self = <climada.engine.unsequa.test.test_unsequa.TestOutput testMethod=test_save_load_pass>

    def test_save_load_pass(self):
        """Test save and load output data"""
    
        exp_unc, impf_unc, _ = make_input_vars()
        haz = haz_dem()
        unc_calc = CalcImpact(exp_unc, impf_unc, haz)
    
        unc_data_save = unc_calc.make_sample(
            N=2, sampling_kwargs={"calc_second_order": True}
        )
        filename = unc_data_save.to_hdf5()
        unc_data_load = UncOutput.from_hdf5(filename)
        for attr_save, val_save in unc_data_save.__dict__.items():
            if isinstance(val_save, pd.DataFrame):
                df_load = getattr(unc_data_load, attr_save)
                self.assertTrue(df_load.equals(val_save))
        self.assertEqual(unc_data_load.sampling_method, unc_data_save.sampling_method)
        self.assertEqual(unc_data_load.sampling_kwargs, unc_data_save.sampling_kwargs)
        filename.unlink()
    
        unc_data_save = unc_calc.uncertainty(
            unc_data_save, calc_eai_exp=True, calc_at_event=False
        )
        filename = unc_data_save.to_hdf5()
        unc_data_load = UncOutput.from_hdf5(filename)
        for attr_save, val_save in unc_data_save.__dict__.items():
            if isinstance(val_save, pd.DataFrame):
                df_load = getattr(unc_data_load, attr_save)
                self.assertTrue(df_load.equals(val_save))
        self.assertEqual(unc_data_load.sampling_method, unc_data_save.sampling_method)
        self.assertEqual(unc_data_load.sampling_kwargs, unc_data_save.sampling_kwargs)
        filename.unlink()
    
        unc_data_save = unc_calc.sensitivity(
            unc_data_save, sensitivity_kwargs={"calc_second_order": True}
        )
        filename = unc_data_save.to_hdf5()
>       unc_data_load = UncOutput.from_hdf5(filename)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

climada/engine/unsequa/test/test_unsequa.py:308: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
climada/engine/unsequa/unc_output.py:1206: in from_hdf5
    setattr(unc_data, var_name[1:], store.get(var_name))
                                    ^^^^^^^^^^^^^^^^^^^
../../../../../miniforge3/envs/climada_env/lib/python3.11/site-packages/pandas/io/pytables.py:812: in get
    return self._read_group(group)
           ^^^^^^^^^^^^^^^^^^^^^^^
../../../../../miniforge3/envs/climada_env/lib/python3.11/site-packages/pandas/io/pytables.py:1879: in _read_group
    return s.read()
           ^^^^^^^^
../../../../../miniforge3/envs/climada_env/lib/python3.11/site-packages/pandas/io/pytables.py:3292: in read
    columns = items[items.get_indexer(blk_items)]
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Index([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0],
      dtype='int64')
target = Index([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0],
      dtype='int64')
method = None, limit = None, tolerance = None

    @Appender(_index_shared_docs["get_indexer"] % _index_doc_kwargs)
    @final
    def get_indexer(
        self,
        target,
        method: ReindexMethod | None = None,
        limit: int | None = None,
        tolerance=None,
    ) -> npt.NDArray[np.intp]:
        method = clean_reindex_fill_method(method)
        orig_target = target
        target = self._maybe_cast_listlike_indexer(target)
    
        self._check_indexing_method(method, limit, tolerance)
    
        if not self._index_as_unique:
>           raise InvalidIndexError(self._requires_unique_msg)
E           pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects

../../../../../miniforge3/envs/climada_env/lib/python3.11/site-packages/pandas/core/indexes/base.py:3875: InvalidIndexError