File python3-scipy.spec of Package python3-scipy

#
# spec file for package python3-scipy
#
# Copyright (c) 2016 SUSE LINUX 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 with_atlas 0

Name:           python3-scipy
Version:        0.17.1
Release:        32.4
Summary:        Scientific Tools for Python
License:        BSD-3-Clause and LGPL-2.0+
Group:          Development/Libraries/Python
Url:            http://www.scipy.org
Source0:        https://files.pythonhosted.org/packages/source/s/scipy/scipy-%{version}.tar.gz
# BuildRequires:  blas-devel
BuildRequires:  fdupes
BuildRequires:  fftw-devel
BuildRequires:  gcc-c++
# BuildRequires:  gcc-fortran
# BuildRequires:  lapack-devel
BuildRequires:  python3-base
BuildRequires:  python3-Cython >= 0.19
BuildRequires:  python3-devel
BuildRequires:  python3-numpy-f2py
# BuildRequires:  python3-numpy-devel >= 1.5.1
# BuildRequires:  suitesparse-devel-static
# BuildRequires:  swig
# %if %{with_atlas} == 1
# BuildRequires:  libatlas3-devel
# %endif
Requires:       python3-numpy >= 1.5.1
BuildRoot:      %{_tmppath}/%{name}-%{version}-build

%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.

%prep
%setup -q -n scipy-%{version}
find . -type f -name "*.py" -exec sed -i "s|#!/usr/bin/env python||" {} \;

cat > site.cfg << EOF
[amd]
library_dirs = %{_libdir}
include_dirs = /usr/include/suitesparse:/usr/include/ufsparse
amd_libs = amd

[umfpack]
library_dirs = %{_libdir}
include_dirs = /usr/include/suitesparse:/usr/include/ufsparse
umfpack_libs = umfpack
EOF

%build
# %if %{with_atlas} == 1
# export ATLAS=%{_libdir}/atlas
# %endif
CFLAGS="%{optflags} -fno-strict-aliasing" \
FFTW=%{_libdir} \
BLAS= \
LAPACK= \
python3 setup.py config_fc --fcompiler=f2py --noarch build

%install
python3 setup.py install --prefix=%{_prefix} --root=%{buildroot}
find %{buildroot}%{python3_sitearch}/scipy -type d -name tests | xargs rm -rf # Don't ship tests
# Don't ship weave examples, they're marked as documentation:
find %{buildroot}%{python3_sitearch}/scipy/weave -type d -name examples | xargs rm -rf
%fdupes %{buildroot}%{python3_sitearch}
# fix executability issue
chmod +x %{buildroot}%{python3_sitearch}/scipy/io/arff/arffread.py
chmod +x %{buildroot}%{python3_sitearch}/scipy/special/spfun_stats.py
chmod +x %{buildroot}%{python3_sitearch}/scipy/special/generate_ufuncs.py

%files
%defattr(-,root,root,-)
%doc LICENSE.txt
%{python3_sitearch}/scipy/
%{python3_sitearch}/scipy-%{version}-py*.egg-info

%changelog
* Sat May 14 2016 arun@gmx.de
- update to version 0.17.1:
  * #5817: BUG: skew, kurtosis return np.nan instead of "propagate"
  * #5850: Test failed with sgelsy
  * #5898: interpolate.interp1d crashes using float128
  * #5953: Massive performance regression in cKDTree.query with L_inf
    distance...
  * #6062: mannwhitneyu breaks backward compatibility in 0.17.0
  * #6134: T test does not handle nans
  * #5902: BUG: interpolate: make interp1d handle np.float128 again
  * #5957: BUG: slow down with p=np.inf in 0.17 cKDTree.query
  * #5970: Actually propagate nans through stats functions with
    nan_policy="propagate"
  * #5971: BUG: linalg: fix lwork check in *gelsy
  * #6074: BUG: special: fixed violation of strict aliasing rules.
  * #6083: BUG: Fix dtype for sum of linear operators
  * #6100: BUG: Fix mannwhitneyu to be backward compatible
  * #6135: Don't pass null pointers to LAPACK, even during workspace
    queries.
