Usage : ./map_csv_to_corticalROI.py -i lh.r.aparc.a2009s.annot.gii --csv stats.csv -o test.tex

map_csv_to_corticalROI.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

def map_values(parc, csv):
    from soma import aims
    import numpy as np
    labels2009 = ['G_and_S_frontomargin', 'G_and_S_occipital_inf', 'G_and_S_paracentral', 'G_and_S_subcentral', 'G_and_S_transv_frontopol', 'G_and_S_cingul-Ant', 'G_and_S_cingul-Mid-Ant', 'G_and_S_cingul-Mid-Post', 'G_cingul-Post-dorsal', 'G_cingul-Post-ventral', 'G_cuneus', 'G_front_inf-Opercular', 'G_front_inf-Orbital', 'G_front_inf-Triangul', 'G_front_middle', 'G_front_sup', 'G_Ins_lg_and_S_cent_ins', 'G_insular_short', 'G_occipital_middle', 'G_occipital_sup', 'G_oc-temp_lat-fusifor', 'G_oc-temp_med-Lingual', 'G_oc-temp_med-Parahip', 'G_orbital', 'G_pariet_inf-Angular', 'G_pariet_inf-Supramar', 'G_parietal_sup', 'G_postcentral', 'G_precentral', 'G_precuneus', 'G_rectus', 'G_subcallosal', 'G_temp_sup-G_T_transv', 'G_temp_sup-Lateral', 'G_temp_sup-Plan_polar', 'G_temp_sup-Plan_tempo', 'G_temporal_inf', 'G_temporal_middle', 'Lat_Fis-ant-Horizont', 'Lat_Fis-ant-Vertical', 'Lat_Fis-post', 'Pole_occipital', 'Pole_temporal', 'S_calcarine', 'S_central', 'S_cingul-Marginalis', 'S_circular_insula_ant', 'S_circular_insula_inf', 'S_circular_insula_sup', 'S_collat_transv_ant', 'S_collat_transv_post', 'S_front_inf', 'S_front_middle', 'S_front_sup', 'S_interm_prim-Jensen', 'S_intrapariet_and_P_trans', 'S_oc_middle_and_Lunatus', 'S_oc_sup_and_transversal', 'S_occipital_ant', 'S_oc-temp_lat', 'S_oc-temp_med_and_Lingual', 'S_orbital_lateral', 'S_orbital_med-olfact', 'S_orbital-H_Shaped', 'S_parieto_occipital', 'S_pericallosal', 'S_postcentral', 'S_precentral-inf-part', 'S_precentral-sup-part', 'S_suborbital', 'S_subparietal', 'S_temporal_inf', 'S_temporal_sup', 'S_temporal_transverse']
    df = {}
    f = [e.rstrip('\n') for e in open('/tmp/stats.csv').readlines()]
    f.pop(0)
    for each in f:
        k,v = each.split(',')
        df[k] = v

    p = aims.read(parc)
    t = aims.TimeTexture_FLOAT(1, len(p))
    d = []
    for i, each in enumerate(p[0]):
       label = int(round(each))
       if label != 0:
           lab = labels2009[label-2]
           v = float(df[lab])
           d.append(v)
       else:
           d.append(0.0)

    t[0] = aims.Texture_FLOAT(np.array(d, dtype=np.float32, order='F'))
    return t

if __name__ == "__main__":
    import argparse, os, os.path as osp
    parser = argparse.ArgumentParser(description='map values to cortical surface')
    parser.add_argument("-i", dest="tex",  help="gyral parcellation texture", type=str, required=True)
    parser.add_argument("--csv", dest='csvfile', type=str, help="csv", required=True)
    parser.add_argument("-o", dest='output', type=str, help="output texture", required=True)

    args = parser.parse_args()
    if not osp.isfile(args.tex):
        raise Exception('%s should be a valid file'%args.tex)
    if not osp.isfile(args.csvfile):
        raise Exception('%s should be a valid file'%args.csvfile)
    args.tex = osp.abspath(args.tex)
    args.csvfile = osp.abspath(args.csvfile)

    tex = map_values(args.tex, args.csvfile)

    from soma import aims
    aims.write(tex, args.output)

stats.csv