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        "author": "The PEtab developers",
        "author_email": "daniel.weindl@helmholtz-muenchen.de",
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        "description": "[![Build Status](https://travis-ci.com/ICB-DCM/PEtab.svg?branch=master)](https://travis-ci.com/ICB-DCM/PEtab)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/c1e34bb9ade14fe6bce54199be18535c)](https://www.codacy.com/app/dweindl/PEtab?utm_source=github.com&utm_medium=referral&utm_content=ICB-DCM/PEtab&utm_campaign=Badge_Grade)\n[![codecov](https://codecov.io/gh/ICB-DCM/PEtab/branch/master/graph/badge.svg)](https://codecov.io/gh/ICB-DCM/PEtab)\n[![PyPI version](https://badge.fury.io/py/petab.svg)](https://badge.fury.io/py/petab)\n\n\n# PEtab --- a data format for specifying parameter estimation problems in systems biology\n\n![Logo](doc/logo/PEtab.png)\n\nThis repository describes *PEtab* --- a data format for specifying parameter \nestimation problems in systems biology, provides a Python library for easy \naccess and validation of *PEtab* files. See \n[doc/documentation_data_format.md](doc/documentation_data_format.md) for more \ninfo.\n\n## About PEtab\n\nPEtab is built around [SBML](http://sbml.org/) and based on tab-separated values \n(TSV) files. It is meant as a standardized way to provide information for \nparameter estimation which is out of the current scope of SBML. This includes\nfor example:\n\n  - Specifying and linking measurements to models\n\n    - Defining model outputs\n\n    - Specifying noise models\n\n  - Specifying parameter bounds for optimization\n\n  - Specifying multiple simulation condition with potentially shared parameters\n \n## References\n\nWhere PEtab is used / supported:\n\n  - Within the systems biology optimization \n    [benchmark problem collection](https://github.com/LoosC/Benchmark-Models)\n\n  - [pyPESTO](https://github.com/ICB-DCM/pyPESTO/)\n\n  - [AMICI](https://github.com/ICB-DCM/AMICI/)\n\nPEtab support for [D2D](https://github.com/Data2Dynamics/d2d/) and\n[AMIGO2](https://sites.google.com/site/amigo2toolbox/) is under development.\n\nIf your project or tool is using PEtab, and you would like to have it listed\nhere, please let us know.\n\n## Using PEtab\n\nIf you would like to use PEtab yourself, please have a look at \n[doc/documentation_data_format.md](doc/documentation_data_format.md) or at\nthe example models provided in the \n[benchmark problem collection](https://github.com/LoosC/Benchmark-Models).\n\nTo convert your existing parameter estimation problem to the PEtab format, you \nwill have to:\n\n1. Specify your model in SBML\n\n1. Set up model outputs and noise model using `AssignmentRule`s as described in \n  the PEtab documentation\n\n1. Create a condition table, if appropriate\n\n1. Create a table of measurements\n\n1. Create a parameter table\n\nIf you are using Python, some handy functions of the PEtab library can help \nyou with that. This include also a PEtab validator called `petablint.py` which\nyou can use to check if your files adhere to the PEtab standard. If you have \nfurther questions regarding PEtab, feel free to post an \n[issue](https://github.com/ICB-DCM/PEtab/issues) at our github repository.\n\n## PEtab Python library\n\nPEtab comes with a Python package for creating, checking, and working with \nPEtab files. This library is available on pypi and the easiest way to install \nit is running\n\n    pip3 install petab\n    \nIt will require Python3.6 to run.\n\nWhen setting up a new parameter estimation problem, the most useful tools will\nbe:\n\n  - The PEtab validator, which is now automatically installed using Python entrypoints to \n    be available as a shell command from anywhere called `petablint`\n\n  - `petab.core.create_parameter_df` to create the parameter table, once you\n    have set up the model, condition table and measurement table\n\n\n## Extending PEtab\n\nWe are aware of the fact that PEtab may not serve everybody's needs. If you \nhave a suggestion of how to extend PEtab, feel free to post an issue at our \ngithub repository.",
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