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| -rw-r--r-- | var/spack/repos/builtin/packages/py-numexpr3/package.py | 48 |
1 files changed, 48 insertions, 0 deletions
diff --git a/var/spack/repos/builtin/packages/py-numexpr3/package.py b/var/spack/repos/builtin/packages/py-numexpr3/package.py new file mode 100644 index 0000000000..3b27238ef1 --- /dev/null +++ b/var/spack/repos/builtin/packages/py-numexpr3/package.py @@ -0,0 +1,48 @@ +############################################################################## +# Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC. +# Produced at the Lawrence Livermore National Laboratory. +# +# This file is part of Spack. +# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. +# LLNL-CODE-647188 +# +# For details, see https://github.com/spack/spack +# Please also see the NOTICE and LICENSE files for our notice and the LGPL. +# +# This program is free software; you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License (as +# published by the Free Software Foundation) version 2.1, February 1999. +# +# This program is distributed in the hope that it will be useful, but +# WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and +# conditions of the GNU Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this program; if not, write to the Free Software +# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA +############################################################################## +from spack import * + + +class PyNumexpr3(PythonPackage): + """Numexpr3 is a fast numerical expression evaluator for NumPy. With it, + expressions that operate on arrays (like "3*a+4*b") are accelerated and + use less memory than doing the same calculation in Python. + In addition, its multi-threaded capabilities can make use of all your + cores, which may accelerate computations, most specially if they are not + memory-bounded (e.g. those using transcendental functions). + Compared to NumExpr 2.6, functions have been re-written in a fashion such + that gcc can auto-vectorize them with SIMD instruction sets such as + SSE2 or AVX2, if your processor supports them. Use of a newer version of + gcc such as 5.4 is strongly recommended.""" + homepage = "https://github.com/pydata/numexpr/tree/numexpr-3.0" + url = "https://pypi.io/packages/source/n/numexpr3/numexpr3-3.0.1a1.tar.gz" + + version('3.0.1.a1', '9fa8dc59b149aa1956fc755f982a78ad') + # TODO: Add CMake build system for better control of passing flags related + # to CPU ISA. + + depends_on('python@2.6:2.8,3.3:', type=('build', 'run')) + depends_on('py-numpy@1.7:', type=('build', 'run')) + depends_on('py-setuptools@18.2:', type='build') |