old_contents: # Copyright 2013-2019 Lawrence Livermore National Security, LLC and other
  # Spack Project Developers. See the top-level COPYRIGHT file for details.
  #
  # SPDX-License-Identifier: (Apache-2.0 OR MIT)
  
  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', sha256sum='de06f1b4206704b5bc19ea09b5c94350b97c211c26bc866f275252a8461b87e6')
      # 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')
  
new_contents: # Copyright 2013-2019 Lawrence Livermore National Security, LLC and other
  # Spack Project Developers. See the top-level COPYRIGHT file for details.
  #
  # SPDX-License-Identifier: (Apache-2.0 OR MIT)
  
  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('3.0.1a1', sha256='de06f1b4206704b5bc19ea09b5c94350b97c211c26bc866f275252a8461b87e6')
      # 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')
  
subject: fix version (#13781)