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.

- 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

- 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