  * #6148: stats: fix handling of nan values in T tests and kendalltau
* Sun May  8 2016 arun@gmx.de
- specfile:
  * updated source url to files.pythonhosted.org
* Sun Jan 24 2016 arun@gmx.de
- specfile:
  * update copyright year
- update to version 0.17.0:
  (see http://scipy.github.io/devdocs/release.0.17.0.html for full changelog)
  * Highlights
    + New functions for linear and nonlinear least squares
    optimization with constraints: scipy.optimize.lsq_linear and
    scipy.optimize.least_squares
    + Support for fitting with bounds in scipy.optimize.curve_fit.
    + Significant improvements to scipy.stats, providing many
    functions with better handing of inputs which have NaNs or are
    empty, improved documentation, and consistent behavior between
    scipy.stats and scipy.stats.mstats.
    + Significant performance improvements and new functionality in
    scipy.spatial.cKDTree.
* Sun Oct 25 2015 arun@gmx.de
- update to version 0.16.1:
  * SciPy 0.16.1 is a bug-fix release with no new features compared to
    0.16.0.
* Mon Jul 27 2015 toddrme2178@gmail.com
- Remove Cython subpackage.  The sources are not as cleanly
  separated as the changelog implied.
* Mon Jul 27 2015 toddrme2178@gmail.com
- Update to 0.16.0
  * Highlights of this release include:
  - A Cython API for BLAS/LAPACK in scipy.linalg
  - A new benchmark suite.  It's now straightforward to add new benchmarks,
    and they're routinely included with performance enhancement PRs.
  - Support for the second order sections (SOS) format in scipy.signal.
  * New features
  - Benchmark suite
    + The benchmark suite has switched to using Airspeed Velocity
    for benchmarking.
  - scipy.linalg improvements
    + A full set of Cython wrappers for BLAS and LAPACK has been added in
    the modules scipy.linalg.cython_blas and scipy.linalg.cython_lapack.
    In Cython, these wrappers can now be cimported from their
    corresponding modules and used without linking directly against BLAS
    or LAPACK.
    + The functions scipy.linalg.qr_delete, scipy.linalg.qr_insert and
    scipy.linalg.qr_update for updating QR decompositions were added.
    + The function scipy.linalg.solve_circulant solves a linear system with
    a circulant coefficient matrix.
    + The function scipy.linalg.invpascal computes the inverse of a Pascal matrix.
    + The function scipy.linalg.solve_toeplitz, a Levinson-Durbin Toeplitz solver,
    was added.
    + Added wrapper for potentially useful LAPACK function *lasd4.  It computes
    the square root of the i-th updated eigenvalue of a positive symmetric rank-one
    modification to a positive diagonal matrix. See its LAPACK documentation and
    unit tests for it to get more info.
    + Added two extra wrappers for LAPACK least-square solvers. Namely, they are
  * gelsd and *gelsy.
    + Wrappers for the LAPACK *lange functions, which calculate various matrix
    norms, were added.
    + Wrappers for *gtsv and *ptsv, which solve A*X = B for tri-diagonal
    matrix A, were added.
  - scipy.signal improvements
    + Support for second order sections (SOS) as a format for IIR filters
    was added.  The new functions are:
  * scipy.signal.sosfilt
  * scipy.signal.sosfilt_zi,
  * scipy.signal.sos2tf
  * scipy.signal.sos2zpk
  * scipy.signal.tf2sos
  * scipy.signal.zpk2sos.
    + Additionally, the filter design functions iirdesign, iirfilter, butter,
    cheby1, cheby2, ellip, and bessel can return the filter in the SOS
    format.
    + The function scipy.signal.place_poles, which provides two methods to place
    poles for linear systems, was added.
    + The option to use Gustafsson's method for choosing the initial conditions
    of the forward and backward passes was added to scipy.signal.filtfilt.
    + New classes TransferFunction, StateSpace and ZerosPolesGain were
    added.  These classes are now returned when instantiating scipy.signal.lti.
    Conversion between those classes can be done explicitly now.
    + An exponential (Poisson) window was added as scipy.signal.exponential, and a
    Tukey window was added as scipy.signal.tukey.
    + The function for computing digital filter group delay was added as
    scipy.signal.group_delay.
