From a3767ed8f8964a55423038bbef08d40a3b926bdb Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 20 Jun 2023 05:11:03 +0000 Subject: automatic import of python-do-mpc --- .gitignore | 1 + python-do-mpc.spec | 192 +++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 194 insertions(+) create mode 100644 python-do-mpc.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..f6e0ba0 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/do_mpc-4.6.0.tar.gz diff --git a/python-do-mpc.spec b/python-do-mpc.spec new file mode 100644 index 0000000..f87e02f --- /dev/null +++ b/python-do-mpc.spec @@ -0,0 +1,192 @@ +%global _empty_manifest_terminate_build 0 +Name: python-do-mpc +Version: 4.6.0 +Release: 1 +Summary: please add a summary manually as the author left a blank one +License: GNU Lesser General Public License version 3 +URL: https://www.do-mpc.com +Source0: https://mirrors.aliyun.com/pypi/web/packages/fe/2f/031e03849149aa72d51df399cd98b63477f34317fb10e1e6915cdedbbbec/do_mpc-4.6.0.tar.gz +BuildArch: noarch + +Requires: python3-casadi +Requires: python3-numpy +Requires: python3-matplotlib + +%description + + +# Model predictive control python toolbox + +[![Documentation Status](https://readthedocs.org/projects/do-mpc/badge/?version=latest)](https://www.do-mpc.com) +[![Build Status](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml/badge.svg?branch=develop)](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml) +[![PyPI version](https://badge.fury.io/py/do-mpc.svg)](https://badge.fury.io/py/do-mpc) +[![awesome](https://img.shields.io/badge/awesome-yes-brightgreen.svg?style=flat-square)](https://github.com/do-mpc/do-mpc) + +**do-mpc** is a comprehensive open-source toolbox for robust **model predictive control (MPC)** +and **moving horizon estimation (MHE)**. +**do-mpc** enables the efficient formulation and solution of control and estimation problems for nonlinear systems, +including tools to deal with uncertainty and time discretization. +The modular structure of **do-mpc** contains simulation, estimation and control components +that can be easily extended and combined to fit many different applications. + +In summary, **do-mpc** offers the following features: + +* nonlinear and economic model predictive control +* support for differential algebraic equations (DAE) +* time discretization with orthogonal collocation on finite elements +* robust multi-stage model predictive control +* moving horizon state and parameter estimation +* modular design that can be easily extended + +The **do-mpc** software is Python based and works therefore on any OS with a Python 3.x distribution. **do-mpc** has been developed by Sergio Lucia and Alexandru Tatulea at the DYN chair of the TU Dortmund lead by Sebastian Engell. The development is continued at the [Laboratory of Process Automation Systems](https://pas.bci.tu-dortmund.de) (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia. + +## Installation instructions +Installation instructions are given [here](https://www.do-mpc.com/en/latest/installation.html). + +## Documentation +Please visit our extensive [documentation](https://www.do-mpc.com), kindly hosted on readthedocs. + +## Citing **do-mpc** +If you use **do-mpc** for published work please cite it as: + +S. Lucia, A. Tatulea-Codrean, C. Schoppmeyer, and S. Engell. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Engineering Practice, 60:51-62, 2017 + +Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...) + + +%package -n python3-do-mpc +Summary: please add a summary manually as the author left a blank one +Provides: python-do-mpc +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-do-mpc + + +# Model predictive control python toolbox + +[![Documentation Status](https://readthedocs.org/projects/do-mpc/badge/?version=latest)](https://www.do-mpc.com) +[![Build Status](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml/badge.svg?branch=develop)](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml) +[![PyPI version](https://badge.fury.io/py/do-mpc.svg)](https://badge.fury.io/py/do-mpc) +[![awesome](https://img.shields.io/badge/awesome-yes-brightgreen.svg?style=flat-square)](https://github.com/do-mpc/do-mpc) + +**do-mpc** is a comprehensive open-source toolbox for robust **model predictive control (MPC)** +and **moving horizon estimation (MHE)**. +**do-mpc** enables the efficient formulation and solution of control and estimation