AArch64 | |

ppc64le | |

s390x | |

x86-64 |

python-scipy

python-scipy-weave

- 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