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Remove non-package-related files, update links (#41)
Remove everything that is not related to the petab Python package. Closes #39 Co-authored-by: Yannik Schälte <31767307+yannikschaelte@users.noreply.github.com>
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.github/ISSUE_TEMPLATE/petab-specification-changes.md

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.github/workflows/ci_tests.yml

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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.8]
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python-version: [3.7, 3.8, 3.9]
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steps:
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- name: Check out repository

CONTRIBUTING.md

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# PEtab contribution guide
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# petab contribution guide
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We are happy about contributions to PEtab of any form.
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This includes, but is not limited to:
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* Extending / improving
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[documentation and examples](https://petab.readthedocs.io/en/latest/)
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* New library functionality
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* Additional
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[PEtab application examples](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab)
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* Additional minimal
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[PEtab test cases](https://github.com/PEtab-dev/petab_test_suite)
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## Extending PEtab
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We are aware of the fact that PEtab may not serve everybody's needs. If you
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have a suggestion of how to extend PEtab, feel free to post an
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[issue](https://github.com/PEtab-dev/PEtab/issues) at our GitHub repository.
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## Contributions to this repository
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General:
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*By creating a pull request you agree with your contribution being made
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available under the license terms specified in
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[https://github.com/PEtab-dev/PEtab/blob/master/LICENSE](https://github.com/PEtab-dev/PEtab/blob/master/LICENSE).*
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[https://github.com/PEtab-dev/libpetab-python/blob/master/LICENSE](https://github.com/PEtab-dev/libpetab-python/blob/master/LICENSE).*
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* Assign a reviewer, or let us know otherwise, that your pull request is ready
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for review.

LICENSE

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MIT License
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Copyright (c) 2018 Data-driven Computational Modelling
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Copyright (c) 2018 petab package developers
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