problems for nonlinear systems, +including tools to deal with uncertainty and time discretization. +The modular structure of **do-mpc** contains simulation, estimation and control components +that can be easily extended and combined to fit many different applications. + +In summary, **do-mpc** offers the following features: + +* nonlinear and economic model predictive control +* support for differential algebraic equations (DAE) +* time discretization with orthogonal collocation on finite elements +* robust multi-stage model predictive control +* moving horizon state and parameter estimation +* modular design that can be easily extended + +The **do-mpc** software is Python based and works therefore on any OS with a Python 3.x distribution. **do-mpc** has been developed by Sergio Lucia and Alexandru Tatulea at the DYN chair of the TU Dortmund lead by Sebastian Engell. The development is continued at the [Laboratory of Process Automation Systems](https://pas.bci.tu-dortmund.de) (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia. + +## Installation instructions +Installation instructions are given [here](https://www.do-mpc.com/en/latest/installation.html). + +## Documentation +Please visit our extensive [documentation](https://www.do-mpc.com), kindly hosted on readthedocs. + +## Citing **do-mpc** +If you use **do-mpc** for published work please cite it as: + +S. Lucia, A. Tatulea-Codrean, C. Schoppmeyer, and S. Engell. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Engineering Practice, 60:51-62, 2017 + +Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...) + + +%package help +Summary: Development documents and examples for do-mpc +Provides: python3-do-mpc-doc +%description help + + +# Model predictive control python toolbox + +[![Documentation Status](https://readthedocs.org/projects/do-mpc/badge/?version=latest)](https://www.do-mpc.com) +[![Build Status](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml/badge.svg?branch=develop)](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml) +[![PyPI version](https://badge.fury.io/py/do-mpc.svg)](https://badge.fury.io/py/do-mpc) +[![awesome](https://img.shields.io/badge/awesome-yes-brightgreen.svg?style=flat-square)](https://github.com/do-mpc/do-mpc) + +**do-mpc** is a comprehensive open-source toolbox for robust **model predictive control (MPC)** +and **moving horizon estimation (MHE)**. +**do-mpc** enables the efficient formulation and solution of control and estimation problems for nonlinear systems, +including tools to deal with uncertainty and time discretization. +The modular structure of **do-mpc** contains simulation, estimation and control components +that can be easily extended and combined to fit many different applications. + +In summary, **do-mpc** offers the following features: + +* nonlinear and economic model predictive control +* support for differential algebraic equations (DAE) +* time discretization with orthogonal collocation on finite elements +* robust multi-stage model predictive control +* moving horizon state and parameter estimation +* modular design that can be easily extended + +The **do-mpc** software is Python based and works therefore on any OS with a Python 3.x distribution. **do-mpc** has been developed by Sergio Lucia and Alexandru Tatulea at the DYN chair of the TU Dortmund lead by Sebastian Engell. The development is continued at the [Laboratory of Process Automation Systems](https://pas.bci.tu-dortmund.de) (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia. + +## Installation instructions +Installation instructions are given [here](https://www.do-mpc.com/en/latest/installation.html). + +## Documentation +Please visit our extensive [documentation](https://www.do-mpc.com), kindly hosted on readthedocs. + +## Citing **do-mpc** +If you use **do-mpc** for published work please cite it as: + +S. Lucia, A. Tatulea-Codrean, C. Schoppmeyer, and S. Engell. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Engineering Practice, 60:51-62, 2017 + +Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...) + + +%prep +%autosetup -n do_mpc-4.6.0 + +%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-do-mpc -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot - 4.6.0-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..5be0a27 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +db4b8feb06813d9f68addd76e1629d98 do_mpc-4.6.0.tar.gz -- cgit v1.2.3