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@@ -0,0 +1 @@ +/optlang-1.6.1.tar.gz diff --git a/python-optlang.spec b/python-optlang.spec new file mode 100644 index 0000000..58c8f3c --- /dev/null +++ b/python-optlang.spec @@ -0,0 +1,252 @@ +%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 @@ -0,0 +1 @@ +c38f9f33bca1dfa436bfcdd9d501f670 optlang-1.6.1.tar.gz |