    + The functionality for spectral analysis and spectral density estimation has
    been significantly improved: scipy.signal.welch became ~8x faster and the
    functions scipy.signal.spectrogram, scipy.signal.coherence and
    scipy.signal.csd (cross-spectral density) were added.
    + scipy.signal.lsim was rewritten - all known issues are fixed, so this
    function can now be used instead of lsim2; lsim is orders of magnitude
    faster than lsim2 in most cases.
  - scipy.sparse improvements
    + The function scipy.sparse.norm, which computes sparse matrix norms, was
    added.
    + The function scipy.sparse.random, which allows to draw random variates from
    an arbitrary distribution, was added.
  - scipy.spatial improvements
    + scipy.spatial.cKDTree has seen a major rewrite, which improved the
    performance of the query method significantly, added support for parallel
    queries, pickling, and options that affect the tree layout.  See pull request
    4374 for more details.
    + The function scipy.spatial.procrustes for Procrustes analysis (statistical
    shape analysis) was added.
  - scipy.stats improvements
    + The Wishart distribution and its inverse have been added, as
    scipy.stats.wishart and scipy.stats.invwishart.
    + The Exponentially Modified Normal distribution has been
    added as scipy.stats.exponnorm.
    + The Generalized Normal distribution has been added as scipy.stats.gennorm.
    + All distributions now contain a random_state property and allow specifying a
    specific numpy.random.RandomState random number generator when generating
    random variates.
    + Many statistical tests and other scipy.stats functions that have multiple
    return values now return namedtuples.  See pull request 4709 for details.
  - scipy.optimize improvements
    + A new derivative-free method DF-SANE has been added to the nonlinear equation
    system solving function scipy.optimize.root.
  * Deprecated features
  - scipy.stats.pdf_fromgamma is deprecated.  This function was undocumented,
    untested and rarely used.  Statsmodels provides equivalent functionality
    with statsmodels.distributions.ExpandedNormal.
  - scipy.stats.fastsort is deprecated.  This function is unnecessary,
    numpy.argsort can be used instead.
  - scipy.stats.signaltonoise and scipy.stats.mstats.signaltonoise are
    deprecated.  These functions did not belong in scipy.stats and are rarely
    used.  See issue #609 for details.
  - scipy.stats.histogram2 is deprecated. This function is unnecessary,
    numpy.histogram2d can be used instead.
  * Backwards incompatible changes
  - The deprecated global optimizer scipy.optimize.anneal was removed.
  - The following deprecated modules have been removed. They had been deprecated
    since Scipy 0.12.0, the functionality should be accessed as scipy.linalg.blas
    and scipy.linalg.lapack.
    + scipy.lib.blas
    + scipy.lib.lapack
    + scipy.linalg.cblas
    + scipy.linalg.fblas
    + scipy.linalg.clapack
    + scipy.linalg.flapack.
  - The deprecated function scipy.special.all_mat has been removed.
  - These deprecated functions have been removed from scipy.stats:
    + scipy.stats.fprob
    + scipy.stats.ksprob
    + scipy.stats.zprob
    + scipy.stats.randwcdf
    + scipy.stats.randwppf
  * Other changes
  - The version numbering for development builds has been updated to comply with PEP 440.
  - Building with python setup.py develop is now supported.
- Move Cython imports to another package
* Sun Jan 25 2015 arun@gmx.de
- specfile: update copyright year
- update to version 0.15.1:
  * #4413: BUG: Tests too strict, f2py doesn't have to overwrite this array
  * #4417: BLD: avoid using NPY_API_VERSION to check not using deprecated...
  * #4418: Restore and deprecate scipy.linalg.calc_work
* Mon Jan 12 2015 toddrme2178@gmail.com
- Update to 0.15.0
  * New features
  * scipy.optimize improvements
  * scipy.optimize.linprog now provides a generic
    linear programming similar to the way scipy.optimize.minimize
    provides a generic interface to nonlinear programming optimizers.
    Currently the only method supported is simplex which provides
    a two-phase, dense-matrix-based simplex algorithm. Callbacks
    functions are supported,allowing the user to monitor the progress
    of the algorithm.
