ANN: SciPy 0.11.0 release
Hi, I am pleased to announce the availability of SciPy 0.11.0. For this release many new features have been added, and over 120 tickets and pull requests have been closed. Also noteworthy is that the number of contributors for this release has risen to over 50. We hope to see this number continuing to increase! The highlights of this release are:  A new module, sparse.csgraph, has been added which provides a number of common sparse graph algorithms.  New unified interfaces to the existing optimization and root finding functions have been added. Sources and binaries can be found at http://sourceforge.net/projects/scipy/files/scipy/0.11.0/, release notes are copied below. Thanks to everyone who contributed to this release, Ralf ========================== SciPy 0.11.0 Release Notes ========================== .. contents:: SciPy 0.11.0 is the culmination of 8 months of hard work. It contains many new features, numerous bugfixes, improved test coverage and better documentation. Highlights of this release are:  A new module has been added which provides a number of common sparse graph algorithms.  New unified interfaces to the existing optimization and root finding functions have been added. All users are encouraged to upgrade to this release, as there are a large number of bugfixes and optimizations. Our development attention will now shift to bugfix releases on the 0.11.x branch, and on adding new features on the master branch. This release requires Python 2.42.7 or 3.13.2 and NumPy 1.5.1 or greater. New features ============ Sparse Graph Submodule  The new submodule :mod:`scipy.sparse.csgraph` implements a number of efficient graph algorithms for graphs stored as sparse adjacency matrices. Available routines are:  :func:`connected_components`  determine connected components of a graph  :func:`laplacian`  compute the laplacian of a graph  :func:`shortest_path`  compute the shortest path between points on a positive graph  :func:`dijkstra`  use Dijkstra's algorithm for shortest path  :func:`floyd_warshall`  use the FloydWarshall algorithm for shortest path  :func:`breadth_first_order`  compute a breadthfirst order of nodes  :func:`depth_first_order`  compute a depthfirst order of nodes  :func:`breadth_first_tree`  construct the breadthfirst tree from a given node  :func:`depth_first_tree`  construct a depthfirst tree from a given node  :func:`minimum_spanning_tree`  construct the minimum spanning tree of a graph ``scipy.optimize`` improvements  The optimize module has received a lot of attention this release. In addition to added tests, documentation improvements, bug fixes and code cleanup, the following improvements were made:  A unified interface to minimizers of univariate and multivariate functions has been added.  A unified interface to root finding algorithms for multivariate functions has been added.  The LBFGSB algorithm has been updated to version 3.0. Unified interfaces to minimizers ```````````````````````````````` Two new functions ``scipy.optimize.minimize`` and ``scipy.optimize.minimize_scalar`` were added to provide a common interface to minimizers of multivariate and univariate functions respectively. For multivariate functions, ``scipy.optimize.minimize`` provides an interface to methods for unconstrained optimization (`fmin`, `fmin_powell`, `fmin_cg`, `fmin_ncg`, `fmin_bfgs` and `anneal`) or constrained optimization (`fmin_l_bfgs_b`, `fmin_tnc`, `fmin_cobyla` and `fmin_slsqp`). For univariate functions, ``scipy.optimize.minimize_scalar`` provides an interface to methods for unconstrained and bounded optimization (`brent`, `golden`, `fminbound`). This allows for easier comparing and switching between solvers. Unified interface to root finding algorithms ```````````````````````````````````````````` The new function ``scipy.optimize.root`` provides a common interface to root finding algorithms for multivariate functions, embeding `fsolve`, `leastsq` and `nonlin` solvers. ``scipy.linalg`` improvements  New matrix equation solvers ``````````````````````````` Solvers for the Sylvester equation (``scipy.linalg.solve_sylvester``, discrete and continuous Lyapunov equations (``scipy.linalg.solve_lyapunov``, ``scipy.linalg.solve_discrete_lyapunov``) and discrete and continuous algebraic Riccati equations (``scipy.linalg.solve_continuous_are``, ``scipy.linalg.solve_discrete_are``) have been added to ``scipy.linalg``. These solvers are often used in the field of linear control theory. QZ and QR Decomposition ```````````````````````` It is now possible to calculate the QZ, or Generalized Schur, decomposition using ``scipy.linalg.qz``. This function wraps the LAPACK routines sgges, dgges, cgges, and zgges. The function ``scipy.linalg.qr_multiply``, which allows efficient computation of the matrix product of Q (from a QR decompostion) and a vector, has been added. Pascal matrices ``````````````` A function for creating Pascal matrices, ``scipy.linalg.pascal``, was added. Sparse matrix construction and operations  Two new functions, ``scipy.sparse.diags`` and ``scipy.sparse.block_diag``, were added to easily construct diagonal and blockdiagonal sparse matrices respectively. ``scipy.sparse.csc_matrix`` and ``csr_matrix`` now support the operations ``sin``, ``tan``, ``arcsin``, ``arctan``, ``sinh``, ``tanh``, ``arcsinh``, ``arctanh``, ``rint``, ``sign``, ``expm1``, ``log1p``, ``deg2rad``, ``rad2deg``, ``floor``, ``ceil`` and ``trunc``. Previously, these operations had to be performed by operating on the matrices' ``data`` attribute. LSMR iterative solver  LSMR, an iterative method for solving (sparse) linear and linear leastsquares systems, was added as ``scipy.sparse.linalg.lsmr``. Discrete Sine Transform  Bindings for the discrete sine transform functions have been added to ``scipy.fftpack``. ``scipy.interpolate`` improvements  For interpolation in spherical coordinates, the three classes ``scipy.interpolate.SmoothSphereBivariateSpline``, ``scipy.interpolate.LSQSphereBivariateSpline``, and ``scipy.interpolate.RectSphereBivariateSpline`` have been added. Binned statistics (``scipy.stats``)  The stats module has gained functions to do binned statistics, which are a generalization of histograms, in 1D, 2D and multiple dimensions: ``scipy.stats.binned_statistic``, ``scipy.stats.binned_statistic_2d`` and ``scipy.stats.binned_statistic_dd``. Deprecated features =================== ``scipy.sparse.cs_graph_components`` has been made a part of the sparse graph submodule, and renamed to ``scipy.sparse.csgraph.connected_components``. Calling the former routine will result in a deprecation warning. ``scipy.misc.radon`` has been deprecated. A more fullfeatured radon transform can be found in scikitsimage. ``scipy.io.save_as_module`` has been deprecated. A better way to save multiple Numpy arrays is the ``numpy.savez`` function. The `xa` and `xb` parameters for all distributions in ``scipy.stats.distributions`` already weren't used; they have now been deprecated. Backwards incompatible changes ============================== Removal of ``scipy.maxentropy``  The ``scipy.maxentropy`` module, which was deprecated in the 0.10.0 release, has been removed. Logistic regression in scikits.learn is a good and modern alternative for this functionality. Minor change in behavior of ``splev``  The spline evaluation function now behaves similarly to ``interp1d`` for size1 arrays. Previous behavior:: >>> from scipy.interpolate import splev, splrep, interp1d >>> x = [1,2,3,4,5] >>> y = [4,5,6,7,8] >>> tck = splrep(x, y) >>> splev([1], tck) 4. >>> splev(1, tck) 4. Corrected behavior:: >>> splev([1], tck) array([ 4.]) >>> splev(1, tck) array(4.) This affects also the ``UnivariateSpline`` classes. Behavior of ``scipy.integrate.complex_ode``  The behavior of the ``y`` attribute of ``complex_ode`` is changed. Previously, it expressed the complexvalued solution in the form:: z = ode.y[::2] + 1j * ode.y[1::2] Now, it is directly the complexvalued solution:: z = ode.y Minor change in behavior of Ttests  The Ttests ``scipy.stats.ttest_ind``, ``scipy.stats.ttest_rel`` and ``scipy.stats.ttest_1samp`` have been changed so that 0 / 0 now returns NaN instead of 1. Other changes ============= The SuperLU sources in ``scipy.sparse.linalg`` have been updated to version 4.3 from upstream. The function ``scipy.signal.bode``, which calculates magnitude and phase data for a continuoustime system, has been added. The twosample Ttest ``scipy.stats.ttest_ind`` gained an option to compare samples with unequal variances, i.e. Welch's Ttest. ``scipy.misc.logsumexp`` now takes an optional ``axis`` keyword argument. Authors ======= This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order): * Jeff Armstrong * Chad Baker * Brandon Beacher + * behrisch + * borishim + * Matthew Brett * Lars Buitinck * Luis Pedro Coelho + * Johann CohenTanugi * David Cournapeau * dougal + * Ali Ebrahim + * endolith + * Bjørn Forsman + * Robert Gantner + * Sebastian Gassner + * Christoph Gohlke * Ralf Gommers * Yaroslav Halchenko * Charles Harris * Jonathan Helmus + * Andreas Hilboll + * Marc Honnorat + * Jonathan Hunt + * Maxim Ivanov + * Thouis (Ray) Jones * Christopher Kuster + * Josh Lawrence + * Denis Laxalde + * Travis Oliphant * Joonas Paalasmaa + * Fabian Pedregosa * Josef Perktold * Gavin Price + * Jim Radford + * Andrew Schein + * Skipper Seabold * Jacob Silterra + * Scott Sinclair * Alexis Tabary + * Martin Teichmann * Matt Terry + * Nicky van Foreest + * Jacob Vanderplas * Patrick Varilly + * Pauli Virtanen * Nils Wagner + * Darryl Wally + * Stefan van der Walt * Liming Wang + * David WardeFarley + * Warren Weckesser * Sebastian Werk + * Mike Wimmer + * Tony S Yu + A total of 55 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
participants (1)

Ralf Gommers