def _getSmartIndenter(indenterName, qpart, indenter):
    """Get indenter by name.
    Available indenters are none, normal, cstyle, haskell, lilypond, lisp, python, ruby, xml
    Indenter name is not case sensitive
    Raise KeyError if not found
    indentText is indentation, which shall be used. i.e. '\t' for tabs, '    ' for 4 space symbols
    """
    indenterName = indenterName.lower()

    if indenterName in ('haskell', 'lilypond'):  # not supported yet
        logger.warning('Smart indentation for %s not supported yet. But you could be a hero who implemented it' % indenterName)
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'none' == indenterName:
        from qutepart.indenter.base import IndentAlgBase as indenterClass
    elif 'normal' == indenterName:
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'cstyle' == indenterName:
        from qutepart.indenter.cstyle import IndentAlgCStyle as indenterClass
    elif 'python' == indenterName:
        from qutepart.indenter.python import IndentAlgPython as indenterClass
    elif 'ruby' == indenterName:
        from qutepart.indenter.ruby import IndentAlgRuby as indenterClass
    elif 'xml' == indenterName:
        from qutepart.indenter.xmlindent import IndentAlgXml as indenterClass
    elif 'haskell' == indenterName:
        from qutepart.indenter.haskell import IndenterHaskell as indenterClass
    elif 'lilypond' == indenterName:
        from qutepart.indenter.lilypond import IndenterLilypond as indenterClass
    elif 'lisp' == indenterName:
        from qutepart.indenter.lisp import IndentAlgLisp as indenterClass
    elif 'scheme' == indenterName:
        from qutepart.indenter.scheme import IndentAlgScheme as indenterClass
    else:
        raise KeyError("Indenter %s not found" % indenterName)

    return indenterClass(qpart, indenter)
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]