update the specfile for python3 build [release 1.10.4-2mamba;Thu Mar 17 2016]
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# python-numpy
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NumPy is the fundamental package needed for scientific computing with Python. It contains among other things:
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NumPy is the fundamental package needed for scientific computing with Python.
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It contains among other things:
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* a powerful N-dimensional array object
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* sophisticated (broadcasting) functions
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* tools for integrating C/C++ and Fortran code
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* useful linear algebra, Fourier transform, and random number capabilities.
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Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
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Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.
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Arbitrary data-types can be defined.
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This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
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@ -1,7 +1,7 @@
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Name: python-numpy
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Version: 1.10.4
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Release: 1mamba
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Summary: NumPy: array processing for numbers, strings, records, and objects
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Release: 2mamba
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Summary: NumPy: Scientific computing with Python NumPy
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Group: System/Libraries/Python
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Vendor: openmamba
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Distribution: openmamba
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@ -11,32 +11,32 @@ Source: http://downloads.sourceforge.net/project/numpy/NumPy/%{version}/n
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License: BSD
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## AUTOBUILDREQ-BEGIN
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BuildRequires: glibc-devel
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BuildRequires: libblas-devel
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%ifnarch arm
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BuildRequires: libquadmath-devel
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%endif
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BuildRequires: liblapack-devel
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BuildRequires: libpython-devel
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## AUTOBUILDREQ-END
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BuildRequires: gcc-fortran
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%py_requires
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Requires: python >= %python_version
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BuildRoot: %{_tmppath}/%{name}-%{version}-root
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%description
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NumPy is the fundamental package needed for scientific computing with Python. It contains among other things:
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NumPy is the fundamental package needed for scientific computing with Python.
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It contains among other things:
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* a powerful N-dimensional array object
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* sophisticated (broadcasting) functions
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* tools for integrating C/C++ and Fortran code
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* useful linear algebra, Fourier transform, and random number capabilities.
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Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
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Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.
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Arbitrary data-types can be defined.
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This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
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%if "%?with_pyver"
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%define pyver %{with_pyver}
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%define __python %(rpm --eval %{__python%{with_pyver}})
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%define python_sitearch %(rpm --eval %{python%{with_pyver}_sitearch})
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%define python_sitelib %(rpm --eval %{python%{with_pyver}_sitelib})
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%define python_inc %(rpm --eval %{python%{with_pyver}_inc})
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%define python_version %(rpm --eval %{python%{with_pyver}_version})
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%define python_provides %(rpm --eval %{python%{with_pyver}_provides})
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%define python_requires %(rpm --eval %{python%{with_pyver}_requires})
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%define py_requires %(rpm --eval %{py%{with_pyver}_requires})
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%if 0%{?with_pyver}
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%pyver_package
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%endif
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%package py%{pyver}
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Group: System/Libraries/Python
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@ -44,15 +44,16 @@ Summary: NumPy: array processing for numbers, strings, records, and object
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%py_requires
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%description py%{pyver}
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NumPy is the fundamental package needed for scientific computing with Python. It contains among other things:
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NumPy is the fundamental package needed for scientific computing with Python.
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It contains among other things:
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* a powerful N-dimensional array object
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* sophisticated (broadcasting) functions
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* tools for integrating C/C++ and Fortran code
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* useful linear algebra, Fourier transform, and random number capabilities.
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Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
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%endif
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Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.
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Arbitrary data-types can be defined.
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This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
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%prep
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%setup -q -n numpy-%{version}
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@ -64,24 +65,26 @@ CFLAGS="%{optflags}" %{__python} setup.py build
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[ "%{buildroot}" != / ] && rm -rf "%{buildroot}"
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%{__python} setup.py install \
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--root="%{buildroot}" \
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--install-headers=%{_includedir}/python \
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--install-headers=%{python_inc} \
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--install-lib=%{python_sitearch}
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install -D -p -m 0644 doc/f2py/f2py.1 %{buildroot}%{_mandir}/man1/f2py%{pyver}.1
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%clean
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[ "%{buildroot}" != / ] && rm -rf "%{buildroot}"
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%if "%?with_pyver"
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%files py%{pyver}
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%else
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%files
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%endif
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%files %{?pyappend}
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%defattr(-,root,root)
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%{_bindir}/f2py*
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%{_mandir}/man1/f2py*
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%dir %{python_sitearch}/numpy
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%{python_sitearch}/numpy/*
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%{python_sitearch}/numpy-%{version}-py*.egg-info
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%changelog
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* Thu Mar 17 2016 Davide Madrisan <davide.madrisan@gmail.com> 1.10.4-2mamba
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- update the specfile for python3 build
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* Thu Jan 14 2016 Automatic Build System <autodist@mambasoft.it> 1.10.4-1mamba
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- automatic version update by autodist
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