python-scipy-1.0.0-bp150.2.6

- Update to version 1.0.0
  * Many changes. Please see changelog at:
    https://github.com/scipy/scipy/blob/v1.0.0/doc/release/1.0.0-notes.rst#why-1-0-now
- Rebase no_implicit_decl.patch

- More rpmlint fixes.

- Update to version 0.19.1
  * #7214: Memory use in integrate.quad in scipy-0.19.0
  * #7258: linalg.matrix_balance gives wrong transformation matrix
  * #7262: Segfault in daily testing
  * #7273: scipy.interpolate._bspl.evaluate_spline gets wrong type
  * #7335: scipy.signal.dlti(A,B,C,D).freqresp() fails
  * #7211: BUG: convolve may yield inconsistent dtypes with method changed
  * #7216: BUG: integrate: fix refcounting bug in quad()
  * #7229: MAINT: special: Rewrite a test of wrightomega
  * #7261: FIX: Corrected the transformation matrix permutation
  * #7265: BUG: Fix broken axis handling in spectral functions
  * #7266: FIX 7262: ckdtree crashes in query_knn.
  * #7279: Upcast half- and single-precision floats to doubles in BSpline...
  * #7336: BUG: Fix signal.dfreqresp for StateSpace systems
  * #7419: Fix several issues in sparse.load_npz, save_npz
  * #7420: BUG: stats: allow integers as kappa4 shape parameters
- Add no_implicit_decl.patch
  Fixes implicit-pointer-decl warnings and implicit-fortify-decl error.
- Fix wrong-script-interpreter rpmlint error.

- Update to version 0.19.0
  + Highlights
  * A unified foreign function interface layer, `scipy.LowLevelCallable`.
  * Cython API for scalar, typed versions of the universal functions from
    the `scipy.special` module, via `cimport scipy.special.cython_special`.
- Removed weave subpackage. It was removed upstream in this release.

- Switch to single-spec version
- update to version 0.18.1:
  * #6357: scipy 0.17.1 piecewise cubic hermite interpolation does not
    return...
  * #6420: circmean() changed behaviour from 0.17 to 0.18
  * #6421: scipy.linalg.solve_banded overwrites input 'b' when the
    inversion...
  * #6425: cKDTree INF bug
  * #6435: scipy.stats.ks_2samp returns different values on different
    computers
  * #6458: Error in scipy.integrate.dblquad when using variable
    integration...
  * #6405: BUG: sparse: fix elementwise divide for CSR/CSC
  * #6431: BUG: result for insufficient neighbours from cKDTree is
    wrong.
  * #6432: BUG Issue #6421: scipy.linalg.solve_banded overwrites input
    'b'...
  * #6455: DOC: add links to release notes
  * #6462: BUG: interpolate: fix .roots method of PchipInterpolator
  * #6492: BUG: Fix regression in dblquad: #6458
  * #6543: fix the regression in circmean
  * #6545: Revert gh-5938, restore ks_2samp
  * #6557: Backports for 0.18.1
- update to version 0.18.0:
  (see http://scipy.github.io/devdocs/release.0.18.0.html for full changelog)
  * Highlights of this release include:
    + A new ODE solver for two-point boundary value problems,
    scipy.optimize.solve_bvp.
    + A new class, CubicSpline, for cubic spline interpolation of
    data.
    + N-dimensional tensor product polynomials,
    scipy.interpolate.NdPPoly.
    + Spherical Voronoi diagrams, scipy.spatial.SphericalVoronoi.
    + Support for discrete-time linear systems, scipy.signal.dlti.
- 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
- specfile:
  * updated source url to files.pythonhosted.org
  * require setuptools
  * Add openBLAS support.
    This can improve performance in many situations.
  * Drop ATLAS support.

- 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.

- Update to 0.16.1
  SciPy 0.16.1 is a bug-fix release with no new features compared
  to 0.16.0.

- Remove Cython subpackage.  The sources are not as cleanly
  separated as the changelog implied.

- Remove Cython subpackage.  The sources are not as cleanly
  separated as the changelog implied.

- 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

- 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.
- Update copyright year

- Switch to pypi download location
- Minor spec file cleanups

- Mark python-scipy-weave as deprecated.
  Please use python-weave package instead.

- 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.

- 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.

- 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

- 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

- 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

- 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).

- 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

- Disable broken libatlas3

- Add suitesparse buildrequires
- Remove blas/lapack tests since these build successfully on all
  targets now

- license update: BSD-3-Clause
  No LGPL licenses found in the package

- Don't build against libatlas on factory since libatlas doesn't
  work there

- Fix rmplint warnings
- Clean up spec file formatting