Looking for the latest version? Download scipy-0.14.0.tar.gz (10.2 MB)
Home / scipy / 0.13.0b1
Name Modified Size Downloads / Week Status
Parent folder
Totals: 3 Items   21.5 MB 7
scipy-0.13.0b1.zip 2013-08-22 11.4 MB 22 weekly downloads
scipy-0.13.0b1.tar.gz 2013-08-22 10.1 MB 33 weekly downloads
README.rst 2013-08-22 11.1 kB 22 weekly downloads

SciPy 0.13.0 Release Notes

Note

Scipy 0.13.0 is not released yet!

SciPy 0.13.0 is the culmination of 5 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 0.13.x branch, and on adding new features on the master branch.

This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or greater.

New features

scipy.integrate improvements

N-dimensional numerical integration

A new function scipy.integrate.nquad, which provides N-dimensional integration functionality with a more flexible interface than dblquad and tplquad, has been added.

dopri* improvements

The intermediate results from the dopri family of ODE solvers can now be accessed by a solout callback function.

scipy.linalg improvements

Interpolative decompositions

Scipy now includes a new module scipy.linalg.interpolative containing routines for computing interpolative matrix decompositions (ID). This feature is based on the ID software package by P.G. Martinsson, V. Rokhlin, Y. Shkolnisky, and M. Tygert, previously adapted for Python in the PymatrixId package by K.L. Ho.

Polar decomposition

A new function scipy.linalg.polar, to compute the polar decomposition of a matrix, was added.

BLAS level 3 functions

The BLAS functions symm, syrk, syr2k, hemm, herk and her2k are now wrapped in scipy.linalg.

Matrix functions

Several matrix function algorithms have been implemented or updated following detailed descriptions in recent papers of Nick Higham and his co-authors. These include the matrix square root (sqrtm), the matrix logarithm (logm), the matrix exponential (expm) and its Frechet derivative (expm_frechet), and fractional matrix powers (fractional_matrix_power).

scipy.optimize improvements

Trust-region unconstrained minimization algorithms

The minimize function gained two trust-region solvers for unconstrained minimization: dogleg and trust-ncg.

scipy.sparse improvements

Boolean comparisons and sparse matrices

All sparse matrix types now support boolean data, and boolean operations. Two sparse matrices A and B can be compared in all the expected ways A < B, A >= B, A != B, producing similar results as dense Numpy arrays. Comparisons with dense matrices and scalars are also supported.

CSR and CSC fancy indexing

Compressed sparse row and column sparse matrix types now support fancy indexing with boolean matrices, slices, and lists. So where A is a (CSC or CSR) sparse matrix, you can do things like:

>>> A[A > 0.5] = 1  # since Boolean sparse matrices work
>>> A[:2, :3] = 2
>>> A[[1,2], 2] = 3

scipy.sparse.linalg improvements

The new function onenormest provides a lower bound of the 1-norm of a linear operator and has been implemented according to Higham and Tisseur (2000). This function is not only useful for sparse matrices, but can also be used to estimate the norm of products or powers of dense matrices without explictly building the intermediate matrix.

The multiplicative action of the matrix exponential of a linear operator (expm_multiply) has been implemented following the description in Al-Mohy and Higham (2011).

Abstract linear operators (scipy.sparse.linalg.LinearOperator) can now be multiplied, added to each other, and exponentiated, producing new linear operators. This enables easier construction of composite linear operations.

scipy.spatial improvements

The vertices of a ConvexHull can now be accessed via the vertices attribute, which gives proper orientation in 2-D.

scipy.signal improvements

The cosine window function scipy.signal.cosine was added.

scipy.special improvements

New functions scipy.special.xlogy and scipy.special.xlog1py were added. These functions can simplify and speed up code that has to calculate x * log(y) and give 0 when x == 0.

scipy.io improvements

Unformatted Fortran file reader

The new class scipy.io.FortranFile facilitates reading unformatted sequential files written by Fortran code.

scipy.io.wavfile enhancements

scipy.io.wavfile.write now accepts a file buffer. Previously it only accepted a filename.

scipy.io.wavfile.read and scipy.io.wavfile.write can now handle floating point WAV files.

scipy.interpolate improvements

B-spline derivatives and antiderivatives

scipy.interpolate.splder and scipy.interpolate.splantider functions for computing B-splines that represent derivatives and antiderivatives of B-splines were added. These functions are also available in the class-based FITPACK interface as UnivariateSpline.derivative and UnivariateSpline.antiderivative.

scipy.stats improvements

Distributions now allow using keyword parameters in addition to positional parameters in all methods.

The function scipy.stats.power_divergence has been added for the Cressie-Read power divergence statistic and goodness of fit test. Included in this family of statistics is the "G-test" (http://en.wikipedia.org/wiki/G-test).

scipy.stats.mood now accepts multidimensional input.

