topapprox

License: MIT

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

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.

Project links

Data verified by PyPI on Apr 19, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

2 maintainers

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Avatar for mvlier from gravatar.com mvlier Avatar for shizuo-kaji from gravatar.com shizuo-kaji

Credits

Author: Junyan Chu, Sebastían Elías Graiff Zurita

GitHub Statistics

Data verified by PyPI on Apr 19, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT License)

Requires

Python >=3.10

Provides Extra

plot numba test dev

Tags

persistent homology topological data analysis signal processing pybind11

Classifiers

Report project as malware

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0.2.1 This release

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