Fast, python-ish way of ranking chunks of 1's in numpy array?

Question

I have a numpy array consisting of 0\'s and 1\'s. Each sequence of 1\'s within the array stands for occurrence of one event. I want to

Answer 1

A really cool way to do this is using the DBSCAN clustering algorithm. It might not be the most efficient for this specific task, BUT it is resilient in case you want to give a minimum number of 1's per event, or allow a gap of a certain number of zeroes within an event.

from sklearn.cluster import DBSCAN
import numpy as np

max_gap = 1
min_samples = 1

# Get indices of every element that belongs to a certain event
input_values = np.array([0,0,0,1,1,1,0,0,0,1,1,0,0,0,1,1,1,1])  
positives_indices = np.where(input_values > 0)[0]

# Turn the indices into a 2D array of so called 'examples'
X = positives_indices.reshape(-1, 1)

# Train a model and transform the data in one
clustering = DBSCAN(eps=max_gap, min_samples=min_samples) \
    .fit_predict(X)

# Get results, yields (index, event_id)
zip(X, clustering)