Metadata-Version: 2.1
Name: amici
Version: 0.11.13
Summary: Advanced multi-language Interface to CVODES and IDAS
Home-page: https://github.com/AMICI-dev/AMICI
Author: Fabian Froehlich, Jan Hasenauer, Daniel Weindl and Paul Stapor
Author-email: fabian_froehlich@hms.harvard.edu
License: BSD 3-Clause License
Project-URL: Bug Reports, https://github.com/AMICI-dev/AMICI/issues
Project-URL: Source, https://github.com/AMICI-dev/AMICI
Project-URL: Documentation, https://amici.readthedocs.io/en/latest/
Description: <img src="https://raw.githubusercontent.com/AMICI-dev/AMICI/master/documentation/gfx/banner.png" height="60" align="left" alt="AMICI logo">
        
        ## Advanced Multilanguage Interface for CVODES and IDAS
        
        ## About 
        
        AMICI provides a multi-language (Python, C++, Matlab) interface for the
        [SUNDIALS](https://computing.llnl.gov/projects/sundials/) solvers
        [CVODES](https://computing.llnl.gov/projects/sundials/cvodes)
        (for ordinary differential equations) and
        [IDAS](https://computing.llnl.gov/projects/sundials/idas)
        (for algebraic differential equations). AMICI allows the user to read
        differential equation models specified as [SBML](http://sbml.org/)
        or [PySB](http://pysb.org/)
        and automatically compiles such models into Python modules, C++ libraries or
        Matlab `.mex` simulation files.
        
        In contrast to the (no longer maintained)
        [sundialsTB](https://computing.llnl.gov/projects/sundials/sundials-software)
        Matlab interface, all necessary functions are transformed into native
        C++ code, which allows for a significantly faster simulation.
        
        Beyond forward integration, the compiled simulation file also allows for
        forward sensitivity analysis, steady state sensitivity analysis and
        adjoint sensitivity analysis for likelihood-based output functions.
        
        The interface was designed to provide routines for efficient gradient
        computation in parameter estimation of biochemical reaction models but
        it is also applicable to a wider range of differential equation
        constrained optimization problems.
        
        ## Current build status
        
        <a href="https://badge.fury.io/py/amici">
          <img src="https://badge.fury.io/py/amici.svg" alt="PyPI version"></a>
        <a href="https://travis-ci.com/AMICI-dev/AMICI">
          <img src="https://travis-ci.com/AMICI-dev/AMICI.svg?branch=master" alt="Build Status"></a>
        <a href="https://codecov.io/gh/AMICI-dev/AMICI">
          <img src="https://codecov.io/gh/AMICI-dev/AMICI/branch/master/graph/badge.svg" alt="Code coverage"></a>
        <a href="https://sonarcloud.io/dashboard?id=ICB-DCM_AMICI&branch=master">
          <img src="https://sonarcloud.io/api/project_badges/measure?branch=master&project=ICB-DCM_AMICI&metric=sqale_index" alt="SonarCloud technical debt"></a>
        <a href="https://zenodo.org/badge/latestdoi/43677177">
          <img src="https://zenodo.org/badge/43677177.svg" alt="Zenodo DOI"></a>
        <a href="https://amici.readthedocs.io/en/latest/?badge=latest">
         <img src="https://readthedocs.org/projects/amici/badge/?version=latest" alt="ReadTheDocs status"></a>
        <a href="https://bestpractices.coreinfrastructure.org/projects/3780">
          <img src="https://bestpractices.coreinfrastructure.org/projects/3780/badge" alt="coreinfrastructure bestpractices badge"></a>
        
        ## Features
        
        * SBML import
        * PySB import
        * Generation of C++ code for model simulation and sensitivity
          computation
        * Access to and high customizability of CVODES and IDAS solver
        * Python, C++, Matlab interface
        * Sensitivity analysis
          * forward
          * steady state
          * adjoint
          * first- and second-order
        * Pre-equilibration and pre-simulation conditions
        * Support for
          [discrete events and logical operations](https://academic.oup.com/bioinformatics/article/33/7/1049/2769435)
          (Matlab-only)
        
        ## Interfaces & workflow
        
        The AMICI workflow starts with importing a model from either
        [SBML](http://sbml.org/) (Matlab, Python), [PySB](http://pysb.org/) (Python),
        or a Matlab definition of the model (Matlab-only). From this input,
        all equations for model simulation
        are derived symbolically and C++ code is generated. This code is then
        compiled into a C++ library, a Python module, or a Matlab `.mex` file and
        is then used for model simulation.
        
