[SciPy-User] ANN: Scipy 0.14.0 beta 1 release

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[SciPy-User] ANN: Scipy 0.14.0 beta 1 release


I'm pleased to announce the availability of the first beta release of Scipy 0.14.0. Please try this beta and report any issues on the scipy-dev mailing list.

Source tarballs, binaries and the full release notes can be found at http://sourceforge.net/projects/scipy/files/scipy/0.14.0b1/. Part of the release notes copied below.

A big thank you to everyone who contributed to this release!


SciPy 0.14.0 is the culmination of 8 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and
better documentation.  There have been a number of deprecations and
API changes in this release, which are documented below.  All users
are encouraged to upgrade to this release, as there are a large number
of bug-fixes and optimizations.  Moreover, our development attention
will now shift to bug-fix releases on the 0.14.x branch, and on adding
new features on the master branch.

This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or greater.

New features

``scipy.interpolate`` improvements

A new wrapper function `scipy.interpolate.interpn` for interpolation on regular
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`` 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

Deprecated features


The global minimization function `scipy.optimize.anneal` is deprecated.
All users should use the `scipy.optimize.basinhopping` function instead.


``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.


``PiecewisePolynomial`` class is deprecated.

Backwards incompatible changes


``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.


Eigenvectors in the case of generalized eigenvalue problem are normalized to
unit vectors in 2-norm, rather than following the LAPACK normalization

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.


The deprecated functions ``glm``, ``oneway`` and ``cmedian`` have been removed
from ``scipy.stats``.

``stats.scoreatpercentile`` now returns an array instead of a list of


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.


* Marc Abramowitz +
* andbo +
* Vincent Arel-Bundock +
* Petr Baudis +
* Max Bolingbroke
* François Boulogne
* Matthew Brett
* Lars Buitinck
* Evgeni Burovski
* CJ Carey +
* Thomas A Caswell +
* Pawel Chojnacki +
* Phillip Cloud +
* Stefano Costa +
* David Cournapeau
* Dapid +
* Matthieu Dartiailh +
* Christoph Deil +
* Jörg Dietrich +
* endolith
* Francisco de la Peña +
* Ben FrantzDale +
* Jim Garrison +
* André Gaul
* Christoph Gohlke
* Ralf Gommers
* Robert David Grant
* Alex Griffing
* Blake Griffith
* Yaroslav Halchenko
* Andreas Hilboll
* Kat Huang
* Gert-Ludwig Ingold
* jamestwebber +
* Dorota Jarecka +
* Todd Jennings +
* Thouis (Ray) Jones
* Juan Luis Cano Rodríguez
* ktritz +
* Jacques Kvam +
* Eric Larson +
* Justin Lavoie +
* Denis Laxalde
* Jussi Leinonen +
* lemonlaug +
* Tim Leslie
* Alain Leufroy +
* George Lewis +
* Max Linke +
* Brandon Liu +
* Benny Malengier +
* Matthias Kümmerer +
* Cimarron Mittelsteadt +
* Eric Moore
* Andrew Nelson +
* Niklas Hambüchen +
* Joel Nothman +
* Clemens Novak
* Emanuele Olivetti +
* Stefan Otte +
* peb +
* Josef Perktold
* pjwerneck
* Andrew Sczesnak +
* poolio
* Jérôme Roy +
* Carl Sandrock +
* Shauna +
* Fabrice Silva
* Daniel B. Smith
* Patrick Snape +
* Thomas Spura +
* Jacob Stevenson
* Julian Taylor
* Tomas Tomecek
* Richard Tsai
* Joris Vankerschaver +
* Pauli Virtanen
* Warren Weckesser

A total of 78 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

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