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add a variable to select the clustering algorithm in busmap_for_country. Default k-manes

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This commit is contained in:
Arnaud Leroy 2018-11-26 16:10:32 +01:00
parent 5adf2ccd6c
commit 8a7ffbdb4e
1 changed files with 17 additions and 6 deletions
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23
scripts/cluster_network.py
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@ -111,7 +111,7 @@ def distribute_clusters_optim(n, n_clusters, solver_name=None):
return pd.Series(m.n.get_values(), index=L.index).astype(int)
def busmap_for_n_clusters(n, n_clusters):
def busmap_for_n_clusters(n, n_clusters,algo_cluster):
n.determine_network_topology()
if 'snakemake' in globals():
@ -121,13 +121,24 @@ def busmap_for_n_clusters(n, n_clusters):
n_clusters = distribute_clusters_optim(n, n_clusters, solver_name=solver_name)
def busmap_for_country(x):
def busmap_for_country(x,algo_cluster):
prefix = x.name[0] + x.name[1] + ' '
if len(x) == 1:
return pd.Series(prefix + '0', index=x.index)
weight = weighting_for_country(n, x)
return prefix + busmap_by_kmeans(n, weight, n_clusters[x.name], buses_i=x.index, n_init=1000, max_iter=30000, tol=1e-6)
return n.buses.groupby(['country', 'sub_network'], group_keys=False).apply(busmap_for_country)
if algo_cluster=="kmeans":
return prefix + busmap_by_kmeans(n, weight, n_clusters[x.name], buses_i=x.index, n_init=1000, max_iter=30000, tol=1e-6)
elif algo_cluster=="spectral":
n2=pypsa.Network()
n2.buses=x
n2.lines=n.lines.loc[n.lines.bus0.isin(x.index)].loc[n.lines.bus1.isin(x.index)]
return prefix + busmap_by_spectral_clustering(n2, n_clusters[x.name])
elif algo_cluster=="louvain":
n2=pypsa.Network()
n2.buses=x
n2.lines=n.lines.loc[n.lines.bus0.isin(x.index)].loc[n.lines.bus1.isin(x.index)]
return prefix + busmap_by_louvain(n2, n_clusters[x.name])
return n.buses.groupby(['country', 'sub_network'], group_keys=False).apply(busmap_for_country, algo_cluster)
def plot_busmap_for_n_clusters(n, n_clusters=50):
busmap = busmap_for_n_clusters(n, n_clusters)
@ -136,12 +147,12 @@ def plot_busmap_for_n_clusters(n, n_clusters=50):
n.plot(bus_colors=busmap.map(dict(zip(cs, cr))))
del cs, cr
def clustering_for_n_clusters(n, n_clusters, aggregate_renewables=True, line_length_factor=1.25):
def clustering_for_n_clusters(n, n_clusters, aggregate_renewables=True, line_length_factor=1.25,algo_cluster="kmeans"):
aggregate_generators_carriers = (None if aggregate_renewables
else (pd.Index(n.generators.carrier.unique())
.difference(['onwind', 'offwind', 'solar'])))
clustering = get_clustering_from_busmap(
n, busmap_for_n_clusters(n, n_clusters),
n, busmap_for_n_clusters(n, n_clusters,algo_cluster),
bus_strategies=dict(country=_make_consense("Bus", "country")),
aggregate_generators_weighted=True,
aggregate_generators_carriers=aggregate_generators_carriers,