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61 lines
2.3 KiB
Tcl
61 lines
2.3 KiB
Tcl
# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4
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PortSystem 1.0
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PortGroup python 1.0
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name py-dask
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version 2021.4.0
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revision 0
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categories-append devel
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platforms darwin
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license BSD
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supported_archs noarch
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python.versions 27 35 36 37 38 39
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maintainers {stromnov @stromnov} openmaintainer
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description Minimal task scheduling abstraction.
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long_description Dask provides multi-core execution on larger-than-memory \
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datasets using blocked algorithms and task scheduling. \
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It maps high-level NumPy, Pandas, and list operations on \
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large datasets on to many operations on small in-memory \
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datasets. It then executes these graphs in parallel on a \
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single machine. Dask lets us use traditional NumPy, \
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Pandas, and list programming while operating on \
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inconveniently large data in a small amount of space.
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homepage https://github.com/dask/dask/
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checksums rmd160 3a22dcb238caedb768dc9084fc267df62e1f260d \
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sha256 4f7e52ddedbffdf1cfd5525f4f901689e45f8769e059e7b7ecb332f8496f7f34 \
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size 3689501
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if {${name} ne ${subport}} {
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depends_build-append \
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port:py${python.version}-setuptools
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if {${python.version} eq 27} {
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version 1.2.2
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revision 0
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epoch 1
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distname ${python.rootname}-${version}
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checksums rmd160 94cce1974d6ad2830a6fd3d745cbe5d4100ff5ba \
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sha256 5e7876bae2a01b355d1969b73aeafa23310febd8c353163910b73e93dc7e492c \
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size 2404729
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}
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if {${python.version} eq 35} {
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version 2.6.0
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revision 0
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epoch 1
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distname ${python.rootname}-${version}
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checksums rmd160 29c49c566dfbb45c0908dd40d969538390ad231a \
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sha256 81c7891f0d2e7ac03d1f7fabf1f639360a1db52c03a7155ba9b08e9ee6280f2b \
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size 2479716
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}
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livecheck.type none
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}
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