Hello community, here is the log from the commit of package python-scipy for openSUSE:Factory checked in at 2014-05-09 08:52:06 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Comparing /work/SRC/openSUSE:Factory/python-scipy (Old) and /work/SRC/openSUSE:Factory/.python-scipy.new (New) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Package is "python-scipy" Changes: -------- --- /work/SRC/openSUSE:Factory/python-scipy/python-scipy.changes 2014-03-02 18:22:39.000000000 +0100 +++ /work/SRC/openSUSE:Factory/.python-scipy.new/python-scipy.changes 2014-05-09 08:52:08.000000000 +0200 @@ -1,0 +2,128 @@ +Thu May 8 10:16:00 UTC 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. + +------------------------------------------------------------------- Old: ---- scipy-0.13.3.tar.gz New: ---- scipy-0.14.0.tar.gz ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-scipy.spec ++++++ --- /var/tmp/diff_new_pack.QO7f31/_old 2014-05-09 08:52:10.000000000 +0200 +++ /var/tmp/diff_new_pack.QO7f31/_new 2014-05-09 08:52:10.000000000 +0200 @@ -20,7 +20,7 @@ %define modname scipy Name: python-%{modname} -Version: 0.13.3 +Version: 0.14.0 Release: 0 Summary: Scientific Tools for Python License: BSD-3-Clause ++++++ scipy-0.13.3.tar.gz -> scipy-0.14.0.tar.gz ++++++ /work/SRC/openSUSE:Factory/python-scipy/scipy-0.13.3.tar.gz /work/SRC/openSUSE:Factory/.python-scipy.new/scipy-0.14.0.tar.gz differ: char 5, line 1 -- To unsubscribe, e-mail: opensuse-commit+unsubscribe@opensuse.org For additional commands, e-mail: opensuse-commit+help@opensuse.org