How to use the pytesmo.metrics.bias function in pytesmo

def test_apply():

    df = pd.DataFrame(index=pd.date_range(start='2000-01-01', end='2000-12-31', freq='D'),
                      data={'ds0': np.repeat(0, 366), 'ds1': np.repeat(1, 366)})
    bias_matrix_old = df_metrics.pairwise_apply(df, bias)
    bias_matrix_new = df_metrics.nwise_apply(df, bias,  n=2, as_df=True)
    assert bias_matrix_old.equals(bias_matrix_new)

    # check if dict implementation and matrix implementation have same result
    bias_new = df_metrics.nwise_apply(df, bias,  n=2, as_df=False)
    for i, v in bias_new.items():
        assert bias_matrix_new.loc[i] == v

"""
    # example 1
    x = np.arange(10)
    y = np.arange(10) + 2

    b_pred = -2
    b_obs = met.bias(x, y)

    nptest.assert_equal(b_obs, b_pred)

    # example 2
    x = np.arange(10)
    y = np.arange(20, 30)

    b_pred = 20.
    b_obs = met.bias(y, x)

    nptest.assert_equal(b_obs, b_pred)

def test_bias():
    """
    Test for bias
    """
    # example 1
    x = np.arange(10)
    y = np.arange(10) + 2

    b_pred = -2
    b_obs = met.bias(x, y)

    nptest.assert_equal(b_obs, b_pred)

    # example 2
    x = np.arange(10)
    y = np.arange(20, 30)

    b_pred = 20.
    b_obs = met.bias(y, x)

    nptest.assert_equal(b_obs, b_pred)

def bias(df):
    """Bias

    Returns
    -------
    bias : pandas.Dataframe
        of shape (len(df.columns),len(df.columns))
    See Also
    --------
    pytesmo.metrics.bias
    """
    return _dict_to_namedtuple(nwise_apply(df, metrics.bias, n=2, comm=False),
                               'bias')

-----
        Kendall tau is calculation is optional at the moment
        because the scipy implementation is very slow which is problematic for
        global comparisons

        """
        dataset = super(BasicMetrics, self).calc_metrics(data, gpi_info)

        if len(data) < 10:
            return dataset

        x, y = data['ref'].values, data[self.other_name].values
        R, p_R = metrics.pearsonr(x, y)
        rho, p_rho = metrics.spearmanr(x, y)
        RMSD = metrics.rmsd(x, y)
        BIAS = metrics.bias(x, y)

        dataset['R'][0], dataset['p_R'][0] = R, p_R
        dataset['rho'][0], dataset['p_rho'][0] = rho, p_rho
        dataset['RMSD'][0] = RMSD
        dataset['BIAS'][0] = BIAS
        dataset['n_obs'][0] = len(data)

        if self.calc_tau:
            tau, p_tau = metrics.kendalltau(x, y)
            dataset['tau'][0], dataset['p_tau'][0] = tau, p_tau

        return dataset

x, y = scaled_data[label_ascat].values, scaled_data[label_insitu].values
        
        print "ISMN time series:", ISMN_time_series
        print "compared to"
        print ascat_time_series
        print "Results:"
        
        # df_metrics takes a DataFrame as input and automatically
        # calculates the metric on all combinations of columns
        # returns a named tuple for easy printing
        print df_metrics.pearsonr(scaled_data)
        print "Spearman's (rho,p_value)", metrics.spearmanr(x, y)
        print "Kendalls's (tau,p_value)", metrics.kendalltau(x, y)
        print df_metrics.kendalltau(scaled_data)
        print df_metrics.rmsd(scaled_data)
        print "Bias", metrics.bias(x, y)
        print "Nash Sutcliffe", metrics.nash_sutcliffe(x, y)
        
        
    i += 1
    
    # only show the first 2 stations, otherwise this program would run a long time
    # and produce a lot of plots
    if i >= 2:
        break

x, y = scaled_data[label_ascat].values, scaled_data[label_insitu].values
        
        print("ISMN time series:", ISMN_time_series)
        print("compared to")
        print(ascat_time_series)
        print("Results:")
        
        # df_metrics takes a DataFrame as input and automatically
        # calculates the metric on all combinations of columns
        # returns a named tuple for easy printing
        print(df_metrics.pearsonr(scaled_data))
        print("Spearman's (rho,p_value)", metrics.spearmanr(x, y))
        print("Kendalls's (tau,p_value)", metrics.kendalltau(x, y))
        print(df_metrics.kendalltau(scaled_data))
        print(df_metrics.rmsd(scaled_data))
        print("Bias", metrics.bias(x, y))
        print("Nash Sutcliffe", metrics.nash_sutcliffe(x, y))
        
        
    i += 1

    # only show the first 2 stations, otherwise this program would run a long time
    # and produce a lot of plots
    if i >= 2:
        break