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%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 <Python_Bot@openeuler.org> - 0.11.0-1
- Package Spec generated
|
