def _remodel_optics(model, target="xi", **kwargs):
if target == "xi":
xi = kwargs.get("xi", 0.03)
min_cluster_size = kwargs.get("min_cluster_size", 0.01)
min_samples = kwargs.get("min_samples", 0.03)
labels = sk_cluster.cluster_optics_xi(
min_samples=min_samples,
min_cluster_size=min_cluster_size,
xi=xi,
reachability=model.reachability_,
predecessor=model.predecessor_,
ordering=model.ordering_)
else:
eps = kwargs.get("eps", 0.5)
labels = sk_cluster.cluster_optics_dbscan(
eps=eps,
reachability=model.reachability_,
core_distances=model.core_distances_,
ordering=model.ordering_,
)
return sort_labels(labels)
noisecolor = 'gray' # color for plotting of noisy points
#%% Load the OPTICS result
dirr = '/Users/nooteboom/Documents/GitHub/cluster_TM/cluster_SP/density/dens/results/'
ff = np.load(dirr+'OPTICS_sp%d_smin%d.npz'%(sp, mins))
lon0 = ff['lon']
lat0 = ff['lat']
reachability = ff['reachability'] / 1000
ordering = ff['ordering']
predecessor = ff['predecessor']
core_distances = ff['core_distances']
#%% Create the clusters from the reachabilities, given the xi value
labels = []
for op in opts:
m, c = op[0], op[1]
if m == "xi":
l, _ = cluster_optics_xi(reachability, predecessor, ordering, mins, xi=c)
else:
l = cluster_optics_dbscan(reachability=reachability,
core_distances=core_distances,
ordering=ordering, eps=c)
labels.append(l)
norms = []
for l in labels:
bounds = np.arange(-.5,np.max(l)+1.5,1)
norms.append(matplotlib.colors.BoundaryNorm(bounds, len(bounds)))
#%%
exte=[18, 360-70, -75, 0]; latlines=[-75,-50, -25, 0, 25, 50, 75, 100];
# Read Foram data
OPTICS
"""
if P["OPTICS"]:
optics_clustering = OPTICS(min_samples=P["MinPts"], metric="euclidean").fit(X_embedding)
reachability = optics_clustering.reachability_
core_distances = optics_clustering.core_distances_
ordering = optics_clustering.ordering_
predecessor = optics_clustering.predecessor_
labels = []
for op in P["optics_params"]:
m, c = op[0], op[1]
if m == "xi":
l, _ = cluster_optics_xi(reachability, predecessor, ordering, P["MinPts"], xi=c)
else:
l = cluster_optics_dbscan(reachability=reachability,
core_distances=core_distances,
ordering=ordering, eps=c)
l = np.array([li % 20 if li>=0 else li for li in l])
labels.append(l) #cmap with 20 colors
norms = []
for l in labels:
bounds = np.arange(-.5,np.max(l)+1.5,1)
norms.append(matplotlib.colors.BoundaryNorm(bounds, len(bounds)))
#Optics results
f = plt.figure(constrained_layout=True, figsize = (10, 2*len(labels)))
gs = f.add_gridspec(len(labels), 3)