check_inputs.py

import numpy as np


def check_inputs(data, design):
    """
    check that the data and design meet the required
    specifications for glm single trial estimates.

    Arguments
    _________

        data (list): could be x,y,z,t or XYZ,t
        design (list of runs or single run): design matrix

    Returns:
    ________

        data (list): flattened XYZ data format
        design (list): design matrix with a list entry per run
    """
    # massage <design> and sanity-check it
    if type(design) is not list:
        design = [design]

    numcond = design[0].shape[1]
    for p in range(len(design)):
        np.testing.assert_array_equal(
            np.unique(design),
            [0, 1],
            err_msg='<design> must consist of 0s and 1s')
        condmsg = \
            'all runs in <design> should have equal number of conditions'
        np.testing.assert_equal(
            design[p].shape[1],
            numcond,
            err_msg=condmsg)
        # if the design happened to be a sparse?
        # design[p] = np.full(design[p])

    # massage <data> and sanity-check it
    if type(data) is not list:
        data = [data]

    # make sure it is single
    for p in range(len(data)):
        data[p] = data[p].astype(np.float32, copy=False)

    np.testing.assert_equal(
        np.all(np.isfinite(data[0].flatten())),
        True,
        err_msg='We checked the first run and '
        'found some non-finite values (e.g. NaN, Inf).'
        'Please fix and re-run.')
    np.testing.assert_equal(
        len(design),
        len(data),
        err_msg='<design> and <data> should have '
        'the same number of runs')

    # reshape data in 2D mode.
    is3d = data[0].ndim > 2  # is this the X Y Z T case?
    if is3d:
        xyz = data[0].shape[:3]
        n_times = data[0].shape[3]
        for p in range(len(data)):
            data[p] = np.reshape(
                data[p],
                [np.prod(xyz), n_times])
            # force to XYZ x T for convenience
    else:
        xyz = False

    # check number of time points and truncate if necessary
    for run_i in np.arange(len(data)):
        if data[run_i].shape[1] > design[run_i].shape[0]:
            print(
                f'WARNING: run {run_i} has more time points'
                'in <data> than <design>. We are truncating'
                '<data>.\n')
            data[run_i] = data[run_i][:, np.arange(
                design.shape[0])]

        if data[run_i].shape[1] < design[run_i].shape[0]:
            print(
                f'WARNING: run {run_i} has more time points in <design>'
                'than <data>. We are truncating <design>.\n')
            design[run_i] = design[run_i][np.arange(
                data[run_i].shape[0]), :]

    return data, design, xyz