def calc_msit_stcs_diff(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
data_per_task=True,
contrast='interference',
cleaning_method='nTSSS'))
smooth = False
fname_format, fname_format_cond, conditions = meg.init(args.subject[0], args, args.mri_subject[0])
stc_template_name = meg.STC_HEMI_SMOOTH if smooth else meg.STC_HEMI
stc_fnames = [stc_template_name.format(cond=cond, method=args.inverse_method[0], hemi='lh') for cond in conditions.keys()]
output_fname = stc_template_name.format(cond='diff', method=args.inverse_method[0], hemi='lh')
meg.calc_stc_diff(*stc_fnames, output_fname)
def calc_mne_python_sample_data_stcs_diff(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
contrast = 'audvis',
fname_format = '{subject}_audvis-{ana_type}.{file_type}',
fname_format_cond = '{subject}_audvis_{cond}-{ana_type}.{file_type}',
conditions = ['LA', 'RA']
))
smooth = False
fname_format, fname_format_cond, conditions = meg.init(args.subject[0], args, args.mri_subject[0])
stc_template_name = meg.STC_HEMI_SMOOTH if smooth else meg.STC_HEMI
stc_fnames = [stc_template_name.format(cond=cond, method=args.inverse_method[0], hemi='lh') for cond in conditions.keys()]
output_fname = stc_template_name.format(cond='diff', method=args.inverse_method[0], hemi='lh')
meg.calc_stc_diff(*stc_fnames, output_fname)