tedana.selection.component_selector.ComponentSelector

class ComponentSelector(tree, component_table, cross_component_metrics={}, status_table=None)

Load and classify components based on a specified tree.

__init__(tree, component_table, cross_component_metrics={}, status_table=None)

Initialize the class using the info specified in the json file tree.

Parameters:

• tree (str) – The named tree or path to a JSON file that defines one.

• 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

Initializing the ComponentSelector confirms tree is valid and loads all information in the tree json file into ComponentSelector.

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 kundu and minimal trees

n_volsint

Number of volumes (time points) in the fMRI data Required for kundu tree

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

Methods

add_manual(indices, classification)	Add nodes that will manually classify components.
are_all_components_accepted_or_rejected()	After the tree has finished executing, check if all component classifications are either "accepted" or "rejected".
are_only_necessary_metrics_used()	Check if all metrics that are declared as necessary are actually used and if any used_metrics weren't explicitly declared necessary.
check_null(params, fcn)	Checks that all required parameters for selection node functions are attributes in the class.
select()	Apply the decision tree to data.
to_files(io_generator)	Convert this selector into component files.

Attributes

accepted_comps	A boolean pandas.Series of components that are accepted.
likely_bold_comps	A boolean pandas.Series of components that are tagged "Likely BOLD".
n_accepted_comps	The number of components that are accepted.
n_comps	The number of components in the component table.
n_likely_bold_comps	The number of components that are tagged "Likely BOLD".
rejected_comps	A boolean pandas.Series of components that are rejected.