Metadata-Version: 2.4
Name: pepper-lab
Version: 1.2.0
Summary: PEPPER is a package developed by the Fenner Labs for analyzing and modeling persistence of micropollutants in different environments.
License: MIT License
         
         Copyright (c) 2023 FennerLabs - Swiss Federal Institute of Aquatic Science and Technology (Eawag) and University of Zurich (UZH)
         
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License-File: LICENSE
Author: Jose Cordero
Author-email: jose.cordero@eawag.ch
Requires-Python: >=3.11
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: emcee (>=3.1.6,<4.0.0)
Requires-Dist: envipath-python (>=0.2.4,<0.3.0)
Requires-Dist: matplotlib (>=3.9.4,<4.0.0)
Requires-Dist: mlxtend (>=0.23.4,<0.24.0)
Requires-Dist: mordredcommunity (>=2.0.6,<3.0.0)
Requires-Dist: numpy (>=2.2.3,<3.0.0)
Requires-Dist: padelpy (>=0.1.14,<0.2.0)
Requires-Dist: pandas (>=2.2.0,<3.0.0)
Requires-Dist: pubchempy (>=1.0.4,<2.0.0)
Requires-Dist: pyyaml (>=6.0.2,<7.0.0)
Requires-Dist: rdkit (>=2024.9.5,<2025.0.0)
Requires-Dist: scikit-learn (>=1.6.1,<1.7.0)
Requires-Dist: seaborn (>=0.13.2,<0.14.0)
Requires-Dist: sklearn-genetic (>=0.6.0,<0.7.0)
Requires-Dist: tabulate (>=0.9.0,<1.0.0)
Requires-Dist: tqdm (>=4.67.1,<5.0.0)
Project-URL: Repository, https://github.com/FennerLabs/pepper.git
Description-Content-Type: text/markdown

# PEPPER - Predict Environmental Pollutant PERsistence

PEPPER is a package developed by the Fenner Labs for analyzing and modeling persistence of micropollutants in different environments.

## Installation 

The PEPPER library may be installed using:
```
pip install pepper-lab
```


## Projects
Follow these steps to reproduce the workflows and results from previous publications:

Clone the repository
```
git clone https://github.com/FennerLabs/pepper
cd pepper
```

Fetch the files from github
```
git lfs fetch --all
git lfs pull
```

We also recommend creating a dedicated virtual environment with python 3.12 as base 

```
python -m venv pepper_env
source pepper_env/bin/activate
```

We have included all requirements in the pyproject.toml file so all dependencies may be installed as follows

``` 
pip install .
```


### Bayesian inference for soil biotransformation half-lives - Hafner et al., 2023
Here's how to reproduce the data and the figures from the publication:
```
cd scripts
python bayesian_inference_main.py
```

### Current Opportunities and Limitations in Predicting Micropollutant Removal in Wastewater Treatment based on Molecular Structure - Cordero et al., 2025
In this project we include methods to model the breakthrough of micropollutants in wastewater treatment plants.
Main results can be reproduced as follows:
```
cd scripts
predict_breakthrough_wwtp.py
```

Please refer to the main publication for further details

### Confidently uncertain: Probabilistic machine learning to predict soil biotransformation half-lives - Salz et al., 2026
Code to reproduce the predictions from the publication:
```
cd scripts
python predict_soil_halflives.py
```
The code to reproduce analyses, tables and figures can be found in the jupyter notebook:
```
jupyter notebook soil_half_lives_additional_analyses.ipynb
```

## Sessions 
Use this link to start a session and test PEPPER
[![launch - renku](https://renkulab.io/renku-badge.svg)](https://renkulab.io/projects/fenner-labs/projects/pepper/sessions/new?autostart=1)

## Related Projects
We also have a pepper_app 
[<img alt="launch - streamlit" height="20" src="https://streamlit.io/images/brand/streamlit-mark-color.svg" title="Launch pepper_app" width="20"/>](https://pepper-app.streamlit.app)
to predict several endpoints of interest related to environmental persistence

