How to use the tigramite.data_processing.quantile_bin_array function in tigramite

T = 100000
        numpy.random.seed(None)
        array = numpy.random.randn(3, T)

        cov = numpy.array([[1., val_ana],[val_ana, 1.]])
        array[:2, :] = numpy.random.multivariate_normal(
                        mean=numpy.zeros(2),
                        cov=cov, size=T).T

        # Generate some confounding
        if len(array) > 2:
            array[0] += 0.5* array[2:].sum(axis=0)
            array[1] += 0.7* array[2:].sum(axis=0)

        # Transform to symbolic data
        array = pp.quantile_bin_array(array.T, bins=16).T

        dim, T = array.shape
        xyz = numpy.array([0,1,2,2,2])

        val_est = ci_cmi_symb.get_dependence_measure(array, xyz)
        
        print(val_est)
        print(_par_corr_to_cmi(val_ana))

        numpy.testing.assert_allclose(numpy.array(_par_corr_to_cmi(val_ana)), 
                                   numpy.array(val_est),
                                   atol=0.02)

def test_cmi_symb(cmi_symb, data_sample_d):
    # Get the data sample values
    small_array, _, corr_val, xyz, dim, T = data_sample_d
    # Generate the full array
    dim = 3
    array = np.random.randn(dim, T)
    array[:2, :] = small_array
    # Generate some confounding
    array[0] += 0.5* array[2:].sum(axis=0)
    array[1] += 0.7* array[2:].sum(axis=0)
    # Transform to symbolic data
    array = pp.quantile_bin_array(array.T, bins=16).T
    # Reset the dimension
    xyz = np.array([0, 1, 2, 2, 2])
    # Get the estimated value
    val_est = cmi_symb.get_dependence_measure(array, xyz)
    np.testing.assert_allclose(np.array(_par_corr_to_cmi(corr_val)),
                               np.array(val_est),
                               atol=0.02)