nwo
stringlengths
5
91
sha
stringlengths
40
40
path
stringlengths
5
174
language
stringclasses
1 value
identifier
stringlengths
1
120
parameters
stringlengths
0
3.15k
argument_list
stringclasses
1 value
return_statement
stringlengths
0
24.1k
docstring
stringlengths
0
27.3k
docstring_summary
stringlengths
0
13.8k
docstring_tokens
sequence
function
stringlengths
22
139k
function_tokens
sequence
url
stringlengths
87
283
kubernetes-client/python
47b9da9de2d02b2b7a34fbe05afb44afd130d73a
kubernetes/client/api/rbac_authorization_v1_api.py
python
RbacAuthorizationV1Api.list_cluster_role
(self, **kwargs)
return self.list_cluster_role_with_http_info(**kwargs)
list_cluster_role # noqa: E501 list or watch objects of kind ClusterRole # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_cluster_role(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str pretty: If 'true', then the output is pretty printed. :param bool allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. :param str _continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications. :param str field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. :param str label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. :param int limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned. :param str resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset :param str resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset :param int timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. :param bool watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: V1ClusterRoleList If the method is called asynchronously, returns the request thread.
list_cluster_role # noqa: E501
[ "list_cluster_role", "#", "noqa", ":", "E501" ]
def list_cluster_role(self, **kwargs): # noqa: E501 """list_cluster_role # noqa: E501 list or watch objects of kind ClusterRole # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_cluster_role(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str pretty: If 'true', then the output is pretty printed. :param bool allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. :param str _continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications. :param str field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. :param str label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. :param int limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned. :param str resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset :param str resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset :param int timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. :param bool watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: V1ClusterRoleList If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.list_cluster_role_with_http_info(**kwargs)
[ "def", "list_cluster_role", "(", "self", ",", "*", "*", "kwargs", ")", ":", "# noqa: E501", "kwargs", "[", "'_return_http_data_only'", "]", "=", "True", "return", "self", ".", "list_cluster_role_with_http_info", "(", "*", "*", "kwargs", ")" ]
https://github.com/kubernetes-client/python/blob/47b9da9de2d02b2b7a34fbe05afb44afd130d73a/kubernetes/client/api/rbac_authorization_v1_api.py#L1970-L2002
SanPen/GridCal
d3f4566d2d72c11c7e910c9d162538ef0e60df31
src/GridCal/Gui/Main/GridCalMain.py
python
MainGUI.post_contingency_analysis
(self)
Action performed after the short circuit. Returns:
Action performed after the short circuit. Returns:
[ "Action", "performed", "after", "the", "short", "circuit", ".", "Returns", ":" ]
def post_contingency_analysis(self): """ Action performed after the short circuit. Returns: """ drv, results = self.session.get_driver_results(sim.SimulationTypes.ContingencyAnalysis_run) self.remove_simulation(sim.SimulationTypes.ContingencyAnalysis_run) # update the results in the circuit structures if not drv.__cancel__: if results is not None: self.ui.progress_label.setText('Colouring contingency analysis results in the grid...') QtGui.QGuiApplication.processEvents() if self.ui.draw_schematic_checkBox.isChecked(): if results.S.shape[0] > 0: viz.colour_the_schematic(circuit=self.circuit, Sbus=results.S[0, :], # same injection for all the contingencies Sf=np.abs(results.Sf).max(axis=1), voltages=results.voltage.max(axis=0), loadings=np.abs(results.loading).max(axis=1), types=results.bus_types, use_flow_based_width=self.ui.branch_width_based_on_flow_checkBox.isChecked(), min_branch_width=self.ui.min_branch_size_spinBox.value(), max_branch_width=self.ui.max_branch_size_spinBox.value(), min_bus_width=self.ui.min_node_size_spinBox.value(), max_bus_width=self.ui.max_node_size_spinBox.value() ) else: info_msg('Cannot colour because there are no branches :/') self.update_available_results() self.colour_now() else: error_msg('Something went wrong, There are no contingency analysis results.') if not self.session.is_anything_running(): self.UNLOCK()
[ "def", "post_contingency_analysis", "(", "self", ")", ":", "drv", ",", "results", "=", "self", ".", "session", ".", "get_driver_results", "(", "sim", ".", "SimulationTypes", ".", "ContingencyAnalysis_run", ")", "self", ".", "remove_simulation", "(", "sim", ".", "SimulationTypes", ".", "ContingencyAnalysis_run", ")", "# update the results in the circuit structures", "if", "not", "drv", ".", "__cancel__", ":", "if", "results", "is", "not", "None", ":", "self", ".", "ui", ".", "progress_label", ".", "setText", "(", "'Colouring contingency analysis results in the grid...'", ")", "QtGui", ".", "QGuiApplication", ".", "processEvents", "(", ")", "if", "self", ".", "ui", ".", "draw_schematic_checkBox", ".", "isChecked", "(", ")", ":", "if", "results", ".", "S", ".", "shape", "[", "0", "]", ">", "0", ":", "viz", ".", "colour_the_schematic", "(", "circuit", "=", "self", ".", "circuit", ",", "Sbus", "=", "results", ".", "S", "[", "0", ",", ":", "]", ",", "# same injection for all the contingencies", "Sf", "=", "np", ".", "abs", "(", "results", ".", "Sf", ")", ".", "max", "(", "axis", "=", "1", ")", ",", "voltages", "=", "results", ".", "voltage", ".", "max", "(", "axis", "=", "0", ")", ",", "loadings", "=", "np", ".", "abs", "(", "results", ".", "loading", ")", ".", "max", "(", "axis", "=", "1", ")", ",", "types", "=", "results", ".", "bus_types", ",", "use_flow_based_width", "=", "self", ".", "ui", ".", "branch_width_based_on_flow_checkBox", ".", "isChecked", "(", ")", ",", "min_branch_width", "=", "self", ".", "ui", ".", "min_branch_size_spinBox", ".", "value", "(", ")", ",", "max_branch_width", "=", "self", ".", "ui", ".", "max_branch_size_spinBox", ".", "value", "(", ")", ",", "min_bus_width", "=", "self", ".", "ui", ".", "min_node_size_spinBox", ".", "value", "(", ")", ",", "max_bus_width", "=", "self", ".", "ui", ".", "max_node_size_spinBox", ".", "value", "(", ")", ")", "else", ":", "info_msg", "(", "'Cannot colour because there are no branches :/'", ")", "self", ".", "update_available_results", "(", ")", "self", ".", "colour_now", "(", ")", "else", ":", "error_msg", "(", "'Something went wrong, There are no contingency analysis results.'", ")", "if", "not", "self", ".", "session", ".", "is_anything_running", "(", ")", ":", "self", ".", "UNLOCK", "(", ")" ]
https://github.com/SanPen/GridCal/blob/d3f4566d2d72c11c7e910c9d162538ef0e60df31/src/GridCal/Gui/Main/GridCalMain.py#L2800-L2838
earthgecko/skyline
12754424de72593e29eb21009fb1ae3f07f3abff
skyline/custom_algorithms/m66.py
python
m66
(current_skyline_app, parent_pid, timeseries, algorithm_parameters)
return (anomalous, anomalyScore)
A time series data points are anomalous if the 6th median is 6 standard deviations (six-sigma) from the time series 6th median standard deviation and persists for x_windows, where `x_windows = int(window / 2)`. This algorithm finds SIGNIFICANT cahngepoints in a time series, similar to PELT and Bayesian Online Changepoint Detection, however it is more robust to instaneous outliers and more conditionally selective of changepoints. :param current_skyline_app: the Skyline app executing the algorithm. This will be passed to the algorithm by Skyline. This is **required** for error handling and logging. You do not have to worry about handling the argument in the scope of the custom algorithm itself, but the algorithm must accept it as the first agrument. :param parent_pid: the parent pid which is executing the algorithm, this is **required** for error handling and logging. You do not have to worry about handling this argument in the scope of algorithm, but the algorithm must accept it as the second argument. :param timeseries: the time series as a list e.g. ``[[1578916800.0, 29.0], [1578920400.0, 55.0], ... [1580353200.0, 55.0]]`` :param algorithm_parameters: a dictionary of any required parameters for the custom_algorithm and algorithm itself for example: ``algorithm_parameters={ 'nth_median': 6, 'sigma': 6, 'window': 5, 'return_anomalies' = True, }`` :type current_skyline_app: str :type parent_pid: int :type timeseries: list :type algorithm_parameters: dict :return: True, False or Non :rtype: boolean Example CUSTOM_ALGORITHMS configuration: 'm66': { 'namespaces': [ 'skyline.analyzer.run_time', 'skyline.analyzer.total_metrics', 'skyline.analyzer.exceptions' ], 'algorithm_source': '/opt/skyline/github/skyline/skyline/custom_algorithms/m66.py', 'algorithm_parameters': { 'nth_median': 6, 'sigma': 6, 'window': 5, 'resolution': 60, 'minimum_sparsity': 0, 'determine_duration': False, 'return_anomalies': True, 'save_plots_to': False, 'save_plots_to_absolute_dir': False, 'filename_prefix': False }, 'max_execution_time': 1.0 'consensus': 1, 'algorithms_allowed_in_consensus': ['m66'], 'run_3sigma_algorithms': False, 'run_before_3sigma': False, 'run_only_if_consensus': False, 'use_with': ['crucible', 'luminosity'], 'debug_logging': False, },
A time series data points are anomalous if the 6th median is 6 standard deviations (six-sigma) from the time series 6th median standard deviation and persists for x_windows, where `x_windows = int(window / 2)`. This algorithm finds SIGNIFICANT cahngepoints in a time series, similar to PELT and Bayesian Online Changepoint Detection, however it is more robust to instaneous outliers and more conditionally selective of changepoints.
[ "A", "time", "series", "data", "points", "are", "anomalous", "if", "the", "6th", "median", "is", "6", "standard", "deviations", "(", "six", "-", "sigma", ")", "from", "the", "time", "series", "6th", "median", "standard", "deviation", "and", "persists", "for", "x_windows", "where", "x_windows", "=", "int", "(", "window", "/", "2", ")", ".", "This", "algorithm", "finds", "SIGNIFICANT", "cahngepoints", "in", "a", "time", "series", "similar", "to", "PELT", "and", "Bayesian", "Online", "Changepoint", "Detection", "however", "it", "is", "more", "robust", "to", "instaneous", "outliers", "and", "more", "conditionally", "selective", "of", "changepoints", "." ]
def m66(current_skyline_app, parent_pid, timeseries, algorithm_parameters): """ A time series data points are anomalous if the 6th median is 6 standard deviations (six-sigma) from the time series 6th median standard deviation and persists for x_windows, where `x_windows = int(window / 2)`. This algorithm finds SIGNIFICANT cahngepoints in a time series, similar to PELT and Bayesian Online Changepoint Detection, however it is more robust to instaneous outliers and more conditionally selective of changepoints. :param current_skyline_app: the Skyline app executing the algorithm. This will be passed to the algorithm by Skyline. This is **required** for error handling and logging. You do not have to worry about handling the argument in the scope of the custom algorithm itself, but the algorithm must accept it as the first agrument. :param parent_pid: the parent pid which is executing the algorithm, this is **required** for error handling and logging. You do not have to worry about handling this argument in the scope of algorithm, but the algorithm must accept it as the second argument. :param timeseries: the time series as a list e.g. ``[[1578916800.0, 29.0], [1578920400.0, 55.0], ... [1580353200.0, 55.0]]`` :param algorithm_parameters: a dictionary of any required parameters for the custom_algorithm and algorithm itself for example: ``algorithm_parameters={ 'nth_median': 6, 'sigma': 6, 'window': 5, 'return_anomalies' = True, }`` :type current_skyline_app: str :type parent_pid: int :type timeseries: list :type algorithm_parameters: dict :return: True, False or Non :rtype: boolean Example CUSTOM_ALGORITHMS configuration: 'm66': { 'namespaces': [ 'skyline.analyzer.run_time', 'skyline.analyzer.total_metrics', 'skyline.analyzer.exceptions' ], 'algorithm_source': '/opt/skyline/github/skyline/skyline/custom_algorithms/m66.py', 'algorithm_parameters': { 'nth_median': 6, 'sigma': 6, 'window': 5, 'resolution': 60, 'minimum_sparsity': 0, 'determine_duration': False, 'return_anomalies': True, 'save_plots_to': False, 'save_plots_to_absolute_dir': False, 'filename_prefix': False }, 'max_execution_time': 1.0 'consensus': 1, 'algorithms_allowed_in_consensus': ['m66'], 'run_3sigma_algorithms': False, 'run_before_3sigma': False, 'run_only_if_consensus': False, 'use_with': ['crucible', 'luminosity'], 'debug_logging': False, }, """ # You MUST define the algorithm_name algorithm_name = 'm66' # Define the default state of None and None, anomalous does not default to # False as that is not correct, False is only correct if the algorithm # determines the data point is not anomalous. The same is true for the # anomalyScore. anomalous = None anomalyScore = None return_anomalies = False anomalies = [] anomalies_dict = {} anomalies_dict['algorithm'] = algorithm_name realtime_analysis = False current_logger = None dev_null = None # If you wanted to log, you can but this should only be done during # testing and development def get_log(current_skyline_app): current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger(current_skyline_app_logger) return current_logger start = timer() # Use the algorithm_parameters to determine the sample_period debug_logging = None try: debug_logging = algorithm_parameters['debug_logging'] except: debug_logging = False if debug_logging: try: current_logger = get_log(current_skyline_app) current_logger.debug('debug :: %s :: debug_logging enabled with algorithm_parameters - %s' % ( algorithm_name, str(algorithm_parameters))) except Exception as e: # This except pattern MUST be used in ALL custom algortihms to # facilitate the traceback from any errors. The algorithm we want to # run super fast and without spamming the log with lots of errors. # But we do not want the function returning and not reporting # anything to the log, so the pythonic except is used to "sample" any # algorithm errors to a tmp file and report once per run rather than # spewing tons of errors into the log e.g. analyzer.log dev_null = e record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) # Return None and None as the algorithm could not determine True or False del dev_null if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) # Allow the m66 parameters to be passed in the algorithm_parameters window = 6 try: window = algorithm_parameters['window'] except KeyError: window = 6 except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e nth_median = 6 try: nth_median = algorithm_parameters['nth_median'] except KeyError: nth_median = 6 except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e n_sigma = 6 try: n_sigma = algorithm_parameters['sigma'] except KeyError: n_sigma = 6 except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e resolution = 0 try: resolution = algorithm_parameters['resolution'] except KeyError: resolution = 0 except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e determine_duration = False try: determine_duration = algorithm_parameters['determine_duration'] except KeyError: determine_duration = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e minimum_sparsity = 0 try: minimum_sparsity = algorithm_parameters['minimum_sparsity'] except KeyError: minimum_sparsity = 0 except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e shift_to_start_of_window = True try: shift_to_start_of_window = algorithm_parameters['shift_to_start_of_window'] except KeyError: shift_to_start_of_window = True except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e save_plots_to = False try: save_plots_to = algorithm_parameters['save_plots_to'] except KeyError: save_plots_to = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e save_plots_to_absolute_dir = False try: save_plots_to_absolute_dir = algorithm_parameters['save_plots_to_absolute_dir'] except KeyError: save_plots_to_absolute_dir = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e filename_prefix = False try: filename_prefix = algorithm_parameters['filename_prefix'] except KeyError: filename_prefix = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e if debug_logging: current_logger.debug('debug :: algorithm_parameters :: %s' % ( str(algorithm_parameters))) return_anomalies = False try: return_anomalies = algorithm_parameters['return_anomalies'] except KeyError: return_anomalies = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e try: realtime_analysis = algorithm_parameters['realtime_analysis'] except KeyError: realtime_analysis = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e save_plots_to = False try: save_plots_to = algorithm_parameters['save_plots_to'] except KeyError: save_plots_to = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e save_plots_to_absolute_dir = False try: save_plots_to_absolute_dir = algorithm_parameters['save_plots_to_absolute_dir'] except KeyError: save_plots_to = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e filename_prefix = False try: filename_prefix = algorithm_parameters['filename_prefix'] except KeyError: filename_prefix = False except Exception as e: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) dev_null = e try: base_name = algorithm_parameters['base_name'] except Exception as e: # This except pattern MUST be used in ALL custom algortihms to # facilitate the traceback from any errors. The algorithm we want to # run super fast and without spamming the log with lots of errors. # But we do not want the function returning and not reporting # anything to the log, so the pythonic except is used to "sample" any # algorithm errors to a tmp file and report once per run rather than # spewing tons of errors into the log e.g. analyzer.log record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) # Return None and None as the algorithm could not determine True or False dev_null = e del dev_null if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (False, None, anomalies) return (False, None) if debug_logging: current_logger.debug('debug :: %s :: base_name - %s' % ( algorithm_name, str(base_name))) anomalies_dict['metric'] = base_name anomalies_dict['anomalies'] = {} use_bottleneck = True if save_plots_to: use_bottleneck = False if use_bottleneck: import bottleneck as bn # ALWAYS WRAP YOUR ALGORITHM IN try and the BELOW except try: start_preprocessing = timer() # INFO: Sorting time series of 10079 data points took 0.002215 seconds timeseries = sorted(timeseries, key=lambda x: x[0]) if debug_logging: current_logger.debug('debug :: %s :: time series of length - %s' % ( algorithm_name, str(len(timeseries)))) # Testing the data to ensure it meets minimum requirements, in the case # of Skyline's use of the m66 algorithm this means that: # - the time series must have at least 75% of its full_duration do_not_use_sparse_data = False if current_skyline_app == 'luminosity': do_not_use_sparse_data = True if minimum_sparsity == 0: do_not_use_sparse_data = False total_period = 0 total_datapoints = 0 calculate_variables = False if do_not_use_sparse_data: calculate_variables = True if determine_duration: calculate_variables = True if calculate_variables: try: start_timestamp = int(timeseries[0][0]) end_timestamp = int(timeseries[-1][0]) total_period = end_timestamp - start_timestamp total_datapoints = len(timeseries) except SystemExit as e: if debug_logging: current_logger.debug('debug_logging :: %s :: SystemExit called, exiting - %s' % ( algorithm_name, e)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except: traceback_msg = traceback.format_exc() record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback_msg) if debug_logging: current_logger.error(traceback_msg) current_logger.error('error :: debug_logging :: %s :: failed to determine total_period and total_datapoints' % ( algorithm_name)) timeseries = [] if not timeseries: if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) if current_skyline_app == 'analyzer': # Default for analyzer at required period to 18 hours period_required = int(FULL_DURATION * 0.75) else: # Determine from timeseries if total_period < FULL_DURATION: period_required = int(FULL_DURATION * 0.75) else: period_required = int(total_period * 0.75) if determine_duration: period_required = int(total_period * 0.75) if do_not_use_sparse_data: # If the time series does not have 75% of its full_duration it does # not have sufficient data to sample try: if total_period < period_required: if debug_logging: current_logger.debug('debug :: %s :: time series does not have sufficient data' % ( algorithm_name)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except SystemExit as e: if debug_logging: current_logger.debug('debug_logging :: %s :: SystemExit called, exiting - %s' % ( algorithm_name, e)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except: traceback_msg = traceback.format_exc() record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback_msg) if debug_logging: current_logger.error(traceback_msg) current_logger.error('error :: debug_logging :: %s :: falied to determine if time series has sufficient data' % ( algorithm_name)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) # If the time series does not have 75% of its full_duration # datapoints it does not have sufficient data to sample # Determine resolution from the last 30 data points # INFO took 0.002060 seconds if not resolution: resolution_timestamps = [] metric_resolution = False for metric_datapoint in timeseries[-30:]: timestamp = int(metric_datapoint[0]) resolution_timestamps.append(timestamp) timestamp_resolutions = [] if resolution_timestamps: last_timestamp = None for timestamp in resolution_timestamps: if last_timestamp: resolution = timestamp - last_timestamp timestamp_resolutions.append(resolution) last_timestamp = timestamp else: last_timestamp = timestamp try: del resolution_timestamps except: pass if timestamp_resolutions: try: timestamp_resolutions_count = Counter(timestamp_resolutions) ordered_timestamp_resolutions_count = timestamp_resolutions_count.most_common() metric_resolution = int(ordered_timestamp_resolutions_count[0][0]) except SystemExit as e: if debug_logging: current_logger.debug('debug_logging :: %s :: SystemExit called, exiting - %s' % ( algorithm_name, e)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except: traceback_msg = traceback.format_exc() record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback_msg) if debug_logging: current_logger.error(traceback_msg) current_logger.error('error :: debug_logging :: %s :: failed to determine if time series has sufficient data' % ( algorithm_name)) try: del timestamp_resolutions except: pass else: metric_resolution = resolution minimum_datapoints = None if metric_resolution: minimum_datapoints = int(period_required / metric_resolution) if minimum_datapoints: if total_datapoints < minimum_datapoints: if debug_logging: current_logger.debug('debug :: %s :: time series does not have sufficient data, minimum_datapoints required is %s and time series has %s' % ( algorithm_name, str(minimum_datapoints), str(total_datapoints))) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) # Is the time series fully populated? # full_duration_datapoints = int(full_duration / metric_resolution) total_period_datapoints = int(total_period / metric_resolution) # minimum_percentage_sparsity = 95 minimum_percentage_sparsity = 90 sparsity = int(total_datapoints / (total_period_datapoints / 100)) if sparsity < minimum_percentage_sparsity: if debug_logging: current_logger.debug('debug :: %s :: time series does not have sufficient data, minimum_percentage_sparsity required is %s and time series has %s' % ( algorithm_name, str(minimum_percentage_sparsity), str(sparsity))) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) if len(set(item[1] for item in timeseries)) == 1: if debug_logging: current_logger.debug('debug :: %s :: time series does not have sufficient variability, all the values are the same' % algorithm_name) anomalous = False anomalyScore = 0.0 if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) end_preprocessing = timer() preprocessing_runtime = end_preprocessing - start_preprocessing if debug_logging: current_logger.debug('debug :: %s :: preprocessing took %.6f seconds' % ( algorithm_name, preprocessing_runtime)) if not timeseries: if debug_logging: current_logger.debug('debug :: %s :: m66 not run as no data' % ( algorithm_name)) anomalies = [] if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) if debug_logging: current_logger.debug('debug :: %s :: timeseries length: %s' % ( algorithm_name, str(len(timeseries)))) anomalies_dict['timestamp'] = int(timeseries[-1][0]) anomalies_dict['from_timestamp'] = int(timeseries[0][0]) start_analysis = timer() try: # bottleneck is used because it is much faster # pd dataframe method (1445 data point - 24hrs): took 0.077915 seconds # bottleneck method (1445 data point - 24hrs): took 0.005692 seconds # numpy and pandas rolling # 2021-07-30 12:37:31 :: 2827897 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 136.93 seconds # 2021-07-30 12:44:53 :: 2855884 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 148.82 seconds # 2021-07-30 12:48:41 :: 2870822 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 145.62 seconds # 2021-07-30 12:55:00 :: 2893634 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 139.00 seconds # 2021-07-30 12:59:31 :: 2910443 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 144.80 seconds # 2021-07-30 13:02:31 :: 2922928 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 143.35 seconds # 2021-07-30 14:12:56 :: 3132457 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 129.25 seconds # 2021-07-30 14:22:35 :: 3164370 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 125.72 seconds # 2021-07-30 14:28:24 :: 3179687 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 222.43 seconds # 2021-07-30 14:33:45 :: 3179687 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 244.00 seconds # 2021-07-30 14:36:27 :: 3214047 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 141.10 seconds # numpy and bottleneck # 2021-07-30 16:41:52 :: 3585162 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 73.92 seconds # 2021-07-30 16:46:46 :: 3585162 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 68.84 seconds # 2021-07-30 16:51:48 :: 3585162 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 70.55 seconds # numpy and bottleneck (passing resolution and not calculating in m66) # 2021-07-30 16:57:46 :: 3643253 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 65.59 seconds if use_bottleneck: if len(timeseries) < 10: if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) x_np = np.asarray([x[1] for x in timeseries]) # Fast Min-Max scaling data = (x_np - x_np.min()) / (x_np.max() - x_np.min()) # m66 - calculate to nth_median median_count = 0 while median_count < nth_median: median_count += 1 rolling_median_s = bn.move_median(data, window=window) median = rolling_median_s.tolist() data = median if median_count == nth_median: break # m66 - calculate the moving standard deviation for the # nth_median array rolling_std_s = bn.move_std(data, window=window) std_nth_median_array = np.nan_to_num(rolling_std_s, copy=False, nan=0.0, posinf=None, neginf=None) std_nth_median = std_nth_median_array.tolist() if debug_logging: current_logger.debug('debug :: %s :: std_nth_median calculated with bn' % ( algorithm_name)) else: df = pd.DataFrame(timeseries, columns=['date', 'value']) df['date'] = pd.to_datetime(df['date'], unit='s') datetime_index = pd.DatetimeIndex(df['date'].values) df = df.set_index(datetime_index) df.drop('date', axis=1, inplace=True) original_df = df.copy() # MinMax scale df = (df - df.min()) / (df.max() - df.min()) # window = 6 data = df['value'].tolist() if len(data) < 10: if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) # m66 - calculate to nth_median median_count = 0 while median_count < nth_median: median_count += 1 s = pd.Series(data) rolling_median_s = s.rolling(window).median() median = rolling_median_s.tolist() data = median if median_count == nth_median: break # m66 - calculate the moving standard deviation for the # nth_median array s = pd.Series(data) rolling_std_s = s.rolling(window).std() nth_median_column = 'std_nth_median_%s' % str(nth_median) df[nth_median_column] = rolling_std_s.tolist() std_nth_median = df[nth_median_column].fillna(0).tolist() # m66 - calculate the standard deviation for the entire nth_median # array metric_stddev = np.std(std_nth_median) std_nth_median_n_sigma = [] anomalies_found = False for value in std_nth_median: # m66 - if the value in the 6th median array is > six-sigma of # the metric_stddev the datapoint is anomalous if value > (metric_stddev * n_sigma): std_nth_median_n_sigma.append(1) anomalies_found = True else: std_nth_median_n_sigma.append(0) std_nth_median_n_sigma_column = 'std_median_%s_%s_sigma' % (str(nth_median), str(n_sigma)) if not use_bottleneck: df[std_nth_median_n_sigma_column] = std_nth_median_n_sigma anomalies = [] # m66 - only label anomalous if the n_sigma triggers are persisted # for (window / 2) if anomalies_found: current_triggers = [] for index, item in enumerate(timeseries): if std_nth_median_n_sigma[index] == 1: current_triggers.append(index) else: if len(current_triggers) > int(window / 2): for trigger_index in current_triggers: # Shift the anomaly back to the beginning of the # window if shift_to_start_of_window: anomalies.append(timeseries[(trigger_index - (window * int((nth_median / 2))))]) else: anomalies.append(timeseries[trigger_index]) current_triggers = [] # Process any remaining current_triggers if len(current_triggers) > int(window / 2): for trigger_index in current_triggers: # Shift the anomaly back to the beginning of the # window if shift_to_start_of_window: anomalies.append(timeseries[(trigger_index - (window * int((nth_median / 2))))]) else: anomalies.append(timeseries[trigger_index]) if not anomalies: anomalous = False if anomalies: anomalous = True anomalies_data = [] anomaly_timestamps = [int(item[0]) for item in anomalies] for item in timeseries: if int(item[0]) in anomaly_timestamps: anomalies_data.append(1) else: anomalies_data.append(0) if not use_bottleneck: df['anomalies'] = anomalies_data anomalies_list = [] for ts, value in timeseries: if int(ts) in anomaly_timestamps: anomalies_list.append([int(ts), value]) anomalies_dict['anomalies'][int(ts)] = value if anomalies and save_plots_to: try: from adtk.visualization import plot metric_dir = base_name.replace('.', '/') timestamp_dir = str(int(timeseries[-1][0])) save_path = '%s/%s/%s/%s' % ( save_plots_to, algorithm_name, metric_dir, timestamp_dir) if save_plots_to_absolute_dir: save_path = '%s' % save_plots_to anomalies_dict['file_path'] = save_path save_to_file = '%s/%s.%s.png' % ( save_path, algorithm_name, base_name) if filename_prefix: save_to_file = '%s/%s.%s.%s.png' % ( save_path, filename_prefix, algorithm_name, base_name) save_to_path = os_path_dirname(save_to_file) title = '%s\n%s - median %s %s-sigma persisted (window=%s)' % ( base_name, algorithm_name, str(nth_median), str(n_sigma), str(window)) if not os_path_exists(save_to_path): try: mkdir_p(save_to_path) except Exception as e: current_logger.error('error :: %s :: failed to create dir - %s - %s' % ( algorithm_name, save_to_path, e)) if os_path_exists(save_to_path): try: plot(original_df['value'], anomaly=df['anomalies'], anomaly_color='red', title=title, save_to_file=save_to_file) if debug_logging: current_logger.debug('debug :: %s :: plot saved to - %s' % ( algorithm_name, save_to_file)) anomalies_dict['image'] = save_to_file except Exception as e: current_logger.error('error :: %s :: failed to plot - %s - %s' % ( algorithm_name, base_name, e)) anomalies_file = '%s/%s.%s.anomalies_list.txt' % ( save_path, algorithm_name, base_name) with open(anomalies_file, 'w') as fh: fh.write(str(anomalies_list)) # os.chmod(anomalies_file, mode=0o644) data_file = '%s/data.txt' % (save_path) with open(data_file, 'w') as fh: fh.write(str(anomalies_dict)) except SystemExit as e: if debug_logging: current_logger.debug('debug_logging :: %s :: SystemExit called during save plot, exiting - %s' % ( algorithm_name, e)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except Exception as e: traceback_msg = traceback.format_exc() record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback_msg) if debug_logging: current_logger.error(traceback_msg) current_logger.error('error :: %s :: failed to plot or save anomalies file - %s - %s' % ( algorithm_name, base_name, e)) try: del df except: pass except SystemExit as e: if debug_logging: current_logger.debug('debug_logging :: %s :: SystemExit called, during analysis, exiting - %s' % ( algorithm_name, e)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except: traceback_msg = traceback.format_exc() record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback_msg) if debug_logging: current_logger.error(traceback_msg) current_logger.error('error :: debug_logging :: %s :: failed to run on ts' % ( algorithm_name)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) end_analysis = timer() analysis_runtime = end_analysis - start_analysis if debug_logging: current_logger.debug('debug :: analysis with %s took %.6f seconds' % ( algorithm_name, analysis_runtime)) if anomalous: anomalyScore = 1.0 else: anomalyScore = 0.0 if debug_logging: current_logger.info('%s :: anomalous - %s, anomalyScore - %s' % ( algorithm_name, str(anomalous), str(anomalyScore))) if debug_logging: end = timer() processing_runtime = end - start current_logger.info('%s :: completed in %.6f seconds' % ( algorithm_name, processing_runtime)) try: del timeseries except: pass if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except SystemExit as e: if debug_logging: current_logger.debug('debug_logging :: %s :: SystemExit called (before StopIteration), exiting - %s' % ( algorithm_name, e)) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore) except StopIteration: # This except pattern MUST be used in ALL custom algortihms to # facilitate the traceback from any errors. The algorithm we want to # run super fast and without spamming the log with lots of errors. # But we do not want the function returning and not reporting # anything to the log, so the pythonic except is used to "sample" any # algorithm errors to a tmp file and report once per run rather than # spewing tons of errors into the log e.g. analyzer.log if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (False, None, anomalies) return (False, None) except: record_algorithm_error(current_skyline_app, parent_pid, algorithm_name, traceback.format_exc()) # Return None and None as the algorithm could not determine True or False if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (False, None, anomalies) return (False, None) if current_skyline_app == 'webapp': return (anomalous, anomalyScore, anomalies, anomalies_dict) if return_anomalies: return (anomalous, anomalyScore, anomalies) return (anomalous, anomalyScore)
