def _get_inferred_data_res(column, age):
    """
    Calculate Resolution and m/m/m/m for column values.
    :param dict column: Column data
    :param list age: Age values
    :return dict column: Column data - modified
    """
    try:
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            # Get the values for this column
            values = column["values"]
            # Make sure that age and values are numpy arrays
            _values = np.array(copy.copy(values), dtype=float)
            # _values = _values[np.where(~np.isnan(_values))[0]]
            _age = np.array(age, dtype=float)
            # _age = _age[np.where(~np.isnan(_age))[0]]

            # If we have values, keep going
            if len(_values) != 0:
                # Get the resolution for this age and column values data
                res = _get_resolution(_age, _values)
                # If we have successful resolution data, keep going
                if len(res) != 0:
                    column["hasResolution"] = __get_inferred_data_res_2(res)

                # Remove the NaNs from the values list.
                _values = _values[np.where(~np.isnan(_values))[0]]
                # Calculate column non-resolution data, update the column with the results.
                column.update(__get_inferred_data_res_2(_values))

    except KeyError as e:
        logger_inferred_data.debug("get_inferred_data_column: KeyError: {}".format(e))
    except Exception as e:
        logger_inferred_data.debug("get_inferred_data_column: Exception: {}".format(e))

    return column