tedana.selection.component_selector.ComponentSelector.select

ComponentSelector.select(component_table, cross_component_metrics={}, status_table=None)

Apply the decision tree to data.

Using the validated tree in ComponentSelector to run the decision tree functions to calculate cross_component metrics and classify each component as accepted or rejected.

Parameters:

• component_table ((C x M) pandas.DataFrame) – Component metric table. One row for each component, with a column for each metric; the index should be the component number.

• cross_component_metrics (dict) – Metrics that are each a single value calculated across components. Default is empty dictionary.

• status_table (pandas.DataFrame) – A table tracking the status of each component at each step. Pass a status table if running additional steps on a decision tree that was already executed. Default=None.

Notes

Adds to the ComponentSelector:

• component_status_table_: empty dataframe or contents of inputted status_table

• cross_component_metrics_: empty dict or contents of inputed values

• used_metrics: empty set

Any parameter that is used by a decision tree node function can be passed as a parameter in the ComponentSelector initialization or can be included in the json file that defines the decision tree. If a parameter is set in the json file, that will take precedence. As a style rule, a parameter that is the same regardless of the inputted data should be defined in the decision tree json file. A parameter that is dataset-specific should be passed through the initialization function. Dataset-specific parameters that may need to be passed during initialization include:

n_echosint

Number of echos in multi-echo fMRI data. Required for tedana_orig, meica, and minimal trees

n_volsint

Number of volumes (time points) in the fMRI data Required for tedana_orig and meica trees

An example initialization with these options would look like selector = selector.select(comptable, n_echos=n_echos, n_vols=n_vols)

The selection process uses previously calculated parameters stored in component_table for each ICA component such as Kappa (a T2* weighting metric), Rho (an S0 weighting metric), and variance explained. If a necessary metric is not calculated, this will not run. See tedana.metrics for more detail on the calculated metrics

This can be used on a component_table with no component classifications or to alter classifications on a component_table that was already run (i.e. for manual classificaiton changes after visual inspection)

When this is run, multiple elements in ComponentSelector will change including:

• component_table_: classification column with accepted or rejected labels and classification_tags column with can hold multiple comma-separated labels explaining why a classification happened

• cross_component_metrics_: Any values that were calculated based on the metric values across components or by direct user input

• component_status_table_: Contains the classification statuses at each node in the decision tree

• used_metrics: A list of metrics used in the selection process

• nodes: The original tree definition with an added outputs key listing everything that changed in each node

• current_node_idx_: The total number of nodes run in ComponentSelector