Skip to main content

python-based Parameter EStimation TOolbox

Project description

pyPESTO - Parameter EStimation TOolbox for python

pyPESTO logo

pyPESTO is a widely applicable and highly customizable toolbox for parameter estimation.

PyPI CI Coverage Documentation DOI

Feature overview

pyPESTO features include:

  • Multi-start local optimization
  • Profile computation
  • Result visualization
  • Interface to AMICI for efficient simulation and sensitivity analysis of ordinary differential equation (ODE) models (example)
  • Parameter estimation pipeline for systems biology problems specified in SBML and PEtab (example)
  • Parameter estimation with ordinal data as described in Schmiester et al. (2020) and Schmiester et al. (2021). (example)
  • Parameter estimation with censored data. (example)
  • Parameter estimation with nonlinear-monotone data. (example)

Quick install

The simplest way to install pyPESTO is via pip:

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 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

Publications

Citeable DOI for the latest pyPESTO release: DOI

There is a list of publications using pyPESTO. If you used pyPESTO in your work, we are happy to include your project, please let us know via a GitHub issue.

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 (arXiv:2305.01821).

When presenting work that employs pyPESTO, feel free to use one of the icons in doc/logo/:

AMICI Logo

References

pyPESTO supersedes PESTO a parameter estimation toolbox for MATLAB, whose development is discontinued.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pypesto-0.4.0.tar.gz (281.3 kB view details)

Uploaded Source

Built Distribution

pypesto-0.4.0-py3-none-any.whl (351.6 kB view details)

Uploaded Python 3

File details

Details for the file pypesto-0.4.0.tar.gz.

File metadata

  • Download URL: pypesto-0.4.0.tar.gz
  • Upload date:
  • Size: 281.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for pypesto-0.4.0.tar.gz
Algorithm Hash digest
SHA256 564488904fa6f970c61887b3d0391ae604346956d9f1ae58032d6ea5347fc821
MD5 9d96e49db1961bcb872164f4358d4c18
BLAKE2b-256 ef27c1a7f3a10f311658dd17228c7f49ca4a2cd54a504856f435b44f80d70547

See more details on using hashes here.

File details

Details for the file pypesto-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: pypesto-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 351.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for pypesto-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 aba2d3b2a4bec1cbe08f91faa001c1ffa55e78e23859979086a85ea2e45273e4
MD5 84b0f8b54d7a0b6698d8db656b8a07cb
BLAKE2b-256 477b21397774f1c1eb792758fa7e2f741d06f1ffa79750e915b65ec1e9569958

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page