[
](https://circleci.com/gh/pnsaevik/effluent)
[](https://doi.org/10.21105/joss.05554)
# effluent
Effluent is a python package for simulating the dispersion of effluent
discharges from wastewater pipes. The underlying model is based on
Lee, Joseph H. W., and Chu, Vincent H.: *Turbulent Jets and Plumes*.
Boston, MA: Springer US, 2003.
[doi:10.1007/978-1-4615-0407-8](https://doi.org/10.1007/978-1-4615-0407-8).
The package is mainly intended for research purposes, and does not contain
any convenience plotting or statistics functionality. It is expected that
these analyses are conducted in post-processing stages using other packages.
## Installation
The package is installed using pip:
pip install effluent
Alternatively, the package can be installed to an isolated conda environment
using the in-repo file `environment.yml` as follows:
conda env create -f environment.yml
## Usage
The software is invoked as a command line script:
effluent config.toml
where `config.toml` is the configuration file specifying the simulation
setup. Examples of valid configuration files are given in the repository
directory `docs/source/examples`, and in the
[online documentation](https://effluent.readthedocs.io/en/latest/).
## Citation
If you use the software in a publication or report, please cite it as follows:
Sævik, P. N., (2023). _Effluent: A Python package for modelling effluent discharge_.
Journal of Open Source Software, **8**(89),
[https://doi.org/10.21105/joss.05554](https://doi.org/10.21105/joss.05554).
Consider also citing the work which this package is based on:
Lee, J. H. W., and Chu, V. H. (2003).
_Turbulent Jets and Plumes - A Lagrangian Approach_. Boston, MA: Springer US.
[https://doi.org/10.1007/978-1-4615-0407-8](https://doi.org/10.1007/978-1-4615-0407-8).
## Documentation
Check out the
[online documentation](https://effluent.readthedocs.io/en/latest/) for a
detailed description of the algorithm and software features.
## How to contribute
Use the GitHub issue tracking system to report problems with the software, seek
support, or suggest improvements. Code contributions can be suggested using
GitHub pull requests. Check documentation for details.
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