62 lines
3.3 KiB
Python
62 lines
3.3 KiB
Python
##############################################################################
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# Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC.
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# Produced at the Lawrence Livermore National Laboratory.
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#
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# This file is part of Spack.
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# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
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# LLNL-CODE-647188
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#
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# For details, see https://github.com/spack/spack
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# Please also see the NOTICE and LICENSE files for our notice and the LGPL.
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#
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU Lesser General Public License (as
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# published by the Free Software Foundation) version 2.1, February 1999.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and
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# conditions of the GNU Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with this program; if not, write to the Free Software
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# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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##############################################################################
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from spack import *
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class RDelayedarray(RPackage):
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"""Wrapping an array-like object (typically an on-disk object) in a
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DelayedArray object allows one to perform common array operations on it
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without loading the object in memory. In order to reduce memory usage
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and optimize performance, operations on the object are either delayed
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or executed using a block processing mechanism. Note that this also
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works on in-memory array-like objects like DataFrame objects (typically
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with Rle columns), Matrix objects, and ordinary arrays and data frames.
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Wrapping an array-like object (typically an on-disk object) in a
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DelayedArray object allows one to perform common array operations on it
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without loading the object in memory. In order to reduce memory usage
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and optimize performance, operations on the object are either delayed
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or executed using a block processing mechanism. Note that this also
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works on in-memory array-like objects like DataFrame objects (typically
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with Rle columns), Matrix objects, and ordinary arrays and data
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frames."""
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homepage = "https://bioconductor.org/packages/DelayedArray/"
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git = "https://git.bioconductor.org/packages/DelayedArray.git"
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version('0.6.5', commit='7d1cb6477cb024c38bf1ee0c9155e010249ed94e')
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version('0.4.1', commit='ffe932ef8c255614340e4856fc6e0b44128a27a1')
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version('0.2.7', commit='909c2ce1665ebae2543172ead50abbe10bd42bc4')
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depends_on('r-biocgenerics', type=('build', 'run'))
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depends_on('r-biocgenerics@0.25.1:', when='@0.6.5', type=('build', 'run'))
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depends_on('r-s4vectors@0.14.3:', when='@0.2.7', type=('build', 'run'))
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depends_on('r-s4vectors@0.15.3:', when='@0.4.1', type=('build', 'run'))
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depends_on('r-s4vectors@0.17.43:', when='@0.6.5', type=('build', 'run'))
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depends_on('r-iranges', type=('build', 'run'))
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depends_on('r-iranges@2.11.17:', when='@0.4.1:', type=('build', 'run'))
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depends_on('r-matrixstats', type=('build', 'run'))
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depends_on('r@3.4.0:3.4.9', when='@0.2.7', type=('build', 'run'))
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depends_on('r@3.5.0:3.5.9', when='@0.6.5', type=('build', 'run'))
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