def busmap_for_country(x):
prefix = x.name[0] + x.name[1] + ' '
logger.debug("Determining busmap for country {}".format(prefix[:-1]))
if len(x) == 1:
return pd.Series(prefix + '0', index=x.index)
weight = weighting_for_country(n, x)
if algorithm == "kmeans":
return prefix + busmap_by_kmeans(n, weight, n_clusters[x.name], buses_i=x.index, **algorithm_kwds)
elif algorithm == "spectral":
return prefix + busmap_by_spectral_clustering(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
elif algorithm == "louvain":
return prefix + busmap_by_louvain(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
else:
raise ValueError(f"`algorithm` must be one of 'kmeans', 'spectral' or 'louvain'. Is {algorithm}.")
def busmap_for_country(x):
if isinstance(n_clusters, pd.Series):
n_cluster_c = n_clusters[x.name]
else:
n_cluster_c = n_clusters
prefix = x.name[0] + x.name[1] + " "
logger.debug(f"Determining busmap for country {prefix[:-1]}")
if len(x) == 1:
return pd.Series(prefix + "0", index=x.index)
weight = weighting_for_country(n, x)
if algorithm == "kmeans":
return prefix + busmap_by_kmeans(
n, weight, n_cluster_c, buses_i=x.index, **algorithm_kwds)
elif algorithm == "spectral":
return prefix + busmap_by_spectral_clustering(
reduce_network(n, x), n_cluster_c, **algorithm_kwds)
else:
raise ValueError(
f"`algorithm` must be one of 'kmeans', 'spectral' or 'louvain'. Is {algorithm}."
)