number (or id) that this calls to Kwargs: timeout (int): HTTP timeout in seconds Returns: AuthServiceProxy. convenience object for making RPC calls. """ proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url # store URL on proxy for info coverage_logfile = coverage.get_filename( COVERAGE_DIR, node_number) if COVERAGE_DIR else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def p2p_port(n): assert(n <= MAX_NODES) return PORT_MIN + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_port(n): return PORT_MIN + PORT_RANGE + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def sync_blocks(rpc_connections, wait=1, timeout=60): """ Wait until everybody has the same tip """ while timeout > 0: tips = [ x.getbestblockhash() for x in rpc_connections ] if tips == [ tips[0] ]*len(tips): return True time.sleep(wait) timeout -= wait raise AssertionError("Block sync failed") def sync_mempools(rpc_connections, wait=1, timeout=60): """ Wait until everybody has the same transactions in their memory pools """ while timeout > 0: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): return True time.sleep(wait) timeout -= wait raise AssertionError("Mempool sync failed") vpubd_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) rpc_u, rpc_p = rpc_auth_pair(n) with open(os.path.join(datadir, "vpub.conf"), 'w', encoding='utf8') as f: f.write("regtest=1\n") f.write("server=1\n") f.write("rpcuser=" + rpc_u + "\n") f.write("rpcpassword=" + rpc_p + "\n") f.write("port="+str(p2p_port(n))+"\n") f.write("rpcport="+str(rpc_port(n))+"\n") f.write("listenonion=0\n") return datadir def rpc_auth_pair(n): return 'rpcuser' + str(n), 'rpcpass' + str(n) def rpc_url(i, rpchost=None): rpc_u, rpc_p = rpc_auth_pair(i) host = '127.0.0.1' port = rpc_port(i) if rpchost: parts = rpchost.split(':') if len(parts) == 2: host, port = parts else: host = rpchost return "http://%s:%s@%s:%d" % (rpc_u, rpc_p, host, int(port)) def wait_for_vpubd_start(process, url, i): ''' Wait for vpubd to start. This means that RPC is accessible and fully initialized. Raise an exception if vpubd exits during initialization. ''' while True: if process.poll() is not None: raise Exception('vpubd exited with status %i during initialization' % process.returncode) try: rpc = get_rpc_proxy(url, i) blocks = rpc.getblockcount() break # break out of loop on success except IOError as e: if e.errno != errno.ECONNREFUSED: # Port not yet open? raise # unknown IO error except JSONRPCException as e: # Initialization phase if e.error['code'] != -28: # RPC in warmup? raise # unkown JSON RPC exception time.sleep(0.25) def initialize_chain(test_dir, num_nodes): """ Create a cache of a 200-block-long chain (with wallet) for MAX_NODES Afterward, create num_nodes copies from the cache """ assert num_nodes <= MAX_NODES create_cache = False for i in range(MAX_NODES): if not os.path.isdir(os.path.join('cache', 'node'+str(i))): create_cache = True break if create_cache: #find and delete old cache directories if any exist for i in range(MAX_NODES): if os.path.isdir(os.path.join("cache","node"+str(i))): shutil.rmtree(os.path.join("cache","node"+str(i))) # Create cache directories, run vpubds: for i in range(MAX_NODES): datadir=initialize_datadir("cache", i) args = [ os.getenv("VPUBD", "vpubd"), "-server", "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: vpubd started, waiting for RPC to come up") wait_for_vpubd_start(vpubd_processes[i], rpc_url(i), i) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: RPC succesfully started") rpcs = [] for i in range(MAX_NODES): try: rpcs.append(get_rpc_proxy(rpc_url(i), i)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) # Create a 200-block-long chain; each of the 4 first nodes # gets 25 mature blocks and 25 immature. # Note: To preserve compatibility with older versions of # initialize_chain, only 4 nodes will generate coins. # # blocks are created with timestamps 10 minutes apart # starting from 2010 minutes in the past enable_mocktime() block_time = get_mocktime() - (201 * 10 * 60) for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].setgenerate(True, 1) block_time += 10*60 # Must sync before next peer starts generating blocks sync_blocks(rpcs) # Shut them down, and clean up cache directories: stop_nodes(rpcs) disable_mocktime() for i in range(MAX_NODES): os.remove(log_filename("cache", i, "debug.log")) os.remove(log_filename("cache", i, "db.log")) os.remove(log_filename("cache", i, "peers.dat")) os.remove(log_filename("cache", i, "fee_estimates.dat")) for i in range(num_nodes): from_dir = os.path.join("cache", "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) # Overwrite port/rpcport in vpub.conf def initialize_chain_clean(test_dir, num_nodes): """ Create an empty blockchain and num_nodes wallets. Useful if a test case wants complete control over initialization. """ for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): '''Convert optional IP:port spec to rpcconnect/rpcport args''' if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): # remove IPv6 [...] wrapping rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start a vpubd and return RPC connection to it """ datadir = os.path.join(dirname, "node"+str(i)) if binary is None: binary = os.getenv("VPUBD", "vpubd") args = [ binary, "-datadir="+datadir, "-server", "-keypool=1", "-discover=0", "-rest", "-mocktime="+str(get_mocktime()), "-regtest", "-sporkkey=923EhWh2bJHynX6d4Tqt2Q75bhTDCT1b4kff3qzDKDZHZ6pkQs7"] if extra_args is not None: args.extend(extra_args) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("start_node: phroed started, waiting for RPC to come up") url = rpc_url(i, rpchost) wait_for_vpubd_start(vpubd_processes[i], url, i) if os.getenv("PYTHON_DEBUG", ""): print("start_node: RPC succesfully started") proxy = get_rpc_proxy(url, i, timeout=timewait) if COVERAGE_DIR: coverage.write_all_rpc_commands(COVERAGE_DIR, proxy) return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start multiple vpubds, return RPC connections to them """ if extra_args is None: extra_args = [ None for _ in range(num_nodes) ] if binary is None: binary = [ None for _ in range(num_nodes) ] rpcs = [] try: for i in range(num_nodes): rpcs.append(start_node(i, dirname, extra_args[i], rpchost, timewait=timewait, binary=binary[i])) except: # If one node failed to start, stop the others stop_nodes(rpcs) raise return rpcs def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) vpubd_processes[i].wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) del vpubd_processes[i] def stop_nodes(nodes): for node in nodes: try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) del nodes[:] # Emptying array closes connections as a side effect wait_vpubds() def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def wait_vpubds(): # Wait for all vpubds to cleanly exit for vpubd in vpubd_processes.values(): vpubd.wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) vpubd_processes.clear() def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): """ Return index to output of txid with value amount Raises exception if there is none. """ txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): """ Return a random set of unspent txouts that are enough to pay amount_needed """ assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): """ Create change output(s), return them """ outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): """ Create&broadcast a zero-priority transaction. Returns (txid, hex-encoded-txdata) Ensures transaction is zero-priority by first creating a send-to-self, then using its output """ # Create a send-to-self with confirmed inputs: self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) # Now immediately spend the output to create a 1-input, 1-output # zero-priority transaction: inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random zero-priority transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_fee_amount(fee, tx_size, fee_per_kB): """Assert the fee was in range""" target_fee = tx_size * fee_per_kB / 1000 if fee < target_fee: raise AssertionError("Fee of %s BTC too low! (Should be %s BTC)"%(str(fee), str(target_fee))) # allow the wallet's estimation to be at most 2 bytes off if fee > (tx_size + 2) * fee_per_kB / 1000: raise AssertionError("Fee of %s BTC too high! (Should be %s BTC)"%(str(fee), str(target_fee))) def assert_equal(thing1, thing2): if thing1 != thing2: raise AssertionError("%s != %s"%(str(thing1),str(thing2))) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): assert_raises_message(exc, None, fun, *args, **kwds) def assert_raises_message(exc, message, fun, *args, **kwds): try: fun(*args, **kwds) except exc as e: if message is not None and message not in e.error['message']: raise AssertionError("Expected substring not found:"+e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, str): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find = False): """ Pass in array of JSON objects, a dictionary with key/value pairs to match against, and another dictionary with expected key/value pairs. If the should_not_find flag is true, to_match should not be found in object_array """ if should_not_find == True: assert_equal(expected, { }) num_matched = 0 for item in object_array: all_match = True for key,value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find == True: num_matched = num_matched+1 for key,value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0 and should_not_find != True: raise AssertionError("No objects matched %s"%(str(to_match))) if num_matched > 0 and should_not_find == True: raise AssertionError("Objects were found %s"%(str(to_match))) def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): node.setgenerate(True, int(0.5*count)+101) utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in range(iterations): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value/2) outputs[addr2] = satoshi_round(send_value/2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransaction(raw_tx)["hex"] txid = node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.setgenerate(True, 1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) # create one script_pubkey script_pubkey = "6a4d0200" #OP_RETURN OP_PUSH2 512 bytes for i in range (512): script_pubkey = script_pubkey + "01" # concatenate 128 txouts of above script_pubkey which we'll insert before the txout for change txouts = "81" for k in range(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts def create_tx(node, coinbase, to_address, amount): inputs = [{ "txid" : coinbase, "vout" : 0}] outputs = { to_address : amount } rawtx = node.createrawtransaction(inputs, outputs) signresult = node.signrawtransaction(rawtx) assert_equal(signresult["complete"], True) return signresult["hex"] # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, fee): addr = node.getnewaddress() txids = [] for i in range(len(utxos)): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr] = satoshi_round(send_value) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransaction(newtx, None, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def get_bip9_status(node, key): info = node.getblockchaininfo() return info['bip9_softforks'][key]

35.713415

201

0.651742

import os import sys from binascii import hexlify, unhexlify from base64 import b64encode from decimal import Decimal, ROUND_DOWN import json import http.client import random import shutil import subprocess import time import re import errno from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException COVERAGE_DIR = None MAX_NODES = 8 PORT_MIN = 11000 # The number of ports to "reserve" for p2p and rpc, each PORT_RANGE = 5000 VPUBD_PROC_WAIT_TIMEOUT = 60 class PortSeed: # Must be initialized with a unique integer for each process n = None #Set Mocktime default to OFF. #MOCKTIME is only needed for scripts that use the #cached version of the blockchain. If the cached #version of the blockchain is used without MOCKTIME #then the mempools will not sync due to IBD. MOCKTIME = 0 def enable_mocktime(): #For backwared compatibility of the python scripts #with previous versions of the cache, set MOCKTIME #to Jan 1, 2014 + (201 * 10 * 60) global MOCKTIME MOCKTIME = 1388534400 + (201 * 10 * 60) def disable_mocktime(): global MOCKTIME MOCKTIME = 0 def get_mocktime(): return MOCKTIME def enable_coverage(dirname): global COVERAGE_DIR COVERAGE_DIR = dirname def get_rpc_proxy(url, node_number, timeout=None): proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url # store URL on proxy for info coverage_logfile = coverage.get_filename( COVERAGE_DIR, node_number) if COVERAGE_DIR else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def p2p_port(n): assert(n <= MAX_NODES) return PORT_MIN + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_port(n): return PORT_MIN + PORT_RANGE + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def check_json_precision(): n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def sync_blocks(rpc_connections, wait=1, timeout=60): while timeout > 0: tips = [ x.getbestblockhash() for x in rpc_connections ] if tips == [ tips[0] ]*len(tips): return True time.sleep(wait) timeout -= wait raise AssertionError("Block sync failed") def sync_mempools(rpc_connections, wait=1, timeout=60): while timeout > 0: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): return True time.sleep(wait) timeout -= wait raise AssertionError("Mempool sync failed") vpubd_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) rpc_u, rpc_p = rpc_auth_pair(n) with open(os.path.join(datadir, "vpub.conf"), 'w', encoding='utf8') as f: f.write("regtest=1\n") f.write("server=1\n") f.write("rpcuser=" + rpc_u + "\n") f.write("rpcpassword=" + rpc_p + "\n") f.write("port="+str(p2p_port(n))+"\n") f.write("rpcport="+str(rpc_port(n))+"\n") f.write("listenonion=0\n") return datadir def rpc_auth_pair(n): return 'rpcuser' + str(n), 'rpcpass' + str(n) def rpc_url(i, rpchost=None): rpc_u, rpc_p = rpc_auth_pair(i) host = '127.0.0.1' port = rpc_port(i) if rpchost: parts = rpchost.split(':') if len(parts) == 2: host, port = parts else: host = rpchost return "http://%s:%s@%s:%d" % (rpc_u, rpc_p, host, int(port)) def wait_for_vpubd_start(process, url, i): while True: if process.poll() is not None: raise Exception('vpubd exited with status %i during initialization' % process.returncode) try: rpc = get_rpc_proxy(url, i) blocks = rpc.getblockcount() break # break out of loop on success except IOError as e: if e.errno != errno.ECONNREFUSED: # Port not yet open? raise # unknown IO error except JSONRPCException as e: # Initialization phase if e.error['code'] != -28: # RPC in warmup? raise # unkown JSON RPC exception time.sleep(0.25) def initialize_chain(test_dir, num_nodes): assert num_nodes <= MAX_NODES create_cache = False for i in range(MAX_NODES): if not os.path.isdir(os.path.join('cache', 'node'+str(i))): create_cache = True break if create_cache: #find and delete old cache directories if any exist for i in range(MAX_NODES): if os.path.isdir(os.path.join("cache","node"+str(i))): shutil.rmtree(os.path.join("cache","node"+str(i))) # Create cache directories, run vpubds: for i in range(MAX_NODES): datadir=initialize_datadir("cache", i) args = [ os.getenv("VPUBD", "vpubd"), "-server", "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: vpubd started, waiting for RPC to come up") wait_for_vpubd_start(vpubd_processes[i], rpc_url(i), i) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: RPC succesfully started") rpcs = [] for i in range(MAX_NODES): try: rpcs.append(get_rpc_proxy(rpc_url(i), i)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) # Create a 200-block-long chain; each of the 4 first nodes # gets 25 mature blocks and 25 immature. # Note: To preserve compatibility with older versions of # initialize_chain, only 4 nodes will generate coins. # # blocks are created with timestamps 10 minutes apart # starting from 2010 minutes in the past enable_mocktime() block_time = get_mocktime() - (201 * 10 * 60) for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].setgenerate(True, 1) block_time += 10*60 # Must sync before next peer starts generating blocks sync_blocks(rpcs) # Shut them down, and clean up cache directories: stop_nodes(rpcs) disable_mocktime() for i in range(MAX_NODES): os.remove(log_filename("cache", i, "debug.log")) os.remove(log_filename("cache", i, "db.log")) os.remove(log_filename("cache", i, "peers.dat")) os.remove(log_filename("cache", i, "fee_estimates.dat")) for i in range(num_nodes): from_dir = os.path.join("cache", "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) # Overwrite port/rpcport in vpub.conf def initialize_chain_clean(test_dir, num_nodes): for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): # remove IPv6 [...] wrapping rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): datadir = os.path.join(dirname, "node"+str(i)) if binary is None: binary = os.getenv("VPUBD", "vpubd") args = [ binary, "-datadir="+datadir, "-server", "-keypool=1", "-discover=0", "-rest", "-mocktime="+str(get_mocktime()), "-regtest", "-sporkkey=923EhWh2bJHynX6d4Tqt2Q75bhTDCT1b4kff3qzDKDZHZ6pkQs7"] if extra_args is not None: args.extend(extra_args) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("start_node: phroed started, waiting for RPC to come up") url = rpc_url(i, rpchost) wait_for_vpubd_start(vpubd_processes[i], url, i) if os.getenv("PYTHON_DEBUG", ""): print("start_node: RPC succesfully started") proxy = get_rpc_proxy(url, i, timeout=timewait) if COVERAGE_DIR: coverage.write_all_rpc_commands(COVERAGE_DIR, proxy) return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): if extra_args is None: extra_args = [ None for _ in range(num_nodes) ] if binary is None: binary = [ None for _ in range(num_nodes) ] rpcs = [] try: for i in range(num_nodes): rpcs.append(start_node(i, dirname, extra_args[i], rpchost, timewait=timewait, binary=binary[i])) except: # If one node failed to start, stop the others stop_nodes(rpcs) raise return rpcs def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) vpubd_processes[i].wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) del vpubd_processes[i] def stop_nodes(nodes): for node in nodes: try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) del nodes[:] # Emptying array closes connections as a side effect wait_vpubds() def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def wait_vpubds(): # Wait for all vpubds to cleanly exit for vpubd in vpubd_processes.values(): vpubd.wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) vpubd_processes.clear() def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): # Create a send-to-self with confirmed inputs: self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) # Now immediately spend the output to create a 1-input, 1-output # zero-priority transaction: inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_fee_amount(fee, tx_size, fee_per_kB): target_fee = tx_size * fee_per_kB / 1000 if fee < target_fee: raise AssertionError("Fee of %s BTC too low! (Should be %s BTC)"%(str(fee), str(target_fee))) # allow the wallet's estimation to be at most 2 bytes off if fee > (tx_size + 2) * fee_per_kB / 1000: raise AssertionError("Fee of %s BTC too high! (Should be %s BTC)"%(str(fee), str(target_fee))) def assert_equal(thing1, thing2): if thing1 != thing2: raise AssertionError("%s != %s"%(str(thing1),str(thing2))) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): assert_raises_message(exc, None, fun, *args, **kwds) def assert_raises_message(exc, message, fun, *args, **kwds): try: fun(*args, **kwds) except exc as e: if message is not None and message not in e.error['message']: raise AssertionError("Expected substring not found:"+e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, str): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find = False): if should_not_find == True: assert_equal(expected, { }) num_matched = 0 for item in object_array: all_match = True for key,value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find == True: num_matched = num_matched+1 for key,value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0 and should_not_find != True: raise AssertionError("No objects matched %s"%(str(to_match))) if num_matched > 0 and should_not_find == True: raise AssertionError("Objects were found %s"%(str(to_match))) def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): node.setgenerate(True, int(0.5*count)+101) utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in range(iterations): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value/2) outputs[addr2] = satoshi_round(send_value/2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransaction(raw_tx)["hex"] txid = node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.setgenerate(True, 1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) script_pubkey = "6a4d0200" for i in range (512): script_pubkey = script_pubkey + "01" txouts = "81" for k in range(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts def create_tx(node, coinbase, to_address, amount): inputs = [{ "txid" : coinbase, "vout" : 0}] outputs = { to_address : amount } rawtx = node.createrawtransaction(inputs, outputs) signresult = node.signrawtransaction(rawtx) assert_equal(signresult["complete"], True) return signresult["hex"] # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, fee): addr = node.getnewaddress() txids = [] for i in range(len(utxos)): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr] = satoshi_round(send_value) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransaction(newtx, None, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def get_bip9_status(node, key): info = node.getblockchaininfo() return info['bip9_softforks'][key]

true

f71b49b3ed0e3ba6b9a8f90ca5e35100450fb249

13,385

py

Python

skimage/segmentation/tests/test_random_walker.py

thewtex/scikit-image

22bb6b94698b8889cbdf26b25d9e4fdb8b968d97

[ "BSD-3-Clause" ]

1

2019-01-12T13:17:32.000Z

skimage/segmentation/tests/test_random_walker.py

thewtex/scikit-image

22bb6b94698b8889cbdf26b25d9e4fdb8b968d97

[ "BSD-3-Clause" ]

null

skimage/segmentation/tests/test_random_walker.py

thewtex/scikit-image

22bb6b94698b8889cbdf26b25d9e4fdb8b968d97

[ "BSD-3-Clause" ]

null

import numpy as np from skimage.segmentation import random_walker from skimage.transform import resize from skimage._shared._warnings import expected_warnings from skimage._shared import testing # older versions of scipy raise a warning with new NumPy because they use # numpy.rank() instead of arr.ndim or numpy.linalg.matrix_rank. SCIPY_EXPECTED = 'numpy.linalg.matrix_rank|\A\Z' PYAMG_EXPECTED_WARNING = 'pyamg|\A\Z' PYAMG_SCIPY_EXPECTED = SCIPY_EXPECTED + '|' + PYAMG_EXPECTED_WARNING def make_2d_syntheticdata(lx, ly=None): if ly is None: ly = lx np.random.seed(1234) data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1] = ( 0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2)) data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2 return data, seeds def make_3d_syntheticdata(lx, ly=None, lz=None): if ly is None: ly = lx if lz is None: lz = lx np.random.seed(1234) data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l, lz // 2 - small_l:lz // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1, lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0 # make a hole hole_size = np.max([1, small_l // 8]) data[lx // 2 - small_l, ly // 2 - hole_size:ly // 2 + hole_size, lz // 2 - hole_size:lz // 2 + hole_size] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5, lz // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 2 - small_l // 4] = 2 return data, seeds def test_2d_bf(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert data.shape == labels.shape # Now test with more than two labels labels[55, 80] = 3 full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert len(full_prob_bf) == 3 assert data.shape == labels.shape def test_2d_cg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_cg = random_walker(data, labels, beta=90, mode='cg') assert (labels_cg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): full_prob = random_walker(data, labels, beta=90, mode='cg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg def test_2d_cg_mg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) expected = 'scipy.sparse.sparsetools|%s' % PYAMG_SCIPY_EXPECTED with expected_warnings([expected]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings([expected]): full_prob = random_walker(data, labels, beta=90, mode='cg_mg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg_mg def test_types(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) data = 255 * (data - data.min()) // (data.max() - data.min()) data = data.astype(np.uint8) with expected_warnings([PYAMG_SCIPY_EXPECTED]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_cg_mg def test_reorder_labels(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[labels == 2] = 4 labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_bf def test_2d_inactive(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[10:20, 10:20] = -1 labels[46:50, 33:38] = -2 labels = random_walker(data, labels, beta=90) assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels def test_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels def test_3d_inactive(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) old_labels = np.copy(labels) labels[5:25, 26:29, 26:29] = -1 after_labels = np.copy(labels) with expected_warnings(['"cg" mode|CObject type' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels, old_labels, after_labels def test_multispectral_2d(): lx, ly = 70, 100 data, labels = make_2d_syntheticdata(lx, ly) data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_multispectral_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_spacing_0(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) # Rescale `data` along Z axis data_aniso = np.zeros((n, n, n // 2)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n, n // 2), mode='constant', anti_aliasing=False) # Generate new labels small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 4 - small_l // 8] = 2 # Test with `spacing` kwarg with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 1., 0.5)) assert (labels_aniso[13:17, 13:17, 7:9] == 2).all() def test_spacing_1(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) # Rescale `data` along Y axis # `resize` is not yet 3D capable, so this must be done by looping in 2D. data_aniso = np.zeros((n, n * 2, n)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n * 2, n), mode='constant', anti_aliasing=False) # Generate new labels small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly - small_l // 2, lz // 2 - small_l // 4] = 2 # Test with `spacing` kwarg # First, anisotropic along Y with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 2., 1.)) assert (labels_aniso[13:17, 26:34, 13:17] == 2).all() # Rescale `data` along X axis # `resize` is not yet 3D capable, so this must be done by looping in 2D. data_aniso = np.zeros((n, n * 2, n)) for i in range(data.shape[1]): data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n), mode='constant', anti_aliasing=False) # Generate new labels small_l = int(lx // 5) labels_aniso2 = np.zeros_like(data_aniso) labels_aniso2[lx // 5, ly // 5, lz // 5] = 1 labels_aniso2[lx - small_l // 2, ly // 2 + small_l // 4, lz // 2 - small_l // 4] = 2 # Anisotropic along X with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso2 = random_walker(data_aniso, labels_aniso2, mode='cg', spacing=(2., 1., 1.)) assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all() def test_trivial_cases(): # When all voxels are labeled img = np.ones((10, 10)) labels = np.ones((10, 10)) with expected_warnings(["Returning provided labels"]): pass_through = random_walker(img, labels) np.testing.assert_array_equal(pass_through, labels) # When all voxels are labeled AND return_full_prob is True labels[:, :5] = 3 expected = np.concatenate(((labels == 1)[..., np.newaxis], (labels == 3)[..., np.newaxis]), axis=2) with expected_warnings(["Returning provided labels"]): test = random_walker(img, labels, return_full_prob=True) np.testing.assert_array_equal(test, expected) def test_length2_spacing(): # If this passes without raising an exception (warnings OK), the new # spacing code is working properly. np.random.seed(42) img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10)) labels = np.zeros((10, 10), dtype=np.uint8) labels[2, 4] = 1 labels[6, 8] = 4 random_walker(img, labels, spacing=(1., 2.)) def test_bad_inputs(): # Too few dimensions img = np.ones(10) labels = np.arange(10) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) # Too many dimensions np.random.seed(42) img = np.random.normal(size=(3, 3, 3, 3, 3)) labels = np.arange(3 ** 5).reshape(img.shape) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) # Spacing incorrect length img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) labels[2, 4] = 2 labels[6, 8] = 5 with testing.raises(ValueError): random_walker(img, labels, spacing=(1,)) # Invalid mode img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) with testing.raises(ValueError): random_walker(img, labels, mode='bad') def test_isolated_seeds(): np.random.seed(0) a = np.random.random((7, 7)) mask = - np.ones(a.shape) # This pixel is an isolated seed mask[1, 1] = 1 # Unlabeled pixels mask[3:, 3:] = 0 # Seeds connected to unlabeled pixels mask[4, 4] = 2 mask[6, 6] = 1 # Test that no error is raised, and that labels of isolated seeds are OK res = random_walker(a, mask) assert res[1, 1] == 1 res = random_walker(a, mask, return_full_prob=True) assert res[0, 1, 1] == 1 assert res[1, 1, 1] == 0

