%global _empty_manifest_terminate_build 0 Name: python-PyNN Version: 0.11.0 Release: 1 Summary: A Python package for simulator-independent specification of neuronal network models License: CeCILL http://www.cecill.info URL: https://pypi.org/project/PyNN/ Source0: https://mirrors.aliyun.com/pypi/web/packages/5e/55/4a904b95ecb1c50e716e0ba48e0918daf522c9c6bac5b75e0c08c1030217/PyNN-0.11.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-lazyarray Requires: python3-neo Requires: python3-quantities Requires: python3-mpi4py Requires: python3-brian2 Requires: python3-sphinx Requires: python3-matplotlib Requires: python3-scipy Requires: python3-neuron Requires: python3-matplotlib Requires: python3-scipy Requires: python3-h5py Requires: python3-pytest Requires: python3-wheel Requires: python3-mpi4py Requires: python3-scipy Requires: python3-matplotlib Requires: python3-Cheetah3 Requires: python3-h5py %description PyNN (pronounced '*pine*') is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian 2) and on a number of neuromorphic hardware systems. The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way. Even if you don't wish to run simulations on multiple simulators, you may benefit from writing your simulation code using PyNN's powerful, high-level interface. In this case, you can use any neuron or synapse model supported by your simulator, and are not restricted to the standard models. - Home page: http://neuralensemble.org/PyNN/ - Documentation: http://neuralensemble.org/docs/PyNN/ - Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble - Bug reports: https://github.com/NeuralEnsemble/PyNN/issues %package -n python3-PyNN Summary: A Python package for simulator-independent specification of neuronal network models Provides: python-PyNN BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-PyNN PyNN (pronounced '*pine*') is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian 2) and on a number of neuromorphic hardware systems. The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way. Even if you don't wish to run simulations on multiple simulators, you may benefit from writing your simulation code using PyNN's powerful, high-level interface. In this case, you can use any neuron or synapse model supported by your simulator, and are not restricted to the standard models. - Home page: http://neuralensemble.org/PyNN/ - Documentation: http://neuralensemble.org/docs/PyNN/ - Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble - Bug reports: https://github.com/NeuralEnsemble/PyNN/issues %package help Summary: Development documents and examples for PyNN Provides: python3-PyNN-doc %description help PyNN (pronounced '*pine*') is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian 2) and on a number of neuromorphic hardware systems. The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way. Even if you don't wish to run simulations on multiple simulators, you may benefit from writing your simulation code using PyNN's powerful, high-level interface. In this case, you can use any neuron or synapse model supported by your simulator, and are not restricted to the standard models. - Home page: http://neuralensemble.org/PyNN/ - Documentation: http://neuralensemble.org/docs/PyNN/ - Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble - Bug reports: https://github.com/NeuralEnsemble/PyNN/issues %prep %autosetup -n PyNN-0.11.0 %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-PyNN -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 0.11.0-1 - Package Spec generated