@verbose
def spatio_temporal_cluster_test(X, threshold=1.67, n_permutations=1024, tail=0, stat_fun=f_oneway, connectivity=None, verbose=None, n_jobs=1, seed=None, max_step=1, spatial_exclude=None, step_down_p=0, t_power=1, out_type='indices', check_disjoint=False, buffer_size=1000):
 (n_samples, n_times, n_vertices) = X[0].shape
 if (spatial_exclude is not None):
  exclude = _st_mask_from_s_inds(n_times, n_vertices, spatial_exclude, True)
 else:
  exclude = None
 out = permutation_cluster_test(X, threshold=threshold, stat_fun=stat_fun, tail=tail, n_permutations=n_permutations, connectivity=connectivity, n_jobs=n_jobs, seed=seed, max_step=max_step, exclude=exclude, step_down_p=step_down_p, t_power=t_power, out_type=out_type, check_disjoint=check_disjoint, buffer_size=buffer_size)
 return out

@ verbose def spatio_temporal_cluster_test ( X , threshold = 1.67 , n_permutations = 1024 , tail = 0 , stat_fun = f_oneway , connectivity = None , verbose = None , n_jobs = 1 , seed = None , max_step = 1 , spatial_exclude = None , step_down_p = 0 , t_power = 1 , out_type = 'indices' , check_disjoint = False , buffer_size = 1000 ) : ( n_samples , n_times , n_vertices ) = X [ 0 ] . shape if ( spatial_exclude is not None ) : exclude = _st_mask_from_s_inds ( n_times , n_vertices , spatial_exclude , True ) else : exclude = None out = permutation_cluster_test ( X , threshold = threshold , stat_fun = stat_fun , tail = tail , n_permutations = n_permutations , connectivity = connectivity , n_jobs = n_jobs , seed = seed , max_step = max_step , exclude = exclude , step_down_p = step_down_p , t_power = t_power , out_type = out_type , check_disjoint = check_disjoint , buffer_size = buffer_size ) return out
non-parametric cluster-level test for spatio-temporal data .