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Common-basis analysis of three-dimensional ECT sensor and measurement designs

This repository contains the Python analysis workflow associated with the manuscript:

Common-basis analysis reveals task-dependent trade-offs in three-dimensional electrical capacitance tomography sensor and measurement design

The workflow provides a reproducible computational framework for comparing three-dimensional electrical capacitance tomography (ECT) sensitivity models represented by heterogeneous finite-element discretisations.

It performs:

  • volume integration and mapping of native FEM sensitivity matrices onto a shared equal-volume cylindrical basis;
  • regularised spectral and spatial sensitivity analysis;
  • electrode-pair classification and class-wise analysis;
  • greedy regularised D-optimal measurement-channel selection;
  • matched-model spherical-phantom reconstruction benchmarking;
  • paired statistical comparisons;
  • automated verification and provenance tracking.

Software environment

The publication workflow was developed and executed using Python 3.12.3.

The main package versions used in the reported analysis are:

  • NumPy 2.4.6
  • SciPy 1.16.3
  • pandas 2.3.3
  • h5py 3.15.1
  • Matplotlib 3.10.7

Exact runtime dependencies are listed in requirements.txt and pyproject.toml.

Archival release

The exact software version associated with the manuscript has been archived as release v1.0.0 in Zenodo:

https://doi.org/10.5281/zenodo.22209591

This archived release provides a permanent and immutable record of the analysis code corresponding to the reported study. The GitHub repository remains the actively maintained development repository.

Citation

If you use this software, please cite the archived Zenodo release:

Banasiak, R. (2026). Common-basis analysis of three-dimensional ECT sensor and measurement designs (Version 1.0.0). Zenodo. https://doi.org/10.5281/zenodo.22209591

Citation metadata are also provided in CITATION.cff.

Licence

This software is released under the BSD 3-Clause License. See LICENSE for details.

Installation

Create a Python 3.12 virtual environment and install the required packages:

pip install -r requirements.txt

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