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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. Nevertheless, note that presently scikit-umfpack
is not available on SlackBuilds.org.
NOTE: If you use this SlackBuild, numpy will run with the python version
provided by Slackware Linux, which is presently 2.7.xx. If you'd like to
use python 3.x then you have to install the numpy3 SlackBuild.
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.
THUS: This SlackBuild conflicts with the numpy-legacy SlackBuild
which installs versions < 1.9.0!
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