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+/amici-0.16.1.tar.gz
diff --git a/python-amici.spec b/python-amici.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-amici
+Version:	0.16.1
+Release:	1
+Summary:	Advanced multi-language Interface to CVODES and IDAS
+License:	BSD 3-Clause License
+URL:		https://github.com/AMICI-dev/AMICI
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a3/01/dda1b86559667af98c446493de359c119dc776128c43310d14a9200d0d56/amici-0.16.1.tar.gz
+BuildArch:	noarch
+
+
+%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+In contrast to the (no longer maintained)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://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml">
+  <img src="https://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml/badge.svg" alt="PyPI installation"></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)
+
+## 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)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AMICI-dev/AMICI/develop?labpath=documentation%2FGettingStarted.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., 2021.
+  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227,
+  [DOI:10.1093/bioinformatics/btab227](https://doi.org/10.1093/bioinformatics/btab227).
+```
+@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 = {Bioinformatics},
+  year = {2021},
+  month = {04},
+  issn = {1367-4803},
+  doi = {10.1093/bioinformatics/btab227},
+  note = {btab227},
+  eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab227/36866220/btab227.pdf},
+}
+```
+  
+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>
+
+
+%package -n python3-amici
+Summary:	Advanced multi-language Interface to CVODES and IDAS
+Provides:	python-amici
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-amici
+<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://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml">
+  <img src="https://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml/badge.svg" alt="PyPI installation"></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)
+
+## 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)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AMICI-dev/AMICI/develop?labpath=documentation%2FGettingStarted.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., 2021.
+  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227,
+  [DOI:10.1093/bioinformatics/btab227](https://doi.org/10.1093/bioinformatics/btab227).
+```
+@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 = {Bioinformatics},
+  year = {2021},
+  month = {04},
+  issn = {1367-4803},
+  doi = {10.1093/bioinformatics/btab227},
+  note = {btab227},
+  eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab227/36866220/btab227.pdf},
+}
+```
+  
+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>
+
+
+%package help
+Summary:	Development documents and examples for amici
+Provides:	python3-amici-doc
+%description help
+<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://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml">
+  <img src="https://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml/badge.svg" alt="PyPI installation"></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)
+
+## 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)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AMICI-dev/AMICI/develop?labpath=documentation%2FGettingStarted.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., 2021.
+  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227,
+  [DOI:10.1093/bioinformatics/btab227](https://doi.org/10.1093/bioinformatics/btab227).
+```
+@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 = {Bioinformatics},
+  year = {2021},
+  month = {04},
+  issn = {1367-4803},
+  doi = {10.1093/bioinformatics/btab227},
+  note = {btab227},
+  eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab227/36866220/btab227.pdf},
+}
+```
+  
+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>
+
+
+%prep
+%autosetup -n amici-0.16.1
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-amici -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.16.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..16222f9
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+7945da68eeed52ea2a1f7526af5a44a4  amici-0.16.1.tar.gz