def cluster_data(data, ov_min=10, ov_min1=0.2):
cl = Clustering(ov_min, ov_min1)
cl.fill_clusters(data['x1'], data['x2'])
c_labels = cl.clabels
nhits = len(c_labels)
n_clust = cl.ncl
x1cl = cl.cluster_x1
x2cl = cl.cluster_x2
ycl = []
pcentcl = []
namecl = []
detailcl = []
x1tcl = []
x2tcl = []
dxtcl = []
for i in range(0,n_clust):
ycl.append(float(i+1)/2.)
for j in range(0,nhits):
if c_labels[j]==i:
pcentcl.append(data['pcent'][j])
namecl.append(data['name'][j])
detailcl.append(data['detail'][j])
x1tcl.append(data['x1t'][j])
x2tcl.append(data['x2t'][j])
dxtcl.append(data['dxt'][j])
break
new_data = dict(x1=x1cl, x2=x2cl, dx=[end-beg for beg,end in zip(x1cl,x2cl)],
xm=[(beg+end)/2 for beg,end in zip(x1cl,x2cl)],
x1t=x1tcl, x2t=x2tcl, dxt=dxtcl,
y=ycl, nhits=cl.nhits, name=namecl, pcent=pcentcl, detail=detailcl)
return new_data, n_clust
def filter_repeated_hits(data):
cl = Clustering(10, 0.1) # cluster with default values
cl.fill_clusters(data['x1'], data['x2'])
toDelete = [False]*len(cl.clabels)
for icl in range(cl.ncl): # loop over clusters
if cl.nhits[icl]>50:
counter = 0
for i in range(len(cl.clabels)): # loop over hits
if cl.clabels[i]==icl:
counter = counter + 1
if counter>50:
toDelete[i] = True
for key in data.keys():
data[key][:] = [value for value,flag in zip(data[key],toDelete) if not flag]
return data
def cluster_pred(x1l, x2l, ov_min=10, ov_min1=0.2):
cl = Clustering(ov_min, ov_min1)
cl.fill_clusters(x1l, x2l)
return cl.clabels, cl.ncl
