def _prepare_metric_operation(self, sim_operation):
# type: (Operation) -> Operation
metric_algo = dao.get_algorithm_by_module(MEASURE_METRICS_MODULE, MEASURE_METRICS_CLASS)
datatype_index = h5.REGISTRY.get_index_for_datatype(TimeSeries)
time_series_index = dao.get_generic_entity(datatype_index, sim_operation.id, 'fk_from_operation')[0]
ga = self._prepare_metadata(metric_algo.algorithm_category, {}, None, time_series_index.fk_parent_burst)
ga.visible = False
view_model = get_class_by_name("{}.{}".format(MEASURE_METRICS_MODULE, MEASURE_METRICS_MODEL_CLASS))()
view_model.time_series = time_series_index.gid
view_model.algorithms = tuple(choices.values())
view_model.generic_attributes = ga
parent_burst = dao.get_generic_entity(BurstConfiguration, time_series_index.fk_parent_burst, 'gid')[0]
metric_operation_group_id = parent_burst.fk_metric_operation_group
range_values = sim_operation.range_values
metric_operation = Operation(sim_operation.fk_launched_by, sim_operation.fk_launched_in, metric_algo.id,
json.dumps({'gid': view_model.gid.hex}), user_group=ga.operation_tag,
op_group_id=metric_operation_group_id, range_values=range_values)
metric_operation.visible = False
metric_operation = dao.store_entity(metric_operation)
metrics_datatype_group = dao.get_generic_entity(DataTypeGroup, metric_operation_group_id,
'fk_operation_group')[0]
if metrics_datatype_group.fk_from_operation is None:
metrics_datatype_group.fk_from_operation = metric_operation.id
self._store_view_model(metric_operation, sim_operation.project, view_model)
return metric_operation
class TimeseriesMetricsAdapterModel(ViewModel, BaseTimeseriesMetricAlgorithm):
time_series = DataTypeGidAttr(
linked_datatype=TimeSeries,
label="Time Series",
required=True,
doc="The TimeSeries for which the metric(s) will be computed.")
algorithms = List(
of=str,
choices=tuple(choices.values()),
label='Selected metrics to be applied',
doc=
'The selected algorithms will all be applied on the input TimeSeries')