NumPy is a general-purpose array-processing package designed to
efficiently manipulate large multi-dimensional arrays of arbitrary
records without sacrificing too much speed for small multi-dimensional
arrays.  NumPy is built on the Numeric code base and adds features
introduced by numarray as well as an extended C-API and the ability to
create arrays of arbitrary type which also makes NumPy suitable for
interfacing with general-purpose data-base applications.

There are also basic facilities for discrete fourier transform, basic
linear algebra and random number generation.

If you need to build numpy for debugging, set DEBUG=y. If you use
software which is having problems with numpy's new relaxed strides
checking, set NPY_RSC=0.

It is highly recommended to install libraries implementing BLAS and
LAPACK before installing numpy. You may choose between:
   a) BLAS and LAPACK (reference but unoptimized and thus slow)
   b) OpenBLAS (optimized, provides LAPACK too)
   c) ATLAS and LAPACK (optimized), good to read README.ATLAS
All these are available on SlackBuilds.org.

If you want to use the UMFPACK library instead of SuperLU to solve
unsymmetric sparse linear systems, then run this Slackbuild with
NO_UMFPACK set to "no" and then install scikit-umfpack on top of
scipy. In this context, UMFPACK is an optional dependency for
numpy.

IMPORTANT: The version installed by this SlackBuild does NOT include the
           oldnumeric and numarray compatibility modules since
           starting with version 1.9.0 these modules got removed by
           the numpy developers.  If you need these compatibility
           modules please consider the numpy-legacy SlackBuild which
           is available for python2 only and does not conflict with this
           installation of numpy.

This python3-numpy SlackBuild creates bindings for python3 and can be
installed without conflict alongside the python2-numpy SlackBuild.