automatic import of python-do-mpcopeneuler20.03

3 files changed, 194 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/do_mpc-4.6.0.tar.gz
diff --git a/python-do-mpc.spec b/python-do-mpc.spec
new file mode 100644
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+%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
+<img align="left" width="30%" hspace="2%" src="https://raw.githubusercontent.com/do-mpc/do-mpc/master/documentation/source/static/dompc_var_02_rtd_blue.png">
+
+# 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
+<img align="left" width="30%" hspace="2%" src="https://raw.githubusercontent.com/do-mpc/do-mpc/master/documentation/source/static/dompc_var_02_rtd_blue.png">
+
+# 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
+<img align="left" width="30%" hspace="2%" src="https://raw.githubusercontent.com/do-mpc/do-mpc/master/documentation/source/static/dompc_var_02_rtd_blue.png">
+
+# 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 <Python_Bot@openeuler.org> - 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