def get_nstd(X, y, order, n_azi_buckets, param_var):
basisfns, b, B = setup_azi_basisfns(order, n_azi_buckets, param_var)
lbm1 = LinearBasisModel(X=X, y=y, basisfns = basisfns, param_mean=b, param_covar=B, noise_std=1.0, sta="FITZ")
pred = lbm1.predict(X)
nstd = np.std(y-pred)
return nstd
