Tutorial 5: Changing Dtype With `change_dtype`#

The dtype of the all participant’s NumPy arrays can be changed to assist with memory usage.

import numpy as np
from neurocaps.analysis import change_dtype

subject_timeseries = {str(x): {f"run-{y}": np.random.rand(50, 100) for y in range(1, 3)} for x in range(1, 3)}
converted_subject_timeseries = change_dtype(subject_timeseries_list=[subject_timeseries], dtype=np.float32)
for subj_id in subject_timeseries:
    for run in subject_timeseries[subj_id]:
        print(
            f"""
              subj-{subj_id}; {run}:
              dtype before conversion {subject_timeseries[subj_id][run].dtype}
              dtype after conversion: {converted_subject_timeseries["dict_0"][subj_id][run].dtype}
              """
        )

subj-1; run-1:
dtype before conversion float64
dtype after conversion: float32


subj-1; run-2:
dtype before conversion float64
dtype after conversion: float32


subj-2; run-1:
dtype before conversion float64
dtype after conversion: float32


subj-2; run-2:
dtype before conversion float64
dtype after conversion: float32

Tutorial 5: Changing Dtype With change_dtype#

Tutorial 5: Changing Dtype With `change_dtype`#