SciPy: Scientific Library for Python

========================= SciPy 0.9.0 Release Notes ========================= .. contents:: 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. .. _Qhull: http://www.qhull.org/ 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. ``scipy.sparse.linalg.arpack.speigs`` ------------------------------------- A duplicated interface to the ARPACK library was removed. Other changes ============= ARPACK interface changes ------------------------ The interface to the ARPACK eigenvalue routines in ``scipy.sparse.linalg`` was changed for more robustness. The eigenvalue and SVD routines now raise ``ArpackNoConvergence`` if the eigenvalue iteration fails to converge. If partially converged results are desired, they can be accessed as follows:: 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 Checksums ========= e7e2c21ea20dfffe2cdc127d8512e5d9 scipy-0.9.0-py2.5-python.org-macosx10.3.dmg 6bf8b80b235554fdb4e208f8d521b587 scipy-0.9.0-py2.6-python.org-macosx10.3.dmg 17d21570b77d3ebc574c00a1df3bef17 scipy-0.9.0-py2.7-python.org-macosx10.3.dmg 4fe35e6feae6fd6219d2cca07831565e scipy-0.9.0-py2.7-python.org-macosx10.6.dmg 6dc01734e32a061eddf325080fa07ea7 scipy-0.9.0-win32-superpack-python2.5.exe 561ffb4ab19026f7020121767f0ac362 scipy-0.9.0-win32-superpack-python2.6.exe 1fd6e02756305d7d71a8739adb6998fa scipy-0.9.0-win32-superpack-python2.7.exe 3323926658dbeb1ef0f32b152aa095a4 scipy-0.9.0-win32-superpack-python3.1.exe ebfef6e8e82d15c875a4ee6a46d4e1cd scipy-0.9.0.tar.gz a37933c9e3c4fdf8d087624cd7dcb47d scipy-0.9.0.zip