36.175676

80

0.582219

import numpy as np from skimage.segmentation import random_walker from skimage.transform import resize from skimage._shared._warnings import expected_warnings from skimage._shared import testing SCIPY_EXPECTED = 'numpy.linalg.matrix_rank|\A\Z' PYAMG_EXPECTED_WARNING = 'pyamg|\A\Z' PYAMG_SCIPY_EXPECTED = SCIPY_EXPECTED + '|' + PYAMG_EXPECTED_WARNING def make_2d_syntheticdata(lx, ly=None): if ly is None: ly = lx np.random.seed(1234) data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1] = ( 0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2)) data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2 return data, seeds def make_3d_syntheticdata(lx, ly=None, lz=None): if ly is None: ly = lx if lz is None: lz = lx np.random.seed(1234) data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l, lz // 2 - small_l:lz // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1, lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0 hole_size = np.max([1, small_l // 8]) data[lx // 2 - small_l, ly // 2 - hole_size:ly // 2 + hole_size, lz // 2 - hole_size:lz // 2 + hole_size] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5, lz // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 2 - small_l // 4] = 2 return data, seeds def test_2d_bf(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert data.shape == labels.shape labels[55, 80] = 3 full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert len(full_prob_bf) == 3 assert data.shape == labels.shape def test_2d_cg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_cg = random_walker(data, labels, beta=90, mode='cg') assert (labels_cg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): full_prob = random_walker(data, labels, beta=90, mode='cg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg def test_2d_cg_mg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) expected = 'scipy.sparse.sparsetools|%s' % PYAMG_SCIPY_EXPECTED with expected_warnings([expected]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings([expected]): full_prob = random_walker(data, labels, beta=90, mode='cg_mg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg_mg def test_types(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) data = 255 * (data - data.min()) // (data.max() - data.min()) data = data.astype(np.uint8) with expected_warnings([PYAMG_SCIPY_EXPECTED]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_cg_mg def test_reorder_labels(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[labels == 2] = 4 labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_bf def test_2d_inactive(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[10:20, 10:20] = -1 labels[46:50, 33:38] = -2 labels = random_walker(data, labels, beta=90) assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels def test_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels def test_3d_inactive(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) old_labels = np.copy(labels) labels[5:25, 26:29, 26:29] = -1 after_labels = np.copy(labels) with expected_warnings(['"cg" mode|CObject type' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels, old_labels, after_labels def test_multispectral_2d(): lx, ly = 70, 100 data, labels = make_2d_syntheticdata(lx, ly) data = data[..., np.newaxis].repeat(2, axis=-1) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_multispectral_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) data = data[..., np.newaxis].repeat(2, axis=-1) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_spacing_0(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) data_aniso = np.zeros((n, n, n // 2)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n, n // 2), mode='constant', anti_aliasing=False) small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 4 - small_l // 8] = 2 with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 1., 0.5)) assert (labels_aniso[13:17, 13:17, 7:9] == 2).all() def test_spacing_1(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) data_aniso = np.zeros((n, n * 2, n)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n * 2, n), mode='constant', anti_aliasing=False) small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly - small_l // 2, lz // 2 - small_l // 4] = 2 with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 2., 1.)) assert (labels_aniso[13:17, 26:34, 13:17] == 2).all() data_aniso = np.zeros((n, n * 2, n)) for i in range(data.shape[1]): data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n), mode='constant', anti_aliasing=False) small_l = int(lx // 5) labels_aniso2 = np.zeros_like(data_aniso) labels_aniso2[lx // 5, ly // 5, lz // 5] = 1 labels_aniso2[lx - small_l // 2, ly // 2 + small_l // 4, lz // 2 - small_l // 4] = 2 with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso2 = random_walker(data_aniso, labels_aniso2, mode='cg', spacing=(2., 1., 1.)) assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all() def test_trivial_cases(): img = np.ones((10, 10)) labels = np.ones((10, 10)) with expected_warnings(["Returning provided labels"]): pass_through = random_walker(img, labels) np.testing.assert_array_equal(pass_through, labels) labels[:, :5] = 3 expected = np.concatenate(((labels == 1)[..., np.newaxis], (labels == 3)[..., np.newaxis]), axis=2) with expected_warnings(["Returning provided labels"]): test = random_walker(img, labels, return_full_prob=True) np.testing.assert_array_equal(test, expected) def test_length2_spacing(): np.random.seed(42) img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10)) labels = np.zeros((10, 10), dtype=np.uint8) labels[2, 4] = 1 labels[6, 8] = 4 random_walker(img, labels, spacing=(1., 2.)) def test_bad_inputs(): img = np.ones(10) labels = np.arange(10) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) np.random.seed(42) img = np.random.normal(size=(3, 3, 3, 3, 3)) labels = np.arange(3 ** 5).reshape(img.shape) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) labels[2, 4] = 2 labels[6, 8] = 5 with testing.raises(ValueError): random_walker(img, labels, spacing=(1,)) img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) with testing.raises(ValueError): random_walker(img, labels, mode='bad') def test_isolated_seeds(): np.random.seed(0) a = np.random.random((7, 7)) mask = - np.ones(a.shape) mask[1, 1] = 1 mask[3:, 3:] = 0 mask[4, 4] = 2 mask[6, 6] = 1 res = random_walker(a, mask) assert res[1, 1] == 1 res = random_walker(a, mask, return_full_prob=True) assert res[0, 1, 1] == 1 assert res[1, 1, 1] == 0

true

f71b4ba03c952835c47e87d25a7a9beba942d977

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py

Python

mmdet/models/necks/mscatfpn.py

JHuang-CV/OD

290bf90a5f210199b6a3750c88152f7dd2fbc276

[ "Apache-2.0" ]

null

mmdet/models/necks/mscatfpn.py

JHuang-CV/OD

290bf90a5f210199b6a3750c88152f7dd2fbc276

[ "Apache-2.0" ]

null

mmdet/models/necks/mscatfpn.py

JHuang-CV/OD

290bf90a5f210199b6a3750c88152f7dd2fbc276

[ "Apache-2.0" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import xavier_init from mmdet.core import auto_fp16 from ..registry import NECKS from ..utils import ConvModule from mmdet.ops.context_block import ContextBlock from mmdet.models.plugins.squeeze_excitation import ChannelSELayer @NECKS.register_module class MSCATFPN(nn.Module): def __init__(self, in_channels, out_channels, num_outs, start_level=0, end_level=-1, add_extra_convs=False, extra_convs_on_inputs=True, relu_before_extra_convs=False, no_norm_on_lateral=False, conv_cfg=None, norm_cfg=None, activation=None): super(MSCATFPN, self).__init__() assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) self.num_outs = num_outs self.activation = activation self.relu_before_extra_convs = relu_before_extra_convs self.no_norm_on_lateral = no_norm_on_lateral self.norm_cfg = norm_cfg self.fp16_enabled = False self.epsilon = 1e-4 self.se = ChannelSELayer(768) if end_level == -1: self.backbone_end_level = self.num_ins assert num_outs >= self.num_ins - start_level else: # if end_level < inputs, no extra level is allowed self.backbone_end_level = end_level assert end_level <= len(in_channels) assert num_outs == end_level - start_level self.start_level = start_level self.end_level = end_level self.add_extra_convs = add_extra_convs self.extra_convs_on_inputs = extra_convs_on_inputs self.lateral_convs = nn.ModuleList() self.fpn_convs = nn.ModuleList() self.cat_convs = nn.ModuleList() self.add_convs = nn.ModuleList() #self.gc_block = nn.ModuleList() self.relu = nn.ReLU() self.gc_block1 = ContextBlock(inplanes=256, ratio=1./4.) self.gc_block2 = ContextBlock(inplanes=256, ratio=1. / 4.) self.scat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.c3_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c4_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c5_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) for i in range(self.start_level, self.backbone_end_level): l_conv = ConvModule( in_channels[i], out_channels, 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, activation=self.activation, inplace=False) cat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) add_conv = ConvModule( out_channels, out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.cat_convs.append(cat_conv) self.lateral_convs.append(l_conv) self.add_convs.append(add_conv) #self.gc_block.append(ContextBlock(inplanes=256, ratio=1./4.)) # add extra conv layers (e.g., RetinaNet) extra_levels = num_outs - self.backbone_end_level + self.start_level if add_extra_convs and extra_levels >= 1: for i in range(extra_levels): if i == 0 and self.extra_convs_on_inputs: in_channels = self.in_channels[self.backbone_end_level - 1] else: in_channels = out_channels extra_fpn_conv = ConvModule( in_channels, out_channels, 3, stride=2, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.fpn_convs.append(extra_fpn_conv) # default init_weights for conv(msra) and norm in ConvModule def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m, distribution='uniform') @auto_fp16() def forward(self, inputs): assert len(inputs) == len(self.in_channels) # build laterals laterals = [ lateral_conv(inputs[i + self.start_level]) for i, lateral_conv in enumerate(self.lateral_convs) ] used_backbone_levels = len(laterals) mulscale_per_level = [] for i in range(used_backbone_levels): level = [] m = i - 0 n = used_backbone_levels - 1 - i level.append(laterals[i]) for x in range(m): level.insert(0, F.interpolate(level[0], scale_factor=2, mode='nearest')) for y in range(n): level.append(F.max_pool2d(level[-1], 2, stride=2)) mulscale_per_level.append(level) sglscale_per_level = list(zip(*mulscale_per_level)) feat_cat = [torch.cat(scale, 1)for scale in sglscale_per_level] #channel_se = [self.se(cat_ft) for cat_ft in feat_cat] mcat = [cat_conv(feat_cat[i]) for i, cat_conv in enumerate(self.cat_convs)] #outs = [gc(outs[i]) for i, gc in enumerate(self.gc_block)] mcat = [self.gc_block1(ft) for ft in mcat] single_list = [] level = used_backbone_levels // 2 for i in range(used_backbone_levels): if i < level: single_list.append(F.max_pool2d(laterals[i], 2, stride=2)) elif i == level: single_list.append(laterals[i]) else: single_list.append(F.interpolate(laterals[i], scale_factor=2, mode='nearest')) single_cat = torch.cat(single_list, 1) single_cat = self.scat_conv(single_cat) single_cat = self.gc_block2(single_cat) m = level - 0 n = used_backbone_levels - 1 - level scat = [single_cat] for x in range(m): scat.insert(0, F.interpolate(scat[0], scale_factor=2, mode='nearest')) for y in range(n): scat.append(F.max_pool2d(scat[-1], 2, stride=2)) # outs = [scat[i]+lateral for i, lateral in enumerate(laterals)] # outs = [add_conv(outs[i]) for i, add_conv in enumerate(self.add_convs)] outs = [] for i, (m, s, l) in enumerate(zip(mcat, scat, laterals)): outs.append( self.add_convs[i](m.sigmoid()*s/2 + l / 2) ) if self.num_outs > used_backbone_levels: if not self.add_extra_convs: for i in range(self.num_outs - used_backbone_levels): outs.append(F.max_pool2d(outs[-1], 1, stride=2)) else: if self.extra_convs_on_inputs: orig = inputs[self.backbone_end_level - 1] outs.append(self.fpn_convs[0](orig)) else: outs.append(self.fpn_convs[0](outs[-1])) for i in range(1, self.num_outs-used_backbone_levels): if self.relu_before_extra_convs: outs.append(self.fpn_convs[i](F.relu(outs[-1]))) else: outs.append(self.fpn_convs[i](outs[-1])) return tuple(outs)

37.768182

94

0.564087

import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import xavier_init from mmdet.core import auto_fp16 from ..registry import NECKS from ..utils import ConvModule from mmdet.ops.context_block import ContextBlock from mmdet.models.plugins.squeeze_excitation import ChannelSELayer @NECKS.register_module class MSCATFPN(nn.Module): def __init__(self, in_channels, out_channels, num_outs, start_level=0, end_level=-1, add_extra_convs=False, extra_convs_on_inputs=True, relu_before_extra_convs=False, no_norm_on_lateral=False, conv_cfg=None, norm_cfg=None, activation=None): super(MSCATFPN, self).__init__() assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) self.num_outs = num_outs self.activation = activation self.relu_before_extra_convs = relu_before_extra_convs self.no_norm_on_lateral = no_norm_on_lateral self.norm_cfg = norm_cfg self.fp16_enabled = False self.epsilon = 1e-4 self.se = ChannelSELayer(768) if end_level == -1: self.backbone_end_level = self.num_ins assert num_outs >= self.num_ins - start_level else: self.backbone_end_level = end_level assert end_level <= len(in_channels) assert num_outs == end_level - start_level self.start_level = start_level self.end_level = end_level self.add_extra_convs = add_extra_convs self.extra_convs_on_inputs = extra_convs_on_inputs self.lateral_convs = nn.ModuleList() self.fpn_convs = nn.ModuleList() self.cat_convs = nn.ModuleList() self.add_convs = nn.ModuleList() self.relu = nn.ReLU() self.gc_block1 = ContextBlock(inplanes=256, ratio=1./4.) self.gc_block2 = ContextBlock(inplanes=256, ratio=1. / 4.) self.scat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.c3_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c4_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c5_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) for i in range(self.start_level, self.backbone_end_level): l_conv = ConvModule( in_channels[i], out_channels, 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, activation=self.activation, inplace=False) cat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) add_conv = ConvModule( out_channels, out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.cat_convs.append(cat_conv) self.lateral_convs.append(l_conv) self.add_convs.append(add_conv) extra_levels = num_outs - self.backbone_end_level + self.start_level if add_extra_convs and extra_levels >= 1: for i in range(extra_levels): if i == 0 and self.extra_convs_on_inputs: in_channels = self.in_channels[self.backbone_end_level - 1] else: in_channels = out_channels extra_fpn_conv = ConvModule( in_channels, out_channels, 3, stride=2, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.fpn_convs.append(extra_fpn_conv) def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m, distribution='uniform') @auto_fp16() def forward(self, inputs): assert len(inputs) == len(self.in_channels) laterals = [ lateral_conv(inputs[i + self.start_level]) for i, lateral_conv in enumerate(self.lateral_convs) ] used_backbone_levels = len(laterals) mulscale_per_level = [] for i in range(used_backbone_levels): level = [] m = i - 0 n = used_backbone_levels - 1 - i level.append(laterals[i]) for x in range(m): level.insert(0, F.interpolate(level[0], scale_factor=2, mode='nearest')) for y in range(n): level.append(F.max_pool2d(level[-1], 2, stride=2)) mulscale_per_level.append(level) sglscale_per_level = list(zip(*mulscale_per_level)) feat_cat = [torch.cat(scale, 1)for scale in sglscale_per_level] mcat = [cat_conv(feat_cat[i]) for i, cat_conv in enumerate(self.cat_convs)] mcat = [self.gc_block1(ft) for ft in mcat] single_list = [] level = used_backbone_levels // 2 for i in range(used_backbone_levels): if i < level: single_list.append(F.max_pool2d(laterals[i], 2, stride=2)) elif i == level: single_list.append(laterals[i]) else: single_list.append(F.interpolate(laterals[i], scale_factor=2, mode='nearest')) single_cat = torch.cat(single_list, 1) single_cat = self.scat_conv(single_cat) single_cat = self.gc_block2(single_cat) m = level - 0 n = used_backbone_levels - 1 - level scat = [single_cat] for x in range(m): scat.insert(0, F.interpolate(scat[0], scale_factor=2, mode='nearest')) for y in range(n): scat.append(F.max_pool2d(scat[-1], 2, stride=2)) outs = [] for i, (m, s, l) in enumerate(zip(mcat, scat, laterals)): outs.append( self.add_convs[i](m.sigmoid()*s/2 + l / 2) ) if self.num_outs > used_backbone_levels: if not self.add_extra_convs: for i in range(self.num_outs - used_backbone_levels): outs.append(F.max_pool2d(outs[-1], 1, stride=2)) else: if self.extra_convs_on_inputs: orig = inputs[self.backbone_end_level - 1] outs.append(self.fpn_convs[0](orig)) else: outs.append(self.fpn_convs[0](outs[-1])) for i in range(1, self.num_outs-used_backbone_levels): if self.relu_before_extra_convs: outs.append(self.fpn_convs[i](F.relu(outs[-1]))) else: outs.append(self.fpn_convs[i](outs[-1])) return tuple(outs)

true

f71b4bb600bb418ed1ef7e86a5615b6ad8bfabf3

2,753

py

Python

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

[ "Apache-2.0" ]

2

2020-07-24T19:26:51.000Z

2021-08-21T21:04:11.000Z

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

[ "Apache-2.0" ]

null

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

[ "Apache-2.0" ]

1

2021-03-09T12:42:46.000Z

"""MXNet Module for Attention-based Graph Neural Network layer""" # pylint: disable= no-member, arguments-differ, invalid-name import mxnet as mx from mxnet.gluon import nn from .... import function as fn from ..softmax import edge_softmax from ..utils import normalize from ....utils import expand_as_pair class AGNNConv(nn.Block): r"""Attention-based Graph Neural Network layer from paper `Attention-based Graph Neural Network for Semi-Supervised Learning <https://arxiv.org/abs/1803.03735>`__. .. math:: H^{l+1} = P H^{l} where :math:`P` is computed as: .. math:: P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l)) Parameters ---------- init_beta : float, optional The :math:`\beta` in the formula. learn_beta : bool, optional If True, :math:`\beta` will be learnable parameter. """ def __init__(self, init_beta=1., learn_beta=True): super(AGNNConv, self).__init__() with self.name_scope(): self.beta = self.params.get('beta', shape=(1,), grad_req='write' if learn_beta else 'null', init=mx.init.Constant(init_beta)) def forward(self, graph, feat): r"""Compute AGNN Layer. Parameters ---------- graph : DGLGraph The graph. feat : mxnet.NDArray The input feature of shape :math:`(N, *)` :math:`N` is the number of nodes, and :math:`*` could be of any shape. If a pair of mxnet.NDArray is given, the pair must contain two tensors of shape :math:`(N_{in}, *)` and :math:`(N_{out}, *})`, the the :math:`*` in the later tensor must equal the previous one. Returns ------- mxnet.NDArray The output feature of shape :math:`(N, *)` where :math:`*` should be the same as input shape. """ with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata['h'] = feat_src graph.srcdata['norm_h'] = normalize(feat_src, p=2, axis=-1) if isinstance(feat, tuple) or graph.is_block: graph.dstdata['norm_h'] = normalize(feat_dst, p=2, axis=-1) # compute cosine distance graph.apply_edges(fn.u_dot_v('norm_h', 'norm_h', 'cos')) cos = graph.edata.pop('cos') e = self.beta.data(feat_src.context) * cos graph.edata['p'] = edge_softmax(graph, e) graph.update_all(fn.u_mul_e('h', 'p', 'm'), fn.sum('m', 'h')) return graph.dstdata.pop('h')

36.706667

91

0.553578

import mxnet as mx from mxnet.gluon import nn from .... import function as fn from ..softmax import edge_softmax from ..utils import normalize from ....utils import expand_as_pair class AGNNConv(nn.Block): def __init__(self, init_beta=1., learn_beta=True): super(AGNNConv, self).__init__() with self.name_scope(): self.beta = self.params.get('beta', shape=(1,), grad_req='write' if learn_beta else 'null', init=mx.init.Constant(init_beta)) def forward(self, graph, feat): with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata['h'] = feat_src graph.srcdata['norm_h'] = normalize(feat_src, p=2, axis=-1) if isinstance(feat, tuple) or graph.is_block: graph.dstdata['norm_h'] = normalize(feat_dst, p=2, axis=-1) graph.apply_edges(fn.u_dot_v('norm_h', 'norm_h', 'cos')) cos = graph.edata.pop('cos') e = self.beta.data(feat_src.context) * cos graph.edata['p'] = edge_softmax(graph, e) graph.update_all(fn.u_mul_e('h', 'p', 'm'), fn.sum('m', 'h')) return graph.dstdata.pop('h')

true

f71b4d5f7826768bee64b7feec106bd5368db512

1,099

py

Python

test/test_arrow_result.py

mariusvniekerk/snowflake-connector-python

4c6b728f9ca7ac9c8a318741924a963a5574e216

[ "Apache-2.0" ]

null

test/test_arrow_result.py

mariusvniekerk/snowflake-connector-python

4c6b728f9ca7ac9c8a318741924a963a5574e216

[ "Apache-2.0" ]

null

test/test_arrow_result.py

mariusvniekerk/snowflake-connector-python

4c6b728f9ca7ac9c8a318741924a963a5574e216

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (c) 2012-2019 Snowflake Computing Inc. All right reserved. # import pytest @pytest.mark.skip( reason="Cython is not enabled in build env") def test_select_with_num(conn_cnx): with conn_cnx() as json_cnx: with conn_cnx() as arrow_cnx: row_count = 50000 sql_text = ("select seq4() as c1, uniform(1, 10, random(12)) as c2 from " + "table(generator(rowcount=>50000)) order by c1") cursor_json = json_cnx.cursor() cursor_json.execute("alter session set query_result_format='JSON'") cursor_json.execute(sql_text) cursor_arrow = arrow_cnx.cursor() cursor_arrow.execute("alter session set query_result_format='ARROW_FORCE'") cursor_arrow.execute(sql_text) for i in range(0, row_count): (json_c1, json_c2) = cursor_json.fetchone() (arrow_c1, arrow_c2) = cursor_arrow.fetchone() assert json_c1 == arrow_c1 assert json_c2 == arrow_c2

36.633333

87

0.610555

import pytest @pytest.mark.skip( reason="Cython is not enabled in build env") def test_select_with_num(conn_cnx): with conn_cnx() as json_cnx: with conn_cnx() as arrow_cnx: row_count = 50000 sql_text = ("select seq4() as c1, uniform(1, 10, random(12)) as c2 from " + "table(generator(rowcount=>50000)) order by c1") cursor_json = json_cnx.cursor() cursor_json.execute("alter session set query_result_format='JSON'") cursor_json.execute(sql_text) cursor_arrow = arrow_cnx.cursor() cursor_arrow.execute("alter session set query_result_format='ARROW_FORCE'") cursor_arrow.execute(sql_text) for i in range(0, row_count): (json_c1, json_c2) = cursor_json.fetchone() (arrow_c1, arrow_c2) = cursor_arrow.fetchone() assert json_c1 == arrow_c1 assert json_c2 == arrow_c2

true

f71b4eb0cc83cbb94a84bbec221dd9f3a3147026

25,712

py

Python

draco/core/io.py

sjforeman/draco

b0ab40b6984637642b28a5485af1c09c9cf183f2

[ "MIT" ]

null

draco/core/io.py

sjforeman/draco

b0ab40b6984637642b28a5485af1c09c9cf183f2

[ "MIT" ]

null

draco/core/io.py

sjforeman/draco

b0ab40b6984637642b28a5485af1c09c9cf183f2

[ "MIT" ]

null

"""Tasks for reading and writing data. Tasks ===== .. autosummary:: :toctree: LoadFiles LoadMaps LoadFilesFromParams Save Print LoadBeamTransfer File Groups =========== Several tasks accept groups of files as arguments. These are specified in the YAML file as a dictionary like below. .. code-block:: yaml list_of_file_groups: - tag: first_group # An optional tag naming the group files: - 'file1.h5' - 'file[3-4].h5' # Globs are processed - 'file7.h5' - files: # No tag specified, implicitly gets the tag 'group_2' - 'another_file1.h5' - 'another_file2.h5' single_group: files: ['file1.h5', 'file2.h5'] """ import os.path import h5py import numpy as np from yaml import dump as yamldump from caput import pipeline from caput import config from cora.util import units from . import task from ..util.truncate import bit_truncate_weights, bit_truncate_fixed from .containers import SiderealStream, TimeStream, TrackBeam TRUNC_SPEC = { SiderealStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TimeStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TrackBeam: { "dataset": ["beam", "weight"], "weight_dataset": ["weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, } def _list_of_filelists(files): # Take in a list of lists/glob patterns of filenames import glob f2 = [] for filelist in files: if isinstance(filelist, str): filelist = glob.glob(filelist) elif isinstance(filelist, list): pass else: raise Exception("Must be list or glob pattern.") f2.append(filelist) return f2 def _list_or_glob(files): # Take in a list of lists/glob patterns of filenames import glob if isinstance(files, str): files = sorted(glob.glob(files)) elif isinstance(files, list): pass else: raise ValueError("Argument must be list or glob pattern, got %s" % repr(files)) return files def _list_of_filegroups(groups): # Process a file group/groups import glob # Convert to list if the group was not included in a list if not isinstance(groups, list): groups = [groups] # Iterate over groups, set the tag if needed, and process the file list # through glob for gi, group in enumerate(groups): files = group["files"] if "tag" not in group: group["tag"] = "group_%i" % gi flist = [] for fname in files: flist += glob.glob(fname) if not len(flist): raise RuntimeError("No files in group exist (%s)." % files) group["files"] = flist return groups class LoadMaps(task.MPILoggedTask): """Load a series of maps from files given in the tasks parameters. Maps are given as one, or a list of `File Groups` (see :mod:`draco.core.io`). Maps within the same group are added together before being passed on. Attributes ---------- maps : list or dict A dictionary specifying a file group, or a list of them. """ maps = config.Property(proptype=_list_of_filegroups) def next(self): """Load the groups of maps from disk and pass them on. Returns ------- map : :class:`containers.Map` """ from . import containers # Exit this task if we have eaten all the file groups if len(self.maps) == 0: raise pipeline.PipelineStopIteration group = self.maps.pop(0) map_stack = None # Iterate over all the files in the group, load them into a Map # container and add them all together for mfile in group["files"]: self.log.debug("Loading file %s", mfile) current_map = containers.Map.from_file(mfile, distributed=True) current_map.redistribute("freq") # Start the stack if needed if map_stack is None: map_stack = current_map # Otherwise, check that the new map has consistent frequencies, # nside and pol and stack up. else: if (current_map.freq != map_stack.freq).all(): raise RuntimeError("Maps do not have consistent frequencies.") if (current_map.index_map["pol"] != map_stack.index_map["pol"]).all(): raise RuntimeError("Maps do not have the same polarisations.") if ( current_map.index_map["pixel"] != map_stack.index_map["pixel"] ).all(): raise RuntimeError("Maps do not have the same pixelisation.") map_stack.map[:] += current_map.map[:] # Assign a tag to the stack of maps map_stack.attrs["tag"] = group["tag"] return map_stack class LoadFITSCatalog(task.SingleTask): """Load an SDSS-style FITS source catalog. Catalogs are given as one, or a list of `File Groups` (see :mod:`draco.core.io`). Catalogs within the same group are combined together before being passed on. Attributes ---------- catalogs : list or dict A dictionary specifying a file group, or a list of them. z_range : list, optional Select only sources with a redshift within the given range. freq_range : list, optional Select only sources with a 21cm line freq within the given range. Overrides `z_range`. """ catalogs = config.Property(proptype=_list_of_filegroups) z_range = config.list_type(type_=float, length=2, default=None) freq_range = config.list_type(type_=float, length=2, default=None) def process(self): """Load the groups of catalogs from disk, concatenate them and pass them on. Returns ------- catalog : :class:`containers.SpectroscopicCatalog` """ from astropy.io import fits from . import containers # Exit this task if we have eaten all the file groups if len(self.catalogs) == 0: raise pipeline.PipelineStopIteration group = self.catalogs.pop(0) # Set the redshift selection if self.freq_range: zl = units.nu21 / self.freq_range[1] - 1 zh = units.nu21 / self.freq_range[0] - 1 self.z_range = (zl, zh) if self.z_range: zl, zh = self.z_range self.log.info(f"Applying redshift selection {zl:.2f} <= z <= {zh:.2f}") # Load the data only on rank=0 and then broadcast if self.comm.rank == 0: # Iterate over all the files in the group, load them into a Map # container and add them all together catalog_stack = [] for cfile in group["files"]: self.log.debug("Loading file %s", cfile) # TODO: read out the weights from the catalogs with fits.open(cfile, mode="readonly") as cat: pos = np.array([cat[1].data[col] for col in ["RA", "DEC", "Z"]]) # Apply any redshift selection to the objects if self.z_range: zsel = (pos[2] >= self.z_range[0]) & (pos[2] <= self.z_range[1]) pos = pos[:, zsel] catalog_stack.append(pos) # NOTE: this one is tricky, for some reason the concatenate in here # produces a non C contiguous array, so we need to ensure that otherwise # the broadcasting will get very confused catalog_array = np.concatenate(catalog_stack, axis=-1).astype(np.float64) catalog_array = np.ascontiguousarray(catalog_array) num_objects = catalog_array.shape[-1] else: num_objects = None catalog_array = None # Broadcast the size of the catalog to all ranks, create the target array and # broadcast into it num_objects = self.comm.bcast(num_objects, root=0) self.log.debug(f"Constructing catalog with {num_objects} objects.") if self.comm.rank != 0: catalog_array = np.zeros((3, num_objects), dtype=np.float64) self.comm.Bcast(catalog_array, root=0) catalog = containers.SpectroscopicCatalog(object_id=num_objects) catalog["position"]["ra"] = catalog_array[0] catalog["position"]["dec"] = catalog_array[1] catalog["redshift"]["z"] = catalog_array[2] catalog["redshift"]["z_error"] = 0 # Assign a tag to the stack of maps catalog.attrs["tag"] = group["tag"] return catalog class LoadFilesFromParams(task.SingleTask): """Load data from files given in the tasks parameters. Attributes ---------- files : glob pattern, or list Can either be a glob pattern, or lists of actual files. distributed : bool, optional Whether the file should be loaded distributed across ranks. convert_strings : bool, optional Convert strings to unicode when loading. selections : dict, optional A dictionary of axis selections. See the section below for details. Selections ---------- Selections can be given to limit the data read to specified subsets. They can be given for any named axis in the container. Selections can be given as a slice with an `<axis name>_range` key with either `[start, stop]` or `[start, stop, step]` as the value. Alternatively a list of explicit indices to extract can be given with the `<axis name>_index` key, and the value is a list of the indices. If both `<axis name>_range` and `<axis name>_index` keys are given the former will take precedence, but you should clearly avoid doing this. Additionally index based selections currently don't work for distributed reads. Here's an example in the YAML format that the pipeline uses: .. code-block:: yaml selections: freq_range: [256, 512, 4] # A strided slice stack_index: [1, 2, 4, 9, 16, 25, 36, 49, 64] # A sparse selection stack_range: [1, 14] # Will override the selection above """ files = config.Property(proptype=_list_or_glob) distributed = config.Property(proptype=bool, default=True) convert_strings = config.Property(proptype=bool, default=True) selections = config.Property(proptype=dict, default=None) def setup(self): """Resolve the selections.""" self._sel = self._resolve_sel() def process(self): """Load the given files in turn and pass on. Returns ------- cont : subclass of `memh5.BasicCont` """ from caput import memh5 # Garbage collect to workaround leaking memory from containers. # TODO: find actual source of leak import gc gc.collect() if len(self.files) == 0: raise pipeline.PipelineStopIteration # Fetch and remove the first item in the list file_ = self.files.pop(0) self.log.info(f"Loading file {file_}") self.log.debug(f"Reading with selections: {self._sel}") # If we are applying selections we need to dispatch the `from_file` via the # correct subclass, rather than relying on the internal detection of the # subclass. To minimise the number of files being opened this is only done on # rank=0 and is then broadcast if self._sel: if self.comm.rank == 0: with h5py.File(file_, "r") as fh: clspath = memh5.MemDiskGroup._detect_subclass_path(fh) else: clspath = None clspath = self.comm.bcast(clspath, root=0) new_cls = memh5.MemDiskGroup._resolve_subclass(clspath) else: new_cls = memh5.BasicCont cont = new_cls.from_file( file_, distributed=self.distributed, comm=self.comm, convert_attribute_strings=self.convert_strings, convert_dataset_strings=self.convert_strings, **self._sel, ) if "tag" not in cont.attrs: # Get the first part of the actual filename and use it as the tag tag = os.path.splitext(os.path.basename(file_))[0] cont.attrs["tag"] = tag return cont def _resolve_sel(self): # Turn the selection parameters into actual selectable types sel = {} sel_parsers = {"range": self._parse_range, "index": self._parse_index} # To enforce the precedence of range vs index selections, we rely on the fact # that a sort will place the axis_range keys after axis_index keys for k in sorted(self.selections or []): # Parse the key to get the axis name and type, accounting for the fact the # axis name may contain an underscore *axis, type_ = k.split("_") axis_name = "_".join(axis) if type_ not in sel_parsers: raise ValueError( f'Unsupported selection type "{type_}", or invalid key "{k}"' ) sel[f"{axis_name}_sel"] = sel_parsers[type_](self.selections[k]) return sel def _parse_range(self, x): # Parse and validate a range type selection if not isinstance(x, (list, tuple)) or len(x) > 3 or len(x) < 2: raise ValueError( f"Range spec must be a length 2 or 3 list or tuple. Got {x}." ) for v in x: if not isinstance(v, int): raise ValueError(f"All elements of range spec must be ints. Got {x}") return slice(*x) def _parse_index(self, x): # Parse and validate an index type selection if not isinstance(x, (list, tuple)) or len(x) == 0: raise ValueError(f"Index spec must be a non-empty list or tuple. Got {x}.") for v in x: if not isinstance(v, int): raise ValueError(f"All elements of index spec must be ints. Got {x}") return list(x) # Define alias for old code LoadBasicCont = LoadFilesFromParams class FindFiles(pipeline.TaskBase): """Take a glob or list of files specified as a parameter in the configuration file and pass on to other tasks. Parameters ---------- files : list or glob """ files = config.Property(proptype=_list_or_glob) def setup(self): """Return list of files specified in the parameters.""" if not isinstance(self.files, (list, tuple)): raise RuntimeError("Argument must be list of files.") return self.files class LoadFiles(LoadFilesFromParams): """Load data from files passed into the setup routine. File must be a serialised subclass of :class:`memh5.BasicCont`. """ files = None def setup(self, files): """Set the list of files to load. Parameters ---------- files : list """ # Call the baseclass setup to resolve any selections super().setup() if not isinstance(files, (list, tuple)): raise RuntimeError(f'Argument must be list of files. Got "{files}"') self.files = files class Save(pipeline.TaskBase): """Save out the input, and pass it on. Assumes that the input has a `to_hdf5` method. Appends a *tag* if there is a `tag` entry in the attributes, otherwise just uses a count. Attributes ---------- root : str Root of the file name to output to. """ root = config.Property(proptype=str) count = 0 def next(self, data): """Write out the data file. Assumes it has an MPIDataset interface. Parameters ---------- data : mpidataset.MPIDataset Data to write out. """ if "tag" not in data.attrs: tag = self.count self.count += 1 else: tag = data.attrs["tag"] fname = "%s_%s.h5" % (self.root, str(tag)) data.to_hdf5(fname) return data class Print(pipeline.TaskBase): """Stupid module which just prints whatever it gets. Good for debugging.""" def next(self, input_): print(input_) return input_ class LoadBeamTransfer(pipeline.TaskBase): """Loads a beam transfer manager from disk. Attributes ---------- product_directory : str Path to the saved Beam Transfer products. """ product_directory = config.Property(proptype=str) def setup(self): """Load the beam transfer matrices. Returns ------- tel : TransitTelescope Object describing the telescope. bt : BeamTransfer BeamTransfer manager. feed_info : list, optional Optional list providing additional information about each feed. """ import os from drift.core import beamtransfer if not os.path.exists(self.product_directory): raise RuntimeError("BeamTransfers do not exist.") bt = beamtransfer.BeamTransfer(self.product_directory) tel = bt.telescope try: return tel, bt, tel.feeds except AttributeError: return tel, bt class LoadProductManager(pipeline.TaskBase): """Loads a driftscan product manager from disk. Attributes ---------- product_directory : str Path to the root of the products. This is the same as the output directory used by ``drift-makeproducts``. """ product_directory = config.Property(proptype=str) def setup(self): """Load the beam transfer matrices. Returns ------- manager : ProductManager Object describing the telescope. """ import os from drift.core import manager if not os.path.exists(self.product_directory): raise RuntimeError("Products do not exist.") # Load ProductManager and Timestream pm = manager.ProductManager.from_config(self.product_directory) return pm class Truncate(task.SingleTask): """Precision truncate data prior to saving with bitshuffle compression. If no configuration is provided, will look for preset values for the input container. Any properties defined in the config will override the presets. If available, each specified dataset will be truncated relative to a (specified) weight dataset with the truncation increasing the variance up to the specified maximum in `variance_increase`. If there is no specified weight dataset then the truncation falls back to using the `fixed_precision`. Attributes ---------- dataset : list of str Datasets to truncate. weight_dataset : list of str Datasets to use as inverse variance for truncation precision. fixed_precision : float Relative precision to truncate to (default 1e-4). variance_increase : float Maximum fractional increase in variance from numerical truncation. """ dataset = config.Property(proptype=list, default=None) weight_dataset = config.Property(proptype=list, default=None) fixed_precision = config.Property(proptype=float, default=None) variance_increase = config.Property(proptype=float, default=None) def _get_params(self, container): """Load truncation parameters from config or container defaults.""" if container in TRUNC_SPEC: self.log.info("Truncating from preset for container {}".format(container)) for key in [ "dataset", "weight_dataset", "fixed_precision", "variance_increase", ]: attr = getattr(self, key) if attr is None: setattr(self, key, TRUNC_SPEC[container][key]) else: self.log.info("Overriding container default for '{}'.".format(key)) else: if ( self.dataset is None or self.fixed_precision is None or self.variance_increase is None ): raise pipeline.PipelineConfigError( "Container {} has no preset values. You must define all of 'dataset', " "'fixed_precision', and 'variance_increase' properties.".format( container ) ) # Factor of 3 for variance over uniform distribution of truncation errors self.variance_increase *= 3 def process(self, data): """Truncate the incoming data. The truncation is done *in place*. Parameters ---------- data : containers.ContainerBase Data to truncate. Returns ------- truncated_data : containers.ContainerBase Truncated data. """ # get truncation parameters from config or container defaults self._get_params(type(data)) if self.weight_dataset is None: self.weight_dataset = [None] * len(self.dataset) for dset, wgt in zip(self.dataset, self.weight_dataset): old_shape = data[dset].local_shape val = np.ndarray.reshape(data[dset][:], data[dset][:].size) if wgt is None: if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_fixed( val.real, self.fixed_precision ).reshape(old_shape) data[dset][:].imag = bit_truncate_fixed( val.imag, self.fixed_precision ).reshape(old_shape) else: data[dset][:] = bit_truncate_fixed( val, self.fixed_precision ).reshape(old_shape) else: if data[dset][:].shape != data[wgt][:].shape: raise pipeline.PipelineRuntimeError( "Dataset and weight arrays must have same shape ({} != {})".format( data[dset].shape, data[wgt].shape ) ) invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size) if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_weights( val.real, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) data[dset][:].imag = bit_truncate_weights( val.imag, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) else: data[dset][:] = bit_truncate_weights( val, invvar / self.variance_increase, self.fixed_precision ).reshape(old_shape) return data class SaveModuleVersions(task.SingleTask): """Write module versions to a YAML file. The list of modules should be added to the configuration under key 'save_versions'. The version strings are written to a YAML file. Attributes ---------- root : str Root of the file name to output to. """ root = config.Property(proptype=str) done = True def setup(self): """Save module versions.""" fname = "{}_versions.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.versions)) f.close() self.done = True def process(self): """Do nothing.""" self.done = True return class SaveConfig(task.SingleTask): """Write pipeline config to a text file. Yaml configuration document is written to a text file. Attributes ---------- root : str Root of the file name to output to. """ root = config.Property(proptype=str) done = True def setup(self): """Save module versions.""" fname = "{}_config.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.pipeline_config)) f.close() self.done = True def process(self): """Do nothing.""" self.done = True return def get_telescope(obj): """Return a telescope object out of the input (either `ProductManager`, `BeamTransfer` or `TransitTelescope`). """ from drift.core import telescope try: return get_beamtransfer(obj).telescope except RuntimeError: if isinstance(obj, telescope.TransitTelescope): return obj raise RuntimeError("Could not get telescope instance out of %s" % repr(obj)) def get_beamtransfer(obj): """Return a BeamTransfer object out of the input (either `ProductManager`, `BeamTransfer`). """ from drift.core import manager, beamtransfer if isinstance(obj, beamtransfer.BeamTransfer): return obj if isinstance(obj, manager.ProductManager): return obj.beamtransfer raise RuntimeError("Could not get BeamTransfer instance out of %s" % repr(obj))