  * The differential_evolution function is available from the scipy.optimize
    module.  Differential Evolution is an algorithm used for finding the global
    minimum of multivariate functions. It is stochastic in nature (does not use
    gradient methods), and can search large areas of candidate space, but often
    requires larger numbers of function evaluations than conventional gradient
    based techniques.
  * scipy.signal improvements
  * The function max_len_seq was added, which computes a Maximum
    Length Sequence (MLS) signal.
  * scipy.integrate improvements
  * The interface between the scipy.integrate module and the QUADPACK library was
    redesigned. It is now possible to use scipy.integrate to integrate
    multivariate ctypes functions, thus avoiding callbacks to Python and providing
    better performance, especially for complex integrand functions.
  * scipy.sparse improvements
  * scipy.sparse.linalg.svds now takes a LinearOperator as its main input.
  * scipy.stats improvements
  * Added a Dirichlet distribution as multivariate distribution.
  * The new function `scipy.stats.median_test` computes Mood's median test.
  * `scipy.stats.describe` returns a namedtuple rather than a tuple, allowing
    users to access results by index or by name.
  * Deprecated features
  * The scipy.weave module is deprecated.  It was the only module never ported
    to Python 3.x, and is not recommended to be used for new code - use Cython
    instead.  In order to support existing code, scipy.weave has been packaged
    separately: https://github.com/scipy/weave.  It is a pure Python package, so
    can easily be installed with pip install weave.
  * scipy.special.bessel_diff_formula is deprecated.  It is a private function,
    and therefore will be removed from the public API in a following release.
  * Backwards incompatible changes
  * scipy.ndimage
  * The functions scipy.ndimage.minimum_positions,
    scipy.ndimage.maximum_positions and scipy.ndimage.extrema return
    positions as ints instead of floats.
  * Other changes
  * scipy.integrate
  * The OPTPACK and QUADPACK code has been changed to use the LAPACK matrix
    solvers rather than the bundled LINPACK code. This means that there is no
    longer any need for the bundled LINPACK routines, so they have been removed.
* Sat Jan 10 2015 arun@gmx.de
- specfile:
  * update copyright year
  * remove python(abi) requirement
* Mon Aug 11 2014 toddrme2178@gmail.com
- Switch to pypi download location
- Minor spec file cleanups
* Thu May  8 2014 toddrme2178@gmail.com
- Update to version 0.14.0
  * New features
  * scipy.interpolate improvements
  * A new wrapper function `scipy.interpolate.interpn` for
    interpolation onregular grids has been added. `interpn`
    supports linear and nearest-neighbor interpolation in
    arbitrary dimensions and spline interpolation in two
    dimensions.
  * Faster implementations of piecewise polynomials in power
    and Bernstein polynomial bases have been added as
    `scipy.interpolate.PPoly` and `scipy.interpolate.BPoly`.
    New users should use these in favor of
    `scipy.interpolate.PiecewisePolynomial`.
  * `scipy.interpolate.interp1d` now accepts non-monotonic
    inputs and sorts them.  If performance is critical, sorting
    can be turned off by using the new ``assume_sorted``
    keyword.
  * Functionality for evaluation of bivariate spline
    derivatives in ``scipy.interpolate`` has been added.
  * The new class `scipy.interpolate.Akima1DInterpolator`
    implements the piecewise cubic polynomial interpolation
    scheme devised by H. Akima.
  * Functionality for fast interpolation on regular, unevenly
    spaced grids in arbitrary dimensions has been added as
    `scipy.interpolate.RegularGridInterpolator` .
  * ``scipy.linalg`` improvements
  * The new function `scipy.linalg.dft` computes the matrix of
    the discrete Fourier transform.
  * A condition number estimation function for matrix
    exponential, `scipy.linalg.expm_cond`, has been added.
  * ``scipy.optimize`` improvements
  * A set of benchmarks for optimize, which can be run with
    ``optimize.bench()``, has been added.
  * `scipy.optimize.curve_fit` now has more controllable error
    estimation via the ``absolute_sigma`` keyword.
  * Support for passing custom minimization methods to
    ``optimize.minimize()``  and ``optimize.minimize_scalar()``
    has been added, currently useful especially for combining
    ``optimize.basinhopping()`` with custom local optimizer
    routines.
