test_get_restore_numpy_array.py 文件源码

def test_restores_collected_horizontal_dimensions(self):
        random = np.random.RandomState(0)
        T_array = random.randn(3, 2, 4)
        input_state = {
            'air_temperature': DataArray(
                T_array,
                dims=['x', 'y', 'z'],
                attrs={'units': 'degK'},
            )
        }
        input_properties = {
            'air_temperature': {
                'dims': ['z', '*'],
                'units': 'degK',
            }
        }
        raw_arrays = get_numpy_arrays_with_properties(input_state, input_properties)
        raw_arrays = {key + '_tendency': value for key, value in raw_arrays.items()}
        output_properties = {
            'air_temperature_tendency': {
                'dims_like': 'air_temperature',
                'units': 'degK/s',
            }
        }
        return_value = restore_data_arrays_with_properties(
            raw_arrays, output_properties, input_state, input_properties
        )
        assert isinstance(return_value, dict)
        assert len(return_value.keys()) == 1
        assert isinstance(return_value['air_temperature_tendency'], DataArray)
        assert return_value['air_temperature_tendency'].attrs['units'] is 'degK/s'
        assert np.byte_bounds(
            return_value['air_temperature_tendency'].values) == np.byte_bounds(
            input_state['air_temperature'].values)
        assert (return_value['air_temperature_tendency'].values.base is
                input_state['air_temperature'].values)
        assert return_value['air_temperature_tendency'].shape == (3, 2, 4)
        assert np.all(return_value['air_temperature_tendency'] == T_array)
        assert return_value['air_temperature_tendency'].dims == input_state['air_temperature'].dims