[ "def", "m66", "(", "current_skyline_app", ",", "parent_pid", ",", "timeseries", ",", "algorithm_parameters", ")", ":", "# You MUST define the algorithm_name", "algorithm_name", "=", "'m66'", "# Define the default state of None and None, anomalous does not default to", "# False as that is not correct, False is only correct if the algorithm", "# determines the data point is not anomalous. The same is true for the", "# anomalyScore.", "anomalous", "=", "None", "anomalyScore", "=", "None", "return_anomalies", "=", "False", "anomalies", "=", "[", "]", "anomalies_dict", "=", "{", "}", "anomalies_dict", "[", "'algorithm'", "]", "=", "algorithm_name", "realtime_analysis", "=", "False", "current_logger", "=", "None", "dev_null", "=", "None", "# If you wanted to log, you can but this should only be done during", "# testing and development", "def", "get_log", "(", "current_skyline_app", ")", ":", "current_skyline_app_logger", "=", "current_skyline_app", "+", "'Log'", "current_logger", "=", "logging", ".", "getLogger", "(", "current_skyline_app_logger", ")", "return", "current_logger", "start", "=", "timer", "(", ")", "# Use the algorithm_parameters to determine the sample_period", "debug_logging", "=", "None", "try", ":", "debug_logging", "=", "algorithm_parameters", "[", "'debug_logging'", "]", "except", ":", "debug_logging", "=", "False", "if", "debug_logging", ":", "try", ":", "current_logger", "=", "get_log", "(", "current_skyline_app", ")", "current_logger", ".", "debug", "(", "'debug :: %s :: debug_logging enabled with algorithm_parameters - %s'", "%", "(", "algorithm_name", ",", "str", "(", "algorithm_parameters", ")", ")", ")", "except", "Exception", "as", "e", ":", "# This except pattern MUST be used in ALL custom algortihms to", "# facilitate the traceback from any errors. The algorithm we want to", "# run super fast and without spamming the log with lots of errors.", "# But we do not want the function returning and not reporting", "# anything to the log, so the pythonic except is used to \"sample\" any", "# algorithm errors to a tmp file and report once per run rather than", "# spewing tons of errors into the log e.g. analyzer.log", "dev_null", "=", "e", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "# Return None and None as the algorithm could not determine True or False", "del", "dev_null", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "# Allow the m66 parameters to be passed in the algorithm_parameters", "window", "=", "6", "try", ":", "window", "=", "algorithm_parameters", "[", "'window'", "]", "except", "KeyError", ":", "window", "=", "6", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "nth_median", "=", "6", "try", ":", "nth_median", "=", "algorithm_parameters", "[", "'nth_median'", "]", "except", "KeyError", ":", "nth_median", "=", "6", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "n_sigma", "=", "6", "try", ":", "n_sigma", "=", "algorithm_parameters", "[", "'sigma'", "]", "except", "KeyError", ":", "n_sigma", "=", "6", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "resolution", "=", "0", "try", ":", "resolution", "=", "algorithm_parameters", "[", "'resolution'", "]", "except", "KeyError", ":", "resolution", "=", "0", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "determine_duration", "=", "False", "try", ":", "determine_duration", "=", "algorithm_parameters", "[", "'determine_duration'", "]", "except", "KeyError", ":", "determine_duration", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "minimum_sparsity", "=", "0", "try", ":", "minimum_sparsity", "=", "algorithm_parameters", "[", "'minimum_sparsity'", "]", "except", "KeyError", ":", "minimum_sparsity", "=", "0", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "shift_to_start_of_window", "=", "True", "try", ":", "shift_to_start_of_window", "=", "algorithm_parameters", "[", "'shift_to_start_of_window'", "]", "except", "KeyError", ":", "shift_to_start_of_window", "=", "True", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "save_plots_to", "=", "False", "try", ":", "save_plots_to", "=", "algorithm_parameters", "[", "'save_plots_to'", "]", "except", "KeyError", ":", "save_plots_to", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "save_plots_to_absolute_dir", "=", "False", "try", ":", "save_plots_to_absolute_dir", "=", "algorithm_parameters", "[", "'save_plots_to_absolute_dir'", "]", "except", "KeyError", ":", "save_plots_to_absolute_dir", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "filename_prefix", "=", "False", "try", ":", "filename_prefix", "=", "algorithm_parameters", "[", "'filename_prefix'", "]", "except", "KeyError", ":", "filename_prefix", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: algorithm_parameters :: %s'", "%", "(", "str", "(", "algorithm_parameters", ")", ")", ")", "return_anomalies", "=", "False", "try", ":", "return_anomalies", "=", "algorithm_parameters", "[", "'return_anomalies'", "]", "except", "KeyError", ":", "return_anomalies", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "try", ":", "realtime_analysis", "=", "algorithm_parameters", "[", "'realtime_analysis'", "]", "except", "KeyError", ":", "realtime_analysis", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "save_plots_to", "=", "False", "try", ":", "save_plots_to", "=", "algorithm_parameters", "[", "'save_plots_to'", "]", "except", "KeyError", ":", "save_plots_to", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "save_plots_to_absolute_dir", "=", "False", "try", ":", "save_plots_to_absolute_dir", "=", "algorithm_parameters", "[", "'save_plots_to_absolute_dir'", "]", "except", "KeyError", ":", "save_plots_to", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "filename_prefix", "=", "False", "try", ":", "filename_prefix", "=", "algorithm_parameters", "[", "'filename_prefix'", "]", "except", "KeyError", ":", "filename_prefix", "=", "False", "except", "Exception", "as", "e", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "dev_null", "=", "e", "try", ":", "base_name", "=", "algorithm_parameters", "[", "'base_name'", "]", "except", "Exception", "as", "e", ":", "# This except pattern MUST be used in ALL custom algortihms to", "# facilitate the traceback from any errors. The algorithm we want to", "# run super fast and without spamming the log with lots of errors.", "# But we do not want the function returning and not reporting", "# anything to the log, so the pythonic except is used to \"sample\" any", "# algorithm errors to a tmp file and report once per run rather than", "# spewing tons of errors into the log e.g. analyzer.log", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "# Return None and None as the algorithm could not determine True or False", "dev_null", "=", "e", "del", "dev_null", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "False", ",", "None", ",", "anomalies", ")", "return", "(", "False", ",", "None", ")", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: base_name - %s'", "%", "(", "algorithm_name", ",", "str", "(", "base_name", ")", ")", ")", "anomalies_dict", "[", "'metric'", "]", "=", "base_name", "anomalies_dict", "[", "'anomalies'", "]", "=", "{", "}", "use_bottleneck", "=", "True", "if", "save_plots_to", ":", "use_bottleneck", "=", "False", "if", "use_bottleneck", ":", "import", "bottleneck", "as", "bn", "# ALWAYS WRAP YOUR ALGORITHM IN try and the BELOW except", "try", ":", "start_preprocessing", "=", "timer", "(", ")", "# INFO: Sorting time series of 10079 data points took 0.002215 seconds", "timeseries", "=", "sorted", "(", "timeseries", ",", "key", "=", "lambda", "x", ":", "x", "[", "0", "]", ")", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: time series of length - %s'", "%", "(", "algorithm_name", ",", "str", "(", "len", "(", "timeseries", ")", ")", ")", ")", "# Testing the data to ensure it meets minimum requirements, in the case", "# of Skyline's use of the m66 algorithm this means that:", "# - the time series must have at least 75% of its full_duration", "do_not_use_sparse_data", "=", "False", "if", "current_skyline_app", "==", "'luminosity'", ":", "do_not_use_sparse_data", "=", "True", "if", "minimum_sparsity", "==", "0", ":", "do_not_use_sparse_data", "=", "False", "total_period", "=", "0", "total_datapoints", "=", "0", "calculate_variables", "=", "False", "if", "do_not_use_sparse_data", ":", "calculate_variables", "=", "True", "if", "determine_duration", ":", "calculate_variables", "=", "True", "if", "calculate_variables", ":", "try", ":", "start_timestamp", "=", "int", "(", "timeseries", "[", "0", "]", "[", "0", "]", ")", "end_timestamp", "=", "int", "(", "timeseries", "[", "-", "1", "]", "[", "0", "]", ")", "total_period", "=", "end_timestamp", "-", "start_timestamp", "total_datapoints", "=", "len", "(", "timeseries", ")", "except", "SystemExit", "as", "e", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug_logging :: %s :: SystemExit called, exiting - %s'", "%", "(", "algorithm_name", ",", "e", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", ":", "traceback_msg", "=", "traceback", ".", "format_exc", "(", ")", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback_msg", ")", "if", "debug_logging", ":", "current_logger", ".", "error", "(", "traceback_msg", ")", "current_logger", ".", "error", "(", "'error :: debug_logging :: %s :: failed to determine total_period and total_datapoints'", "%", "(", "algorithm_name", ")", ")", "timeseries", "=", "[", "]", "if", "not", "timeseries", ":", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "if", "current_skyline_app", "==", "'analyzer'", ":", "# Default for analyzer at required period to 18 hours", "period_required", "=", "int", "(", "FULL_DURATION", "*", "0.75", ")", "else", ":", "# Determine from timeseries", "if", "total_period", "<", "FULL_DURATION", ":", "period_required", "=", "int", "(", "FULL_DURATION", "*", "0.75", ")", "else", ":", "period_required", "=", "int", "(", "total_period", "*", "0.75", ")", "if", "determine_duration", ":", "period_required", "=", "int", "(", "total_period", "*", "0.75", ")", "if", "do_not_use_sparse_data", ":", "# If the time series does not have 75% of its full_duration it does", "# not have sufficient data to sample", "try", ":", "if", "total_period", "<", "period_required", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: time series does not have sufficient data'", "%", "(", "algorithm_name", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", "SystemExit", "as", "e", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug_logging :: %s :: SystemExit called, exiting - %s'", "%", "(", "algorithm_name", ",", "e", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", ":", "traceback_msg", "=", "traceback", ".", "format_exc", "(", ")", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback_msg", ")", "if", "debug_logging", ":", "current_logger", ".", "error", "(", "traceback_msg", ")", "current_logger", ".", "error", "(", "'error :: debug_logging :: %s :: falied to determine if time series has sufficient data'", "%", "(", "algorithm_name", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "# If the time series does not have 75% of its full_duration", "# datapoints it does not have sufficient data to sample", "# Determine resolution from the last 30 data points", "# INFO took 0.002060 seconds", "if", "not", "resolution", ":", "resolution_timestamps", "=", "[", "]", "metric_resolution", "=", "False", "for", "metric_datapoint", "in", "timeseries", "[", "-", "30", ":", "]", ":", "timestamp", "=", "int", "(", "metric_datapoint", "[", "0", "]", ")", "resolution_timestamps", ".", "append", "(", "timestamp", ")", "timestamp_resolutions", "=", "[", "]", "if", "resolution_timestamps", ":", "last_timestamp", "=", "None", "for", "timestamp", "in", "resolution_timestamps", ":", "if", "last_timestamp", ":", "resolution", "=", "timestamp", "-", "last_timestamp", "timestamp_resolutions", ".", "append", "(", "resolution", ")", "last_timestamp", "=", "timestamp", "else", ":", "last_timestamp", "=", "timestamp", "try", ":", "del", "resolution_timestamps", "except", ":", "pass", "if", "timestamp_resolutions", ":", "try", ":", "timestamp_resolutions_count", "=", "Counter", "(", "timestamp_resolutions", ")", "ordered_timestamp_resolutions_count", "=", "timestamp_resolutions_count", ".", "most_common", "(", ")", "metric_resolution", "=", "int", "(", "ordered_timestamp_resolutions_count", "[", "0", "]", "[", "0", "]", ")", "except", "SystemExit", "as", "e", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug_logging :: %s :: SystemExit called, exiting - %s'", "%", "(", "algorithm_name", ",", "e", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", ":", "traceback_msg", "=", "traceback", ".", "format_exc", "(", ")", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback_msg", ")", "if", "debug_logging", ":", "current_logger", ".", "error", "(", "traceback_msg", ")", "current_logger", ".", "error", "(", "'error :: debug_logging :: %s :: failed to determine if time series has sufficient data'", "%", "(", "algorithm_name", ")", ")", "try", ":", "del", "timestamp_resolutions", "except", ":", "pass", "else", ":", "metric_resolution", "=", "resolution", "minimum_datapoints", "=", "None", "if", "metric_resolution", ":", "minimum_datapoints", "=", "int", "(", "period_required", "/", "metric_resolution", ")", "if", "minimum_datapoints", ":", "if", "total_datapoints", "<", "minimum_datapoints", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: time series does not have sufficient data, minimum_datapoints required is %s and time series has %s'", "%", "(", "algorithm_name", ",", "str", "(", "minimum_datapoints", ")", ",", "str", "(", "total_datapoints", ")", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "# Is the time series fully populated?", "# full_duration_datapoints = int(full_duration / metric_resolution)", "total_period_datapoints", "=", "int", "(", "total_period", "/", "metric_resolution", ")", "# minimum_percentage_sparsity = 95", "minimum_percentage_sparsity", "=", "90", "sparsity", "=", "int", "(", "total_datapoints", "/", "(", "total_period_datapoints", "/", "100", ")", ")", "if", "sparsity", "<", "minimum_percentage_sparsity", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: time series does not have sufficient data, minimum_percentage_sparsity required is %s and time series has %s'", "%", "(", "algorithm_name", ",", "str", "(", "minimum_percentage_sparsity", ")", ",", "str", "(", "sparsity", ")", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "if", "len", "(", "set", "(", "item", "[", "1", "]", "for", "item", "in", "timeseries", ")", ")", "==", "1", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: time series does not have sufficient variability, all the values are the same'", "%", "algorithm_name", ")", "anomalous", "=", "False", "anomalyScore", "=", "0.0", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "end_preprocessing", "=", "timer", "(", ")", "preprocessing_runtime", "=", "end_preprocessing", "-", "start_preprocessing", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: preprocessing took %.6f seconds'", "%", "(", "algorithm_name", ",", "preprocessing_runtime", ")", ")", "if", "not", "timeseries", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: m66 not run as no data'", "%", "(", "algorithm_name", ")", ")", "anomalies", "=", "[", "]", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: timeseries length: %s'", "%", "(", "algorithm_name", ",", "str", "(", "len", "(", "timeseries", ")", ")", ")", ")", "anomalies_dict", "[", "'timestamp'", "]", "=", "int", "(", "timeseries", "[", "-", "1", "]", "[", "0", "]", ")", "anomalies_dict", "[", "'from_timestamp'", "]", "=", "int", "(", "timeseries", "[", "0", "]", "[", "0", "]", ")", "start_analysis", "=", "timer", "(", ")", "try", ":", "# bottleneck is used because it is much faster", "# pd dataframe method (1445 data point - 24hrs): took 0.077915 seconds", "# bottleneck method (1445 data point - 24hrs): took 0.005692 seconds", "# numpy and pandas rolling", "# 2021-07-30 12:37:31 :: 2827897 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 136.93 seconds", "# 2021-07-30 12:44:53 :: 2855884 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 148.82 seconds", "# 2021-07-30 12:48:41 :: 2870822 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 145.62 seconds", "# 2021-07-30 12:55:00 :: 2893634 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 139.00 seconds", "# 2021-07-30 12:59:31 :: 2910443 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 144.80 seconds", "# 2021-07-30 13:02:31 :: 2922928 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 143.35 seconds", "# 2021-07-30 14:12:56 :: 3132457 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 129.25 seconds", "# 2021-07-30 14:22:35 :: 3164370 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 125.72 seconds", "# 2021-07-30 14:28:24 :: 3179687 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 222.43 seconds", "# 2021-07-30 14:33:45 :: 3179687 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 244.00 seconds", "# 2021-07-30 14:36:27 :: 3214047 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 141.10 seconds", "# numpy and bottleneck", "# 2021-07-30 16:41:52 :: 3585162 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 73.92 seconds", "# 2021-07-30 16:46:46 :: 3585162 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 68.84 seconds", "# 2021-07-30 16:51:48 :: 3585162 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 70.55 seconds", "# numpy and bottleneck (passing resolution and not calculating in m66)", "# 2021-07-30 16:57:46 :: 3643253 :: cloudbursts :: find_cloudbursts completed on 1530 metrics in 65.59 seconds", "if", "use_bottleneck", ":", "if", "len", "(", "timeseries", ")", "<", "10", ":", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "x_np", "=", "np", ".", "asarray", "(", "[", "x", "[", "1", "]", "for", "x", "in", "timeseries", "]", ")", "# Fast Min-Max scaling", "data", "=", "(", "x_np", "-", "x_np", ".", "min", "(", ")", ")", "/", "(", "x_np", ".", "max", "(", ")", "-", "x_np", ".", "min", "(", ")", ")", "# m66 - calculate to nth_median", "median_count", "=", "0", "while", "median_count", "<", "nth_median", ":", "median_count", "+=", "1", "rolling_median_s", "=", "bn", ".", "move_median", "(", "data", ",", "window", "=", "window", ")", "median", "=", "rolling_median_s", ".", "tolist", "(", ")", "data", "=", "median", "if", "median_count", "==", "nth_median", ":", "break", "# m66 - calculate the moving standard deviation for the", "# nth_median array", "rolling_std_s", "=", "bn", ".", "move_std", "(", "data", ",", "window", "=", "window", ")", "std_nth_median_array", "=", "np", ".", "nan_to_num", "(", "rolling_std_s", ",", "copy", "=", "False", ",", "nan", "=", "0.0", ",", "posinf", "=", "None", ",", "neginf", "=", "None", ")", "std_nth_median", "=", "std_nth_median_array", ".", "tolist", "(", ")", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: std_nth_median calculated with bn'", "%", "(", "algorithm_name", ")", ")", "else", ":", "df", "=", "pd", ".", "DataFrame", "(", "timeseries", ",", "columns", "=", "[", "'date'", ",", "'value'", "]", ")", "df", "[", "'date'", "]", "=", "pd", ".", "to_datetime", "(", "df", "[", "'date'", "]", ",", "unit", "=", "'s'", ")", "datetime_index", "=", "pd", ".", "DatetimeIndex", "(", "df", "[", "'date'", "]", ".", "values", ")", "df", "=", "df", ".", "set_index", "(", "datetime_index", ")", "df", ".", "drop", "(", "'date'", ",", "axis", "=", "1", ",", "inplace", "=", "True", ")", "original_df", "=", "df", ".", "copy", "(", ")", "# MinMax scale", "df", "=", "(", "df", "-", "df", ".", "min", "(", ")", ")", "/", "(", "df", ".", "max", "(", ")", "-", "df", ".", "min", "(", ")", ")", "# window = 6", "data", "=", "df", "[", "'value'", "]", ".", "tolist", "(", ")", "if", "len", "(", "data", ")", "<", "10", ":", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "# m66 - calculate to nth_median", "median_count", "=", "0", "while", "median_count", "<", "nth_median", ":", "median_count", "+=", "1", "s", "=", "pd", ".", "Series", "(", "data", ")", "rolling_median_s", "=", "s", ".", "rolling", "(", "window", ")", ".", "median", "(", ")", "median", "=", "rolling_median_s", ".", "tolist", "(", ")", "data", "=", "median", "if", "median_count", "==", "nth_median", ":", "break", "# m66 - calculate the moving standard deviation for the", "# nth_median array", "s", "=", "pd", ".", "Series", "(", "data", ")", "rolling_std_s", "=", "s", ".", "rolling", "(", "window", ")", ".", "std", "(", ")", "nth_median_column", "=", "'std_nth_median_%s'", "%", "str", "(", "nth_median", ")", "df", "[", "nth_median_column", "]", "=", "rolling_std_s", ".", "tolist", "(", ")", "std_nth_median", "=", "df", "[", "nth_median_column", "]", ".", "fillna", "(", "0", ")", ".", "tolist", "(", ")", "# m66 - calculate the standard deviation for the entire nth_median", "# array", "metric_stddev", "=", "np", ".", "std", "(", "std_nth_median", ")", "std_nth_median_n_sigma", "=", "[", "]", "anomalies_found", "=", "False", "for", "value", "in", "std_nth_median", ":", "# m66 - if the value in the 6th median array is > six-sigma of", "# the metric_stddev the datapoint is anomalous", "if", "value", ">", "(", "metric_stddev", "*", "n_sigma", ")", ":", "std_nth_median_n_sigma", ".", "append", "(", "1", ")", "anomalies_found", "=", "True", "else", ":", "std_nth_median_n_sigma", ".", "append", "(", "0", ")", "std_nth_median_n_sigma_column", "=", "'std_median_%s_%s_sigma'", "%", "(", "str", "(", "nth_median", ")", ",", "str", "(", "n_sigma", ")", ")", "if", "not", "use_bottleneck", ":", "df", "[", "std_nth_median_n_sigma_column", "]", "=", "std_nth_median_n_sigma", "anomalies", "=", "[", "]", "# m66 - only label anomalous if the n_sigma triggers are persisted", "# for (window / 2)", "if", "anomalies_found", ":", "current_triggers", "=", "[", "]", "for", "index", ",", "item", "in", "enumerate", "(", "timeseries", ")", ":", "if", "std_nth_median_n_sigma", "[", "index", "]", "==", "1", ":", "current_triggers", ".", "append", "(", "index", ")", "else", ":", "if", "len", "(", "current_triggers", ")", ">", "int", "(", "window", "/", "2", ")", ":", "for", "trigger_index", "in", "current_triggers", ":", "# Shift the anomaly back to the beginning of the", "# window", "if", "shift_to_start_of_window", ":", "anomalies", ".", "append", "(", "timeseries", "[", "(", "trigger_index", "-", "(", "window", "*", "int", "(", "(", "nth_median", "/", "2", ")", ")", ")", ")", "]", ")", "else", ":", "anomalies", ".", "append", "(", "timeseries", "[", "trigger_index", "]", ")", "current_triggers", "=", "[", "]", "# Process any remaining current_triggers", "if", "len", "(", "current_triggers", ")", ">", "int", "(", "window", "/", "2", ")", ":", "for", "trigger_index", "in", "current_triggers", ":", "# Shift the anomaly back to the beginning of the", "# window", "if", "shift_to_start_of_window", ":", "anomalies", ".", "append", "(", "timeseries", "[", "(", "trigger_index", "-", "(", "window", "*", "int", "(", "(", "nth_median", "/", "2", ")", ")", ")", ")", "]", ")", "else", ":", "anomalies", ".", "append", "(", "timeseries", "[", "trigger_index", "]", ")", "if", "not", "anomalies", ":", "anomalous", "=", "False", "if", "anomalies", ":", "anomalous", "=", "True", "anomalies_data", "=", "[", "]", "anomaly_timestamps", "=", "[", "int", "(", "item", "[", "0", "]", ")", "for", "item", "in", "anomalies", "]", "for", "item", "in", "timeseries", ":", "if", "int", "(", "item", "[", "0", "]", ")", "in", "anomaly_timestamps", ":", "anomalies_data", ".", "append", "(", "1", ")", "else", ":", "anomalies_data", ".", "append", "(", "0", ")", "if", "not", "use_bottleneck", ":", "df", "[", "'anomalies'", "]", "=", "anomalies_data", "anomalies_list", "=", "[", "]", "for", "ts", ",", "value", "in", "timeseries", ":", "if", "int", "(", "ts", ")", "in", "anomaly_timestamps", ":", "anomalies_list", ".", "append", "(", "[", "int", "(", "ts", ")", ",", "value", "]", ")", "anomalies_dict", "[", "'anomalies'", "]", "[", "int", "(", "ts", ")", "]", "=", "value", "if", "anomalies", "and", "save_plots_to", ":", "try", ":", "from", "adtk", ".", "visualization", "import", "plot", "metric_dir", "=", "base_name", ".", "replace", "(", "'.'", ",", "'/'", ")", "timestamp_dir", "=", "str", "(", "int", "(", "timeseries", "[", "-", "1", "]", "[", "0", "]", ")", ")", "save_path", "=", "'%s/%s/%s/%s'", "%", "(", "save_plots_to", ",", "algorithm_name", ",", "metric_dir", ",", "timestamp_dir", ")", "if", "save_plots_to_absolute_dir", ":", "save_path", "=", "'%s'", "%", "save_plots_to", "anomalies_dict", "[", "'file_path'", "]", "=", "save_path", "save_to_file", "=", "'%s/%s.%s.png'", "%", "(", "save_path", ",", "algorithm_name", ",", "base_name", ")", "if", "filename_prefix", ":", "save_to_file", "=", "'%s/%s.%s.%s.png'", "%", "(", "save_path", ",", "filename_prefix", ",", "algorithm_name", ",", "base_name", ")", "save_to_path", "=", "os_path_dirname", "(", "save_to_file", ")", "title", "=", "'%s\\n%s - median %s %s-sigma persisted (window=%s)'", "%", "(", "base_name", ",", "algorithm_name", ",", "str", "(", "nth_median", ")", ",", "str", "(", "n_sigma", ")", ",", "str", "(", "window", ")", ")", "if", "not", "os_path_exists", "(", "save_to_path", ")", ":", "try", ":", "mkdir_p", "(", "save_to_path", ")", "except", "Exception", "as", "e", ":", "current_logger", ".", "error", "(", "'error :: %s :: failed to create dir - %s - %s'", "%", "(", "algorithm_name", ",", "save_to_path", ",", "e", ")", ")", "if", "os_path_exists", "(", "save_to_path", ")", ":", "try", ":", "plot", "(", "original_df", "[", "'value'", "]", ",", "anomaly", "=", "df", "[", "'anomalies'", "]", ",", "anomaly_color", "=", "'red'", ",", "title", "=", "title", ",", "save_to_file", "=", "save_to_file", ")", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: %s :: plot saved to - %s'", "%", "(", "algorithm_name", ",", "save_to_file", ")", ")", "anomalies_dict", "[", "'image'", "]", "=", "save_to_file", "except", "Exception", "as", "e", ":", "current_logger", ".", "error", "(", "'error :: %s :: failed to plot - %s - %s'", "%", "(", "algorithm_name", ",", "base_name", ",", "e", ")", ")", "anomalies_file", "=", "'%s/%s.%s.anomalies_list.txt'", "%", "(", "save_path", ",", "algorithm_name", ",", "base_name", ")", "with", "open", "(", "anomalies_file", ",", "'w'", ")", "as", "fh", ":", "fh", ".", "write", "(", "str", "(", "anomalies_list", ")", ")", "# os.chmod(anomalies_file, mode=0o644)", "data_file", "=", "'%s/data.txt'", "%", "(", "save_path", ")", "with", "open", "(", "data_file", ",", "'w'", ")", "as", "fh", ":", "fh", ".", "write", "(", "str", "(", "anomalies_dict", ")", ")", "except", "SystemExit", "as", "e", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug_logging :: %s :: SystemExit called during save plot, exiting - %s'", "%", "(", "algorithm_name", ",", "e", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", "Exception", "as", "e", ":", "traceback_msg", "=", "traceback", ".", "format_exc", "(", ")", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback_msg", ")", "if", "debug_logging", ":", "current_logger", ".", "error", "(", "traceback_msg", ")", "current_logger", ".", "error", "(", "'error :: %s :: failed to plot or save anomalies file - %s - %s'", "%", "(", "algorithm_name", ",", "base_name", ",", "e", ")", ")", "try", ":", "del", "df", "except", ":", "pass", "except", "SystemExit", "as", "e", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug_logging :: %s :: SystemExit called, during analysis, exiting - %s'", "%", "(", "algorithm_name", ",", "e", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", ":", "traceback_msg", "=", "traceback", ".", "format_exc", "(", ")", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback_msg", ")", "if", "debug_logging", ":", "current_logger", ".", "error", "(", "traceback_msg", ")", "current_logger", ".", "error", "(", "'error :: debug_logging :: %s :: failed to run on ts'", "%", "(", "algorithm_name", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "end_analysis", "=", "timer", "(", ")", "analysis_runtime", "=", "end_analysis", "-", "start_analysis", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug :: analysis with %s took %.6f seconds'", "%", "(", "algorithm_name", ",", "analysis_runtime", ")", ")", "if", "anomalous", ":", "anomalyScore", "=", "1.0", "else", ":", "anomalyScore", "=", "0.0", "if", "debug_logging", ":", "current_logger", ".", "info", "(", "'%s :: anomalous - %s, anomalyScore - %s'", "%", "(", "algorithm_name", ",", "str", "(", "anomalous", ")", ",", "str", "(", "anomalyScore", ")", ")", ")", "if", "debug_logging", ":", "end", "=", "timer", "(", ")", "processing_runtime", "=", "end", "-", "start", "current_logger", ".", "info", "(", "'%s :: completed in %.6f seconds'", "%", "(", "algorithm_name", ",", "processing_runtime", ")", ")", "try", ":", "del", "timeseries", "except", ":", "pass", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", "SystemExit", "as", "e", ":", "if", "debug_logging", ":", "current_logger", ".", "debug", "(", "'debug_logging :: %s :: SystemExit called (before StopIteration), exiting - %s'", "%", "(", "algorithm_name", ",", "e", ")", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")", "except", "StopIteration", ":", "# This except pattern MUST be used in ALL custom algortihms to", "# facilitate the traceback from any errors. The algorithm we want to", "# run super fast and without spamming the log with lots of errors.", "# But we do not want the function returning and not reporting", "# anything to the log, so the pythonic except is used to \"sample\" any", "# algorithm errors to a tmp file and report once per run rather than", "# spewing tons of errors into the log e.g. analyzer.log", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "False", ",", "None", ",", "anomalies", ")", "return", "(", "False", ",", "None", ")", "except", ":", "record_algorithm_error", "(", "current_skyline_app", ",", "parent_pid", ",", "algorithm_name", ",", "traceback", ".", "format_exc", "(", ")", ")", "# Return None and None as the algorithm could not determine True or False", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "False", ",", "None", ",", "anomalies", ")", "return", "(", "False", ",", "None", ")", "if", "current_skyline_app", "==", "'webapp'", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ",", "anomalies_dict", ")", "if", "return_anomalies", ":", "return", "(", "anomalous", ",", "anomalyScore", ",", "anomalies", ")", "return", "(", "anomalous", ",", "anomalyScore", ")" ]
https://github.com/earthgecko/skyline/blob/12754424de72593e29eb21009fb1ae3f07f3abff/skyline/custom_algorithms/m66.py#L35-L859
robotlearn/pyrobolearn
9cd7c060723fda7d2779fa255ac998c2c82b8436
pyrobolearn/simulators/gazebo_ros.py
python
GazeboROSEnv.get_obs
(self)
Return the observation. Example: The observation could be an image, while the state could be the position (and velocity) of a target on the picture. The state is used to compute the reward function.
Return the observation.
[ "Return", "the", "observation", "." ]
def get_obs(self): """ Return the observation. Example: The observation could be an image, while the state could be the position (and velocity) of a target on the picture. The state is used to compute the reward function. """ raise NotImplementedError("This function needs to be overwritten...")
[ "def", "get_obs", "(", "self", ")", ":", "raise", "NotImplementedError", "(", "\"This function needs to be overwritten...\"", ")" ]
https://github.com/robotlearn/pyrobolearn/blob/9cd7c060723fda7d2779fa255ac998c2c82b8436/pyrobolearn/simulators/gazebo_ros.py#L462-L471
JiYou/openstack
8607dd488bde0905044b303eb6e52bdea6806923
packages/source/nova/nova/network/floating_ips.py
python
FloatingIP._disassociate_floating_ip
(self, context, address, interface, instance_uuid)
Performs db and driver calls to disassociate floating ip.
Performs db and driver calls to disassociate floating ip.
[ "Performs", "db", "and", "driver", "calls", "to", "disassociate", "floating", "ip", "." ]
def _disassociate_floating_ip(self, context, address, interface, instance_uuid): """Performs db and driver calls to disassociate floating ip.""" interface = CONF.public_interface or interface @lockutils.synchronized(unicode(address), 'nova-') def do_disassociate(): # NOTE(vish): Note that we are disassociating in the db before we # actually remove the ip address on the host. We are # safe from races on this host due to the decorator, # but another host might grab the ip right away. We # don't worry about this case because the minuscule # window where the ip is on both hosts shouldn't cause # any problems. fixed = self.db.floating_ip_disassociate(context, address) if not fixed: # NOTE(vish): ip was already disassociated return if interface: # go go driver time self.l3driver.remove_floating_ip(address, fixed['address'], interface, fixed['network']) payload = dict(project_id=context.project_id, instance_id=instance_uuid, floating_ip=address) notifier.notify(context, notifier.publisher_id("network"), 'network.floating_ip.disassociate', notifier.INFO, payload=payload) do_disassociate()
[ "def", "_disassociate_floating_ip", "(", "self", ",", "context", ",", "address", ",", "interface", ",", "instance_uuid", ")", ":", "interface", "=", "CONF", ".", "public_interface", "or", "interface", "@", "lockutils", ".", "synchronized", "(", "unicode", "(", "address", ")", ",", "'nova-'", ")", "def", "do_disassociate", "(", ")", ":", "# NOTE(vish): Note that we are disassociating in the db before we", "# actually remove the ip address on the host. We are", "# safe from races on this host due to the decorator,", "# but another host might grab the ip right away. We", "# don't worry about this case because the minuscule", "# window where the ip is on both hosts shouldn't cause", "# any problems.", "fixed", "=", "self", ".", "db", ".", "floating_ip_disassociate", "(", "context", ",", "address", ")", "if", "not", "fixed", ":", "# NOTE(vish): ip was already disassociated", "return", "if", "interface", ":", "# go go driver time", "self", ".", "l3driver", ".", "remove_floating_ip", "(", "address", ",", "fixed", "[", "'address'", "]", ",", "interface", ",", "fixed", "[", "'network'", "]", ")", "payload", "=", "dict", "(", "project_id", "=", "context", ".", "project_id", ",", "instance_id", "=", "instance_uuid", ",", "floating_ip", "=", "address", ")", "notifier", ".", "notify", "(", "context", ",", "notifier", ".", "publisher_id", "(", "\"network\"", ")", ",", "'network.floating_ip.disassociate'", ",", "notifier", ".", "INFO", ",", "payload", "=", "payload", ")", "do_disassociate", "(", ")" ]
https://github.com/JiYou/openstack/blob/8607dd488bde0905044b303eb6e52bdea6806923/packages/source/nova/nova/network/floating_ips.py#L439-L469
facebookresearch/mobile-vision
f40401a44e86bb3ba9c1b66e7700e15f96b880cb
mobile_cv/common/misc/py.py
python
dynamic_import
(obj_full_name)
return ret
Dynamically import an object (class or function or global variable). Args: obj_full_name: full name of the object, eg. ExampleClass/example_foo is defined inside a/b.py, the obj_full_name is "a.b.ExampleClass" or "a.b.example_foo". Returns: The imported object.
Dynamically import an object (class or function or global variable).
[ "Dynamically", "import", "an", "object", "(", "class", "or", "function", "or", "global", "variable", ")", "." ]
def dynamic_import(obj_full_name): """ Dynamically import an object (class or function or global variable). Args: obj_full_name: full name of the object, eg. ExampleClass/example_foo is defined inside a/b.py, the obj_full_name is "a.b.ExampleClass" or "a.b.example_foo". Returns: The imported object. """ import importlib import pydoc ret = pydoc.locate(obj_full_name) if ret is None: # pydoc.locate imports in forward order, sometimes causing circular import, # fallback to use importlib if pydoc.locate doesn't work module_name, obj_name = obj_full_name.rsplit(".", 1) module = importlib.import_module(module_name) ret = getattr(module, obj_name) return ret
[ "def", "dynamic_import", "(", "obj_full_name", ")", ":", "import", "importlib", "import", "pydoc", "ret", "=", "pydoc", ".", "locate", "(", "obj_full_name", ")", "if", "ret", "is", "None", ":", "# pydoc.locate imports in forward order, sometimes causing circular import,", "# fallback to use importlib if pydoc.locate doesn't work", "module_name", ",", "obj_name", "=", "obj_full_name", ".", "rsplit", "(", "\".\"", ",", "1", ")", "module", "=", "importlib", ".", "import_module", "(", "module_name", ")", "ret", "=", "getattr", "(", "module", ",", "obj_name", ")", "return", "ret" ]
https://github.com/facebookresearch/mobile-vision/blob/f40401a44e86bb3ba9c1b66e7700e15f96b880cb/mobile_cv/common/misc/py.py#L37-L57
titusjan/argos
5a9c31a8a9a2ca825bbf821aa1e685740e3682d7
argos/repo/rtiplugins/exdir.py
python
ExdirScalarRti.unit
(self)
return dataSetUnit(self._exdirDataset)
Returns the unit of the RTI by calling dataSetUnit on the underlying dataset
Returns the unit of the RTI by calling dataSetUnit on the underlying dataset
[ "Returns", "the", "unit", "of", "the", "RTI", "by", "calling", "dataSetUnit", "on", "the", "underlying", "dataset" ]
def unit(self): """ Returns the unit of the RTI by calling dataSetUnit on the underlying dataset """ return dataSetUnit(self._exdirDataset)
[ "def", "unit", "(", "self", ")", ":", "return", "dataSetUnit", "(", "self", ".", "_exdirDataset", ")" ]
https://github.com/titusjan/argos/blob/5a9c31a8a9a2ca825bbf821aa1e685740e3682d7/argos/repo/rtiplugins/exdir.py#L178-L181
mit-ll/LL-Fuzzer
7c532a55cfd7dba9445afa39bd25574a320e1a69
sulley/legos/dcerpc.py
python
ndr_conformant_array.render
(self)
return self.rendered
We overload and extend the render routine in order to properly pad and prefix the string. [dword length][array][pad]
We overload and extend the render routine in order to properly pad and prefix the string.
