automatic import of python-optlangopeneuler20.03

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+/optlang-1.6.1.tar.gz
diff --git a/python-optlang.spec b/python-optlang.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-optlang
+Version:	1.6.1
+Release:	1
+Summary:	Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK).
+License:	Apache-2.0
+URL:		https://github.com/opencobra/optlang
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/90/9b/5343d9a18a7e5e4d87fcb18058044eb4e94e0a44e160d43ac9132cf5deec/optlang-1.6.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-six
+Requires:	python3-swiglpk
+Requires:	python3-sympy
+Requires:	python3-black
+Requires:	python3-isort
+Requires:	python3-tox
+
+%description
+*Sympy based mathematical programming language*
+|PyPI| |Python Versions| |License| |Code of Conduct| |GitHub Actions| |Coverage Status| |Documentation Status| |Gitter| |JOSS| |DOI|
+Optlang is a Python package for solving mathematical optimization
+problems, i.e. maximizing or minimizing an objective function over a set
+of variables subject to a number of constraints. Optlang provides a
+common interface to a series of optimization tools, so different solver
+backends can be changed in a transparent way.
+Optlang's object-oriented API takes advantage of the symbolic math library
+`sympy <http://sympy.org/en/index.html>`__ to allow objective functions
+and constraints to be easily formulated from symbolic expressions of
+variables (see examples).
+Show us some love by staring this repo if you find optlang useful!
+Also, please use the GitHub `issue tracker <https://github.com/biosustain/optlang/issues>`_
+to let us know about bugs or feature requests, or our `gitter channel <https://gitter.im/biosustain/optlang>`_ if you have problems or questions regarding optlang.
+Installation
+~~~~~~~~~~~~
+Install using pip
+    pip install optlang
+This will also install `swiglpk <https://github.com/biosustain/swiglpk>`_, an interface to the open source (mixed integer) LP solver `GLPK <https://www.gnu.org/software/glpk/>`_.
+Quadratic programming (and MIQP) is supported through additional optional solvers (see below).
+Dependencies
+~~~~~~~~~~~~
+The following dependencies are needed.
+-  `sympy >= 1.0.0 <http://sympy.org/en/index.html>`__
+-  `six >= 1.9.0 <https://pypi.python.org/pypi/six>`__
+-  `swiglpk >= 1.4.3 <https://pypi.python.org/pypi/swiglpk>`__
+The following are optional dependencies that allow other solvers to be used.
+-  `cplex <https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__ (LP, MILP, QP, MIQP)
+-  `gurobipy <http://www.gurobi.com>`__ (LP, MILP, QP, MIQP)
+-  `scipy <http://www.scipy.org>`__ (LP)
+-  `osqp <https://osqp.org/>`__ (LP, QP)
+Example
+~~~~~~~
+Formulating and solving the problem is straightforward (example taken
+from `GLPK documentation <http://www.gnu.org/software/glpk>`__):
+    from __future__ import print_function
+    from optlang import Model, Variable, Constraint, Objective
+    # All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound.
+    x1 = Variable('x1', lb=0)
+    x2 = Variable('x2', lb=0)
+    x3 = Variable('x3', lb=0)
+    # A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub).
+    c1 = Constraint(x1 + x2 + x3, ub=100)
+    c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600)
+    c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300)
+    # An objective can be formulated
+    obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')
+    # Variables, constraints and objective are combined in a Model object, which can subsequently be optimized.
+    model = Model(name='Simple model')
+    model.objective = obj
+    model.add([c1, c2, c3])
+    status = model.optimize()
+    print("status:", model.status)
+    print("objective value:", model.objective.value)
+    print("----------")
+    for var_name, var in model.variables.iteritems():
+        print(var_name, "=", var.primal)
+The example will produce the following output:
+    status: optimal
+
+%package -n python3-optlang
+Summary:	Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK).
+Provides:	python-optlang
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-optlang
+*Sympy based mathematical programming language*
+|PyPI| |Python Versions| |License| |Code of Conduct| |GitHub Actions| |Coverage Status| |Documentation Status| |Gitter| |JOSS| |DOI|
+Optlang is a Python package for solving mathematical optimization
+problems, i.e. maximizing or minimizing an objective function over a set
+of variables subject to a number of constraints. Optlang provides a
+common interface to a series of optimization tools, so different solver
+backends can be changed in a transparent way.
+Optlang's object-oriented API takes advantage of the symbolic math library
+`sympy <http://sympy.org/en/index.html>`__ to allow objective functions
+and constraints to be easily formulated from symbolic expressions of
+variables (see examples).
+Show us some love by staring this repo if you find optlang useful!
+Also, please use the GitHub `issue tracker <https://github.com/biosustain/optlang/issues>`_
+to let us know about bugs or feature requests, or our `gitter channel <https://gitter.im/biosustain/optlang>`_ if you have problems or questions regarding optlang.
+Installation
+~~~~~~~~~~~~
+Install using pip
+    pip install optlang
+This will also install `swiglpk <https://github.com/biosustain/swiglpk>`_, an interface to the open source (mixed integer) LP solver `GLPK <https://www.gnu.org/software/glpk/>`_.
+Quadratic programming (and MIQP) is supported through additional optional solvers (see below).
