Metadata-Version: 2.4
Name: efa-gui
Version: 1.0.1
Summary: Interactive Streamlit app for computational elliptic Fourier analysis of particle shapes.
Author: Computational Elliptic Fourier Analysis contributors
Keywords: elliptic-fourier-analysis,efa,particle-shape-analysis,streamlit
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: streamlit
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: plotly
Requires-Dist: scipy

# Computational Elliptic Fourier Analysis

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://computational-elliptic-fourier-analysis-1.streamlit.app/)

An interactive **Streamlit web app** for particle shape analysis using EFA.

## Quick Start

1. **Launch App**: [computational-elliptic-fourier-analysis-1.streamlit.app](https://computational-elliptic-fourier-analysis-1.streamlit.app/)
2. **Download Test Data**: [`test_data.csv`](./test_data.csv) (set px/mm = 77)
3. **Read Manual**: [`User Manual (PDF)`](./User%20Manual.pdf)

## Install from PyPI

```powershell
pip install efa-gui
efa-gui
```

## Publish to PyPI

Create a PyPI API token, then run these two commands from the project root:

```powershell
$env:TWINE_PASSWORD='pypi-your-token-here'
powershell -ExecutionPolicy Bypass -File .\publish.ps1
```


## Citation

If you use this app or code in your work, please cite:

Boribayeva, A., Sultaniyar, S., Lukmanov, I., Baigarina, A., Rojas-Solórzano, L. R., Curtis, J. S., Govender N. & Golman, B. (2026). Integrated characterization, classification, and quasi-3D reconstruction of highly irregular particles using multiscale shape descriptors for predictive DEM flow simulation. Powder Technology, 122435.