[ "We", "overload", "and", "extend", "the", "render", "routine", "in", "order", "to", "properly", "pad", "and", "prefix", "the", "string", "." ]
def render (self): ''' We overload and extend the render routine in order to properly pad and prefix the string. [dword length][array][pad] ''' # let the parent do the initial render. blocks.block.render(self) # encode the empty string correctly: if self.rendered == "": self.rendered = "\x00\x00\x00\x00" else: self.rendered = struct.pack("<L", len(self.rendered)) + self.rendered + ndr_pad(self.rendered) return self.rendered
[ "def", "render", "(", "self", ")", ":", "# let the parent do the initial render.", "blocks", ".", "block", ".", "render", "(", "self", ")", "# encode the empty string correctly:", "if", "self", ".", "rendered", "==", "\"\"", ":", "self", ".", "rendered", "=", "\"\\x00\\x00\\x00\\x00\"", "else", ":", "self", ".", "rendered", "=", "struct", ".", "pack", "(", "\"<L\"", ",", "len", "(", "self", ".", "rendered", ")", ")", "+", "self", ".", "rendered", "+", "ndr_pad", "(", "self", ".", "rendered", ")", "return", "self", ".", "rendered" ]
https://github.com/mit-ll/LL-Fuzzer/blob/7c532a55cfd7dba9445afa39bd25574a320e1a69/sulley/legos/dcerpc.py#L33-L49
AnalogJ/lexicon
c7bedfed6ed34c96950954933b07ca3ce081d0e5
lexicon/providers/namesilo.py
python
provider_parser
(subparser)
Configure provider parser for Namesilo
Configure provider parser for Namesilo
[ "Configure", "provider", "parser", "for", "Namesilo" ]
def provider_parser(subparser): """Configure provider parser for Namesilo""" subparser.add_argument("--auth-token", help="specify key for authentication")
[ "def", "provider_parser", "(", "subparser", ")", ":", "subparser", ".", "add_argument", "(", "\"--auth-token\"", ",", "help", "=", "\"specify key for authentication\"", ")" ]
https://github.com/AnalogJ/lexicon/blob/c7bedfed6ed34c96950954933b07ca3ce081d0e5/lexicon/providers/namesilo.py#L15-L17
jgagneastro/coffeegrindsize
22661ebd21831dba4cf32bfc6ba59fe3d49f879c
App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/scipy/optimize/_hessian_update_strategy.py
python
HessianUpdateStrategy.dot
(self, p)
Compute the product of the internal matrix with the given vector. Parameters ---------- p : array_like 1-d array representing a vector. Returns ------- Hp : array 1-d represents the result of multiplying the approximation matrix by vector p.
Compute the product of the internal matrix with the given vector.
[ "Compute", "the", "product", "of", "the", "internal", "matrix", "with", "the", "given", "vector", "." ]
def dot(self, p): """Compute the product of the internal matrix with the given vector. Parameters ---------- p : array_like 1-d array representing a vector. Returns ------- Hp : array 1-d represents the result of multiplying the approximation matrix by vector p. """ raise NotImplementedError("The method ``dot(p)``" " is not implemented.")
[ "def", "dot", "(", "self", ",", "p", ")", ":", "raise", "NotImplementedError", "(", "\"The method ``dot(p)``\"", "\" is not implemented.\"", ")" ]
https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/scipy/optimize/_hessian_update_strategy.py#L73-L88
sametmax/Django--an-app-at-a-time
99eddf12ead76e6dfbeb09ce0bae61e282e22f8a
ignore_this_directory/django/core/serializers/__init__.py
python
unregister_serializer
(format)
Unregister a given serializer. This is not a thread-safe operation.
Unregister a given serializer. This is not a thread-safe operation.
[ "Unregister", "a", "given", "serializer", ".", "This", "is", "not", "a", "thread", "-", "safe", "operation", "." ]
def unregister_serializer(format): "Unregister a given serializer. This is not a thread-safe operation." if not _serializers: _load_serializers() if format not in _serializers: raise SerializerDoesNotExist(format) del _serializers[format]
[ "def", "unregister_serializer", "(", "format", ")", ":", "if", "not", "_serializers", ":", "_load_serializers", "(", ")", "if", "format", "not", "in", "_serializers", ":", "raise", "SerializerDoesNotExist", "(", "format", ")", "del", "_serializers", "[", "format", "]" ]
https://github.com/sametmax/Django--an-app-at-a-time/blob/99eddf12ead76e6dfbeb09ce0bae61e282e22f8a/ignore_this_directory/django/core/serializers/__init__.py#L85-L91
ales-tsurko/cells
4cf7e395cd433762bea70cdc863a346f3a6fe1d0
packaging/macos/python/lib/python3.7/zipfile.py
python
ZipInfo.from_file
(cls, filename, arcname=None)
return zinfo
Construct an appropriate ZipInfo for a file on the filesystem. filename should be the path to a file or directory on the filesystem. arcname is the name which it will have within the archive (by default, this will be the same as filename, but without a drive letter and with leading path separators removed).
Construct an appropriate ZipInfo for a file on the filesystem.
[ "Construct", "an", "appropriate", "ZipInfo", "for", "a", "file", "on", "the", "filesystem", "." ]
def from_file(cls, filename, arcname=None): """Construct an appropriate ZipInfo for a file on the filesystem. filename should be the path to a file or directory on the filesystem. arcname is the name which it will have within the archive (by default, this will be the same as filename, but without a drive letter and with leading path separators removed). """ if isinstance(filename, os.PathLike): filename = os.fspath(filename) st = os.stat(filename) isdir = stat.S_ISDIR(st.st_mode) mtime = time.localtime(st.st_mtime) date_time = mtime[0:6] # Create ZipInfo instance to store file information if arcname is None: arcname = filename arcname = os.path.normpath(os.path.splitdrive(arcname)[1]) while arcname[0] in (os.sep, os.altsep): arcname = arcname[1:] if isdir: arcname += '/' zinfo = cls(arcname, date_time) zinfo.external_attr = (st.st_mode & 0xFFFF) << 16 # Unix attributes if isdir: zinfo.file_size = 0 zinfo.external_attr |= 0x10 # MS-DOS directory flag else: zinfo.file_size = st.st_size return zinfo
[ "def", "from_file", "(", "cls", ",", "filename", ",", "arcname", "=", "None", ")", ":", "if", "isinstance", "(", "filename", ",", "os", ".", "PathLike", ")", ":", "filename", "=", "os", ".", "fspath", "(", "filename", ")", "st", "=", "os", ".", "stat", "(", "filename", ")", "isdir", "=", "stat", ".", "S_ISDIR", "(", "st", ".", "st_mode", ")", "mtime", "=", "time", ".", "localtime", "(", "st", ".", "st_mtime", ")", "date_time", "=", "mtime", "[", "0", ":", "6", "]", "# Create ZipInfo instance to store file information", "if", "arcname", "is", "None", ":", "arcname", "=", "filename", "arcname", "=", "os", ".", "path", ".", "normpath", "(", "os", ".", "path", ".", "splitdrive", "(", "arcname", ")", "[", "1", "]", ")", "while", "arcname", "[", "0", "]", "in", "(", "os", ".", "sep", ",", "os", ".", "altsep", ")", ":", "arcname", "=", "arcname", "[", "1", ":", "]", "if", "isdir", ":", "arcname", "+=", "'/'", "zinfo", "=", "cls", "(", "arcname", ",", "date_time", ")", "zinfo", ".", "external_attr", "=", "(", "st", ".", "st_mode", "&", "0xFFFF", ")", "<<", "16", "# Unix attributes", "if", "isdir", ":", "zinfo", ".", "file_size", "=", "0", "zinfo", ".", "external_attr", "|=", "0x10", "# MS-DOS directory flag", "else", ":", "zinfo", ".", "file_size", "=", "st", ".", "st_size", "return", "zinfo" ]
https://github.com/ales-tsurko/cells/blob/4cf7e395cd433762bea70cdc863a346f3a6fe1d0/packaging/macos/python/lib/python3.7/zipfile.py#L495-L526
demisto/content
5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07
Packs/Kafka/Integrations/Kafka_V2/Kafka_V2.py
python
check_latest_offset
(topic, partition_number=None)
return latest_offset - 1
:param topic: topic to check the latest offset :type topic: :class:`pykafka.topic.Topic` :param partition_number: partition to take latest offset from :type partition_number: int, str :return latest_offset: last message offset :rtype: int
:param topic: topic to check the latest offset :type topic: :class:`pykafka.topic.Topic` :param partition_number: partition to take latest offset from :type partition_number: int, str :return latest_offset: last message offset :rtype: int
[ ":", "param", "topic", ":", "topic", "to", "check", "the", "latest", "offset", ":", "type", "topic", ":", ":", "class", ":", "pykafka", ".", "topic", ".", "Topic", ":", "param", "partition_number", ":", "partition", "to", "take", "latest", "offset", "from", ":", "type", "partition_number", ":", "int", "str", ":", "return", "latest_offset", ":", "last", "message", "offset", ":", "rtype", ":", "int" ]
def check_latest_offset(topic, partition_number=None): """ :param topic: topic to check the latest offset :type topic: :class:`pykafka.topic.Topic` :param partition_number: partition to take latest offset from :type partition_number: int, str :return latest_offset: last message offset :rtype: int """ partitions = topic.latest_available_offsets() latest_offset = 0 if partition_number is not None: partition = partitions.get(str(partition_number)) if partitions: latest_offset = partition[0][0] else: return_error('Partition does not exist') else: for partition in partitions.values(): if latest_offset < partition[0][0]: latest_offset = partition[0][0] return latest_offset - 1
[ "def", "check_latest_offset", "(", "topic", ",", "partition_number", "=", "None", ")", ":", "partitions", "=", "topic", ".", "latest_available_offsets", "(", ")", "latest_offset", "=", "0", "if", "partition_number", "is", "not", "None", ":", "partition", "=", "partitions", ".", "get", "(", "str", "(", "partition_number", ")", ")", "if", "partitions", ":", "latest_offset", "=", "partition", "[", "0", "]", "[", "0", "]", "else", ":", "return_error", "(", "'Partition does not exist'", ")", "else", ":", "for", "partition", "in", "partitions", ".", "values", "(", ")", ":", "if", "latest_offset", "<", "partition", "[", "0", "]", "[", "0", "]", ":", "latest_offset", "=", "partition", "[", "0", "]", "[", "0", "]", "return", "latest_offset", "-", "1" ]
https://github.com/demisto/content/blob/5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07/Packs/Kafka/Integrations/Kafka_V2/Kafka_V2.py#L104-L125
virus-warnning/twnews
4c7ef436018480d07b5f3f5f474f3843af46eb99
bin/publish.py
python
wheel_check
()
檢查 wheel 是否能正常運作在各個 Python 版本環境上
檢查 wheel 是否能正常運作在各個 Python 版本環境上
[ "檢查", "wheel", "是否能正常運作在各個", "Python", "版本環境上" ]
def wheel_check(): """ 檢查 wheel 是否能正常運作在各個 Python 版本環境上 """ """ print('檢查 logging.ini') config = configparser.ConfigParser() config.read('twnews/conf/logging.ini') if config['handler_stdout']['level'] != 'CRITICAL': print('handler_stdout 忘記切換成 CRITICAL level') exit(1) """ print('檢查程式碼品質') ret = os.system('pylint -f colorized twnews') if ret != 0: print('檢查沒通過,停止封裝') exit(ret) print('檢查 README.rst') ret = os.system('rstcheck README.rst') if ret != 0: print('檢查沒通過,停止封裝') exit(ret) print('偵測可用的測試環境') os.system('rm -rf sandbox/*') wheel = get_wheel() latest_python = get_latest_python() if len(latest_python) == 0: print('沒有任何可用的測試環境') exit(1) for pyver in latest_python: print('測試 Python %s' % pyver) test_in_virtualenv(pyver, wheel)
[ "def", "wheel_check", "(", ")", ":", "\"\"\"\n print('檢查 logging.ini')\n config = configparser.ConfigParser()\n config.read('twnews/conf/logging.ini')\n if config['handler_stdout']['level'] != 'CRITICAL':\n print('handler_stdout 忘記切換成 CRITICAL level')\n exit(1)\n \"\"\"", "print", "(", "'檢查程式碼品質')", "", "ret", "=", "os", ".", "system", "(", "'pylint -f colorized twnews'", ")", "if", "ret", "!=", "0", ":", "print", "(", "'檢查沒通過,停止封裝')", "", "exit", "(", "ret", ")", "print", "(", "'檢查 README.rst')", "", "ret", "=", "os", ".", "system", "(", "'rstcheck README.rst'", ")", "if", "ret", "!=", "0", ":", "print", "(", "'檢查沒通過,停止封裝')", "", "exit", "(", "ret", ")", "print", "(", "'偵測可用的測試環境')", "", "os", ".", "system", "(", "'rm -rf sandbox/*'", ")", "wheel", "=", "get_wheel", "(", ")", "latest_python", "=", "get_latest_python", "(", ")", "if", "len", "(", "latest_python", ")", "==", "0", ":", "print", "(", "'沒有任何可用的測試環境')", "", "exit", "(", "1", ")", "for", "pyver", "in", "latest_python", ":", "print", "(", "'測試 Python %s' % p", "v", "r)", "", "test_in_virtualenv", "(", "pyver", ",", "wheel", ")" ]
https://github.com/virus-warnning/twnews/blob/4c7ef436018480d07b5f3f5f474f3843af46eb99/bin/publish.py#L81-L115
XKNX/xknx
1deeeb3dc0978aebacf14492a84e1f1eaf0970ed
xknx/devices/cover.py
python
Cover.supports_angle
(self)
return self.angle.initialized
Return if cover supports tilt angle.
Return if cover supports tilt angle.
[ "Return", "if", "cover", "supports", "tilt", "angle", "." ]
def supports_angle(self) -> bool: """Return if cover supports tilt angle.""" return self.angle.initialized
[ "def", "supports_angle", "(", "self", ")", "->", "bool", ":", "return", "self", ".", "angle", ".", "initialized" ]
https://github.com/XKNX/xknx/blob/1deeeb3dc0978aebacf14492a84e1f1eaf0970ed/xknx/devices/cover.py#L357-L359
oilshell/oil
94388e7d44a9ad879b12615f6203b38596b5a2d3
core/state.py
python
MutableOpts.SetShoptOption
(self, opt_name, b)
For shopt -s/-u and sh -O/+O.
For shopt -s/-u and sh -O/+O.
[ "For", "shopt", "-", "s", "/", "-", "u", "and", "sh", "-", "O", "/", "+", "O", "." ]
def SetShoptOption(self, opt_name, b): # type: (str, bool) -> None """ For shopt -s/-u and sh -O/+O. """ # shopt -s all:oil turns on all Oil options, which includes all strict # # options if opt_name == 'oil:basic': self._SetGroup(consts.OIL_BASIC, b) self.SetDeferredErrExit(b) # Special case return if opt_name == 'oil:all': self._SetGroup(consts.OIL_ALL, b) self.SetDeferredErrExit(b) # Special case return if opt_name == 'strict:all': self._SetGroup(consts.STRICT_ALL, b) return opt_num = _ShoptOptionNum(opt_name) if opt_num == option_i.errexit: self.SetDeferredErrExit(b) return self._SetArrayByNum(opt_num, b)
[ "def", "SetShoptOption", "(", "self", ",", "opt_name", ",", "b", ")", ":", "# type: (str, bool) -> None", "# shopt -s all:oil turns on all Oil options, which includes all strict #", "# options", "if", "opt_name", "==", "'oil:basic'", ":", "self", ".", "_SetGroup", "(", "consts", ".", "OIL_BASIC", ",", "b", ")", "self", ".", "SetDeferredErrExit", "(", "b", ")", "# Special case", "return", "if", "opt_name", "==", "'oil:all'", ":", "self", ".", "_SetGroup", "(", "consts", ".", "OIL_ALL", ",", "b", ")", "self", ".", "SetDeferredErrExit", "(", "b", ")", "# Special case", "return", "if", "opt_name", "==", "'strict:all'", ":", "self", ".", "_SetGroup", "(", "consts", ".", "STRICT_ALL", ",", "b", ")", "return", "opt_num", "=", "_ShoptOptionNum", "(", "opt_name", ")", "if", "opt_num", "==", "option_i", ".", "errexit", ":", "self", ".", "SetDeferredErrExit", "(", "b", ")", "return", "self", ".", "_SetArrayByNum", "(", "opt_num", ",", "b", ")" ]
https://github.com/oilshell/oil/blob/94388e7d44a9ad879b12615f6203b38596b5a2d3/core/state.py#L523-L549
scikit-hep/awkward-0.x
dd885bef15814f588b58944d2505296df4aaae0e
awkward0/array/masked.py
python
MaskedArray.counts
(self)
return out
[]
def counts(self): self._valid() content = self._util_counts(self._content) out = self.numpy.full(self.shape, -1, dtype=content.dtype) mask = self.boolmask(maskedwhen=False) out[mask] = content[mask] return out
[ "def", "counts", "(", "self", ")", ":", "self", ".", "_valid", "(", ")", "content", "=", "self", ".", "_util_counts", "(", "self", ".", "_content", ")", "out", "=", "self", ".", "numpy", ".", "full", "(", "self", ".", "shape", ",", "-", "1", ",", "dtype", "=", "content", ".", "dtype", ")", "mask", "=", "self", ".", "boolmask", "(", "maskedwhen", "=", "False", ")", "out", "[", "mask", "]", "=", "content", "[", "mask", "]", "return", "out" ]
https://github.com/scikit-hep/awkward-0.x/blob/dd885bef15814f588b58944d2505296df4aaae0e/awkward0/array/masked.py#L256-L262
oracle/oci-python-sdk
3c1604e4e212008fb6718e2f68cdb5ef71fd5793
src/oci/data_safe/data_safe_client.py
python
DataSafeClient.__init__
(self, config, **kwargs)
Creates a new service client :param dict config: Configuration keys and values as per `SDK and Tool Configuration <https://docs.cloud.oracle.com/Content/API/Concepts/sdkconfig.htm>`__. The :py:meth:`~oci.config.from_file` method can be used to load configuration from a file. Alternatively, a ``dict`` can be passed. You can validate_config the dict using :py:meth:`~oci.config.validate_config` :param str service_endpoint: (optional) The endpoint of the service to call using this client. For example ``https://iaas.us-ashburn-1.oraclecloud.com``. If this keyword argument is not provided then it will be derived using the region in the config parameter. You should only provide this keyword argument if you have an explicit need to specify a service endpoint. :param timeout: (optional) The connection and read timeouts for the client. The default values are connection timeout 10 seconds and read timeout 60 seconds. This keyword argument can be provided as a single float, in which case the value provided is used for both the read and connection timeouts, or as a tuple of two floats. If a tuple is provided then the first value is used as the connection timeout and the second value as the read timeout. :type timeout: float or tuple(float, float) :param signer: (optional) The signer to use when signing requests made by the service client. The default is to use a :py:class:`~oci.signer.Signer` based on the values provided in the config parameter. One use case for this parameter is for `Instance Principals authentication <https://docs.cloud.oracle.com/Content/Identity/Tasks/callingservicesfrominstances.htm>`__ by passing an instance of :py:class:`~oci.auth.signers.InstancePrincipalsSecurityTokenSigner` as the value for this keyword argument :type signer: :py:class:`~oci.signer.AbstractBaseSigner` :param obj retry_strategy: (optional) A retry strategy to apply to all calls made by this service client (i.e. at the client level). There is no retry strategy applied by default. Retry strategies can also be applied at the operation level by passing a ``retry_strategy`` keyword argument as part of calling the operation. Any value provided at the operation level will override whatever is specified at the client level. This should be one of the strategies available in the :py:mod:`~oci.retry` module. A convenience :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` is also available. The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__. :param obj circuit_breaker_strategy: (optional) A circuit breaker strategy to apply to all calls made by this service client (i.e. at the client level). This client uses :py:data:`~oci.circuit_breaker.DEFAULT_CIRCUIT_BREAKER_STRATEGY` as default if no circuit breaker strategy is provided. The specifics of circuit breaker strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/circuit_breakers.html>`__. :param function circuit_breaker_callback: (optional) Callback function to receive any exceptions triggerred by the circuit breaker.
Creates a new service client
[ "Creates", "a", "new", "service", "client" ]
def __init__(self, config, **kwargs): """ Creates a new service client :param dict config: Configuration keys and values as per `SDK and Tool Configuration <https://docs.cloud.oracle.com/Content/API/Concepts/sdkconfig.htm>`__. The :py:meth:`~oci.config.from_file` method can be used to load configuration from a file. Alternatively, a ``dict`` can be passed. You can validate_config the dict using :py:meth:`~oci.config.validate_config` :param str service_endpoint: (optional) The endpoint of the service to call using this client. For example ``https://iaas.us-ashburn-1.oraclecloud.com``. If this keyword argument is not provided then it will be derived using the region in the config parameter. You should only provide this keyword argument if you have an explicit need to specify a service endpoint. :param timeout: (optional) The connection and read timeouts for the client. The default values are connection timeout 10 seconds and read timeout 60 seconds. This keyword argument can be provided as a single float, in which case the value provided is used for both the read and connection timeouts, or as a tuple of two floats. If a tuple is provided then the first value is used as the connection timeout and the second value as the read timeout. :type timeout: float or tuple(float, float) :param signer: (optional) The signer to use when signing requests made by the service client. The default is to use a :py:class:`~oci.signer.Signer` based on the values provided in the config parameter. One use case for this parameter is for `Instance Principals authentication <https://docs.cloud.oracle.com/Content/Identity/Tasks/callingservicesfrominstances.htm>`__ by passing an instance of :py:class:`~oci.auth.signers.InstancePrincipalsSecurityTokenSigner` as the value for this keyword argument :type signer: :py:class:`~oci.signer.AbstractBaseSigner` :param obj retry_strategy: (optional) A retry strategy to apply to all calls made by this service client (i.e. at the client level). There is no retry strategy applied by default. Retry strategies can also be applied at the operation level by passing a ``retry_strategy`` keyword argument as part of calling the operation. Any value provided at the operation level will override whatever is specified at the client level. This should be one of the strategies available in the :py:mod:`~oci.retry` module. A convenience :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` is also available. The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__. :param obj circuit_breaker_strategy: (optional) A circuit breaker strategy to apply to all calls made by this service client (i.e. at the client level). This client uses :py:data:`~oci.circuit_breaker.DEFAULT_CIRCUIT_BREAKER_STRATEGY` as default if no circuit breaker strategy is provided. The specifics of circuit breaker strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/circuit_breakers.html>`__. :param function circuit_breaker_callback: (optional) Callback function to receive any exceptions triggerred by the circuit breaker. """ validate_config(config, signer=kwargs.get('signer')) if 'signer' in kwargs: signer = kwargs['signer'] elif AUTHENTICATION_TYPE_FIELD_NAME in config: signer = get_signer_from_authentication_type(config) else: signer = Signer( tenancy=config["tenancy"], user=config["user"], fingerprint=config["fingerprint"], private_key_file_location=config.get("key_file"), pass_phrase=get_config_value_or_default(config, "pass_phrase"), private_key_content=config.get("key_content") ) base_client_init_kwargs = { 'regional_client': True, 'service_endpoint': kwargs.get('service_endpoint'), 'base_path': '/20181201', 'service_endpoint_template': 'https://datasafe.{region}.oci.{secondLevelDomain}', 'skip_deserialization': kwargs.get('skip_deserialization', False), 'circuit_breaker_strategy': kwargs.get('circuit_breaker_strategy', circuit_breaker.GLOBAL_CIRCUIT_BREAKER_STRATEGY) } if 'timeout' in kwargs: base_client_init_kwargs['timeout'] = kwargs.get('timeout') if base_client_init_kwargs.get('circuit_breaker_strategy') is None: base_client_init_kwargs['circuit_breaker_strategy'] = circuit_breaker.DEFAULT_CIRCUIT_BREAKER_STRATEGY self.base_client = BaseClient("data_safe", config, signer, data_safe_type_mapping, **base_client_init_kwargs) self.retry_strategy = kwargs.get('retry_strategy') self.circuit_breaker_callback = kwargs.get('circuit_breaker_callback')
[ "def", "__init__", "(", "self", ",", "config", ",", "*", "*", "kwargs", ")", ":", "validate_config", "(", "config", ",", "signer", "=", "kwargs", ".", "get", "(", "'signer'", ")", ")", "if", "'signer'", "in", "kwargs", ":", "signer", "=", "kwargs", "[", "'signer'", "]", "elif", "AUTHENTICATION_TYPE_FIELD_NAME", "in", "config", ":", "signer", "=", "get_signer_from_authentication_type", "(", "config", ")", "else", ":", "signer", "=", "Signer", "(", "tenancy", "=", "config", "[", "\"tenancy\"", "]", ",", "user", "=", "config", "[", "\"user\"", "]", ",", "fingerprint", "=", "config", "[", "\"fingerprint\"", "]", ",", "private_key_file_location", "=", "config", ".", "get", "(", "\"key_file\"", ")", ",", "pass_phrase", "=", "get_config_value_or_default", "(", "config", ",", "\"pass_phrase\"", ")", ",", "private_key_content", "=", "config", ".", "get", "(", "\"key_content\"", ")", ")", "base_client_init_kwargs", "=", "{", "'regional_client'", ":", "True", ",", "'service_endpoint'", ":", "kwargs", ".", "get", "(", "'service_endpoint'", ")", ",", "'base_path'", ":", "'/20181201'", ",", "'service_endpoint_template'", ":", "'https://datasafe.{region}.oci.{secondLevelDomain}'", ",", "'skip_deserialization'", ":", "kwargs", ".", "get", "(", "'skip_deserialization'", ",", "False", ")", ",", "'circuit_breaker_strategy'", ":", "kwargs", ".", "get", "(", "'circuit_breaker_strategy'", ",", "circuit_breaker", ".", "GLOBAL_CIRCUIT_BREAKER_STRATEGY", ")", "}", "if", "'timeout'", "in", "kwargs", ":", "base_client_init_kwargs", "[", "'timeout'", "]", "=", "kwargs", ".", "get", "(", "'timeout'", ")", "if", "base_client_init_kwargs", ".", "get", "(", "'circuit_breaker_strategy'", ")", "is", "None", ":", "base_client_init_kwargs", "[", "'circuit_breaker_strategy'", "]", "=", "circuit_breaker", ".", "DEFAULT_CIRCUIT_BREAKER_STRATEGY", "self", ".", "base_client", "=", "BaseClient", "(", "\"data_safe\"", ",", "config", ",", "signer", ",", "data_safe_type_mapping", ",", "*", "*", "base_client_init_kwargs", ")", "self", ".", "retry_strategy", "=", "kwargs", ".", "get", "(", "'retry_strategy'", ")", "self", ".", "circuit_breaker_callback", "=", "kwargs", ".", "get", "(", "'circuit_breaker_callback'", ")" ]
https://github.com/oracle/oci-python-sdk/blob/3c1604e4e212008fb6718e2f68cdb5ef71fd5793/src/oci/data_safe/data_safe_client.py#L24-L99
number5/cloud-init
19948dbaf40309355e1a2dbef116efb0ce66245c
cloudinit/distros/debian.py
python
Distro.get_locale
(self)
return ( self.system_locale if self.system_locale else self.default_locale )
Return the default locale if set, else use default locale
Return the default locale if set, else use default locale
[ "Return", "the", "default", "locale", "if", "set", "else", "use", "default", "locale" ]
def get_locale(self): """Return the default locale if set, else use default locale""" # read system locale value if not self.system_locale: self.system_locale = read_system_locale() # Return system_locale setting if valid, else use default locale return ( self.system_locale if self.system_locale else self.default_locale )
[ "def", "get_locale", "(", "self", ")", ":", "# read system locale value", "if", "not", "self", ".", "system_locale", ":", "self", ".", "system_locale", "=", "read_system_locale", "(", ")", "# Return system_locale setting if valid, else use default locale", "return", "(", "self", ".", "system_locale", "if", "self", ".", "system_locale", "else", "self", ".", "default_locale", ")" ]
https://github.com/number5/cloud-init/blob/19948dbaf40309355e1a2dbef116efb0ce66245c/cloudinit/distros/debian.py#L88-L98
MasoniteFramework/masonite
faa448377916e9e0f618ea6bdc82330fa6604efc
src/masonite/response/response.py
python
Response.status
(self, status)
return self
Set the HTTP status code. Arguments: status {string|integer} -- A string or integer with the standardized status code Returns: self
Set the HTTP status code.
[ "Set", "the", "HTTP", "status", "code", "." ]
def status(self, status): """Set the HTTP status code. Arguments: status {string|integer} -- A string or integer with the standardized status code Returns: self """ if isinstance(status, str): self._status = status elif isinstance(status, int): try: self._status = self.statuses[status] except KeyError: raise InvalidHTTPStatusCode return self
[ "def", "status", "(", "self", ",", "status", ")", ":", "if", "isinstance", "(", "status", ",", "str", ")", ":", "self", ".", "_status", "=", "status", "elif", "isinstance", "(", "status", ",", "int", ")", ":", "try", ":", "self", ".", "_status", "=", "self", ".", "statuses", "[", "status", "]", "except", "KeyError", ":", "raise", "InvalidHTTPStatusCode", "return", "self" ]
https://github.com/MasoniteFramework/masonite/blob/faa448377916e9e0f618ea6bdc82330fa6604efc/src/masonite/response/response.py#L87-L103
google-research/electra
8a46635f32083ada044d7e9ad09604742600ee7b
finetune/preprocessing.py
python
Preprocessor._serialize_dataset
(self, tasks, is_training, split)
return input_fn, steps
Write out the dataset as tfrecords.
Write out the dataset as tfrecords.
[ "Write", "out", "the", "dataset", "as", "tfrecords", "." ]
def _serialize_dataset(self, tasks, is_training, split): """Write out the dataset as tfrecords.""" dataset_name = "_".join(sorted([task.name for task in tasks])) dataset_name += "_" + split dataset_prefix = os.path.join( self._config.preprocessed_data_dir, dataset_name) tfrecords_path = dataset_prefix + ".tfrecord" metadata_path = dataset_prefix + ".metadata" batch_size = (self._config.train_batch_size if is_training else self._config.eval_batch_size) utils.log("Loading dataset", dataset_name) n_examples = None if (self._config.use_tfrecords_if_existing and tf.io.gfile.exists(metadata_path)): n_examples = utils.load_json(metadata_path)["n_examples"] if n_examples is None: utils.log("Existing tfrecords not found so creating") examples = [] for task in tasks: task_examples = task.get_examples(split) examples += task_examples if is_training: random.shuffle(examples) utils.mkdir(tfrecords_path.rsplit("/", 1)[0]) n_examples = self.serialize_examples( examples, is_training, tfrecords_path, batch_size) utils.write_json({"n_examples": n_examples}, metadata_path) input_fn = self._input_fn_builder(tfrecords_path, is_training) if is_training: steps = int(n_examples // batch_size * self._config.num_train_epochs) else: steps = n_examples // batch_size return input_fn, steps
[ "def", "_serialize_dataset", "(", "self", ",", "tasks", ",", "is_training", ",", "split", ")", ":", "dataset_name", "=", "\"_\"", ".", "join", "(", "sorted", "(", "[", "task", ".", "name", "for", "task", "in", "tasks", "]", ")", ")", "dataset_name", "+=", "\"_\"", "+", "split", "dataset_prefix", "=", "os", ".", "path", ".", "join", "(", "self", ".", "_config", ".", "preprocessed_data_dir", ",", "dataset_name", ")", "tfrecords_path", "=", "dataset_prefix", "+", "\".tfrecord\"", "metadata_path", "=", "dataset_prefix", "+", "\".metadata\"", "batch_size", "=", "(", "self", ".", "_config", ".", "train_batch_size", "if", "is_training", "else", "self", ".", "_config", ".", "eval_batch_size", ")", "utils", ".", "log", "(", "\"Loading dataset\"", ",", "dataset_name", ")", "n_examples", "=", "None", "if", "(", "self", ".", "_config", ".", "use_tfrecords_if_existing", "and", "tf", ".", "io", ".", "gfile", ".", "exists", "(", "metadata_path", ")", ")", ":", "n_examples", "=", "utils", ".", "load_json", "(", "metadata_path", ")", "[", "\"n_examples\"", "]", "if", "n_examples", "is", "None", ":", "utils", ".", "log", "(", "\"Existing tfrecords not found so creating\"", ")", "examples", "=", "[", "]", "for", "task", "in", "tasks", ":", "task_examples", "=", "task", ".", "get_examples", "(", "split", ")", "examples", "+=", "task_examples", "if", "is_training", ":", "random", ".", "shuffle", "(", "examples", ")", "utils", ".", "mkdir", "(", "tfrecords_path", ".", "rsplit", "(", "\"/\"", ",", "1", ")", "[", "0", "]", ")", "n_examples", "=", "self", ".", "serialize_examples", "(", "examples", ",", "is_training", ",", "tfrecords_path", ",", "batch_size", ")", "utils", ".", "write_json", "(", "{", "\"n_examples\"", ":", "n_examples", "}", ",", "metadata_path", ")", "input_fn", "=", "self", ".", "_input_fn_builder", "(", "tfrecords_path", ",", "is_training", ")", "if", "is_training", ":", "steps", "=", "int", "(", "n_examples", "//", "batch_size", "*", "self", ".", "_config", ".", "num_train_epochs", ")", "else", ":", "steps", "=", "n_examples", "//", "batch_size", "return", "input_fn", ",", "steps" ]
https://github.com/google-research/electra/blob/8a46635f32083ada044d7e9ad09604742600ee7b/finetune/preprocessing.py#L56-L92
jzlianglu/pykaldi2
4d31968f8dff7cccf6a8395b7e69005ae3b2b30a
reader/stream.py
python
SpeechDataStream.utt_id2spk_id
(self, utt_id)
Different corpus usually has different ways to convert utt_id to spk_id So we should overload the class and provide the implementation in the subclasses.
