`neurocaps.analysis`.change_dtype#

class change_dtype(subject_timeseries_list, dtype, return_dicts=True, output_dir=None, filenames=None)[source]#

Perform Participant-wise Dtype Conversion.

Changes the dtypes of each participants NumPy array.

Parameters:

subject_timeseries_list (list[dict[str, dict[str, np.ndarray]]] or list[str]) – A list where each element consist of a dictionary mapping subject IDs to their run IDs and associated timeseries (TRs x ROIs) as a NumPy array. Can also be a list consisting of paths to pickle files containing this same structure. Refer to documentation for SubjectTimeseries in the “See Also” section for an example structure.
dtype (bool or np.floating) – Target data type (e.g “float32” or np.float32) to convert each participant’s NumPy arrays into.
return_dicts (bool, default=True) – If True, returns a single dictionary containing the converted input dictionaries. Keys are named “dict_{0}” where {0} corresponds to the dictionary’s position in the input list.
output_dir (str or None, default=None) – Directory to save the converted subject_timeseries as pickle files. The directory will be created if it does not exist. Dictionaries will not be saved if None.
filenames (list[str] or None, default=None) – A list of names to save the dictionaries with changed dtypes as. Names are matched to dictionaries by position (e.g., a file name in the 0th position will be the file name for the dictionary in the 0th position of subject_timeseries_list). If None and output_dir is specified, uses default file names - “subject_timeseries_{0}_float{1}.pkl” (where {0} indicates the original input order and {1} is the dtype.

Returns:

dict[str, SubjectTimeseries] – A nested dictionary containing the converted subject timeseries if return_dicts is True.

neurocaps.analysis.change_dtype#