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import os.path import h5py import numpy as np from yaml import dump as yamldump from caput import pipeline from caput import config from cora.util import units from . import task from ..util.truncate import bit_truncate_weights, bit_truncate_fixed from .containers import SiderealStream, TimeStream, TrackBeam TRUNC_SPEC = { SiderealStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TimeStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TrackBeam: { "dataset": ["beam", "weight"], "weight_dataset": ["weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, } def _list_of_filelists(files): import glob f2 = [] for filelist in files: if isinstance(filelist, str): filelist = glob.glob(filelist) elif isinstance(filelist, list): pass else: raise Exception("Must be list or glob pattern.") f2.append(filelist) return f2 def _list_or_glob(files): import glob if isinstance(files, str): files = sorted(glob.glob(files)) elif isinstance(files, list): pass else: raise ValueError("Argument must be list or glob pattern, got %s" % repr(files)) return files def _list_of_filegroups(groups): import glob if not isinstance(groups, list): groups = [groups] for gi, group in enumerate(groups): files = group["files"] if "tag" not in group: group["tag"] = "group_%i" % gi flist = [] for fname in files: flist += glob.glob(fname) if not len(flist): raise RuntimeError("No files in group exist (%s)." % files) group["files"] = flist return groups class LoadMaps(task.MPILoggedTask): maps = config.Property(proptype=_list_of_filegroups) def next(self): from . import containers if len(self.maps) == 0: raise pipeline.PipelineStopIteration group = self.maps.pop(0) map_stack = None for mfile in group["files"]: self.log.debug("Loading file %s", mfile) current_map = containers.Map.from_file(mfile, distributed=True) current_map.redistribute("freq") if map_stack is None: map_stack = current_map else: if (current_map.freq != map_stack.freq).all(): raise RuntimeError("Maps do not have consistent frequencies.") if (current_map.index_map["pol"] != map_stack.index_map["pol"]).all(): raise RuntimeError("Maps do not have the same polarisations.") if ( current_map.index_map["pixel"] != map_stack.index_map["pixel"] ).all(): raise RuntimeError("Maps do not have the same pixelisation.") map_stack.map[:] += current_map.map[:] map_stack.attrs["tag"] = group["tag"] return map_stack class LoadFITSCatalog(task.SingleTask): catalogs = config.Property(proptype=_list_of_filegroups) z_range = config.list_type(type_=float, length=2, default=None) freq_range = config.list_type(type_=float, length=2, default=None) def process(self): from astropy.io import fits from . import containers if len(self.catalogs) == 0: raise pipeline.PipelineStopIteration group = self.catalogs.pop(0) if self.freq_range: zl = units.nu21 / self.freq_range[1] - 1 zh = units.nu21 / self.freq_range[0] - 1 self.z_range = (zl, zh) if self.z_range: zl, zh = self.z_range self.log.info(f"Applying redshift selection {zl:.2f} <= z <= {zh:.2f}") if self.comm.rank == 0: catalog_stack = [] for cfile in group["files"]: self.log.debug("Loading file %s", cfile) with fits.open(cfile, mode="readonly") as cat: pos = np.array([cat[1].data[col] for col in ["RA", "DEC", "Z"]]) if self.z_range: zsel = (pos[2] >= self.z_range[0]) & (pos[2] <= self.z_range[1]) pos = pos[:, zsel] catalog_stack.append(pos) catalog_array = np.concatenate(catalog_stack, axis=-1).astype(np.float64) catalog_array = np.ascontiguousarray(catalog_array) num_objects = catalog_array.shape[-1] else: num_objects = None catalog_array = None num_objects = self.comm.bcast(num_objects, root=0) self.log.debug(f"Constructing catalog with {num_objects} objects.") if self.comm.rank != 0: catalog_array = np.zeros((3, num_objects), dtype=np.float64) self.comm.Bcast(catalog_array, root=0) catalog = containers.SpectroscopicCatalog(object_id=num_objects) catalog["position"]["ra"] = catalog_array[0] catalog["position"]["dec"] = catalog_array[1] catalog["redshift"]["z"] = catalog_array[2] catalog["redshift"]["z_error"] = 0 catalog.attrs["tag"] = group["tag"] return catalog class LoadFilesFromParams(task.SingleTask): files = config.Property(proptype=_list_or_glob) distributed = config.Property(proptype=bool, default=True) convert_strings = config.Property(proptype=bool, default=True) selections = config.Property(proptype=dict, default=None) def setup(self): self._sel = self._resolve_sel() def process(self): from caput import memh5 import gc gc.collect() if len(self.files) == 0: raise pipeline.PipelineStopIteration file_ = self.files.pop(0) self.log.info(f"Loading file {file_}") self.log.debug(f"Reading with selections: {self._sel}") if self._sel: if self.comm.rank == 0: with h5py.File(file_, "r") as fh: clspath = memh5.MemDiskGroup._detect_subclass_path(fh) else: clspath = None clspath = self.comm.bcast(clspath, root=0) new_cls = memh5.MemDiskGroup._resolve_subclass(clspath) else: new_cls = memh5.BasicCont cont = new_cls.from_file( file_, distributed=self.distributed, comm=self.comm, convert_attribute_strings=self.convert_strings, convert_dataset_strings=self.convert_strings, **self._sel, ) if "tag" not in cont.attrs: tag = os.path.splitext(os.path.basename(file_))[0] cont.attrs["tag"] = tag return cont def _resolve_sel(self): sel = {} sel_parsers = {"range": self._parse_range, "index": self._parse_index} for k in sorted(self.selections or []): *axis, type_ = k.split("_") axis_name = "_".join(axis) if type_ not in sel_parsers: raise ValueError( f'Unsupported selection type "{type_}", or invalid key "{k}"' ) sel[f"{axis_name}_sel"] = sel_parsers[type_](self.selections[k]) return sel def _parse_range(self, x): if not isinstance(x, (list, tuple)) or len(x) > 3 or len(x) < 2: raise ValueError( f"Range spec must be a length 2 or 3 list or tuple. Got {x}." ) for v in x: if not isinstance(v, int): raise ValueError(f"All elements of range spec must be ints. Got {x}") return slice(*x) def _parse_index(self, x): if not isinstance(x, (list, tuple)) or len(x) == 0: raise ValueError(f"Index spec must be a non-empty list or tuple. Got {x}.") for v in x: if not isinstance(v, int): raise ValueError(f"All elements of index spec must be ints. Got {x}") return list(x) LoadBasicCont = LoadFilesFromParams class FindFiles(pipeline.TaskBase): files = config.Property(proptype=_list_or_glob) def setup(self): if not isinstance(self.files, (list, tuple)): raise RuntimeError("Argument must be list of files.") return self.files class LoadFiles(LoadFilesFromParams): files = None def setup(self, files): super().setup() if not isinstance(files, (list, tuple)): raise RuntimeError(f'Argument must be list of files. Got "{files}"') self.files = files class Save(pipeline.TaskBase): root = config.Property(proptype=str) count = 0 def next(self, data): if "tag" not in data.attrs: tag = self.count self.count += 1 else: tag = data.attrs["tag"] fname = "%s_%s.h5" % (self.root, str(tag)) data.to_hdf5(fname) return data class Print(pipeline.TaskBase): def next(self, input_): print(input_) return input_ class LoadBeamTransfer(pipeline.TaskBase): product_directory = config.Property(proptype=str) def setup(self): import os from drift.core import beamtransfer if not os.path.exists(self.product_directory): raise RuntimeError("BeamTransfers do not exist.") bt = beamtransfer.BeamTransfer(self.product_directory) tel = bt.telescope try: return tel, bt, tel.feeds except AttributeError: return tel, bt class LoadProductManager(pipeline.TaskBase): product_directory = config.Property(proptype=str) def setup(self): import os from drift.core import manager if not os.path.exists(self.product_directory): raise RuntimeError("Products do not exist.") pm = manager.ProductManager.from_config(self.product_directory) return pm class Truncate(task.SingleTask): dataset = config.Property(proptype=list, default=None) weight_dataset = config.Property(proptype=list, default=None) fixed_precision = config.Property(proptype=float, default=None) variance_increase = config.Property(proptype=float, default=None) def _get_params(self, container): if container in TRUNC_SPEC: self.log.info("Truncating from preset for container {}".format(container)) for key in [ "dataset", "weight_dataset", "fixed_precision", "variance_increase", ]: attr = getattr(self, key) if attr is None: setattr(self, key, TRUNC_SPEC[container][key]) else: self.log.info("Overriding container default for '{}'.".format(key)) else: if ( self.dataset is None or self.fixed_precision is None or self.variance_increase is None ): raise pipeline.PipelineConfigError( "Container {} has no preset values. You must define all of 'dataset', " "'fixed_precision', and 'variance_increase' properties.".format( container ) ) self.variance_increase *= 3 def process(self, data): self._get_params(type(data)) if self.weight_dataset is None: self.weight_dataset = [None] * len(self.dataset) for dset, wgt in zip(self.dataset, self.weight_dataset): old_shape = data[dset].local_shape val = np.ndarray.reshape(data[dset][:], data[dset][:].size) if wgt is None: if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_fixed( val.real, self.fixed_precision ).reshape(old_shape) data[dset][:].imag = bit_truncate_fixed( val.imag, self.fixed_precision ).reshape(old_shape) else: data[dset][:] = bit_truncate_fixed( val, self.fixed_precision ).reshape(old_shape) else: if data[dset][:].shape != data[wgt][:].shape: raise pipeline.PipelineRuntimeError( "Dataset and weight arrays must have same shape ({} != {})".format( data[dset].shape, data[wgt].shape ) ) invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size) if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_weights( val.real, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) data[dset][:].imag = bit_truncate_weights( val.imag, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) else: data[dset][:] = bit_truncate_weights( val, invvar / self.variance_increase, self.fixed_precision ).reshape(old_shape) return data class SaveModuleVersions(task.SingleTask): root = config.Property(proptype=str) done = True def setup(self): fname = "{}_versions.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.versions)) f.close() self.done = True def process(self): self.done = True return class SaveConfig(task.SingleTask): root = config.Property(proptype=str) done = True def setup(self): fname = "{}_config.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.pipeline_config)) f.close() self.done = True def process(self): self.done = True return def get_telescope(obj): from drift.core import telescope try: return get_beamtransfer(obj).telescope except RuntimeError: if isinstance(obj, telescope.TransitTelescope): return obj raise RuntimeError("Could not get telescope instance out of %s" % repr(obj)) def get_beamtransfer(obj): from drift.core import manager, beamtransfer if isinstance(obj, beamtransfer.BeamTransfer): return obj if isinstance(obj, manager.ProductManager): return obj.beamtransfer raise RuntimeError("Could not get BeamTransfer instance out of %s" % repr(obj))

true

f71b4f651dc252f16edc83bd218126c89ab19ffc

2,507

py

Python

scripts/remove_orphans.py

dbaio/portsfallout

2512036a9983b833f4ece2a0801541dca4d8d58c

[ "BSD-2-Clause" ]

6

2020-10-11T07:54:50.000Z

2022-01-25T22:03:18.000Z

scripts/remove_orphans.py

dbaio/portsfallout

2512036a9983b833f4ece2a0801541dca4d8d58c

[ "BSD-2-Clause" ]

null

scripts/remove_orphans.py

dbaio/portsfallout

2512036a9983b833f4ece2a0801541dca4d8d58c

[ "BSD-2-Clause" ]

null

# Copyright (c) 2020-2021 Danilo G. Baio <dbaio@bsd.com.br> # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import os import sys import requests import bz2 sys.path.insert(1, r'../') os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'portsfallout.settings') import django django.setup() from ports.models import Port def fetch_index(): url = "https://www.FreeBSD.org/ports/INDEX-13.bz2" r = requests.get(url, allow_redirects=True) open('INDEX-13.bz2', 'wb').write(r.content) def populate_set(): Ports = Port.objects.all().values('origin').order_by('origin') sPorts = set() for port in Ports: sPorts.add(port['origin']) return sPorts def read_index(sPorts): with bz2.open('INDEX-13.bz2', mode='rt') as index_file: for row in index_file: row_list = row.split("|") p_origin = row_list[1].replace("/usr/ports/", "") if p_origin in sPorts: sPorts.remove(p_origin) return sPorts def remove_orphans(sPortsOrp): for port in sPortsOrp: print('Removing {}'.format(port)) Port.objects.filter(origin=port).delete() if __name__ == "__main__": fetch_index() sPorts = populate_set() sPortsOrp = read_index(sPorts) remove_orphans(sPortsOrp)

32.986842

81

0.723574

import os import sys import requests import bz2 sys.path.insert(1, r'../') os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'portsfallout.settings') import django django.setup() from ports.models import Port def fetch_index(): url = "https://www.FreeBSD.org/ports/INDEX-13.bz2" r = requests.get(url, allow_redirects=True) open('INDEX-13.bz2', 'wb').write(r.content) def populate_set(): Ports = Port.objects.all().values('origin').order_by('origin') sPorts = set() for port in Ports: sPorts.add(port['origin']) return sPorts def read_index(sPorts): with bz2.open('INDEX-13.bz2', mode='rt') as index_file: for row in index_file: row_list = row.split("|") p_origin = row_list[1].replace("/usr/ports/", "") if p_origin in sPorts: sPorts.remove(p_origin) return sPorts def remove_orphans(sPortsOrp): for port in sPortsOrp: print('Removing {}'.format(port)) Port.objects.filter(origin=port).delete() if __name__ == "__main__": fetch_index() sPorts = populate_set() sPortsOrp = read_index(sPorts) remove_orphans(sPortsOrp)

true

f71b510ba2e775050928e7d131af51fdd10e9af6

4,481

py

Python

scripts/mergemessages.py

burakozdemir32/django-internationalflavor

4c5d29519050c929a608d2054c14faa44ee273c9

[ "BSD-3-Clause" ]

null

scripts/mergemessages.py

burakozdemir32/django-internationalflavor

4c5d29519050c929a608d2054c14faa44ee273c9

[ "BSD-3-Clause" ]

null

scripts/mergemessages.py

burakozdemir32/django-internationalflavor

4c5d29519050c929a608d2054c14faa44ee273c9

[ "BSD-3-Clause" ]

null

import os import sys import polib import django from django.conf import settings from django.core.management.base import BaseCommand from django.utils import translation # This is almost a management command, but we do not want it to be added to the django-admin namespace for the simple # reason that it is not expected to be executed by package users, only by the package maintainers. # We use a thin __main__ wrapper to make it work (ish) like a management command. MODULE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'internationalflavor') LOCALE_PATH = os.path.join(MODULE_PATH, 'locale') def mark_entry(entry): if 'fuzzy' in entry.flags: entry.flags.remove('fuzzy') entry.comment = "auto-generated from CLDR -- see docs before updating" class Command(BaseCommand): help = 'Updates messages in the PO file with messages from the CLDR' def handle(self, *args, **options): translation.deactivate_all() if options['l']: languages = (options['l'], dict(settings.LANGUAGES)[options['l']]), else: languages = settings.LANGUAGES for lc, language in languages: try: self.stdout.write("Parsing language %s [%s]" % (language, lc)) # Get some files ready # The pofile is our combined file pofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.po')) # The cldrfile contain only messages from CLDR cldrfile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'cldr.po')) # The djangofile will only contain messages not from CLDR try: djangofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) except IOError: djangofile = polib.POFile() djangofile.metadata = pofile.metadata djangofile.header = pofile.header # Merge all non-django messages to the djangofile django_only_messages = polib.POFile() for entry in pofile: if cldrfile.find(entry.msgid) is None and not entry.obsolete and not 'fuzzy' in entry.flags: django_only_messages.append(entry) djangofile.merge(django_only_messages) djangofile.save(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) # Add all entries from the CLDR file to the combined file for entry in cldrfile: e = pofile.find(entry.msgid) if e is None: e = polib.POEntry() e.msgid = entry.msgid pofile.append(e) elif 'manual' in e.tcomment.lower(): self.stdout.write("-- Skipping %s of %s" % (e.msgid, language)) continue e.obsolete = False e.msgstr = entry.msgstr e.comment = entry.comment if 'fuzzy' in e.flags: e.flags.remove('fuzzy') # Add entries from the Django file to the combined file for entry in djangofile: e = pofile.find(entry.msgid) # If not in main file, then skip if e is None: continue e.obsolete = entry.obsolete e.msgstr = entry.msgstr e.comment = entry.comment e.flags = entry.flags # We copy over the header and metadata from the djangofile. pofile.metadata = djangofile.metadata pofile.header = djangofile.header pofile.save() pofile.save_as_mofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.mo')) except IOError as e: self.stderr.write("Error while handling %s: %s (possibly no valid .po file)" % (language, e)) except Exception as e: self.stderr.write("Error while handling %s: %s" % (language, e)) def add_arguments(self, parser): parser.add_argument('-l') if __name__ == '__main__': settings.configure() django.setup() Command().run_from_argv(["django-admin.py", "mergemessages"] + sys.argv[1:])

41.110092

117

0.567061

import os import sys import polib import django from django.conf import settings from django.core.management.base import BaseCommand from django.utils import translation MODULE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'internationalflavor') LOCALE_PATH = os.path.join(MODULE_PATH, 'locale') def mark_entry(entry): if 'fuzzy' in entry.flags: entry.flags.remove('fuzzy') entry.comment = "auto-generated from CLDR -- see docs before updating" class Command(BaseCommand): help = 'Updates messages in the PO file with messages from the CLDR' def handle(self, *args, **options): translation.deactivate_all() if options['l']: languages = (options['l'], dict(settings.LANGUAGES)[options['l']]), else: languages = settings.LANGUAGES for lc, language in languages: try: self.stdout.write("Parsing language %s [%s]" % (language, lc)) pofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.po')) cldrfile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'cldr.po')) try: djangofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) except IOError: djangofile = polib.POFile() djangofile.metadata = pofile.metadata djangofile.header = pofile.header django_only_messages = polib.POFile() for entry in pofile: if cldrfile.find(entry.msgid) is None and not entry.obsolete and not 'fuzzy' in entry.flags: django_only_messages.append(entry) djangofile.merge(django_only_messages) djangofile.save(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) for entry in cldrfile: e = pofile.find(entry.msgid) if e is None: e = polib.POEntry() e.msgid = entry.msgid pofile.append(e) elif 'manual' in e.tcomment.lower(): self.stdout.write("-- Skipping %s of %s" % (e.msgid, language)) continue e.obsolete = False e.msgstr = entry.msgstr e.comment = entry.comment if 'fuzzy' in e.flags: e.flags.remove('fuzzy') for entry in djangofile: e = pofile.find(entry.msgid) if e is None: continue e.obsolete = entry.obsolete e.msgstr = entry.msgstr e.comment = entry.comment e.flags = entry.flags pofile.metadata = djangofile.metadata pofile.header = djangofile.header pofile.save() pofile.save_as_mofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.mo')) except IOError as e: self.stderr.write("Error while handling %s: %s (possibly no valid .po file)" % (language, e)) except Exception as e: self.stderr.write("Error while handling %s: %s" % (language, e)) def add_arguments(self, parser): parser.add_argument('-l') if __name__ == '__main__': settings.configure() django.setup() Command().run_from_argv(["django-admin.py", "mergemessages"] + sys.argv[1:])

true

f71b518086dd6b504569820b3ca2a1c860242389

213

py

Python

wadeem/wadeem/doctype/coordinators/test_coordinators.py

siddhantsinha-oodles/Wadeem-app

eee05dead7ccee8878cf3630d3cdf32adb155c7f

[ "MIT" ]

null

wadeem/wadeem/doctype/coordinators/test_coordinators.py

siddhantsinha-oodles/Wadeem-app

eee05dead7ccee8878cf3630d3cdf32adb155c7f

[ "MIT" ]

null

wadeem/wadeem/doctype/coordinators/test_coordinators.py

siddhantsinha-oodles/Wadeem-app

eee05dead7ccee8878cf3630d3cdf32adb155c7f

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2020, Siddhant and Contributors # See license.txt from __future__ import unicode_literals # import frappe import unittest class TestCoordinators(unittest.TestCase): pass

19.363636

47

0.765258

from __future__ import unicode_literals import unittest class TestCoordinators(unittest.TestCase): pass

true

f71b5347247e075de1cdb3afefef4dff57d2b7aa

802

py

Python

multiqc_npm/modules/npm_calculate_callability.py

c-BIG/MultiQC_NPM

c8dbf15c2b2ce03f2932db0bb50d49dcab865d75

[ "MIT" ]

null

multiqc_npm/modules/npm_calculate_callability.py

c-BIG/MultiQC_NPM

c8dbf15c2b2ce03f2932db0bb50d49dcab865d75

[ "MIT" ]

null

multiqc_npm/modules/npm_calculate_callability.py

c-BIG/MultiQC_NPM

c8dbf15c2b2ce03f2932db0bb50d49dcab865d75

[ "MIT" ]

null

#!/usr/bin/env python3 """ Parser for calculate_callability.py """ import logging import json from multiqc.utils import report log = logging.getLogger(__name__) def parse_reports(self): # Set up vars self.calculate_callability = dict() # Collect metrics for f in self.find_log_files('multiqc_npm/calculate_callability'): parsed_data = json.loads(f["f"]) # Save results s_name = f["s_name"] self.calculate_callability[s_name] = parsed_data # Write results if len(self.calculate_callability) > 0: # Write parsed data to a file self.write_data_file(self.calculate_callability, 'multiqc_npm_calculate_callability') # Return the number of detected samples to the parent module return len(self.calculate_callability)