        ![AMICI workflow](https://raw.githubusercontent.com/AMICI-dev/AMICI/master/documentation/gfx/amici_workflow.png)
        
        ## Getting started
        
        The AMICI source code is available at https://github.com/AMICI-dev/AMICI/.
        To install AMICI, first read the installation instructions for
        [Python](https://amici.readthedocs.io/en/latest/python_installation.html),
        [C++](https://amici.readthedocs.io/en/develop/cpp_installation.html) or
        [Matlab](https://amici.readthedocs.io/en/develop/matlab_installation.html).
        
        To get you started with Python-AMICI, the best way might be checking out this
        [Jupyter notebook](https://github.com/AMICI-dev/AMICI/blob/master/documentation/GettingStarted.ipynb).
        
        To get started with Matlab-AMICI, various examples are available
        in [matlab/examples/](https://github.com/AMICI-dev/AMICI/tree/master/matlab/examples).
        
        Comprehensive documentation is available at
        [https://amici.readthedocs.io/en/latest/](https://amici.readthedocs.io/en/latest/).
        
        Any [contributions](https://amici.readthedocs.io/en/develop/CONTRIBUTING.html)
        to AMICI are welcome (code, bug reports, suggestions for improvements, ...).
        
        
        ## Getting help
        
        In case of questions or problems with using AMICI, feel free to post an
        [issue](https://github.com/AMICI-dev/AMICI/issues) on GitHub. We are trying to
        get back to you quickly.
        
        ## Projects using AMICI
        
        There are several tools for parameter estimation offering good integration
        with AMICI:
        
        * [pyPESTO](https://github.com/ICB-DCM/pyPESTO): Python library for
          optimization, sampling and uncertainty analysis
        * [pyABC](https://github.com/ICB-DCM/pyABC): Python library for
          parallel and scalable ABC-SMC (Approximate Bayesian Computation - Sequential
          Monte Carlo)
        * [parPE](https://github.com/ICB-DCM/parPE): C++ library for parameter
          estimation of ODE models offering distributed memory parallelism with focus
          on problems with many simulation conditions.
        
        ## Publications
        
        **Citeable DOI for the latest AMICI release:**
        [![DOI](https://zenodo.org/badge/43677177.svg)](https://zenodo.org/badge/latestdoi/43677177)
        
        There is a list of [publications using AMICI](https://amici.readthedocs.io/en/latest/references.html).
        If you used AMICI in your work, we are happy to include
        your project, please let us know via a Github issue.
        
        When using AMICI in your project, please cite
        * Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2020. 
          AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. arXiv preprint [arXiv:2012.09122](https://arxiv.org/abs/2012.09122).
        ```
        @article{frohlich2020amici,
          title={AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models},
          author={Fr{\"o}hlich, Fabian and Weindl, Daniel and Sch{\"a}lte, Yannik and Pathirana, Dilan and Paszkowski, {\L}ukasz and Lines, Glenn Terje and Stapor, Paul and Hasenauer, Jan},
          journal={arXiv preprint arXiv:2012.09122},
          year={2020}
        }
        ```
          
        When presenting work that employs AMICI, feel free to use one of the icons in 
        [documentation/gfx/](https://github.com/AMICI-dev/AMICI/tree/master/documentation/gfx),
        which are available under a
        [CC0](https://github.com/AMICI-dev/AMICI/tree/master/documentation/gfx/LICENSE.md)
        license:
        
        <p align="center">
          <img src="https://raw.githubusercontent.com/AMICI-dev/AMICI/master/documentation/gfx/logo_text.png" height="75" alt="AMICI Logo">
        </p>
        
Keywords: differential equations,simulation,ode,cvodes,systems biology,sensitivity analysis,sbml,pysb,petab
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python
Classifier: Programming Language :: C++
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: petab
Provides-Extra: pysb
