docs: 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