22.914286

93

0.706983

import logging import json from multiqc.utils import report log = logging.getLogger(__name__) def parse_reports(self): self.calculate_callability = dict() for f in self.find_log_files('multiqc_npm/calculate_callability'): parsed_data = json.loads(f["f"]) s_name = f["s_name"] self.calculate_callability[s_name] = parsed_data if len(self.calculate_callability) > 0: self.write_data_file(self.calculate_callability, 'multiqc_npm_calculate_callability') return len(self.calculate_callability)

true

f71b54183262fd12198dc0255f4b1059568df90a

14,252

py

Python

airflow/models/connection.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

null

airflow/models/connection.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

1

2019-05-14T14:32:40.000Z

airflow/models/connection.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 from urllib.parse import parse_qsl, quote, unquote, urlencode, urlparse from sqlalchemy import Boolean, Column, Integer, String from sqlalchemy.ext.declarative import declared_attr from sqlalchemy.orm import synonym from airflow.exceptions import AirflowException from airflow.models.base import ID_LEN, Base from airflow.models.crypto import get_fernet from airflow.utils.log.logging_mixin import LoggingMixin # Python automatically converts all letters to lowercase in hostname # See: https://issues.apache.org/jira/browse/AIRFLOW-3615 def parse_netloc_to_hostname(uri_parts): hostname = unquote(uri_parts.hostname or '') if '/' in hostname: hostname = uri_parts.netloc if "@" in hostname: hostname = hostname.rsplit("@", 1)[1] if ":" in hostname: hostname = hostname.split(":", 1)[0] hostname = unquote(hostname) return hostname class Connection(Base, LoggingMixin): """ Placeholder to store information about different database instances connection information. The idea here is that scripts use references to database instances (conn_id) instead of hard coding hostname, logins and passwords when using operators or hooks. """ __tablename__ = "connection" id = Column(Integer(), primary_key=True) conn_id = Column(String(ID_LEN)) conn_type = Column(String(500)) host = Column(String(500)) schema = Column(String(500)) login = Column(String(500)) _password = Column('password', String(5000)) port = Column(Integer()) is_encrypted = Column(Boolean, unique=False, default=False) is_extra_encrypted = Column(Boolean, unique=False, default=False) _extra = Column('extra', String(5000)) _types = [ ('docker', 'Docker Registry'), ('fs', 'File (path)'), ('ftp', 'FTP'), ('google_cloud_platform', 'Google Cloud Platform'), ('hdfs', 'HDFS'), ('http', 'HTTP'), ('pig_cli', 'Pig Client Wrapper'), ('hive_cli', 'Hive Client Wrapper'), ('hive_metastore', 'Hive Metastore Thrift'), ('hiveserver2', 'Hive Server 2 Thrift'), ('jdbc', 'JDBC Connection'), ('odbc', 'ODBC Connection'), ('jenkins', 'Jenkins'), ('mysql', 'MySQL'), ('postgres', 'Postgres'), ('oracle', 'Oracle'), ('vertica', 'Vertica'), ('presto', 'Presto'), ('s3', 'S3'), ('samba', 'Samba'), ('sqlite', 'Sqlite'), ('ssh', 'SSH'), ('cloudant', 'IBM Cloudant'), ('mssql', 'Microsoft SQL Server'), ('mesos_framework-id', 'Mesos Framework ID'), ('jira', 'JIRA'), ('redis', 'Redis'), ('wasb', 'Azure Blob Storage'), ('databricks', 'Databricks'), ('aws', 'Amazon Web Services'), ('emr', 'Elastic MapReduce'), ('snowflake', 'Snowflake'), ('segment', 'Segment'), ('sqoop', 'Sqoop'), ('azure_data_lake', 'Azure Data Lake'), ('azure_container_instances', 'Azure Container Instances'), ('azure_cosmos', 'Azure CosmosDB'), ('cassandra', 'Cassandra'), ('qubole', 'Qubole'), ('mongo', 'MongoDB'), ('gcpcloudsql', 'Google Cloud SQL'), ('grpc', 'GRPC Connection'), ] def __init__( self, conn_id=None, conn_type=None, host=None, login=None, password=None, schema=None, port=None, extra=None, uri=None): self.conn_id = conn_id if uri: self.parse_from_uri(uri) else: self.conn_type = conn_type self.host = host self.login = login self.password = password self.schema = schema self.port = port self.extra = extra def parse_from_uri(self, uri): uri_parts = urlparse(uri) conn_type = uri_parts.scheme if conn_type == 'postgresql': conn_type = 'postgres' elif '-' in conn_type: conn_type = conn_type.replace('-', '_') self.conn_type = conn_type self.host = parse_netloc_to_hostname(uri_parts) quoted_schema = uri_parts.path[1:] self.schema = unquote(quoted_schema) if quoted_schema else quoted_schema self.login = unquote(uri_parts.username) \ if uri_parts.username else uri_parts.username self.password = unquote(uri_parts.password) \ if uri_parts.password else uri_parts.password self.port = uri_parts.port if uri_parts.query: self.extra = json.dumps(dict(parse_qsl(uri_parts.query, keep_blank_values=True))) def get_uri(self) -> str: uri = '{}://'.format(str(self.conn_type).lower().replace('_', '-')) authority_block = '' if self.login is not None: authority_block += quote(self.login, safe='') if self.password is not None: authority_block += ':' + quote(self.password, safe='') if authority_block > '': authority_block += '@' uri += authority_block host_block = '' if self.host: host_block += quote(self.host, safe='') if self.port: if host_block > '': host_block += ':{}'.format(self.port) else: host_block += '@:{}'.format(self.port) if self.schema: host_block += '/{}'.format(quote(self.schema, safe='')) uri += host_block if self.extra_dejson: uri += '?{}'.format(urlencode(self.extra_dejson)) return uri def get_password(self): if self._password and self.is_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt encrypted password for login={}, \ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._password, 'utf-8')).decode() else: return self._password def set_password(self, value): if value: fernet = get_fernet() self._password = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_encrypted = fernet.is_encrypted @declared_attr def password(cls): return synonym('_password', descriptor=property(cls.get_password, cls.set_password)) def get_extra(self): if self._extra and self.is_extra_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt `extra` params for login={},\ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._extra, 'utf-8')).decode() else: return self._extra def set_extra(self, value): if value: fernet = get_fernet() self._extra = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_extra_encrypted = fernet.is_encrypted else: self._extra = value self.is_extra_encrypted = False @declared_attr def extra(cls): return synonym('_extra', descriptor=property(cls.get_extra, cls.set_extra)) def rotate_fernet_key(self): fernet = get_fernet() if self._password and self.is_encrypted: self._password = fernet.rotate(self._password.encode('utf-8')).decode() if self._extra and self.is_extra_encrypted: self._extra = fernet.rotate(self._extra.encode('utf-8')).decode() def get_hook(self): if self.conn_type == 'mysql': from airflow.providers.mysql.hooks.mysql import MySqlHook return MySqlHook(mysql_conn_id=self.conn_id) elif self.conn_type == 'google_cloud_platform': from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook return BigQueryHook(bigquery_conn_id=self.conn_id) elif self.conn_type == 'postgres': from airflow.providers.postgres.hooks.postgres import PostgresHook return PostgresHook(postgres_conn_id=self.conn_id) elif self.conn_type == 'pig_cli': from airflow.providers.apache.pig.hooks.pig import PigCliHook return PigCliHook(pig_cli_conn_id=self.conn_id) elif self.conn_type == 'hive_cli': from airflow.providers.apache.hive.hooks.hive import HiveCliHook return HiveCliHook(hive_cli_conn_id=self.conn_id) elif self.conn_type == 'presto': from airflow.providers.presto.hooks.presto import PrestoHook return PrestoHook(presto_conn_id=self.conn_id) elif self.conn_type == 'hiveserver2': from airflow.providers.apache.hive.hooks.hive import HiveServer2Hook return HiveServer2Hook(hiveserver2_conn_id=self.conn_id) elif self.conn_type == 'sqlite': from airflow.providers.sqlite.hooks.sqlite import SqliteHook return SqliteHook(sqlite_conn_id=self.conn_id) elif self.conn_type == 'jdbc': from airflow.providers.jdbc.hooks.jdbc import JdbcHook return JdbcHook(jdbc_conn_id=self.conn_id) elif self.conn_type == 'mssql': from airflow.providers.microsoft.mssql.hooks.mssql import MsSqlHook return MsSqlHook(mssql_conn_id=self.conn_id) elif self.conn_type == 'odbc': from airflow.providers.odbc.hooks.odbc import OdbcHook return OdbcHook(odbc_conn_id=self.conn_id) elif self.conn_type == 'oracle': from airflow.providers.oracle.hooks.oracle import OracleHook return OracleHook(oracle_conn_id=self.conn_id) elif self.conn_type == 'vertica': from airflow.providers.vertica.hooks.vertica import VerticaHook return VerticaHook(vertica_conn_id=self.conn_id) elif self.conn_type == 'cloudant': from airflow.providers.cloudant.hooks.cloudant import CloudantHook return CloudantHook(cloudant_conn_id=self.conn_id) elif self.conn_type == 'jira': from airflow.providers.jira.hooks.jira import JiraHook return JiraHook(jira_conn_id=self.conn_id) elif self.conn_type == 'redis': from airflow.providers.redis.hooks.redis import RedisHook return RedisHook(redis_conn_id=self.conn_id) elif self.conn_type == 'wasb': from airflow.providers.microsoft.azure.hooks.wasb import WasbHook return WasbHook(wasb_conn_id=self.conn_id) elif self.conn_type == 'docker': from airflow.providers.docker.hooks.docker import DockerHook return DockerHook(docker_conn_id=self.conn_id) elif self.conn_type == 'azure_data_lake': from airflow.providers.microsoft.azure.hooks.azure_data_lake import AzureDataLakeHook return AzureDataLakeHook(azure_data_lake_conn_id=self.conn_id) elif self.conn_type == 'azure_cosmos': from airflow.providers.microsoft.azure.hooks.azure_cosmos import AzureCosmosDBHook return AzureCosmosDBHook(azure_cosmos_conn_id=self.conn_id) elif self.conn_type == 'cassandra': from airflow.providers.apache.cassandra.hooks.cassandra import CassandraHook return CassandraHook(cassandra_conn_id=self.conn_id) elif self.conn_type == 'mongo': from airflow.providers.mongo.hooks.mongo import MongoHook return MongoHook(conn_id=self.conn_id) elif self.conn_type == 'gcpcloudsql': from airflow.providers.google.cloud.hooks.cloud_sql import CloudSQLDatabaseHook return CloudSQLDatabaseHook(gcp_cloudsql_conn_id=self.conn_id) elif self.conn_type == 'grpc': from airflow.providers.grpc.hooks.grpc import GrpcHook return GrpcHook(grpc_conn_id=self.conn_id) raise AirflowException("Unknown hook type {}".format(self.conn_type)) def __repr__(self): return self.conn_id def log_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, "XXXXXXXX" if self.extra_dejson else None)) def debug_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, self.extra_dejson)) @property def extra_dejson(self): """Returns the extra property by deserializing json.""" obj = {} if self.extra: try: obj = json.loads(self.extra) except Exception as e: self.log.exception(e) self.log.error("Failed parsing the json for conn_id %s", self.conn_id) return obj

40.72

97

0.613388

import json from urllib.parse import parse_qsl, quote, unquote, urlencode, urlparse from sqlalchemy import Boolean, Column, Integer, String from sqlalchemy.ext.declarative import declared_attr from sqlalchemy.orm import synonym from airflow.exceptions import AirflowException from airflow.models.base import ID_LEN, Base from airflow.models.crypto import get_fernet from airflow.utils.log.logging_mixin import LoggingMixin def parse_netloc_to_hostname(uri_parts): hostname = unquote(uri_parts.hostname or '') if '/' in hostname: hostname = uri_parts.netloc if "@" in hostname: hostname = hostname.rsplit("@", 1)[1] if ":" in hostname: hostname = hostname.split(":", 1)[0] hostname = unquote(hostname) return hostname class Connection(Base, LoggingMixin): __tablename__ = "connection" id = Column(Integer(), primary_key=True) conn_id = Column(String(ID_LEN)) conn_type = Column(String(500)) host = Column(String(500)) schema = Column(String(500)) login = Column(String(500)) _password = Column('password', String(5000)) port = Column(Integer()) is_encrypted = Column(Boolean, unique=False, default=False) is_extra_encrypted = Column(Boolean, unique=False, default=False) _extra = Column('extra', String(5000)) _types = [ ('docker', 'Docker Registry'), ('fs', 'File (path)'), ('ftp', 'FTP'), ('google_cloud_platform', 'Google Cloud Platform'), ('hdfs', 'HDFS'), ('http', 'HTTP'), ('pig_cli', 'Pig Client Wrapper'), ('hive_cli', 'Hive Client Wrapper'), ('hive_metastore', 'Hive Metastore Thrift'), ('hiveserver2', 'Hive Server 2 Thrift'), ('jdbc', 'JDBC Connection'), ('odbc', 'ODBC Connection'), ('jenkins', 'Jenkins'), ('mysql', 'MySQL'), ('postgres', 'Postgres'), ('oracle', 'Oracle'), ('vertica', 'Vertica'), ('presto', 'Presto'), ('s3', 'S3'), ('samba', 'Samba'), ('sqlite', 'Sqlite'), ('ssh', 'SSH'), ('cloudant', 'IBM Cloudant'), ('mssql', 'Microsoft SQL Server'), ('mesos_framework-id', 'Mesos Framework ID'), ('jira', 'JIRA'), ('redis', 'Redis'), ('wasb', 'Azure Blob Storage'), ('databricks', 'Databricks'), ('aws', 'Amazon Web Services'), ('emr', 'Elastic MapReduce'), ('snowflake', 'Snowflake'), ('segment', 'Segment'), ('sqoop', 'Sqoop'), ('azure_data_lake', 'Azure Data Lake'), ('azure_container_instances', 'Azure Container Instances'), ('azure_cosmos', 'Azure CosmosDB'), ('cassandra', 'Cassandra'), ('qubole', 'Qubole'), ('mongo', 'MongoDB'), ('gcpcloudsql', 'Google Cloud SQL'), ('grpc', 'GRPC Connection'), ] def __init__( self, conn_id=None, conn_type=None, host=None, login=None, password=None, schema=None, port=None, extra=None, uri=None): self.conn_id = conn_id if uri: self.parse_from_uri(uri) else: self.conn_type = conn_type self.host = host self.login = login self.password = password self.schema = schema self.port = port self.extra = extra def parse_from_uri(self, uri): uri_parts = urlparse(uri) conn_type = uri_parts.scheme if conn_type == 'postgresql': conn_type = 'postgres' elif '-' in conn_type: conn_type = conn_type.replace('-', '_') self.conn_type = conn_type self.host = parse_netloc_to_hostname(uri_parts) quoted_schema = uri_parts.path[1:] self.schema = unquote(quoted_schema) if quoted_schema else quoted_schema self.login = unquote(uri_parts.username) \ if uri_parts.username else uri_parts.username self.password = unquote(uri_parts.password) \ if uri_parts.password else uri_parts.password self.port = uri_parts.port if uri_parts.query: self.extra = json.dumps(dict(parse_qsl(uri_parts.query, keep_blank_values=True))) def get_uri(self) -> str: uri = '{}://'.format(str(self.conn_type).lower().replace('_', '-')) authority_block = '' if self.login is not None: authority_block += quote(self.login, safe='') if self.password is not None: authority_block += ':' + quote(self.password, safe='') if authority_block > '': authority_block += '@' uri += authority_block host_block = '' if self.host: host_block += quote(self.host, safe='') if self.port: if host_block > '': host_block += ':{}'.format(self.port) else: host_block += '@:{}'.format(self.port) if self.schema: host_block += '/{}'.format(quote(self.schema, safe='')) uri += host_block if self.extra_dejson: uri += '?{}'.format(urlencode(self.extra_dejson)) return uri def get_password(self): if self._password and self.is_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt encrypted password for login={}, \ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._password, 'utf-8')).decode() else: return self._password def set_password(self, value): if value: fernet = get_fernet() self._password = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_encrypted = fernet.is_encrypted @declared_attr def password(cls): return synonym('_password', descriptor=property(cls.get_password, cls.set_password)) def get_extra(self): if self._extra and self.is_extra_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt `extra` params for login={},\ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._extra, 'utf-8')).decode() else: return self._extra def set_extra(self, value): if value: fernet = get_fernet() self._extra = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_extra_encrypted = fernet.is_encrypted else: self._extra = value self.is_extra_encrypted = False @declared_attr def extra(cls): return synonym('_extra', descriptor=property(cls.get_extra, cls.set_extra)) def rotate_fernet_key(self): fernet = get_fernet() if self._password and self.is_encrypted: self._password = fernet.rotate(self._password.encode('utf-8')).decode() if self._extra and self.is_extra_encrypted: self._extra = fernet.rotate(self._extra.encode('utf-8')).decode() def get_hook(self): if self.conn_type == 'mysql': from airflow.providers.mysql.hooks.mysql import MySqlHook return MySqlHook(mysql_conn_id=self.conn_id) elif self.conn_type == 'google_cloud_platform': from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook return BigQueryHook(bigquery_conn_id=self.conn_id) elif self.conn_type == 'postgres': from airflow.providers.postgres.hooks.postgres import PostgresHook return PostgresHook(postgres_conn_id=self.conn_id) elif self.conn_type == 'pig_cli': from airflow.providers.apache.pig.hooks.pig import PigCliHook return PigCliHook(pig_cli_conn_id=self.conn_id) elif self.conn_type == 'hive_cli': from airflow.providers.apache.hive.hooks.hive import HiveCliHook return HiveCliHook(hive_cli_conn_id=self.conn_id) elif self.conn_type == 'presto': from airflow.providers.presto.hooks.presto import PrestoHook return PrestoHook(presto_conn_id=self.conn_id) elif self.conn_type == 'hiveserver2': from airflow.providers.apache.hive.hooks.hive import HiveServer2Hook return HiveServer2Hook(hiveserver2_conn_id=self.conn_id) elif self.conn_type == 'sqlite': from airflow.providers.sqlite.hooks.sqlite import SqliteHook return SqliteHook(sqlite_conn_id=self.conn_id) elif self.conn_type == 'jdbc': from airflow.providers.jdbc.hooks.jdbc import JdbcHook return JdbcHook(jdbc_conn_id=self.conn_id) elif self.conn_type == 'mssql': from airflow.providers.microsoft.mssql.hooks.mssql import MsSqlHook return MsSqlHook(mssql_conn_id=self.conn_id) elif self.conn_type == 'odbc': from airflow.providers.odbc.hooks.odbc import OdbcHook return OdbcHook(odbc_conn_id=self.conn_id) elif self.conn_type == 'oracle': from airflow.providers.oracle.hooks.oracle import OracleHook return OracleHook(oracle_conn_id=self.conn_id) elif self.conn_type == 'vertica': from airflow.providers.vertica.hooks.vertica import VerticaHook return VerticaHook(vertica_conn_id=self.conn_id) elif self.conn_type == 'cloudant': from airflow.providers.cloudant.hooks.cloudant import CloudantHook return CloudantHook(cloudant_conn_id=self.conn_id) elif self.conn_type == 'jira': from airflow.providers.jira.hooks.jira import JiraHook return JiraHook(jira_conn_id=self.conn_id) elif self.conn_type == 'redis': from airflow.providers.redis.hooks.redis import RedisHook return RedisHook(redis_conn_id=self.conn_id) elif self.conn_type == 'wasb': from airflow.providers.microsoft.azure.hooks.wasb import WasbHook return WasbHook(wasb_conn_id=self.conn_id) elif self.conn_type == 'docker': from airflow.providers.docker.hooks.docker import DockerHook return DockerHook(docker_conn_id=self.conn_id) elif self.conn_type == 'azure_data_lake': from airflow.providers.microsoft.azure.hooks.azure_data_lake import AzureDataLakeHook return AzureDataLakeHook(azure_data_lake_conn_id=self.conn_id) elif self.conn_type == 'azure_cosmos': from airflow.providers.microsoft.azure.hooks.azure_cosmos import AzureCosmosDBHook return AzureCosmosDBHook(azure_cosmos_conn_id=self.conn_id) elif self.conn_type == 'cassandra': from airflow.providers.apache.cassandra.hooks.cassandra import CassandraHook return CassandraHook(cassandra_conn_id=self.conn_id) elif self.conn_type == 'mongo': from airflow.providers.mongo.hooks.mongo import MongoHook return MongoHook(conn_id=self.conn_id) elif self.conn_type == 'gcpcloudsql': from airflow.providers.google.cloud.hooks.cloud_sql import CloudSQLDatabaseHook return CloudSQLDatabaseHook(gcp_cloudsql_conn_id=self.conn_id) elif self.conn_type == 'grpc': from airflow.providers.grpc.hooks.grpc import GrpcHook return GrpcHook(grpc_conn_id=self.conn_id) raise AirflowException("Unknown hook type {}".format(self.conn_type)) def __repr__(self): return self.conn_id def log_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, "XXXXXXXX" if self.extra_dejson else None)) def debug_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, self.extra_dejson)) @property def extra_dejson(self): obj = {} if self.extra: try: obj = json.loads(self.extra) except Exception as e: self.log.exception(e) self.log.error("Failed parsing the json for conn_id %s", self.conn_id) return obj

true

f71b5426abe6a21a2aafe3e9e5c4721319783d05

5,751

py

Python

original_author_notes/yolo_video.py

adam-blinzler/simple-lane-detection

8814e0aaf7ac56b7e5be59634e363ca17839effb

[ "MIT" ]

null

original_author_notes/yolo_video.py

adam-blinzler/simple-lane-detection

8814e0aaf7ac56b7e5be59634e363ca17839effb

[ "MIT" ]

null

original_author_notes/yolo_video.py

adam-blinzler/simple-lane-detection

8814e0aaf7ac56b7e5be59634e363ca17839effb

[ "MIT" ]

null

# USAGE # python yolo_video.py --input videos/airport.mp4 --output output/airport_output.avi --object_detection object_detection-coco # import the necessary packages import numpy as np import argparse import imutils import time import cv2 import os # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--input", required=True, help="path to input video") ap.add_argument("-o", "--output", required=True, help="path to output video") ap.add_argument("-y", "--object_detection", required=True, help="base path to YOLO directory") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applyong non-maxima suppression") args = vars(ap.parse_args()) # load the COCO class labels our YOLO model was trained on labelsPath = os.path.sep.join([args["object_detection"], "coco.names"]) LABELS = open(labelsPath).read().strip().split("\n") # initialize a list of colors to represent each possible class label np.random.seed(42) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") # derive the paths to the YOLO weights and model configuration weightsPath = os.path.sep.join([args["object_detection"], "yolov3.weights"]) configPath = os.path.sep.join([args["object_detection"], "yolov3.cfg"]) # load our YOLO object detector trained on COCO dataset (80 classes) # and determine only the *output* layer names that we need from YOLO print("[INFO] loading YOLO from disk...") net = cv2.dnn.readNetFromDarknet(configPath, weightsPath) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # initialize the video stream, pointer to output video file, and # frame dimensions vs = cv2.VideoCapture(args["input"]) writer = None (W, H) = (None, None) # try to determine the total number of frames in the video file try: prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \ else cv2.CAP_PROP_FRAME_COUNT total = int(vs.get(prop)) print("[INFO] {} total frames in video".format(total)) # an error occurred while trying to determine the total # number of frames in the video file except: print("[INFO] could not determine # of frames in video") print("[INFO] no approx. completion time can be provided") total = -1 # loop over frames from the video file stream while True: # read the next frame from the file (grabbed, frame) = vs.read() # if the frame was not grabbed, then we have reached the end # of the stream if not grabbed: break # if the frame dimensions are empty, grab them if W is None or H is None: (H, W) = frame.shape[:2] # construct a blob from the input frame and then perform a forward # pass of the YOLO object detector, giving us our bounding boxes # and associated probabilities blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) end = time.time() # initialize our lists of detected bounding boxes, confidences, # and class IDs, respectively boxes = [] confidences = [] classIDs = [] # loop over each of the layer outputs for output in layerOutputs: # loop over each of the detections for detection in output: # extract the class ID and confidence (i.e., probability) # of the current object detection scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] # filter out weak predictions by ensuring the detected # probability is greater than the minimum probability if confidence > args["confidence"]: # scale the bounding box coordinates back relative to # the size of the image, keeping in mind that YOLO # actually returns the center (x, y)-coordinates of # the bounding box followed by the boxes' width and # height box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top # and and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, # confidences, and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # apply non-maxima suppression to suppress weak, overlapping # bounding boxes idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"]) # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) # draw a bounding box rectangle and label on the frame color = [int(c) for c in COLORS[classIDs[i]]] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) # check if the video writer is None if writer is None: # initialize our video writer fourcc = cv2.VideoWriter_fourcc(*"MJPG") writer = cv2.VideoWriter(args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True) # some information on processing single frame if total > 0: elap = (end - start) print("[INFO] single frame took {:.4f} seconds".format(elap)) print("[INFO] estimated total time to finish: {:.4f}".format( elap * total)) # write the output frame to disk writer.write(frame) # release the file pointers print("[INFO] cleaning up...") writer.release() vs.release()

34.029586

125

0.704051

import numpy as np import argparse import imutils import time import cv2 import os ap = argparse.ArgumentParser() ap.add_argument("-i", "--input", required=True, help="path to input video") ap.add_argument("-o", "--output", required=True, help="path to output video") ap.add_argument("-y", "--object_detection", required=True, help="base path to YOLO directory") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applyong non-maxima suppression") args = vars(ap.parse_args()) labelsPath = os.path.sep.join([args["object_detection"], "coco.names"]) LABELS = open(labelsPath).read().strip().split("\n") np.random.seed(42) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") weightsPath = os.path.sep.join([args["object_detection"], "yolov3.weights"]) configPath = os.path.sep.join([args["object_detection"], "yolov3.cfg"]) print("[INFO] loading YOLO from disk...") net = cv2.dnn.readNetFromDarknet(configPath, weightsPath) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] vs = cv2.VideoCapture(args["input"]) writer = None (W, H) = (None, None) try: prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \ else cv2.CAP_PROP_FRAME_COUNT total = int(vs.get(prop)) print("[INFO] {} total frames in video".format(total)) except: print("[INFO] could not determine # of frames in video") print("[INFO] no approx. completion time can be provided") total = -1 while True: (grabbed, frame) = vs.read() if not grabbed: break if W is None or H is None: (H, W) = frame.shape[:2] blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) end = time.time() boxes = [] confidences = [] classIDs = [] for output in layerOutputs: for detection in output: scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] if confidence > args["confidence"]: # height box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top # and and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, # confidences, and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # apply non-maxima suppression to suppress weak, overlapping # bounding boxes idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"]) # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) # draw a bounding box rectangle and label on the frame color = [int(c) for c in COLORS[classIDs[i]]] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) # check if the video writer is None if writer is None: # initialize our video writer fourcc = cv2.VideoWriter_fourcc(*"MJPG") writer = cv2.VideoWriter(args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True) # some information on processing single frame if total > 0: elap = (end - start) print("[INFO] single frame took {:.4f} seconds".format(elap)) print("[INFO] estimated total time to finish: {:.4f}".format( elap * total)) # write the output frame to disk writer.write(frame) # release the file pointers print("[INFO] cleaning up...") writer.release() vs.release()

true

f71b5468bb09f935a4b8dd8609a936248498eb63

7,498

py

Python

tessia/server/api/views/auth.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

