目錄樹:
e161958bab
11.0
12.0
12.1
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14.0
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bedlam
bedlam-name-fixes
dive
erik
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python-renames
rworkman
urchlay
user/aclemons/updates
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user/anddt/development/glm
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user/anddt/graphics/maim
user/anddt/ham/chirp
user/anddt/ham/hamlib
user/anddt/libraries/girara
user/anddt/libraries/goffice
user/anddt/libraries/gtksourceview4
user/anddt/libraries/imlib2
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user/anddt/misc/discount
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user/anddt/network/putty
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user/anddt/network/sshguard
user/anddt/network/strongswan
user/anddt/network/umurmur
user/anddt/network/yt-dlp
user/anddt/network/znc
user/anddt/office/gnumeric
user/anddt/office/sigil
user/anddt/system/FontAwesome
user/anddt/system/conky
user/anddt/system/fakeroot
user/anddt/system/hplip-plugin
user/anddt/system/postgresql
user/anddt/system/testdisk
user/anddt/system/worker
user/anddt/system/xen
user/anddt/system/zzzfm
user/belka/updates
user/chris.willing/updates
user/dslackw/updates
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user/ponce/updates
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willysr
wxWidgets-wip
GPG-KEY
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Heinz Wiesinger
63daf9f79a
All: Support $PRINT_PACKAGE_NAME env var
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3 年之前 | |
|---|---|---|
| .. | ||
| README | 8f71e0e0b1 development/numpy: Fix README. | 4 年之前 |
| README.ATLAS | b87bb0c167 development/numpy: Updated for version 1.11.1. | 8 年之前 |
| numpy.SlackBuild | 63daf9f79a All: Support $PRINT_PACKAGE_NAME env var | 3 年之前 |
| numpy.info | 9db42ae9a6 development/numpy: Fixed homepage. | 6 年之前 |
| slack-desc | 9db42ae9a6 development/numpy: Fixed homepage. | 6 年之前 |
README
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!
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!