neurocaps.analysis.change_dtype

change_dtype(subject_timeseries_list, dtype, return_dicts=True, output_dir=None, filenames=None)

Perform Participant-wise Dtype Conversion.

Changes the dtypes of each participants NumPy array. This function uses the .astype() method from NumPy. This function can help reduce memory usage. For example, converting a NumPy array from "float64" to "float32" can halve the memory required, which is particularly useful when analyzing large datasets on a local machine.

Parameters:

• subject_timeseries_list (list[dict[str, dict[str, np.ndarray]]] or list[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
          }
      }
  

• 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 (os.PathLike 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.

  Changed in version 0.19.0: file_names to filenames

Returns:

dict[str, dict[str, dict[str, np.ndarray]]] -- A nested dictionary containing the converted subject timeseries.

Warning

Floating Point Precision: The minimum recommended floating-point dtype is float32, as lower precision may introduce rounding errors that affect calculations.