def cluster_ordered_agglomerative (dataframe,
n_clusters=2,
affinity='euclidean',
memory=None,
connectivity=None,
compute_full_tree='auto',
linkage='ward',
rankstat='median',
cmapcolors='Blues',
z_score=False):
if z_score: dataframe = dataframe.copy().apply(zscore)
clusters = AgglomerativeClustering(n_clusters=n_clusters,
affinity=affinity,
memory=memory,
connectivity=connectivity,
compute_full_tree=compute_full_tree,
linkage=linkage).\
fit_predict(dataframe)
clusters = pd.DataFrame({'unordered_id':clusters})
clusters.index = dataframe.index
rowsums = pd.DataFrame(dataframe.sum(1)).rename(columns={0:'rankstat'})
clusters = clusters.merge(rowsums,left_index=True,right_index=True)
rankfunc = None
if rankstat == 'median': rankfunc = np.median
elif rankstat == 'mean': rankfunc = np.mean
else: raise ValueError("unsupported rankstat: "+str(rankstat))
ordered = list(clusters.groupby('unordered_id').\
apply(lambda x: rankfunc(x['rankstat'])).sort_values().index)
converter = dict(zip(ordered,range(0,n_clusters)))
cluster_ids = clusters.apply(lambda x: converter[x['unordered_id']],1)
clusters['cluster_id'] = cluster_ids
colorconverter = sns.color_palette(cmapcolors,n_clusters)
cluster_colors = clusters.apply(lambda x: colorconverter[int(x['cluster_id'])],1)
clusters['cluster_colors'] = cluster_colors
ordered_index = clusters.sort_values(['cluster_id','rankstat']).index
dfo = pd.DataFrame({'ordered_index':ordered_index})
dfo['order_index'] = range(0,dfo.shape[0])
dfo = dfo.set_index('ordered_index')
saveindex = clusters.index
clusters = clusters.merge(dfo,left_index=True,right_index=True)
clusters = clusters.loc[saveindex]
return clusters.drop(columns=['unordered_id'])
