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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 while its dependencies are.
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!
This numpy3 SlackBuild creates bindings for python3 and can be installed
without conflict alongside the standard numpy SlackBuild.
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