Package Release Info


Update Info: openSUSE-2016-1107
Available in Package Hub : 12 GA-SP5





Change Logs

* Thu Jan 28 2016
- specfile:
  * update copyright year
- update to version 0.17.0:
  (see for full changelog)
  * Highlights
    + New functions for linear and nonlinear least squares
    optimization with constraints: scipy.optimize.lsq_linear and
    + 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
* Fri Oct 30 2015
- Update to 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
- Remove Cython subpackage.  The sources are not as cleanly
  separated as the changelog implied.
* Mon Jul 27 2015
- Remove Cython subpackage.  The sources are not as cleanly
  separated as the changelog implied.
* Mon Mar 02 2015
- 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
- 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:  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.
- Update copyright year
* Mon Aug 11 2014
- Switch to pypi download location
- Minor spec file cleanups
* Fri May 30 2014
- Mark python-scipy-weave as deprecated.
  Please use python-weave package instead.
* Thu May 08 2014
- 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
  * 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.interp1d` now accepts non-monotonic
    inputs and sorts them.  If performance is critical, sorting
    can be turned off by using the new ``assume_sorted``
  * 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
  * ``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
  * ``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.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
  * 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 02 2014
- 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
- 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 python-Cython >= 0.19
* Tue Nov 19 2013
- Update to version 0.13.1
  + ``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
- 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
  * improvements
  * Unformatted Fortran file reader
  * 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
  * 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
* Sun Apr 14 2013
- 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).
* Mon Oct 01 2012
- 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
- Disable broken libatlas3
* Mon Jun 04 2012
- Add suitesparse buildrequires
- Remove blas/lapack tests since these build successfully on all
  targets now
* Tue May 29 2012
- license update: BSD-3-Clause
  No LGPL licenses found in the package
* Tue May 29 2012
- Don't build against libatlas on factory since libatlas doesn't
  work there
* Fri May 18 2012
- Fix rmplint warnings
- Clean up spec file formatting