Different corpus usually has different ways to convert utt_id to spk_id So we should overload the class and provide the implementation in the subclasses.
[ "Different", "corpus", "usually", "has", "different", "ways", "to", "convert", "utt_id", "to", "spk_id", "So", "we", "should", "overload", "the", "class", "and", "provide", "the", "implementation", "in", "the", "subclasses", "." ]
def utt_id2spk_id(self, utt_id): """Different corpus usually has different ways to convert utt_id to spk_id So we should overload the class and provide the implementation in the subclasses.""" pass
[ "def", "utt_id2spk_id", "(", "self", ",", "utt_id", ")", ":", "pass" ]
https://github.com/jzlianglu/pykaldi2/blob/4d31968f8dff7cccf6a8395b7e69005ae3b2b30a/reader/stream.py#L483-L486
zhl2008/awd-platform
0416b31abea29743387b10b3914581fbe8e7da5e
web_flaskbb/lib/python2.7/site-packages/PIL/Image.py
python
Image._dump
(self, file=None, format=None, **options)
return filename
[]
def _dump(self, file=None, format=None, **options): import tempfile suffix = '' if format: suffix = '.'+format if not file: f, filename = tempfile.mkstemp(suffix) os.close(f) else: filename = file if not filename.endswith(suffix): filename = filename + suffix self.load() if not format or format == "PPM": self.im.save_ppm(filename) else: self.save(filename, format, **options) return filename
[ "def", "_dump", "(", "self", ",", "file", "=", "None", ",", "format", "=", "None", ",", "*", "*", "options", ")", ":", "import", "tempfile", "suffix", "=", "''", "if", "format", ":", "suffix", "=", "'.'", "+", "format", "if", "not", "file", ":", "f", ",", "filename", "=", "tempfile", ".", "mkstemp", "(", "suffix", ")", "os", ".", "close", "(", "f", ")", "else", ":", "filename", "=", "file", "if", "not", "filename", ".", "endswith", "(", "suffix", ")", ":", "filename", "=", "filename", "+", "suffix", "self", ".", "load", "(", ")", "if", "not", "format", "or", "format", "==", "\"PPM\"", ":", "self", ".", "im", ".", "save_ppm", "(", "filename", ")", "else", ":", "self", ".", "save", "(", "filename", ",", "format", ",", "*", "*", "options", ")", "return", "filename" ]
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/PIL/Image.py#L606-L628
ifwe/digsby
f5fe00244744aa131e07f09348d10563f3d8fa99
digsby/src/gui/imwin/imwin_ctrl.py
python
ImWinCtrl.message
(self, messageobj, convo = None, mode = 'im', meta = None)
Called by imhub.py with incoming messages.
Called by imhub.py with incoming messages.
[ "Called", "by", "imhub", ".", "py", "with", "incoming", "messages", "." ]
def message(self, messageobj, convo = None, mode = 'im', meta = None): "Called by imhub.py with incoming messages." info('%r', self) info_s(' messageobj: %r', messageobj) info(' convo: %r', convo) info(' mode: %r', mode) info(' meta: %r', meta) assert wx.IsMainThread() if messageobj is None: # starting a new conversation--no message self.set_conversation(convo) self.set_mode(mode) self.IMControl.SetConvo(convo) if convo.ischat: self.show_roomlist(True) elif (messageobj.get('sms', False) or getattr(convo.buddy, 'sms', None)) and not profile.blist.on_buddylist(convo.buddy): # an incoming SMS message if self.convo is None: self.set_conversation(convo) if self.mode != 'sms': self.set_mode('sms') # just show it self.show_message(messageobj) else: convo = messageobj.conversation if self.mode is None: self.set_mode(mode) self.show_message(messageobj) self.set_conversation(convo)
[ "def", "message", "(", "self", ",", "messageobj", ",", "convo", "=", "None", ",", "mode", "=", "'im'", ",", "meta", "=", "None", ")", ":", "info", "(", "'%r'", ",", "self", ")", "info_s", "(", "' messageobj: %r'", ",", "messageobj", ")", "info", "(", "' convo: %r'", ",", "convo", ")", "info", "(", "' mode: %r'", ",", "mode", ")", "info", "(", "' meta: %r'", ",", "meta", ")", "assert", "wx", ".", "IsMainThread", "(", ")", "if", "messageobj", "is", "None", ":", "# starting a new conversation--no message", "self", ".", "set_conversation", "(", "convo", ")", "self", ".", "set_mode", "(", "mode", ")", "self", ".", "IMControl", ".", "SetConvo", "(", "convo", ")", "if", "convo", ".", "ischat", ":", "self", ".", "show_roomlist", "(", "True", ")", "elif", "(", "messageobj", ".", "get", "(", "'sms'", ",", "False", ")", "or", "getattr", "(", "convo", ".", "buddy", ",", "'sms'", ",", "None", ")", ")", "and", "not", "profile", ".", "blist", ".", "on_buddylist", "(", "convo", ".", "buddy", ")", ":", "# an incoming SMS message", "if", "self", ".", "convo", "is", "None", ":", "self", ".", "set_conversation", "(", "convo", ")", "if", "self", ".", "mode", "!=", "'sms'", ":", "self", ".", "set_mode", "(", "'sms'", ")", "# just show it", "self", ".", "show_message", "(", "messageobj", ")", "else", ":", "convo", "=", "messageobj", ".", "conversation", "if", "self", ".", "mode", "is", "None", ":", "self", ".", "set_mode", "(", "mode", ")", "self", ".", "show_message", "(", "messageobj", ")", "self", ".", "set_conversation", "(", "convo", ")" ]
https://github.com/ifwe/digsby/blob/f5fe00244744aa131e07f09348d10563f3d8fa99/digsby/src/gui/imwin/imwin_ctrl.py#L73-L108
blasty/moneyshot
0541356cca38e57ec03a30b6dff1d38c0c7dfd00
shell.py
python
main
(args)
[]
def main(args): if (len(args) != 2): print "usage: moneyshot shell <host> <port>" exit() target = (args[0], int(args[1])) s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect(target) old_settings = termios.tcgetattr(0) try: tty.setcbreak(0) c = True while c: for i in select.select([0, s.fileno()], [], [], 0)[0]: c = os.read(i, 1024) if c: os.write(s.fileno() if i == 0 else 1, c) except KeyboardInterrupt: pass finally: termios.tcsetattr(0, termios.TCSADRAIN, old_settings)
[ "def", "main", "(", "args", ")", ":", "if", "(", "len", "(", "args", ")", "!=", "2", ")", ":", "print", "\"usage: moneyshot shell <host> <port>\"", "exit", "(", ")", "target", "=", "(", "args", "[", "0", "]", ",", "int", "(", "args", "[", "1", "]", ")", ")", "s", "=", "socket", ".", "socket", "(", "socket", ".", "AF_INET", ",", "socket", ".", "SOCK_STREAM", ")", "s", ".", "connect", "(", "target", ")", "old_settings", "=", "termios", ".", "tcgetattr", "(", "0", ")", "try", ":", "tty", ".", "setcbreak", "(", "0", ")", "c", "=", "True", "while", "c", ":", "for", "i", "in", "select", ".", "select", "(", "[", "0", ",", "s", ".", "fileno", "(", ")", "]", ",", "[", "]", ",", "[", "]", ",", "0", ")", "[", "0", "]", ":", "c", "=", "os", ".", "read", "(", "i", ",", "1024", ")", "if", "c", ":", "os", ".", "write", "(", "s", ".", "fileno", "(", ")", "if", "i", "==", "0", "else", "1", ",", "c", ")", "except", "KeyboardInterrupt", ":", "pass", "finally", ":", "termios", ".", "tcsetattr", "(", "0", ",", "termios", ".", "TCSADRAIN", ",", "old_settings", ")" ]
https://github.com/blasty/moneyshot/blob/0541356cca38e57ec03a30b6dff1d38c0c7dfd00/shell.py#L5-L24
FSecureLABS/drozer
df11e6e63fbaefa9b58ed1e42533ddf76241d7e1
src/drozer/repoman/repository_builder.py
python
RepositoryBuilder.__find_sources
(self)
Searches the source folder, to identify source files and packages, and isolate them ready for building.
Searches the source folder, to identify source files and packages, and isolate them ready for building.
[ "Searches", "the", "source", "folder", "to", "identify", "source", "files", "and", "packages", "and", "isolate", "them", "ready", "for", "building", "." ]
def __find_sources(self): """ Searches the source folder, to identify source files and packages, and isolate them ready for building. """ for root, folders, files in os.walk(self.source): self.__skip_folders(folders) if ".drozer_package" in files: yield SourcePackage(self.source, root, files) else: for f in files: if f.endswith(".py") and not f == "__init__.py": yield SourceFile(self.source, os.sep.join([root, f]))
[ "def", "__find_sources", "(", "self", ")", ":", "for", "root", ",", "folders", ",", "files", "in", "os", ".", "walk", "(", "self", ".", "source", ")", ":", "self", ".", "__skip_folders", "(", "folders", ")", "if", "\".drozer_package\"", "in", "files", ":", "yield", "SourcePackage", "(", "self", ".", "source", ",", "root", ",", "files", ")", "else", ":", "for", "f", "in", "files", ":", "if", "f", ".", "endswith", "(", "\".py\"", ")", "and", "not", "f", "==", "\"__init__.py\"", ":", "yield", "SourceFile", "(", "self", ".", "source", ",", "os", ".", "sep", ".", "join", "(", "[", "root", ",", "f", "]", ")", ")" ]
https://github.com/FSecureLABS/drozer/blob/df11e6e63fbaefa9b58ed1e42533ddf76241d7e1/src/drozer/repoman/repository_builder.py#L52-L66
psychopy/psychopy
01b674094f38d0e0bd51c45a6f66f671d7041696
psychopy/app/coder/coder.py
python
CodeEditor.setLexer
(self, lexer=None)
Lexer is a simple string (e.g. 'python', 'html') that will be converted to use the right STC_LEXER_XXXX value
Lexer is a simple string (e.g. 'python', 'html') that will be converted to use the right STC_LEXER_XXXX value
[ "Lexer", "is", "a", "simple", "string", "(", "e", ".", "g", ".", "python", "html", ")", "that", "will", "be", "converted", "to", "use", "the", "right", "STC_LEXER_XXXX", "value" ]
def setLexer(self, lexer=None): """Lexer is a simple string (e.g. 'python', 'html') that will be converted to use the right STC_LEXER_XXXX value """ lexer = 'null' if lexer is None else lexer try: lex = getattr(wx.stc, "STC_LEX_%s" % (lexer.upper())) except AttributeError: logging.warn("Unknown lexer %r. Using plain text." % lexer) lex = wx.stc.STC_LEX_NULL lexer = 'null' # then actually set it self.SetLexer(lex) self.setFonts() if lexer == 'python': self.SetIndentationGuides(self.coder.appData['showIndentGuides']) self.SetProperty("fold", "1") # allow folding self.SetProperty("tab.timmy.whinge.level", "1") elif lexer.lower() == 'html': self.SetProperty("fold", "1") # allow folding # 4 means 'tabs are bad'; 1 means 'flag inconsistency' self.SetProperty("tab.timmy.whinge.level", "1") elif lexer == 'cpp': # JS, C/C++, GLSL, mex, arduino self.SetIndentationGuides(self.coder.appData['showIndentGuides']) self.SetProperty("fold", "1") self.SetProperty("tab.timmy.whinge.level", "1") # don't grey out preprocessor lines self.SetProperty("lexer.cpp.track.preprocessor", "0") elif lexer == 'R': self.SetIndentationGuides(self.coder.appData['showIndentGuides']) self.SetProperty("fold", "1") self.SetProperty("tab.timmy.whinge.level", "1") else: self.SetIndentationGuides(0) self.SetProperty("tab.timmy.whinge.level", "0") # deprecated in newer versions of Scintilla self.SetStyleBits(self.GetStyleBitsNeeded()) # keep text from being squashed and hard to read spacing = self.coder.prefs['lineSpacing'] / 2. self.SetExtraAscent(int(spacing)) self.SetExtraDescent(int(spacing)) self.Colourise(0, -1) self._applyAppTheme()
[ "def", "setLexer", "(", "self", ",", "lexer", "=", "None", ")", ":", "lexer", "=", "'null'", "if", "lexer", "is", "None", "else", "lexer", "try", ":", "lex", "=", "getattr", "(", "wx", ".", "stc", ",", "\"STC_LEX_%s\"", "%", "(", "lexer", ".", "upper", "(", ")", ")", ")", "except", "AttributeError", ":", "logging", ".", "warn", "(", "\"Unknown lexer %r. Using plain text.\"", "%", "lexer", ")", "lex", "=", "wx", ".", "stc", ".", "STC_LEX_NULL", "lexer", "=", "'null'", "# then actually set it", "self", ".", "SetLexer", "(", "lex", ")", "self", ".", "setFonts", "(", ")", "if", "lexer", "==", "'python'", ":", "self", ".", "SetIndentationGuides", "(", "self", ".", "coder", ".", "appData", "[", "'showIndentGuides'", "]", ")", "self", ".", "SetProperty", "(", "\"fold\"", ",", "\"1\"", ")", "# allow folding", "self", ".", "SetProperty", "(", "\"tab.timmy.whinge.level\"", ",", "\"1\"", ")", "elif", "lexer", ".", "lower", "(", ")", "==", "'html'", ":", "self", ".", "SetProperty", "(", "\"fold\"", ",", "\"1\"", ")", "# allow folding", "# 4 means 'tabs are bad'; 1 means 'flag inconsistency'", "self", ".", "SetProperty", "(", "\"tab.timmy.whinge.level\"", ",", "\"1\"", ")", "elif", "lexer", "==", "'cpp'", ":", "# JS, C/C++, GLSL, mex, arduino", "self", ".", "SetIndentationGuides", "(", "self", ".", "coder", ".", "appData", "[", "'showIndentGuides'", "]", ")", "self", ".", "SetProperty", "(", "\"fold\"", ",", "\"1\"", ")", "self", ".", "SetProperty", "(", "\"tab.timmy.whinge.level\"", ",", "\"1\"", ")", "# don't grey out preprocessor lines", "self", ".", "SetProperty", "(", "\"lexer.cpp.track.preprocessor\"", ",", "\"0\"", ")", "elif", "lexer", "==", "'R'", ":", "self", ".", "SetIndentationGuides", "(", "self", ".", "coder", ".", "appData", "[", "'showIndentGuides'", "]", ")", "self", ".", "SetProperty", "(", "\"fold\"", ",", "\"1\"", ")", "self", ".", "SetProperty", "(", "\"tab.timmy.whinge.level\"", ",", "\"1\"", ")", "else", ":", "self", ".", "SetIndentationGuides", "(", "0", ")", "self", ".", "SetProperty", "(", "\"tab.timmy.whinge.level\"", ",", "\"0\"", ")", "# deprecated in newer versions of Scintilla", "self", ".", "SetStyleBits", "(", "self", ".", "GetStyleBitsNeeded", "(", ")", ")", "# keep text from being squashed and hard to read", "spacing", "=", "self", ".", "coder", ".", "prefs", "[", "'lineSpacing'", "]", "/", "2.", "self", ".", "SetExtraAscent", "(", "int", "(", "spacing", ")", ")", "self", ".", "SetExtraDescent", "(", "int", "(", "spacing", ")", ")", "self", ".", "Colourise", "(", "0", ",", "-", "1", ")", "self", ".", "_applyAppTheme", "(", ")" ]
https://github.com/psychopy/psychopy/blob/01b674094f38d0e0bd51c45a6f66f671d7041696/psychopy/app/coder/coder.py#L1044-L1091
xiaoyufenfei/Efficient-Segmentation-Networks
0f0c32e7af3463d381cb184a158ff60e16f7fb9a
dataset/camvid.py
python
CamVidTrainInform.__init__
(self, data_dir='', classes=11, train_set_file="", inform_data_file="", normVal=1.10)
Args: data_dir: directory where the dataset is kept classes: number of classes in the dataset inform_data_file: location where cached file has to be stored normVal: normalization value, as defined in ERFNet paper
Args: data_dir: directory where the dataset is kept classes: number of classes in the dataset inform_data_file: location where cached file has to be stored normVal: normalization value, as defined in ERFNet paper
[ "Args", ":", "data_dir", ":", "directory", "where", "the", "dataset", "is", "kept", "classes", ":", "number", "of", "classes", "in", "the", "dataset", "inform_data_file", ":", "location", "where", "cached", "file", "has", "to", "be", "stored", "normVal", ":", "normalization", "value", "as", "defined", "in", "ERFNet", "paper" ]
def __init__(self, data_dir='', classes=11, train_set_file="", inform_data_file="", normVal=1.10): """ Args: data_dir: directory where the dataset is kept classes: number of classes in the dataset inform_data_file: location where cached file has to be stored normVal: normalization value, as defined in ERFNet paper """ self.data_dir = data_dir self.classes = classes self.classWeights = np.ones(self.classes, dtype=np.float32) self.normVal = normVal self.mean = np.zeros(3, dtype=np.float32) self.std = np.zeros(3, dtype=np.float32) self.train_set_file = train_set_file self.inform_data_file = inform_data_file
[ "def", "__init__", "(", "self", ",", "data_dir", "=", "''", ",", "classes", "=", "11", ",", "train_set_file", "=", "\"\"", ",", "inform_data_file", "=", "\"\"", ",", "normVal", "=", "1.10", ")", ":", "self", ".", "data_dir", "=", "data_dir", "self", ".", "classes", "=", "classes", "self", ".", "classWeights", "=", "np", ".", "ones", "(", "self", ".", "classes", ",", "dtype", "=", "np", ".", "float32", ")", "self", ".", "normVal", "=", "normVal", "self", ".", "mean", "=", "np", ".", "zeros", "(", "3", ",", "dtype", "=", "np", ".", "float32", ")", "self", ".", "std", "=", "np", ".", "zeros", "(", "3", ",", "dtype", "=", "np", ".", "float32", ")", "self", ".", "train_set_file", "=", "train_set_file", "self", ".", "inform_data_file", "=", "inform_data_file" ]
https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks/blob/0f0c32e7af3463d381cb184a158ff60e16f7fb9a/dataset/camvid.py#L213-L229
gramps-project/gramps
04d4651a43eb210192f40a9f8c2bad8ee8fa3753
gramps/plugins/drawreport/statisticschart.py
python
StatisticsChart.index_items
(self, data, sort, reverse)
return index
creates & stores a sorted index for the items
creates & stores a sorted index for the items
[ "creates", "&", "stores", "a", "sorted", "index", "for", "the", "items" ]
def index_items(self, data, sort, reverse): """creates & stores a sorted index for the items""" # sort by item keys index = sorted(data, reverse=True if reverse else False) if sort == _options.SORT_VALUE: # set for the sorting function self.lookup_items = data # then sort by value index.sort(key=lambda x: self.lookup_items[x], reverse=True if reverse else False) return index
[ "def", "index_items", "(", "self", ",", "data", ",", "sort", ",", "reverse", ")", ":", "# sort by item keys", "index", "=", "sorted", "(", "data", ",", "reverse", "=", "True", "if", "reverse", "else", "False", ")", "if", "sort", "==", "_options", ".", "SORT_VALUE", ":", "# set for the sorting function", "self", ".", "lookup_items", "=", "data", "# then sort by value", "index", ".", "sort", "(", "key", "=", "lambda", "x", ":", "self", ".", "lookup_items", "[", "x", "]", ",", "reverse", "=", "True", "if", "reverse", "else", "False", ")", "return", "index" ]
https://github.com/gramps-project/gramps/blob/04d4651a43eb210192f40a9f8c2bad8ee8fa3753/gramps/plugins/drawreport/statisticschart.py#L837-L851
sametmax/Django--an-app-at-a-time
99eddf12ead76e6dfbeb09ce0bae61e282e22f8a
ignore_this_directory/django/contrib/messages/storage/session.py
python
SessionStorage._store
(self, messages, response, *args, **kwargs)
return []
Store a list of messages to the request's session.
Store a list of messages to the request's session.
[ "Store", "a", "list", "of", "messages", "to", "the", "request", "s", "session", "." ]
def _store(self, messages, response, *args, **kwargs): """ Store a list of messages to the request's session. """ if messages: self.request.session[self.session_key] = self.serialize_messages(messages) else: self.request.session.pop(self.session_key, None) return []
[ "def", "_store", "(", "self", ",", "messages", ",", "response", ",", "*", "args", ",", "*", "*", "kwargs", ")", ":", "if", "messages", ":", "self", ".", "request", ".", "session", "[", "self", ".", "session_key", "]", "=", "self", ".", "serialize_messages", "(", "messages", ")", "else", ":", "self", ".", "request", ".", "session", ".", "pop", "(", "self", ".", "session_key", ",", "None", ")", "return", "[", "]" ]
https://github.com/sametmax/Django--an-app-at-a-time/blob/99eddf12ead76e6dfbeb09ce0bae61e282e22f8a/ignore_this_directory/django/contrib/messages/storage/session.py#L31-L39
zutianbiao/baize
cf78a4a59b1fed29e825fe94c31dd093d7a747be
APP/APP_web/views.py
python
bussiness_manage_delete
(request)
return HttpResponse(json.dumps(json_response_data), content_type="application/json; charset=utf-8")
任务删除
任务删除
[ "任务删除" ]
def bussiness_manage_delete(request): """ 任务删除 """ id = request.POST.get('id', '') try: bussiness = Bussiness.objects.get(id=id) bussiness.delete() except Exception, e: json_response_data = { "success": False, "msg": u"业务已经不存在", 'data': None } return HttpResponse(json.dumps(json_response_data), content_type="application/json; charset=utf-8") data = { "id": id } json_response_data = { "success": True, "msg": u"删除业务成功", 'data': data } return HttpResponse(json.dumps(json_response_data), content_type="application/json; charset=utf-8")
[ "def", "bussiness_manage_delete", "(", "request", ")", ":", "id", "=", "request", ".", "POST", ".", "get", "(", "'id'", ",", "''", ")", "try", ":", "bussiness", "=", "Bussiness", ".", "objects", ".", "get", "(", "id", "=", "id", ")", "bussiness", ".", "delete", "(", ")", "except", "Exception", ",", "e", ":", "json_response_data", "=", "{", "\"success\"", ":", "False", ",", "\"msg\"", ":", "u\"业务已经不存在\",", "", "'data'", ":", "None", "}", "return", "HttpResponse", "(", "json", ".", "dumps", "(", "json_response_data", ")", ",", "content_type", "=", "\"application/json; charset=utf-8\"", ")", "data", "=", "{", "\"id\"", ":", "id", "}", "json_response_data", "=", "{", "\"success\"", ":", "True", ",", "\"msg\"", ":", "u\"删除业务成功\",", "", "'data'", ":", "data", "}", "return", "HttpResponse", "(", "json", ".", "dumps", "(", "json_response_data", ")", ",", "content_type", "=", "\"application/json; charset=utf-8\"", ")" ]
https://github.com/zutianbiao/baize/blob/cf78a4a59b1fed29e825fe94c31dd093d7a747be/APP/APP_web/views.py#L4437-L4458
michaelhush/M-LOOP
cd0bf2d0de0bfe7f533156399a94b576f7f34a35
mloop/learners.py
python
GaussianProcessLearner.find_next_parameters
(self)
return next_params
Returns next parameters to find. Increments counters and bias function appropriately. Return: next_params (array): Returns next parameters from biased cost search.
Returns next parameters to find. Increments counters and bias function appropriately.
[ "Returns", "next", "parameters", "to", "find", ".", "Increments", "counters", "and", "bias", "function", "appropriately", "." ]
def find_next_parameters(self): ''' Returns next parameters to find. Increments counters and bias function appropriately. Return: next_params (array): Returns next parameters from biased cost search. ''' self.params_count += 1 self.update_bias_function() self.update_search_params() next_params = None next_cost = float('inf') for start_params in self.search_params: result = so.minimize(self.predict_biased_cost, start_params, bounds = self.search_region, tol=self.search_precision) if result.fun < next_cost: next_params = result.x next_cost = result.fun return next_params
[ "def", "find_next_parameters", "(", "self", ")", ":", "self", ".", "params_count", "+=", "1", "self", ".", "update_bias_function", "(", ")", "self", ".", "update_search_params", "(", ")", "next_params", "=", "None", "next_cost", "=", "float", "(", "'inf'", ")", "for", "start_params", "in", "self", ".", "search_params", ":", "result", "=", "so", ".", "minimize", "(", "self", ".", "predict_biased_cost", ",", "start_params", ",", "bounds", "=", "self", ".", "search_region", ",", "tol", "=", "self", ".", "search_precision", ")", "if", "result", ".", "fun", "<", "next_cost", ":", "next_params", "=", "result", ".", "x", "next_cost", "=", "result", ".", "fun", "return", "next_params" ]
https://github.com/michaelhush/M-LOOP/blob/cd0bf2d0de0bfe7f533156399a94b576f7f34a35/mloop/learners.py#L1998-L2015
daavoo/pyntcloud
1bd6d0a6409ae13147c54c132cd9ffaa6455d398
pyntcloud/io/las.py
python
read_las
(filename, xyz_dtype="float32", rgb_dtype="uint8", backend="pylas")
return data
Read a .las/laz file and store elements in pandas DataFrame. Parameters ---------- filename: str Path to the filename xyz_dtype: str Defines the data type of the xyz coordinate rgb_dtype: str Defines the data type of the color Returns ------- data: dict Elements as pandas DataFrames.
Read a .las/laz file and store elements in pandas DataFrame.
[ "Read", "a", ".", "las", "/", "laz", "file", "and", "store", "elements", "in", "pandas", "DataFrame", "." ]
def read_las(filename, xyz_dtype="float32", rgb_dtype="uint8", backend="pylas"): """Read a .las/laz file and store elements in pandas DataFrame. Parameters ---------- filename: str Path to the filename xyz_dtype: str Defines the data type of the xyz coordinate rgb_dtype: str Defines the data type of the color Returns ------- data: dict Elements as pandas DataFrames. """ if backend == "pylas": data = read_las_with_pylas(filename) elif backend == "laspy": data = read_las_with_laspy(filename) else: raise ValueError(f"Unsupported backend. Expected one of ['pylas', 'laspy'] but got {backend}") data = convert_location_to_dtype(data, xyz_dtype) data = convert_color_to_dtype(data, rgb_dtype) return data
[ "def", "read_las", "(", "filename", ",", "xyz_dtype", "=", "\"float32\"", ",", "rgb_dtype", "=", "\"uint8\"", ",", "backend", "=", "\"pylas\"", ")", ":", "if", "backend", "==", "\"pylas\"", ":", "data", "=", "read_las_with_pylas", "(", "filename", ")", "elif", "backend", "==", "\"laspy\"", ":", "data", "=", "read_las_with_laspy", "(", "filename", ")", "else", ":", "raise", "ValueError", "(", "f\"Unsupported backend. Expected one of ['pylas', 'laspy'] but got {backend}\"", ")", "data", "=", "convert_location_to_dtype", "(", "data", ",", "xyz_dtype", ")", "data", "=", "convert_color_to_dtype", "(", "data", ",", "rgb_dtype", ")", "return", "data" ]
https://github.com/daavoo/pyntcloud/blob/1bd6d0a6409ae13147c54c132cd9ffaa6455d398/pyntcloud/io/las.py#L73-L97
openedx/edx-platform
68dd185a0ab45862a2a61e0f803d7e03d2be71b5
common/lib/xmodule/xmodule/modulestore/mixed.py
python
MixedModuleStore.get_courses
(self, **kwargs)
return list(courses.values())
Returns a list containing the top level XModuleDescriptors of the courses in this modulestore.
Returns a list containing the top level XModuleDescriptors of the courses in this modulestore.
[ "Returns", "a", "list", "containing", "the", "top", "level", "XModuleDescriptors", "of", "the", "courses", "in", "this", "modulestore", "." ]
def get_courses(self, **kwargs): ''' Returns a list containing the top level XModuleDescriptors of the courses in this modulestore. ''' courses = {} for store in self.modulestores: # filter out ones which were fetched from earlier stores but locations may not be == for course in store.get_courses(**kwargs): course_id = self._clean_locator_for_mapping(course.id) if course_id not in courses: # course is indeed unique. save it in result courses[course_id] = course return list(courses.values())
[ "def", "get_courses", "(", "self", ",", "*", "*", "kwargs", ")", ":", "courses", "=", "{", "}", "for", "store", "in", "self", ".", "modulestores", ":", "# filter out ones which were fetched from earlier stores but locations may not be ==", "for", "course", "in", "store", ".", "get_courses", "(", "*", "*", "kwargs", ")", ":", "course_id", "=", "self", ".", "_clean_locator_for_mapping", "(", "course", ".", "id", ")", "if", "course_id", "not", "in", "courses", ":", "# course is indeed unique. save it in result", "courses", "[", "course_id", "]", "=", "course", "return", "list", "(", "courses", ".", "values", "(", ")", ")" ]
https://github.com/openedx/edx-platform/blob/68dd185a0ab45862a2a61e0f803d7e03d2be71b5/common/lib/xmodule/xmodule/modulestore/mixed.py#L303-L315
fortharris/Pcode
147962d160a834c219e12cb456abc130826468e4
pyflakes/checker.py
python
Checker.handleNodeStore
(self, node)
[]
def handleNodeStore(self, node): name = getNodeName(node) if not name: return # if the name hasn't already been defined in the current scope if isinstance(self.scope, FunctionScope) and name not in self.scope: # for each function or module scope above us for scope in self.scopeStack[:-1]: if not isinstance(scope, (FunctionScope, ModuleScope)): continue # if the name was defined in that scope, and the name has # been accessed already in the current scope, and hasn't # been declared global used = name in scope and scope[name].used if used and used[0] is self.scope and name not in self.scope.globals: # then it's probably a mistake self.report(messages.UndefinedLocal, scope[name].used[1], name, scope[name].source) break parent_stmt = self.getParent(node) if isinstance(parent_stmt, (ast.For, ast.comprehension)) or ( parent_stmt != node.parent and not self.isLiteralTupleUnpacking(parent_stmt)): binding = Binding(name, node) elif name == '__all__' and isinstance(self.scope, ModuleScope): binding = ExportBinding(name, node.parent, self.scope) else: binding = Assignment(name, node) if name in self.scope: binding.used = self.scope[name].used self.addBinding(node, binding)
[ "def", "handleNodeStore", "(", "self", ",", "node", ")", ":", "name", "=", "getNodeName", "(", "node", ")", "if", "not", "name", ":", "return", "# if the name hasn't already been defined in the current scope", "if", "isinstance", "(", "self", ".", "scope", ",", "FunctionScope", ")", "and", "name", "not", "in", "self", ".", "scope", ":", "# for each function or module scope above us", "for", "scope", "in", "self", ".", "scopeStack", "[", ":", "-", "1", "]", ":", "if", "not", "isinstance", "(", "scope", ",", "(", "FunctionScope", ",", "ModuleScope", ")", ")", ":", "continue", "# if the name was defined in that scope, and the name has", "# been accessed already in the current scope, and hasn't", "# been declared global", "used", "=", "name", "in", "scope", "and", "scope", "[", "name", "]", ".", "used", "if", "used", "and", "used", "[", "0", "]", "is", "self", ".", "scope", "and", "name", "not", "in", "self", ".", "scope", ".", "globals", ":", "# then it's probably a mistake", "self", ".", "report", "(", "messages", ".", "UndefinedLocal", ",", "scope", "[", "name", "]", ".", "used", "[", "1", "]", ",", "name", ",", "scope", "[", "name", "]", ".", "source", ")", "break", "parent_stmt", "=", "self", ".", "getParent", "(", "node", ")", "if", "isinstance", "(", "parent_stmt", ",", "(", "ast", ".", "For", ",", "ast", ".", "comprehension", ")", ")", "or", "(", "parent_stmt", "!=", "node", ".", "parent", "and", "not", "self", ".", "isLiteralTupleUnpacking", "(", "parent_stmt", ")", ")", ":", "binding", "=", "Binding", "(", "name", ",", "node", ")", "elif", "name", "==", "'__all__'", "and", "isinstance", "(", "self", ".", "scope", ",", "ModuleScope", ")", ":", "binding", "=", "ExportBinding", "(", "name", ",", "node", ".", "parent", ",", "self", ".", "scope", ")", "else", ":", "binding", "=", "Assignment", "(", "name", ",", "node", ")", "if", "name", "in", "self", ".", "scope", ":", "binding", ".", "used", "=", "self", ".", "scope", "[", "name", "]", ".", "used", "self", ".", "addBinding", "(", "node", ",", "binding", ")" ]
https://github.com/fortharris/Pcode/blob/147962d160a834c219e12cb456abc130826468e4/pyflakes/checker.py#L500-L531
mherrmann/selenium-python-helium
02f9a5a872871999d683c84461ac0d0b3e9da192
helium/__init__.py
python
drag_file
(file_path, to)
Simulates the dragging of a file from the computer over the browser window and dropping it over the given element. This allows, for example, to attach files to emails in Gmail:: click("COMPOSE") write("example@gmail.com", into="To") write("Email subject", into="Subject") drag_file(r"C:\\Documents\\notes.txt", to="Drop files here")
Simulates the dragging of a file from the computer over the browser window and dropping it over the given element. This allows, for example, to attach files to emails in Gmail::
[ "Simulates", "the", "dragging", "of", "a", "file", "from", "the", "computer", "over", "the", "browser", "window", "and", "dropping", "it", "over", "the", "given", "element", ".", "This", "allows", "for", "example", "to", "attach", "files", "to", "emails", "in", "Gmail", "::" ]
def drag_file(file_path, to): """ Simulates the dragging of a file from the computer over the browser window and dropping it over the given element. This allows, for example, to attach files to emails in Gmail:: click("COMPOSE") write("example@gmail.com", into="To") write("Email subject", into="Subject") drag_file(r"C:\\Documents\\notes.txt", to="Drop files here") """ _get_api_impl().drag_file_impl(file_path, to)
[ "def", "drag_file", "(", "file_path", ",", "to", ")", ":", "_get_api_impl", "(", ")", ".", "drag_file_impl", "(", "file_path", ",", "to", ")" ]
https://github.com/mherrmann/selenium-python-helium/blob/02f9a5a872871999d683c84461ac0d0b3e9da192/helium/__init__.py#L413-L424
deepmipt/DeepPavlov
08555428388fed3c7b036c0a82a70a25efcabcff
deeppavlov/models/multitask_bert/multitask_bert.py
python
MTBertTask.get_sess_run_train_args
(self, *args)
Returns fetches and feed_dict for task ``train_on_batch`` method. Overriding methods take task inputs as positional arguments. ATTENTION! Let ``get_sess_run_infer_args`` method have ``n_x_args`` arguments. Then the order of first ``n_x_args`` arguments of ``get_sess_run_train_args`` method arguments has to match the order of ``get_sess_run_infer_args`` arguments. Args: args: task inputs followed by expect outputs. Returns: fetches and feed_dict
Returns fetches and feed_dict for task ``train_on_batch`` method.
