[](https://pypi.org/project/scikit-ferm/)
# scikit-ferm
**scikit-ferm** is a Python package designed to generate synthetic fermentation datasets and model microbial growth dynamics. Whether you're studying food fermentation (like yogurt production) or simulating microbial behavior for research and development, scikit-ferm provides flexible tools to create realistic datasets based on established growth models.
<a href="https://aschwins.github.io/scikit-lego/"><img src="images/logo.png" width="60%" height="60%" align="center" /></a>
The official documentation is hosted [here](https://aschwins.github.io/scikit-ferm/).
## Installation
Install scikit-ferm via pip with:
```bash
uv pip install scikit-ferm
```
Alternatively, to edit and contribute you can fork/clone and run:
```bash
git clone https://github.com/Aschwins/scikit-ferm.git
uv sync
```
## Use cases
| Use Case | Modules | Notebook | Documentation |
|----------|---------|----------|---------------|
| Generate synthetic fermentation datasets | •[`skferm.datasets.generate_synthetic_growth`](skferm/datasets.py)<br> •[`skferm.datasets.rheolaser`](skferm/datasets/rheolaser.py) | [📓 Notebook](notebooks/01-curve-smoothing.ipynb) | [📚 Docs](https://aschwins.github.io/scikit-ferm/usage.html#datasets) |
| Growth modeling | • [`skferm.growth_models.gompertz`](skferm/growth_models/gompertz.py)<br>• [`skferm.growth_models.logistic`](skferm/growth_models/logistic.py) | [📓 Notebook](notebooks/02-gompertz-model.ipynb) | [📚 Docs](https://aschwins.github.io/scikit-ferm/usage.html#growth_models) |
| Curve smoothing | • [`skferm.curve_smoothing.smooth`](skferm/curve_smoothing/smooth.py) | [📓 Notebook](notebooks/03-curve-smoothing.ipynb) | [📚 Docs](https://aschwins.github.io/scikit-ferm/usage.html#curve_smoothing) |
http://172.18.195.64:8000/
## Examples
Jupyter notebooks are used to demonstrate examples. You can find the notebooks in the `notebooks` directory. Each example describes a use case. To run the examples you need to install scikit-ferm with an additional dependencies and start Jupyter Lab.
```bash
uv sync
jupyter lab
```
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