tedana.selection.selection_nodes.dec_reclassify_high_var_comps

dec_reclassify_high_var_comps(selector, new_classification, decide_comps, log_extra_info='', custom_node_label='', only_used_metrics=False, tag=None)[source]

Identify and reclassify a couple components with the largest gaps in variance.

Parameters:

  • selector (ComponentSelector) – If only_used_metrics is False, the updated selector is returned.

  • new_classification (str) – Assign all components identified in decide_comps the classification in new_classification.

  • decide_comps (str or list[str]) – What classification(s) to operate on. using default or intermediate_classification labels. For example: decide_comps=’unclassified’ means to operate only on unclassified components. Use ‘all’ to include all components.

  • log_extra_info (str) – Additional text to the information log. Default=””.

  • custom_node_label (str) – A short label to describe what happens in this step. If “” then a label is automatically generated. Default=””.

  • only_used_metrics (bool) – If True, only return the component_table metrics that would be used. Default=False.

  • tag (str) – A classification tag to assign to all components being reclassified. This should be one of the tags defined by classification_tags in the decision tree specification. Default=””.

Returns:

  • selector (ComponentSelector) – If only_used_metrics is False, the updated selector is returned.

  • used_metrics (set(str)) – If only_used_metrics is True, the names of the metrics used in the function are returned.

Note

This function should not exist, but with the goal of maintaining the results of the original MEICA decision tree it is necessary, so here it is. It is a quirky and brittle step that is used to remove a few higher variance components from the calculation of the rho elbow. In the kundu decision tree, these components are also excluded from being provisionally accepted if kappa>kappa_elbow and rho<rho_elbow.