<metapackage xmlns:os="http://opensuse.org/Standards/One_Click_Install" xmlns="http://opensuse.org/Standards/One_Click_Install">
  <group>
    <name>ohpc-python-libs-gnu</name>
    <summary>ohpc-python-libs-gnu</summary>
    <description>OpenHPC python related library builds for use with GNU compiler toolchain</description>
    <repositories>
      <repository recommended="true">
        <name>OpenHPC:1.2</name>
        <summary>1.2 - Base (November 12, 2016)</summary>
        <description></description>
        <url>http://build.openhpc.community/OpenHPC:/1.2/SLE_12_SP1/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE.org:SUSE:SLE-12-SP1:GA</name>
        <url>http://build.openhpc.community/openSUSE.org:/SUSE:/SLE-12-SP1:/GA/standard/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE.org:SUSE:SLE-12:Update</name>
        <url>http://build.openhpc.community/openSUSE.org:/SUSE:/SLE-12:/Update/standard/</url>
      </repository>
      <repository recommended="false">
        <name>openSUSE.org:SUSE:SLE-12:GA</name>
        <url>http://build.openhpc.community/openSUSE.org:/SUSE:/SLE-12:/GA/standard/</url>
      </repository>
    </repositories>
    <software>
      <item>
        <name>python-numpy-gnu-ohpc</name>
        <summary>NumPy array processing for numbers, strings, records and objects</summary>
        <description>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.</description>
      </item>
      <item>
        <name>python-scipy-gnu-mpich-ohpc</name>
        <summary>Scientific Tools for Python</summary>
        <description>Scipy is open-source software for mathematics, science, and
engineering. The core library is NumPy which provides convenient and
fast N-dimensional array manipulation. The SciPy library is built to
work with NumPy arrays, and provides many user-friendly and efficient
numerical routines such as routines for numerical integration and
optimization. Together, they run on all popular operating systems, are
quick to install, and are free of charge. NumPy and SciPy are easy to
use, but powerful enough to be depended upon by some of the world's
leading scientists and engineers.</description>
      </item>
      <item>
        <name>python-scipy-gnu-mvapich2-ohpc</name>
        <summary>Scientific Tools for Python</summary>
        <description>Scipy is open-source software for mathematics, science, and
engineering. The core library is NumPy which provides convenient and
fast N-dimensional array manipulation. The SciPy library is built to
work with NumPy arrays, and provides many user-friendly and efficient
numerical routines such as routines for numerical integration and
optimization. Together, they run on all popular operating systems, are
quick to install, and are free of charge. NumPy and SciPy are easy to
use, but powerful enough to be depended upon by some of the world's
leading scientists and engineers.</description>
      </item>
      <item>
        <name>python-scipy-gnu-openmpi-ohpc</name>
        <summary>Scientific Tools for Python</summary>
        <description>Scipy is open-source software for mathematics, science, and
engineering. The core library is NumPy which provides convenient and
fast N-dimensional array manipulation. The SciPy library is built to
work with NumPy arrays, and provides many user-friendly and efficient
numerical routines such as routines for numerical integration and
optimization. Together, they run on all popular operating systems, are
quick to install, and are free of charge. NumPy and SciPy are easy to
use, but powerful enough to be depended upon by some of the world's
leading scientists and engineers.</description>
      </item>
    </software>
  </group>
</metapackage>
