  Given the ubiquity of lattice models in physics, it is imperative for
researchers to possess robust methods for quantifying clusters on the lattice
--- whether they be Ising spins or clumps of molecules. Inspired by biophysical
studies, we present Python code for handling clusters on a 2D periodic lattice.
Properties of individual clusters, such as their area, can be obtained with a
few function calls. Our code invokes an unsupervised machine learning method
called hierarchical clustering, which is simultaneously effective for the
present problem and simple enough for non-experts to grasp qualitatively.
Moreover, our code transparently merges clusters neighboring each other across
periodic boundaries using breadth-first search (BFS), an algorithm
well-documented in computer science pedagogy. The fact that our code is written
in Python --- instead of proprietary languages --- further enhances its value
for reproducible science.