+Dependencies
+~~~~~~~~~~~~
+The following dependencies are needed.
+-  `sympy >= 1.0.0 <http://sympy.org/en/index.html>`__
+-  `six >= 1.9.0 <https://pypi.python.org/pypi/six>`__
+-  `swiglpk >= 1.4.3 <https://pypi.python.org/pypi/swiglpk>`__
+The following are optional dependencies that allow other solvers to be used.
+-  `cplex <https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__ (LP, MILP, QP, MIQP)
+-  `gurobipy <http://www.gurobi.com>`__ (LP, MILP, QP, MIQP)
+-  `scipy <http://www.scipy.org>`__ (LP)
+-  `osqp <https://osqp.org/>`__ (LP, QP)
+Example
+~~~~~~~
+Formulating and solving the problem is straightforward (example taken
+from `GLPK documentation <http://www.gnu.org/software/glpk>`__):
+    from __future__ import print_function
+    from optlang import Model, Variable, Constraint, Objective
+    # All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound.
+    x1 = Variable('x1', lb=0)
+    x2 = Variable('x2', lb=0)
+    x3 = Variable('x3', lb=0)
+    # A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub).
+    c1 = Constraint(x1 + x2 + x3, ub=100)
+    c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600)
+    c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300)
+    # An objective can be formulated
+    obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')
+    # Variables, constraints and objective are combined in a Model object, which can subsequently be optimized.
+    model = Model(name='Simple model')
+    model.objective = obj
+    model.add([c1, c2, c3])
+    status = model.optimize()
+    print("status:", model.status)
+    print("objective value:", model.objective.value)
+    print("----------")
+    for var_name, var in model.variables.iteritems():
+        print(var_name, "=", var.primal)
+The example will produce the following output:
+    status: optimal
+
+%package help
+Summary:	Development documents and examples for optlang
+Provides:	python3-optlang-doc
+%description help
+*Sympy based mathematical programming language*
+|PyPI| |Python Versions| |License| |Code of Conduct| |GitHub Actions| |Coverage Status| |Documentation Status| |Gitter| |JOSS| |DOI|
+Optlang is a Python package for solving mathematical optimization
+problems, i.e. maximizing or minimizing an objective function over a set
+of variables subject to a number of constraints. Optlang provides a
+common interface to a series of optimization tools, so different solver
+backends can be changed in a transparent way.
+Optlang's object-oriented API takes advantage of the symbolic math library
+`sympy <http://sympy.org/en/index.html>`__ to allow objective functions
+and constraints to be easily formulated from symbolic expressions of
+variables (see examples).
+Show us some love by staring this repo if you find optlang useful!
+Also, please use the GitHub `issue tracker <https://github.com/biosustain/optlang/issues>`_
+to let us know about bugs or feature requests, or our `gitter channel <https://gitter.im/biosustain/optlang>`_ if you have problems or questions regarding optlang.
+Installation
+~~~~~~~~~~~~
+Install using pip
+    pip install optlang
+This will also install `swiglpk <https://github.com/biosustain/swiglpk>`_, an interface to the open source (mixed integer) LP solver `GLPK <https://www.gnu.org/software/glpk/>`_.
+Quadratic programming (and MIQP) is supported through additional optional solvers (see below).
+Dependencies
+~~~~~~~~~~~~
+The following dependencies are needed.
+-  `sympy >= 1.0.0 <http://sympy.org/en/index.html>`__
+-  `six >= 1.9.0 <https://pypi.python.org/pypi/six>`__
+-  `swiglpk >= 1.4.3 <https://pypi.python.org/pypi/swiglpk>`__
+The following are optional dependencies that allow other solvers to be used.
+-  `cplex <https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__ (LP, MILP, QP, MIQP)
+-  `gurobipy <http://www.gurobi.com>`__ (LP, MILP, QP, MIQP)
+-  `scipy <http://www.scipy.org>`__ (LP)
+-  `osqp <https://osqp.org/>`__ (LP, QP)
+Example
+~~~~~~~
+Formulating and solving the problem is straightforward (example taken
+from `GLPK documentation <http://www.gnu.org/software/glpk>`__):
+    from __future__ import print_function
+    from optlang import Model, Variable, Constraint, Objective
+    # All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound.
+    x1 = Variable('x1', lb=0)
+    x2 = Variable('x2', lb=0)
+    x3 = Variable('x3', lb=0)
+    # A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub).
+    c1 = Constraint(x1 + x2 + x3, ub=100)
+    c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600)
+    c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300)
+    # An objective can be formulated
+    obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')
+    # Variables, constraints and objective are combined in a Model object, which can subsequently be optimized.
+    model = Model(name='Simple model')
+    model.objective = obj
+    model.add([c1, c2, c3])
+    status = model.optimize()
+    print("status:", model.status)
+    print("objective value:", model.objective.value)
+    print("----------")
+    for var_name, var in model.variables.iteritems():
+        print(var_name, "=", var.primal)
+The example will produce the following output:
+    status: optimal
+
+%prep
+%autosetup -n optlang-1.6.1
+
+%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-optlang -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.6.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
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@@ -0,0 +1 @@
+c38f9f33bca1dfa436bfcdd9d501f670  optlang-1.6.1.tar.gz