How to use the pytesmo.validation_framework.data_manager.get_result_names function in pytesmo

def test_get_result_names():

    tst_ds_dict = {'DS1': ['soil moisture'],
                   'DS2': ['sm'],
                   'DS3': ['sm', 'sm2']}
    result_names = get_result_names(tst_ds_dict, 'DS1', 3)
    assert result_names == [(('DS1', 'soil moisture'), ('DS2', 'sm'), ('DS3', 'sm')),
                            (('DS1', 'soil moisture'), ('DS2', 'sm'), ('DS3', 'sm2'))]

    result_names = get_result_names(tst_ds_dict, 'DS1', 2)
    assert result_names == [(('DS1', 'soil moisture'), ('DS2', 'sm')),
                            (('DS1', 'soil moisture'), ('DS3', 'sm')),
                            (('DS1', 'soil moisture'), ('DS3', 'sm2'))]

    result_names = get_result_names(tst_ds_dict, 'DS2', 2)
    assert result_names == [(('DS2', 'sm'), ('DS1', 'soil moisture')),
                            (('DS2', 'sm'), ('DS3', 'sm')),
                            (('DS2', 'sm'), ('DS3', 'sm2'))]

def test_get_result_names():

    tst_ds_dict = {'DS1': ['soil moisture'],
                   'DS2': ['sm'],
                   'DS3': ['sm', 'sm2']}
    result_names = get_result_names(tst_ds_dict, 'DS1', 3)
    assert result_names == [(('DS1', 'soil moisture'), ('DS2', 'sm'), ('DS3', 'sm')),
                            (('DS1', 'soil moisture'), ('DS2', 'sm'), ('DS3', 'sm2'))]

    result_names = get_result_names(tst_ds_dict, 'DS1', 2)
    assert result_names == [(('DS1', 'soil moisture'), ('DS2', 'sm')),
                            (('DS1', 'soil moisture'), ('DS3', 'sm')),
                            (('DS1', 'soil moisture'), ('DS3', 'sm2'))]

    result_names = get_result_names(tst_ds_dict, 'DS2', 2)
    assert result_names == [(('DS2', 'sm'), ('DS1', 'soil moisture')),
                            (('DS2', 'sm'), ('DS3', 'sm')),
                            (('DS2', 'sm'), ('DS3', 'sm2'))]

def test_get_result_names():

    tst_ds_dict = {'DS1': ['soil moisture'],
                   'DS2': ['sm'],
                   'DS3': ['sm', 'sm2']}
    result_names = get_result_names(tst_ds_dict, 'DS1', 3)
    assert result_names == [(('DS1', 'soil moisture'), ('DS2', 'sm'), ('DS3', 'sm')),
                            (('DS1', 'soil moisture'), ('DS2', 'sm'), ('DS3', 'sm2'))]

    result_names = get_result_names(tst_ds_dict, 'DS1', 2)
    assert result_names == [(('DS1', 'soil moisture'), ('DS2', 'sm')),
                            (('DS1', 'soil moisture'), ('DS3', 'sm')),
                            (('DS1', 'soil moisture'), ('DS3', 'sm2'))]

    result_names = get_result_names(tst_ds_dict, 'DS2', 2)
    assert result_names == [(('DS2', 'sm'), ('DS1', 'soil moisture')),
                            (('DS2', 'sm'), ('DS3', 'sm')),
                            (('DS2', 'sm'), ('DS3', 'sm2'))]

ref_key: basestring
        Name of the reference dataset
    datasets: dict
        Dictionary of dictionaries as provided to the validation framework
        in order to perform the validation process.

    Returns
    -------
    dataset_names: list
        List of the dataset names in correct order

    """
    ds_dict = {}
    for ds in datasets.keys():
        ds_dict[ds] = datasets[ds]['columns']
    ds_names = get_result_names(ds_dict, ref_key, n)
    dataset_names = []
    for name in ds_names[0]:
        dataset_names.append(name[0])

    return dataset_names

Parameters
        ----------
        gpi_info: tuple
            tuple of at least, (gpi, lon, lat)

        Returns
        -------
        mask: numpy.ndarray
            boolean array of the size of the temporal reference read
        """

        matched_masking = self.temporal_match_masking_data(ref_df, gpi_info)
        # this will only be one element since n is the same as the
        # number of masking datasets
        result_names = get_result_names(self.masking_dm.ds_dict,
                                        '_reference',
                                        n=2)
        choose_all = pd.DataFrame(index=ref_df.index)
        for data, result in self.k_datasets_from(matched_masking,
                                                 result_names):
            if len(data) == 0:
                continue

            for key in result:
                if key[0] != '_reference':
                    # this is necessary since the boolean datatype might have
                    # been changed to float 1.0 and 0.0 issue with temporal
                    # resampling that is not easily resolved since most
                    # datatypes have no nan representation.
                    choose = pd.Series((data[key] == False), index=data.index)
                    choose = choose.reindex(index=choose_all.index,

if self.masking_dm is not None:
            ref_df = df_dict[self.temporal_ref]
            masked_ref_df = self.mask_dataset(ref_df,
                                              gpi_info)
            if len(masked_ref_df) == 0:
                return matched_n, results, used_data

            df_dict[self.temporal_ref] = masked_ref_df

        matched_n = self.temporal_match_datasets(df_dict)

        for n, k in self.metrics_c:
            n_matched_data = matched_n[(n, k)]
            if len(n_matched_data) == 0:
                continue
            result_names = get_result_names(self.data_manager.ds_dict,
                                            self.temporal_ref,
                                            n=k)
            for data, result_key in self.k_datasets_from(n_matched_data,
                                                         result_names):

                if len(data) == 0:
                    continue

                # at this stage we can drop the column multiindex and just use
                # the dataset name
                if LooseVersion(pd.__version__) < LooseVersion('0.23'):
                    data.columns = data.columns.droplevel(level=1)
                else:
                    data = data.rename(columns=lambda x: x[0])

                if self.scaling is not None: