%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 * Mon Apr 10 2023 Python_Bot - 1.1.0-1 - Package Spec generated