[ "Returns", "fetches", "and", "feed_dict", "for", "task", "train_on_batch", "method", "." ]
def get_sess_run_train_args(self, *args) -> Tuple[List[tf.Tensor], Dict[tf.placeholder, Any]]: """Returns fetches and feed_dict for task ``train_on_batch`` method. Overriding methods take task inputs as positional arguments. ATTENTION! Let ``get_sess_run_infer_args`` method have ``n_x_args`` arguments. Then the order of first ``n_x_args`` arguments of ``get_sess_run_train_args`` method arguments has to match the order of ``get_sess_run_infer_args`` arguments. Args: args: task inputs followed by expect outputs. Returns: fetches and feed_dict """ pass
[ "def", "get_sess_run_train_args", "(", "self", ",", "*", "args", ")", "->", "Tuple", "[", "List", "[", "tf", ".", "Tensor", "]", ",", "Dict", "[", "tf", ".", "placeholder", ",", "Any", "]", "]", ":", "pass" ]
https://github.com/deepmipt/DeepPavlov/blob/08555428388fed3c7b036c0a82a70a25efcabcff/deeppavlov/models/multitask_bert/multitask_bert.py#L242-L257
kuri65536/python-for-android
26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891
python-modules/twisted/twisted/conch/ssh/keys.py
python
lenSig
(obj)
return obj.size()/8
Return the length of the signature in bytes for a key object. @type obj: C{Crypto.PublicKey.pubkey.pubkey} @rtype: C{long}
Return the length of the signature in bytes for a key object.
[ "Return", "the", "length", "of", "the", "signature", "in", "bytes", "for", "a", "key", "object", "." ]
def lenSig(obj): """ Return the length of the signature in bytes for a key object. @type obj: C{Crypto.PublicKey.pubkey.pubkey} @rtype: C{long} """ return obj.size()/8
[ "def", "lenSig", "(", "obj", ")", ":", "return", "obj", ".", "size", "(", ")", "/", "8" ]
https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-modules/twisted/twisted/conch/ssh/keys.py#L770-L777
robcarver17/pysystemtrade
b0385705b7135c52d39cb6d2400feece881bcca9
systems/accounts/account_buffering_subsystem.py
python
accountBufferingSubSystemLevel.get_buffers_for_subsystem_position
(self, instrument_code: str)
return self.parent.positionSize.get_buffers_for_subsystem_position(instrument_code)
Get the buffered position from a previous module :param instrument_code: instrument to get values for :type instrument_code: str :returns: Tx2 pd.DataFrame: columns top_pos, bot_pos KEY INPUT
Get the buffered position from a previous module
[ "Get", "the", "buffered", "position", "from", "a", "previous", "module" ]
def get_buffers_for_subsystem_position(self, instrument_code: str) -> pd.DataFrame: """ Get the buffered position from a previous module :param instrument_code: instrument to get values for :type instrument_code: str :returns: Tx2 pd.DataFrame: columns top_pos, bot_pos KEY INPUT """ return self.parent.positionSize.get_buffers_for_subsystem_position(instrument_code)
[ "def", "get_buffers_for_subsystem_position", "(", "self", ",", "instrument_code", ":", "str", ")", "->", "pd", ".", "DataFrame", ":", "return", "self", ".", "parent", ".", "positionSize", ".", "get_buffers_for_subsystem_position", "(", "instrument_code", ")" ]
https://github.com/robcarver17/pysystemtrade/blob/b0385705b7135c52d39cb6d2400feece881bcca9/systems/accounts/account_buffering_subsystem.py#L89-L101
mchristopher/PokemonGo-DesktopMap
ec37575f2776ee7d64456e2a1f6b6b78830b4fe0
app/pylibs/osx64/Cryptodome/Hash/SHAKE256.py
python
SHAKE256_XOF.read
(self, length)
return get_raw_buffer(bfr)
Return the next ``length`` bytes of **binary** (non-printable) digest for the message. You cannot use ``update`` anymore after the first call to ``read``. :Return: A byte string of `length` bytes.
Return the next ``length`` bytes of **binary** (non-printable) digest for the message.
[ "Return", "the", "next", "length", "bytes", "of", "**", "binary", "**", "(", "non", "-", "printable", ")", "digest", "for", "the", "message", "." ]
def read(self, length): """Return the next ``length`` bytes of **binary** (non-printable) digest for the message. You cannot use ``update`` anymore after the first call to ``read``. :Return: A byte string of `length` bytes. """ self._is_squeezing = True bfr = create_string_buffer(length) result = _raw_keccak_lib.keccak_squeeze(self._state.get(), bfr, c_size_t(length)) if result: raise ValueError("Error %d while extracting from SHAKE256" % result) return get_raw_buffer(bfr)
[ "def", "read", "(", "self", ",", "length", ")", ":", "self", ".", "_is_squeezing", "=", "True", "bfr", "=", "create_string_buffer", "(", "length", ")", "result", "=", "_raw_keccak_lib", ".", "keccak_squeeze", "(", "self", ".", "_state", ".", "get", "(", ")", ",", "bfr", ",", "c_size_t", "(", "length", ")", ")", "if", "result", ":", "raise", "ValueError", "(", "\"Error %d while extracting from SHAKE256\"", "%", "result", ")", "return", "get_raw_buffer", "(", "bfr", ")" ]
https://github.com/mchristopher/PokemonGo-DesktopMap/blob/ec37575f2776ee7d64456e2a1f6b6b78830b4fe0/app/pylibs/osx64/Cryptodome/Hash/SHAKE256.py#L116-L134
UCL-INGI/INGInious
60f10cb4c375ce207471043e76bd813220b95399
inginious/frontend/user_manager.py
python
UserManager.set_session_token
(self, token)
Sets the token of the current user in the session, if one is open.
Sets the token of the current user in the session, if one is open.
[ "Sets", "the", "token", "of", "the", "current", "user", "in", "the", "session", "if", "one", "is", "open", "." ]
def set_session_token(self, token): """ Sets the token of the current user in the session, if one is open.""" if self.session_logged_in(): self._session["token"] = token
[ "def", "set_session_token", "(", "self", ",", "token", ")", ":", "if", "self", ".", "session_logged_in", "(", ")", ":", "self", ".", "_session", "[", "\"token\"", "]", "=", "token" ]
https://github.com/UCL-INGI/INGInious/blob/60f10cb4c375ce207471043e76bd813220b95399/inginious/frontend/user_manager.py#L185-L188
KhronosGroup/OpenXR-SDK-Source
76756e2e7849b15466d29bee7d80cada92865550
external/python/jinja2/runtime.py
python
Context.super
(self, name, current)
return BlockReference(name, self, blocks, index)
Render a parent block.
Render a parent block.
[ "Render", "a", "parent", "block", "." ]
def super(self, name, current): """Render a parent block.""" try: blocks = self.blocks[name] index = blocks.index(current) + 1 blocks[index] except LookupError: return self.environment.undefined('there is no parent block ' 'called %r.' % name, name='super') return BlockReference(name, self, blocks, index)
[ "def", "super", "(", "self", ",", "name", ",", "current", ")", ":", "try", ":", "blocks", "=", "self", ".", "blocks", "[", "name", "]", "index", "=", "blocks", ".", "index", "(", "current", ")", "+", "1", "blocks", "[", "index", "]", "except", "LookupError", ":", "return", "self", ".", "environment", ".", "undefined", "(", "'there is no parent block '", "'called %r.'", "%", "name", ",", "name", "=", "'super'", ")", "return", "BlockReference", "(", "name", ",", "self", ",", "blocks", ",", "index", ")" ]
https://github.com/KhronosGroup/OpenXR-SDK-Source/blob/76756e2e7849b15466d29bee7d80cada92865550/external/python/jinja2/runtime.py#L175-L185
tkarras/progressive_growing_of_gans
2504c3f3cb98ca58751610ad61fa1097313152bd
util_scripts.py
python
generate_fake_images
(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8)
[]
def generate_fake_images(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8): network_pkl = misc.locate_network_pkl(run_id, snapshot) if png_prefix is None: png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-' random_state = np.random.RandomState(random_seed) print('Loading network from "%s"...' % network_pkl) G, D, Gs = misc.load_network_pkl(run_id, snapshot) result_subdir = misc.create_result_subdir(config.result_dir, config.desc) for png_idx in range(num_pngs): print('Generating png %d / %d...' % (png_idx, num_pngs)) latents = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state) labels = np.zeros([latents.shape[0], 0], np.float32) images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8) misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size) open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
[ "def", "generate_fake_images", "(", "run_id", ",", "snapshot", "=", "None", ",", "grid_size", "=", "[", "1", ",", "1", "]", ",", "num_pngs", "=", "1", ",", "image_shrink", "=", "1", ",", "png_prefix", "=", "None", ",", "random_seed", "=", "1000", ",", "minibatch_size", "=", "8", ")", ":", "network_pkl", "=", "misc", ".", "locate_network_pkl", "(", "run_id", ",", "snapshot", ")", "if", "png_prefix", "is", "None", ":", "png_prefix", "=", "misc", ".", "get_id_string_for_network_pkl", "(", "network_pkl", ")", "+", "'-'", "random_state", "=", "np", ".", "random", ".", "RandomState", "(", "random_seed", ")", "print", "(", "'Loading network from \"%s\"...'", "%", "network_pkl", ")", "G", ",", "D", ",", "Gs", "=", "misc", ".", "load_network_pkl", "(", "run_id", ",", "snapshot", ")", "result_subdir", "=", "misc", ".", "create_result_subdir", "(", "config", ".", "result_dir", ",", "config", ".", "desc", ")", "for", "png_idx", "in", "range", "(", "num_pngs", ")", ":", "print", "(", "'Generating png %d / %d...'", "%", "(", "png_idx", ",", "num_pngs", ")", ")", "latents", "=", "misc", ".", "random_latents", "(", "np", ".", "prod", "(", "grid_size", ")", ",", "Gs", ",", "random_state", "=", "random_state", ")", "labels", "=", "np", ".", "zeros", "(", "[", "latents", ".", "shape", "[", "0", "]", ",", "0", "]", ",", "np", ".", "float32", ")", "images", "=", "Gs", ".", "run", "(", "latents", ",", "labels", ",", "minibatch_size", "=", "minibatch_size", ",", "num_gpus", "=", "config", ".", "num_gpus", ",", "out_mul", "=", "127.5", ",", "out_add", "=", "127.5", ",", "out_shrink", "=", "image_shrink", ",", "out_dtype", "=", "np", ".", "uint8", ")", "misc", ".", "save_image_grid", "(", "images", ",", "os", ".", "path", ".", "join", "(", "result_subdir", ",", "'%s%06d.png'", "%", "(", "png_prefix", ",", "png_idx", ")", ")", ",", "[", "0", ",", "255", "]", ",", "grid_size", ")", "open", "(", "os", ".", "path", ".", "join", "(", "result_subdir", ",", "'_done.txt'", ")", ",", "'wt'", ")", ".", "close", "(", ")" ]
https://github.com/tkarras/progressive_growing_of_gans/blob/2504c3f3cb98ca58751610ad61fa1097313152bd/util_scripts.py#L28-L44
marcoeilers/nagini
a2a19df7d833e67841e03c9885869c3dddef3327
src/nagini_translation/lib/util.py
python
contains_stmt
(container: Any, contained: ast.AST)
Checks if 'contained' is a part of the partial AST whose root is 'container'.
Checks if 'contained' is a part of the partial AST whose root is 'container'.
[ "Checks", "if", "contained", "is", "a", "part", "of", "the", "partial", "AST", "whose", "root", "is", "container", "." ]
def contains_stmt(container: Any, contained: ast.AST) -> bool: """ Checks if 'contained' is a part of the partial AST whose root is 'container'. """ if container is contained: return True if isinstance(container, list): for stmt in container: if contains_stmt(stmt, contained): return True return False elif isinstance(container, ast.AST): for field in container._fields: if contains_stmt(getattr(container, field), contained): return True return False else: return False
[ "def", "contains_stmt", "(", "container", ":", "Any", ",", "contained", ":", "ast", ".", "AST", ")", "->", "bool", ":", "if", "container", "is", "contained", ":", "return", "True", "if", "isinstance", "(", "container", ",", "list", ")", ":", "for", "stmt", "in", "container", ":", "if", "contains_stmt", "(", "stmt", ",", "contained", ")", ":", "return", "True", "return", "False", "elif", "isinstance", "(", "container", ",", "ast", ".", "AST", ")", ":", "for", "field", "in", "container", ".", "_fields", ":", "if", "contains_stmt", "(", "getattr", "(", "container", ",", "field", ")", ",", "contained", ")", ":", "return", "True", "return", "False", "else", ":", "return", "False" ]
https://github.com/marcoeilers/nagini/blob/a2a19df7d833e67841e03c9885869c3dddef3327/src/nagini_translation/lib/util.py#L128-L146
facelessuser/ColorHelper
cfed17c35dbae4db49a14165ef222407c48a3014
lib/coloraide/color.py
python
Color.space
(self)
return self._space.NAME
The current color space.
The current color space.
[ "The", "current", "color", "space", "." ]
def space(self) -> str: """The current color space.""" return self._space.NAME
[ "def", "space", "(", "self", ")", "->", "str", ":", "return", "self", ".", "_space", ".", "NAME" ]
https://github.com/facelessuser/ColorHelper/blob/cfed17c35dbae4db49a14165ef222407c48a3014/lib/coloraide/color.py#L359-L362
facebookresearch/pytorch_GAN_zoo
b75dee40918caabb4fe7ec561522717bf096a8cb
visualization/np_visualizer.py
python
publishTensors
(data, out_size_image, caption="", window_token=None, env="main")
return None
[]
def publishTensors(data, out_size_image, caption="", window_token=None, env="main"): return None
[ "def", "publishTensors", "(", "data", ",", "out_size_image", ",", "caption", "=", "\"\"", ",", "window_token", "=", "None", ",", "env", "=", "\"main\"", ")", ":", "return", "None" ]
https://github.com/facebookresearch/pytorch_GAN_zoo/blob/b75dee40918caabb4fe7ec561522717bf096a8cb/visualization/np_visualizer.py#L75-L76
NervanaSystems/neon
8c3fb8a93b4a89303467b25817c60536542d08bd
neon/transforms/cost.py
python
LogLoss.__init__
(self)
[]
def __init__(self): self.correctProbs = self.be.iobuf(1) self.metric_names = ['LogLoss']
[ "def", "__init__", "(", "self", ")", ":", "self", ".", "correctProbs", "=", "self", ".", "be", ".", "iobuf", "(", "1", ")", "self", ".", "metric_names", "=", "[", "'LogLoss'", "]" ]
https://github.com/NervanaSystems/neon/blob/8c3fb8a93b4a89303467b25817c60536542d08bd/neon/transforms/cost.py#L329-L331
maqp/tfc
4bb13da1f19671e1e723db7e8a21be58847209af
src/common/db_contacts.py
python
ContactList.remove_contact_by_pub_key
(self, onion_pub_key: bytes)
Remove the contact that has a matching Onion Service public key. If the contact was found and removed, write changes to the database.
Remove the contact that has a matching Onion Service public key.
[ "Remove", "the", "contact", "that", "has", "a", "matching", "Onion", "Service", "public", "key", "." ]
def remove_contact_by_pub_key(self, onion_pub_key: bytes) -> None: """Remove the contact that has a matching Onion Service public key. If the contact was found and removed, write changes to the database. """ for i, c in enumerate(self.contacts): if c.onion_pub_key == onion_pub_key: del self.contacts[i] self.store_contacts() break
[ "def", "remove_contact_by_pub_key", "(", "self", ",", "onion_pub_key", ":", "bytes", ")", "->", "None", ":", "for", "i", ",", "c", "in", "enumerate", "(", "self", ".", "contacts", ")", ":", "if", "c", ".", "onion_pub_key", "==", "onion_pub_key", ":", "del", "self", ".", "contacts", "[", "i", "]", "self", ".", "store_contacts", "(", ")", "break" ]
https://github.com/maqp/tfc/blob/4bb13da1f19671e1e723db7e8a21be58847209af/src/common/db_contacts.py#L367-L376
elastic/elasticsearch-py
6ef1adfa3c840a84afda7369cd8e43ae7dc45cdb
elasticsearch/_sync/client/cat.py
python
CatClient.aliases
( self, *, name: Optional[Any] = None, error_trace: Optional[bool] = None, expand_wildcards: Optional[Any] = None, filter_path: Optional[Union[List[str], str]] = None, format: Optional[str] = None, h: Optional[Any] = None, help: Optional[bool] = None, human: Optional[bool] = None, local: Optional[bool] = None, master_timeout: Optional[Any] = None, pretty: Optional[bool] = None, s: Optional[List[str]] = None, v: Optional[bool] = None, )
return self._perform_request("GET", __target, headers=__headers)
Shows information about currently configured aliases to indices including filter and routing infos. `<https://www.elastic.co/guide/en/elasticsearch/reference/master/cat-alias.html>`_ :param name: A comma-separated list of alias names to return :param expand_wildcards: Whether to expand wildcard expression to concrete indices that are open, closed or both. :param format: Specifies the format to return the columnar data in, can be set to `text`, `json`, `cbor`, `yaml`, or `smile`. :param h: List of columns to appear in the response. Supports simple wildcards. :param help: When set to `true` will output available columns. This option can't be combined with any other query string option. :param local: If `true`, the request computes the list of selected nodes from the local cluster state. If `false` the list of selected nodes are computed from the cluster state of the master node. In both cases the coordinating node will send requests for further information to each selected node. :param master_timeout: Period to wait for a connection to the master node. :param s: List of columns that determine how the table should be sorted. Sorting defaults to ascending and can be changed by setting `:asc` or `:desc` as a suffix to the column name. :param v: When set to `true` will enable verbose output.
Shows information about currently configured aliases to indices including filter and routing infos.
[ "Shows", "information", "about", "currently", "configured", "aliases", "to", "indices", "including", "filter", "and", "routing", "infos", "." ]
def aliases( self, *, name: Optional[Any] = None, error_trace: Optional[bool] = None, expand_wildcards: Optional[Any] = None, filter_path: Optional[Union[List[str], str]] = None, format: Optional[str] = None, h: Optional[Any] = None, help: Optional[bool] = None, human: Optional[bool] = None, local: Optional[bool] = None, master_timeout: Optional[Any] = None, pretty: Optional[bool] = None, s: Optional[List[str]] = None, v: Optional[bool] = None, ) -> Union[ObjectApiResponse[Any], TextApiResponse]: """ Shows information about currently configured aliases to indices including filter and routing infos. `<https://www.elastic.co/guide/en/elasticsearch/reference/master/cat-alias.html>`_ :param name: A comma-separated list of alias names to return :param expand_wildcards: Whether to expand wildcard expression to concrete indices that are open, closed or both. :param format: Specifies the format to return the columnar data in, can be set to `text`, `json`, `cbor`, `yaml`, or `smile`. :param h: List of columns to appear in the response. Supports simple wildcards. :param help: When set to `true` will output available columns. This option can't be combined with any other query string option. :param local: If `true`, the request computes the list of selected nodes from the local cluster state. If `false` the list of selected nodes are computed from the cluster state of the master node. In both cases the coordinating node will send requests for further information to each selected node. :param master_timeout: Period to wait for a connection to the master node. :param s: List of columns that determine how the table should be sorted. Sorting defaults to ascending and can be changed by setting `:asc` or `:desc` as a suffix to the column name. :param v: When set to `true` will enable verbose output. """ if name not in SKIP_IN_PATH: __path = f"/_cat/aliases/{_quote(name)}" else: __path = "/_cat/aliases" __query: Dict[str, Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path if format is not None: __query["format"] = format if h is not None: __query["h"] = h if help is not None: __query["help"] = help if human is not None: __query["human"] = human if local is not None: __query["local"] = local if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty if s is not None: __query["s"] = s if v is not None: __query["v"] = v if __query: __target = f"{__path}?{_quote_query(__query)}" else: __target = __path __headers = {"accept": "text/plain,application/json"} return self._perform_request("GET", __target, headers=__headers)
[ "def", "aliases", "(", "self", ",", "*", ",", "name", ":", "Optional", "[", "Any", "]", "=", "None", ",", "error_trace", ":", "Optional", "[", "bool", "]", "=", "None", ",", "expand_wildcards", ":", "Optional", "[", "Any", "]", "=", "None", ",", "filter_path", ":", "Optional", "[", "Union", "[", "List", "[", "str", "]", ",", "str", "]", "]", "=", "None", ",", "format", ":", "Optional", "[", "str", "]", "=", "None", ",", "h", ":", "Optional", "[", "Any", "]", "=", "None", ",", "help", ":", "Optional", "[", "bool", "]", "=", "None", ",", "human", ":", "Optional", "[", "bool", "]", "=", "None", ",", "local", ":", "Optional", "[", "bool", "]", "=", "None", ",", "master_timeout", ":", "Optional", "[", "Any", "]", "=", "None", ",", "pretty", ":", "Optional", "[", "bool", "]", "=", "None", ",", "s", ":", "Optional", "[", "List", "[", "str", "]", "]", "=", "None", ",", "v", ":", "Optional", "[", "bool", "]", "=", "None", ",", ")", "->", "Union", "[", "ObjectApiResponse", "[", "Any", "]", ",", "TextApiResponse", "]", ":", "if", "name", "not", "in", "SKIP_IN_PATH", ":", "__path", "=", "f\"/_cat/aliases/{_quote(name)}\"", "else", ":", "__path", "=", "\"/_cat/aliases\"", "__query", ":", "Dict", "[", "str", ",", "Any", "]", "=", "{", "}", "if", "error_trace", "is", "not", "None", ":", "__query", "[", "\"error_trace\"", "]", "=", "error_trace", "if", "expand_wildcards", "is", "not", "None", ":", "__query", "[", "\"expand_wildcards\"", "]", "=", "expand_wildcards", "if", "filter_path", "is", "not", "None", ":", "__query", "[", "\"filter_path\"", "]", "=", "filter_path", "if", "format", "is", "not", "None", ":", "__query", "[", "\"format\"", "]", "=", "format", "if", "h", "is", "not", "None", ":", "__query", "[", "\"h\"", "]", "=", "h", "if", "help", "is", "not", "None", ":", "__query", "[", "\"help\"", "]", "=", "help", "if", "human", "is", "not", "None", ":", "__query", "[", "\"human\"", "]", "=", "human", "if", "local", "is", "not", "None", ":", "__query", "[", "\"local\"", "]", "=", "local", "if", "master_timeout", "is", "not", "None", ":", "__query", "[", "\"master_timeout\"", "]", "=", "master_timeout", "if", "pretty", "is", "not", "None", ":", "__query", "[", "\"pretty\"", "]", "=", "pretty", "if", "s", "is", "not", "None", ":", "__query", "[", "\"s\"", "]", "=", "s", "if", "v", "is", "not", "None", ":", "__query", "[", "\"v\"", "]", "=", "v", "if", "__query", ":", "__target", "=", "f\"{__path}?{_quote_query(__query)}\"", "else", ":", "__target", "=", "__path", "__headers", "=", "{", "\"accept\"", ":", "\"text/plain,application/json\"", "}", "return", "self", ".", "_perform_request", "(", "\"GET\"", ",", "__target", ",", "headers", "=", "__headers", ")" ]
https://github.com/elastic/elasticsearch-py/blob/6ef1adfa3c840a84afda7369cd8e43ae7dc45cdb/elasticsearch/_sync/client/cat.py#L28-L103
wxWidgets/Phoenix
b2199e299a6ca6d866aa6f3d0888499136ead9d6
wx/py/crustslices.py
python
CrustSlicesFrame.bufferNew
(self)
return cancel
Create new buffer.
Create new buffer.
[ "Create", "new", "buffer", "." ]
def bufferNew(self): """Create new buffer.""" cancel = self.bufferSuggestSave() if cancel: return cancel self.sliceshell.clear() self.SetTitle( 'PySlices') self.sliceshell.NeedsCheckForSave=False self.sliceshell.SetSavePoint() self.buffer.doc = document.Document() self.buffer.name = 'This shell' self.buffer.modulename = self.buffer.doc.filebase #self.bufferCreate() cancel = False return cancel
[ "def", "bufferNew", "(", "self", ")", ":", "cancel", "=", "self", ".", "bufferSuggestSave", "(", ")", "if", "cancel", ":", "return", "cancel", "self", ".", "sliceshell", ".", "clear", "(", ")", "self", ".", "SetTitle", "(", "'PySlices'", ")", "self", ".", "sliceshell", ".", "NeedsCheckForSave", "=", "False", "self", ".", "sliceshell", ".", "SetSavePoint", "(", ")", "self", ".", "buffer", ".", "doc", "=", "document", ".", "Document", "(", ")", "self", ".", "buffer", ".", "name", "=", "'This shell'", "self", ".", "buffer", ".", "modulename", "=", "self", ".", "buffer", ".", "doc", ".", "filebase", "#self.bufferCreate()", "cancel", "=", "False", "return", "cancel" ]
https://github.com/wxWidgets/Phoenix/blob/b2199e299a6ca6d866aa6f3d0888499136ead9d6/wx/py/crustslices.py#L262-L276
kamalgill/flask-appengine-template
11760f83faccbb0d0afe416fc58e67ecfb4643c2
src/lib/click/decorators.py
python
make_pass_decorator
(object_type, ensure=False)
return decorator
Given an object type this creates a decorator that will work similar to :func:`pass_obj` but instead of passing the object of the current context, it will find the innermost context of type :func:`object_type`. This generates a decorator that works roughly like this:: from functools import update_wrapper def decorator(f): @pass_context def new_func(ctx, *args, **kwargs): obj = ctx.find_object(object_type) return ctx.invoke(f, obj, *args, **kwargs) return update_wrapper(new_func, f) return decorator :param object_type: the type of the object to pass. :param ensure: if set to `True`, a new object will be created and remembered on the context if it's not there yet.
Given an object type this creates a decorator that will work similar to :func:`pass_obj` but instead of passing the object of the current context, it will find the innermost context of type :func:`object_type`.
[ "Given", "an", "object", "type", "this", "creates", "a", "decorator", "that", "will", "work", "similar", "to", ":", "func", ":", "pass_obj", "but", "instead", "of", "passing", "the", "object", "of", "the", "current", "context", "it", "will", "find", "the", "innermost", "context", "of", "type", ":", "func", ":", "object_type", "." ]
def make_pass_decorator(object_type, ensure=False): """Given an object type this creates a decorator that will work similar to :func:`pass_obj` but instead of passing the object of the current context, it will find the innermost context of type :func:`object_type`. This generates a decorator that works roughly like this:: from functools import update_wrapper def decorator(f): @pass_context def new_func(ctx, *args, **kwargs): obj = ctx.find_object(object_type) return ctx.invoke(f, obj, *args, **kwargs) return update_wrapper(new_func, f) return decorator :param object_type: the type of the object to pass. :param ensure: if set to `True`, a new object will be created and remembered on the context if it's not there yet. """ def decorator(f): def new_func(*args, **kwargs): ctx = get_current_context() if ensure: obj = ctx.ensure_object(object_type) else: obj = ctx.find_object(object_type) if obj is None: raise RuntimeError('Managed to invoke callback without a ' 'context object of type %r existing' % object_type.__name__) return ctx.invoke(f, obj, *args[1:], **kwargs) return update_wrapper(new_func, f) return decorator
[ "def", "make_pass_decorator", "(", "object_type", ",", "ensure", "=", "False", ")", ":", "def", "decorator", "(", "f", ")", ":", "def", "new_func", "(", "*", "args", ",", "*", "*", "kwargs", ")", ":", "ctx", "=", "get_current_context", "(", ")", "if", "ensure", ":", "obj", "=", "ctx", ".", "ensure_object", "(", "object_type", ")", "else", ":", "obj", "=", "ctx", ".", "find_object", "(", "object_type", ")", "if", "obj", "is", "None", ":", "raise", "RuntimeError", "(", "'Managed to invoke callback without a '", "'context object of type %r existing'", "%", "object_type", ".", "__name__", ")", "return", "ctx", ".", "invoke", "(", "f", ",", "obj", ",", "*", "args", "[", "1", ":", "]", ",", "*", "*", "kwargs", ")", "return", "update_wrapper", "(", "new_func", ",", "f", ")", "return", "decorator" ]
https://github.com/kamalgill/flask-appengine-template/blob/11760f83faccbb0d0afe416fc58e67ecfb4643c2/src/lib/click/decorators.py#L31-L66
PythonCharmers/python-future
80523f383fbba1c6de0551e19d0277e73e69573c
src/future/standard_library/__init__.py
python
cache_py2_modules
()
Currently this function is unneeded, as we are not attempting to provide import hooks for modules with ambiguous names: email, urllib, pickle.
Currently this function is unneeded, as we are not attempting to provide import hooks for modules with ambiguous names: email, urllib, pickle.
[ "Currently", "this", "function", "is", "unneeded", "as", "we", "are", "not", "attempting", "to", "provide", "import", "hooks", "for", "modules", "with", "ambiguous", "names", ":", "email", "urllib", "pickle", "." ]
def cache_py2_modules(): """ Currently this function is unneeded, as we are not attempting to provide import hooks for modules with ambiguous names: email, urllib, pickle. """ if len(sys.py2_modules) != 0: return assert not detect_hooks() import urllib sys.py2_modules['urllib'] = urllib import email sys.py2_modules['email'] = email import pickle sys.py2_modules['pickle'] = pickle
[ "def", "cache_py2_modules", "(", ")", ":", "if", "len", "(", "sys", ".", "py2_modules", ")", "!=", "0", ":", "return", "assert", "not", "detect_hooks", "(", ")", "import", "urllib", "sys", ".", "py2_modules", "[", "'urllib'", "]", "=", "urllib", "import", "email", "sys", ".", "py2_modules", "[", "'email'", "]", "=", "email", "import", "pickle", "sys", ".", "py2_modules", "[", "'pickle'", "]", "=", "pickle" ]
https://github.com/PythonCharmers/python-future/blob/80523f383fbba1c6de0551e19d0277e73e69573c/src/future/standard_library/__init__.py#L600-L615
chribsen/simple-machine-learning-examples
dc94e52a4cebdc8bb959ff88b81ff8cfeca25022
venv/lib/python2.7/site-packages/sklearn/model_selection/_search.py
python
BaseSearchCV.transform
(self, X)
return self.best_estimator_.transform(X)
Call transform on the estimator with the best found parameters. Only available if the underlying estimator supports ``transform`` and ``refit=True``. Parameters ----------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.
Call transform on the estimator with the best found parameters.
[ "Call", "transform", "on", "the", "estimator", "with", "the", "best", "found", "parameters", "." ]
def transform(self, X): """Call transform on the estimator with the best found parameters. Only available if the underlying estimator supports ``transform`` and ``refit=True``. Parameters ----------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. """ self._check_is_fitted('transform') return self.best_estimator_.transform(X)
[ "def", "transform", "(", "self", ",", "X", ")", ":", "self", ".", "_check_is_fitted", "(", "'transform'", ")", "return", "self", ".", "best_estimator_", ".", "transform", "(", "X", ")" ]
https://github.com/chribsen/simple-machine-learning-examples/blob/dc94e52a4cebdc8bb959ff88b81ff8cfeca25022/venv/lib/python2.7/site-packages/sklearn/model_selection/_search.py#L502-L516
sagemath/sage
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
src/sage/rings/polynomial/convolution.py
python
_combine
(L, m, k)
return [L[0][j] for j in range(half_K)] + \ [L[i+1][j] + L[i][j+half_K] \ for i in range(M-1) for j in range(half_K)]
r""" Assumes L is a list of length `2^m`, each entry a list of length `2^k`. Combines together into a single list, effectively inverting ``_split()``, but overlaying coefficients, i.e. list #i gets added in starting at position `2^{k-1} i`. Note that the second half of the last list is ignored.
r""" Assumes L is a list of length `2^m`, each entry a list of length `2^k`. Combines together into a single list, effectively inverting ``_split()``, but overlaying coefficients, i.e. list #i gets added in starting at position `2^{k-1} i`. Note that the second half of the last list is ignored.
