wip/py-tinyarray: import py37-tinyarray-1.2.2
Tinyarrays are similar to NumPy arrays, but optimized for small sizes. Common operations on very small arrays are to 3-7 times faster than with NumPy (with NumPy 1.6 it used to be up to 35 times), and 3 times less memory is used to store them. Tinyarrays are useful if you need many small arrays of numbers, and cannot combine them into a few large ones. (The resulting code is still much slower than C, but it may now be fast enough.)
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Tinyarrays are similar to NumPy arrays, but optimized for small sizes. Common
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operations on very small arrays are to 3-7 times faster than with NumPy (with
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NumPy 1.6 it used to be up to 35 times), and 3 times less memory is used to
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store them. Tinyarrays are useful if you need many small arrays of numbers,
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and cannot combine them into a few large ones. (The resulting code is still
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much slower than C, but it may now be fast enough.)
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# $NetBSD$
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DISTNAME= tinyarray-1.2.2
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PKGNAME= ${PYPKGPREFIX}-${DISTNAME}
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CATEGORIES= math python
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MASTER_SITES= https://files.pythonhosted.org/packages/5e/3c/f212cbf3fc9b725913b5f63e1918ed3a3c9885e44e451e7a7200b02e987f/
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MAINTAINER= jihbed.research@gmail.com
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HOMEPAGE= https://gitlab.kwant-project.org/kwant/tinyarray
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COMMENT= Arrays of numbers for Python, optimized for small sizes
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LICENSE= 2-clause-bsd
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USE_LANGUAGES= c c++
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.include "../../math/py-numpy/buildlink3.mk"
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.include "../../lang/python/egg.mk"
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.include "../../mk/bsd.pkg.mk"
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@comment $NetBSD$
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${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
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${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
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${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
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${PYSITELIB}/${EGG_INFODIR}/top_level.txt
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${PYSITELIB}/tinyarray.so
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$NetBSD$
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SHA1 (tinyarray-1.2.2.tar.gz) = 42359c2798757339dd490831be596e2a77a00127
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RMD160 (tinyarray-1.2.2.tar.gz) = 87f14a51609fbd27595fffaa19bd76c70916c804
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SHA512 (tinyarray-1.2.2.tar.gz) = 1c9672bf088f81ea8551068c7ede49ee70e41bfea33ea4c154817195ec62802c9937d7b995d3d5d82a1a4d9af8de536b830008fb7775a124100841b5209c5586
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Size (tinyarray-1.2.2.tar.gz) = 35801 bytes
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