5

2020-06-04T10:20:33.000Z

2020-10-26T15:09:19.000Z

tessia/server/api/views/auth.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

null

tessia/server/api/views/auth.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

null

# Copyright 2016, 2017 IBM Corp. # # 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. """ Authentication routines """ # # IMPORTS # from base64 import b64decode from flask import g as flask_global from flask import request as flask_request from sqlalchemy.sql import func from tessia.server import auth from tessia.server.api.db import API_DB from tessia.server.api.exceptions import UnauthorizedError from tessia.server.config import CONF from tessia.server.db.models import User from tessia.server.db.models import UserKey # use the exception directly so that potion custom error handler can catch # it and convert to a valid json response from werkzeug.exceptions import BadRequest # # CONSTANTS AND DEFINITIONS # # # CODE # class _LoginManager: # holds the login manager object _manager = None @classmethod def get_login_manager(cls): """ Return the login manager object, as defined by the auth module. """ if cls._manager is None: cls._manager = auth.get_manager() return cls._manager # get_login_manager() @classmethod def authenticate_basic(cls, auth_value): """ Basic authentication with username and password, validate against the login manager defined in configured file (usually LDAP) Args: auth_value (str): the value part of the Authorization header form username:password base64 encoded Raises: BadRequest: if value is malformed UnauthorizedError: if credentials are invalid Returns: User: instance of User's sqlalchemy model """ try: # http headers are always ascii user, passwd = b64decode(auth_value).decode( 'ascii').split(':', 1) except Exception: raise BadRequest() case_sensitive = CONF.get_config().get( 'auth', {}).get('case_sensitive', False) if not case_sensitive: # logins should be case-insensitive user = user.lower() # user authentication with login provider failed: return unauthorized result = cls.get_login_manager().authenticate(user, passwd) if result is None: raise UnauthorizedError() # find user entry in database user_entry = User.query.filter_by(login=user).first() if user_entry is not None: # update db in case user information has changed changed = False if user_entry.name != result['fullname']: changed = True user_entry.name = result['fullname'] if user_entry.title != result.get('title', None): changed = True user_entry.title = result.get('title', None) if changed: API_DB.db.session.add(user_entry) API_DB.db.session.commit() return user_entry allow_auto_create = CONF.get_config().get( 'auth', {}).get('allow_user_auto_create', False) # auto creation of users not allowed: report unauthorized if not allow_auto_create: raise UnauthorizedError( msg='User authenticated but not registered in database') # create user in database new_user = User() if case_sensitive: new_user.login = result['login'] else: # save login as lowercase to avoid duplicates or user having to # worry about entering the right case new_user.login = result['login'].lower() new_user.name = result['fullname'] # job title is optional new_user.title = result.get('title', None) new_user.restricted = False new_user.admin = False API_DB.db.session.add(new_user) API_DB.db.session.commit() return new_user # authenticate_basic() @classmethod def authenticate_key(cls, auth_value): """ API key-based authentication Args: auth_value (str): the value part of the Authorization header in the form key_id:key_value Raises: BadRequest: if value is malformed UnauthorizedError: if credentials are invalid Returns: User: instance of User's sqlalchemy model """ try: # http headers are always ascii key_id, key_secret = auth_value.split(':', 1) except Exception: raise BadRequest() key_entry = UserKey.query.filter_by( key_id=key_id, key_secret=key_secret).first() if key_entry is None: raise UnauthorizedError() key_entry.last_used = func.now() API_DB.db.session.add(key_entry) API_DB.db.session.commit() return key_entry.user_rel # authenticate_key() # _LoginManager def authorize(decorated_view): """ A decorator view which implements authorization routine. Args: decorated_view (method): the view function to be decorated Returns: method: the authenticate wrapper containing the original view Raises: None """ def authenticate(*args, **kwargs): """ The wrapper that takes the authorization related actions before executing the actual view Args: args: packed args for the decorated view kwargs: packed keyargs for the decorated view Returns: any: the return value of the decorated view Raises: UnauthorizedError: if auth header is missing or invalid """ # no credentials provided: reply that authorization is needed. # The exception takes care of providing the scheme allowed # via WWW-Authenticate response header auth_header = flask_request.headers.get('Authorization', None) if not auth_header: raise UnauthorizedError(auth_provided=False) try: auth_scheme, auth_value = auth_header.split(None, 1) except ValueError: raise UnauthorizedError() auth_scheme = auth_scheme.lower() if auth_scheme == 'basic': user_entry = _LoginManager.authenticate_basic(auth_value) elif auth_scheme == 'x-key': user_entry = _LoginManager.authenticate_key(auth_value) else: # scheme not supported raise UnauthorizedError() # set model as session variable flask_global.auth_user = user_entry # pylint: disable=assigning-non-slot # this might be relevant depending on the nature of the operation. # i.e. api key operations are only allowed after entering password # (basic scheme) flask_global.auth_method = auth_scheme # pylint: disable=assigning-non-slot return decorated_view(*args, **kwargs) # authenticate() return authenticate # authorize()

31.771186

83

0.634169

from base64 import b64decode from flask import g as flask_global from flask import request as flask_request from sqlalchemy.sql import func from tessia.server import auth from tessia.server.api.db import API_DB from tessia.server.api.exceptions import UnauthorizedError from tessia.server.config import CONF from tessia.server.db.models import User from tessia.server.db.models import UserKey from werkzeug.exceptions import BadRequest class _LoginManager: _manager = None @classmethod def get_login_manager(cls): if cls._manager is None: cls._manager = auth.get_manager() return cls._manager @classmethod def authenticate_basic(cls, auth_value): try: user, passwd = b64decode(auth_value).decode( 'ascii').split(':', 1) except Exception: raise BadRequest() case_sensitive = CONF.get_config().get( 'auth', {}).get('case_sensitive', False) if not case_sensitive: user = user.lower() result = cls.get_login_manager().authenticate(user, passwd) if result is None: raise UnauthorizedError() user_entry = User.query.filter_by(login=user).first() if user_entry is not None: changed = False if user_entry.name != result['fullname']: changed = True user_entry.name = result['fullname'] if user_entry.title != result.get('title', None): changed = True user_entry.title = result.get('title', None) if changed: API_DB.db.session.add(user_entry) API_DB.db.session.commit() return user_entry allow_auto_create = CONF.get_config().get( 'auth', {}).get('allow_user_auto_create', False) if not allow_auto_create: raise UnauthorizedError( msg='User authenticated but not registered in database') new_user = User() if case_sensitive: new_user.login = result['login'] else: new_user.login = result['login'].lower() new_user.name = result['fullname'] new_user.title = result.get('title', None) new_user.restricted = False new_user.admin = False API_DB.db.session.add(new_user) API_DB.db.session.commit() return new_user @classmethod def authenticate_key(cls, auth_value): try: key_id, key_secret = auth_value.split(':', 1) except Exception: raise BadRequest() key_entry = UserKey.query.filter_by( key_id=key_id, key_secret=key_secret).first() if key_entry is None: raise UnauthorizedError() key_entry.last_used = func.now() API_DB.db.session.add(key_entry) API_DB.db.session.commit() return key_entry.user_rel def authorize(decorated_view): def authenticate(*args, **kwargs): auth_header = flask_request.headers.get('Authorization', None) if not auth_header: raise UnauthorizedError(auth_provided=False) try: auth_scheme, auth_value = auth_header.split(None, 1) except ValueError: raise UnauthorizedError() auth_scheme = auth_scheme.lower() if auth_scheme == 'basic': user_entry = _LoginManager.authenticate_basic(auth_value) elif auth_scheme == 'x-key': user_entry = _LoginManager.authenticate_key(auth_value) else: raise UnauthorizedError() flask_global.auth_user = user_entry flask_global.auth_method = auth_scheme return decorated_view(*args, **kwargs) return authenticate

true

f71b54b467c00b5f3318a9985c87b77ec8bf71f6

853

py

Python

med2img/tests/test_med2img.py

mohitchandarana/pl-med2img

c8c9df64a5e244d57e2fdb9ffadeea3c455eab23

[ "MIT" ]

4

2017-07-06T20:32:08.000Z

2021-09-18T02:46:55.000Z

med2img/tests/test_med2img.py

mohitchandarana/pl-med2img

c8c9df64a5e244d57e2fdb9ffadeea3c455eab23

[ "MIT" ]

1

2021-11-07T20:17:16.000Z

2021-11-08T20:11:48.000Z

med2img/tests/test_med2img.py

mohitchandarana/pl-med2img

c8c9df64a5e244d57e2fdb9ffadeea3c455eab23

[ "MIT" ]

3

2020-09-01T07:13:19.000Z

2021-03-25T16:06:39.000Z

from unittest import TestCase from unittest import mock from med2img.med2img import Med2img class Med2imgTests(TestCase): """ Test Med2img. """ def setUp(self): self.app = Med2img() def test_run(self): """ Test the run code. """ args = [] if self.app.TYPE == 'ds': args.append('inputdir') # you may want to change this inputdir mock args.append('outputdir') # you may want to change this outputdir mock # you may want to add more of your custom defined optional arguments to test # your app with # eg. # args.append('--custom-int') # args.append(10) options = self.app.parse_args(args) self.app.run(options) # write your own assertions self.assertEqual(options.outputdir, 'outputdir')

25.088235

84

0.594373

from unittest import TestCase from unittest import mock from med2img.med2img import Med2img class Med2imgTests(TestCase): def setUp(self): self.app = Med2img() def test_run(self): args = [] if self.app.TYPE == 'ds': args.append('inputdir') args.append('outputdir') options = self.app.parse_args(args) self.app.run(options) self.assertEqual(options.outputdir, 'outputdir')

true

f71b555a4703e5eb655f1c62d6c59060c0f772cf

6,488

py

Python

MPL/MPL_envs/reach/reach_v0.py

vikashplus/MPL

4a784fd94dc7a5988a1eca85851ee546ca1992f9

[ "Apache-2.0" ]

null

MPL/MPL_envs/reach/reach_v0.py

vikashplus/MPL

4a784fd94dc7a5988a1eca85851ee546ca1992f9

[ "Apache-2.0" ]

null

MPL/MPL_envs/reach/reach_v0.py

vikashplus/MPL

4a784fd94dc7a5988a1eca85851ee546ca1992f9

[ "Apache-2.0" ]

null

import numpy as np from gym import utils from mjrl.envs import mujoco_env from mujoco_py import MjViewer from MPL.MPL_robot.robot import Robot import os # TODO: Action normalization is missing class sallyReachEnv(mujoco_env.MujocoEnv, utils.EzPickle): def __init__(self, noise_scale=0.0): # prep utils.EzPickle.__init__(self) self._noise_scale = noise_scale self.initializing = True curr_dir = os.path.dirname(os.path.abspath(__file__)) self.Rtarget = 0 self.Ltarget = 0 self.Rgrasp = 0 self.Lgrasp = 0 # acquire robot self.mpl = Robot(name='sallyReach', model_file=curr_dir+'/reach_v0.xml', config_file=curr_dir+'/reach_v0.config') # acquire env mujoco_env.MujocoEnv.__init__(self, curr_dir+'/reach_v0.xml', 20) self.Rtarget = self.sim.model.site_name2id('Rtarget') self.Ltarget = self.sim.model.site_name2id('Ltarget') self.Rgrasp = self.sim.model.site_name2id('Rgrasp') self.Lgrasp = self.sim.model.site_name2id('Lgrasp') # env ready self.initializing = False def step(self, a): self.mpl.step(self, a, self.frame_skip*self.sim.model.opt.timestep) obs = self.get_obs() score, reward_dict, solved, done = self._get_score_reward_solved_done(self.obs_dict) # finalize step env_info = { 'time': self.obs_dict['t'], 'obs_dict': self.obs_dict, 'rewards': reward_dict, 'score': score, 'solved': solved } return obs, reward_dict['total'], done, env_info # query robot and populate observations def get_obs(self): # ask robot for sensor data sen = self.mpl.get_sensors(self, noise_scale=self._noise_scale) # parse sensor data into obs dict self.obs_dict = {} self.obs_dict['t'] = sen['time'] self.obs_dict['Tmpl_pos'] = sen['Tmpl_pos'] self.obs_dict['Rmpl_pos'] = sen['Rmpl_pos'] self.obs_dict['Lmpl_pos'] = sen['Lmpl_pos'] self.obs_dict['Tmpl_vel'] = sen['Tmpl_vel'] self.obs_dict['Rmpl_vel'] = sen['Rmpl_vel'] self.obs_dict['Lmpl_vel'] = sen['Lmpl_vel'] self.obs_dict['Rerr'] = self.sim.data.site_xpos[self.Rtarget]-self.sim.data.site_xpos[self.Rgrasp] self.obs_dict['Lerr'] = self.sim.data.site_xpos[self.Ltarget]-self.sim.data.site_xpos[self.Lgrasp] # vectorize observations return np.concatenate([ self.obs_dict['Tmpl_pos'], self.obs_dict['Rmpl_pos'], self.obs_dict['Lmpl_pos'], self.obs_dict['Tmpl_vel'], self.obs_dict['Rmpl_vel'], self.obs_dict['Lmpl_vel'], self.obs_dict['Lerr'], self.obs_dict['Rerr']]) # evaluate observations def _get_score_reward_solved_done(self, obs, act=None): Ldist = np.linalg.norm(obs['Lerr']) Rdist = np.linalg.norm(obs['Rerr']) # print(Rdist, Ldist) done = (bool( Ldist > 1.0) or bool(Rdist>1.0)) \ if not self.initializing else False reward_dict = {} avg_dist = (Ldist+Rdist)/2.0 score = -1.* avg_dist reward_dict["avg_dist"] = score reward_dict["small_bonus"] = 2.0*(Ldist<.1) + 2.0*(Rdist<.1) reward_dict["big_bonus"] = 2.0*(Ldist<.1) * 2.0*(Rdist<.1) reward_dict["total"] = reward_dict["avg_dist"] + reward_dict["small_bonus"] + reward_dict["big_bonus"] - 50.0 * int(done) solved = bool(avg_dist<0.100) return score, reward_dict, solved, done # reset model def reset_model(self): raise NotImplementedError # for child class to define # evaluate a path def compute_path_rewards(self, paths): # path has two keys: observations and actions # path["observations"] : (num_traj, horizon, obs_dim) # path["rewards"] should have shape (num_traj, horizon) obs = paths["observations"] score, rewards, done = self._get_score_reward_solved_done(obs) paths["rewards"] = rewards if rewards.shape[0] > 1 else rewards.ravel() # evaluate policy's success from a collection of paths def evaluate_success(self, paths, logger=None): success = 0.0 for p in paths: if np.mean(p['env_infos']['solved'][-4:]) > 0.0: success += 1.0 success_rate = 100.0*success/len(paths) if logger is None: # nowhere to log so return the value return success_rate else: # log the success # can log multiple statistics here if needed logger.log_kv('success_rate', success_rate) return None # -------------------------------- # get and set states # -------------------------------- def get_env_state(self): return dict(qp=self.data.qpos.copy(), qv=self.data.qvel.copy()) def set_env_state(self, state): self.sim.reset() qp = state['qp'].copy() qv = state['qv'].copy() self.set_state(qp, qv) self.sim.forward() # -------------------------------- # utility functions # -------------------------------- def get_env_infos(self): return dict(state=self.get_env_state()) def mj_viewer_setup(self): self.viewer = MjViewer(self.sim) self.viewer.cam.azimuth = -90 self.viewer.cam.distance = 2.5 self.viewer.cam.elevation = -30 self.sim.forward() def close_env(self): pass # Reach at fixed targets class sallyReachEnvFixed(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = np.array([0.15, 0.2, .6]) self.sim.model.site_pos[self.Ltarget] = np.array([-0.15, 0.2, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs() # Reach at random targets class sallyReachEnvRandom(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = self.np_random.uniform(high=[0.5, .5, .6], low=[0, .1, .3]) self.sim.model.site_pos[self.Ltarget] = self.np_random.uniform(high=[0, .5, .6], low=[-.5, .1, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs()

33.968586

130

0.594945

import numpy as np from gym import utils from mjrl.envs import mujoco_env from mujoco_py import MjViewer from MPL.MPL_robot.robot import Robot import os class sallyReachEnv(mujoco_env.MujocoEnv, utils.EzPickle): def __init__(self, noise_scale=0.0): utils.EzPickle.__init__(self) self._noise_scale = noise_scale self.initializing = True curr_dir = os.path.dirname(os.path.abspath(__file__)) self.Rtarget = 0 self.Ltarget = 0 self.Rgrasp = 0 self.Lgrasp = 0 self.mpl = Robot(name='sallyReach', model_file=curr_dir+'/reach_v0.xml', config_file=curr_dir+'/reach_v0.config') mujoco_env.MujocoEnv.__init__(self, curr_dir+'/reach_v0.xml', 20) self.Rtarget = self.sim.model.site_name2id('Rtarget') self.Ltarget = self.sim.model.site_name2id('Ltarget') self.Rgrasp = self.sim.model.site_name2id('Rgrasp') self.Lgrasp = self.sim.model.site_name2id('Lgrasp') self.initializing = False def step(self, a): self.mpl.step(self, a, self.frame_skip*self.sim.model.opt.timestep) obs = self.get_obs() score, reward_dict, solved, done = self._get_score_reward_solved_done(self.obs_dict) env_info = { 'time': self.obs_dict['t'], 'obs_dict': self.obs_dict, 'rewards': reward_dict, 'score': score, 'solved': solved } return obs, reward_dict['total'], done, env_info def get_obs(self): sen = self.mpl.get_sensors(self, noise_scale=self._noise_scale) self.obs_dict = {} self.obs_dict['t'] = sen['time'] self.obs_dict['Tmpl_pos'] = sen['Tmpl_pos'] self.obs_dict['Rmpl_pos'] = sen['Rmpl_pos'] self.obs_dict['Lmpl_pos'] = sen['Lmpl_pos'] self.obs_dict['Tmpl_vel'] = sen['Tmpl_vel'] self.obs_dict['Rmpl_vel'] = sen['Rmpl_vel'] self.obs_dict['Lmpl_vel'] = sen['Lmpl_vel'] self.obs_dict['Rerr'] = self.sim.data.site_xpos[self.Rtarget]-self.sim.data.site_xpos[self.Rgrasp] self.obs_dict['Lerr'] = self.sim.data.site_xpos[self.Ltarget]-self.sim.data.site_xpos[self.Lgrasp] return np.concatenate([ self.obs_dict['Tmpl_pos'], self.obs_dict['Rmpl_pos'], self.obs_dict['Lmpl_pos'], self.obs_dict['Tmpl_vel'], self.obs_dict['Rmpl_vel'], self.obs_dict['Lmpl_vel'], self.obs_dict['Lerr'], self.obs_dict['Rerr']]) def _get_score_reward_solved_done(self, obs, act=None): Ldist = np.linalg.norm(obs['Lerr']) Rdist = np.linalg.norm(obs['Rerr']) done = (bool( Ldist > 1.0) or bool(Rdist>1.0)) \ if not self.initializing else False reward_dict = {} avg_dist = (Ldist+Rdist)/2.0 score = -1.* avg_dist reward_dict["avg_dist"] = score reward_dict["small_bonus"] = 2.0*(Ldist<.1) + 2.0*(Rdist<.1) reward_dict["big_bonus"] = 2.0*(Ldist<.1) * 2.0*(Rdist<.1) reward_dict["total"] = reward_dict["avg_dist"] + reward_dict["small_bonus"] + reward_dict["big_bonus"] - 50.0 * int(done) solved = bool(avg_dist<0.100) return score, reward_dict, solved, done def reset_model(self): raise NotImplementedError def compute_path_rewards(self, paths): obs = paths["observations"] score, rewards, done = self._get_score_reward_solved_done(obs) paths["rewards"] = rewards if rewards.shape[0] > 1 else rewards.ravel() def evaluate_success(self, paths, logger=None): success = 0.0 for p in paths: if np.mean(p['env_infos']['solved'][-4:]) > 0.0: success += 1.0 success_rate = 100.0*success/len(paths) if logger is None: # nowhere to log so return the value return success_rate else: # log the success # can log multiple statistics here if needed logger.log_kv('success_rate', success_rate) return None # -------------------------------- # get and set states # -------------------------------- def get_env_state(self): return dict(qp=self.data.qpos.copy(), qv=self.data.qvel.copy()) def set_env_state(self, state): self.sim.reset() qp = state['qp'].copy() qv = state['qv'].copy() self.set_state(qp, qv) self.sim.forward() # -------------------------------- # utility functions # -------------------------------- def get_env_infos(self): return dict(state=self.get_env_state()) def mj_viewer_setup(self): self.viewer = MjViewer(self.sim) self.viewer.cam.azimuth = -90 self.viewer.cam.distance = 2.5 self.viewer.cam.elevation = -30 self.sim.forward() def close_env(self): pass # Reach at fixed targets class sallyReachEnvFixed(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = np.array([0.15, 0.2, .6]) self.sim.model.site_pos[self.Ltarget] = np.array([-0.15, 0.2, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs() # Reach at random targets class sallyReachEnvRandom(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = self.np_random.uniform(high=[0.5, .5, .6], low=[0, .1, .3]) self.sim.model.site_pos[self.Ltarget] = self.np_random.uniform(high=[0, .5, .6], low=[-.5, .1, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs()

true

f71b560fdbc2811c07e38208065e693e4befa940

40,786

py

Python

metaflow/plugins/aws/step_functions/step_functions.py

cclauss/metaflow

5186c6c5bba36d9e77077413ee2495dc79da3dca

[ "Apache-2.0" ]

1

2021-11-29T22:37:54.000Z

metaflow/plugins/aws/step_functions/step_functions.py

sappier/metaflow

dfe1a216e342555d5fb127607b35491a4ef11627

[ "Apache-2.0" ]

null

metaflow/plugins/aws/step_functions/step_functions.py

sappier/metaflow

dfe1a216e342555d5fb127607b35491a4ef11627

[ "Apache-2.0" ]