[ "r", "Assumes", "L", "is", "a", "list", "of", "length", "2^m", "each", "entry", "a", "list", "of", "length", "2^k", ".", "Combines", "together", "into", "a", "single", "list", "effectively", "inverting", "_split", "()", "but", "overlaying", "coefficients", "i", ".", "e", ".", "list", "#i", "gets", "added", "in", "starting", "at", "position", "2^", "{", "k", "-", "1", "}", "i", ".", "Note", "that", "the", "second", "half", "of", "the", "last", "list", "is", "ignored", "." ]
def _combine(L, m, k): r""" Assumes L is a list of length `2^m`, each entry a list of length `2^k`. Combines together into a single list, effectively inverting ``_split()``, but overlaying coefficients, i.e. list #i gets added in starting at position `2^{k-1} i`. Note that the second half of the last list is ignored. """ M = 1 << m half_K = 1 << (k-1) return [L[0][j] for j in range(half_K)] + \ [L[i+1][j] + L[i][j+half_K] \ for i in range(M-1) for j in range(half_K)]
[ "def", "_combine", "(", "L", ",", "m", ",", "k", ")", ":", "M", "=", "1", "<<", "m", "half_K", "=", "1", "<<", "(", "k", "-", "1", ")", "return", "[", "L", "[", "0", "]", "[", "j", "]", "for", "j", "in", "range", "(", "half_K", ")", "]", "+", "[", "L", "[", "i", "+", "1", "]", "[", "j", "]", "+", "L", "[", "i", "]", "[", "j", "+", "half_K", "]", "for", "i", "in", "range", "(", "M", "-", "1", ")", "for", "j", "in", "range", "(", "half_K", ")", "]" ]
https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/rings/polynomial/convolution.py#L264-L277
zhl2008/awd-platform
0416b31abea29743387b10b3914581fbe8e7da5e
web_flaskbb/lib/python2.7/site-packages/redis/client.py
python
StrictRedis.hexists
(self, name, key)
return self.execute_command('HEXISTS', name, key)
Returns a boolean indicating if ``key`` exists within hash ``name``
Returns a boolean indicating if ``key`` exists within hash ``name``
[ "Returns", "a", "boolean", "indicating", "if", "key", "exists", "within", "hash", "name" ]
def hexists(self, name, key): "Returns a boolean indicating if ``key`` exists within hash ``name``" return self.execute_command('HEXISTS', name, key)
[ "def", "hexists", "(", "self", ",", "name", ",", "key", ")", ":", "return", "self", ".", "execute_command", "(", "'HEXISTS'", ",", "name", ",", "key", ")" ]
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/redis/client.py#L1957-L1959
thunlp/HATT-Proto
8630f048ecc52714dda45e3d731ec68156439b4f
fewshot_re_kit/framework.py
python
FewShotREFramework.eval
(self, model, B, N, K, Q, eval_iter, ckpt=None, noise_rate=0)
return iter_right / iter_sample
model: a FewShotREModel instance B: Batch size N: Num of classes for each batch K: Num of instances for each class in the support set Q: Num of instances for each class in the query set eval_iter: Num of iterations ckpt: Checkpoint path. Set as None if using current model parameters. return: Accuracy
model: a FewShotREModel instance B: Batch size N: Num of classes for each batch K: Num of instances for each class in the support set Q: Num of instances for each class in the query set eval_iter: Num of iterations ckpt: Checkpoint path. Set as None if using current model parameters. return: Accuracy
[ "model", ":", "a", "FewShotREModel", "instance", "B", ":", "Batch", "size", "N", ":", "Num", "of", "classes", "for", "each", "batch", "K", ":", "Num", "of", "instances", "for", "each", "class", "in", "the", "support", "set", "Q", ":", "Num", "of", "instances", "for", "each", "class", "in", "the", "query", "set", "eval_iter", ":", "Num", "of", "iterations", "ckpt", ":", "Checkpoint", "path", ".", "Set", "as", "None", "if", "using", "current", "model", "parameters", ".", "return", ":", "Accuracy" ]
def eval(self, model, B, N, K, Q, eval_iter, ckpt=None, noise_rate=0): ''' model: a FewShotREModel instance B: Batch size N: Num of classes for each batch K: Num of instances for each class in the support set Q: Num of instances for each class in the query set eval_iter: Num of iterations ckpt: Checkpoint path. Set as None if using current model parameters. return: Accuracy ''' print("") model.eval() if ckpt is None: eval_dataset = self.val_data_loader else: checkpoint = self.__load_model__(ckpt) model.load_state_dict(checkpoint['state_dict']) eval_dataset = self.test_data_loader iter_right = 0.0 iter_sample = 0.0 for it in range(eval_iter): support, query, label = eval_dataset.next_batch(B, N, K, Q, noise_rate=noise_rate) logits, pred = model(support, query, N, K, Q) right = model.accuracy(pred, label) iter_right += self.item(right.data) iter_sample += 1 sys.stdout.write('[EVAL] step: {0:4} | accuracy: {1:3.2f}%'.format(it + 1, 100 * iter_right / iter_sample) +'\r') sys.stdout.flush() print("") return iter_right / iter_sample
[ "def", "eval", "(", "self", ",", "model", ",", "B", ",", "N", ",", "K", ",", "Q", ",", "eval_iter", ",", "ckpt", "=", "None", ",", "noise_rate", "=", "0", ")", ":", "print", "(", "\"\"", ")", "model", ".", "eval", "(", ")", "if", "ckpt", "is", "None", ":", "eval_dataset", "=", "self", ".", "val_data_loader", "else", ":", "checkpoint", "=", "self", ".", "__load_model__", "(", "ckpt", ")", "model", ".", "load_state_dict", "(", "checkpoint", "[", "'state_dict'", "]", ")", "eval_dataset", "=", "self", ".", "test_data_loader", "iter_right", "=", "0.0", "iter_sample", "=", "0.0", "for", "it", "in", "range", "(", "eval_iter", ")", ":", "support", ",", "query", ",", "label", "=", "eval_dataset", ".", "next_batch", "(", "B", ",", "N", ",", "K", ",", "Q", ",", "noise_rate", "=", "noise_rate", ")", "logits", ",", "pred", "=", "model", "(", "support", ",", "query", ",", "N", ",", "K", ",", "Q", ")", "right", "=", "model", ".", "accuracy", "(", "pred", ",", "label", ")", "iter_right", "+=", "self", ".", "item", "(", "right", ".", "data", ")", "iter_sample", "+=", "1", "sys", ".", "stdout", ".", "write", "(", "'[EVAL] step: {0:4} | accuracy: {1:3.2f}%'", ".", "format", "(", "it", "+", "1", ",", "100", "*", "iter_right", "/", "iter_sample", ")", "+", "'\\r'", ")", "sys", ".", "stdout", ".", "flush", "(", ")", "print", "(", "\"\"", ")", "return", "iter_right", "/", "iter_sample" ]
https://github.com/thunlp/HATT-Proto/blob/8630f048ecc52714dda45e3d731ec68156439b4f/fewshot_re_kit/framework.py#L182-L219
oilshell/oil
94388e7d44a9ad879b12615f6203b38596b5a2d3
oil_lang/funcs_builtin.py
python
_Append
(L, arg)
[]
def _Append(L, arg): L.append(arg)
[ "def", "_Append", "(", "L", ",", "arg", ")", ":", "L", ".", "append", "(", "arg", ")" ]
https://github.com/oilshell/oil/blob/94388e7d44a9ad879b12615f6203b38596b5a2d3/oil_lang/funcs_builtin.py#L58-L59
sao-eht/eat
26062fe552fc304a3efc26274e8ed56ddbda323d
eat/aips/__init__.py
python
_source
(script, replace=None, update=True)
Source variables from a shell script import them in the environment (if update==True)
Source variables from a shell script import them in the environment (if update==True)
[ "Source", "variables", "from", "a", "shell", "script", "import", "them", "in", "the", "environment", "(", "if", "update", "==", "True", ")" ]
def _source(script, replace=None, update=True): """ Source variables from a shell script import them in the environment (if update==True) """ from subprocess import Popen, PIPE from os import environ import os if os.path.isfile(script) is False: errmsg = "'%s' is not available"%(script) raise ValueError(errmsg) else: print((" Reading Environmental Variables from %s"%(script))) pipe = Popen(". %s > /dev/null 2>&1; env" % script, stdout=PIPE, shell=True) data = str(pipe.communicate()[0]) env = dict((line.split("=", 1) for line in data.splitlines())) if replace is not None: for key in list(env.keys()): value = env[key] if replace[0] in value: env[key] = env[key].replace(replace[0], replace[1]) if update: environ.update(env) else: return env
[ "def", "_source", "(", "script", ",", "replace", "=", "None", ",", "update", "=", "True", ")", ":", "from", "subprocess", "import", "Popen", ",", "PIPE", "from", "os", "import", "environ", "import", "os", "if", "os", ".", "path", ".", "isfile", "(", "script", ")", "is", "False", ":", "errmsg", "=", "\"'%s' is not available\"", "%", "(", "script", ")", "raise", "ValueError", "(", "errmsg", ")", "else", ":", "print", "(", "(", "\" Reading Environmental Variables from %s\"", "%", "(", "script", ")", ")", ")", "pipe", "=", "Popen", "(", "\". %s > /dev/null 2>&1; env\"", "%", "script", ",", "stdout", "=", "PIPE", ",", "shell", "=", "True", ")", "data", "=", "str", "(", "pipe", ".", "communicate", "(", ")", "[", "0", "]", ")", "env", "=", "dict", "(", "(", "line", ".", "split", "(", "\"=\"", ",", "1", ")", "for", "line", "in", "data", ".", "splitlines", "(", ")", ")", ")", "if", "replace", "is", "not", "None", ":", "for", "key", "in", "list", "(", "env", ".", "keys", "(", ")", ")", ":", "value", "=", "env", "[", "key", "]", "if", "replace", "[", "0", "]", "in", "value", ":", "env", "[", "key", "]", "=", "env", "[", "key", "]", ".", "replace", "(", "replace", "[", "0", "]", ",", "replace", "[", "1", "]", ")", "if", "update", ":", "environ", ".", "update", "(", "env", ")", "else", ":", "return", "env" ]
https://github.com/sao-eht/eat/blob/26062fe552fc304a3efc26274e8ed56ddbda323d/eat/aips/__init__.py#L125-L152
dagwieers/mrepo
a55cbc737d8bade92070d38e4dbb9a24be4b477f
up2date_client/repoBackends/aptRepo.py
python
AptRepoSource.getHeader
(self, package, msgCallback = None, progressCallback = None)
return None
[]
def getHeader(self, package, msgCallback = None, progressCallback = None): # there are weird cases where this can happen, mostly as a result of # mucking with things in /var/spool/up2date # # not a particularly effiencent way to get the header, but we should # not get hit very often return None
[ "def", "getHeader", "(", "self", ",", "package", ",", "msgCallback", "=", "None", ",", "progressCallback", "=", "None", ")", ":", "# there are weird cases where this can happen, mostly as a result of", "# mucking with things in /var/spool/up2date", "#", "# not a particularly effiencent way to get the header, but we should", "# not get hit very often", "return", "None" ]
https://github.com/dagwieers/mrepo/blob/a55cbc737d8bade92070d38e4dbb9a24be4b477f/up2date_client/repoBackends/aptRepo.py#L198-L205
pyparallel/pyparallel
11e8c6072d48c8f13641925d17b147bf36ee0ba3
Lib/site-packages/ipython-4.0.0-py3.3.egg/IPython/utils/data.py
python
flatten
(seq)
return [x for subseq in seq for x in subseq]
Flatten a list of lists (NOT recursive, only works for 2d lists).
Flatten a list of lists (NOT recursive, only works for 2d lists).
[ "Flatten", "a", "list", "of", "lists", "(", "NOT", "recursive", "only", "works", "for", "2d", "lists", ")", "." ]
def flatten(seq): """Flatten a list of lists (NOT recursive, only works for 2d lists).""" return [x for subseq in seq for x in subseq]
[ "def", "flatten", "(", "seq", ")", ":", "return", "[", "x", "for", "subseq", "in", "seq", "for", "x", "in", "subseq", "]" ]
https://github.com/pyparallel/pyparallel/blob/11e8c6072d48c8f13641925d17b147bf36ee0ba3/Lib/site-packages/ipython-4.0.0-py3.3.egg/IPython/utils/data.py#L27-L30
mozman/ezdxf
59d0fc2ea63f5cf82293428f5931da7e9f9718e9
src/ezdxf/audit.py
python
Auditor.purge
(self, codes: Set[int])
Remove error messages defined by integer error `codes`. This is useful to remove errors which are not important for a specific file usage.
Remove error messages defined by integer error `codes`.
[ "Remove", "error", "messages", "defined", "by", "integer", "error", "codes", "." ]
def purge(self, codes: Set[int]): """Remove error messages defined by integer error `codes`. This is useful to remove errors which are not important for a specific file usage. """ self.errors = [err for err in self.errors if err.code in codes]
[ "def", "purge", "(", "self", ",", "codes", ":", "Set", "[", "int", "]", ")", ":", "self", ".", "errors", "=", "[", "err", "for", "err", "in", "self", ".", "errors", "if", "err", ".", "code", "in", "codes", "]" ]
https://github.com/mozman/ezdxf/blob/59d0fc2ea63f5cf82293428f5931da7e9f9718e9/src/ezdxf/audit.py#L219-L226
ZENGXH/DMM_Net
a6308688cbcf411db9072aa68efbe485dde02a9b
dmm/modules/match_model.py
python
MatchModel.forward
(self, proposed_feature, proposed_mask, template_feature, mask_last_occurence, proposal_score, targets=None)
return full_outmask, match_score, det_score, full_outmask, match_loss
matching layer compute the cost matrix and perform matching Arguments" proposed_feature: p,256; PHW proposed_mask: p,h.w: PHW template_feature: list of template_feature [o,256]: mask_last_occurence: PHW proposal_score targets: OHW return:
matching layer compute the cost matrix and perform matching Arguments" proposed_feature: p,256; PHW proposed_mask: p,h.w: PHW template_feature: list of template_feature [o,256]: mask_last_occurence: PHW proposal_score targets: OHW return:
[ "matching", "layer", "compute", "the", "cost", "matrix", "and", "perform", "matching", "Arguments", "proposed_feature", ":", "p", "256", ";", "PHW", "proposed_mask", ":", "p", "h", ".", "w", ":", "PHW", "template_feature", ":", "list", "of", "template_feature", "[", "o", "256", "]", ":", "mask_last_occurence", ":", "PHW", "proposal_score", "targets", ":", "OHW", "return", ":" ]
def forward(self, proposed_feature, proposed_mask, template_feature, mask_last_occurence, proposal_score, targets=None): """ matching layer compute the cost matrix and perform matching Arguments" proposed_feature: p,256; PHW proposed_mask: p,h.w: PHW template_feature: list of template_feature [o,256]: mask_last_occurence: PHW proposal_score targets: OHW return: """ features = {'proposed': proposed_feature, # PXX "template":template_feature} # OXX mask = {'proposed': proposed_mask, # PHW 'template':mask_last_occurence}# OHW scores = {'proposal_score':proposal_score} # loss = {} # compute Cosine Distance target_sim_matrix, num_K, num_Q, match_loss = self.compute_cost_matrix(features, mask, scores, targets) n_prop = proposed_mask.shape[0] # P,H,W n_tplt = template_feature[0].shape[0] # O,D full_outmask, match_score, det_score, logic_mask, assign_matrix = self.match_with_first_frame( target_sim_matrix, n_prop, n_tplt, proposed_mask.float(), proposal_score, mask_last_occurence) return full_outmask, match_score, det_score, full_outmask, match_loss
[ "def", "forward", "(", "self", ",", "proposed_feature", ",", "proposed_mask", ",", "template_feature", ",", "mask_last_occurence", ",", "proposal_score", ",", "targets", "=", "None", ")", ":", "features", "=", "{", "'proposed'", ":", "proposed_feature", ",", "# PXX", "\"template\"", ":", "template_feature", "}", "# OXX ", "mask", "=", "{", "'proposed'", ":", "proposed_mask", ",", "# PHW ", "'template'", ":", "mask_last_occurence", "}", "# OHW", "scores", "=", "{", "'proposal_score'", ":", "proposal_score", "}", "# loss = {}", "# compute Cosine Distance ", "target_sim_matrix", ",", "num_K", ",", "num_Q", ",", "match_loss", "=", "self", ".", "compute_cost_matrix", "(", "features", ",", "mask", ",", "scores", ",", "targets", ")", "n_prop", "=", "proposed_mask", ".", "shape", "[", "0", "]", "# P,H,W", "n_tplt", "=", "template_feature", "[", "0", "]", ".", "shape", "[", "0", "]", "# O,D", "full_outmask", ",", "match_score", ",", "det_score", ",", "logic_mask", ",", "assign_matrix", "=", "self", ".", "match_with_first_frame", "(", "target_sim_matrix", ",", "n_prop", ",", "n_tplt", ",", "proposed_mask", ".", "float", "(", ")", ",", "proposal_score", ",", "mask_last_occurence", ")", "return", "full_outmask", ",", "match_score", ",", "det_score", ",", "full_outmask", ",", "match_loss" ]
https://github.com/ZENGXH/DMM_Net/blob/a6308688cbcf411db9072aa68efbe485dde02a9b/dmm/modules/match_model.py#L24-L47
Pymol-Scripts/Pymol-script-repo
bcd7bb7812dc6db1595953dfa4471fa15fb68c77
modules/pdb2pqr/src/quatfit.py
python
qchichange
(initcoords, refcoords, angle)
return newcoords
Change the chiangle of the reference coordinate using the initcoords and the given angle Parameters initcoords: Coordinates based on the point and basis atoms (one dimensional list) difchi : The angle to use (float) refcoords : The atoms to analyze (list of many coordinates) Returns newcoords : The new coordinates of the atoms (list of many coords)
Change the chiangle of the reference coordinate using the initcoords and the given angle
[ "Change", "the", "chiangle", "of", "the", "reference", "coordinate", "using", "the", "initcoords", "and", "the", "given", "angle" ]
def qchichange(initcoords, refcoords, angle): """ Change the chiangle of the reference coordinate using the initcoords and the given angle Parameters initcoords: Coordinates based on the point and basis atoms (one dimensional list) difchi : The angle to use (float) refcoords : The atoms to analyze (list of many coordinates) Returns newcoords : The new coordinates of the atoms (list of many coords) """ # Initialize L,R = [],[] for i in range(3): L.append(0.0) R.append([0.0,0.0,0.0]) # Convert to radians and normalize radangle = math.pi * angle/180.0 normalized = normalize(initcoords) L[0] = normalized[0] L[1] = normalized[1] L[2] = normalized[2] # Construct the rotation matrix R[0][0] = math.cos(radangle) + L[0]*L[0] * (1.0 - math.cos(radangle)) R[1][1] = math.cos(radangle) + L[1]*L[1] * (1.0 - math.cos(radangle)) R[2][2] = math.cos(radangle) + L[2]*L[2] * (1.0 - math.cos(radangle)) R[1][0] = L[0]*L[1]*(1.0 - math.cos(radangle)) - L[2] * math.sin(radangle) R[2][0] = L[0]*L[2]*(1.0 - math.cos(radangle)) + L[1] * math.sin(radangle) R[0][1] = L[1]*L[0]*(1.0 - math.cos(radangle)) + L[2] * math.sin(radangle) R[2][1] = L[1]*L[2]*(1.0 - math.cos(radangle)) - L[0] * math.sin(radangle) R[0][2] = L[2]*L[0]*(1.0 - math.cos(radangle)) - L[1] * math.sin(radangle) R[1][2] = L[2]*L[1]*(1.0 - math.cos(radangle)) + L[0] * math.sin(radangle) numpoints = len(refcoords) newcoords = rotmol(numpoints, refcoords, R) return newcoords
[ "def", "qchichange", "(", "initcoords", ",", "refcoords", ",", "angle", ")", ":", "# Initialize", "L", ",", "R", "=", "[", "]", ",", "[", "]", "for", "i", "in", "range", "(", "3", ")", ":", "L", ".", "append", "(", "0.0", ")", "R", ".", "append", "(", "[", "0.0", ",", "0.0", ",", "0.0", "]", ")", "# Convert to radians and normalize", "radangle", "=", "math", ".", "pi", "*", "angle", "/", "180.0", "normalized", "=", "normalize", "(", "initcoords", ")", "L", "[", "0", "]", "=", "normalized", "[", "0", "]", "L", "[", "1", "]", "=", "normalized", "[", "1", "]", "L", "[", "2", "]", "=", "normalized", "[", "2", "]", "# Construct the rotation matrix", "R", "[", "0", "]", "[", "0", "]", "=", "math", ".", "cos", "(", "radangle", ")", "+", "L", "[", "0", "]", "*", "L", "[", "0", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "R", "[", "1", "]", "[", "1", "]", "=", "math", ".", "cos", "(", "radangle", ")", "+", "L", "[", "1", "]", "*", "L", "[", "1", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "R", "[", "2", "]", "[", "2", "]", "=", "math", ".", "cos", "(", "radangle", ")", "+", "L", "[", "2", "]", "*", "L", "[", "2", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "R", "[", "1", "]", "[", "0", "]", "=", "L", "[", "0", "]", "*", "L", "[", "1", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "-", "L", "[", "2", "]", "*", "math", ".", "sin", "(", "radangle", ")", "R", "[", "2", "]", "[", "0", "]", "=", "L", "[", "0", "]", "*", "L", "[", "2", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "+", "L", "[", "1", "]", "*", "math", ".", "sin", "(", "radangle", ")", "R", "[", "0", "]", "[", "1", "]", "=", "L", "[", "1", "]", "*", "L", "[", "0", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "+", "L", "[", "2", "]", "*", "math", ".", "sin", "(", "radangle", ")", "R", "[", "2", "]", "[", "1", "]", "=", "L", "[", "1", "]", "*", "L", "[", "2", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "-", "L", "[", "0", "]", "*", "math", ".", "sin", "(", "radangle", ")", "R", "[", "0", "]", "[", "2", "]", "=", "L", "[", "2", "]", "*", "L", "[", "0", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "-", "L", "[", "1", "]", "*", "math", ".", "sin", "(", "radangle", ")", "R", "[", "1", "]", "[", "2", "]", "=", "L", "[", "2", "]", "*", "L", "[", "1", "]", "*", "(", "1.0", "-", "math", ".", "cos", "(", "radangle", ")", ")", "+", "L", "[", "0", "]", "*", "math", ".", "sin", "(", "radangle", ")", "numpoints", "=", "len", "(", "refcoords", ")", "newcoords", "=", "rotmol", "(", "numpoints", ",", "refcoords", ",", "R", ")", "return", "newcoords" ]
https://github.com/Pymol-Scripts/Pymol-script-repo/blob/bcd7bb7812dc6db1595953dfa4471fa15fb68c77/modules/pdb2pqr/src/quatfit.py#L137-L182
CPqD/RouteFlow
3f406b9c1a0796f40a86eb1194990cdd2c955f4d
pox/pox/openflow/topology.py
python
OFSyncFlowTable._handle_FlowRemoved
(self, event)
return EventContinue
process a flow removed event -- remove the matching flow from the table.
process a flow removed event -- remove the matching flow from the table.
[ "process", "a", "flow", "removed", "event", "--", "remove", "the", "matching", "flow", "from", "the", "table", "." ]
def _handle_FlowRemoved (self, event): """ process a flow removed event -- remove the matching flow from the table. """ flow_removed = event.ofp for entry in self.flow_table.entries: if (flow_removed.match == entry.match and flow_removed.priority == entry.priority): self.flow_table.remove_entry(entry) self.raiseEvent(FlowTableModification(removed=[entry])) return EventHalt return EventContinue
[ "def", "_handle_FlowRemoved", "(", "self", ",", "event", ")", ":", "flow_removed", "=", "event", ".", "ofp", "for", "entry", "in", "self", ".", "flow_table", ".", "entries", ":", "if", "(", "flow_removed", ".", "match", "==", "entry", ".", "match", "and", "flow_removed", ".", "priority", "==", "entry", ".", "priority", ")", ":", "self", ".", "flow_table", ".", "remove_entry", "(", "entry", ")", "self", ".", "raiseEvent", "(", "FlowTableModification", "(", "removed", "=", "[", "entry", "]", ")", ")", "return", "EventHalt", "return", "EventContinue" ]
https://github.com/CPqD/RouteFlow/blob/3f406b9c1a0796f40a86eb1194990cdd2c955f4d/pox/pox/openflow/topology.py#L449-L460
krintoxi/NoobSec-Toolkit
38738541cbc03cedb9a3b3ed13b629f781ad64f6
NoobSecToolkit /scripts/sshbackdoors/backdoors/shell/pupy/pupy/modules/search.py
python
SearchModule.init_argparse
(self)
[]
def init_argparse(self): self.arg_parser = PupyArgumentParser(prog="search", description=self.__doc__) self.arg_parser.add_argument('path', help='path') self.arg_parser.add_argument('-e','--extensions',metavar='ext1,ext2,...', help='limit to some extensions') self.arg_parser.add_argument('strings', nargs='+', metavar='string', help='strings to search') self.arg_parser.add_argument('-m','--max-size', type=int, default=None, help='max file size')
[ "def", "init_argparse", "(", "self", ")", ":", "self", ".", "arg_parser", "=", "PupyArgumentParser", "(", "prog", "=", "\"search\"", ",", "description", "=", "self", ".", "__doc__", ")", "self", ".", "arg_parser", ".", "add_argument", "(", "'path'", ",", "help", "=", "'path'", ")", "self", ".", "arg_parser", ".", "add_argument", "(", "'-e'", ",", "'--extensions'", ",", "metavar", "=", "'ext1,ext2,...'", ",", "help", "=", "'limit to some extensions'", ")", "self", ".", "arg_parser", ".", "add_argument", "(", "'strings'", ",", "nargs", "=", "'+'", ",", "metavar", "=", "'string'", ",", "help", "=", "'strings to search'", ")", "self", ".", "arg_parser", ".", "add_argument", "(", "'-m'", ",", "'--max-size'", ",", "type", "=", "int", ",", "default", "=", "None", ",", "help", "=", "'max file size'", ")" ]
https://github.com/krintoxi/NoobSec-Toolkit/blob/38738541cbc03cedb9a3b3ed13b629f781ad64f6/NoobSecToolkit /scripts/sshbackdoors/backdoors/shell/pupy/pupy/modules/search.py#L9-L14
openshift/openshift-tools
1188778e728a6e4781acf728123e5b356380fe6f
openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_user.py
python
OCUser.group_update
(self)
return rval
update group membership
update group membership
[ "update", "group", "membership" ]
def group_update(self): ''' update group membership ''' rval = {'returncode': 0} cmd = ['get', 'groups', '-o', 'json'] all_groups = self.openshift_cmd(cmd, output=True) # pylint misindentifying all_groups['results']['items'] type # pylint: disable=invalid-sequence-index for group in all_groups['results']['items']: # If we're supposed to be in this group if group['metadata']['name'] in self.groups \ and (group['users'] is None or self.config.username not in group['users']): cmd = ['groups', 'add-users', group['metadata']['name'], self.config.username] rval = self.openshift_cmd(cmd, oadm=True) if rval['returncode'] != 0: return rval # else if we're in the group, but aren't supposed to be elif group['users'] != None and self.config.username in group['users'] \ and group['metadata']['name'] not in self.groups: cmd = ['groups', 'remove-users', group['metadata']['name'], self.config.username] rval = self.openshift_cmd(cmd, oadm=True) if rval['returncode'] != 0: return rval return rval
[ "def", "group_update", "(", "self", ")", ":", "rval", "=", "{", "'returncode'", ":", "0", "}", "cmd", "=", "[", "'get'", ",", "'groups'", ",", "'-o'", ",", "'json'", "]", "all_groups", "=", "self", ".", "openshift_cmd", "(", "cmd", ",", "output", "=", "True", ")", "# pylint misindentifying all_groups['results']['items'] type", "# pylint: disable=invalid-sequence-index", "for", "group", "in", "all_groups", "[", "'results'", "]", "[", "'items'", "]", ":", "# If we're supposed to be in this group", "if", "group", "[", "'metadata'", "]", "[", "'name'", "]", "in", "self", ".", "groups", "and", "(", "group", "[", "'users'", "]", "is", "None", "or", "self", ".", "config", ".", "username", "not", "in", "group", "[", "'users'", "]", ")", ":", "cmd", "=", "[", "'groups'", ",", "'add-users'", ",", "group", "[", "'metadata'", "]", "[", "'name'", "]", ",", "self", ".", "config", ".", "username", "]", "rval", "=", "self", ".", "openshift_cmd", "(", "cmd", ",", "oadm", "=", "True", ")", "if", "rval", "[", "'returncode'", "]", "!=", "0", ":", "return", "rval", "# else if we're in the group, but aren't supposed to be", "elif", "group", "[", "'users'", "]", "!=", "None", "and", "self", ".", "config", ".", "username", "in", "group", "[", "'users'", "]", "and", "group", "[", "'metadata'", "]", "[", "'name'", "]", "not", "in", "self", ".", "groups", ":", "cmd", "=", "[", "'groups'", ",", "'remove-users'", ",", "group", "[", "'metadata'", "]", "[", "'name'", "]", ",", "self", ".", "config", ".", "username", "]", "rval", "=", "self", ".", "openshift_cmd", "(", "cmd", ",", "oadm", "=", "True", ")", "if", "rval", "[", "'returncode'", "]", "!=", "0", ":", "return", "rval", "return", "rval" ]
https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_user.py#L1629-L1655
mrkipling/maraschino
c6be9286937783ae01df2d6d8cebfc8b2734a7d7
lib/werkzeug/debug/__init__.py
python
DebuggedApplication.get_resource
(self, request, filename)
return Response('Not Found', status=404)
Return a static resource from the shared folder.
Return a static resource from the shared folder.
[ "Return", "a", "static", "resource", "from", "the", "shared", "folder", "." ]
def get_resource(self, request, filename): """Return a static resource from the shared folder.""" filename = join(dirname(__file__), 'shared', basename(filename)) if isfile(filename): mimetype = mimetypes.guess_type(filename)[0] \ or 'application/octet-stream' f = file(filename, 'rb') try: return Response(f.read(), mimetype=mimetype) finally: f.close() return Response('Not Found', status=404)
[ "def", "get_resource", "(", "self", ",", "request", ",", "filename", ")", ":", "filename", "=", "join", "(", "dirname", "(", "__file__", ")", ",", "'shared'", ",", "basename", "(", "filename", ")", ")", "if", "isfile", "(", "filename", ")", ":", "mimetype", "=", "mimetypes", ".", "guess_type", "(", "filename", ")", "[", "0", "]", "or", "'application/octet-stream'", "f", "=", "file", "(", "filename", ",", "'rb'", ")", "try", ":", "return", "Response", "(", "f", ".", "read", "(", ")", ",", "mimetype", "=", "mimetype", ")", "finally", ":", "f", ".", "close", "(", ")", "return", "Response", "(", "'Not Found'", ",", "status", "=", "404", ")" ]
https://github.com/mrkipling/maraschino/blob/c6be9286937783ae01df2d6d8cebfc8b2734a7d7/lib/werkzeug/debug/__init__.py#L146-L157
ntoll/drogulus
d74b78d0bf0220b91f075dbd3f9a06c2663b474e
drogulus/dht/validators.py
python
validate_timestamp
(val)
return (isinstance(val, float) and val >= 0.0)
Returns a boolean indication that a field is a valid timestamp - a floating point number representing the time in seconds since the Epoch (so called POSIX time, see https://en.wikipedia.org/wiki/Unix_time).
Returns a boolean indication that a field is a valid timestamp - a floating point number representing the time in seconds since the Epoch (so called POSIX time, see https://en.wikipedia.org/wiki/Unix_time).
[ "Returns", "a", "boolean", "indication", "that", "a", "field", "is", "a", "valid", "timestamp", "-", "a", "floating", "point", "number", "representing", "the", "time", "in", "seconds", "since", "the", "Epoch", "(", "so", "called", "POSIX", "time", "see", "https", ":", "//", "en", ".", "wikipedia", ".", "org", "/", "wiki", "/", "Unix_time", ")", "." ]
def validate_timestamp(val): """ Returns a boolean indication that a field is a valid timestamp - a floating point number representing the time in seconds since the Epoch (so called POSIX time, see https://en.wikipedia.org/wiki/Unix_time). """ return (isinstance(val, float) and val >= 0.0)
[ "def", "validate_timestamp", "(", "val", ")", ":", "return", "(", "isinstance", "(", "val", ",", "float", ")", "and", "val", ">=", "0.0", ")" ]
https://github.com/ntoll/drogulus/blob/d74b78d0bf0220b91f075dbd3f9a06c2663b474e/drogulus/dht/validators.py#L8-L14
scikit-learn/scikit-learn
1d1aadd0711b87d2a11c80aad15df6f8cf156712
sklearn/tree/_classes.py
python
DecisionTreeRegressor.fit
(self, X, y, sample_weight=None, check_input=True)
return self
Build a decision tree regressor from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (real numbers). Use ``dtype=np.float64`` and ``order='C'`` for maximum efficiency. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- self : DecisionTreeRegressor Fitted estimator.
Build a decision tree regressor from the training set (X, y).