  * ``scipy.stats`` improvements
  * A new class `scipy.stats.multivariate_normal` with
    functionality for  multivariate normal random variables
    has been added.
  * A lot of work on the ``scipy.stats`` distribution framework
    has been done.  Moment calculations (skew and kurtosis
    mainly) are fixed and verified, all examples are now
    runnable, and many small accuracy and performance
    improvements for individual distributions were merged.
  * The new function `scipy.stats.anderson_ksamp` computes the
    k-sample Anderson-Darling test for the null hypothesis that
    k samples come from the same parent population.
  * ``scipy.signal`` improvements
  * ``scipy.signal.iirfilter`` and related functions to design
    Butterworth, Chebyshev, elliptical and Bessel IIR filters
    now all use pole-zero ("zpk") format internally instead of
    using transformations to numerator/denominator format.
    The accuracy of the produced filters, especially high-order
    ones, is improved significantly as a result.
  * The new function `scipy.signal.vectorstrength` computes the
    vector strength, a measure of phase synchrony, of a set of
    events.
  * ``scipy.special`` improvements
  * The functions `scipy.special.boxcox` and
    `scipy.special.boxcox1p`, which compute the
    Box-Cox  transformation, have been added.
  * ``scipy.sparse`` improvements
  * Significant performance improvement in CSR, CSC, and DOK
    indexing speed.
  * When using Numpy >= 1.9 (to be released in MM 2014), sparse
    matrices function correctly when given to arguments of
    ``np.dot``, ``np.multiply`` and other ufuncs.
    With earlier Numpy and Scipy versions, the results of such
    operations are undefined and usually unexpected.
  * Sparse matrices are no longer limited to ``2^31`` nonzero
    elements.  They automatically switch to using 64-bit index
    data type for matrices containing more elements.  User code
    written assuming the sparse matrices use int32 as the index
    data type will continue to work, except for such large
    matrices. Code dealing with larger matrices needs to accept
    either int32 or int64 indices.
  * Deprecated features
  * ``anneal``
  * The global minimization function `scipy.optimize.anneal` is
    deprecated.  All users should use the
    `scipy.optimize.basinhopping` function instead.
  * ``scipy.stats``
  * ``randwcdf`` and ``randwppf`` functions are deprecated.
    All users should use distribution-specific ``rvs`` methods
    instead.
  * Probability calculation aliases ``zprob``, ``fprob`` and
    ``ksprob`` are deprecated. Use instead the ``sf`` methods
    of the corresponding distributions or the ``special``
    functions directly.
  * ``scipy.interpolate``
  * ``PiecewisePolynomial`` class is deprecated.
  * Backwards incompatible changes
  * scipy.special.lpmn
  * ``lpmn`` no longer accepts complex-valued arguments. A new
    function ``clpmn`` with uniform complex analytic behavior
    has been added, and it should be used instead.
  * scipy.sparse.linalg
  * Eigenvectors in the case of generalized eigenvalue problem
    are normalized to unit vectors in 2-norm, rather than
    following the LAPACK normalization convention.
  * The deprecated UMFPACK wrapper in ``scipy.sparse.linalg``
    has been removed due to license and install issues.  If
    available, ``scikits.umfpack`` is still used transparently
    in the ``spsolve`` and ``factorized`` functions.
    Otherwise, SuperLU is used instead in these functions.
  * scipy.stats
  * The deprecated functions ``glm``, ``oneway`` and
    ``cmedian`` have been removed from ``scipy.stats``.
  * ``stats.scoreatpercentile`` now returns an array instead of
    a list of percentiles.
  * scipy.interpolate
  * The API for computing derivatives of a monotone piecewise
    interpolation has changed: if `p` is a
    ``PchipInterpolator`` object, `p.derivative(der)`
    returns a callable object representing the derivative of
    `p`. For in-place derivatives use the second argument of
    the `__call__` method: `p(0.1, der=2)` evaluates the
    second derivative of `p` at `x=0.1`.
  * The method `p.derivatives` has been removed.
* Sun Mar  2 2014 arun@gmx.de
- updated to version 0.13.3
  Issues fixed:
  * 3148: fix a memory leak in ``ndimage.label``.
