# pyPESTO - Parameter EStimation TOolbox for python
<img src="https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo_wordmark.png" width="50%" alt="pyPESTO logo"/>
**pyPESTO** is a widely applicable and highly customizable toolbox for
parameter estimation.
[](https://badge.fury.io/py/pypesto)
[](https://github.com/ICB-DCM/pyPESTO/actions)
[](https://codecov.io/gh/ICB-DCM/pyPESTO)
[](https://pypesto.readthedocs.io)
[](https://doi.org/10.5281/zenodo.2553546)
## Feature overview
pyPESTO features include:
* Multi-start local optimization
* Profile computation
* Result visualization
* Interface to [AMICI](https://github.com/AMICI-dev/AMICI/) for efficient
simulation and sensitivity analysis of ordinary differential equation (ODE)
models
([example](https://github.com/ICB-DCM/pyPESTO/blob/main/doc/example/amici.ipynb))
* Parameter estimation pipeline for systems biology problems specified in
[SBML](http://sbml.org/) and [PEtab](https://github.com/PEtab-dev/PEtab)
([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/petab_import.ipynb))
* Parameter estimation with ordinal data as described in
[Schmiester et al. (2020)](https://doi.org/10.1007/s00285-020-01522-w) and
[Schmiester et al. (2021)](https://doi.org/10.1093/bioinformatics/btab512).
([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/ordinal.ipynb))
* Parameter estimation with censored data. ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/censored.ipynb))
* Parameter estimation with nonlinear-monotone data. ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/nonlinear_monotone.ipynb))
## Quick install
The simplest way to install **pyPESTO** is via pip:
```shell
pip3 install pypesto
```
More information is available here:
https://pypesto.readthedocs.io/en/latest/install.html
## Documentation
The documentation is hosted on readthedocs.io:
<https://pypesto.readthedocs.io>
## Examples
Multiple use cases are discussed in the documentation. In particular, there are
jupyter notebooks in the [doc/example](doc/example) directory.
## Contributing
We are happy about any contributions. For more information on how to contribute
to pyPESTO check out
<https://pypesto.readthedocs.io/en/latest/contribute.html>
## How to Cite
**Citeable DOI for the latest pyPESTO release:**
[](https://doi.org/10.5281/zenodo.2553546)
When using pyPESTO in your project, please cite
* Schälte, Y., Fröhlich, F., Jost, P. J., Vanhoefer, J., Pathirana, D., Stapor, P.,
Lakrisenko, P., Wang, D., Raimúndez, E., Merkt, S., Schmiester, L., Städter, P.,
Grein, S., Dudkin, E., Doresic, D., Weindl, D., & Hasenauer, J. (2023). pyPESTO: A
modular and scalable tool for parameter estimation for dynamic models,
Bioinformatics, 2023, btad711, [doi:10.1093/bioinformatics/btad711](https://doi.org/10.1093/bioinformatics/btad711)
When presenting work that employs pyPESTO, feel free to use one of the icons in
[doc/logo/](https://github.com/ICB-DCM/pyPESTO/tree/main/doc/logo):
<p align="center">
<img src="https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo.png" height="75" alt="AMICI Logo">
</p>
There is a list of [publications using pyPESTO](https://pypesto.readthedocs.io/en/latest/references.html).
If you used pyPESTO in your work, we are happy to include
your project, please let us know via a GitHub issue.
## References
pyPESTO supersedes [**PESTO**](https://github.com/ICB-DCM/PESTO/) a parameter estimation
toolbox for MATLAB, whose development is discontinued.
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