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 run(
            self, field, query_string='', query_dsl='',
            supported_charts='table', start_time='', end_time='', limit=10):
        """Run the aggregation.

        Args:
            field (str): this denotes the event attribute that is used
                for aggregation.
            query_string (str): the query field to run on all documents prior to
                aggregating the results.
            query_dsl (str): the query DSL field to run on all documents prior
                to aggregating the results. Either a query string or a query
                DSL has to be present.
            supported_charts: Chart type to render. Defaults to table.
            start_time: Optional ISO formatted date string that limits the time
                range for the aggregation.
            end_time: Optional ISO formatted date string that limits the time
                range for the aggregation.
            limit (int): How many buckets to return, defaults to 10.

        Returns:
            Instance of interface.AggregationResult with aggregation result.

        Raises:
            ValueError: if neither query_string or query_dsl is provided.
        """
        if not (query_string or query_dsl):
            raise ValueError('Both query_string and query_dsl are missing')

        self.field = field
        formatted_field_name = self.format_field_by_type(field)

        aggregation_spec = get_spec(
            field=formatted_field_name, limit=limit, query=query_string,
            query_dsl=query_dsl)

        aggregation_spec = self._add_query_to_aggregation_spec(
            aggregation_spec, start_time=start_time, end_time=end_time)

        # Encoding information for Vega-Lite.
        encoding = {
            'x': {
                'field': field,
                'type': 'nominal',
                'sort': {
                    'op': 'sum',
                    'field': 'count',
                    'order': 'descending'
                }
            },
            'y': {'field': 'count', 'type': 'quantitative'},
            'tooltip': [
                {'field': field, 'type': 'nominal'},
                {'field': 'count', 'type': 'quantitative'}],
        }

        response = self.opensearch_aggregation(aggregation_spec)
        aggregations = response.get('aggregations', {})
        aggregation = aggregations.get('aggregation', {})

        buckets = aggregation.get('buckets', [])
        values = []
        for bucket in buckets:
            d = {
                field: bucket.get('key', 'N/A'),
                'count': bucket.get('doc_count', 0)
            }
            values.append(d)

        if query_string:
            extra_query_url = 'AND {0:s}'.format(query_string)
        else:
            extra_query_url = ''

        return interface.AggregationResult(
            encoding=encoding, values=values, chart_type=supported_charts,
            sketch_url=self._sketch_url, field=field,
            extra_query_url=extra_query_url)