An option was added to scipy.stats.wilcoxon for continuity correction.

scipy.stats.chisquare now has an axis argument.

scipy.stats.mstats.chisquare now has axis and ddof arguments.

Deprecated features

expm2 and expm3

The matrix exponential functions scipy.linalg.expm2 and scipy.linalg.expm3 are deprecated. All users should use the numerically more robust scipy.linalg.expm function instead.

scipy.stats functions

scipy.stats.oneway is deprecated; scipy.stats.f_oneway should be used instead.

scipy.stats.glm is deprecated. scipy.stats.ttest_ind is an equivalent function; more full-featured general (and generalized) linear model implementations can be found in statsmodels.

scipy.stats.cmedian is deprecated; numpy.median should be used instead.

Backwards incompatible changes

LIL matrix assignment

Assigning values to LIL matrices with two index arrays now works similarly as assigning into ndarrays:

>>> x = lil_matrix((3, 3))
>>> x[[0,1,2],[0,1,2]]=[0,1,2]
>>> x.todense()
matrix([[ 0.,  0.,  0.],
        [ 0.,  1.,  0.],
        [ 0.,  0.,  2.]])

rather than giving the result:

>>> x.todense()
matrix([[ 0.,  1.,  2.],
        [ 0.,  1.,  2.],
        [ 0.,  1.,  2.]])

Users relying on the previous behavior will need to revisit their code. The previous behavior is obtained by ``x[numpy.ix_([0,1,2],[0,1,2])] = ...`.

Deprecated radon function removed

The misc.radon function, which was deprecated in scipy 0.11.0, has been removed. Users can find a more full-featured radon function in scikit-image.

Removed deprecated keywords xa and xb from stats.distributions

The keywords xa and xb, which were deprecated since 0.11.0, have been removed from the distributions in scipy.stats.

Changes to MATLAB file readers / writers

The major change is that 1D arrays in numpy now become row vectors (shape 1, N) when saved to a MATLAB 5 format file. Previously 1D arrays saved as column vectors (N, 1). This is to harmonize the behavior of writing MATLAB 4 and 5 formats, and adapt to the defaults of numpy and MATLAB - for example np.atleast_2d returns 1D arrays as row vectors.

Trying to save arrays of greater than 2 dimensions in MATLAB 4 format now raises an error instead of silently reshaping the array as 2D.

scipy.io.loadmat('afile') used to look for afile on the Python system path (sys.path); now loadmat only looks in the current directory for a relative path filename.

Other changes

Cython is now required to build unreleased versions of scipy. The C files generated from Cython sources are not included in the git repo anymore. They are however still shipped in source releases.

The code base received a fairly large PEP8 cleanup. A tox pep8 command has been added; new code should pass this test command.

Authors

This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order):

  • Jorge Cañardo Alastuey +
  • Tom Aldcroft +
  • Max Bolingbroke +
  • Joseph Jon Booker +
  • François Boulogne
  • Matthew Brett
  • Christian Brodbeck +
  • Per Brodtkorb +
  • Christian Brueffer +
  • Lars Buitinck
  • Evgeni Burovski +
  • Tim Cera
  • Lawrence Chan +
  • David Cournapeau
  • Dražen Lučanin +
  • Alexander J. Dunlap +
  • endolith
  • André Gaul +
  • Ralf Gommers
  • Alex Griffing +
  • Blake Griffith +
  • Charles Harris
  • Bob Helmbold +
  • Andreas Hilboll
  • Kat Huang +
  • Oleksandr (Sasha) Huziy +
  • Gert-Ludwig Ingold +
  • Thouis (Ray) Jones
  • Juan Luis Cano Rodríguez +
  • Robert Kern
  • Andreas Kloeckner +
  • Sytse Knypstra +
  • Gustav Larsson +
  • Denis Laxalde
  • Christopher Lee
  • Tim Leslie
  • Wendy Liu +
  • Clemens Novak +
  • Takuya Oshima +
  • Josef Perktold
  • Illia Polosukhin +
  • Przemek Porebski +
  • Steve Richardson +
  • Branden Rolston +
  • Skipper Seabold
  • Fazlul Shahriar
  • Leo Singer +
  • Rohit Sivaprasad +
  • Daniel B. Smith +
  • Julian Taylor
  • Louis Thibault +
  • Tomas Tomecek +
  • John Travers
  • Richard Tsai +
  • Jacob Vanderplas
  • Patrick Varilly
  • Pauli Virtanen
  • Stefan van der Walt
  • Warren Weckesser
  • Nils Werner +
  • Michael Wimmer +
  • Nathan Woods +
  • Tony S. Yu +

A total of 63 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Checksums

d51c84f63542fd610c0344af424c563b release/installers/scipy-0.13.0b1.tar.gz c233a43a8abc17cf45f6248bd376375a release/installers/scipy-0.13.0b1.zip

Source: README.rst, updated 2013-08-22