def create_datatype_measure(self, analyzed_entity, operation=None, storage_path=None):
"""
:return: persisted DatatypeMeasure
"""
if operation is None:
operation, _, storage_path = self.__create_operation()
measure = DatatypeMeasure(storage_path=storage_path, metrics=self.DATATYPE_MEASURE_METRIC)
measure.analyzed_datatype = analyzed_entity
adapter_instance = StoreAdapter([measure])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return measure
def create_datatype_measure(self, analyzed_entity, operation=None, storage_path=None):
"""
:return: persisted DatatypeMeasure
"""
if operation is None:
operation, _, storage_path = self.__create_operation()
measure = DatatypeMeasure(storage_path=storage_path, metrics=self.DATATYPE_MEASURE_METRIC)
measure.analyzed_datatype = analyzed_entity
adapter_instance = StoreAdapter([measure])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return measure
def launch(self, time_series, algorithms=None):
"""
Launch algorithm and build results.
:param time_series: the time series on which the algorithms are run
:param algorithms: the algorithms to be run for computing measures on the time series
:type algorithms: any subclass of BaseTimeseriesMetricAlgorithm (KuramotoIndex, \
GlobalVariance, VarianceNodeVariance)
:rtype: `DatatypeMeasure`
"""
if algorithms is None:
algorithms = self.available_algorithms.keys()
shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
metrics_results = {}
for algorithm_name in algorithms:
##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
node_slice = [
slice(shape[0]),
slice(shape[1]),
slice(shape[2]),
slice(shape[3])
]
##---------- Iterate over slices and compose final result ------------##
unstored_ts = TimeSeries(use_storage=False)
unstored_ts.data = time_series.read_data_slice(tuple(node_slice))
##-------------------- Fill Algorithm for Analysis -------------------##
algorithm = self.available_algorithms[algorithm_name](
time_series=unstored_ts)
## Validate that current algorithm's filter is valid.
if (algorithm.accept_filter is not None and not algorithm.
accept_filter.get_python_filter_equivalent(time_series)):
LOG.warning(
'Measure algorithm will not be computed because of incompatibility on input. '
'Filters failed on algo: ' + str(algorithm_name))
continue
else:
LOG.debug("Applying measure: " + str(algorithm_name))
unstored_result = algorithm.evaluate()
##----------------- Prepare a Float object for result ----------------##
metrics_results[algorithm_name] = unstored_result
result = DatatypeMeasure(analyzed_datatype=time_series,
storage_path=self.storage_path,
data_name=self._ui_name,
metrics=metrics_results)
return result