How to use the pytesmo.df_metrics.n_combinations function in pytesmo

def test_n_combinations():
    coll = [1,2,3,4]
    combs = df_metrics.n_combinations(coll, n=2, must_include=[1], permutations=False)
    assert combs == [(1,2), (1,3), (1,4)]

    coll = [1, 2, 3, 4]
    combs = df_metrics.n_combinations(coll, n=3, permutations=False)
    assert combs == [(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)]

def test_n_combinations():
    coll = [1,2,3,4]
    combs = df_metrics.n_combinations(coll, n=2, must_include=[1], permutations=False)
    assert combs == [(1,2), (1,3), (1,4)]

    coll = [1, 2, 3, 4]
    combs = df_metrics.n_combinations(coll, n=3, permutations=False)
    assert combs == [(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)]

def _make_names(self):
        tds_names, thds_names = [], []
        combis_2 = n_combinations(
            self.df_columns, 2, must_include=[self.ref_name])
        combis_3 = n_combinations(
            self.df_columns, 3, must_include=[self.ref_name])

        for combi in combis_2:
            tds_names.append(self.ds_names_split.join(combi))

        for combi in combis_3:
            thds_names.append("{1}{0}{2}{0}{3}".format(
                self.ds_names_split, *combi))

        return tds_names, thds_names

def _make_names(self):
        tds_names, thds_names = [], []
        combis_2 = n_combinations(
            self.df_columns, 2, must_include=[self.ref_name])
        combis_3 = n_combinations(
            self.df_columns, 3, must_include=[self.ref_name])

        for combi in combis_2:
            tds_names.append(self.ds_names_split.join(combi))

        for combi in combis_3:
            thds_names.append("{1}{0}{2}{0}{3}".format(
                self.ds_names_split, *combi))

        return tds_names, thds_names

self.ds_names_split, self.metric_ds_split = '_and_', '_between_'

        self.df_columns = ['ref'] + self.other_name

        self.calc_tau = calc_tau

        if dataset_names is None:
            self.ds_names = self.df_columns
        else:
            self.ds_names = dataset_names

        self.ds_names_lut = {}
        for name, col in zip(self.ds_names, self.df_columns):
            self.ds_names_lut[col] = name

        combis = n_combinations(self.df_columns, 2, must_include='ref')
        self.tds_names = []
        for combi in combis:
            self.tds_names.append("{1}{0}{2}".format(
                self.ds_names_split, *combi))

        # metrics that are equal for all datasets
        metrics_common = ['n_obs']
        # metrics that are calculated between dataset pairs
        metrics_tds = ['R', 'p_R', 'rho', 'p_rho', 'BIAS', 'RMSD', 'mse', 'RSS',
                       'mse_corr', 'mse_bias', 'urmsd', 'mse_var', 'tau', 'p_tau']

        metrics_common = _get_metric_template(metrics_common)
        metrics_tds = _get_metric_template(metrics_tds)

        for metric in metrics_common.keys():
            self.result_template[metric] = metrics_common[metric].copy()