%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 - 0.4.2-1 - Package Spec generated