408f12dec2ff56559a26873a848a09e4c8facfeb,brainiak/isc.py,,isc,#Any#Any#Any#Any#,76

Before Change

        elif pairwise:
            iscs = squareform(np.corrcoef(voxel_data), checks=False)
        elif not pairwise:
            iscs = np.array([pearsonr(subject,
                                      mean(np.delete(voxel_data,
                                                     s, axis=0),
                                           axis=0))[0]
                             for s, subject in enumerate(voxel_data)])
        voxel_iscs.append(iscs)
    iscs_stack = np.column_stack(voxel_iscs)

    // Get ISCs back into correct shape after masking out NaNs

After Change

    elif not pairwise:

        // Loop through left-out subjects
        iscs_stack = []
        for s in np.arange(n_subjects):

            // Correlation between left-out subject and mean of others
            iscs_stack.append(array_correlation(
                data[..., s],
                mean(np.delete(data, s, axis=2), axis=2)))

        iscs_stack = np.array(iscs_stack)

    // Get ISCs back into correct shape after masking out NaNs
    iscs = np.full((iscs_stack.shape[0], n_voxels), np.nan)

Instances