phun-reps 0.1.1

A fun Python package for creating topological descriptors for nanoporous materials with persistent homology using nets (PHuN).

PHuN representations

This is a fun and computationally efficient Python package for computing persistent homology using nets (PHuN) representations of nanoporous materials. It enables the extraction of topological information for use as machine learning descriptors.

Installation Guide

Prerequisites

PHuN requires the CrystalNets.jl Julia interface, which is used to identify and extract the topological nets of nanoporous materials.

Install phun_reps using pip

pip install phun_reps

This package was built and tested with Python 3.10.13, and will automatically install its dependencies: ase==3.26.0, Cython==3.2.0, juliacall==0.9.28, pandas==2.3.3, and ripser==0.6.12.

Usage

PHuN provides tools to compute persistent homology diagrams for nanoporous materials using either:

• Atomic coordinates

• Topological nets derived from CrystalNets.jl.

It integrates with Ripser to compute persistence diagrams and can extract topological descriptors for machine learning.

PHuN can be used to:

• Generate persistent diagrams/images from CIF files

• Visualize persistence diagrams/images

• Extract topological descriptors (persistent image features and persistent statistics features) that can be used for machine learning

For a complete example of usage, see the Example Usage section below.

Example Usage

Initial setup

import os
# Folder containing .cif files to process
folder = "test-cif"

# Folder where CrystalNets.jl outputs will be saved
# Default is /tmp if not specified
export_folder = "/tmp"

# Clustering option for CrystalNets.jl
# Determines how topological nets are identified
clustering = 'SingleNodes'

Load .cif files

# Load .cif files from the specified folder
files = cp.get_cif_files(folder)

Build dataset

# Build dataset:
# - Uses CrystalNets.jl to identify topological nets based on clustering option. If ACPH features are wanted, set clustering to 'input'
dataset, top_nets, names = cp.build_dataset(files, export_folder, clustering)

Compute persistent diagrams

# Compute persistent homology diagrams from the dataset
diagrams_tuples = cp.get_persistent_diagrams(
    dataset, names, top_nets,
    maxdim=2, coeff=2,
    save_file=f"diagrams_{folder}_{clustering}.pkl"
)

Extract persistent image features from diagrams using persim

image_features_df = fe.get_persistent_image_features(
    diagrams_tuples,
    output_image_size=(30, 30),
    savefig=True,
    export_folder="test_images"
)

Extract statistical features from persistent diagrams

stats_features_df = fe.get_persistent_stats_features(diagrams_tuples)