  * 3216: fix weave issue with too long file names for MSVC.
  Other changes:
  * Update Sphinx theme used for html docs so ``>>>`` in examples can be toggled.
* Wed Dec 11 2013 toddrme2178@gmail.com
- Update to version 0.13.2
  + require Cython 0.19, earlier versions have memory leaks in
    fused types
  + ndimage.label fix swapped 64-bitness test
  + optimize.fmin_slsqp constraint violation
- Require python3-Cython >= 0.19
* Tue Dec  3 2013 arun@gmx.de
- update to 0.13.1 (bugfix release)
  - `ndimage.label` returns incorrect results in scipy 0.13.0
  - `ndimage.label` return type changed from int32 to uint32
  - `ndimage.find_objects` doesn't work with int32 input in some cases
* Fri Oct 25 2013 toddrme2178@gmail.com
- Update to 0.13.0
  * Highlights
  * support for fancy indexing and boolean comparisons with
    sparse matrices
  * interpolative decompositions and matrix functions in the
    linalg module
  * two new trust-region solvers for unconstrained minimization
  * scipy.integrate improvements
  * N-dimensional numerical integration
  * dopri* improvements
  * scipy.linalg improvements
  * Interpolative decompositions
  * Polar decomposition
  * BLAS level 3 functions
  * Matrix functions
  * scipy.optimize improvements
  * Trust-region unconstrained minimization algorithms
  * scipy.sparse improvements
  * Boolean comparisons and sparse matrices
  * CSR and CSC fancy indexing
  * scipy.io improvements
  * Unformatted Fortran file reader
  * scipy.io.wavfile enhancements
  * scipy.interpolate improvements
  * B-spline derivatives and antiderivatives
  * Deprecated features
  * expm2 and expm3
  * scipy.stats functions
  * Backwards incompatible changes
  * LIL matrix assignment
  * Deprecated radon function removed
  * Removed deprecated keywords xa and xb from
    stats.distributions
  * Changes to MATLAB file readers / writers
- Add a new flag to easily enable/disable atlas support for if it
  ever gets fixed in the future
- Added numpy version number to requires and buildrequires
- Updated rpmlint fixes
- Disabled weave package, it is not available in python 3 yet
* Sun Apr 14 2013 termim@gmail.com
- Update to version 0.12.0
  Some of the highlights of this release are:
  * Completed QHull wrappers in scipy.spatial.
  * cKDTree now a drop-in replacement for KDTree.
  * A new global optimizer, basinhopping.
  * Support for Python 2 and Python 3 from the same code base (no more 2to3).
* Thu Nov 22 2012 toddrme2178@gmail.com
- Removed openSUSE 11.4 spec file workarounds
* Mon Oct  1 2012 toddrme2178@gmail.com
- Update to version 0.11.0:
  * Sparse Graph Submodule
  * scipy.optimize improvements
  * A unified interface to minimizers of univariate and
    multivariate functions has been added.
  * A unified interface to root finding algorithms for
    multivariate functions has been added.
  * The L-BFGS-B algorithm has been updated to version 3.0.
  * scipy.linalg improvements
  * New matrix equation solvers
  * QZ and QR Decomposition
  * Pascal matrices
  * Sparse matrix construction and operations
  * LSMR iterative solver
  * Discrete Sine Transform
  * scipy.interpolate improvements
  * Interpolation in spherical coordinates
  * scipy.stats improvements
  * Binned statistics
- Remove upstreamed patches
* Mon Aug 27 2012 toddrme2178@gmail.com
- Disable broken libatlas3
* Mon Jun  4 2012 toddrme2178@gmail.com
- Add suitesparse buildrequires
- Remove blas/lapack tests since these build successfully on all
  targets now
* Tue May 29 2012 toddrme2178@gmail.com
- Don't build against libatlas on factory since libatlas doesn't
  work there
* Fri May 18 2012 toddrme2178@gmail.com
- Fix rmplint warnings
- Clean up spec file formatting
* Mon Apr 30 2012 toddrme2178@gmail.com
- Removed tests for unsupported openSUSE versions
* Tue Apr 24 2012 toddrme2178@gmail.com
- Add python 3 package