>>> ch1_data[0:5]
[[ 11743. 17707.7]
[ 15850.9 16474. ]
[ 21205.1 17580.2]
[ 11763.2 17712.5]
[ 14094.9 16369.7]]

from sklearn.cluster import DBSCAN

db1 = DBSCAN(eps=20, min_samples=2, metric='euclidean').fit(ch1_data)
db1_labels = db1.labels_
db1n_clusters_ = len(set(db1_labels)) - (1 if -1 in db1_labels else 0)
print('Estimated number of clusters: %d' % db1n_clusters_)
labels_ch1 = db1.labels_
ch1_counts = np.bincount(labels_ch1[labels_ch1>=0])
print('Number of particles per cluster:', ch1_counts)