Metadata-Version: 2.1
Name: amici
Version: 0.10.11
Summary: Advanced multi-language Interface to CVODES and IDAS (%s)
Home-page: https://github.com/ICB-DCM/AMICI
Author: Fabian Froehlich, Jan Hasenauer, Daniel Weindl and Paul Stapor
Author-email: fabian_froehlich@hms.harvard.edu
License: BSD
Description: # About AMICI
        
        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/)
        and automatically compiles such models as `.mex` simulation files
        (Matlab), C++ executables or Python modules.
        
        In contrast to the (no longer maintained)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.
        
        
        ## Features
        
        * SBML import (see details below)
        * 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)
        
        
        ## Interfaces & workflow
        
        The AMICI workflow starts with importing a model from either
        [SBML](http://sbml.org/) (Matlab, 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/ICB-DCM/AMICI/master/documentation/gfx/amici_workflow.png)
        
        ## Getting started
        
        AMICI installation instructions are provided
        [here](http://icb-dcm.github.io/AMICI/md__i_n_s_t_a_l_l.html).
        
        To get you started with Python-AMICI the best way might be this
        [Jupyter notebook](https://github.com/ICB-DCM/AMICI/blob/master/python/examples/example_steadystate/ExampleSteadystate.ipynb).
        
        For Matlab, various examples are available
        [here](https://github.com/ICB-DCM/AMICI/tree/master/matlab/examples).
        
        
        Comprehensive documentation on installation and usage of AMICI is available
        online at [http://icb-dcm.github.io/AMICI/](http://icb-dcm.github.io/AMICI/).
        
        Any contributions to AMICI are welcome, read more contributing
        [here](http://icb-dcm.github.io/AMICI/md__c_o_n_t_r_i_b_u_t_i_n_g.html).
        
        
        ### Getting help
        
        In case of questions or problems with using AMICI, feel free to post an
        [issue](https://github.com/ICB-DCM/AMICI/issues) on Github. We are trying to
        get back to you quickly.
        
        ## 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](documentation/references.md).
        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., Kaltenbacher, B., Theis, F. J., & Hasenauer, J. (2017). Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.   Plos Computational Biology, 13(1), e1005331. doi: 10.1371/journal.pcbi.1005331](https://doi.org/10.1371/journal.pcbi.1005331)
        and/or
        * [Fröhlich, F., Theis, F. J., Rädler, J. O., & Hasenauer, J. (2017). Parameter estimation for dynamical systems with discrete events and logical operations. Bioinformatics, 33(7), 1049-1056. doi: 10.1093/bioinformatics/btw764](https://doi.org/10.1093/bioinformatics/btw764)
        
        
        ## Status of SBML support in Python-AMICI
        
        Python-AMICI currently passes 494 out of the 1780 (~28%) test cases from
        the semantic
        [SBML Test Suite](https://github.com/sbmlteam/sbml-test-suite/).
        
        In additional, we currently plan to add support for the following features
        (see corresponding issues for details and progress):
        
        - Events (currently Matlab-only)
        - Rate rules
        - Algebraic rules
        - Species assignment rules
        - Compartment assignment rules
        - Models without species
        - Logical operators
        
        contributions are welcome.
        
        However, the following features are unlikely to be supported:
        
        - SBML extensions
        - `factorial()`, `ceil()`, `floor()`, due to incompatibility with
          symbolic sensitivity computations
        - initial assignments for parameters
        - `delay()` due to missing SUNDIALS solver support
        
        
        ## Current build status
        
        <a href="https://badge.fury.io/py/amici" alt="PyPI version">
          <img src="https://badge.fury.io/py/amici.svg"></a>
        <a href="https://travis-ci.com/ICB-DCM/AMICI" alt="Build Status">
          <img src="https://travis-ci.com/ICB-DCM/AMICI.svg?branch=master"></a>
        <a href="https://codecov.io/gh/ICB-DCM/AMICI" alt="CodeCov">
          <img src="https://codecov.io/gh/ICB-DCM/AMICI/branch/master/graph/badge.svg"></a>
        <a href="https://www.codacy.com/app/FFroehlich/AMICI" alt="Codacy">
          <img src="https://api.codacy.com/project/badge/Grade/945235766e344a7fa36278feab915ff6"></a>
        
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: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: petab
Provides-Extra: wurlitzer
