# save zstat map
res_dir = sp.path('rsa', f'{res_name}_{name}')
if not os.path.exists(res_dir):
os.makedirs(res_dir)
filepath = os.path.join(res_dir, 'zstat.nii.gz')
nifti = map2nifti(ds, sl_map[i])
nifti.to_filename(filepath)
# save mask of included voxels
include_file = os.path.join(res_dir, 'included.nii.gz')
nifti_include.to_filename(include_file)
if __name__ == '__main__':
parser = su.SubjParser(include_log=False)
parser.add_argument('mask', help="name of mask for searchlight centers")
parser.add_argument('feature_mask',
help="name of mask for included voxels")
parser.add_argument('models',
help="models to include (e.g., hmax-wiki_w2v)")
parser.add_argument('category', help="category to include (face,scene)")
parser.add_argument('res_name', help="name of results directory")
parser.add_argument('--suffix',
'-b',
default='_stim2',
help="suffix for beta images (_stim2)")
parser.add_argument('--radius',
'-r',
type=int,
default=3,
stat = prsa.perm_partial(data_vec, perm['model_mats'][i],
perm['model_resid'][i])
rho[i] = stat[0]
zstat[i] = prsa.perm_z(stat)
# save results
res_dir = os.path.join(study_dir, 'batch', 'prsa', res_name, roi)
if not os.path.exists(res_dir):
os.makedirs(res_dir, exist_ok=True)
df = pd.DataFrame({'rho': rho, 'zstat': zstat}, index=model_names)
res_file = os.path.join(res_dir, f'zstat_{subject}.csv')
df.to_csv(res_file)
if __name__ == '__main__':
parser = subjutil.SubjParser(include_log=False)
parser.add_argument('beh_dir', help='path to behavioral data directory')
parser.add_argument('rsa_name', help='name for rsa results')
parser.add_argument('roi', help='name of roi to analyze')
parser.add_argument('res_name', help='name for results')
parser.add_argument('--block',
'-b',
help='block to include (walk, random)')
parser.add_argument('--n-perm',
'-p',
type=int,
default=1000,
help="number of permutations to run (1000)")
args = parser.parse_args()
main(args.subject,
args.study_dir,