# # spec file for package python-numpy-doc # # Copyright (c) 2016 SUSE LINUX Products GmbH, Nuernberg, Germany. # # All modifications and additions to the file contributed by third parties # remain the property of their copyright owners, unless otherwise agreed # upon. The license for this file, and modifications and additions to the # file, is the same license as for the pristine package itself (unless the # license for the pristine package is not an Open Source License, in which # case the license is the MIT License). An "Open Source License" is a # license that conforms to the Open Source Definition (Version 1.9) # published by the Open Source Initiative. # Please submit bugfixes or comments via http://bugs.opensuse.org/ # %define modname numpy Name: python-numpy-doc %define docname numpydoc Version: 1.10.4 Release: 0 %define docvers 0.4 Url: http://www.numpy.org/ Summary: Documentation for python-numpy License: BSD-3-Clause Group: Development/Libraries/Python Source: https://pypi.python.org/packages/source/n/numpy/numpy-%{version}.tar.gz # PATCH-FIX-OPENSUSE numpy-buildfix.patch -- openSUSE-specific build fixes Patch0: numpy-buildfix.patch # PATCH-FIX-OPENSUSE numpy-1.9.0-remove-__declspec.patch -- fix for spurious compiler warnings that cause build failure Patch1: numpy-1.9.0-remove-__declspec.patch BuildRoot: %{_tmppath}/%{name}-%{version}-build BuildRequires: blas-devel BuildRequires: lapack-devel BuildRequires: python-Sphinx BuildRequires: python-devel BuildRequires: python-matplotlib BuildRequires: python-numpy-devel = %{version} BuildRequires: python-numpydoc BuildRequires: zip # LaTeX requirements, not available on SLES %if 0%{?suse_version} > 1110 && 0%{?suse_version} != 1315 BuildRequires: python-Sphinx-latex BuildRequires: tex(a4wide.sty) BuildRequires: tex(abstract.sty) BuildRequires: tex(amsmath.sty) BuildRequires: tex(epsfig.sty) BuildRequires: tex(expdlist.sty) BuildRequires: tex(verbatim.sty) BuildRequires: tex(xspace.sty) BuildRequires: tex(article.cls) %endif %if 0%{?suse_version} BuildRequires: fdupes BuildRequires: gcc-fortran %if 0%{?suse_version} <= 1110 %{!?python_sitelib: %global python_sitelib %(python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())")} %else BuildArch: noarch %py_requires %endif %else BuildRequires: gcc-gfortran %endif %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. This package provides the documentation for NumPy %package html Summary: HTML documentation for python-numpy Group: Development/Libraries/Python Recommends: python-numpy = %{version} %description html 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. This package provides the HTML documentation for NumPy %package pdf Summary: PDF documentation for python-numpy Group: Development/Libraries/Python Recommends: python-numpy = %{version} %description pdf 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. This package provides the PDF documentation for NumPy %prep %setup -q -n numpy-%{version} %patch0 -p1 %patch1 -p1 %build # make the documentation pushd doc make html PYVER=%{py_ver} popd # LaTeX building currently broken # %if 0%{?suse_version} > 1110 && 0%{?suse_version} != 1315 # pushd doc # make latex PYVER=%{py_ver} # make -C build/latex all-pdf PYVER=%{py_ver} # popd # %endif %install # install the documentation mkdir -p %{buildroot}%{_docdir}/python-numpy/ pushd doc/build cp -r html %{buildroot}%{_docdir}/python-numpy/ popd %fdupes %{buildroot}%{_docdir}/python-numpy/html/ # LaTeX building currently broken # %if 0%{?suse_version} > 1110 && 0%{?suse_version} != 1315 # pushd doc/build # cp -r pdf %{buildroot}%{_docdir}/python-numpy/ # popd # %fdupes %{buildroot}%{_docdir}/python-numpy/pdf/ # %endif %files html %defattr(-,root,root) %{_docdir}/python-numpy/html/ # LaTeX building currently broken # %if 0%{?suse_version} > 1110 && 0%{?suse_version} != 1315 # %files pdf # %defattr(-,root,root) # %{_docdir}/python-numpy/pdf/ # %endif %changelog