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 groupby(cls, dataset, dimensions, container_type, group_type, **kwargs):
        # Get dimensions information
        dimensions = [dataset.get_dimension(d).name for d in dimensions]
        kdims = [kdim for kdim in dataset.kdims if kdim not in dimensions]

        # Update the kwargs appropriately for Element group types
        group_kwargs = {}
        group_type = dict if group_type == 'raw' else group_type
        if issubclass(group_type, Element):
            group_kwargs.update(util.get_param_values(dataset))
            group_kwargs['kdims'] = kdims
        group_kwargs.update(kwargs)

        # Propagate dataset
        group_kwargs['dataset'] = dataset.dataset

        # Find all the keys along supplied dimensions
        keys = product(*(dataset.data[dimensions[0]].unique().values_host for d in dimensions))

        # Iterate over the unique entries applying selection masks
        grouped_data = []
        for unique_key in util.unique_iterator(keys):
            group_data = dataset.select(**dict(zip(dimensions, unique_key)))
            if not len(group_data):
                continue
            group_data = group_type(group_data, **group_kwargs)
            grouped_data.append((unique_key, group_data))

        if issubclass(container_type, NdMapping):
            with item_check(False), sorted_context(False):
                kdims = [dataset.get_dimension(d) for d in dimensions]
                return container_type(grouped_data, kdims=kdims)
        else:
            return container_type(grouped_data)