topapprox 0.2.1

Persistent-homology-based filtering for arrays and graph signals.

topapprox

Authors: Matias de Jong van Lier, Junyan Chu, Sebastían Elías Graiff Zurita, Shizuo Kaji

topapprox is a Python package for persistent-homology-based filtering of scalar signals on:

• 1D, 2D, and 3D arrays
• graphs with faces

Low-persistence filtering removes topological features that are small relative to a chosen threshold epsilon.

In practice:

1. Build the persistence diagram of the input signal.
2. Identify features whose persistence is smaller than epsilon.
3. Modify the signal so those low-persistence features disappear, while the more significant ones remain.

This makes the filter useful for denoising and simplification: small oscillations, shallow basins, and weak cycles are removed, while large-scale structure is preserved. For array signals, the filtered output remains within epsilon in the L^inf norm of the original input.

Installation

From PyPI:

pip install topapprox

From source:

python -m pip install -e ".[dev]"

Quick Start

import numpy as np
import topapprox as ta
from topapprox.persistence import get_PD_gwf

img = np.array([[0, 5, 3], [5, 6, 4], [2, 5, 1]], dtype=float)

image_filter = ta.ImageFilter(img, method="python")
filtered_h0 = image_filter.low_pers_filter(1.5)

dual_filter = ta.ImageFilter(img, dual=True, method="python")
filtered_h1 = dual_filter.low_pers_filter(1.5)

faces = [[0, 1, 2, 3]]
holes = [[0, 1, 2, 3]]
signal = np.array([0.0, 1.0, 0.5, 0.2])

graph_filter = ta.GraphFilter(method="python")
graph_filter.compute_gwf(F=faces, H=holes, signal=signal)
pd0, pd1 = graph_filter.get_diagram()

persistence_filter = ta.PersistenceFilter().load_signal(img)
filtered_01 = persistence_filter.low_pers_filter(1.5, iteration_order="01", method="python")

pd0_fn, pd1_fn = get_PD_gwf(faces, holes, signal, method="python")

Documentation & Examples

• Interactive Tutorial
• BHT Basin Visualization
• Reproducing Paper Examples
• Notebook/API migration memo
• Original paper on arXiv

Development

Run the full test suite:

python -m pytest -q

Build distributions locally:

python -m build --wheel --sdist

Binary wheels are built with cibuildwheel, and the native extension is compiled through scikit-build-core + CMake.

Citation

If you use this package in your work, please cite:

Matias de Jong van Lier, Sebastían Elías Graiff Zurita, Shizuo Kaji. Topological filtering of a signal over a network (2024). arXiv:2408.14109

@article{vanlier2024topological,
  title={Topological filtering of a signal over a network},
  author={de Jong van Lier, Matias and Graiff Zurita, Sebastían Elías and Kaji, Shizuo},
  journal={arXiv preprint arXiv:2408.14109},
  year={2024}
}

Migration Notes

The refactor intentionally simplified the public surface and build system. If you are adapting older notebooks, see docs/migration.md.