def select_by_rois_proximity(self, time_series, proximity, proximity_th=None, percentile=None, n_signals=None):
initial_selection = range(time_series.number_of_labels)
selection = []
for prox in proximity:
selection += (
np.array(initial_selection)[select_greater_values_array_inds(prox, proximity_th,
percentile, n_signals)]).tolist()
selection = np.unique(selection)
return time_series.get_subspace_by_index(selection), selection
def select_by_metric(self,
time_series,
metric,
metric_th=None,
metric_percentile=None,
nvals=None):
selection = np.unique(
select_greater_values_array_inds(metric, metric_th,
metric_percentile, nvals))
return time_series.get_subspace_by_index(selection), selection