[ "Build", "a", "decision", "tree", "regressor", "from", "the", "training", "set", "(", "X", "y", ")", "." ]
def fit(self, X, y, sample_weight=None, check_input=True): """Build a decision tree regressor from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (real numbers). Use ``dtype=np.float64`` and ``order='C'`` for maximum efficiency. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- self : DecisionTreeRegressor Fitted estimator. """ super().fit( X, y, sample_weight=sample_weight, check_input=check_input, ) return self
[ "def", "fit", "(", "self", ",", "X", ",", "y", ",", "sample_weight", "=", "None", ",", "check_input", "=", "True", ")", ":", "super", "(", ")", ".", "fit", "(", "X", ",", "y", ",", "sample_weight", "=", "sample_weight", ",", "check_input", "=", "check_input", ",", ")", "return", "self" ]
https://github.com/scikit-learn/scikit-learn/blob/1d1aadd0711b87d2a11c80aad15df6f8cf156712/sklearn/tree/_classes.py#L1257-L1292
TencentCloud/tencentcloud-sdk-python
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
tencentcloud/cwp/v20180228/models.py
python
SecurityTrend.__init__
(self)
r""" :param Date: 事件时间。 :type Date: str :param EventNum: 事件数量。 :type EventNum: int
r""" :param Date: 事件时间。 :type Date: str :param EventNum: 事件数量。 :type EventNum: int
[ "r", ":", "param", "Date", ":", "事件时间。", ":", "type", "Date", ":", "str", ":", "param", "EventNum", ":", "事件数量。", ":", "type", "EventNum", ":", "int" ]
def __init__(self): r""" :param Date: 事件时间。 :type Date: str :param EventNum: 事件数量。 :type EventNum: int """ self.Date = None self.EventNum = None
[ "def", "__init__", "(", "self", ")", ":", "self", ".", "Date", "=", "None", "self", ".", "EventNum", "=", "None" ]
https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/cwp/v20180228/models.py#L18499-L18507
OCA/l10n-brazil
6faefc04c7b0de3de3810a7ab137493d933fb579
l10n_br_fiscal/models/tax.py
python
Tax.compute_taxes
(self, **kwargs)
return result_amounts
arguments: company, partner, product, price_unit, quantity, uom_id, fiscal_price, fiscal_quantity, uot_id, discount_value, insurance_value, other_value, freight_value, ncm, nbs, nbm, cest, operation_line, icmssn_range, icms_origin, return { 'amount_included': float 'amount_not_included': float 'amount_withholding': float 'taxes': dict }
arguments: company, partner, product, price_unit, quantity, uom_id, fiscal_price, fiscal_quantity, uot_id, discount_value, insurance_value, other_value, freight_value, ncm, nbs, nbm, cest, operation_line, icmssn_range, icms_origin, return { 'amount_included': float 'amount_not_included': float 'amount_withholding': float 'taxes': dict }
[ "arguments", ":", "company", "partner", "product", "price_unit", "quantity", "uom_id", "fiscal_price", "fiscal_quantity", "uot_id", "discount_value", "insurance_value", "other_value", "freight_value", "ncm", "nbs", "nbm", "cest", "operation_line", "icmssn_range", "icms_origin", "return", "{", "amount_included", ":", "float", "amount_not_included", ":", "float", "amount_withholding", ":", "float", "taxes", ":", "dict", "}" ]
def compute_taxes(self, **kwargs): """ arguments: company, partner, product, price_unit, quantity, uom_id, fiscal_price, fiscal_quantity, uot_id, discount_value, insurance_value, other_value, freight_value, ncm, nbs, nbm, cest, operation_line, icmssn_range, icms_origin, return { 'amount_included': float 'amount_not_included': float 'amount_withholding': float 'taxes': dict } """ result_amounts = { "amount_included": 0.00, "amount_not_included": 0.00, "amount_withholding": 0.00, "estimate_tax": 0.00, "taxes": {}, } taxes = {} for tax in self.sorted(key=lambda t: t.compute_sequence): taxes[tax.tax_domain] = dict(TAX_DICT_VALUES) try: # Define CST FROM TAX operation_line = kwargs.get("operation_line") fiscal_operation_type = ( operation_line.fiscal_operation_type or FISCAL_OUT ) kwargs.update({"cst": tax.cst_from_tax(fiscal_operation_type)}) compute_method = getattr(self, "_compute_%s" % tax.tax_domain) taxes[tax.tax_domain].update(compute_method(tax, taxes, **kwargs)) if taxes[tax.tax_domain]["tax_include"]: result_amounts["amount_included"] += taxes[tax.tax_domain].get( "tax_value", 0.00 ) else: result_amounts["amount_not_included"] += taxes[tax.tax_domain].get( "tax_value", 0.00 ) if taxes[tax.tax_domain]["tax_withholding"]: result_amounts["amount_withholding"] += taxes[tax.tax_domain].get( "tax_value", 0.00 ) except AttributeError: taxes[tax.tax_domain].update(tax._compute_generic(tax, taxes, **kwargs)) # Caso não exista campos especificos dos impostos # no documento fiscal, os mesmos são calculados. continue # Estimate taxes result_amounts["estimate_tax"] = self._compute_estimate_taxes(**kwargs) result_amounts["taxes"] = taxes return result_amounts
[ "def", "compute_taxes", "(", "self", ",", "*", "*", "kwargs", ")", ":", "result_amounts", "=", "{", "\"amount_included\"", ":", "0.00", ",", "\"amount_not_included\"", ":", "0.00", ",", "\"amount_withholding\"", ":", "0.00", ",", "\"estimate_tax\"", ":", "0.00", ",", "\"taxes\"", ":", "{", "}", ",", "}", "taxes", "=", "{", "}", "for", "tax", "in", "self", ".", "sorted", "(", "key", "=", "lambda", "t", ":", "t", ".", "compute_sequence", ")", ":", "taxes", "[", "tax", ".", "tax_domain", "]", "=", "dict", "(", "TAX_DICT_VALUES", ")", "try", ":", "# Define CST FROM TAX", "operation_line", "=", "kwargs", ".", "get", "(", "\"operation_line\"", ")", "fiscal_operation_type", "=", "(", "operation_line", ".", "fiscal_operation_type", "or", "FISCAL_OUT", ")", "kwargs", ".", "update", "(", "{", "\"cst\"", ":", "tax", ".", "cst_from_tax", "(", "fiscal_operation_type", ")", "}", ")", "compute_method", "=", "getattr", "(", "self", ",", "\"_compute_%s\"", "%", "tax", ".", "tax_domain", ")", "taxes", "[", "tax", ".", "tax_domain", "]", ".", "update", "(", "compute_method", "(", "tax", ",", "taxes", ",", "*", "*", "kwargs", ")", ")", "if", "taxes", "[", "tax", ".", "tax_domain", "]", "[", "\"tax_include\"", "]", ":", "result_amounts", "[", "\"amount_included\"", "]", "+=", "taxes", "[", "tax", ".", "tax_domain", "]", ".", "get", "(", "\"tax_value\"", ",", "0.00", ")", "else", ":", "result_amounts", "[", "\"amount_not_included\"", "]", "+=", "taxes", "[", "tax", ".", "tax_domain", "]", ".", "get", "(", "\"tax_value\"", ",", "0.00", ")", "if", "taxes", "[", "tax", ".", "tax_domain", "]", "[", "\"tax_withholding\"", "]", ":", "result_amounts", "[", "\"amount_withholding\"", "]", "+=", "taxes", "[", "tax", ".", "tax_domain", "]", ".", "get", "(", "\"tax_value\"", ",", "0.00", ")", "except", "AttributeError", ":", "taxes", "[", "tax", ".", "tax_domain", "]", ".", "update", "(", "tax", ".", "_compute_generic", "(", "tax", ",", "taxes", ",", "*", "*", "kwargs", ")", ")", "# Caso não exista campos especificos dos impostos", "# no documento fiscal, os mesmos são calculados.", "continue", "# Estimate taxes", "result_amounts", "[", "\"estimate_tax\"", "]", "=", "self", ".", "_compute_estimate_taxes", "(", "*", "*", "kwargs", ")", "result_amounts", "[", "\"taxes\"", "]", "=", "taxes", "return", "result_amounts" ]
https://github.com/OCA/l10n-brazil/blob/6faefc04c7b0de3de3810a7ab137493d933fb579/l10n_br_fiscal/models/tax.py#L658-L733
YutingZhang/lmdis-rep
e1b210477fec4010e4e47112a48e6239f2dc73c1
net_modules/spatial_transformer.py
python
transformer
(U, theta, out_size, name='SpatialTransformer', **kwargs)
Spatial Transformer Layer Implements a spatial transformer layer as described in [1]_. Based on [2]_ and edited by David Dao for Tensorflow. Parameters ---------- U : float The output of a convolutional net should have the shape [num_batch, height, width, num_channels]. theta: float The output of the localisation network should be [num_batch, 6]. out_size: tuple of two ints The size of the output of the network (height, width) References ---------- .. [1] Spatial Transformer Networks Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu Submitted on 5 Jun 2015 .. [2] https://github.com/skaae/transformer_network/blob/master/transformerlayer.py Notes ----- To initialize the network to the identity transform init ``theta`` to : identity = np.array([[1., 0., 0.], [0., 1., 0.]]) identity = identity.flatten() theta = tf.Variable(initial_value=identity)
Spatial Transformer Layer
[ "Spatial", "Transformer", "Layer" ]
def transformer(U, theta, out_size, name='SpatialTransformer', **kwargs): """Spatial Transformer Layer Implements a spatial transformer layer as described in [1]_. Based on [2]_ and edited by David Dao for Tensorflow. Parameters ---------- U : float The output of a convolutional net should have the shape [num_batch, height, width, num_channels]. theta: float The output of the localisation network should be [num_batch, 6]. out_size: tuple of two ints The size of the output of the network (height, width) References ---------- .. [1] Spatial Transformer Networks Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu Submitted on 5 Jun 2015 .. [2] https://github.com/skaae/transformer_network/blob/master/transformerlayer.py Notes ----- To initialize the network to the identity transform init ``theta`` to : identity = np.array([[1., 0., 0.], [0., 1., 0.]]) identity = identity.flatten() theta = tf.Variable(initial_value=identity) """ with tf.variable_scope(name): output = _transform(theta, U, out_size) return output
[ "def", "transformer", "(", "U", ",", "theta", ",", "out_size", ",", "name", "=", "'SpatialTransformer'", ",", "*", "*", "kwargs", ")", ":", "with", "tf", ".", "variable_scope", "(", "name", ")", ":", "output", "=", "_transform", "(", "theta", ",", "U", ",", "out_size", ")", "return", "output" ]
https://github.com/YutingZhang/lmdis-rep/blob/e1b210477fec4010e4e47112a48e6239f2dc73c1/net_modules/spatial_transformer.py#L149-L186
ansible-collections/community.general
3faffe8f47968a2400ba3c896c8901c03001a194
plugins/modules/files/filesize.py
python
size_spec
(args)
return args['size_spec']
Return a dictionary with size specifications, especially the size in bytes (after rounding it to an integer number of blocks).
Return a dictionary with size specifications, especially the size in bytes (after rounding it to an integer number of blocks).
[ "Return", "a", "dictionary", "with", "size", "specifications", "especially", "the", "size", "in", "bytes", "(", "after", "rounding", "it", "to", "an", "integer", "number", "of", "blocks", ")", "." ]
def size_spec(args): """Return a dictionary with size specifications, especially the size in bytes (after rounding it to an integer number of blocks). """ blocksize_in_bytes = split_size_unit(args['blocksize'], True)[2] if blocksize_in_bytes == 0: raise AssertionError("block size cannot be equal to zero") size_value, size_unit, size_result = split_size_unit(args['size']) if not size_unit: blocks = int(math.ceil(size_value)) else: blocksize_in_bytes = smart_blocksize(size_value, size_unit, size_result, blocksize_in_bytes) blocks = int(math.ceil(size_result / blocksize_in_bytes)) args['size_diff'] = round_bytes = int(blocks * blocksize_in_bytes) args['size_spec'] = dict(blocks=blocks, blocksize=blocksize_in_bytes, bytes=round_bytes, iec=bytes_to_human(round_bytes, True), si=bytes_to_human(round_bytes)) return args['size_spec']
[ "def", "size_spec", "(", "args", ")", ":", "blocksize_in_bytes", "=", "split_size_unit", "(", "args", "[", "'blocksize'", "]", ",", "True", ")", "[", "2", "]", "if", "blocksize_in_bytes", "==", "0", ":", "raise", "AssertionError", "(", "\"block size cannot be equal to zero\"", ")", "size_value", ",", "size_unit", ",", "size_result", "=", "split_size_unit", "(", "args", "[", "'size'", "]", ")", "if", "not", "size_unit", ":", "blocks", "=", "int", "(", "math", ".", "ceil", "(", "size_value", ")", ")", "else", ":", "blocksize_in_bytes", "=", "smart_blocksize", "(", "size_value", ",", "size_unit", ",", "size_result", ",", "blocksize_in_bytes", ")", "blocks", "=", "int", "(", "math", ".", "ceil", "(", "size_result", "/", "blocksize_in_bytes", ")", ")", "args", "[", "'size_diff'", "]", "=", "round_bytes", "=", "int", "(", "blocks", "*", "blocksize_in_bytes", ")", "args", "[", "'size_spec'", "]", "=", "dict", "(", "blocks", "=", "blocks", ",", "blocksize", "=", "blocksize_in_bytes", ",", "bytes", "=", "round_bytes", ",", "iec", "=", "bytes_to_human", "(", "round_bytes", ",", "True", ")", ",", "si", "=", "bytes_to_human", "(", "round_bytes", ")", ")", "return", "args", "[", "'size_spec'", "]" ]
https://github.com/ansible-collections/community.general/blob/3faffe8f47968a2400ba3c896c8901c03001a194/plugins/modules/files/filesize.py#L339-L358
tp4a/teleport
1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad
server/www/packages/packages-darwin/x64/psutil/_pswindows.py
python
sensors_battery
()
return _common.sbattery(percent, secsleft, power_plugged)
Return battery information.
Return battery information.
[ "Return", "battery", "information", "." ]
def sensors_battery(): """Return battery information.""" # For constants meaning see: # https://msdn.microsoft.com/en-us/library/windows/desktop/ # aa373232(v=vs.85).aspx acline_status, flags, percent, secsleft = cext.sensors_battery() power_plugged = acline_status == 1 no_battery = bool(flags & 128) charging = bool(flags & 8) if no_battery: return None if power_plugged or charging: secsleft = _common.POWER_TIME_UNLIMITED elif secsleft == -1: secsleft = _common.POWER_TIME_UNKNOWN return _common.sbattery(percent, secsleft, power_plugged)
[ "def", "sensors_battery", "(", ")", ":", "# For constants meaning see:", "# https://msdn.microsoft.com/en-us/library/windows/desktop/", "# aa373232(v=vs.85).aspx", "acline_status", ",", "flags", ",", "percent", ",", "secsleft", "=", "cext", ".", "sensors_battery", "(", ")", "power_plugged", "=", "acline_status", "==", "1", "no_battery", "=", "bool", "(", "flags", "&", "128", ")", "charging", "=", "bool", "(", "flags", "&", "8", ")", "if", "no_battery", ":", "return", "None", "if", "power_plugged", "or", "charging", ":", "secsleft", "=", "_common", ".", "POWER_TIME_UNLIMITED", "elif", "secsleft", "==", "-", "1", ":", "secsleft", "=", "_common", ".", "POWER_TIME_UNKNOWN", "return", "_common", ".", "sbattery", "(", "percent", ",", "secsleft", ",", "power_plugged", ")" ]
https://github.com/tp4a/teleport/blob/1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad/server/www/packages/packages-darwin/x64/psutil/_pswindows.py#L444-L461
xonsh/xonsh
b76d6f994f22a4078f602f8b386f4ec280c8461f
xonsh/parsers/base.py
python
BaseParser.p_expr
(self, p)
expr : xor_expr | xor_expr pipe_xor_expr_list
expr : xor_expr | xor_expr pipe_xor_expr_list
[ "expr", ":", "xor_expr", "|", "xor_expr", "pipe_xor_expr_list" ]
def p_expr(self, p): """ expr : xor_expr | xor_expr pipe_xor_expr_list """ p[0] = self._binop_combine(p[1], p[2] if len(p) > 2 else None)
[ "def", "p_expr", "(", "self", ",", "p", ")", ":", "p", "[", "0", "]", "=", "self", ".", "_binop_combine", "(", "p", "[", "1", "]", ",", "p", "[", "2", "]", "if", "len", "(", "p", ")", ">", "2", "else", "None", ")" ]
https://github.com/xonsh/xonsh/blob/b76d6f994f22a4078f602f8b386f4ec280c8461f/xonsh/parsers/base.py#L2008-L2013
optuna/optuna
2c44c1a405ba059efd53f4b9c8e849d20fb95c0a
optuna/storages/_rdb/storage.py
python
RDBStorage._get_prepared_new_trial
( self, study_id: int, template_trial: Optional[FrozenTrial], session: orm.Session )
return trial
[]
def _get_prepared_new_trial( self, study_id: int, template_trial: Optional[FrozenTrial], session: orm.Session ) -> models.TrialModel: if template_trial is None: trial = models.TrialModel( study_id=study_id, number=None, state=TrialState.RUNNING, datetime_start=datetime.now(), ) else: # Because only `RUNNING` trials can be updated, # we temporarily set the state of the new trial to `RUNNING`. # After all fields of the trial have been updated, # the state is set to `template_trial.state`. temp_state = TrialState.RUNNING trial = models.TrialModel( study_id=study_id, number=None, state=temp_state, datetime_start=template_trial.datetime_start, datetime_complete=template_trial.datetime_complete, ) session.add(trial) # Flush the session cache to reflect the above addition operation to # the current RDB transaction. # # Without flushing, the following operations (e.g, `_set_trial_param_without_commit`) # will fail because the target trial doesn't exist in the storage yet. session.flush() if template_trial is not None: if template_trial.values is not None and len(template_trial.values) > 1: for objective, value in enumerate(template_trial.values): self._set_trial_value_without_commit(session, trial.trial_id, objective, value) elif template_trial.value is not None: self._set_trial_value_without_commit( session, trial.trial_id, 0, template_trial.value ) for param_name, param_value in template_trial.params.items(): distribution = template_trial.distributions[param_name] param_value_in_internal_repr = distribution.to_internal_repr(param_value) self._set_trial_param_without_commit( session, trial.trial_id, param_name, param_value_in_internal_repr, distribution ) for key, value in template_trial.user_attrs.items(): self._set_trial_user_attr_without_commit(session, trial.trial_id, key, value) for key, value in template_trial.system_attrs.items(): self._set_trial_system_attr_without_commit(session, trial.trial_id, key, value) for step, intermediate_value in template_trial.intermediate_values.items(): self._set_trial_intermediate_value_without_commit( session, trial.trial_id, step, intermediate_value ) trial.state = template_trial.state trial.number = trial.count_past_trials(session) session.add(trial) return trial
[ "def", "_get_prepared_new_trial", "(", "self", ",", "study_id", ":", "int", ",", "template_trial", ":", "Optional", "[", "FrozenTrial", "]", ",", "session", ":", "orm", ".", "Session", ")", "->", "models", ".", "TrialModel", ":", "if", "template_trial", "is", "None", ":", "trial", "=", "models", ".", "TrialModel", "(", "study_id", "=", "study_id", ",", "number", "=", "None", ",", "state", "=", "TrialState", ".", "RUNNING", ",", "datetime_start", "=", "datetime", ".", "now", "(", ")", ",", ")", "else", ":", "# Because only `RUNNING` trials can be updated,", "# we temporarily set the state of the new trial to `RUNNING`.", "# After all fields of the trial have been updated,", "# the state is set to `template_trial.state`.", "temp_state", "=", "TrialState", ".", "RUNNING", "trial", "=", "models", ".", "TrialModel", "(", "study_id", "=", "study_id", ",", "number", "=", "None", ",", "state", "=", "temp_state", ",", "datetime_start", "=", "template_trial", ".", "datetime_start", ",", "datetime_complete", "=", "template_trial", ".", "datetime_complete", ",", ")", "session", ".", "add", "(", "trial", ")", "# Flush the session cache to reflect the above addition operation to", "# the current RDB transaction.", "#", "# Without flushing, the following operations (e.g, `_set_trial_param_without_commit`)", "# will fail because the target trial doesn't exist in the storage yet.", "session", ".", "flush", "(", ")", "if", "template_trial", "is", "not", "None", ":", "if", "template_trial", ".", "values", "is", "not", "None", "and", "len", "(", "template_trial", ".", "values", ")", ">", "1", ":", "for", "objective", ",", "value", "in", "enumerate", "(", "template_trial", ".", "values", ")", ":", "self", ".", "_set_trial_value_without_commit", "(", "session", ",", "trial", ".", "trial_id", ",", "objective", ",", "value", ")", "elif", "template_trial", ".", "value", "is", "not", "None", ":", "self", ".", "_set_trial_value_without_commit", "(", "session", ",", "trial", ".", "trial_id", ",", "0", ",", "template_trial", ".", "value", ")", "for", "param_name", ",", "param_value", "in", "template_trial", ".", "params", ".", "items", "(", ")", ":", "distribution", "=", "template_trial", ".", "distributions", "[", "param_name", "]", "param_value_in_internal_repr", "=", "distribution", ".", "to_internal_repr", "(", "param_value", ")", "self", ".", "_set_trial_param_without_commit", "(", "session", ",", "trial", ".", "trial_id", ",", "param_name", ",", "param_value_in_internal_repr", ",", "distribution", ")", "for", "key", ",", "value", "in", "template_trial", ".", "user_attrs", ".", "items", "(", ")", ":", "self", ".", "_set_trial_user_attr_without_commit", "(", "session", ",", "trial", ".", "trial_id", ",", "key", ",", "value", ")", "for", "key", ",", "value", "in", "template_trial", ".", "system_attrs", ".", "items", "(", ")", ":", "self", ".", "_set_trial_system_attr_without_commit", "(", "session", ",", "trial", ".", "trial_id", ",", "key", ",", "value", ")", "for", "step", ",", "intermediate_value", "in", "template_trial", ".", "intermediate_values", ".", "items", "(", ")", ":", "self", ".", "_set_trial_intermediate_value_without_commit", "(", "session", ",", "trial", ".", "trial_id", ",", "step", ",", "intermediate_value", ")", "trial", ".", "state", "=", "template_trial", ".", "state", "trial", ".", "number", "=", "trial", ".", "count_past_trials", "(", "session", ")", "session", ".", "add", "(", "trial", ")", "return", "trial" ]
https://github.com/optuna/optuna/blob/2c44c1a405ba059efd53f4b9c8e849d20fb95c0a/optuna/storages/_rdb/storage.py#L565-L631
mu-editor/mu
5a5d7723405db588f67718a63a0ec0ecabebae33
mu/modes/base.py
python
REPLConnection._on_serial_read
(self)
Called when data is ready to be send from the device
Called when data is ready to be send from the device
[ "Called", "when", "data", "is", "ready", "to", "be", "send", "from", "the", "device" ]
def _on_serial_read(self): """ Called when data is ready to be send from the device """ data = bytes(self.serial.readAll()) self.data_received.emit(data)
[ "def", "_on_serial_read", "(", "self", ")", ":", "data", "=", "bytes", "(", "self", ".", "serial", ".", "readAll", "(", ")", ")", "self", ".", "data_received", ".", "emit", "(", "data", ")" ]
https://github.com/mu-editor/mu/blob/5a5d7723405db588f67718a63a0ec0ecabebae33/mu/modes/base.py#L141-L146
610265158/face_landmark
cae5e3a4434c2d76974bc4dec28e7ead74feae76
lib/core/base_trainer/net_work.py
python
Train.train_step
(self, inputs)
return loss
One train step. Args: inputs: one batch input. Returns: loss: Scaled loss.
One train step. Args: inputs: one batch input. Returns: loss: Scaled loss.
[ "One", "train", "step", ".", "Args", ":", "inputs", ":", "one", "batch", "input", ".", "Returns", ":", "loss", ":", "Scaled", "loss", "." ]
def train_step(self, inputs): """One train step. Args: inputs: one batch input. Returns: loss: Scaled loss. """ image, label = inputs with tf.GradientTape() as tape: predictions = self.model(image, training=True) loss = self.compute_loss(predictions,label,training=True) gradients = tape.gradient(loss, self.model.trainable_variables) gradients = [(tf.clip_by_value(grad, -5.0, 5.0)) for grad in gradients] self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables)) return loss
[ "def", "train_step", "(", "self", ",", "inputs", ")", ":", "image", ",", "label", "=", "inputs", "with", "tf", ".", "GradientTape", "(", ")", "as", "tape", ":", "predictions", "=", "self", ".", "model", "(", "image", ",", "training", "=", "True", ")", "loss", "=", "self", ".", "compute_loss", "(", "predictions", ",", "label", ",", "training", "=", "True", ")", "gradients", "=", "tape", ".", "gradient", "(", "loss", ",", "self", ".", "model", ".", "trainable_variables", ")", "gradients", "=", "[", "(", "tf", ".", "clip_by_value", "(", "grad", ",", "-", "5.0", ",", "5.0", ")", ")", "for", "grad", "in", "gradients", "]", "self", ".", "optimizer", ".", "apply_gradients", "(", "zip", "(", "gradients", ",", "self", ".", "model", ".", "trainable_variables", ")", ")", "return", "loss" ]
https://github.com/610265158/face_landmark/blob/cae5e3a4434c2d76974bc4dec28e7ead74feae76/lib/core/base_trainer/net_work.py#L96-L116
chribsen/simple-machine-learning-examples
dc94e52a4cebdc8bb959ff88b81ff8cfeca25022
venv/lib/python2.7/site-packages/sklearn/model_selection/_split.py
python
check_cv
(cv=3, y=None, classifier=False)
return cv
Input checker utility for building a cross-validator Parameters ---------- cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. For integer/None inputs, if classifier is True and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. y : array-like, optional The target variable for supervised learning problems. classifier : boolean, optional, default False Whether the task is a classification task, in which case stratified KFold will be used. Returns ------- checked_cv : a cross-validator instance. The return value is a cross-validator which generates the train/test splits via the ``split`` method.
Input checker utility for building a cross-validator
[ "Input", "checker", "utility", "for", "building", "a", "cross", "-", "validator" ]
def check_cv(cv=3, y=None, classifier=False): """Input checker utility for building a cross-validator Parameters ---------- cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. For integer/None inputs, if classifier is True and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. y : array-like, optional The target variable for supervised learning problems. classifier : boolean, optional, default False Whether the task is a classification task, in which case stratified KFold will be used. Returns ------- checked_cv : a cross-validator instance. The return value is a cross-validator which generates the train/test splits via the ``split`` method. """ if cv is None: cv = 3 if isinstance(cv, numbers.Integral): if (classifier and (y is not None) and (type_of_target(y) in ('binary', 'multiclass'))): return StratifiedKFold(cv) else: return KFold(cv) if not hasattr(cv, 'split') or isinstance(cv, str): if not isinstance(cv, Iterable) or isinstance(cv, str): raise ValueError("Expected cv as an integer, cross-validation " "object (from sklearn.model_selection) " "or an iterable. Got %s." % cv) return _CVIterableWrapper(cv) return cv
[ "def", "check_cv", "(", "cv", "=", "3", ",", "y", "=", "None", ",", "classifier", "=", "False", ")", ":", "if", "cv", "is", "None", ":", "cv", "=", "3", "if", "isinstance", "(", "cv", ",", "numbers", ".", "Integral", ")", ":", "if", "(", "classifier", "and", "(", "y", "is", "not", "None", ")", "and", "(", "type_of_target", "(", "y", ")", "in", "(", "'binary'", ",", "'multiclass'", ")", ")", ")", ":", "return", "StratifiedKFold", "(", "cv", ")", "else", ":", "return", "KFold", "(", "cv", ")", "if", "not", "hasattr", "(", "cv", ",", "'split'", ")", "or", "isinstance", "(", "cv", ",", "str", ")", ":", "if", "not", "isinstance", "(", "cv", ",", "Iterable", ")", "or", "isinstance", "(", "cv", ",", "str", ")", ":", "raise", "ValueError", "(", "\"Expected cv as an integer, cross-validation \"", "\"object (from sklearn.model_selection) \"", "\"or an iterable. Got %s.\"", "%", "cv", ")", "return", "_CVIterableWrapper", "(", "cv", ")", "return", "cv" ]
https://github.com/chribsen/simple-machine-learning-examples/blob/dc94e52a4cebdc8bb959ff88b81ff8cfeca25022/venv/lib/python2.7/site-packages/sklearn/model_selection/_split.py#L1546-L1596
blawar/nut
2cf351400418399a70164987e28670309f6c9cb5
Fs/File.py
python
BaseFile.write
(self, value, size=None)
return self.f.write(value)
[]
def write(self, value, size=None): if size is not None: value = value + '\0x00' * (size - len(value)) #Print.info('writing to ' + hex(self.f.tell()) + ' ' + self.f.__class__.__name__) # Hex.dump(value) return self.f.write(value)
[ "def", "write", "(", "self", ",", "value", ",", "size", "=", "None", ")", ":", "if", "size", "is", "not", "None", ":", "value", "=", "value", "+", "'\\0x00'", "*", "(", "size", "-", "len", "(", "value", ")", ")", "#Print.info('writing to ' + hex(self.f.tell()) + ' ' + self.f.__class__.__name__)", "# Hex.dump(value)", "return", "self", ".", "f", ".", "write", "(", "value", ")" ]
https://github.com/blawar/nut/blob/2cf351400418399a70164987e28670309f6c9cb5/Fs/File.py#L116-L121
caiiiac/Machine-Learning-with-Python
1a26c4467da41ca4ebc3d5bd789ea942ef79422f
MachineLearning/venv/lib/python3.5/site-packages/pip/_vendor/distlib/_backport/sysconfig.py
python
get_scheme_names
()
return tuple(sorted(_SCHEMES.sections()))
Return a tuple containing the schemes names.
Return a tuple containing the schemes names.
[ "Return", "a", "tuple", "containing", "the", "schemes", "names", "." ]
def get_scheme_names(): """Return a tuple containing the schemes names.""" return tuple(sorted(_SCHEMES.sections()))
[ "def", "get_scheme_names", "(", ")", ":", "return", "tuple", "(", "sorted", "(", "_SCHEMES", ".", "sections", "(", ")", ")", ")" ]
https://github.com/caiiiac/Machine-Learning-with-Python/blob/1a26c4467da41ca4ebc3d5bd789ea942ef79422f/MachineLearning/venv/lib/python3.5/site-packages/pip/_vendor/distlib/_backport/sysconfig.py#L431-L433
krintoxi/NoobSec-Toolkit
38738541cbc03cedb9a3b3ed13b629f781ad64f6
NoobSecToolkit /tools/inject/thirdparty/beautifulsoup/beautifulsoup.py
python
Tag.setString
(self, string)
Replace the contents of the tag with a string
Replace the contents of the tag with a string
[ "Replace", "the", "contents", "of", "the", "tag", "with", "a", "string" ]
def setString(self, string): """Replace the contents of the tag with a string""" self.clear() self.append(string)
[ "def", "setString", "(", "self", ",", "string", ")", ":", "self", ".", "clear", "(", ")", "self", ".", "append", "(", "string", ")" ]
https://github.com/krintoxi/NoobSec-Toolkit/blob/38738541cbc03cedb9a3b3ed13b629f781ad64f6/NoobSecToolkit /tools/inject/thirdparty/beautifulsoup/beautifulsoup.py#L557-L560
andresriancho/w3af
cd22e5252243a87aaa6d0ddea47cf58dacfe00a9
w3af/core/data/url/handlers/cache_backend/cached_response.py
python
CachedResponse.init
()
Takes all the actions needed for the CachedResponse class to work, in most cases this means creating a file, directory or database.
Takes all the actions needed for the CachedResponse class to work, in most cases this means creating a file, directory or database.
[ "Takes", "all", "the", "actions", "needed", "for", "the", "CachedResponse", "class", "to", "work", "in", "most", "cases", "this", "means", "creating", "a", "file", "directory", "or", "database", "." ]
def init(): """ Takes all the actions needed for the CachedResponse class to work, in most cases this means creating a file, directory or database. """ raise NotImplementedError
[ "def", "init", "(", ")", ":", "raise", "NotImplementedError" ]
https://github.com/andresriancho/w3af/blob/cd22e5252243a87aaa6d0ddea47cf58dacfe00a9/w3af/core/data/url/handlers/cache_backend/cached_response.py#L149-L154
getkeops/keops
fbe73a5de07dabc7c20df9cbb5a7e5e5ad360524
pykeops/torch/lazytensor/LazyTensor.py
python
Vj
(x_or_ind, dim=None)
return Var(x_or_ind, dim, 1)
r""" Simple wrapper that returns an instantiation of :class:`LazyTensor` of type 1.
r""" Simple wrapper that returns an instantiation of :class:`LazyTensor` of type 1.
