def __str__(self):
        return "".join([self.package, self.version or ""])
def _check_target_vars(target_names):
    """Error-checks list of target variables.

    Target variables must all have the same mean lead time (average of min and
    max lead times) and event type (tornado or wind).

    :param target_names: 1-D list with names of target variables.  Each must be
        accepted by `target_val_utils.target_name_to_params`.
    :return: mean_lead_time_seconds: Mean lead time (shared by all target
        variables).
    :return: event_type_string: Event type.
    :raises: ValueError: if target variables do not all have the same mean lead
        time or event type.
    """

    error_checking.assert_is_string_list(target_names)
    error_checking.assert_is_numpy_array(
        numpy.array(target_names), num_dimensions=1
    )

    num_target_vars = len(target_names)
    mean_lead_times = numpy.full(num_target_vars, -1, dtype=int)
    event_type_strings = numpy.full(num_target_vars, '', dtype=object)

    for k in range(num_target_vars):
        this_param_dict = target_val_utils.target_name_to_params(
            target_names[k]
        )

        event_type_strings[k] = this_param_dict[target_val_utils.EVENT_TYPE_KEY]

        mean_lead_times[k] = int(numpy.round(
            (this_param_dict[target_val_utils.MAX_LEAD_TIME_KEY] +
             this_param_dict[target_val_utils.MIN_LEAD_TIME_KEY])
            / 2
        ))

    if len(numpy.unique(mean_lead_times)) != 1:
        error_string = (
            'Target variables (listed below) have different mean lead times.'
            '\n{0:s}'
        ).format(str(target_names))

        raise ValueError(error_string)

    if len(numpy.unique(event_type_strings)) != 1:
        error_string = (
            'Target variables (listed below) have different event types.\n{0:s}'
        ).format(str(target_names))

        raise ValueError(error_string)

    return mean_lead_times[0], event_type_strings[0]