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%global _empty_manifest_terminate_build 0
Name: python-spyking-circus
Version: 1.1.0
Release: 1
Summary: Fast spike sorting by template matching
License: License :: OSI Approved :: CeCILL-2.1
URL: http://spyking-circus.rtfd.org
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a9/cf/c16b5eedf2710972f29c6e561289e5774a010bd65fb89355922358201ab2/spyking-circus-1.1.0.tar.gz
BuildArch: noarch
Requires: python3-mpi4py
Requires: python3-numpy
Requires: python3-cython
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-h5py
Requires: python3-colorama
Requires: python3-psutil
Requires: python3-tqdm
Requires: python3-blosc
Requires: python3-statsmodels
Requires: python3-setuptools
Requires: python3-scikit-learn
%description
*A fast and scalable solution for spike sorting of large-scale extracellular recordings*
SpyKING CIRCUS is a python code to allow fast spike sorting on multi channel recordings.
A publication on the algorithm can be found at https://elifesciences.org/articles/34518
It has been tested on datasets coming from *in vitro* retina
with 252 electrodes MEA, from *in vivo* hippocampus with tetrodes, *in vivo* and *in vitro* cortex
data with 30 and up to 4225 channels, with good results. Synthetic tests on these data show
that cells firing at more than 0.5Hz can be detected, and their spikes recovered with error
rates at around 1%, even resolving overlapping spikes and synchronous firing. It seems to
be compatible with optogenetic stimulation, based on experimental data obtained in the retina.
SpyKING CIRCUS is currently still under development. Please do not hesitate to report issues with the issue tracker
* Documentation can be found at http://spyking-circus.rtfd.org
* A Google group can be found at http://groups.google.com/forum/#!forum/spyking-circus-users
* A bug tracker can be found at https://github.com/spyking-circus/spyking-circus/issues
* Open source ground-truth datasets used in the paper https://zenodo.org/record/1205233#.WrTFtXXwaV4
%package -n python3-spyking-circus
Summary: Fast spike sorting by template matching
Provides: python-spyking-circus
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-spyking-circus
*A fast and scalable solution for spike sorting of large-scale extracellular recordings*
SpyKING CIRCUS is a python code to allow fast spike sorting on multi channel recordings.
A publication on the algorithm can be found at https://elifesciences.org/articles/34518
It has been tested on datasets coming from *in vitro* retina
with 252 electrodes MEA, from *in vivo* hippocampus with tetrodes, *in vivo* and *in vitro* cortex
data with 30 and up to 4225 channels, with good results. Synthetic tests on these data show
that cells firing at more than 0.5Hz can be detected, and their spikes recovered with error
rates at around 1%, even resolving overlapping spikes and synchronous firing. It seems to
be compatible with optogenetic stimulation, based on experimental data obtained in the retina.
SpyKING CIRCUS is currently still under development. Please do not hesitate to report issues with the issue tracker
* Documentation can be found at http://spyking-circus.rtfd.org
* A Google group can be found at http://groups.google.com/forum/#!forum/spyking-circus-users
* A bug tracker can be found at https://github.com/spyking-circus/spyking-circus/issues
* Open source ground-truth datasets used in the paper https://zenodo.org/record/1205233#.WrTFtXXwaV4
%package help
Summary: Development documents and examples for spyking-circus
Provides: python3-spyking-circus-doc
%description help
*A fast and scalable solution for spike sorting of large-scale extracellular recordings*
SpyKING CIRCUS is a python code to allow fast spike sorting on multi channel recordings.
A publication on the algorithm can be found at https://elifesciences.org/articles/34518
It has been tested on datasets coming from *in vitro* retina
with 252 electrodes MEA, from *in vivo* hippocampus with tetrodes, *in vivo* and *in vitro* cortex
data with 30 and up to 4225 channels, with good results. Synthetic tests on these data show
that cells firing at more than 0.5Hz can be detected, and their spikes recovered with error
rates at around 1%, even resolving overlapping spikes and synchronous firing. It seems to
be compatible with optogenetic stimulation, based on experimental data obtained in the retina.
SpyKING CIRCUS is currently still under development. Please do not hesitate to report issues with the issue tracker
* Documentation can be found at http://spyking-circus.rtfd.org
* A Google group can be found at http://groups.google.com/forum/#!forum/spyking-circus-users
* A bug tracker can be found at https://github.com/spyking-circus/spyking-circus/issues
* Open source ground-truth datasets used in the paper https://zenodo.org/record/1205233#.WrTFtXXwaV4
%prep
%autosetup -n spyking-circus-1.1.0
%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-spyking-circus -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1
- Package Spec generated
|
