neurocaps.analysis.standardize
- standardize(subject_timeseries_list, return_dicts=True, output_dir=None, file_names=None)[source]
Perform Participant-wise Timeseries Standardization.
Standardizes the columns/ROIs of each run independently for all subjects in the subject timeseries. This function uses sample standard deviation, meaning Bessel's correction, n-1 is used in the denominator. Note, this function is intended for use when standardization was not performed during timeseries extraction using
TimeseriesExtractor.get_bold, as requested by the user.- Parameters:
subject_timeseries_list (
list[dict[str, dict[str, np.ndarray]]]orlist[os.PathLike]) --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. The expected structure of each dictionary is as follows:
subject_timeseries = { "101": { "run-0": np.array([...]), # Shape: TRs x ROIs "run-1": np.array([...]), # Shape: TRs x ROIs "run-2": np.array([...]), # Shape: TRs x ROIs }, "102": { "run-0": np.array([...]), # Shape: TRs x ROIs "run-1": np.array([...]), # Shape: TRs x ROIs } }
return_dicts (
bool, default=True) -- If True, returns a single dictionary containing the standardized input dictionaries. Keys are named "dict_{0}" where {0} corresponds to the dictionary's position in the input list.output_dir (
os.PathLikeorNone, default=None) -- Directory to save the standardized dictionaries as pickle files. The directory will be created if it does not exist. Dictionaries will not be saved if None.file_names (
list[str]orNone, default=None) -- A list of names to save the standardized dictionaries 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 ofsubject_timeseries_list). If None andoutput_diris specified, uses default file names - "subject_timeseries_{0}_standardized.pkl" (where {0} indicates the original input order).
- Returns:
dict[str, dict[str, dict[str, np.ndarray]]]