Stereological Tools for Analysis of Microstructural Parameters
Project description
STAMP is a scientific Python package for quantitative 2-D microstructural analysis. It provides tools to load grain or precipitate measurements, apply stereological corrections to recover 3-D size distributions, compute descriptive statistics with confidence intervals, and generate publication-ready figures.
Modules
| Module | Purpose |
|---|---|
stamp.io |
Load CSV, Excel, or TXT/TSV files and MIPAR feature-measurement exports into a pd.DataFrame |
stamp.stereo |
Stereological corrections: ECD conversion, Fullman (1953) linear intercept, Saltykov/Wicksell (1925/1967) matrix unfolding, two-step lognormal fitting (Lopez-Sanchez & Llana-Funez 2016) |
stamp.stats |
Descriptive statistics with confidence intervals: arithmetic mean (ASTM E112, GCI, mCox), geometric mean (CLT, Bayesian), median (Hollander–Wolfe), KDE mode, MLE distribution fitting with KS goodness-of-fit |
stamp.plot |
Publication-ready figures: histogram + KDE, Saltykov dual-panel (frequency + volume CDF), two-step fit with ±3σ band, PDF/CDF profile, Q-Q plot |
stamp.pipeline |
Batch processing across multiple material states; produces a PipelineResult with per-state statistics, a summary DataFrame, and optional auto-saved box plot and CSV |
Installation
pip install nanoshot-stamp
To also install JupyterLab for running the example notebooks:
pip install "nanoshot-stamp[notebooks]"
Or with uv:
uv add "nanoshot-stamp[notebooks]"
Documentation
Full documentation is available at stamp.readthedocs.io.
Citation
If you use STAMP in your research, please cite it:
@software{westraadt_stamp_2026,
author = {Westraadt, Johan},
title = {STAMP: Stereological Tools for Analysis of Microstructural Parameters},
year = {2026},
url = {https://github.com/jwestraadt/STAMP},
license = {MIT}
}
Or use GitHub's Cite this repository button (powered by CITATION.cff).
Contributing
Contributions are welcome! See CONTRIBUTING.md for details.
License
MIT — see LICENSE for details.
Project details
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