%global _empty_manifest_terminate_build 0 Name: python-pyodesys Version: 0.14.2 Release: 1 Summary: Straightforward numerical integration of ODE systems from Python. License: BSD URL: https://github.com/bjodah/pyodesys Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dc/4e/227dbb03fa5c305ff18c8ce29cd12cbdca01ea786e218a33f8b59adda818/pyodesys-0.14.2.tar.gz BuildArch: noarch %description ``pyodesys`` provides a straightforward way of numerically integrating systems of ordinary differential equations (initial value problems). It unifies the interface of several libraries for performing the numerical integration as well as several libraries for symbolic representation. It also provides a convenience class for representing and integrating ODE systems defined by symbolic expressions, e.g. `SymPy `_ expressions. This allows the user to write concise code and rely on ``pyodesys`` to handle the subtle differences between libraries. The numerical integration is performed using either: - `scipy.integrate.ode `_ - pygslodeiv2_ - pyodeint_ - pycvodes_ Note that implicit steppers require a user supplied callback for calculating the Jacobian. ``pyodesys.SymbolicSys`` derives the Jacobian automatically. The symbolic representation is usually in the form of SymPy expressions, but the user may choose another symbolic back-end (see `sym `_). When performance is of utmost importance, e.g. in model fitting where results are needed for a large set of initial conditions and parameters, the user may transparently rely on compiled native code (classes in ``pyodesys.native.native_sys`` can generate optimal C++ code). The major benefit is that there is no need to manually rewrite the corresponding expressions in another programming language. %package -n python3-pyodesys Summary: Straightforward numerical integration of ODE systems from Python. Provides: python-pyodesys BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyodesys ``pyodesys`` provides a straightforward way of numerically integrating systems of ordinary differential equations (initial value problems). It unifies the interface of several libraries for performing the numerical integration as well as several libraries for symbolic representation. It also provides a convenience class for representing and integrating ODE systems defined by symbolic expressions, e.g. `SymPy `_ expressions. This allows the user to write concise code and rely on ``pyodesys`` to handle the subtle differences between libraries. The numerical integration is performed using either: - `scipy.integrate.ode `_ - pygslodeiv2_ - pyodeint_ - pycvodes_ Note that implicit steppers require a user supplied callback for calculating the Jacobian. ``pyodesys.SymbolicSys`` derives the Jacobian automatically. The symbolic representation is usually in the form of SymPy expressions, but the user may choose another symbolic back-end (see `sym `_). When performance is of utmost importance, e.g. in model fitting where results are needed for a large set of initial conditions and parameters, the user may transparently rely on compiled native code (classes in ``pyodesys.native.native_sys`` can generate optimal C++ code). The major benefit is that there is no need to manually rewrite the corresponding expressions in another programming language. %package help Summary: Development documents and examples for pyodesys Provides: python3-pyodesys-doc %description help ``pyodesys`` provides a straightforward way of numerically integrating systems of ordinary differential equations (initial value problems). It unifies the interface of several libraries for performing the numerical integration as well as several libraries for symbolic representation. It also provides a convenience class for representing and integrating ODE systems defined by symbolic expressions, e.g. `SymPy `_ expressions. This allows the user to write concise code and rely on ``pyodesys`` to handle the subtle differences between libraries. The numerical integration is performed using either: - `scipy.integrate.ode `_ - pygslodeiv2_ - pyodeint_ - pycvodes_ Note that implicit steppers require a user supplied callback for calculating the Jacobian. ``pyodesys.SymbolicSys`` derives the Jacobian automatically. The symbolic representation is usually in the form of SymPy expressions, but the user may choose another symbolic back-end (see `sym `_). When performance is of utmost importance, e.g. in model fitting where results are needed for a large set of initial conditions and parameters, the user may transparently rely on compiled native code (classes in ``pyodesys.native.native_sys`` can generate optimal C++ code). The major benefit is that there is no need to manually rewrite the corresponding expressions in another programming language. %prep %autosetup -n pyodesys-0.14.2 %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-pyodesys -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.14.2-1 - Package Spec generated