%global _empty_manifest_terminate_build 0 Name: python-filterpy Version: 1.4.5 Release: 1 Summary: Kalman filtering and optimal estimation library License: MIT URL: https://github.com/rlabbe/filterpy Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f6/1d/ac8914360460fafa1990890259b7fa5ef7ba4cd59014e782e4ab3ab144d8/filterpy-1.4.5.zip BuildArch: noarch %description **NOTE**: Imminent drop of support of Python 2.7, 3.4. See section below for details. This library provides Kalman filtering and various related optimal and non-optimal filtering software written in Python. It contains Kalman filters, Extended Kalman filters, Unscented Kalman filters, Kalman smoothers, Least Squares filters, fading memory filters, g-h filters, discrete Bayes, and more. This is code I am developing in conjunction with my book Kalman and Bayesian Filter in Python, which you can read/download at https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/ My aim is largely pedalogical - I opt for clear code that matches the equations in the relevant texts on a 1-to-1 basis, even when that has a performance cost. There are places where this tradeoff is unclear - for example, I find it somewhat clearer to write a small set of equations using linear algebra, but numpy's overhead on small matrices makes it run slower than writing each equation out by hand. Furthermore, books such Zarchan present the written out form, not the linear algebra form. It is hard for me to choose which presentation is 'clearer' - it depends on the audience. In that case I usually opt for the faster implementation. I use NumPy and SciPy for all of the computations. I have experimented with Numba and it yields impressive speed ups with minimal costs, but I am not convinced that I want to add that requirement to my project. It is still on my list of things to figure out, however. Sphinx generated documentation lives at http://filterpy.readthedocs.org/. Generation is triggered by git when I do a check in, so this will always be bleeding edge development version - it will often be ahead of the released version. %package -n python3-filterpy Summary: Kalman filtering and optimal estimation library Provides: python-filterpy BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-filterpy **NOTE**: Imminent drop of support of Python 2.7, 3.4. See section below for details. This library provides Kalman filtering and various related optimal and non-optimal filtering software written in Python. It contains Kalman filters, Extended Kalman filters, Unscented Kalman filters, Kalman smoothers, Least Squares filters, fading memory filters, g-h filters, discrete Bayes, and more. This is code I am developing in conjunction with my book Kalman and Bayesian Filter in Python, which you can read/download at https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/ My aim is largely pedalogical - I opt for clear code that matches the equations in the relevant texts on a 1-to-1 basis, even when that has a performance cost. There are places where this tradeoff is unclear - for example, I find it somewhat clearer to write a small set of equations using linear algebra, but numpy's overhead on small matrices makes it run slower than writing each equation out by hand. Furthermore, books such Zarchan present the written out form, not the linear algebra form. It is hard for me to choose which presentation is 'clearer' - it depends on the audience. In that case I usually opt for the faster implementation. I use NumPy and SciPy for all of the computations. I have experimented with Numba and it yields impressive speed ups with minimal costs, but I am not convinced that I want to add that requirement to my project. It is still on my list of things to figure out, however. Sphinx generated documentation lives at http://filterpy.readthedocs.org/. Generation is triggered by git when I do a check in, so this will always be bleeding edge development version - it will often be ahead of the released version. %package help Summary: Development documents and examples for filterpy Provides: python3-filterpy-doc %description help **NOTE**: Imminent drop of support of Python 2.7, 3.4. See section below for details. This library provides Kalman filtering and various related optimal and non-optimal filtering software written in Python. It contains Kalman filters, Extended Kalman filters, Unscented Kalman filters, Kalman smoothers, Least Squares filters, fading memory filters, g-h filters, discrete Bayes, and more. This is code I am developing in conjunction with my book Kalman and Bayesian Filter in Python, which you can read/download at https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/ My aim is largely pedalogical - I opt for clear code that matches the equations in the relevant texts on a 1-to-1 basis, even when that has a performance cost. There are places where this tradeoff is unclear - for example, I find it somewhat clearer to write a small set of equations using linear algebra, but numpy's overhead on small matrices makes it run slower than writing each equation out by hand. Furthermore, books such Zarchan present the written out form, not the linear algebra form. It is hard for me to choose which presentation is 'clearer' - it depends on the audience. In that case I usually opt for the faster implementation. I use NumPy and SciPy for all of the computations. I have experimented with Numba and it yields impressive speed ups with minimal costs, but I am not convinced that I want to add that requirement to my project. It is still on my list of things to figure out, however. Sphinx generated documentation lives at http://filterpy.readthedocs.org/. Generation is triggered by git when I do a check in, so this will always be bleeding edge development version - it will often be ahead of the released version. %prep %autosetup -n filterpy-1.4.5 %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-filterpy -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 1.4.5-1 - Package Spec generated