- SciPy 0.13.0 Release Notes
- New features
- scipy.integrate improvements
- scipy.linalg improvements
- scipy.optimize improvements
- scipy.sparse improvements
- scipy.sparse.linalg improvements
- scipy.spatial improvements
- scipy.signal improvements
- scipy.special improvements
- scipy.io improvements
- scipy.interpolate improvements
- scipy.stats improvements
- Deprecated features
- Backwards incompatible changes
- Other changes
- New features
SciPy 0.13.0 is the culmination of 7 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. Highlights of this release are:
- 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
A new function scipy.integrate.nquad, which provides N-dimensional integration functionality with a more flexible interface than dblquad and tplquad, has been added.
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.
A new function scipy.linalg.polar, to compute the polar decomposition of a matrix, was added.
The BLAS functions symm, syrk, syr2k, hemm, herk and her2k are now wrapped in scipy.linalg.
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).
The minimize function gained two trust-region solvers for unconstrained minimization: dogleg and trust-ncg.
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.
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
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 explicitly 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.
The vertices of a ConvexHull can now be accessed via the vertices attribute, which gives proper orientation in 2-D.
The new class scipy.io.FortranFile facilitates reading unformatted sequential files written by Fortran code.
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.
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.
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.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.
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])] = ...`.
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.
The keywords xa and xb, which were deprecated since 0.11.0, have been removed from the distributions in scipy.stats.
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.
Security fix: scipy.weave previously used temporary directories in an insecure manner under certain circumstances.
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.
Scipy cannot be compiled with gfortran 4.1 anymore (at least on RH5), likely due to that compiler version not supporting entry constructs well.