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

# make numpy array with rows containing the segmenatation candidates (time steps)
        # and columns as dimensions of the
        segmentationCandidates = np.asarray(normalizedTypicalPeriods.loc[i,:])
        # produce adjacency matrix: Each time step is only connected to its preceding and succeeding one
        adjacencyMatrix = np.eye(timeStepsPerPeriod, k=1) + np.eye(timeStepsPerPeriod, k=-1)
        # execute clustering of adjacent time steps
        if noSegments==1:
            clusterOrder = np.asarray([0] * len(segmentationCandidates))
        else:
            clustering = AgglomerativeClustering(n_clusters=noSegments, linkage='ward', connectivity=adjacencyMatrix)
            clusterOrder = clustering.fit_predict(segmentationCandidates)
        # determine the indices where the segments change and the number of time steps in each segment
        segNo, indices, segmentNoOccur = np.unique(clusterOrder, return_index=True, return_counts=True)
        clusterOrderUnique = [clusterOrder[index] for index in sorted(indices)]
        # determine the segments' values
        clusterCenters = meanRepresentation(segmentationCandidates, clusterOrder)
        # predict each time step of the period by representing it with the corresponding segment's values
        predictedSegmentedNormalizedTypicalPeriods = pd.DataFrame(
            clusterCenters,
            columns=normalizedTypicalPeriods.columns).reindex(clusterOrder).reset_index(drop=True)
        # represent the period by the segments in the right order only instead of each time step
        segmentedNormalizedTypicalPeriods = pd.DataFrame(
            clusterCenters,
            columns=normalizedTypicalPeriods.columns).reindex(clusterOrderUnique).set_index(np.sort(indices))
        # keep additional information on the lengths of the segments in the right order
        segmentDuration = pd.DataFrame(segmentNoOccur, columns=['Segment Duration']).reindex(clusterOrderUnique).set_index(np.sort(indices))
        # create DataFrame with reduced number of segments together with three indices per period:
        # 1. The segment number
        # 2. The segment duration
        # 3. The index of the original time step, at which the segment starts
        result=segmentedNormalizedTypicalPeriods.set_index([pd.Index(segNo, name='Segment Step'), segmentDuration['Segment Duration'], pd.Index(np.sort(indices), name='Original Start Step')])
        # append predicted and segmented DataFrame to list to create a big DataFrame for all periods