def fetch_conn_matrices(subject_list, atlas_name, kind):
    '\n        subject_list : list of short subject IDs in string format\n        atlas_name   : the atlas based on which the timeseries are generated e.g. aal, cc200\n        kind         : the kind of correlation used to estimate the matrices, i.e.\n\n    returns:\n        connectivity : list of square connectivity matrices, one for each subject in subject_list\n    '
    conn_files = fetch_filenames(subject_list, (((('_' + atlas_name) + '_') + kind.replace(' ', '_')) + '.mat'))
    conn_matrices = []
    for fl in conn_files:
        print(('Reading connectivity file %s' % fl))
        try:
            mat = sio.loadmat(fl)['connectivity']
            conn_matrices.append(mat)
        except IOError:
            print(('File %s does not exist' % fl))
    return conn_matrices
subject_list : list of short subject IDs in string format
    atlas_name   : the atlas based on which the timeseries are generated e.g. aal, cc200
    kind         : the kind of correlation used to estimate the matrices, i.e.

returns:
    connectivity : list of square connectivity matrices, one for each subject in subject_list
def fetch_conn_matrices(subject_list, atlas_name, kind):
    '\n        subject_list : list of short subject IDs in string format\n        atlas_name   : the atlas based on which the timeseries are generated e.g. aal, cc200\n        kind         : the kind of correlation used to estimate the matrices, i.e.\n\n    returns:\n        connectivity : list of square connectivity matrices, one for each subject in subject_list\n    '
    conn_files = fetch_filenames(subject_list, (((('_' + atlas_name) + '_') + kind.replace(' ', '_')) + '.mat'))
    conn_matrices = []
    for fl in conn_files:
        print(('Reading connectivity file %s' % fl))
        try:
            mat = sio.loadmat(fl)['connectivity']
            conn_matrices.append(mat)
        except IOError:
            print(('File %s does not exist' % fl))
    return conn_matrices