SciPy 0.9.0 Release Notes ¶

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SciPy 0.9.0 Release Notes

SciPy 0.9.0 is the culmination of 6 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 0.9.x branch, and on adding new features on the development trunk.

This release requires Python 2.4 - 2.7 or 3.1 - and NumPy 1.5 or greater.

Please note that SciPy is still considered to have “Beta” status, as we work toward a SciPy 1.0.0 release. The 1.0.0 release will mark a major milestone in the development of SciPy, after which changing the package structure or API will be much more difficult. Whilst these pre-1.0 releases are considered to have “Beta” status, we are committed to making them as bug-free as possible.

However, until the 1.0 release, we are aggressively reviewing and refining the functionality, organization, and interface. This is being done in an effort to make the package as coherent, intuitive, and useful as possible. To achieve this, we need help from the community of users. Specifically, we need feedback regarding all aspects of the project - everything - from which algorithms we implement, to details about our function’s call signatures.

Python 3 ¶

Scipy 0.9.0 is the first SciPy release to support Python 3. The only module that is not yet ported is scipy.weave.

Scipy source code location to be changed ¶

Soon after this release, Scipy will stop using SVN as the version control system, and move to Git. The development source code for Scipy can from then on be found at

http://github.com/scipy/scipy

New features ¶

Delaunay tesselations (`scipy.spatial`)¶

Scipy now includes routines for computing Delaunay tesselations in N dimensions, powered by the Qhull computational geometry library. Such calculations can now make use of the new scipy.spatial.Delaunay interface.

N-dimensional interpolation (`scipy.interpolate`)¶

Support for scattered data interpolation is now significantly improved. This version includes a scipy.interpolate.griddata function that can perform linear and nearest-neighbour interpolation for N-dimensional scattered data, in addition to cubic spline (C1-smooth) interpolation in 2D and 1D. An object-oriented interface to each interpolator type is also available.

Nonlinear equation solvers (`scipy.optimize`)¶

Scipy includes new routines for large-scale nonlinear equation solving in scipy.optimize. The following methods are implemented:

Newton-Krylov (scipy.optimize.newton_krylov)
(Generalized) secant methods:
- Limited-memory Broyden methods (scipy.optimize.broyden1, scipy.optimize.broyden2)
- Anderson method (scipy.optimize.anderson)
Simple iterations (scipy.optimize.diagbroyden, scipy.optimize.excitingmixing, scipy.optimize.linearmixing)

The scipy.optimize.nonlin module was completely rewritten, and some of the functions were deprecated (see above).

New linear algebra routines (`scipy.linalg`)¶

Scipy now contains routines for effectively solving triangular equation systems (scipy.linalg.solve_triangular).

Improved FIR filter design functions (`scipy.signal`)¶

The function scipy.signal.firwin was enhanced to allow the design of highpass, bandpass, bandstop and multi-band FIR filters.

The function scipy.signal.firwin2 was added. This function uses the window method to create a linear phase FIR filter with an arbitrary frequency response.

The functions scipy.signal.kaiser_atten and scipy.signal.kaiser_beta were added.

Improved statistical tests (`scipy.stats`)¶

A new function scipy.stats.fisher_exact was added, that provides Fisher’s exact test for 2x2 contingency tables.

The function scipy.stats.kendalltau was rewritten to make it much faster (O(n log(n)) vs O(n^2)).

Deprecated features ¶

Obsolete nonlinear solvers (in `scipy.optimize`)¶

The following nonlinear solvers from scipy.optimize are deprecated:

broyden_modified (bad performance)
broyden1_modified (bad performance)
broyden_generalized (equivalent to anderson)
anderson2 (equivalent to anderson)
broyden3 (obsoleted by new limited-memory broyden methods)
vackar (renamed to diagbroyden)

Removed features ¶

The deprecated modules helpmod, pexec and ppimport were removed from scipy.misc.

The output_type keyword in many scipy.ndimage interpolation functions has been removed.

The econ keyword in scipy.linalg.qr has been removed. The same functionality is still available by specifying mode='economic'.

Old correlate/convolve behavior (in `scipy.signal`)¶

The old behavior for scipy.signal.convolve, scipy.signal.convolve2d, scipy.signal.correlate and scipy.signal.correlate2d was deprecated in 0.8.0 and has now been removed. Convolve and correlate used to swap their arguments if the second argument has dimensions larger than the first one, and the mode was relative to the input with the largest dimension. The current behavior is to never swap the inputs, which is what most people expect, and is how correlation is usually defined.

`scipy.stats`¶

Many functions in scipy.stats that are either available from numpy or have been superseded, and have been deprecated since version 0.7, have been removed: std, var, mean, median, cov, corrcoef, z, zs, stderr, samplestd, samplevar, pdfapprox, pdf_moments and erfc. These changes are mirrored in scipy.stats.mstats.

`scipy.sparse`¶

Several methods of the sparse matrix classes in scipy.sparse which had been deprecated since version 0.7 were removed: save, rowcol, getdata, listprint, ensure_sorted_indices, matvec, matmat and rmatvec.

The functions spkron, speye, spidentity, lil_eye and lil_diags were removed from scipy.sparse. The first three functions are still available as scipy.sparse.kron, scipy.sparse.eye and scipy.sparse.identity.

The dims and nzmax keywords were removed from the sparse matrix constructor. The colind and rowind attributes were removed from CSR and CSC matrices respectively.

import numpy as np
from scipy.sparse.linalg import eigs, ArpackNoConvergence

m = np.random.randn(30, 30)
try:
    w, v = eigs(m, 6)
except ArpackNoConvergence, err:
    partially_converged_w = err.eigenvalues
    partially_converged_v = err.eigenvectors

Several bugs were also fixed.

The routines were moreover renamed as follows:

eigen –> eigs

eigen_symmetric –> eigsh

svd –> svds