1

2021-11-29T22:37:50.000Z

import os from collections import defaultdict import sys import hashlib import json import time import string import random import uuid from metaflow.exception import MetaflowException, MetaflowInternalError from metaflow.plugins import ResourcesDecorator, BatchDecorator, RetryDecorator from metaflow.parameters import deploy_time_eval from metaflow.decorators import flow_decorators from metaflow.util import compress_list, dict_to_cli_options, to_pascalcase from metaflow.metaflow_config import ( SFN_IAM_ROLE, EVENTS_SFN_ACCESS_IAM_ROLE, SFN_DYNAMO_DB_TABLE, SFN_EXECUTION_LOG_GROUP_ARN, ) from metaflow import R from .step_functions_client import StepFunctionsClient from .event_bridge_client import EventBridgeClient from ..batch.batch import Batch class StepFunctionsException(MetaflowException): headline = "AWS Step Functions error" class StepFunctionsSchedulingException(MetaflowException): headline = "AWS Step Functions scheduling error" class StepFunctions(object): def __init__( self, name, graph, flow, code_package_sha, code_package_url, production_token, metadata, flow_datastore, environment, event_logger, monitor, tags=None, namespace=None, username=None, max_workers=None, workflow_timeout=None, is_project=False, ): self.name = name self.graph = graph self.flow = flow self.code_package_sha = code_package_sha self.code_package_url = code_package_url self.production_token = production_token self.metadata = metadata self.flow_datastore = flow_datastore self.environment = environment self.event_logger = event_logger self.monitor = monitor self.tags = tags self.namespace = namespace self.username = username self.max_workers = max_workers self.workflow_timeout = workflow_timeout self._client = StepFunctionsClient() self._workflow = self._compile() self._cron = self._cron() self._state_machine_arn = None def to_json(self): return self._workflow.to_json(pretty=True) def trigger_explanation(self): if self._cron: # Sometime in the future, we should vendor (or write) a utility # that can translate cron specifications into a human readable # format and push to the user for a better UX, someday. return ( "This workflow triggers automatically " "via a cron schedule *%s* defined in AWS EventBridge." % self.event_bridge_rule ) else: return "No triggers defined. " "You need to launch this workflow manually." def deploy(self, log_execution_history): if SFN_IAM_ROLE is None: raise StepFunctionsException( "No IAM role found for AWS Step " "Functions. You can create one " "following the instructions listed at " "*https://admin-docs.metaflow.org/meta" "flow-on-aws/deployment-guide/manual-d" "eployment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) if log_execution_history: if SFN_EXECUTION_LOG_GROUP_ARN is None: raise StepFunctionsException( "No AWS CloudWatch Logs log " "group ARN found for emitting " "state machine execution logs for " "your workflow. You can set it in " "your environment by using the " "METAFLOW_SFN_EXECUTION_LOG_GROUP_ARN " "environment variable." ) try: self._state_machine_arn = self._client.push( name=self.name, definition=self.to_json(), role_arn=SFN_IAM_ROLE, log_execution_history=log_execution_history, ) except Exception as e: raise StepFunctionsException(repr(e)) def schedule(self): # Scheduling is currently enabled via AWS Event Bridge. if EVENTS_SFN_ACCESS_IAM_ROLE is None: raise StepFunctionsSchedulingException( "No IAM role found for AWS " "Events Bridge. You can " "create one following the " "instructions listed at " "*https://admin-docs.metaflo" "w.org/metaflow-on-aws/deplo" "yment-guide/manual-deployme" "nt#scheduling* and " "re-configure Metaflow " "using *metaflow configure " "aws* on your terminal." ) try: self.event_bridge_rule = ( EventBridgeClient(self.name) .cron(self._cron) .role_arn(EVENTS_SFN_ACCESS_IAM_ROLE) .state_machine_arn(self._state_machine_arn) .schedule() ) except Exception as e: raise StepFunctionsSchedulingException(repr(e)) @classmethod def trigger(cls, name, parameters): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions. Please " "deploy your flow first." % name ) # Dump parameters into `Parameters` input field. input = json.dumps({"Parameters": json.dumps(parameters)}) # AWS Step Functions limits input to be 32KiB, but AWS Batch # has it's own limitation of 30KiB for job specification length. # Reserving 10KiB for rest of the job sprecification leaves 20KiB # for us, which should be enough for most use cases for now. if len(input) > 20480: raise StepFunctionsException( "Length of parameter names and " "values shouldn't exceed 20480 as " "imposed by AWS Step Functions." ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().trigger(state_machine_arn, input) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def list(cls, name, states): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions." % name ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().list_executions(state_machine_arn, states) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def get_existing_deployment(cls, name): workflow = StepFunctionsClient().get(name) if workflow is not None: try: start = json.loads(workflow["definition"])["States"]["start"] parameters = start["Parameters"]["Parameters"] return parameters.get("metaflow.owner"), parameters.get( "metaflow.production_token" ) except KeyError as e: raise StepFunctionsException( "An existing non-metaflow " "workflow with the same name as " "*%s* already exists in AWS Step " "Functions. Please modify the " "name of this flow or delete your " "existing workflow on AWS Step " "Functions." % name ) return None def _compile(self): # Visit every node of the flow and recursively build the state machine. def _visit(node, workflow, exit_node=None): # Assign an AWS Batch job to the AWS Step Functions state # and pass the intermediate state by exposing `JobId` and # `Parameters` to the child job(s) as outputs. `Index` and # `SplitParentTaskId` are populated optionally, when available. # We can't modify the names of keys in AWS Step Functions aside # from a blessed few which are set as `Parameters` for the Map # state. That's why even though `JobId` refers to the parent task # id, we can't call it as such. Similar situation for `Parameters`. state = ( State(node.name) .batch(self._batch(node)) .output_path( "$.['JobId', " "'Parameters', " "'Index', " "'SplitParentTaskId']" ) ) # End the (sub)workflow if we have reached the end of the flow or # the parent step of matching_join of the sub workflow. if node.type == "end" or exit_node in node.out_funcs: workflow.add_state(state.end()) # Continue linear assignment within the (sub)workflow if the node # doesn't branch or fork. elif node.type in ("linear", "join"): workflow.add_state(state.next(node.out_funcs[0])) _visit(self.graph[node.out_funcs[0]], workflow, exit_node) # Create a `Parallel` state and assign sub workflows if the node # branches out. elif node.type == "split-and": branch_name = hashlib.sha224( "&".join(node.out_funcs).encode("utf-8") ).hexdigest() workflow.add_state(state.next(branch_name)) branch = Parallel(branch_name).next(node.matching_join) # Generate as many sub workflows as branches and recurse. for n in node.out_funcs: branch.branch( _visit( self.graph[n], Workflow(n).start_at(n), node.matching_join ) ) workflow.add_state(branch) # Continue the traversal from the matching_join. _visit(self.graph[node.matching_join], workflow, exit_node) # Create a `Map` state and assign sub workflow if the node forks. elif node.type == "foreach": # Fetch runtime cardinality via an AWS DynamoDb Get call before # configuring the node cardinality_state_name = "#%s" % node.out_funcs[0] workflow.add_state(state.next(cardinality_state_name)) cardinality_state = ( State(cardinality_state_name) .dynamo_db(SFN_DYNAMO_DB_TABLE, "$.JobId", "for_each_cardinality") .result_path("$.Result") ) iterator_name = "*%s" % node.out_funcs[0] workflow.add_state(cardinality_state.next(iterator_name)) workflow.add_state( Map(iterator_name) .items_path("$.Result.Item.for_each_cardinality.NS") .parameter("JobId.$", "$.JobId") .parameter("SplitParentTaskId.$", "$.JobId") .parameter("Parameters.$", "$.Parameters") .parameter("Index.$", "$$.Map.Item.Value") .next(node.matching_join) .iterator( _visit( self.graph[node.out_funcs[0]], Workflow(node.out_funcs[0]).start_at(node.out_funcs[0]), node.matching_join, ) ) .max_concurrency(self.max_workers) .output_path("$.[0]") ) # Continue the traversal from the matching_join. _visit(self.graph[node.matching_join], workflow, exit_node) # We shouldn't ideally ever get here. else: raise StepFunctionsException( "Node type *%s* for step *%s* " "is not currently supported by " "AWS Step Functions." % (node.type, node.name) ) return workflow workflow = Workflow(self.name).start_at("start") if self.workflow_timeout: workflow.timeout_seconds(self.workflow_timeout) return _visit(self.graph["start"], workflow) def _cron(self): schedule = self.flow._flow_decorators.get("schedule") if schedule: return schedule.schedule return None def _process_parameters(self): parameters = [] has_schedule = self._cron() is not None seen = set() for var, param in self.flow._get_parameters(): # Throw an exception if the parameter is specified twice. norm = param.name.lower() if norm in seen: raise MetaflowException( "Parameter *%s* is specified twice. " "Note that parameter names are " "case-insensitive." % param.name ) seen.add(norm) is_required = param.kwargs.get("required", False) # Throw an exception if a schedule is set for a flow with required # parameters with no defaults. We currently don't have any notion # of data triggers in AWS Event Bridge. if "default" not in param.kwargs and is_required and has_schedule: raise MetaflowException( "The parameter *%s* does not have a " "default and is required. Scheduling " "such parameters via AWS Event Bridge " "is not currently supported." % param.name ) value = deploy_time_eval(param.kwargs.get("default")) parameters.append(dict(name=param.name, value=value)) return parameters def _batch(self, node): attrs = { # metaflow.user is only used for setting the AWS Job Name. # Since job executions are no longer tied to a specific user # identity, we will just set their user to `SFN`. We still do need # access to the owner of the workflow for production tokens, which # we can stash in metaflow.owner. "metaflow.user": "SFN", "metaflow.owner": self.username, "metaflow.flow_name": self.flow.name, "metaflow.step_name": node.name, "metaflow.run_id.$": "$$.Execution.Name", # Unfortunately we can't set the task id here since AWS Step # Functions lacks any notion of run-scoped task identifiers. We # instead co-opt the AWS Batch job id as the task id. This also # means that the AWS Batch job name will have missing fields since # the job id is determined at job execution, but since the job id is # part of the job description payload, we don't lose much except for # a few ugly looking black fields in the AWS Batch UI. # Also, unfortunately we can't set the retry count since # `$$.State.RetryCount` resolves to an int dynamically and # AWS Batch job specification only accepts strings. We handle # retries/catch within AWS Batch to get around this limitation. "metaflow.version": self.environment.get_environment_info()[ "metaflow_version" ], # We rely on step names and task ids of parent steps to construct # input paths for a task. Since the only information we can pass # between states (via `InputPath` and `ResultPath`) in AWS Step # Functions is the job description, we run the risk of exceeding # 32K state size limit rather quickly if we don't filter the job # description to a minimal set of fields. Unfortunately, the partial # `JsonPath` implementation within AWS Step Functions makes this # work a little non-trivial; it doesn't like dots in keys, so we # have to add the field again. # This pattern is repeated in a lot of other places, where we use # AWS Batch parameters to store AWS Step Functions state # information, since this field is the only field in the AWS Batch # specification that allows us to set key-values. "step_name": node.name, } # Store production token within the `start` step, so that subsequent # `step-functions create` calls can perform a rudimentary authorization # check. if node.name == "start": attrs["metaflow.production_token"] = self.production_token # Add env vars from the optional @environment decorator. env_deco = [deco for deco in node.decorators if deco.name == "environment"] env = {} if env_deco: env = env_deco[0].attributes["vars"] if node.name == "start": # Initialize parameters for the flow in the `start` step. parameters = self._process_parameters() if parameters: # Get user-defined parameters from State Machine Input. # Since AWS Step Functions doesn't allow for optional inputs # currently, we have to unfortunately place an artificial # constraint that every parameterized workflow needs to include # `Parameters` as a key in the input to the workflow. # `step-functions trigger` already takes care of this # requirement, but within the UI, the users will be required to # specify an input with key as `Parameters` and value as a # stringified json of the actual parameters - # {"Parameters": "{\"alpha\": \"beta\"}"} env["METAFLOW_PARAMETERS"] = "$.Parameters" default_parameters = {} for parameter in parameters: if parameter["value"] is not None: default_parameters[parameter["name"]] = parameter["value"] # Dump the default values specified in the flow. env["METAFLOW_DEFAULT_PARAMETERS"] = json.dumps(default_parameters) # `start` step has no upstream input dependencies aside from # parameters. input_paths = None else: # We need to rely on the `InputPath` of the AWS Step Functions # specification to grab task ids and the step names of the parent # to properly construct input_paths at runtime. Thanks to the # JsonPath-foo embedded in the parent states, we have this # information easily available. # Handle foreach join. if ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ): input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/:" "${METAFLOW_PARENT_TASK_IDS}" % node.in_funcs[0] ) # Unfortunately, AWS Batch only allows strings as value types # in it's specification and we don't have any way to concatenate # the task ids array from the parent steps within AWS Step # Functions and pass it down to AWS Batch. We instead have to # rely on publishing the state to DynamoDb and fetching it back # in within the AWS Batch entry point to set # `METAFLOW_PARENT_TASK_IDS`. The state is scoped to the parent # foreach task `METAFLOW_SPLIT_PARENT_TASK_ID`. We decided on # AWS DynamoDb and not AWS Lambdas, because deploying and # debugging Lambdas would be a nightmare as far as OSS support # is concerned. env["METAFLOW_SPLIT_PARENT_TASK_ID"] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) else: # Set appropriate environment variables for runtime replacement. if len(node.in_funcs) == 1: input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/${METAFLOW_PARENT_TASK_ID}" % node.in_funcs[0] ) env["METAFLOW_PARENT_TASK_ID"] = "$.JobId" else: # Generate the input paths in a quasi-compressed format. # See util.decompress_list for why this is written the way # it is. input_paths = "sfn-${METAFLOW_RUN_ID}:" + ",".join( "/${METAFLOW_PARENT_%s_STEP}/" "${METAFLOW_PARENT_%s_TASK_ID}" % (idx, idx) for idx, _ in enumerate(node.in_funcs) ) for idx, _ in enumerate(node.in_funcs): env["METAFLOW_PARENT_%s_TASK_ID" % idx] = "$.[%s].JobId" % idx env["METAFLOW_PARENT_%s_STEP" % idx] = ( "$.[%s].Parameters.step_name" % idx ) env["METAFLOW_INPUT_PATHS"] = input_paths if node.is_inside_foreach: # Set the task id of the parent job of the foreach split in # our favorite dumping ground, the AWS Batch attrs. For # subsequent descendent tasks, this attrs blob becomes the # input to those descendent tasks. We set and propagate the # task ids pointing to split_parents through every state. if any(self.graph[n].type == "foreach" for n in node.in_funcs): attrs[ "split_parent_task_id_%s.$" % node.split_parents[-1] ] = "$.SplitParentTaskId" for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) elif node.type == "join": if self.graph[node.split_parents[-1]].type == "foreach": # A foreach join only gets one set of input from the # parent tasks. We filter the Map state to only output # `$.[0]`, since we don't need any of the other outputs, # that information is available to us from AWS DynamoDB. # This has a nice side-effect of making our foreach # splits infinitely scalable because otherwise we would # be bounded by the 32K state limit for the outputs. So, # instead of referencing `Parameters` fields by index # (like in `split-and`), we can just reference them # directly. attrs["split_parent_task_id_%s.$" % node.split_parents[-1]] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.[0].Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) # Set `METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN` if the # next transition is to a foreach join, so that the # stepfunctions decorator can write the mapping for input path # to DynamoDb. if any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ): env["METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN"] = attrs[ "split_parent_task_id_%s.$" % self.graph[node.out_funcs[0]].split_parents[-1] ] # Set ttl for the values we set in AWS DynamoDB. if node.type == "foreach": if self.workflow_timeout: env["METAFLOW_SFN_WORKFLOW_TIMEOUT"] = self.workflow_timeout # Handle split index for for-each. if any(self.graph[n].type == "foreach" for n in node.in_funcs): env["METAFLOW_SPLIT_INDEX"] = "$.Index" env["METAFLOW_CODE_URL"] = self.code_package_url env["METAFLOW_FLOW_NAME"] = attrs["metaflow.flow_name"] env["METAFLOW_STEP_NAME"] = attrs["metaflow.step_name"] env["METAFLOW_RUN_ID"] = attrs["metaflow.run_id.$"] env["METAFLOW_PRODUCTION_TOKEN"] = self.production_token env["SFN_STATE_MACHINE"] = self.name env["METAFLOW_OWNER"] = attrs["metaflow.owner"] # Can't set `METAFLOW_TASK_ID` due to lack of run-scoped identifiers. # We will instead rely on `AWS_BATCH_JOB_ID` as the task identifier. # Can't set `METAFLOW_RETRY_COUNT` either due to integer casting issue. metadata_env = self.metadata.get_runtime_environment("step-functions") env.update(metadata_env) metaflow_version = self.environment.get_environment_info() metaflow_version["flow_name"] = self.graph.name metaflow_version["production_token"] = self.production_token env["METAFLOW_VERSION"] = json.dumps(metaflow_version) # Set AWS DynamoDb Table Name for state tracking for for-eaches. # There are three instances when metaflow runtime directly interacts # with AWS DynamoDB. # 1. To set the cardinality of foreaches (which are subsequently) # read prior to the instantiation of the Map state by AWS Step # Functions. # 2. To set the input paths from the parent steps of a foreach join. # 3. To read the input paths in a foreach join. if ( node.type == "foreach" or ( node.is_inside_foreach and any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ) ) or ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ) ): if SFN_DYNAMO_DB_TABLE is None: raise StepFunctionsException( "An AWS DynamoDB table is needed " "to support foreach in your flow. " "You can create one following the " "instructions listed at *https://a" "dmin-docs.metaflow.org/metaflow-o" "n-aws/deployment-guide/manual-dep" "loyment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) env["METAFLOW_SFN_DYNAMO_DB_TABLE"] = SFN_DYNAMO_DB_TABLE # Resolve AWS Batch resource requirements. batch_deco = [deco for deco in node.decorators if deco.name == "batch"][0] resources = batch_deco.attributes # Resolve retry strategy. user_code_retries, total_retries = self._get_retries(node) task_spec = { "flow_name": attrs["metaflow.flow_name"], "step_name": attrs["metaflow.step_name"], "run_id": "sfn-$METAFLOW_RUN_ID", # Use AWS Batch job identifier as the globally unique # task identifier. "task_id": "$AWS_BATCH_JOB_ID", # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "retry_count": "$((AWS_BATCH_JOB_ATTEMPT-1))", } return ( Batch(self.metadata, self.environment) .create_job( step_name=node.name, step_cli=self._step_cli( node, input_paths, self.code_package_url, user_code_retries ), task_spec=task_spec, code_package_sha=self.code_package_sha, code_package_url=self.code_package_url, code_package_ds=self.flow_datastore.TYPE, image=resources["image"], queue=resources["queue"], iam_role=resources["iam_role"], execution_role=resources["execution_role"], cpu=resources["cpu"], gpu=resources["gpu"], memory=resources["memory"], run_time_limit=batch_deco.run_time_limit, shared_memory=resources["shared_memory"], max_swap=resources["max_swap"], swappiness=resources["swappiness"], env=env, attrs=attrs, host_volumes=resources["host_volumes"], ) .attempts(total_retries + 1) ) def _get_retries(self, node): max_user_code_retries = 0 max_error_retries = 0 # Different decorators may have different retrying strategies, so take # the max of them. for deco in node.decorators: user_code_retries, error_retries = deco.step_task_retry_count() max_user_code_retries = max(max_user_code_retries, user_code_retries) max_error_retries = max(max_error_retries, error_retries) return max_user_code_retries, max_user_code_retries + max_error_retries def _step_cli(self, node, paths, code_package_url, user_code_retries): cmds = [] script_name = os.path.basename(sys.argv[0]) executable = self.environment.executable(node.name) if R.use_r(): entrypoint = [R.entrypoint()] else: entrypoint = [executable, script_name] # Use AWS Batch job identifier as the globally unique task identifier. task_id = "${AWS_BATCH_JOB_ID}" # FlowDecorators can define their own top-level options. They are # responsible for adding their own top-level options and values through # the get_top_level_options() hook. See similar logic in runtime.py. top_opts_dict = {} for deco in flow_decorators(): top_opts_dict.update(deco.get_top_level_options()) top_opts = list(dict_to_cli_options(top_opts_dict)) if node.name == "start": # We need a separate unique ID for the special _parameters task task_id_params = "%s-params" % task_id # Export user-defined parameters into runtime environment param_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_params = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parameters %s && . `pwd`/%s" % (param_file, param_file) ) params = ( entrypoint + top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=s3", "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "init", "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id_params, ] ) # Assign tags to run objects. if self.tags: params.extend("--tag %s" % tag for tag in self.tags) # If the start step gets retried, we must be careful not to # regenerate multiple parameters tasks. Hence we check first if # _parameters exists already. exists = entrypoint + [ "dump", "--max-value-size=0", "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params), ] cmd = "if ! %s >/dev/null 2>/dev/null; then %s && %s; fi" % ( " ".join(exists), export_params, " ".join(params), ) cmds.append(cmd) paths = "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params) if node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach": parent_tasks_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_parent_tasks = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parent_tasks %s && . `pwd`/%s" % (parent_tasks_file, parent_tasks_file) ) cmds.append(export_parent_tasks) top_level = top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=%s" % self.flow_datastore.TYPE, "--datastore-root=%s" % self.flow_datastore.datastore_root, "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "--with=step_functions_internal", ] step = [ "step", node.name, "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id, # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "--retry-count $((AWS_BATCH_JOB_ATTEMPT-1))", "--max-user-code-retries %d" % user_code_retries, "--input-paths %s" % paths, # Set decorator to batch to execute `task_*` hooks for batch # decorator. "--with=batch", ] if any(self.graph[n].type == "foreach" for n in node.in_funcs): # We set the `METAFLOW_SPLIT_INDEX` through JSONPath-foo # to pass the state from the parent DynamoDb state for for-each. step.append("--split-index $METAFLOW_SPLIT_INDEX") if self.tags: step.extend("--tag %s" % tag for tag in self.tags) if self.namespace is not None: step.append("--namespace=%s" % self.namespace) cmds.append(" ".join(entrypoint + top_level + step)) return " && ".join(cmds) class Workflow(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() def start_at(self, start_at): self.payload["StartAt"] = start_at return self def add_state(self, state): self.payload["States"][state.name] = state.payload return self def timeout_seconds(self, timeout_seconds): self.payload["TimeoutSeconds"] = timeout_seconds return self def to_json(self, pretty=False): return json.dumps(self.payload, indent=4 if pretty else None) class State(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Task" def resource(self, resource): self.payload["Resource"] = resource return self def next(self, state): self.payload["Next"] = state return self def end(self): self.payload["End"] = True return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self def _partition(self): # This is needed to support AWS Gov Cloud and AWS CN regions return SFN_IAM_ROLE.split(":")[1] def batch(self, job): self.resource( "arn:%s:states:::batch:submitJob.sync" % self._partition() ).parameter("JobDefinition", job.payload["jobDefinition"]).parameter( "JobName", job.payload["jobName"] ).parameter( "JobQueue", job.payload["jobQueue"] ).parameter( "Parameters", job.payload["parameters"] ).parameter( "ContainerOverrides", to_pascalcase(job.payload["containerOverrides"]) ).parameter( "RetryStrategy", to_pascalcase(job.payload["retryStrategy"]) ).parameter( "Timeout", to_pascalcase(job.payload["timeout"]) ) # tags may not be present in all scenarios if "tags" in job.payload: self.parameter("Tags", job.payload["tags"]) return self def dynamo_db(self, table_name, primary_key, values): self.resource("arn:%s:states:::dynamodb:getItem" % self._partition()).parameter( "TableName", table_name ).parameter("Key", {"pathspec": {"S.$": primary_key}}).parameter( "ConsistentRead", True ).parameter( "ProjectionExpression", values ) return self class Parallel(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Parallel" def branch(self, workflow): if "Branches" not in self.payload: self.payload["Branches"] = [] self.payload["Branches"].append(workflow.payload) return self def next(self, state): self.payload["Next"] = state return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self class Map(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Map" self.payload["MaxConcurrency"] = 0 def iterator(self, workflow): self.payload["Iterator"] = workflow.payload return self def next(self, state): self.payload["Next"] = state return self def items_path(self, items_path): self.payload["ItemsPath"] = items_path return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def max_concurrency(self, max_concurrency): self.payload["MaxConcurrency"] = max_concurrency return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self

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import os from collections import defaultdict import sys import hashlib import json import time import string import random import uuid from metaflow.exception import MetaflowException, MetaflowInternalError from metaflow.plugins import ResourcesDecorator, BatchDecorator, RetryDecorator from metaflow.parameters import deploy_time_eval from metaflow.decorators import flow_decorators from metaflow.util import compress_list, dict_to_cli_options, to_pascalcase from metaflow.metaflow_config import ( SFN_IAM_ROLE, EVENTS_SFN_ACCESS_IAM_ROLE, SFN_DYNAMO_DB_TABLE, SFN_EXECUTION_LOG_GROUP_ARN, ) from metaflow import R from .step_functions_client import StepFunctionsClient from .event_bridge_client import EventBridgeClient from ..batch.batch import Batch class StepFunctionsException(MetaflowException): headline = "AWS Step Functions error" class StepFunctionsSchedulingException(MetaflowException): headline = "AWS Step Functions scheduling error" class StepFunctions(object): def __init__( self, name, graph, flow, code_package_sha, code_package_url, production_token, metadata, flow_datastore, environment, event_logger, monitor, tags=None, namespace=None, username=None, max_workers=None, workflow_timeout=None, is_project=False, ): self.name = name self.graph = graph self.flow = flow self.code_package_sha = code_package_sha self.code_package_url = code_package_url self.production_token = production_token self.metadata = metadata self.flow_datastore = flow_datastore self.environment = environment self.event_logger = event_logger self.monitor = monitor self.tags = tags self.namespace = namespace self.username = username self.max_workers = max_workers self.workflow_timeout = workflow_timeout self._client = StepFunctionsClient() self._workflow = self._compile() self._cron = self._cron() self._state_machine_arn = None def to_json(self): return self._workflow.to_json(pretty=True) def trigger_explanation(self): if self._cron: return ( "This workflow triggers automatically " "via a cron schedule *%s* defined in AWS EventBridge." % self.event_bridge_rule ) else: return "No triggers defined. " "You need to launch this workflow manually." def deploy(self, log_execution_history): if SFN_IAM_ROLE is None: raise StepFunctionsException( "No IAM role found for AWS Step " "Functions. You can create one " "following the instructions listed at " "*https://admin-docs.metaflow.org/meta" "flow-on-aws/deployment-guide/manual-d" "eployment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) if log_execution_history: if SFN_EXECUTION_LOG_GROUP_ARN is None: raise StepFunctionsException( "No AWS CloudWatch Logs log " "group ARN found for emitting " "state machine execution logs for " "your workflow. You can set it in " "your environment by using the " "METAFLOW_SFN_EXECUTION_LOG_GROUP_ARN " "environment variable." ) try: self._state_machine_arn = self._client.push( name=self.name, definition=self.to_json(), role_arn=SFN_IAM_ROLE, log_execution_history=log_execution_history, ) except Exception as e: raise StepFunctionsException(repr(e)) def schedule(self): if EVENTS_SFN_ACCESS_IAM_ROLE is None: raise StepFunctionsSchedulingException( "No IAM role found for AWS " "Events Bridge. You can " "create one following the " "instructions listed at " "*https://admin-docs.metaflo" "w.org/metaflow-on-aws/deplo" "yment-guide/manual-deployme" "nt#scheduling* and " "re-configure Metaflow " "using *metaflow configure " "aws* on your terminal." ) try: self.event_bridge_rule = ( EventBridgeClient(self.name) .cron(self._cron) .role_arn(EVENTS_SFN_ACCESS_IAM_ROLE) .state_machine_arn(self._state_machine_arn) .schedule() ) except Exception as e: raise StepFunctionsSchedulingException(repr(e)) @classmethod def trigger(cls, name, parameters): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions. Please " "deploy your flow first." % name ) # Dump parameters into `Parameters` input field. input = json.dumps({"Parameters": json.dumps(parameters)}) # AWS Step Functions limits input to be 32KiB, but AWS Batch # has it's own limitation of 30KiB for job specification length. if len(input) > 20480: raise StepFunctionsException( "Length of parameter names and " "values shouldn't exceed 20480 as " "imposed by AWS Step Functions." ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().trigger(state_machine_arn, input) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def list(cls, name, states): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions." % name ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().list_executions(state_machine_arn, states) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def get_existing_deployment(cls, name): workflow = StepFunctionsClient().get(name) if workflow is not None: try: start = json.loads(workflow["definition"])["States"]["start"] parameters = start["Parameters"]["Parameters"] return parameters.get("metaflow.owner"), parameters.get( "metaflow.production_token" ) except KeyError as e: raise StepFunctionsException( "An existing non-metaflow " "workflow with the same name as " "*%s* already exists in AWS Step " "Functions. Please modify the " "name of this flow or delete your " "existing workflow on AWS Step " "Functions." % name ) return None def _compile(self): def _visit(node, workflow, exit_node=None): # from a blessed few which are set as `Parameters` for the Map # state. That's why even though `JobId` refers to the parent task state = ( State(node.name) .batch(self._batch(node)) .output_path( "$.['JobId', " "'Parameters', " "'Index', " "'SplitParentTaskId']" ) ) # End the (sub)workflow if we have reached the end of the flow or # the parent step of matching_join of the sub workflow. if node.type == "end" or exit_node in node.out_funcs: workflow.add_state(state.end()) # Continue linear assignment within the (sub)workflow if the node # doesn't branch or fork. elif node.type in ("linear", "join"): workflow.add_state(state.next(node.out_funcs[0])) _visit(self.graph[node.out_funcs[0]], workflow, exit_node) elif node.type == "split-and": branch_name = hashlib.sha224( "&".join(node.out_funcs).encode("utf-8") ).hexdigest() workflow.add_state(state.next(branch_name)) branch = Parallel(branch_name).next(node.matching_join) for n in node.out_funcs: branch.branch( _visit( self.graph[n], Workflow(n).start_at(n), node.matching_join ) ) workflow.add_state(branch) _visit(self.graph[node.matching_join], workflow, exit_node) elif node.type == "foreach": cardinality_state_name = "#%s" % node.out_funcs[0] workflow.add_state(state.next(cardinality_state_name)) cardinality_state = ( State(cardinality_state_name) .dynamo_db(SFN_DYNAMO_DB_TABLE, "$.JobId", "for_each_cardinality") .result_path("$.Result") ) iterator_name = "*%s" % node.out_funcs[0] workflow.add_state(cardinality_state.next(iterator_name)) workflow.add_state( Map(iterator_name) .items_path("$.Result.Item.for_each_cardinality.NS") .parameter("JobId.$", "$.JobId") .parameter("SplitParentTaskId.$", "$.JobId") .parameter("Parameters.$", "$.Parameters") .parameter("Index.$", "$$.Map.Item.Value") .next(node.matching_join) .iterator( _visit( self.graph[node.out_funcs[0]], Workflow(node.out_funcs[0]).start_at(node.out_funcs[0]), node.matching_join, ) ) .max_concurrency(self.max_workers) .output_path("$.[0]") ) _visit(self.graph[node.matching_join], workflow, exit_node) else: raise StepFunctionsException( "Node type *%s* for step *%s* " "is not currently supported by " "AWS Step Functions." % (node.type, node.name) ) return workflow workflow = Workflow(self.name).start_at("start") if self.workflow_timeout: workflow.timeout_seconds(self.workflow_timeout) return _visit(self.graph["start"], workflow) def _cron(self): schedule = self.flow._flow_decorators.get("schedule") if schedule: return schedule.schedule return None def _process_parameters(self): parameters = [] has_schedule = self._cron() is not None seen = set() for var, param in self.flow._get_parameters(): # Throw an exception if the parameter is specified twice. norm = param.name.lower() if norm in seen: raise MetaflowException( "Parameter *%s* is specified twice. " "Note that parameter names are " "case-insensitive." % param.name ) seen.add(norm) is_required = param.kwargs.get("required", False) # Throw an exception if a schedule is set for a flow with required # parameters with no defaults. We currently don't have any notion if "default" not in param.kwargs and is_required and has_schedule: raise MetaflowException( "The parameter *%s* does not have a " "default and is required. Scheduling " "such parameters via AWS Event Bridge " "is not currently supported." % param.name ) value = deploy_time_eval(param.kwargs.get("default")) parameters.append(dict(name=param.name, value=value)) return parameters def _batch(self, node): attrs = { "metaflow.user": "SFN", "metaflow.owner": self.username, "metaflow.flow_name": self.flow.name, "metaflow.step_name": node.name, "metaflow.run_id.$": "$$.Execution.Name", # Functions lacks any notion of run-scoped task identifiers. We # instead co-opt the AWS Batch job id as the task id. This also # means that the AWS Batch job name will have missing fields since # the job id is determined at job execution, but since the job id is # part of the job description payload, we don't lose much except for # `$$.State.RetryCount` resolves to an int dynamically and # AWS Batch job specification only accepts strings. We handle # retries/catch within AWS Batch to get around this limitation. "metaflow.version": self.environment.get_environment_info()[ "metaflow_version" ], # We rely on step names and task ids of parent steps to construct # input paths for a task. Since the only information we can pass # between states (via `InputPath` and `ResultPath`) in AWS Step # Functions is the job description, we run the risk of exceeding # 32K state size limit rather quickly if we don't filter the job # have to add the field again. # This pattern is repeated in a lot of other places, where we use # AWS Batch parameters to store AWS Step Functions state # information, since this field is the only field in the AWS Batch # specification that allows us to set key-values. "step_name": node.name, } # Store production token within the `start` step, so that subsequent # `step-functions create` calls can perform a rudimentary authorization # check. if node.name == "start": attrs["metaflow.production_token"] = self.production_token # Add env vars from the optional @environment decorator. env_deco = [deco for deco in node.decorators if deco.name == "environment"] env = {} if env_deco: env = env_deco[0].attributes["vars"] if node.name == "start": # Initialize parameters for the flow in the `start` step. parameters = self._process_parameters() if parameters: # Get user-defined parameters from State Machine Input. # Since AWS Step Functions doesn't allow for optional inputs env["METAFLOW_PARAMETERS"] = "$.Parameters" default_parameters = {} for parameter in parameters: if parameter["value"] is not None: default_parameters[parameter["name"]] = parameter["value"] env["METAFLOW_DEFAULT_PARAMETERS"] = json.dumps(default_parameters) input_paths = None else: if ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ): input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/:" "${METAFLOW_PARENT_TASK_IDS}" % node.in_funcs[0] ) env["METAFLOW_SPLIT_PARENT_TASK_ID"] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) else: if len(node.in_funcs) == 1: input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/${METAFLOW_PARENT_TASK_ID}" % node.in_funcs[0] ) env["METAFLOW_PARENT_TASK_ID"] = "$.JobId" else: input_paths = "sfn-${METAFLOW_RUN_ID}:" + ",".join( "/${METAFLOW_PARENT_%s_STEP}/" "${METAFLOW_PARENT_%s_TASK_ID}" % (idx, idx) for idx, _ in enumerate(node.in_funcs) ) for idx, _ in enumerate(node.in_funcs): env["METAFLOW_PARENT_%s_TASK_ID" % idx] = "$.[%s].JobId" % idx env["METAFLOW_PARENT_%s_STEP" % idx] = ( "$.[%s].Parameters.step_name" % idx ) env["METAFLOW_INPUT_PATHS"] = input_paths if node.is_inside_foreach: if any(self.graph[n].type == "foreach" for n in node.in_funcs): attrs[ "split_parent_task_id_%s.$" % node.split_parents[-1] ] = "$.SplitParentTaskId" for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) elif node.type == "join": if self.graph[node.split_parents[-1]].type == "foreach": # that information is available to us from AWS DynamoDB. # This has a nice side-effect of making our foreach # splits infinitely scalable because otherwise we would # be bounded by the 32K state limit for the outputs. So, # instead of referencing `Parameters` fields by index # (like in `split-and`), we can just reference them # directly. attrs["split_parent_task_id_%s.$" % node.split_parents[-1]] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.[0].Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) # Set `METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN` if the # next transition is to a foreach join, so that the # stepfunctions decorator can write the mapping for input path # to DynamoDb. if any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ): env["METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN"] = attrs[ "split_parent_task_id_%s.$" % self.graph[node.out_funcs[0]].split_parents[-1] ] # Set ttl for the values we set in AWS DynamoDB. if node.type == "foreach": if self.workflow_timeout: env["METAFLOW_SFN_WORKFLOW_TIMEOUT"] = self.workflow_timeout # Handle split index for for-each. if any(self.graph[n].type == "foreach" for n in node.in_funcs): env["METAFLOW_SPLIT_INDEX"] = "$.Index" env["METAFLOW_CODE_URL"] = self.code_package_url env["METAFLOW_FLOW_NAME"] = attrs["metaflow.flow_name"] env["METAFLOW_STEP_NAME"] = attrs["metaflow.step_name"] env["METAFLOW_RUN_ID"] = attrs["metaflow.run_id.$"] env["METAFLOW_PRODUCTION_TOKEN"] = self.production_token env["SFN_STATE_MACHINE"] = self.name env["METAFLOW_OWNER"] = attrs["metaflow.owner"] # Can't set `METAFLOW_TASK_ID` due to lack of run-scoped identifiers. metadata_env = self.metadata.get_runtime_environment("step-functions") env.update(metadata_env) metaflow_version = self.environment.get_environment_info() metaflow_version["flow_name"] = self.graph.name metaflow_version["production_token"] = self.production_token env["METAFLOW_VERSION"] = json.dumps(metaflow_version) # Set AWS DynamoDb Table Name for state tracking for for-eaches. # There are three instances when metaflow runtime directly interacts # with AWS DynamoDB. # 1. To set the cardinality of foreaches (which are subsequently) # read prior to the instantiation of the Map state by AWS Step # Functions. # 2. To set the input paths from the parent steps of a foreach join. # 3. To read the input paths in a foreach join. if ( node.type == "foreach" or ( node.is_inside_foreach and any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ) ) or ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ) ): if SFN_DYNAMO_DB_TABLE is None: raise StepFunctionsException( "An AWS DynamoDB table is needed " "to support foreach in your flow. " "You can create one following the " "instructions listed at *https://a" "dmin-docs.metaflow.org/metaflow-o" "n-aws/deployment-guide/manual-dep" "loyment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) env["METAFLOW_SFN_DYNAMO_DB_TABLE"] = SFN_DYNAMO_DB_TABLE # Resolve AWS Batch resource requirements. batch_deco = [deco for deco in node.decorators if deco.name == "batch"][0] resources = batch_deco.attributes # Resolve retry strategy. user_code_retries, total_retries = self._get_retries(node) task_spec = { "flow_name": attrs["metaflow.flow_name"], "step_name": attrs["metaflow.step_name"], "run_id": "sfn-$METAFLOW_RUN_ID", # Use AWS Batch job identifier as the globally unique # task identifier. "task_id": "$AWS_BATCH_JOB_ID", # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "retry_count": "$((AWS_BATCH_JOB_ATTEMPT-1))", } return ( Batch(self.metadata, self.environment) .create_job( step_name=node.name, step_cli=self._step_cli( node, input_paths, self.code_package_url, user_code_retries ), task_spec=task_spec, code_package_sha=self.code_package_sha, code_package_url=self.code_package_url, code_package_ds=self.flow_datastore.TYPE, image=resources["image"], queue=resources["queue"], iam_role=resources["iam_role"], execution_role=resources["execution_role"], cpu=resources["cpu"], gpu=resources["gpu"], memory=resources["memory"], run_time_limit=batch_deco.run_time_limit, shared_memory=resources["shared_memory"], max_swap=resources["max_swap"], swappiness=resources["swappiness"], env=env, attrs=attrs, host_volumes=resources["host_volumes"], ) .attempts(total_retries + 1) ) def _get_retries(self, node): max_user_code_retries = 0 max_error_retries = 0 # Different decorators may have different retrying strategies, so take # the max of them. for deco in node.decorators: user_code_retries, error_retries = deco.step_task_retry_count() max_user_code_retries = max(max_user_code_retries, user_code_retries) max_error_retries = max(max_error_retries, error_retries) return max_user_code_retries, max_user_code_retries + max_error_retries def _step_cli(self, node, paths, code_package_url, user_code_retries): cmds = [] script_name = os.path.basename(sys.argv[0]) executable = self.environment.executable(node.name) if R.use_r(): entrypoint = [R.entrypoint()] else: entrypoint = [executable, script_name] # Use AWS Batch job identifier as the globally unique task identifier. task_id = "${AWS_BATCH_JOB_ID}" # FlowDecorators can define their own top-level options. They are # responsible for adding their own top-level options and values through # the get_top_level_options() hook. See similar logic in runtime.py. top_opts_dict = {} for deco in flow_decorators(): top_opts_dict.update(deco.get_top_level_options()) top_opts = list(dict_to_cli_options(top_opts_dict)) if node.name == "start": # We need a separate unique ID for the special _parameters task task_id_params = "%s-params" % task_id # Export user-defined parameters into runtime environment param_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_params = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parameters %s && . `pwd`/%s" % (param_file, param_file) ) params = ( entrypoint + top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=s3", "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "init", "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id_params, ] ) # Assign tags to run objects. if self.tags: params.extend("--tag %s" % tag for tag in self.tags) # If the start step gets retried, we must be careful not to # regenerate multiple parameters tasks. Hence we check first if # _parameters exists already. exists = entrypoint + [ "dump", "--max-value-size=0", "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params), ] cmd = "if ! %s >/dev/null 2>/dev/null; then %s && %s; fi" % ( " ".join(exists), export_params, " ".join(params), ) cmds.append(cmd) paths = "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params) if node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach": parent_tasks_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_parent_tasks = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parent_tasks %s && . `pwd`/%s" % (parent_tasks_file, parent_tasks_file) ) cmds.append(export_parent_tasks) top_level = top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=%s" % self.flow_datastore.TYPE, "--datastore-root=%s" % self.flow_datastore.datastore_root, "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "--with=step_functions_internal", ] step = [ "step", node.name, "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id, # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "--retry-count $((AWS_BATCH_JOB_ATTEMPT-1))", "--max-user-code-retries %d" % user_code_retries, "--input-paths %s" % paths, # Set decorator to batch to execute `task_*` hooks for batch # decorator. "--with=batch", ] if any(self.graph[n].type == "foreach" for n in node.in_funcs): # We set the `METAFLOW_SPLIT_INDEX` through JSONPath-foo # to pass the state from the parent DynamoDb state for for-each. step.append("--split-index $METAFLOW_SPLIT_INDEX") if self.tags: step.extend("--tag %s" % tag for tag in self.tags) if self.namespace is not None: step.append("--namespace=%s" % self.namespace) cmds.append(" ".join(entrypoint + top_level + step)) return " && ".join(cmds) class Workflow(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() def start_at(self, start_at): self.payload["StartAt"] = start_at return self def add_state(self, state): self.payload["States"][state.name] = state.payload return self def timeout_seconds(self, timeout_seconds): self.payload["TimeoutSeconds"] = timeout_seconds return self def to_json(self, pretty=False): return json.dumps(self.payload, indent=4 if pretty else None) class State(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Task" def resource(self, resource): self.payload["Resource"] = resource return self def next(self, state): self.payload["Next"] = state return self def end(self): self.payload["End"] = True return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self def _partition(self): # This is needed to support AWS Gov Cloud and AWS CN regions return SFN_IAM_ROLE.split(":")[1] def batch(self, job): self.resource( "arn:%s:states:::batch:submitJob.sync" % self._partition() ).parameter("JobDefinition", job.payload["jobDefinition"]).parameter( "JobName", job.payload["jobName"] ).parameter( "JobQueue", job.payload["jobQueue"] ).parameter( "Parameters", job.payload["parameters"] ).parameter( "ContainerOverrides", to_pascalcase(job.payload["containerOverrides"]) ).parameter( "RetryStrategy", to_pascalcase(job.payload["retryStrategy"]) ).parameter( "Timeout", to_pascalcase(job.payload["timeout"]) ) # tags may not be present in all scenarios if "tags" in job.payload: self.parameter("Tags", job.payload["tags"]) return self def dynamo_db(self, table_name, primary_key, values): self.resource("arn:%s:states:::dynamodb:getItem" % self._partition()).parameter( "TableName", table_name ).parameter("Key", {"pathspec": {"S.$": primary_key}}).parameter( "ConsistentRead", True ).parameter( "ProjectionExpression", values ) return self class Parallel(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Parallel" def branch(self, workflow): if "Branches" not in self.payload: self.payload["Branches"] = [] self.payload["Branches"].append(workflow.payload) return self def next(self, state): self.payload["Next"] = state return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self class Map(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Map" self.payload["MaxConcurrency"] = 0 def iterator(self, workflow): self.payload["Iterator"] = workflow.payload return self def next(self, state): self.payload["Next"] = state return self def items_path(self, items_path): self.payload["ItemsPath"] = items_path return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def max_concurrency(self, max_concurrency): self.payload["MaxConcurrency"] = max_concurrency return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self

