automatic import of python-filterpy

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diff --git a/.gitignore b/.gitignore
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+/filterpy-1.4.5.zip
diff --git a/python-filterpy.spec b/python-filterpy.spec
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+%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
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.5-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..1cfe3c7
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+40b17012e98c358e6e6827e05ba02398  filterpy-1.4.5.zip