Source code for unravel.cluster_stats.fstat_sig_vx_mask

#!/usr/bin/env python3

"""
Use ``cstats_fstat_sig_vx_mask`` (``fsvm``) from UNRAVEL to threshold f-statistic 1-p value images and combine them to make a mask for FDR-correction.

Usage:
------
    cstats_fstat_sig_vx_mask -i ['<asterisk>vox_p_fstat<asterisk>.nii.gz'] [-t 0.95] [-o fstat_sig_vx_mask.nii.gz] [-v]
"""

import nibabel as nib
import numpy as np
from pathlib import Path
from rich import print
from rich.traceback import install

from unravel.core.img_io import load_nii
from unravel.core.help_formatter import RichArgumentParser, SuppressMetavar, SM
from unravel.core.config import Configuration
from unravel.core.utils import log_command, match_files, verbose_start_msg, verbose_end_msg




def parse_args():
    parser = RichArgumentParser(formatter_class=SuppressMetavar, add_help=False, docstring=__doc__)

    opts = parser.add_argument_group('Optional arguments')
    opts.add_argument('-i', '--input', help='Glob pattern(s) for ANOVA f statistic 1-p value images. Default: "*vox_p_fstat*.nii.gz"', default='*vox_p_fstat*.nii.gz', nargs='*', action=SM)
    opts.add_argument('-t', '--threshold', help='Threshold for the p-value. Default: 0.95', default=0.95, type=float, action=SM)
    opts.add_argument('-o', '--output', help='Output file path. Default: fstat_sig_vx_mask.nii.gz', default='fstat_sig_vx_mask.nii.gz', action=SM)

    general = parser.add_argument_group('General arguments')
    general.add_argument('-v', '--verbose', help='Increase verbosity. Default: False', action='store_true', default=False)

    return parser.parse_args()


# TODO: Set voxels outside the mask(s) to zero




@log_command
def main():
    install()
    args = parse_args()
    Configuration.verbose = args.verbose
    verbose_start_msg()

    nii_paths = match_files(args.input)

    imgs = [load_nii(path) for path in nii_paths]
    
    # Initialize an empty image to store the sig voxels
    sig_vx_img = np.zeros(imgs[0].shape, dtype=bool)

    # Combine the images to get the sig voxels
    for img in imgs:
        sig_vx_img |= img > args.threshold

    # The dtype for sig_vx_img is bool, so we need to convert it to uint8
    sig_vx_img = sig_vx_img.astype(np.uint8)

    # Save the z-scored image
    output_path = Path(args.output)
    first_nii = nib.load(nii_paths[0])
    sig_vx_nii = nib.Nifti1Image(sig_vx_img, first_nii.affine, first_nii.header)
    sig_vx_nii.header.set_data_dtype(np.uint8)
    nib.save(sig_vx_nii, output_path)

    verbose_end_msg()



if __name__ == '__main__':
    main()