semvec/pypi-packages huggingface dataset:
dojosw 0.6.0
Python Bibliothek für das CoderDojo Schöneweide.
Requires Python: >=3.9

# cdsw-python
Python Bibliotheken für das CoderDojo Schöneweide

## Installation

```sh
python -m venv venv
. venv/bin/activate
pip install -e .
```

## Neue Version veröffentlichen

Version in `pyproject.toml` ändern.

```sh
python -m build
twine upload dist/*
```
scikit-ferm 0.1.2
A scikit-learn compatible package for fermentations.
Requires Python: >=3.12
Keywords: acidification, fermentation, python, scikit-ferm

![sphinx](https://github.com/aschwins/scikit-ferm/actions/workflows/sphinx.yml/badge.svg?branch=main)
![tests](https://github.com/aschwins/scikit-ferm/actions/workflows/pytest.yml/badge.svg?branch=main)
![type_checks](https://github.com/aschwins/scikit-ferm/actions/workflows/pyright.yml/badge.svg?branch=main)
![release](https://github.com/aschwins/scikit-ferm/actions/workflows/release.yml/badge.svg?branch=main)


![](https://img.shields.io/pypi/pyversions/scikit-ferm)
[![Version](https://img.shields.io/pypi/v/scikit-ferm)](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/synthetic.py)<br> •[`skferm.datasets.rheolaser`](skferm/datasets/rheolaser.py) | [📓 Notebook](notebooks/01-curve-smoothing.ipynb) | [📚 Docs](https://aschwins.github.io/scikit-ferm/use-case-datasets.html) |
| 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) |


## 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
```