README.md

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![CI tests](https://github.com/PEtab-dev/PEtab/workflows/CI%20tests/badge.svg)
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[![Codacy Badge](https://api.codacy.com/project/badge/Grade/fd7dd5cee68e449983be5c43f230c7f3)](https://www.codacy.com/gh/PEtab-dev/PEtab?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=PEtab-dev/PEtab&amp;utm_campaign=Badge_Grade)
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[![codecov](https://codecov.io/gh/PEtab-dev/PEtab/branch/master/graph/badge.svg)](https://codecov.io/gh/PEtab-dev/PEtab)
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![CI tests](https://github.com/PEtab-dev/libpetab-python/workflows/CI%20tests/badge.svg)
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[![Codacy Badge](https://api.codacy.com/project/badge/Grade/fd7dd5cee68e449983be5c43f230c7f3)](https://www.codacy.com/gh/PEtab-dev/libpetab-python)
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[![codecov](https://codecov.io/gh/PEtab-dev/libpetab-python/branch/master/graph/badge.svg)](https://codecov.io/gh/PEtab-dev/libpetab-python)
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[![PyPI version](https://badge.fury.io/py/petab.svg)](https://badge.fury.io/py/petab)
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# PEtab -- a data format for specifying parameter estimation problems in systems biology
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# petab - a Python package for handling PEtab files
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![Logo](doc/logo/PEtab.png)
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*PEtab* is a data format for specifying parameter estimation problems in systems biology.
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This repository provides extensive documentation and a Python library for easy
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access and validation of *PEtab* files.
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## About PEtab
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PEtab is built around [SBML](http://sbml.org/) and based on tab-separated values
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(TSV) files. It is meant as a standardized way to provide information for
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parameter estimation, which is out of the current scope of SBML. This includes
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for example:
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- Specifying and linking measurements to models
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- Defining model outputs
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- Specifying noise models
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- Specifying parameter bounds for optimization
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- Specifying multiple simulation condition with potentially shared parameters
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![PEtab files](doc/gfx/petab_files.png)
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[PEtab](https://petab.readthedocs.io/) is a data format for specifying
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parameter estimation problems in systems biology. This repository provides
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the `petab` Python package for reading, writing and validating PEtab files.
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## Documentation
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Documentation of the PEtab data format and Python library is available at
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Documentation of the `petab` Python package is available at
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[https://libpetab-python.readthedocs.io/en/latest/](https://libpetab-python.readthedocs.io/en/latest/).
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Documentation of the PEtab format in general is available at
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[https://petab.readthedocs.io/en/latest/](https://petab.readthedocs.io/en/latest/).
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## Examples
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A wide range of PEtab examples can be found in the systems biology parameter estimation
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[benchmark problem collection](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab).
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## PEtab support in systems biology tools
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Where PEtab is supported (in alphabetical order):
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- [AMICI](https://github.com/ICB-DCM/AMICI/)
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([Example](https://github.com/ICB-DCM/AMICI/blob/master/python/examples/example_petab/petab.ipynb))
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- A PEtab -> [COPASI](http://copasi.org/)
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[converter](https://github.com/copasi/python-petab-importer)
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- [d2d](https://github.com/Data2Dynamics/d2d/)
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([HOWTO](https://github.com/Data2Dynamics/d2d/wiki/Support-for-PEtab))
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- [dMod](https://github.com/dkaschek/dMod/)
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([HOWTO](https://github.com/dkaschek/dMod/wiki/Support-for-PEtab))
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- [MEIGO](https://github.com/gingproc-IIM-CSIC/MEIGO64)
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([HOWTO](https://github.com/gingproc-IIM-CSIC/MEIGO64/tree/master/MEIGO/PEtabMEIGO))
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- [parPE](https://github.com/ICB-DCM/parPE/)
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## Installation
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- [pyABC](https://github.com/ICB-DCM/pyABC/) ([Example](https://pyabc.readthedocs.io/en/latest/examples/petab.html))
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- [pyPESTO](https://github.com/ICB-DCM/pyPESTO/)
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([Example](https://pypesto.readthedocs.io/en/latest/example/petab_import.html))
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- [SBML2Julia](https://github.com/paulflang/SBML2Julia)
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([Tutorial](https://sbml2julia.readthedocs.io/en/latest/python_api.html))
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If your project or tool is using PEtab, and you would like to have it listed
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here, please [let us know](https://github.com/PEtab-dev/PEtab/issues).
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### PEtab features supported in different tools
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The following list provides an overview of supported PEtab features in
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different tools, based on passed test cases of the
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[PEtab test suite](https://github.com/PEtab-dev/petab_test_suite):
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| ID | Test | AMICI<br>`>=0.10.20` | Copasi | D2D | dMod | MEIGO | parPE<br>`develop` | pyABC<br>`>=0.10.1` | pyPESTO<br>`>=0.0.11` | SBML2Julia |
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|----|----------------------------------------------------------------|----------------------|--------|-----|------|-------|-----------------------|-------|------------------------|------------|
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| 1 | Basic simulation | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 2 | Multiple simulation conditions | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 3 | Numeric observable parameter overrides in measurement table | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 4 | Parametric observable parameter overrides in measurement table | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 5 | Parametric overrides in condition table | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 6 | Time-point specific overrides in the measurement table | --- | --- | +++ | +++ | +++ | --- | --- | --- | +++ |
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| 7 | Observable transformations to log10 scale | +-+ | +-- | +++ | ++- | +++ | --+ | +-+ | +-+ | +++ |
