studyforrest-ppa-analysis/code/confounds2onsets.py

#!/usr/bin/python3
'''
created on Mon 18 2020
author: Christian Olaf Haeusler
'''
from glob import glob
import argparse
import os
import shutil
import re

CONFOUNDPATTERN = 'fg_a*seg*.*'

def parse_arguments():
    '''
    '''
    parser = argparse.ArgumentParser(
        description='rename and copy the auditory \
        confound files to onset directories')

    parser.add_argument('-i',
                        default='events/segments',
                        help='directory that contains auditory confound files')

    parser.add_argument('-o',
                        default='events/onsets',
                        help='the output directory for onset files')

    args = parser.parse_args()

    inDir = args.i
    outDir = args.o

    return inDir, outDir


def rms_lrdiff_luminance(confoFpath, outFpath):
    '''
    '''
    try:
        os.makedirs(os.path.dirname(outFpath))
    except OSError:
        if not os.path.isdir(os.path.dirname(outFpath)):
            raise

    # copy that shit
    shutil.copyfile(confoFpath, outFpath)
    print('copied\t', confoFpath)
    print('to:\t', outFpath, '\n')


def perceptual_distance(confoFpath, outFpath):
    '''
    '''
    outFpath = outFpath.replace('.txt', '_pd.txt')

    with open(confoFpath) as f:
        content = f.readlines()

    # delete header
    content = content[1:]
#
    # split lines by comma
    content = [line.split(',') for line in content]
#
    # insert tabs as separator and movie frame duration in between
    pattern = '%s\t0.04\t%s'
    content = [pattern % (line[0], line[1]) for line in content]

    with open(outFpath, 'w') as f:
            f.writelines(content)
    print('wrote modified \t', confoFpath)
    print('to:\t\t', outFpath, '\n')


#### main program #####
if __name__ == "__main__":
    inDir, outDir = parse_arguments()

    inPattern = os.path.join('events/segments', '**',  CONFOUNDPATTERN)
    confoFpathes = sorted(glob(inPattern, recursive=True))

    for confoFpath in confoFpathes:
        # get the segment from the filename
        segString = re.search(r'_seg\d{1}', confoFpath)
        segString = segString.group()
        segment = segString.split('_seg')[1]
        # convert segment number to run number
        run = str(int(segment) + 1)
        # rename name of the input file to correct output file name
        inFname = os.path.basename(confoFpath)
        outFname = inFname.replace(segString, '')
        outFname = outFname.replace(os.path.splitext(outFname)[1], '.txt')

        # handle different output directories for movie and audio-description
        if 'fg_ad_seg' in inFname:
            outFpath = os.path.join(outDir, 'aomovie', 'run-' + run, outFname)
        elif 'fg_av_ger_seg' in inFname:
            outFpath = os.path.join(outDir, 'avmovie', 'run-' + run, outFname)
        else:
            print('unknown stimulus')

        # create the output path in case it does not exist
        try:
            os.makedirs(os.path.dirname(outFpath))
        except OSError:
            if not os.path.isdir(os.path.dirname(outFpath)):
                raise

        # process root mean square power and left-right difference
        if 'rms' in inFname or 'lrdiff' in inFname:
            rms_lrdiff_luminance(confoFpath, outFpath)

        # process luminance confounds
        if '_lr.' in confoFpath or '_ml.' in confoFpath or '_ud.' in confoFpath:
            rms_lrdiff_luminance(confoFpath, outFpath)

        # process perceptual distance (csv files)
        if 'distance' in confoFpath:
            perceptual_distance(confoFpath, outFpath)