true

f71b5640c381e4a1a513cc6857ecd00c92aa7029

279

py

Python

circle_core/writer/base.py

glucoseinc/CircleCore

577f814ce2944efb6e5997f3d7838c71ce9aea6a

[ "MIT" ]

3

2019-01-11T04:30:18.000Z

2019-01-11T04:31:18.000Z

circle_core/writer/base.py

glucoseinc/CircleCore

577f814ce2944efb6e5997f3d7838c71ce9aea6a

[ "MIT" ]

16

2018-11-21T11:47:18.000Z

2021-09-01T03:52:35.000Z

circle_core/writer/base.py

glucoseinc/CircleCore

577f814ce2944efb6e5997f3d7838c71ce9aea6a

[ "MIT" ]

null

import abc class DBWriter(metaclass=abc.ABCMeta): @abc.abstractmethod async def store(self, message_box, message) -> bool: raise NotImplementedError @abc.abstractmethod async def flush(self, flush_all=False) -> None: raise NotImplementedError

21.461538

56

0.706093

import abc class DBWriter(metaclass=abc.ABCMeta): @abc.abstractmethod async def store(self, message_box, message) -> bool: raise NotImplementedError @abc.abstractmethod async def flush(self, flush_all=False) -> None: raise NotImplementedError

true

f71b56535865b78456cbc8ac2192f63ee5287cfd

588

py

Python

src/UI_Code_Q2/Test_code_smaller_parts/PythonWriteUSB.py

KevinEwoudLee/HU3-UI

16d63e0be8c515540daf4f9cfcff2d0a85c1cbab

[ "MIT" ]

1

2019-12-11T15:27:53.000Z

src/UI_Code_Q2/Test_code_smaller_parts/PythonWriteUSB.py

KevinEwoudLee/HU3-UI

16d63e0be8c515540daf4f9cfcff2d0a85c1cbab

[ "MIT" ]

null

src/UI_Code_Q2/Test_code_smaller_parts/PythonWriteUSB.py

KevinEwoudLee/HU3-UI

16d63e0be8c515540daf4f9cfcff2d0a85c1cbab

[ "MIT" ]

1

2019-12-11T15:23:56.000Z

# -*- coding: utf-8 -*- """ Created on Tue Nov 26 09:36:21 2019 @author: kevin """ import os import time from time import sleep from datetime import datetime file = open("E:/test2.csv", "a") i=0 if os.stat("E:/test2.csv").st_size == 0: file.write("Time,Sensor1,Sensor2,Sensor3,Sensor4,Sensor5\n") while True: i=i+1 now = datetime.now() file.write(str(now)+","+str(i)+","+str(-i)+","+str(i-10)+","+str(i+5)+","+str(i*i)+"\n") file.flush() time.sleep(1) if (i>=10): break file.close()

22.615385

97

0.52551

import os import time from time import sleep from datetime import datetime file = open("E:/test2.csv", "a") i=0 if os.stat("E:/test2.csv").st_size == 0: file.write("Time,Sensor1,Sensor2,Sensor3,Sensor4,Sensor5\n") while True: i=i+1 now = datetime.now() file.write(str(now)+","+str(i)+","+str(-i)+","+str(i-10)+","+str(i+5)+","+str(i*i)+"\n") file.flush() time.sleep(1) if (i>=10): break file.close()

true

f71b56ae1d0e79e35e8bd9e7c4c05e6ff33f45bf

3,286

py

Python

src/util/pos_util.py

tiefenauer/ip7-python

512105ba39110ec77d2ea0961dd7c2a42d4ec26d

[ "MIT" ]

null

src/util/pos_util.py

tiefenauer/ip7-python

512105ba39110ec77d2ea0961dd7c2a42d4ec26d

[ "MIT" ]

null

src/util/pos_util.py

tiefenauer/ip7-python

512105ba39110ec77d2ea0961dd7c2a42d4ec26d

[ "MIT" ]

null

import collections from src.importer.known_jobs import KnownJobs from src.preprocessing import preproc from src.util import loe_util, jobtitle_util mw_tokens = ['m/w', 'w/m', 'm/f', 'f/m', 'M/W', 'W/M', 'M/F', 'F/M'] def find_jobs(sentence): jobs = [] # find known jobs for hit in find_job_by_keyword(sentence, KnownJobs()): jobs.append((hit, 'known-job')) # find by m/w patterns sentence_without_percentage = loe_util.remove_percentage(sentence) for hit in find_job_by_keyword(sentence_without_percentage, mw_tokens): jobs.append((hit, 'mw')) # find by percentages sentence_without_mw = jobtitle_util.remove_mw(sentence) for hit in find_job_by_keyword(sentence_without_mw, loe_util.find_all_loe(sentence_without_mw)): jobs.append((hit, 'loe')) # find by gender forms # sentence_without_mw_and_percentage = loe_util.remove_percentage(sentence_without_mw) # jobs += find_job_by_keyword(sentence_without_mw_and_percentage, ['/in', '/-in']) # search by keyword: gender # for match in jobtitle_util.find_all_genderized(sentence): # gender_job = expand_left_right(sentence.split(match[0])[0], sentence) # if gender_job: # yield gender_job return jobs def find_job_by_keyword(sentence, keywords): # job_names = [] for keyword in keywords: if keyword in sentence: job_name = expand_left_right(keyword, sentence) if job_name: yield job_name # job_names.append(job_name) # return job_names def expand_left_right(token, sentence): if token not in sentence: return None job_name_tokens = preproc.to_words(token) sentence_tokens = [word for word in preproc.to_words(sentence) if word not in ['(', ')']] ix_from, ix_to = calculate_positions(job_name_tokens, sentence_tokens) sentence_pos = preproc.pos_tag(sentence_tokens) left = sentence_pos[:ix_from] right = sentence_pos[ix_to:] initial_content = [token] if token not in mw_tokens and not loe_util.is_percentate(token) else [] tokens = collections.deque(initial_content) search_left(left, tokens) search_right(right, tokens) return ' '.join(tokens) def search_left(pos_tagged_words, tokens=collections.deque()): i = len(pos_tagged_words) - 1 while 0 <= i: word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.appendleft(word) else: break i -= 1 return tokens def search_right(pos_tagged_words, tokens=collections.deque()): i = 0 while 0 <= i < len(pos_tagged_words): word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.append(word) else: break i += 1 return tokens def is_part_of_name(word, pos_tag): return is_noun(pos_tag) or word in ['/'] def is_noun(pos_tag): return pos_tag[0] in ['N', 'F'] def is_punctuation(pos_tag): return pos_tag.startswith('$') def calculate_positions(job_name_tokens, sentence_tokens): ix_from = [i for i, word in enumerate(sentence_tokens) if job_name_tokens[0] in word][0] ix_to = ix_from + len(job_name_tokens) return ix_from, ix_to

30.71028

101

0.676811

import collections from src.importer.known_jobs import KnownJobs from src.preprocessing import preproc from src.util import loe_util, jobtitle_util mw_tokens = ['m/w', 'w/m', 'm/f', 'f/m', 'M/W', 'W/M', 'M/F', 'F/M'] def find_jobs(sentence): jobs = [] for hit in find_job_by_keyword(sentence, KnownJobs()): jobs.append((hit, 'known-job')) sentence_without_percentage = loe_util.remove_percentage(sentence) for hit in find_job_by_keyword(sentence_without_percentage, mw_tokens): jobs.append((hit, 'mw')) sentence_without_mw = jobtitle_util.remove_mw(sentence) for hit in find_job_by_keyword(sentence_without_mw, loe_util.find_all_loe(sentence_without_mw)): jobs.append((hit, 'loe')) return jobs def find_job_by_keyword(sentence, keywords): for keyword in keywords: if keyword in sentence: job_name = expand_left_right(keyword, sentence) if job_name: yield job_name def expand_left_right(token, sentence): if token not in sentence: return None job_name_tokens = preproc.to_words(token) sentence_tokens = [word for word in preproc.to_words(sentence) if word not in ['(', ')']] ix_from, ix_to = calculate_positions(job_name_tokens, sentence_tokens) sentence_pos = preproc.pos_tag(sentence_tokens) left = sentence_pos[:ix_from] right = sentence_pos[ix_to:] initial_content = [token] if token not in mw_tokens and not loe_util.is_percentate(token) else [] tokens = collections.deque(initial_content) search_left(left, tokens) search_right(right, tokens) return ' '.join(tokens) def search_left(pos_tagged_words, tokens=collections.deque()): i = len(pos_tagged_words) - 1 while 0 <= i: word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.appendleft(word) else: break i -= 1 return tokens def search_right(pos_tagged_words, tokens=collections.deque()): i = 0 while 0 <= i < len(pos_tagged_words): word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.append(word) else: break i += 1 return tokens def is_part_of_name(word, pos_tag): return is_noun(pos_tag) or word in ['/'] def is_noun(pos_tag): return pos_tag[0] in ['N', 'F'] def is_punctuation(pos_tag): return pos_tag.startswith('$') def calculate_positions(job_name_tokens, sentence_tokens): ix_from = [i for i, word in enumerate(sentence_tokens) if job_name_tokens[0] in word][0] ix_to = ix_from + len(job_name_tokens) return ix_from, ix_to

true

f71b56c4085612ca2aacc209126330286fb3b4f9

2,980

py

Python

webvep/main/settings.py

IanVermes/vep_api

9d9d31eddd969aad1c462278ea1f1fb09153e054

[ "MIT" ]

null

webvep/main/settings.py

IanVermes/vep_api

9d9d31eddd969aad1c462278ea1f1fb09153e054

[ "MIT" ]

1

2020-03-30T10:52:58.000Z

2020-03-30T16:46:31.000Z

webvep/main/settings.py

IanVermes/vep_api

9d9d31eddd969aad1c462278ea1f1fb09153e054

[ "MIT" ]

null

""" Django settings for webvep project. Generated by 'django-admin startproject' using Django 3.0.4. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = "ec)mzu6ls4qaj!8)txrke(uxxtb1gmz^2a_^1$lqe9&ys17^!$" # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ "django.contrib.admin", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.staticfiles", "rest_framework", "webvep_frontend", "webvep_api", ] MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "main.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ], }, }, ] WSGI_APPLICATION = "main.wsgi.application" # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases ## DONT NEED ONE # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", }, {"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",}, {"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",}, {"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",}, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = "/static/"

26.607143

91

0.711409

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = "ec)mzu6ls4qaj!8)txrke(uxxtb1gmz^2a_^1$lqe9&ys17^!$" DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ "django.contrib.admin", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.staticfiles", "rest_framework", "webvep_frontend", "webvep_api", ] MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "main.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ], }, }, ] WSGI_APPLICATION = "main.wsgi.application" # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases ## DONT NEED ONE # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", }, {"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",}, {"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",}, {"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",}, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = "/static/"

true

f71b57614025c4aecf90a7ea7cf4fe3d2b9c8499

77

py

Python

zmon_aws_agent/__main__.py

jrake-revelant/zmon-aws-agent

67ed3d0230f3bec9b3a3950c3eefa48404ee9d6b

[ "Apache-2.0" ]

9

2016-07-28T09:28:20.000Z

2022-02-27T23:40:09.000Z

zmon_aws_agent/__main__.py

jrake-revelant/zmon-aws-agent

67ed3d0230f3bec9b3a3950c3eefa48404ee9d6b

[ "Apache-2.0" ]

146

2016-05-23T13:43:42.000Z

2020-03-10T09:47:07.000Z

zmon_aws_agent/__main__.py

jrake-revelant/zmon-aws-agent

67ed3d0230f3bec9b3a3950c3eefa48404ee9d6b

[ "Apache-2.0" ]

11

2017-02-18T12:46:27.000Z

2020-01-22T13:13:36.000Z

from zmon_aws_agent.main import main if __name__ == '__main__': main()

12.833333

36

0.701299

from zmon_aws_agent.main import main if __name__ == '__main__': main()

true

f71b584d40b3c272f7646b95950ee740aeb0fc1c

5,834

py

Python

tests/model_connectors/test_spawn_ets.py

macintoshpie/geojson-modelica-translator

5ed02d53f06961b8d0f3705343368e4c920e7d7d

[ "BSD-3-Clause" ]

null

tests/model_connectors/test_spawn_ets.py

macintoshpie/geojson-modelica-translator

5ed02d53f06961b8d0f3705343368e4c920e7d7d

[ "BSD-3-Clause" ]

null

tests/model_connectors/test_spawn_ets.py

macintoshpie/geojson-modelica-translator

5ed02d53f06961b8d0f3705343368e4c920e7d7d

[ "BSD-3-Clause" ]

null

""" **************************************************************************************************** :copyright (c) 2019-2020 URBANopt, Alliance for Sustainable Energy, LLC, and other contributors. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. **************************************************************************************************** """ import os import shutil import unittest from pathlib import Path from geojson_modelica_translator.geojson_modelica_translator import ( GeoJsonModelicaTranslator ) from geojson_modelica_translator.model_connectors.spawnBui_ETS_Coupling import ( SpawnConnectorETS ) from geojson_modelica_translator.modelica.modelica_runner import ModelicaRunner from geojson_modelica_translator.system_parameters.system_parameters import ( SystemParameters ) class SpawnModelConnectorSingleBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_single" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) # load in the example geojson with a single offie building filename = os.path.join(self.data_dir, "spawn_geojson_ex1.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) # use the GeoJson translator to scaffold out the directory self.gj.scaffold_directory(self.output_dir, project_name) # load system parameter data filename = os.path.join(self.data_dir, "spawn_system_params_ex1.json") sys_params = SystemParameters(filename) # now test the spawn connector (independent of the larger geojson translator self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_init(self): self.assertIsNotNone(self.spawn) self.assertEqual(self.spawn.system_parameters.get_param("buildings.custom")[0]["load_model"], "Spawn") def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) # make sure the model can run using the ModelicaRunner class mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94020090', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode) class SpawnModelConnectorTwoBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_two_building" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) # load in the example geojson with a single offie building filename = os.path.join(self.data_dir, "spawn_geojson_ex2.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) # use the GeoJson translator to scaffold out the directory self.gj.scaffold_directory(self.output_dir, project_name) # load system parameter data filename = os.path.join(self.data_dir, "spawn_system_params_ex2.json") sys_params = SystemParameters(filename) # now test the spawn connector (independent of the larger geojson translator self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) # make sure the model can run using the ModelicaRunner class mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94021950', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode)

44.876923

114

0.711176

import os import shutil import unittest from pathlib import Path from geojson_modelica_translator.geojson_modelica_translator import ( GeoJsonModelicaTranslator ) from geojson_modelica_translator.model_connectors.spawnBui_ETS_Coupling import ( SpawnConnectorETS ) from geojson_modelica_translator.modelica.modelica_runner import ModelicaRunner from geojson_modelica_translator.system_parameters.system_parameters import ( SystemParameters ) class SpawnModelConnectorSingleBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_single" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) filename = os.path.join(self.data_dir, "spawn_geojson_ex1.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) self.gj.scaffold_directory(self.output_dir, project_name) filename = os.path.join(self.data_dir, "spawn_system_params_ex1.json") sys_params = SystemParameters(filename) self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_init(self): self.assertIsNotNone(self.spawn) self.assertEqual(self.spawn.system_parameters.get_param("buildings.custom")[0]["load_model"], "Spawn") def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94020090', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode) class SpawnModelConnectorTwoBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_two_building" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) filename = os.path.join(self.data_dir, "spawn_geojson_ex2.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) self.gj.scaffold_directory(self.output_dir, project_name) filename = os.path.join(self.data_dir, "spawn_system_params_ex2.json") sys_params = SystemParameters(filename) self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94021950', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode)

true

f71b58bc6969e1f4e15e0b32876c55ba66d9757e

18,715

py

Python

OLD/losses.py

ivankreso/semseg

fcd2889a1e9e03c3d1a71d19d68d15ce25a5dc79

[ "MIT" ]

2

2017-11-17T06:55:44.000Z

2019-06-11T13:07:05.000Z

OLD/losses.py

ivankreso/semseg

fcd2889a1e9e03c3d1a71d19d68d15ce25a5dc79

[ "MIT" ]

null

OLD/losses.py

ivankreso/semseg

fcd2889a1e9e03c3d1a71d19d68d15ce25a5dc79

[ "MIT" ]

