Metadata-Version: 2.4
Name: efa-gui
Version: 1.0.8
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
Provides-Extra: test
Requires-Dist: pytest; extra == "test"

﻿## Install from PyPI

### Option 1: Quick install

```powershell
pip install efa-gui
efa-gui-self-test
efa-gui
```

### Option 2: Recommended install in PowerShell with virtual environment

```powershell
python -m venv env
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\env\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install efa-gui
python -c "import efa_gui; print(efa_gui.__version__)"
efa-gui-self-test
efa-gui
```

### Install in Jupyter / Notebook environment

```powershell
python -m pip install efa-gui
python -c "import efa_gui; print(efa_gui.__version__)"
efa-gui-self-test
efa-gui
```

### Test the app with sample data

To check that the Streamlit app works correctly, download the sample file:

[`test_data.csv`](./test_data.csv)

Download and drop into framework

### Self-test success message

The automated self-test is successful if you see this message:

`Self-test passed: sample CSV loaded, Module 3 ran, and reference indices matched.`

## Citation

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

> Boribayeva, A., Sultaniyar, S., Lukmanov, I., Baigarina, A., Rojas-SolГіrzano, 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.
