How to use the pydruid.utils.filters.Filter.build_filter function in pydruid

client.groupby(**pre_qry)
            query_str += "// Two phase query\n// Phase 1\n"
            query_str += json.dumps(client.query_dict, indent=2) + "\n"
            query_str += "//\nPhase 2 (built based on phase one's results)\n"
            df = client.export_pandas()
            if df is not None and not df.empty:
                dims = qry['dimensions']
                filters = []
                for index, row in df.iterrows():
                    fields = []
                    for dim in dims:
                        f = Filter.build_filter(Dimension(dim) == row[dim])
                        fields.append(f)
                    if len(fields) > 1:
                        filt = Filter(type="and", fields=fields)
                        filters.append(Filter.build_filter(filt))
                    elif fields:
                        filters.append(fields[0])

                if filters:
                    ff = Filter(type="or", fields=filters)
                    if not orig_filters:
                        qry['filter'] = ff
                    else:
                        qry['filter'] = Filter(type="and", fields=[
                            Filter.build_filter(ff),
                            Filter.build_filter(orig_filters)])
                qry['limit_spec'] = None
        if row_limit:
            qry['limit_spec'] = {
                "type": "default",
                "limit": row_limit,

"dimension": metrics[0] if metrics else self.metrics[0],
                    "direction": "descending",
                }],
            }
            client.groupby(**pre_qry)
            query_str += "// Two phase query\n// Phase 1\n"
            query_str += json.dumps(client.query_dict, indent=2) + "\n"
            query_str += "//\nPhase 2 (built based on phase one's results)\n"
            df = client.export_pandas()
            if df is not None and not df.empty:
                dims = qry['dimensions']
                filters = []
                for index, row in df.iterrows():
                    fields = []
                    for dim in dims:
                        f = Filter.build_filter(Dimension(dim) == row[dim])
                        fields.append(f)
                    if len(fields) > 1:
                        filt = Filter(type="and", fields=fields)
                        filters.append(Filter.build_filter(filt))
                    elif fields:
                        filters.append(fields[0])

                if filters:
                    ff = Filter(type="or", fields=filters)
                    if not orig_filters:
                        qry['filter'] = ff
                    else:
                        qry['filter'] = Filter(type="and", fields=[
                            Filter.build_filter(ff),
                            Filter.build_filter(orig_filters)])
                qry['limit_spec'] = None

filters = None
        while True:
            col = args.get("flt_col_" + str(i))
            op = args.get("flt_op_" + str(i))
            eq = args.get("flt_eq_" + str(i))
            if col and op and eq:
                cond = None
                if op == '==':
                    cond = Dimension(col)==eq
                elif op == '!=':
                    cond = ~(Dimension(col)==eq)
                elif op == 'in':
                    fields = []
                    for s in eq.split(','):
                        s = s.strip()
                        fields.append(Filter.build_filter(Dimension(col)==s))
                    cond = Filter(type="or", fields=fields)


                if filters:
                    filters = cond and filters
                else:
                    filters = cond
            else:
                break
            i += 1

        kw = {}
        if filters:
            kw['filter'] = filters
        query.groupby(
            datasource=ds.name,

elif op in ('in', 'not in'):
                fields = []
                splitted = eq.split(',')
                if len(splitted) > 1:
                    for s in eq.split(','):
                        s = s.strip()
                        fields.append(Filter.build_filter(Dimension(col) == s))
                    cond = Filter(type="or", fields=fields)
                else:
                    cond = Dimension(col) == eq
                if op == 'not in':
                    cond = ~cond
            if filters:
                filters = Filter(type="and", fields=[
                    Filter.build_filter(cond),
                    Filter.build_filter(filters)
                ])
            else:
                filters = cond

        if filters:
            qry['filter'] = filters

        client = self.cluster.get_pydruid_client()
        orig_filters = filters
        if timeseries_limit and is_timeseries:
            # Limit on the number of timeseries, doing a two-phases query
            pre_qry = deepcopy(qry)
            pre_qry['granularity'] = "all"
            pre_qry['limit_spec'] = {
                "type": "default",
                "limit": timeseries_limit,

cond = ~(Dimension(col) == eq)
            elif op in ('in', 'not in'):
                fields = []
                splitted = eq.split(',')
                if len(splitted) > 1:
                    for s in eq.split(','):
                        s = s.strip()
                        fields.append(Filter.build_filter(Dimension(col) == s))
                    cond = Filter(type="or", fields=fields)
                else:
                    cond = Dimension(col) == eq
                if op == 'not in':
                    cond = ~cond
            if filters:
                filters = Filter(type="and", fields=[
                    Filter.build_filter(cond),
                    Filter.build_filter(filters)
                ])
            else:
                filters = cond

        if filters:
            qry['filter'] = filters

        client = self.cluster.get_pydruid_client()
        orig_filters = filters
        if timeseries_limit and is_timeseries:
            # Limit on the number of timeseries, doing a two-phases query
            pre_qry = deepcopy(qry)
            pre_qry['granularity'] = "all"
            pre_qry['limit_spec'] = {
                "type": "default",

intervals=from_dttm.isoformat() + '/' + to_dttm.isoformat(),
        )
        filters = None
        for col, op, eq in filter:
            cond = None
            if op == '==':
                cond = Dimension(col) == eq
            elif op == '!=':
                cond = ~(Dimension(col) == eq)
            elif op in ('in', 'not in'):
                fields = []
                splitted = eq.split(',')
                if len(splitted) > 1:
                    for s in eq.split(','):
                        s = s.strip()
                        fields.append(Filter.build_filter(Dimension(col) == s))
                    cond = Filter(type="or", fields=fields)
                else:
                    cond = Dimension(col) == eq
                if op == 'not in':
                    cond = ~cond
            if filters:
                filters = Filter(type="and", fields=[
                    Filter.build_filter(cond),
                    Filter.build_filter(filters)
                ])
            else:
                filters = cond

        if filters:
            qry['filter'] = filters

f = Filter.build_filter(Dimension(dim) == row[dim])
                        fields.append(f)
                    if len(fields) > 1:
                        filt = Filter(type="and", fields=fields)
                        filters.append(Filter.build_filter(filt))
                    elif fields:
                        filters.append(fields[0])

                if filters:
                    ff = Filter(type="or", fields=filters)
                    if not orig_filters:
                        qry['filter'] = ff
                    else:
                        qry['filter'] = Filter(type="and", fields=[
                            Filter.build_filter(ff),
                            Filter.build_filter(orig_filters)])
                qry['limit_spec'] = None
        if row_limit:
            qry['limit_spec'] = {
                "type": "default",
                "limit": row_limit,
                "columns": [{
                    "dimension": metrics[0] if metrics else self.metrics[0],
                    "direction": "descending",
                }],
            }
        client.groupby(**qry)
        query_str += json.dumps(client.query_dict, indent=2)
        df = client.export_pandas()
        if df is None or df.size == 0:
            raise Exception("No data was returned.")