null

import tensorflow as tf import slim FLAGS = tf.app.flags.FLAGS def add_loss_summaries(total_loss): """Add summaries for losses in model. Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: #print(l.op.name) # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. #tf.scalar_summary(l.op.name + ' (raw)', l) #tf.scalar_summary(l.op.name, loss_averages.average(l)) tf.summary.scalar(l.op.name + ' (raw)', l) tf.summary.scalar(l.op.name, loss_averages.average(l)) #tf.scalar_summary([l.op.name + ' (raw)'], l) #tf.scalar_summary([l.op.name], loss_averages.average(l)) return loss_averages_op def total_loss_sum(losses): # Assemble all of the losses for the current tower only. #losses = tf.get_collection(slim.losses.LOSSES_COLLECTION) #print(losses) # Calculate the total loss for the current tower. #regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) #regularization_losses = tf.contrib.losses.get_regularization_losses() regularization_losses = tf.losses.get_regularization_losses() #total_loss = tf.add_n(losses + regularization_losses, name='total_loss') total_loss = tf.add_n(losses + regularization_losses, name='total_loss') return total_loss def cross_entropy_loss(logits, labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) mask = labels < FLAGS.num_classes idx = tf.where(mask) # # labels = tf.reshape(labels, shape=[num_pixels]) # print(idx) labels = tf.to_float(labels) labels = tf.gather_nd(labels, idx) # labels = tf.boolean_mask(labels, mask) labels = tf.to_int32(labels) logits = tf.gather_nd(logits, idx) # logits = tf.boolean_mask(logits, mask) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) # range_idx = tf.range(tf.shape(labels)[0], dtype=tf.int32) # print(range_idx, labels) # labels = tf.reshape(labels, shape=[-1,1]) # range_idx = tf.reshape(range_idx, shape=[-1,1]) # idx = tf.concat([range_idx, labels], axis=1) # print(idx) # probs = tf.nn.softmax(logits) # probs = tf.gather_nd(probs, idx) # print(probs) # xent = tf.square(1 - probs) * xent # # xent = tf.pow(1 - probs, 3) * xent # # xent = (1 - probs) * xent #num_labels = tf.to_float(tf.reduce_sum(num_labels)) #num_labels = tf.reduce_sum(tf.to_float(num_labels)) #class_hist = tf.Print(class_hist, [class_hist], 'hist = ', summarize=30) #num_labels = tf.reduce_sum(onehot_labels) #class_hist = tf.to_float(tf.reduce_sum(class_hist, axis=0)) ##num_labels = tf.Print(num_labels, [num_labels, tf.reduce_sum(onehot_labels)], 'lab = ', summarize=30) #class_weights = num_labels / (class_hist + 1) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) ## we need to append 0 here for ignore pixels #class_weights = tf.concat([class_weights, [0]], axis=0) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) #class_weights = tf.minimum(tf.to_float(max_weight), class_weights) # class_weights = tf.ones([FLAGS.num_classes]) # class_weights = tf.concat([class_weights, [0]], axis=0) # #class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) # weights = tf.gather(class_weights, labels) xent = tf.reduce_mean(xent) return xent def weighted_cross_entropy_loss(logits, labels, class_hist=None, max_weight=1): print('loss: cross-entropy') print('Using balanced loss with max weight = ', max_weight) num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) #num_labels = tf.to_float(tf.reduce_sum(num_labels)) #num_labels = tf.reduce_sum(tf.to_float(num_labels)) #class_hist = tf.Print(class_hist, [class_hist], 'hist = ', summarize=30) num_labels = tf.reduce_sum(onehot_labels) #class_hist = tf.to_float(tf.reduce_sum(class_hist, axis=0)) ##num_labels = tf.Print(num_labels, [num_labels, tf.reduce_sum(onehot_labels)], 'lab = ', summarize=30) #class_weights = num_labels / (class_hist + 1) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) ## we need to append 0 here for ignore pixels #class_weights = tf.concat([class_weights, [0]], axis=0) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) #class_weights = tf.minimum(tf.to_float(max_weight), class_weights) class_weights = tf.ones([FLAGS.num_classes]) class_weights = tf.concat([class_weights, [0]], axis=0) #class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) weights = tf.gather(class_weights, labels) if max_weight > 1: raise ValueError() wgt_sum = tf.reduce_sum(weights) norm_factor = num_labels / wgt_sum # weights need to sum to 1 weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) #num_labels = tf.Print(num_labels, [num_labels, wgt_sum], 'num_labels = ') #xent = tf.Print(xent, [xent], 'num_labels = ') xent = tf.reduce_sum(xent) / num_labels return xent def weighted_cross_entropy_loss_dense(logits, labels, weights=None, num_labels=None, max_weight=100): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) if num_labels is None: num_labels = tf.reduce_sum(onehot_labels) else: num_labels = tf.reduce_sum(num_labels) print('Using balanced loss with max weight = ', max_weight) weights = tf.reshape(weights, shape=[num_pixels]) weights = tf.minimum(tf.to_float(max_weight), weights) wgt_sum = tf.reduce_sum(weights) norm_factor = num_labels / wgt_sum # weights need to sum to 1 weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) #num_labels = tf.Print(num_labels, [num_labels, wgt_sum], 'num_labels = ') #xent = tf.Print(xent, [xent], 'num_labels = ') xent = tf.reduce_sum(xent) / num_labels print(xent) return xent def cross_entropy_loss_old(logits, labels, weights, num_labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels, num_labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) weights = tf.reshape(weights, shape=[num_pixels]) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / tf.reduce_sum(num_labels) print(xent) return xent def mse(yp, yt): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.name_scope('MeanSquareError'): yt = tf.reshape(yt, shape=[num_examples]) yp = tf.reshape(yp, shape=[num_examples]) return tf.reduce_mean(tf.square(yt - yp)) def weighted_cross_entropy_loss_deprecated(logits, labels, weights=None, max_weight=100): #def weighted_cross_entropy_loss(logits, labels, weights=None, max_weight=1e2): #def weighted_cross_entropy_loss(logits, labels, weights=None, max_weight=1e3): print('loss: Weighted Cross Entropy Loss') shape = labels.get_shape().as_list() print(shape) #num_examples = shape[0] * shape[1] num_examples = -1 #num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.name_scope(None, 'WeightedCrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_examples]) #num_labels = tf.to_float(tf.reduce_sum(num_labels)) one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) # todo #log_softmax = tf.log(tf.nn.softmax(logits_1d)) - never do this! log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(-tf.multiply(tf.to_float(one_hot_labels), log_softmax), 1) #weighted_xent = tf.mul(weights, xent) if weights != None: weights = tf.reshape(weights, shape=[num_examples]) xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) #weighted_xent = xent total_loss = tf.div(tf.reduce_sum(xent), tf.to_float(num_labels), name='value') print(total_loss) return total_loss def flip_xent_loss(logits, labels, weights, max_weight=10): print('Loss: Weighted Cross Entropy Loss') assert(FLAGS.batch_size == 2) num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width labels = tf.reshape(labels, shape=[num_examples]) weights = tf.reshape(weights, shape=[num_examples]) #num_labels = tf.to_float(tf.reduce_sum(num_labels)) with tf.name_scope('FlipXentLoss', [logits, labels]): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) #print(logits[].get_shape()) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) # TODO #log_softmax = tf.log(tf.nn.softmax(logits_1d)) log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) #weighted_xent = tf.mul(weights, xent) weighted_xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) #weighted_xent = xent total_loss = - tf.div(tf.reduce_sum(weighted_xent), num_labels, name='value') return total_loss def slim_cross_entropy_loss(logits, labels, num_labels): print('Loss: Cross Entropy Loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) xent_loss = slim.losses.cross_entropy_loss(logits_1d, one_hot_labels) return xent_loss def softmax(logits): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits], None, 'Softmax'): logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) softmax_1d = tf.nn.softmax(logits_1d) softmax_2d = tf.reshape(softmax_1d, [FLAGS.img_height, FLAGS.img_width, FLAGS.num_classes]) return softmax_2d def multiclass_hinge_loss(logits, labels, weights): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width num_classes = FLAGS.num_classes with tf.op_scope([logits, labels], None, 'MulticlassHingeLoss'): #logits = tf.reshape(logits, [num_examples, num_classes]) #labels = tf.reshape(labels, [num_examples]) #weights = tf.reshape(weights, [num_examples]) logits = tf.reshape(logits, [-1, num_classes]) labels = tf.reshape(labels, [-1]) weights = tf.reshape(weights, [-1]) select_mask = tf.greater_equal(labels, 0) logits = tf.boolean_mask(logits, select_mask) labels = tf.boolean_mask(labels, select_mask) weights = tf.boolean_mask(weights, select_mask) num_examples = tf.reduce_sum(tf.to_int32(select_mask)) #num_examples = tf.Print(num_examples, [num_examples, num_labels_old], 'num_examples = ') #print(labels) #print(logits) #print(weights) #print(select_mask) partitions = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0, dtype=tf.int32) #print(partitions) #one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) #one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) #partitions = tf.to_int32(one_hot_labels) num_partitions = 2 scores, score_yt = tf.dynamic_partition(logits, partitions, num_partitions) #scores = tf.reshape(scores, [num_examples, num_classes - 1]) #score_yt = tf.reshape(score_yt, [num_examples, 1]) scores = tf.reshape(scores, [-1, num_classes - 1]) score_yt = tf.reshape(score_yt, [-1, 1]) #print(scores) #print(score_yt) #hinge_loss = tf.maximum(0.0, scores - score_yt + margin) hinge_loss = tf.square(tf.maximum(0.0, scores - score_yt + 1.0)) hinge_loss = tf.reduce_sum(hinge_loss, 1) #total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) #total_loss = tf.div(total_loss, tf.to_float(num_examples), name='value') total_loss = tf.reduce_mean(tf.mul(tf.minimum(100.0, weights), hinge_loss)) #tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None) #tf.nn.l2_loss(t, name=None) return total_loss def metric_hinge_loss(logits, labels, weights, num_labels): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits, labels], None, 'weightedhingeloss'): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) #codes = tf.nn.softmax(logits_1d) codes = tf.nn.l2_normalize(logits_1d, 1) # works worse # l2 loss -> bad! # todo - this is not true svm loss, try it from cs231n l2_dist = tf.sqrt(tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1)) m = 0.2 #l2_dist = tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1) #m = 0.2 ** 2 #m = 0.1 ** 2 #m = 0.3 ** 2 for i in range(num_classes): for j in range(num_classes): raise valueerror(1) hinge_loss = tf.maximum(tf.to_float(0), l2_dist - m) total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) total_loss = tf.div(total_loss, tf.to_float(num_labels), name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) #tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None) #tf.nn.l2_loss(t, name=None) return total_loss #def weighted_hinge_loss(logits, labels, weights, num_labels): # print('Loss: Hinge Loss') # num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width # with tf.op_scope([logits, labels], None, 'WeightedHingeLoss'): # weights = tf.reshape(weights, shape=[num_examples]) # labels = tf.reshape(labels, shape=[num_examples]) # num_labels = tf.to_float(tf.reduce_sum(num_labels)) # one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) # one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) # logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) # #codes = tf.nn.softmax(logits_1d) # codes = tf.nn.l2_normalize(logits_1d, 1) # # works worse # #l2_dist = tf.sqrt(tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1)) # #m = 0.2 # l2_dist = tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1) # m = 0.2 ** 2 # #m = 0.1 ** 2 # #m = 0.3 ** 2 # hinge_loss = tf.maximum(tf.to_float(0), l2_dist - m) # total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) # # total_loss = tf.div(total_loss, tf.to_float(num_labels), name='value') # tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) # # #tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None) # #tf.nn.l2_loss(t, name=None) # return total_loss def flip_xent_loss_symmetric(logits, labels, weights, num_labels): print('Loss: Weighted Cross Entropy Loss') num_examples = FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits, labels], None, 'WeightedCrossEntropyLoss'): labels = tf.reshape(labels, shape=[2, num_examples]) weights = tf.reshape(weights, shape=[2, num_examples]) num_labels = tf.to_float(tf.reduce_sum(num_labels)) #num_labels = tf.to_float(num_labels[0]) logits_flip = logits[1,:,:,:] #weights_flip = weights[1,:] logits = logits[0,:,:,:] weights = weights[0,:] labels = labels[0,:] one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) #logits_orig, logits_flip = tf.split(0, 2, logits) logits_flip = tf.image.flip_left_right(logits_flip) #print(logits[].get_shape()) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) logits_1d_flip = tf.reshape(logits_flip, [num_examples, FLAGS.num_classes]) # TODO log_softmax = tf.nn.log_softmax(logits_1d) #log_softmax_flip = tf.nn.log_softmax(logits_1d_flip) softmax_flip = tf.nn.softmax(logits_1d_flip) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) weighted_xent = tf.mul(tf.minimum(tf.to_float(100), weights), xent) xent_flip = tf.reduce_sum(tf.mul(softmax_flip, log_softmax), 1) xent_flip = tf.mul(tf.minimum(tf.to_float(100), weights), xent_flip) #weighted_xent = tf.mul(weights, xent) #weighted_xent = xent #total_loss = tf.div(- tf.reduce_sum(weighted_xent_flip), # num_labels, name='value') total_loss = - tf.div(tf.reduce_sum(weighted_xent) + tf.reduce_sum(xent_flip), num_labels, name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) return total_loss

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import tensorflow as tf import slim FLAGS = tf.app.flags.FLAGS def add_loss_summaries(total_loss): loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) for l in losses + [total_loss]: tf.summary.scalar(l.op.name + ' (raw)', l) tf.summary.scalar(l.op.name, loss_averages.average(l)) return loss_averages_op def total_loss_sum(losses): regularization_losses = tf.losses.get_regularization_losses() total_loss = tf.add_n(losses + regularization_losses, name='total_loss') return total_loss def cross_entropy_loss(logits, labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) mask = labels < FLAGS.num_classes idx = tf.where(mask) = tf.gather_nd(labels, idx) labels = tf.to_int32(labels) logits = tf.gather_nd(logits, idx) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) num_labels = tf.reduce_sum(onehot_labels) sum weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / num_labels return xent def weighted_cross_entropy_loss_dense(logits, labels, weights=None, num_labels=None, max_weight=100): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) if num_labels is None: num_labels = tf.reduce_sum(onehot_labels) else: num_labels = tf.reduce_sum(num_labels) print('Using balanced loss with max weight = ', max_weight) weights = tf.reshape(weights, shape=[num_pixels]) weights = tf.minimum(tf.to_float(max_weight), weights) wgt_sum = tf.reduce_sum(weights) norm_factor = num_labels / wgt_sum weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / num_labels print(xent) return xent def cross_entropy_loss_old(logits, labels, weights, num_labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels, num_labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) weights = tf.reshape(weights, shape=[num_pixels]) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / tf.reduce_sum(num_labels) print(xent) return xent def mse(yp, yt): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.name_scope('MeanSquareError'): yt = tf.reshape(yt, shape=[num_examples]) yp = tf.reshape(yp, shape=[num_examples]) return tf.reduce_mean(tf.square(yt - yp)) def weighted_cross_entropy_loss_deprecated(logits, labels, weights=None, max_weight=100): print('loss: Weighted Cross Entropy Loss') shape = labels.get_shape().as_list() print(shape) num_examples = -1 with tf.name_scope(None, 'WeightedCrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_examples]) one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(-tf.multiply(tf.to_float(one_hot_labels), log_softmax), 1) if weights != None: weights = tf.reshape(weights, shape=[num_examples]) xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) total_loss = tf.div(tf.reduce_sum(xent), tf.to_float(num_labels), name='value') print(total_loss) return total_loss def flip_xent_loss(logits, labels, weights, max_weight=10): print('Loss: Weighted Cross Entropy Loss') assert(FLAGS.batch_size == 2) num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width labels = tf.reshape(labels, shape=[num_examples]) weights = tf.reshape(weights, shape=[num_examples]) with tf.name_scope('FlipXentLoss', [logits, labels]): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) weighted_xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) total_loss = - tf.div(tf.reduce_sum(weighted_xent), num_labels, name='value') return total_loss def slim_cross_entropy_loss(logits, labels, num_labels): print('Loss: Cross Entropy Loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) xent_loss = slim.losses.cross_entropy_loss(logits_1d, one_hot_labels) return xent_loss def softmax(logits): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits], None, 'Softmax'): logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) softmax_1d = tf.nn.softmax(logits_1d) softmax_2d = tf.reshape(softmax_1d, [FLAGS.img_height, FLAGS.img_width, FLAGS.num_classes]) return softmax_2d def multiclass_hinge_loss(logits, labels, weights): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width num_classes = FLAGS.num_classes with tf.op_scope([logits, labels], None, 'MulticlassHingeLoss'): logits = tf.reshape(logits, [-1, num_classes]) labels = tf.reshape(labels, [-1]) weights = tf.reshape(weights, [-1]) select_mask = tf.greater_equal(labels, 0) logits = tf.boolean_mask(logits, select_mask) labels = tf.boolean_mask(labels, select_mask) weights = tf.boolean_mask(weights, select_mask) num_examples = tf.reduce_sum(tf.to_int32(select_mask)) partitions = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0, dtype=tf.int32) num_partitions = 2 scores, score_yt = tf.dynamic_partition(logits, partitions, num_partitions) scores = tf.reshape(scores, [-1, num_classes - 1]) score_yt = tf.reshape(score_yt, [-1, 1]) hinge_loss = tf.square(tf.maximum(0.0, scores - score_yt + 1.0)) hinge_loss = tf.reduce_sum(hinge_loss, 1) total_loss = tf.reduce_mean(tf.mul(tf.minimum(100.0, weights), hinge_loss)) return total_loss def metric_hinge_loss(logits, labels, weights, num_labels): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits, labels], None, 'weightedhingeloss'): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) codes = tf.nn.l2_normalize(logits_1d, 1) l2_dist = tf.sqrt(tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1)) m = 0.2 for i in range(num_classes): for j in range(num_classes): raise valueerror(1) hinge_loss = tf.maximum(tf.to_float(0), l2_dist - m) total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) total_loss = tf.div(total_loss, tf.to_float(num_labels), name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) return total_loss pyLoss'): labels = tf.reshape(labels, shape=[2, num_examples]) weights = tf.reshape(weights, shape=[2, num_examples]) num_labels = tf.to_float(tf.reduce_sum(num_labels)) logits_flip = logits[1,:,:,:] logits = logits[0,:,:,:] weights = weights[0,:] labels = labels[0,:] one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) logits_flip = tf.image.flip_left_right(logits_flip) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) logits_1d_flip = tf.reshape(logits_flip, [num_examples, FLAGS.num_classes]) log_softmax = tf.nn.log_softmax(logits_1d) softmax_flip = tf.nn.softmax(logits_1d_flip) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) weighted_xent = tf.mul(tf.minimum(tf.to_float(100), weights), xent) xent_flip = tf.reduce_sum(tf.mul(softmax_flip, log_softmax), 1) xent_flip = tf.mul(tf.minimum(tf.to_float(100), weights), xent_flip) total_loss = - tf.div(tf.reduce_sum(weighted_xent) + tf.reduce_sum(xent_flip), num_labels, name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) return total_loss

true

f71b599c49ef3382050c2d01eff0c192906c1d7b

1,580

py

Python

aiLogic/tankAI.py

JoelEager/pyTanks.Player

a35a653e9df2416c63204aba87a95f33e6815b63

[ "MIT" ]

2

2017-03-09T15:32:55.000Z

2017-09-04T11:25:41.000Z

aiLogic/tankAI.py

JoelEager/pyTanks.Player

a35a653e9df2416c63204aba87a95f33e6815b63

[ "MIT" ]

null

aiLogic/tankAI.py

JoelEager/pyTanks.Player

a35a653e9df2416c63204aba87a95f33e6815b63

[ "MIT" ]

4

2017-05-16T15:10:09.000Z

2017-07-06T15:24:50.000Z

""" The player's AI code Functions here are called by clock.py to run the AI code """ import random import math from clientLogic.logging import logPrint from clientLogic import clientData, commands def onConnect(): """ Called when the player initially connects to the server but before the tank first spawns """ commands.setInfo("Python player instance running the example AI.\n" + "Fork me at https://github.com/JoelEager/pyTanks.Player") def onSpawn(): """ Called when the tank spawns in a new game """ pass def onTick(elapsedTime): """ Called once every frame while the tank is alive :param elapsedTime: The time elapsed, in seconds, since the last frame """ gs = clientData.gameState # Collided so try to get moving again if not gs.myTank.moving: commands.turn((math.pi / 4) * random.randint(0, 7)) commands.go() logPrint("Turned and starting moving", 2) # Shooting logic if gs.myTank.canShoot and random.randint(0, 4) == 0: # Select a target random.shuffle(gs.tanks) for target in gs.tanks: if target.alive: # Do the math deltaX = abs(gs.myTank.x - target.x) if deltaX == 0: return deltaY = gs.myTank.y - target.y angle = math.atan(deltaY / deltaX) if target.x < gs.myTank.x: angle = math.pi - angle commands.fire(angle) logPrint("Fired", 2) break

27.719298

92

0.588608

import random import math from clientLogic.logging import logPrint from clientLogic import clientData, commands def onConnect(): commands.setInfo("Python player instance running the example AI.\n" + "Fork me at https://github.com/JoelEager/pyTanks.Player") def onSpawn(): pass def onTick(elapsedTime): gs = clientData.gameState if not gs.myTank.moving: commands.turn((math.pi / 4) * random.randint(0, 7)) commands.go() logPrint("Turned and starting moving", 2) if gs.myTank.canShoot and random.randint(0, 4) == 0: random.shuffle(gs.tanks) for target in gs.tanks: if target.alive: deltaX = abs(gs.myTank.x - target.x) if deltaX == 0: return deltaY = gs.myTank.y - target.y angle = math.atan(deltaY / deltaX) if target.x < gs.myTank.x: angle = math.pi - angle commands.fire(angle) logPrint("Fired", 2) break

true

f71b5a856153c3564ce7371764011afc06ba93ae

12,294

py

Python

tests/unit/common_utils.py

dannielarriola/uai-coursebuilder

fbd440a8bfe1a928ac52985aea2949d5e91ad203

[ "Apache-2.0" ]

null

tests/unit/common_utils.py

dannielarriola/uai-coursebuilder

fbd440a8bfe1a928ac52985aea2949d5e91ad203

[ "Apache-2.0" ]

27

2016-08-31T19:04:46.000Z

2016-09-29T00:22:32.000Z

tests/unit/common_utils.py

dannielarriola/uai-coursebuilder

fbd440a8bfe1a928ac52985aea2949d5e91ad203

[ "Apache-2.0" ]

null

# Copyright 2014 Google Inc. 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. """Unit tests for common.tags.""" __author__ = 'Mike Gainer (mgainer@google.com)' import datetime import os import unittest import appengine_config from common import utils class CommonUnitTests(unittest.TestCase): # --------------------------- String-to-list. def test_list_parsing(self): self.assertListEqual(['foo'], utils.text_to_list('foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\t')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo\t')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\n')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo\n')) self.assertListEqual(['foo'], utils.text_to_list('foo,')) self.assertListEqual(['foo'], utils.text_to_list(',foo')) self.assertListEqual(['foo'], utils.text_to_list(',foo,')) self.assertListEqual(['foo'], utils.text_to_list(' foo ,\n')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo,\t\n')) self.assertListEqual(['foo'], utils.text_to_list(',foo,\n')) self.assertListEqual(['foo'], utils.text_to_list( '[foo]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], \n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar ')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\tfoo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar\t')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\nfoo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\n foo\n bar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' \n foo \n bar \n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '[foo][bar]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( ' [foo] [bar] ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n[foo]\n[bar]\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n,[foo],\n[bar],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) def test_none_split(self): self.assertListEqual([], utils.text_to_list(None)) def test_empty_split(self): self.assertListEqual([], utils.text_to_list('')) def test_all_separators_split(self): self.assertListEqual([], utils.text_to_list(' ,,, \t\t\n\t ')) def test_one_item_split(self): self.assertListEqual(['x'], utils.text_to_list('x')) def test_join_none(self): self.assertEquals('', utils.list_to_text(None)) def test_join_empty(self): self.assertEquals('', utils.list_to_text([])) def test_join_one(self): self.assertEquals('x', utils.list_to_text(['x'])) def test_join_two(self): self.assertEquals('x y', utils.list_to_text(['x', 'y'])) def test_join_split(self): l = ['a', 'b', 'c'] self.assertListEqual(l, utils.text_to_list(utils.list_to_text(l))) def test_split_join(self): text = 'a b c' self.assertEquals(text, utils.list_to_text(utils.text_to_list(text))) class ZipAwareOpenTests(unittest.TestCase): def test_find_in_lib_without_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'babel-0.9.6.zip', 'babel', 'localedata', 'root.dat') with self.assertRaises(IOError): open(path) # This fails. with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(17490, len(data)) data = open(path, 'r').read() self.assertEquals(17490, len(data)) data = open(path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path).read() self.assertEquals(17490, len(data)) with self.assertRaises(IOError): open(path) # This fails again; open has been reset to normal. def test_find_in_lib_with_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'markdown-2.5.zip', 'setup.cfg') with self.assertRaises(IOError): open(path) # This fails. with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(12, len(data)) class ParseTimedeltaTests(unittest.TestCase): def test_parse_empty_string(self): self.assertEquals( utils.parse_timedelta_string(''), datetime.timedelta()) def test_parse_zero(self): self.assertEquals( utils.parse_timedelta_string('0'), datetime.timedelta()) def test_parse_gibberish(self): self.assertEquals( utils.parse_timedelta_string('Amidst the mists and coldest frosts'), datetime.timedelta()) def test_parse_leading_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string( '5 days and a partridge in a pear tree'), datetime.timedelta(days=5)) def test_parse_trailing_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string('we will leave in 5 days'), datetime.timedelta(days=5)) def test_parse_units(self): for unit in ('week', 'day', 'hour', 'minute', 'second'): self._test_parse_units(unit) def _test_parse_units(self, unit): expected1 = datetime.timedelta(**{unit + 's': 1}) expected2 = datetime.timedelta(**{unit + 's': 2}) self.assertEquals( utils.parse_timedelta_string('1%s' % unit[0]), expected1) self.assertEquals( utils.parse_timedelta_string('1%s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2%ss' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit[0]), expected2) self.assertEquals( utils.parse_timedelta_string('1 %s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 \t\t\n %ss' % unit), expected2) def test_parse_out_of_bounds_handled_successfully(self): self.assertEquals( utils.parse_timedelta_string('86400s'), datetime.timedelta(days=1)) self.assertEquals( utils.parse_timedelta_string('19d, 86400s'), datetime.timedelta(weeks=2, days=6)) def test_parse_combinations(self): self.assertEquals( utils.parse_timedelta_string('3w1d3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3w, 1d, 3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 w 1 d 3 m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks 1 day 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks, 1 day, 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) class ValidateTimedeltaTests(unittest.TestCase): def test_blank_is_allowed(self): errors = [] utils.ValidateTimedelta.validate('', errors) self.assertEquals(0, len(errors)) def test_none_is_allowed(self): errors = [] utils.ValidateTimedelta.validate(None, errors) self.assertEquals(0, len(errors)) def test_bare_numbers_not_allowed(self): errors = [] utils.ValidateTimedelta.validate('0', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('-1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('100', errors) self.assertEquals(1, len(errors)) def test_valid_items_allowed(self): errors = [] utils.ValidateTimedelta.validate('1s', errors) utils.ValidateTimedelta.validate('2m', errors) utils.ValidateTimedelta.validate('3h', errors) utils.ValidateTimedelta.validate('4d', errors) utils.ValidateTimedelta.validate('5w', errors) utils.ValidateTimedelta.validate('5 Weeks, 1D,2HOURS 3 seconds', errors) self.assertEquals(0, len(errors)) def test_invalid_items_disallowed(self): errors = [] utils.ValidateTimedelta.validate('1t', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1 year', errors) self.assertEquals(1, len(errors)) def test_parse_months_gives_error(self): errors = [] utils.ValidateTimedelta.validate('3 months', errors) self.assertEquals(1, len(errors))

38.299065

80

0.577843

__author__ = 'Mike Gainer (mgainer@google.com)' import datetime import os import unittest import appengine_config from common import utils class CommonUnitTests(unittest.TestCase): def test_list_parsing(self): self.assertListEqual(['foo'], utils.text_to_list('foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\t')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo\t')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\n')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo\n')) self.assertListEqual(['foo'], utils.text_to_list('foo,')) self.assertListEqual(['foo'], utils.text_to_list(',foo')) self.assertListEqual(['foo'], utils.text_to_list(',foo,')) self.assertListEqual(['foo'], utils.text_to_list(' foo ,\n')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo,\t\n')) self.assertListEqual(['foo'], utils.text_to_list(',foo,\n')) self.assertListEqual(['foo'], utils.text_to_list( '[foo]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], \n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar ')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\tfoo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar\t')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\nfoo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\n foo\n bar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' \n foo \n bar \n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '[foo][bar]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( ' [foo] [bar] ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n[foo]\n[bar]\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n,[foo],\n[bar],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) def test_none_split(self): self.assertListEqual([], utils.text_to_list(None)) def test_empty_split(self): self.assertListEqual([], utils.text_to_list('')) def test_all_separators_split(self): self.assertListEqual([], utils.text_to_list(' ,,, \t\t\n\t ')) def test_one_item_split(self): self.assertListEqual(['x'], utils.text_to_list('x')) def test_join_none(self): self.assertEquals('', utils.list_to_text(None)) def test_join_empty(self): self.assertEquals('', utils.list_to_text([])) def test_join_one(self): self.assertEquals('x', utils.list_to_text(['x'])) def test_join_two(self): self.assertEquals('x y', utils.list_to_text(['x', 'y'])) def test_join_split(self): l = ['a', 'b', 'c'] self.assertListEqual(l, utils.text_to_list(utils.list_to_text(l))) def test_split_join(self): text = 'a b c' self.assertEquals(text, utils.list_to_text(utils.text_to_list(text))) class ZipAwareOpenTests(unittest.TestCase): def test_find_in_lib_without_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'babel-0.9.6.zip', 'babel', 'localedata', 'root.dat') with self.assertRaises(IOError): open(path) with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(17490, len(data)) data = open(path, 'r').read() self.assertEquals(17490, len(data)) data = open(path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path).read() self.assertEquals(17490, len(data)) with self.assertRaises(IOError): open(path) def test_find_in_lib_with_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'markdown-2.5.zip', 'setup.cfg') with self.assertRaises(IOError): open(path) with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(12, len(data)) class ParseTimedeltaTests(unittest.TestCase): def test_parse_empty_string(self): self.assertEquals( utils.parse_timedelta_string(''), datetime.timedelta()) def test_parse_zero(self): self.assertEquals( utils.parse_timedelta_string('0'), datetime.timedelta()) def test_parse_gibberish(self): self.assertEquals( utils.parse_timedelta_string('Amidst the mists and coldest frosts'), datetime.timedelta()) def test_parse_leading_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string( '5 days and a partridge in a pear tree'), datetime.timedelta(days=5)) def test_parse_trailing_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string('we will leave in 5 days'), datetime.timedelta(days=5)) def test_parse_units(self): for unit in ('week', 'day', 'hour', 'minute', 'second'): self._test_parse_units(unit) def _test_parse_units(self, unit): expected1 = datetime.timedelta(**{unit + 's': 1}) expected2 = datetime.timedelta(**{unit + 's': 2}) self.assertEquals( utils.parse_timedelta_string('1%s' % unit[0]), expected1) self.assertEquals( utils.parse_timedelta_string('1%s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2%ss' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit[0]), expected2) self.assertEquals( utils.parse_timedelta_string('1 %s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 \t\t\n %ss' % unit), expected2) def test_parse_out_of_bounds_handled_successfully(self): self.assertEquals( utils.parse_timedelta_string('86400s'), datetime.timedelta(days=1)) self.assertEquals( utils.parse_timedelta_string('19d, 86400s'), datetime.timedelta(weeks=2, days=6)) def test_parse_combinations(self): self.assertEquals( utils.parse_timedelta_string('3w1d3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3w, 1d, 3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 w 1 d 3 m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks 1 day 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks, 1 day, 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) class ValidateTimedeltaTests(unittest.TestCase): def test_blank_is_allowed(self): errors = [] utils.ValidateTimedelta.validate('', errors) self.assertEquals(0, len(errors)) def test_none_is_allowed(self): errors = [] utils.ValidateTimedelta.validate(None, errors) self.assertEquals(0, len(errors)) def test_bare_numbers_not_allowed(self): errors = [] utils.ValidateTimedelta.validate('0', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('-1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('100', errors) self.assertEquals(1, len(errors)) def test_valid_items_allowed(self): errors = [] utils.ValidateTimedelta.validate('1s', errors) utils.ValidateTimedelta.validate('2m', errors) utils.ValidateTimedelta.validate('3h', errors) utils.ValidateTimedelta.validate('4d', errors) utils.ValidateTimedelta.validate('5w', errors) utils.ValidateTimedelta.validate('5 Weeks, 1D,2HOURS 3 seconds', errors) self.assertEquals(0, len(errors)) def test_invalid_items_disallowed(self): errors = [] utils.ValidateTimedelta.validate('1t', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1 year', errors) self.assertEquals(1, len(errors)) def test_parse_months_gives_error(self): errors = [] utils.ValidateTimedelta.validate('3 months', errors) self.assertEquals(1, len(errors))

true

f71b5b1995d1ecc2a1ba880156e9343a02f0c212

578

py

Python

bark_ml/commons/tracer.py

bark-simulator/rl

84f9c74b60becbc4bc758e19b201d85a21880717

[ "MIT" ]

null

bark_ml/commons/tracer.py

bark-simulator/rl

84f9c74b60becbc4bc758e19b201d85a21880717

[ "MIT" ]

null

bark_ml/commons/tracer.py

bark-simulator/rl

84f9c74b60becbc4bc758e19b201d85a21880717

[ "MIT" ]

null

# Copyright (c) 2020 fortiss GmbH # # Authors: Patrick Hart # # This work is licensed under the terms of the MIT license. # For a copy, see <https://opensource.org/licenses/MIT>. import pickle class Tracer: """The tracer can be used to log certain values during episodes.""" def __init__(self, states=None, trace_history=True): self._trace_history = trace_history self._states = [] def Trace(self, eval_dict): """Traces and stores a state.""" if self._trace_history: self._states.append(eval_dict) def Reset(self): self._trace_history = []

25.130435

69

0.698962

import pickle class Tracer: def __init__(self, states=None, trace_history=True): self._trace_history = trace_history self._states = [] def Trace(self, eval_dict): if self._trace_history: self._states.append(eval_dict) def Reset(self): self._trace_history = []

true