tedana.selection.selection_utils.confirm_metrics_exist

confirm_metrics_exist(component_table: DataFrame, necessary_metrics: List[str], function_name: str | None = None) → None | bool[source]

Confirm that all metrics in necessary_metrics are in component_table.

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.

  • necessary_metrics (list[str]) – A list of strings of metric names.

  • function_name (str) – Text identifying the function name that called this function.

Returns:

metrics_are_missing (bool) – If there are no metrics missing, this returns True.

Raises:

ValueError – If metrics_exist is False then raise an error and end the program.

Notes

This doesn’t check if there are data in each metric’s column, just that the columns exist. Also, the string in necessary_metrics and the column labels in component_table will only be matched if they’re identical.