How to use the tsam.timeseriesaggregation.TimeSeriesAggregation function in tsam

# (b) thereby collect the weights which should be considered for each time series as well in a dictionary
        timeSeriesData, weightDict = [], {}
        for mdlName, mdl in self.componentModelingDict.items():
            for compName, comp in mdl.componentsDict.items():
                compTimeSeriesData, compWeightDict = comp.getDataForTimeSeriesAggregation()
                if compTimeSeriesData is not None:
                    timeSeriesData.append(compTimeSeriesData), weightDict.update(compWeightDict)
        timeSeriesData = pd.concat(timeSeriesData, axis=1)
        # Note: Sets index for the time series data. The index is of no further relevance in the energy system model.
        timeSeriesData.index = pd.date_range('2050-01-01 00:30:00', periods=len(self.totalTimeSteps),
                                             freq=(str(self.hoursPerTimeStep) + 'H'), tz='Europe/Berlin')

        # Cluster data with tsam package (the reindex call is here for reproducibility of TimeSeriesAggregation
        # call)
        timeSeriesData = timeSeriesData.reindex(sorted(timeSeriesData.columns), axis=1)
        clusterClass = TimeSeriesAggregation(timeSeries=timeSeriesData, noTypicalPeriods=numberOfTypicalPeriods,
                                             hoursPerPeriod=hoursPerPeriod,
                                             clusterMethod=clusterMethod, sortValues=sortValues, weightDict=weightDict,
                                             **kwargs)

        # Convert the clustered data to a pandas DataFrame and store the respective clustered time series data in the
        # associated components
        data = pd.DataFrame.from_dict(clusterClass.clusterPeriodDict)
        for mdlName, mdl in self.componentModelingDict.items():
            for compName, comp in mdl.componentsDict.items():
                comp.setAggregatedTimeSeriesData(data)

        # Store time series aggregation parameters in class instance
        if storeTSAinstance:
            self.tsaInstance = clusterClass
        self.typicalPeriods = clusterClass.clusterPeriodIdx
        self.timeStepsPerPeriod = list(range(numberOfTimeStepsPerPeriod))