Add numexpr 3 (#8107)

* 	new file:   package.py

* corrected using pep8online.com

* Update package.py

* correct typo

* as requested

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')