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| 8 | Replicate measurements | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 9 | Pre-equilibration | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 10 | Partial pre-equilibration | +++ | --- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 11 | Numeric initial concentration in condition table | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 12 | Numeric initial compartment sizes in condition table | --- | +-- | +++ | +++ | +++ | --- | --- | --- | +++ |
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| 13 | Parametric initial concentrations in condition table | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 14 | Numeric noise parameter overrides in measurement table | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 15 | Parametric noise parameter overrides in measurement table | +++ | +-- | +++ | +++ | +++ | --+ | +++ | +++ | +++ |
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| 16 | Observable transformations to log scale | +-+ | +-- | +++ | ++- | +++ | --+ | +-+ | +-+ | +++ |
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Legend:
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* First character indicates whether computing simulated data is supported and simulations are correct (+) or not (-).
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* Second character indicates whether computing chi2 values of residuals are supported and correct (+) or not (-).
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* Third character indicates whether computing likelihoods is supported and correct (+) or not (-).
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## Using PEtab
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If you would like to use PEtab yourself, please have a look at:
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* [a PEtab tutorial](https://petab.readthedocs.io/en/latest/tutorial.html)
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going through the individual steps of setting up a parameter estimation
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problem in PEtab, independently of any specific software
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* [the PEtab format reference](https://petab.readthedocs.io/en/stable/documentation_data_format.html)
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* the example models provided in the
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[benchmark collection](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab).
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* the tutorials provided with each of the softwares supporting PEtab
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To convert your existing parameter estimation problem to the PEtab format, you
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will have to:
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1. Specify your model in SBML.
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1. Create a condition table.
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1. Create a table of observables.
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1. Create a table of measurements.
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1. Create a parameter table.
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If you are using Python, some handy functions of the
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[PEtab library](https://petab.readthedocs.io/en/latest/modules.html) can help
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you with that. This include also a PEtab validator called `petablint` which
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you can use to check if your files adhere to the PEtab standard. If you have
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further questions regarding PEtab, feel free to post an
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[issue](https://github.com/PEtab-dev/PEtab/issues) at our github repository.
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## PEtab Python library
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PEtab comes with a Python package for creating, checking, visualizing and
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working with PEtab files. This library is available on
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[pypi](https://pypi.org/project/petab/) and the easiest way to install
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it is running
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The PEtab library is available on [pypi](https://pypi.org/project/petab/)
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and the easiest way to install it is running
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pip3 install petab
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It will require Python>=3.7.1 to run.
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Development versions of the PEtab library can be installed using
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pip3 install https://github.com/PEtab-dev/PEtab/archive/develop.zip
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pip3 install https://github.com/PEtab-dev/libpetab-python/archive/develop.zip
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(replace `develop` by the branch or commit you would like to install).
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### Library examples
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## Examples
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* [Validation](https://github.com/PEtab-dev/PEtab/blob/master/doc/example/example_petablint.ipynb)
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* [Visualization](https://github.com/PEtab-dev/PEtab/blob/master/doc/example/example_visualization.ipynb)
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* [Validation](https://github.com/PEtab-dev/libpetab-python/blob/master/doc/example/example_petablint.ipynb)
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* [Visualization](https://github.com/PEtab-dev/libpetab-python/blob/master/doc/example/example_visualization.ipynb)
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## Getting help
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If you have any question or problems with PEtab, feel free to post them at
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our GitHub [issue tracker](https://github.com/PEtab-dev/PEtab/issues/).
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If you have any question or problems with this package, feel free to post them
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at our GitHub [issue tracker](https://github.com/PEtab-dev/libpetab-python/issues/).
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## Contributing to PEtab
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## Contributing
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Contributions and feedback to PEtab are very welcome, see our
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Contributions and feedback to this package are very welcome, see our
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[contribution guide](CONTRIBUTING.md).

doc/compile_doc.sh

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doc/compile_tutorial.sh

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doc/conf.py

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# -- Project information -----------------------------------------------------
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project = 'PEtab'
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copyright = '2019, Daniel Weindl, Yannik Schälte, Dantong Wang, Carolin Loos, Jan Hasenauer, Paul Stapor, Elba Raimúndez Álvarez, Erika Dudkin, Charles Tapley Hoyt, Fabian Fröhlich'
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author = 'Daniel Weindl, Yannik Schälte, Dantong Wang, Carolin Loos, Jan Hasenauer, Paul Stapor, Elba Raimúndez Álvarez, Erika Dudkin, Charles Tapley Hoyt, Fabian Fröhlich'
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copyright = '2018-2021, the PEtab developers'
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author = 'PEtab developers'
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# The full version, including alpha/beta/rc tags
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release = 'latest'
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"github_repo": "petab",
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"github_user": "petab-dev",
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"github_repo": "libpetab-python",
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"github_version": "develop",
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"conf_py_path": "/doc",
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}

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