mapsacman: Mounted Single Particle Compositional and Mineralogical Analyses

mapsacman is a Python toolkit for 3D micro-CT particle analysis, enabling advanced mineralogical and compositional quantification while accounting for partial volume blur (PVB) effects in CT data. It supports:

• Particle property extraction (volume, surface area, Feret diameters, intensity stats)

• Greyscale histogram computation and smoothing

• Peak detection with phase mapping and bias correction for dense minerals

• Multi-phase quantification (liberated to quinary phases)

• Bootstrapping uncertainty estimation for both bulk and surface compositions

• Complete unit/regression testing against reference datasets

Installation

Install directly using pip and git:

pip install git+https://github.com/ShuvamGupta/mspacman.git

Example Usage & Unit Testing

We provide a combined example + unit testing script: mspacman.coreexampleusagewithunit_test.py

This script:

• Demonstrates the complete MSPaCMAn processing pipeline using the publicly available Kemi +850 µm micro-CT dataset.

• Runs regression tests by comparing freshly generated outputs to pre-computed “golden” reference datasets located in: Kemi+850unittestandexampleusage/

Running the Example + Unit Test - Download the sample dataset from Figshare: https://figshare.com/articles/dataset/Kemi850mCTdataandParticle_Labels/29836160

• Download the reference datasets from: https://github.com/ShuvamGupta/mspacman/tree/main/Kemi%2B850unittestandexampleusage

• Run the example script: python mspacman.coreexampleusagewithunit_test.py

The script automatically tests:

• Data upload integrity (shape, index name, etc.)

• Batch-processed outputs vs. reference data

• Individual processing step outputs (properties, histograms, gradients, smoothed histograms)

• Saving and reloading .h5ad and .csv formats without data loss

• Quantification results (bulk, outer, surface)

• Pass criteria: All DataFrames must match exactly with reference datasets (ignoring dtype differences).

All DataFrames must match exactly with reference datasets (ignoring dtype differences).

Dependencies

Built on top of:

• NumPy, SciPy, Pandas, Matplotlib, tifffile – Scientific computing and plotting

• scikit-image – 3D image processing

• Napari – Interactive visualization

• AnnData – .h5ad data handling

• pykuwahara – Edge-aware filtering

• joblib, tqdm – Parallel processing and progress bars

mspacman has been tested with the following package versions. If you encounter issues with other versions, please report them so they can be addressed. {'numpy': '1.26.4', 'scipy': '1.15.2', 'pandas': '2.2.3', 'matplotlib': '3.10.1', 'tifffile': '2025.3.30', 'skimage': '0.25.2', 'napari': '0.4.19.post1', 'anndata': '0.10.7', 'pykuwahara': 'have single version', 'joblib': '1.5.0', 'tqdm': '4.67.1'}

Acknowledge

This project utilizes several open-source Python libraries, including NumPy, SciPy, Pandas, Matplotlib, Napari, tifffile, scikit-image, joblib, anndata, tqdm, pykuwahara, and standard Python libraries (os, glob, re, and gc). We gratefully acknowledge the developers and communities of these tools for enabling efficient scientific computing, visualization, image processing, and data analysis.

Citation

If you use this repository or the method/code provided, please cite the following paper: Gupta, S., Moutinho, V., Godinho, J. R., Guy, B. M., & Gutzmer, J. (2025). 3D mineral quantification of particulate materials with rare earth mineral inclusions: Achieving sub-voxel resolution by considering the partial volume and blurring effect. Tomography of Materials and Structures, 7, 100050. https://doi.org/10.1016/j.tmater.2025.100050

You may also cite this GitLab repository as a secondary reference: Gupta, S. (2025). MSPaCMAn [Python Package]. GitLab. https://gitlab.com/ShuvamGupta1/mspacman Developed while affiliated with Helmholtz Institute Freiberg for Resource Technology, Germany.