[ "r", "Simple", "wrapper", "that", "returns", "an", "instantiation", "of", ":", "class", ":", "LazyTensor", "of", "type", "1", "." ]
def Vj(x_or_ind, dim=None): r""" Simple wrapper that returns an instantiation of :class:`LazyTensor` of type 1. """ return Var(x_or_ind, dim, 1)
[ "def", "Vj", "(", "x_or_ind", ",", "dim", "=", "None", ")", ":", "return", "Var", "(", "x_or_ind", ",", "dim", ",", "1", ")" ]
https://github.com/getkeops/keops/blob/fbe73a5de07dabc7c20df9cbb5a7e5e5ad360524/pykeops/torch/lazytensor/LazyTensor.py#L26-L30
jymcheong/AutoTTP
617128fe71537de4579176d7170a3e8f1680b6a6
EmpireAPIWrapper/wrapper.py
python
modules.module_search
(self, srch_str)
return utilties._postURL(self, full_url, data)
Search modules for passed term \n:param srch_str: Search term \n:type srch_str: str \n:rtype: dict
Search modules for passed term \n:param srch_str: Search term \n:type srch_str: str \n:rtype: dict
[ "Search", "modules", "for", "passed", "term", "\\", "n", ":", "param", "srch_str", ":", "Search", "term", "\\", "n", ":", "type", "srch_str", ":", "str", "\\", "n", ":", "rtype", ":", "dict" ]
def module_search(self, srch_str): """ Search modules for passed term \n:param srch_str: Search term \n:type srch_str: str \n:rtype: dict """ full_url = '/api/modules/search' data = {'term': srch_str} return utilties._postURL(self, full_url, data)
[ "def", "module_search", "(", "self", ",", "srch_str", ")", ":", "full_url", "=", "'/api/modules/search'", "data", "=", "{", "'term'", ":", "srch_str", "}", "return", "utilties", ".", "_postURL", "(", "self", ",", "full_url", ",", "data", ")" ]
https://github.com/jymcheong/AutoTTP/blob/617128fe71537de4579176d7170a3e8f1680b6a6/EmpireAPIWrapper/wrapper.py#L247-L256
angr/angr
4b04d56ace135018083d36d9083805be8146688b
angr/engines/vex/claripy/ccall.py
python
armg_calculate_flag_n
(state, cc_op, cc_dep1, cc_dep2, cc_dep3)
[]
def armg_calculate_flag_n(state, cc_op, cc_dep1, cc_dep2, cc_dep3): concrete_op = op_concretize(cc_op) flag = None if concrete_op == ARMG_CC_OP_COPY: flag = claripy.LShR(cc_dep1, ARMG_CC_SHIFT_N) & 1 elif concrete_op == ARMG_CC_OP_ADD: res = cc_dep1 + cc_dep2 flag = claripy.LShR(res, 31) elif concrete_op == ARMG_CC_OP_SUB: res = cc_dep1 - cc_dep2 flag = claripy.LShR(res, 31) elif concrete_op == ARMG_CC_OP_ADC: res = cc_dep1 + cc_dep2 + cc_dep3 flag = claripy.LShR(res, 31) elif concrete_op == ARMG_CC_OP_SBB: res = cc_dep1 - cc_dep2 - (cc_dep3^1) flag = claripy.LShR(res, 31) elif concrete_op == ARMG_CC_OP_LOGIC: flag = claripy.LShR(cc_dep1, 31) elif concrete_op == ARMG_CC_OP_MUL: flag = claripy.LShR(cc_dep1, 31) elif concrete_op == ARMG_CC_OP_MULL: flag = claripy.LShR(cc_dep2, 31) if flag is not None: return flag l.error("Unknown cc_op %s (armg_calculate_flag_n)", cc_op) raise SimCCallError("Unknown cc_op %s" % cc_op)
[ "def", "armg_calculate_flag_n", "(", "state", ",", "cc_op", ",", "cc_dep1", ",", "cc_dep2", ",", "cc_dep3", ")", ":", "concrete_op", "=", "op_concretize", "(", "cc_op", ")", "flag", "=", "None", "if", "concrete_op", "==", "ARMG_CC_OP_COPY", ":", "flag", "=", "claripy", ".", "LShR", "(", "cc_dep1", ",", "ARMG_CC_SHIFT_N", ")", "&", "1", "elif", "concrete_op", "==", "ARMG_CC_OP_ADD", ":", "res", "=", "cc_dep1", "+", "cc_dep2", "flag", "=", "claripy", ".", "LShR", "(", "res", ",", "31", ")", "elif", "concrete_op", "==", "ARMG_CC_OP_SUB", ":", "res", "=", "cc_dep1", "-", "cc_dep2", "flag", "=", "claripy", ".", "LShR", "(", "res", ",", "31", ")", "elif", "concrete_op", "==", "ARMG_CC_OP_ADC", ":", "res", "=", "cc_dep1", "+", "cc_dep2", "+", "cc_dep3", "flag", "=", "claripy", ".", "LShR", "(", "res", ",", "31", ")", "elif", "concrete_op", "==", "ARMG_CC_OP_SBB", ":", "res", "=", "cc_dep1", "-", "cc_dep2", "-", "(", "cc_dep3", "^", "1", ")", "flag", "=", "claripy", ".", "LShR", "(", "res", ",", "31", ")", "elif", "concrete_op", "==", "ARMG_CC_OP_LOGIC", ":", "flag", "=", "claripy", ".", "LShR", "(", "cc_dep1", ",", "31", ")", "elif", "concrete_op", "==", "ARMG_CC_OP_MUL", ":", "flag", "=", "claripy", ".", "LShR", "(", "cc_dep1", ",", "31", ")", "elif", "concrete_op", "==", "ARMG_CC_OP_MULL", ":", "flag", "=", "claripy", ".", "LShR", "(", "cc_dep2", ",", "31", ")", "if", "flag", "is", "not", "None", ":", "return", "flag", "l", ".", "error", "(", "\"Unknown cc_op %s (armg_calculate_flag_n)\"", ",", "cc_op", ")", "raise", "SimCCallError", "(", "\"Unknown cc_op %s\"", "%", "cc_op", ")" ]
https://github.com/angr/angr/blob/4b04d56ace135018083d36d9083805be8146688b/angr/engines/vex/claripy/ccall.py#L1419-L1447
linxid/Machine_Learning_Study_Path
558e82d13237114bbb8152483977806fc0c222af
Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/pyparsing.py
python
And.__str__
( self )
return self.strRepr
[]
def __str__( self ): if hasattr(self,"name"): return self.name if self.strRepr is None: self.strRepr = "{" + " ".join(_ustr(e) for e in self.exprs) + "}" return self.strRepr
[ "def", "__str__", "(", "self", ")", ":", "if", "hasattr", "(", "self", ",", "\"name\"", ")", ":", "return", "self", ".", "name", "if", "self", ".", "strRepr", "is", "None", ":", "self", ".", "strRepr", "=", "\"{\"", "+", "\" \"", ".", "join", "(", "_ustr", "(", "e", ")", "for", "e", "in", "self", ".", "exprs", ")", "+", "\"}\"", "return", "self", ".", "strRepr" ]
https://github.com/linxid/Machine_Learning_Study_Path/blob/558e82d13237114bbb8152483977806fc0c222af/Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/pyparsing.py#L3393-L3400
zhl2008/awd-platform
0416b31abea29743387b10b3914581fbe8e7da5e
web_flaskbb/lib/python2.7/site-packages/pip/_vendor/distlib/resources.py
python
ZipResourceFinder.__init__
(self, module)
[]
def __init__(self, module): super(ZipResourceFinder, self).__init__(module) archive = self.loader.archive self.prefix_len = 1 + len(archive) # PyPy doesn't have a _files attr on zipimporter, and you can't set one if hasattr(self.loader, '_files'): self._files = self.loader._files else: self._files = zipimport._zip_directory_cache[archive] self.index = sorted(self._files)
[ "def", "__init__", "(", "self", ",", "module", ")", ":", "super", "(", "ZipResourceFinder", ",", "self", ")", ".", "__init__", "(", "module", ")", "archive", "=", "self", ".", "loader", ".", "archive", "self", ".", "prefix_len", "=", "1", "+", "len", "(", "archive", ")", "# PyPy doesn't have a _files attr on zipimporter, and you can't set one", "if", "hasattr", "(", "self", ".", "loader", ",", "'_files'", ")", ":", "self", ".", "_files", "=", "self", ".", "loader", ".", "_files", "else", ":", "self", ".", "_files", "=", "zipimport", ".", "_zip_directory_cache", "[", "archive", "]", "self", ".", "index", "=", "sorted", "(", "self", ".", "_files", ")" ]
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/pip/_vendor/distlib/resources.py#L213-L222
oracle/graalpython
577e02da9755d916056184ec441c26e00b70145c
graalpython/lib-python/3/idlelib/redirector.py
python
OriginalCommand.__repr__
(self)
return "%s(%r, %r)" % (self.__class__.__name__, self.redir, self.operation)
[]
def __repr__(self): return "%s(%r, %r)" % (self.__class__.__name__, self.redir, self.operation)
[ "def", "__repr__", "(", "self", ")", ":", "return", "\"%s(%r, %r)\"", "%", "(", "self", ".", "__class__", ".", "__name__", ",", "self", ".", "redir", ",", "self", ".", "operation", ")" ]
https://github.com/oracle/graalpython/blob/577e02da9755d916056184ec441c26e00b70145c/graalpython/lib-python/3/idlelib/redirector.py#L145-L147
securesystemslab/zippy
ff0e84ac99442c2c55fe1d285332cfd4e185e089
zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/codec/whoosh3.py
python
_vecfield
(fieldname)
return "_%s_vec" % fieldname
[]
def _vecfield(fieldname): return "_%s_vec" % fieldname
[ "def", "_vecfield", "(", "fieldname", ")", ":", "return", "\"_%s_vec\"", "%", "fieldname" ]
https://github.com/securesystemslab/zippy/blob/ff0e84ac99442c2c55fe1d285332cfd4e185e089/zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/codec/whoosh3.py#L144-L145
microsoft/nni
31f11f51249660930824e888af0d4e022823285c
nni/algorithms/hpo/networkmorphism_tuner/graph.py
python
Graph.produce_onnx_model
(self)
return ONNXModel(self)
Build a new ONNX model based on the current graph.
Build a new ONNX model based on the current graph.
[ "Build", "a", "new", "ONNX", "model", "based", "on", "the", "current", "graph", "." ]
def produce_onnx_model(self): """Build a new ONNX model based on the current graph.""" return ONNXModel(self)
[ "def", "produce_onnx_model", "(", "self", ")", ":", "return", "ONNXModel", "(", "self", ")" ]
https://github.com/microsoft/nni/blob/31f11f51249660930824e888af0d4e022823285c/nni/algorithms/hpo/networkmorphism_tuner/graph.py#L645-L647
triaquae/triaquae
bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9
TriAquae/hosts/views.py
python
runCmd
(request)
return HttpResponse('{"TrackMark":%s, "TotalNum":%s}' %(track_mark, task_num))
[]
def runCmd(request): track_mark = MultiRunCounter.AddNumber() user_input = request.POST['command'] user_account = request.POST['UserName'] iplists = request.POST['IPLists'].split(',') task_num = len(set(iplists)) print "user input command is: %s and username is:%s and iplists are: %s" %(user_input,user_account,' '.join(iplists)) cmd = "python %s/TriAquae/backend/multiprocessing_runCMD2.py %s '%s' '%s' %s &" % (tri_config.Working_dir,track_mark,' '.join(iplists),user_input,user_account) os.system(cmd) return HttpResponse('{"TrackMark":%s, "TotalNum":%s}' %(track_mark, task_num))
[ "def", "runCmd", "(", "request", ")", ":", "track_mark", "=", "MultiRunCounter", ".", "AddNumber", "(", ")", "user_input", "=", "request", ".", "POST", "[", "'command'", "]", "user_account", "=", "request", ".", "POST", "[", "'UserName'", "]", "iplists", "=", "request", ".", "POST", "[", "'IPLists'", "]", ".", "split", "(", "','", ")", "task_num", "=", "len", "(", "set", "(", "iplists", ")", ")", "print", "\"user input command is: %s and username is:%s and iplists are: %s\"", "%", "(", "user_input", ",", "user_account", ",", "' '", ".", "join", "(", "iplists", ")", ")", "cmd", "=", "\"python %s/TriAquae/backend/multiprocessing_runCMD2.py %s '%s' '%s' %s &\"", "%", "(", "tri_config", ".", "Working_dir", ",", "track_mark", ",", "' '", ".", "join", "(", "iplists", ")", ",", "user_input", ",", "user_account", ")", "os", ".", "system", "(", "cmd", ")", "return", "HttpResponse", "(", "'{\"TrackMark\":%s, \"TotalNum\":%s}'", "%", "(", "track_mark", ",", "task_num", ")", ")" ]
https://github.com/triaquae/triaquae/blob/bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9/TriAquae/hosts/views.py#L252-L262
inkandswitch/livebook
93c8d467734787366ad084fc3566bf5cbe249c51
public/pypyjs/modules/numpy/random/mtrand.py
python
RandomState.logistic
(self, loc=0.0, scale=1.0, size=None)
return cont2_array(self.internal_state, _mtrand.rk_logistic, size, oloc, oscale)
logistic(loc=0.0, scale=1.0, size=None) Draw samples from a Logistic distribution. Samples are drawn from a Logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0). Parameters ---------- loc : float scale : float > 0. size : {tuple, int} Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Returns ------- samples : {ndarray, scalar} where the values are all integers in [0, n]. See Also -------- scipy.stats.distributions.logistic : probability density function, distribution or cumulative density function, etc. Notes ----- The probability density for the Logistic distribution is .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2}, where :math:`\\mu` = location and :math:`s` = scale. The Logistic distribution is used in Extreme Value problems where it can act as a mixture of Gumbel distributions, in Epidemiology, and by the World Chess Federation (FIDE) where it is used in the Elo ranking system, assuming the performance of each player is a logistically distributed random variable. References ---------- .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme Values, from Insurance, Finance, Hydrology and Other Fields, Birkhauser Verlag, Basel, pp 132-133. .. [2] Weisstein, Eric W. "Logistic Distribution." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/LogisticDistribution.html .. [3] Wikipedia, "Logistic-distribution", http://en.wikipedia.org/wiki/Logistic-distribution Examples -------- Draw samples from the distribution: >>> loc, scale = 10, 1 >>> s = np.random.logistic(loc, scale, 10000) >>> count, bins, ignored = plt.hist(s, bins=50) # plot against distribution >>> def logist(x, loc, scale): ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2) >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\ ... logist(bins, loc, scale).max()) >>> plt.show()
logistic(loc=0.0, scale=1.0, size=None)
[ "logistic", "(", "loc", "=", "0", ".", "0", "scale", "=", "1", ".", "0", "size", "=", "None", ")" ]
def logistic(self, loc=0.0, scale=1.0, size=None): """ logistic(loc=0.0, scale=1.0, size=None) Draw samples from a Logistic distribution. Samples are drawn from a Logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0). Parameters ---------- loc : float scale : float > 0. size : {tuple, int} Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Returns ------- samples : {ndarray, scalar} where the values are all integers in [0, n]. See Also -------- scipy.stats.distributions.logistic : probability density function, distribution or cumulative density function, etc. Notes ----- The probability density for the Logistic distribution is .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2}, where :math:`\\mu` = location and :math:`s` = scale. The Logistic distribution is used in Extreme Value problems where it can act as a mixture of Gumbel distributions, in Epidemiology, and by the World Chess Federation (FIDE) where it is used in the Elo ranking system, assuming the performance of each player is a logistically distributed random variable. References ---------- .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme Values, from Insurance, Finance, Hydrology and Other Fields, Birkhauser Verlag, Basel, pp 132-133. .. [2] Weisstein, Eric W. "Logistic Distribution." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/LogisticDistribution.html .. [3] Wikipedia, "Logistic-distribution", http://en.wikipedia.org/wiki/Logistic-distribution Examples -------- Draw samples from the distribution: >>> loc, scale = 10, 1 >>> s = np.random.logistic(loc, scale, 10000) >>> count, bins, ignored = plt.hist(s, bins=50) # plot against distribution >>> def logist(x, loc, scale): ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2) >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\ ... logist(bins, loc, scale).max()) >>> plt.show() """ try: floc = float(loc) fscale = float(scale) except: pass else: if fscale <= 0: raise ValueError("scale <= 0") return cont2_array_sc(self.internal_state, _mtrand.rk_logistic, size, floc, fscale) oloc = np.array(loc, np.float64) # aligned? oscale = np.array(scale, np.float64) # aligned? if np.any(np.less_equal(oscale, 0.0)): raise ValueError("scale <= 0") return cont2_array(self.internal_state, _mtrand.rk_logistic, size, oloc, oscale)
[ "def", "logistic", "(", "self", ",", "loc", "=", "0.0", ",", "scale", "=", "1.0", ",", "size", "=", "None", ")", ":", "try", ":", "floc", "=", "float", "(", "loc", ")", "fscale", "=", "float", "(", "scale", ")", "except", ":", "pass", "else", ":", "if", "fscale", "<=", "0", ":", "raise", "ValueError", "(", "\"scale <= 0\"", ")", "return", "cont2_array_sc", "(", "self", ".", "internal_state", ",", "_mtrand", ".", "rk_logistic", ",", "size", ",", "floc", ",", "fscale", ")", "oloc", "=", "np", ".", "array", "(", "loc", ",", "np", ".", "float64", ")", "# aligned?", "oscale", "=", "np", ".", "array", "(", "scale", ",", "np", ".", "float64", ")", "# aligned?", "if", "np", ".", "any", "(", "np", ".", "less_equal", "(", "oscale", ",", "0.0", ")", ")", ":", "raise", "ValueError", "(", "\"scale <= 0\"", ")", "return", "cont2_array", "(", "self", ".", "internal_state", ",", "_mtrand", ".", "rk_logistic", ",", "size", ",", "oloc", ",", "oscale", ")" ]
https://github.com/inkandswitch/livebook/blob/93c8d467734787366ad084fc3566bf5cbe249c51/public/pypyjs/modules/numpy/random/mtrand.py#L2496-L2580
pculture/miro
d8e4594441939514dd2ac29812bf37087bb3aea5
tv/lib/libdaap/pybonjour.py
python
DNSServiceRef.close
(self)
Close the connection to the mDNS daemon and terminate any associated browse, resolve, etc. operations.
[]
def close(self): """ Close the connection to the mDNS daemon and terminate any associated browse, resolve, etc. operations. """ if self._valid(): for ref in self._record_refs: ref._invalidate() del self._record_refs _global_lock.acquire() try: _DNSServiceRefDeallocate(self) finally: _global_lock.release() self._invalidate() del self._callbacks
[ "def", "close", "(", "self", ")", ":", "if", "self", ".", "_valid", "(", ")", ":", "for", "ref", "in", "self", ".", "_record_refs", ":", "ref", ".", "_invalidate", "(", ")", "del", "self", ".", "_record_refs", "_global_lock", ".", "acquire", "(", ")", "try", ":", "_DNSServiceRefDeallocate", "(", "self", ")", "finally", ":", "_global_lock", ".", "release", "(", ")", "self", ".", "_invalidate", "(", ")", "del", "self", ".", "_callbacks" ]
https://github.com/pculture/miro/blob/d8e4594441939514dd2ac29812bf37087bb3aea5/tv/lib/libdaap/pybonjour.py#L452-L472
naftaliharris/tauthon
5587ceec329b75f7caf6d65a036db61ac1bae214
Lib/pydoc.py
python
TextDoc.docclass
(self, object, name=None, mod=None, *ignored)
return title + '\n' + self.indent(rstrip(contents), ' | ') + '\n'
Produce text documentation for a given class object.
Produce text documentation for a given class object.
[ "Produce", "text", "documentation", "for", "a", "given", "class", "object", "." ]
def docclass(self, object, name=None, mod=None, *ignored): """Produce text documentation for a given class object.""" realname = object.__name__ name = name or realname bases = object.__bases__ def makename(c, m=object.__module__): return classname(c, m) if name == realname: title = 'class ' + self.bold(realname) else: title = self.bold(name) + ' = class ' + realname if bases: parents = map(makename, bases) title = title + '(%s)' % join(parents, ', ') doc = getdoc(object) contents = doc and [doc + '\n'] or [] push = contents.append # List the mro, if non-trivial. mro = deque(inspect.getmro(object)) if len(mro) > 2: push("Method resolution order:") for base in mro: push(' ' + makename(base)) push('') # Cute little class to pump out a horizontal rule between sections. class HorizontalRule: def __init__(self): self.needone = 0 def maybe(self): if self.needone: push('-' * 70) self.needone = 1 hr = HorizontalRule() def spill(msg, attrs, predicate): ok, attrs = _split_list(attrs, predicate) if ok: hr.maybe() push(msg) for name, kind, homecls, value in ok: try: value = getattr(object, name) except Exception: # Some descriptors may meet a failure in their __get__. # (bug #1785) push(self._docdescriptor(name, value, mod)) else: push(self.document(value, name, mod, object)) return attrs def spilldescriptors(msg, attrs, predicate): ok, attrs = _split_list(attrs, predicate) if ok: hr.maybe() push(msg) for name, kind, homecls, value in ok: push(self._docdescriptor(name, value, mod)) return attrs def spilldata(msg, attrs, predicate): ok, attrs = _split_list(attrs, predicate) if ok: hr.maybe() push(msg) for name, kind, homecls, value in ok: if (hasattr(value, '__call__') or inspect.isdatadescriptor(value)): doc = getdoc(value) else: doc = None push(self.docother(getattr(object, name), name, mod, maxlen=70, doc=doc) + '\n') return attrs attrs = filter(lambda data: visiblename(data[0], obj=object), classify_class_attrs(object)) while attrs: if mro: thisclass = mro.popleft() else: thisclass = attrs[0][2] attrs, inherited = _split_list(attrs, lambda t: t[2] is thisclass) if thisclass is __builtin__.object: attrs = inherited continue elif thisclass is object: tag = "defined here" else: tag = "inherited from %s" % classname(thisclass, object.__module__) # Sort attrs by name. attrs.sort() # Pump out the attrs, segregated by kind. attrs = spill("Methods %s:\n" % tag, attrs, lambda t: t[1] == 'method') attrs = spill("Class methods %s:\n" % tag, attrs, lambda t: t[1] == 'class method') attrs = spill("Static methods %s:\n" % tag, attrs, lambda t: t[1] == 'static method') attrs = spilldescriptors("Data descriptors %s:\n" % tag, attrs, lambda t: t[1] == 'data descriptor') attrs = spilldata("Data and other attributes %s:\n" % tag, attrs, lambda t: t[1] == 'data') assert attrs == [] attrs = inherited contents = '\n'.join(contents) if not contents: return title + '\n' return title + '\n' + self.indent(rstrip(contents), ' | ') + '\n'
[ "def", "docclass", "(", "self", ",", "object", ",", "name", "=", "None", ",", "mod", "=", "None", ",", "*", "ignored", ")", ":", "realname", "=", "object", ".", "__name__", "name", "=", "name", "or", "realname", "bases", "=", "object", ".", "__bases__", "def", "makename", "(", "c", ",", "m", "=", "object", ".", "__module__", ")", ":", "return", "classname", "(", "c", ",", "m", ")", "if", "name", "==", "realname", ":", "title", "=", "'class '", "+", "self", ".", "bold", "(", "realname", ")", "else", ":", "title", "=", "self", ".", "bold", "(", "name", ")", "+", "' = class '", "+", "realname", "if", "bases", ":", "parents", "=", "map", "(", "makename", ",", "bases", ")", "title", "=", "title", "+", "'(%s)'", "%", "join", "(", "parents", ",", "', '", ")", "doc", "=", "getdoc", "(", "object", ")", "contents", "=", "doc", "and", "[", "doc", "+", "'\\n'", "]", "or", "[", "]", "push", "=", "contents", ".", "append", "# List the mro, if non-trivial.", "mro", "=", "deque", "(", "inspect", ".", "getmro", "(", "object", ")", ")", "if", "len", "(", "mro", ")", ">", "2", ":", "push", "(", "\"Method resolution order:\"", ")", "for", "base", "in", "mro", ":", "push", "(", "' '", "+", "makename", "(", "base", ")", ")", "push", "(", "''", ")", "# Cute little class to pump out a horizontal rule between sections.", "class", "HorizontalRule", ":", "def", "__init__", "(", "self", ")", ":", "self", ".", "needone", "=", "0", "def", "maybe", "(", "self", ")", ":", "if", "self", ".", "needone", ":", "push", "(", "'-'", "*", "70", ")", "self", ".", "needone", "=", "1", "hr", "=", "HorizontalRule", "(", ")", "def", "spill", "(", "msg", ",", "attrs", ",", "predicate", ")", ":", "ok", ",", "attrs", "=", "_split_list", "(", "attrs", ",", "predicate", ")", "if", "ok", ":", "hr", ".", "maybe", "(", ")", "push", "(", "msg", ")", "for", "name", ",", "kind", ",", "homecls", ",", "value", "in", "ok", ":", "try", ":", "value", "=", "getattr", "(", "object", ",", "name", ")", "except", "Exception", ":", "# Some descriptors may meet a failure in their __get__.", "# (bug #1785)", "push", "(", "self", ".", "_docdescriptor", "(", "name", ",", "value", ",", "mod", ")", ")", "else", ":", "push", "(", "self", ".", "document", "(", "value", ",", "name", ",", "mod", ",", "object", ")", ")", "return", "attrs", "def", "spilldescriptors", "(", "msg", ",", "attrs", ",", "predicate", ")", ":", "ok", ",", "attrs", "=", "_split_list", "(", "attrs", ",", "predicate", ")", "if", "ok", ":", "hr", ".", "maybe", "(", ")", "push", "(", "msg", ")", "for", "name", ",", "kind", ",", "homecls", ",", "value", "in", "ok", ":", "push", "(", "self", ".", "_docdescriptor", "(", "name", ",", "value", ",", "mod", ")", ")", "return", "attrs", "def", "spilldata", "(", "msg", ",", "attrs", ",", "predicate", ")", ":", "ok", ",", "attrs", "=", "_split_list", "(", "attrs", ",", "predicate", ")", "if", "ok", ":", "hr", ".", "maybe", "(", ")", "push", "(", "msg", ")", "for", "name", ",", "kind", ",", "homecls", ",", "value", "in", "ok", ":", "if", "(", "hasattr", "(", "value", ",", "'__call__'", ")", "or", "inspect", ".", "isdatadescriptor", "(", "value", ")", ")", ":", "doc", "=", "getdoc", "(", "value", ")", "else", ":", "doc", "=", "None", "push", "(", "self", ".", "docother", "(", "getattr", "(", "object", ",", "name", ")", ",", "name", ",", "mod", ",", "maxlen", "=", "70", ",", "doc", "=", "doc", ")", "+", "'\\n'", ")", "return", "attrs", "attrs", "=", "filter", "(", "lambda", "data", ":", "visiblename", "(", "data", "[", "0", "]", ",", "obj", "=", "object", ")", ",", "classify_class_attrs", "(", "object", ")", ")", "while", "attrs", ":", "if", "mro", ":", "thisclass", "=", "mro", ".", "popleft", "(", ")", "else", ":", "thisclass", "=", "attrs", "[", "0", "]", "[", "2", "]", "attrs", ",", "inherited", "=", "_split_list", "(", "attrs", ",", "lambda", "t", ":", "t", "[", "2", "]", "is", "thisclass", ")", "if", "thisclass", "is", "__builtin__", ".", "object", ":", "attrs", "=", "inherited", "continue", "elif", "thisclass", "is", "object", ":", "tag", "=", "\"defined here\"", "else", ":", "tag", "=", "\"inherited from %s\"", "%", "classname", "(", "thisclass", ",", "object", ".", "__module__", ")", "# Sort attrs by name.", "attrs", ".", "sort", "(", ")", "# Pump out the attrs, segregated by kind.", "attrs", "=", "spill", "(", "\"Methods %s:\\n\"", "%", "tag", ",", "attrs", ",", "lambda", "t", ":", "t", "[", "1", "]", "==", "'method'", ")", "attrs", "=", "spill", "(", "\"Class methods %s:\\n\"", "%", "tag", ",", "attrs", ",", "lambda", "t", ":", "t", "[", "1", "]", "==", "'class method'", ")", "attrs", "=", "spill", "(", "\"Static methods %s:\\n\"", "%", "tag", ",", "attrs", ",", "lambda", "t", ":", "t", "[", "1", "]", "==", "'static method'", ")", "attrs", "=", "spilldescriptors", "(", "\"Data descriptors %s:\\n\"", "%", "tag", ",", "attrs", ",", "lambda", "t", ":", "t", "[", "1", "]", "==", "'data descriptor'", ")", "attrs", "=", "spilldata", "(", "\"Data and other attributes %s:\\n\"", "%", "tag", ",", "attrs", ",", "lambda", "t", ":", "t", "[", "1", "]", "==", "'data'", ")", "assert", "attrs", "==", "[", "]", "attrs", "=", "inherited", "contents", "=", "'\\n'", ".", "join", "(", "contents", ")", "if", "not", "contents", ":", "return", "title", "+", "'\\n'", "return", "title", "+", "'\\n'", "+", "self", ".", "indent", "(", "rstrip", "(", "contents", ")", ",", "' | '", ")", "+", "'\\n'" ]
https://github.com/naftaliharris/tauthon/blob/5587ceec329b75f7caf6d65a036db61ac1bae214/Lib/pydoc.py#L1176-L1294
pika/pika
12dcdf15d0932c388790e0fa990810bfd21b1a32
pika/channel.py
python
Channel.add_on_close_callback
(self, callback)
Pass a callback function that will be called when the channel is closed. The callback function will receive the channel and an exception describing why the channel was closed. If the channel is closed by broker via Channel.Close, the callback will receive `ChannelClosedByBroker` as the reason. If graceful user-initiated channel closing completes successfully ( either directly of indirectly by closing a connection containing the channel) and closing concludes gracefully without Channel.Close from the broker and without loss of connection, the callback will receive `ChannelClosedByClient` exception as reason. If channel was closed due to loss of connection, the callback will receive another exception type describing the failure. :param callable callback: The callback, having the signature: callback(Channel, Exception reason)
Pass a callback function that will be called when the channel is closed. The callback function will receive the channel and an exception describing why the channel was closed.
[ "Pass", "a", "callback", "function", "that", "will", "be", "called", "when", "the", "channel", "is", "closed", ".", "The", "callback", "function", "will", "receive", "the", "channel", "and", "an", "exception", "describing", "why", "the", "channel", "was", "closed", "." ]
def add_on_close_callback(self, callback): """Pass a callback function that will be called when the channel is closed. The callback function will receive the channel and an exception describing why the channel was closed. If the channel is closed by broker via Channel.Close, the callback will receive `ChannelClosedByBroker` as the reason. If graceful user-initiated channel closing completes successfully ( either directly of indirectly by closing a connection containing the channel) and closing concludes gracefully without Channel.Close from the broker and without loss of connection, the callback will receive `ChannelClosedByClient` exception as reason. If channel was closed due to loss of connection, the callback will receive another exception type describing the failure. :param callable callback: The callback, having the signature: callback(Channel, Exception reason) """ self.callbacks.add(self.channel_number, '_on_channel_close', callback, False, self)
[ "def", "add_on_close_callback", "(", "self", ",", "callback", ")", ":", "self", ".", "callbacks", ".", "add", "(", "self", ".", "channel_number", ",", "'_on_channel_close'", ",", "callback", ",", "False", ",", "self", ")" ]
https://github.com/pika/pika/blob/12dcdf15d0932c388790e0fa990810bfd21b1a32/pika/channel.py#L134-L156
slinderman/pyhawkes
0df433a40c5e6d8c1dcdb98ffc88fe3a403ac223
pyhawkes/internals/impulses.py
python
ContinuousTimeImpulseResponses.rvs
(self, size=[])
Sample random variables from the Dirichlet impulse response distribution. :param size: :return:
Sample random variables from the Dirichlet impulse response distribution. :param size: :return:
[ "Sample", "random", "variables", "from", "the", "Dirichlet", "impulse", "response", "distribution", ".", ":", "param", "size", ":", ":", "return", ":" ]
def rvs(self, size=[]): """ Sample random variables from the Dirichlet impulse response distribution. :param size: :return: """ pass
[ "def", "rvs", "(", "self", ",", "size", "=", "[", "]", ")", ":", "pass" ]
https://github.com/slinderman/pyhawkes/blob/0df433a40c5e6d8c1dcdb98ffc88fe3a403ac223/pyhawkes/internals/impulses.py#L374-L380
sagemath/sage
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
src/sage/categories/coxeter_groups.py
python
CoxeterGroups.additional_structure
(self)
return None
r""" Return ``None``. Indeed, all the structure Coxeter groups have in addition to groups (simple reflections, ...) is already defined in the super category. .. SEEALSO:: :meth:`Category.additional_structure` EXAMPLES:: sage: CoxeterGroups().additional_structure()
r""" Return ``None``.
[ "r", "Return", "None", "." ]
def additional_structure(self): r""" Return ``None``. Indeed, all the structure Coxeter groups have in addition to groups (simple reflections, ...) is already defined in the super category. .. SEEALSO:: :meth:`Category.additional_structure` EXAMPLES:: sage: CoxeterGroups().additional_structure() """ return None
[ "def", "additional_structure", "(", "self", ")", ":", "return", "None" ]
https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/categories/coxeter_groups.py#L112-L126
Azure/azure-cli
6c1b085a0910c6c2139006fcbd8ade44006eb6dd
src/azure-cli/azure/cli/command_modules/acs/custom.py
python
dcos_install_cli
(cmd, install_location=None, client_version='1.8')
Downloads the dcos command line from Mesosphere
Downloads the dcos command line from Mesosphere
[ "Downloads", "the", "dcos", "command", "line", "from", "Mesosphere" ]
def dcos_install_cli(cmd, install_location=None, client_version='1.8'): """ Downloads the dcos command line from Mesosphere """ system = platform.system() if not install_location: raise CLIError( "No install location specified and it could not be determined from the current platform '{}'".format( system)) base_url = 'https://downloads.dcos.io/binaries/cli/{}/x86-64/dcos-{}/{}' if system == 'Windows': file_url = base_url.format('windows', client_version, 'dcos.exe') elif system == 'Linux': # TODO Support ARM CPU here file_url = base_url.format('linux', client_version, 'dcos') elif system == 'Darwin': file_url = base_url.format('darwin', client_version, 'dcos') else: raise CLIError( 'Proxy server ({}) does not exist on the cluster.'.format(system)) logger.warning('Downloading client to %s', install_location) try: _urlretrieve(file_url, install_location) os.chmod(install_location, os.stat(install_location).st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH) except IOError as err: raise CLIError( 'Connection error while attempting to download client ({})'.format(err))
[ "def", "dcos_install_cli", "(", "cmd", ",", "install_location", "=", "None", ",", "client_version", "=", "'1.8'", ")", ":", "system", "=", "platform", ".", "system", "(", ")", "if", "not", "install_location", ":", "raise", "CLIError", "(", "\"No install location specified and it could not be determined from the current platform '{}'\"", ".", "format", "(", "system", ")", ")", "base_url", "=", "'https://downloads.dcos.io/binaries/cli/{}/x86-64/dcos-{}/{}'", "if", "system", "==", "'Windows'", ":", "file_url", "=", "base_url", ".", "format", "(", "'windows'", ",", "client_version", ",", "'dcos.exe'", ")", "elif", "system", "==", "'Linux'", ":", "# TODO Support ARM CPU here", "file_url", "=", "base_url", ".", "format", "(", "'linux'", ",", "client_version", ",", "'dcos'", ")", "elif", "system", "==", "'Darwin'", ":", "file_url", "=", "base_url", ".", "format", "(", "'darwin'", ",", "client_version", ",", "'dcos'", ")", "else", ":", "raise", "CLIError", "(", "'Proxy server ({}) does not exist on the cluster.'", ".", "format", "(", "system", ")", ")", "logger", ".", "warning", "(", "'Downloading client to %s'", ",", "install_location", ")", "try", ":", "_urlretrieve", "(", "file_url", ",", "install_location", ")", "os", ".", "chmod", "(", "install_location", ",", "os", ".", "stat", "(", "install_location", ")", ".", "st_mode", "|", "stat", ".", "S_IXUSR", "|", "stat", ".", "S_IXGRP", "|", "stat", ".", "S_IXOTH", ")", "except", "IOError", "as", "err", ":", "raise", "CLIError", "(", "'Connection error while attempting to download client ({})'", ".", "format", "(", "err", ")", ")" ]
https://github.com/Azure/azure-cli/blob/6c1b085a0910c6c2139006fcbd8ade44006eb6dd/src/azure-cli/azure/cli/command_modules/acs/custom.py#L324-L353
AppScale/gts
46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9
AppServer/lib/django-1.4/django/views/defaults.py
python
page_not_found
(request, template_name='404.html')
return http.HttpResponseNotFound(t.render(RequestContext(request, {'request_path': request.path})))
Default 404 handler. Templates: `404.html` Context: request_path The path of the requested URL (e.g., '/app/pages/bad_page/')
Default 404 handler.
[ "Default", "404", "handler", "." ]
def page_not_found(request, template_name='404.html'): """ Default 404 handler. Templates: `404.html` Context: request_path The path of the requested URL (e.g., '/app/pages/bad_page/') """ t = loader.get_template(template_name) # You need to create a 404.html template. return http.HttpResponseNotFound(t.render(RequestContext(request, {'request_path': request.path})))
[ "def", "page_not_found", "(", "request", ",", "template_name", "=", "'404.html'", ")", ":", "t", "=", "loader", ".", "get_template", "(", "template_name", ")", "# You need to create a 404.html template.", "return", "http", ".", "HttpResponseNotFound", "(", "t", ".", "render", "(", "RequestContext", "(", "request", ",", "{", "'request_path'", ":", "request", ".", "path", "}", ")", ")", ")" ]
https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/lib/django-1.4/django/views/defaults.py#L11-L21