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|
%global _empty_manifest_terminate_build 0
Name: python-pyfastnoisesimd
Version: 0.4.2
Release: 1
Summary: Python Fast Noise with SIMD
License: https://opensource.org/licenses/BSD-3-Clause
URL: http://github.com/robbmcleod/pyfastnoisesimd
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/66/a9/7f3e010c62593b02fcc8473df8b0487e134c4ecd4b135bc2a6ab6e003a2f/pyfastnoisesimd-0.4.2.tar.gz
Requires: python3-numpy
Requires: python3-setuptools
%description
PyFastNoiseSIMD is a wrapper around Jordan Peck's synthetic noise library
https://github.com/Auburns/FastNoise-SIMD which has been
accelerated with SIMD instruction sets. It may be installed via pip:
pip install pyfastnoisesimd
Parallelism is further enhanced by the use of ``concurrent.futures`` to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
Here is a simple example to generate Perlin-style noise on a 3D rectilinear
grid::
import pyfastnoisesimd as fns
import numpy as np
shape = [512, 512, 512]
seed = np.random.randint(2**31)
N_threads = 4
perlin = fns.Noise(seed=seed, numWorkers=N_threads)
perlin.frequency = 0.02
perlin.noiseType = fns.NoiseType.Perlin
perlin.fractal.octaves = 4
perlin.fractal.lacunarity = 2.1
perlin.fractal.gain = 0.45
perlin.perturb.perturbType = fns.PerturbType.NoPerturb
result = perlin.genAsGrid(shape)
where ``result`` is a 3D ``numpy.ndarray`` of dtype ``'float32'``. Alternatively,
the user can provide coordinates, which is helpful for tasks such as
custom bump-mapping a tessellated surface, via ``Noise.getFromCoords(coords)``.
More extensive examples are found in the ``examples`` folder on the Github repository.
Parallelism is further enhanced by the use of ``concurrent.futures`` to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
%package -n python3-pyfastnoisesimd
Summary: Python Fast Noise with SIMD
Provides: python-pyfastnoisesimd
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-pyfastnoisesimd
PyFastNoiseSIMD is a wrapper around Jordan Peck's synthetic noise library
https://github.com/Auburns/FastNoise-SIMD which has been
accelerated with SIMD instruction sets. It may be installed via pip:
pip install pyfastnoisesimd
Parallelism is further enhanced by the use of ``concurrent.futures`` to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
Here is a simple example to generate Perlin-style noise on a 3D rectilinear
grid::
import pyfastnoisesimd as fns
import numpy as np
shape = [512, 512, 512]
seed = np.random.randint(2**31)
N_threads = 4
perlin = fns.Noise(seed=seed, numWorkers=N_threads)
perlin.frequency = 0.02
perlin.noiseType = fns.NoiseType.Perlin
perlin.fractal.octaves = 4
perlin.fractal.lacunarity = 2.1
perlin.fractal.gain = 0.45
perlin.perturb.perturbType = fns.PerturbType.NoPerturb
result = perlin.genAsGrid(shape)
where ``result`` is a 3D ``numpy.ndarray`` of dtype ``'float32'``. Alternatively,
the user can provide coordinates, which is helpful for tasks such as
custom bump-mapping a tessellated surface, via ``Noise.getFromCoords(coords)``.
More extensive examples are found in the ``examples`` folder on the Github repository.
Parallelism is further enhanced by the use of ``concurrent.futures`` to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
%package help
Summary: Development documents and examples for pyfastnoisesimd
Provides: python3-pyfastnoisesimd-doc
%description help
PyFastNoiseSIMD is a wrapper around Jordan Peck's synthetic noise library
https://github.com/Auburns/FastNoise-SIMD which has been
accelerated with SIMD instruction sets. It may be installed via pip:
pip install pyfastnoisesimd
Parallelism is further enhanced by the use of ``concurrent.futures`` to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
Here is a simple example to generate Perlin-style noise on a 3D rectilinear
grid::
import pyfastnoisesimd as fns
import numpy as np
shape = [512, 512, 512]
seed = np.random.randint(2**31)
N_threads = 4
perlin = fns.Noise(seed=seed, numWorkers=N_threads)
perlin.frequency = 0.02
perlin.noiseType = fns.NoiseType.Perlin
perlin.fractal.octaves = 4
perlin.fractal.lacunarity = 2.1
perlin.fractal.gain = 0.45
perlin.perturb.perturbType = fns.PerturbType.NoPerturb
result = perlin.genAsGrid(shape)
where ``result`` is a 3D ``numpy.ndarray`` of dtype ``'float32'``. Alternatively,
the user can provide coordinates, which is helpful for tasks such as
custom bump-mapping a tessellated surface, via ``Noise.getFromCoords(coords)``.
More extensive examples are found in the ``examples`` folder on the Github repository.
Parallelism is further enhanced by the use of ``concurrent.futures`` to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
%prep
%autosetup -n pyfastnoisesimd-0.4.2
%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-pyfastnoisesimd -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 07 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.2-1
- Package Spec generated
|
