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263 lines
11 KiB
ReStructuredText
Executable file
=========================
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SciPy 0.8.0 Release Notes
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=========================
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.. contents::
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SciPy 0.8.0 is the culmination of 17 months of hard work. It contains
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many new features, numerous bug-fixes, improved test coverage and
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better documentation. There have been a number of deprecations and
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API changes in this release, which are documented below. All users
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are encouraged to upgrade to this release, as there are a large number
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of bug-fixes and optimizations. Moreover, our development attention
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will now shift to bug-fix releases on the 0.8.x branch, and on adding
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new features on the development trunk. This release requires Python
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2.4 - 2.6 and NumPy 1.4.1 or greater.
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Please note that SciPy is still considered to have "Beta" status, as
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we work toward a SciPy 1.0.0 release. The 1.0.0 release will mark a
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major milestone in the development of SciPy, after which changing the
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package structure or API will be much more difficult. Whilst these
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pre-1.0 releases are considered to have "Beta" status, we are
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committed to making them as bug-free as possible.
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However, until the 1.0 release, we are aggressively reviewing and
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refining the functionality, organization, and interface. This is being
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done in an effort to make the package as coherent, intuitive, and
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useful as possible. To achieve this, we need help from the community
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of users. Specifically, we need feedback regarding all aspects of the
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project - everything - from which algorithms we implement, to details
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about our function's call signatures.
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Python 3
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========
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Python 3 compatibility is planned and is currently technically
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feasible, since Numpy has been ported. However, since the Python 3
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compatible Numpy 1.5 has not been released yet, support for Python 3
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in Scipy is not yet included in Scipy 0.8. SciPy 0.9, planned for fall
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2010, will very likely include experimental support for Python 3.
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Major documentation improvements
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================================
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SciPy documentation is greatly improved.
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Deprecated features
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===================
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Swapping inputs for correlation functions (scipy.signal)
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--------------------------------------------------------
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Concern correlate, correlate2d, convolve and convolve2d. If the second input is
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larger than the first input, the inputs are swapped before calling the
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underlying computation routine. This behavior is deprecated, and will be
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removed in scipy 0.9.0.
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Obsolete code deprecated (scipy.misc)
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-------------------------------------
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The modules `helpmod`, `ppimport` and `pexec` from `scipy.misc` are deprecated.
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They will be removed from SciPy in version 0.9.
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Additional deprecations
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-----------------------
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* linalg: The function `solveh_banded` currently returns a tuple containing
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the Cholesky factorization and the solution to the linear system. In
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SciPy 0.9, the return value will be just the solution.
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* The function `constants.codata.find` will generate a DeprecationWarning.
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In Scipy version 0.8.0, the keyword argument 'disp' was added to the
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function, with the default value 'True'. In 0.9.0, the default will be
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'False'.
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* The `qshape` keyword argument of `signal.chirp` is deprecated. Use
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the argument `vertex_zero` instead.
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* Passing the coefficients of a polynomial as the argument `f0` to
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`signal.chirp` is deprecated. Use the function `signal.sweep_poly`
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instead.
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* The `io.recaster` module has been deprecated and will be removed in 0.9.0.
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New features
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============
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DCT support (scipy.fftpack)
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---------------------------
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New realtransforms have been added, namely dct and idct for Discrete Cosine
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Transform; type I, II and III are available.
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Single precision support for fft functions (scipy.fftpack)
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----------------------------------------------------------
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fft functions can now handle single precision inputs as well: fft(x) will
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return a single precision array if x is single precision.
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At the moment, for FFT sizes that are not composites of 2, 3, and 5, the
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transform is computed internally in double precision to avoid rounding error in
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FFTPACK.
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Correlation functions now implement the usual definition (scipy.signal)
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-----------------------------------------------------------------------
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The outputs should now correspond to their matlab and R counterparts, and do
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what most people expect if the old_behavior=False argument is passed:
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* correlate, convolve and their 2d counterparts do not swap their inputs
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depending on their relative shape anymore;
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* correlation functions now conjugate their second argument while computing
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the slided sum-products, which correspond to the usual definition of
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correlation.
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Additions and modification to LTI functions (scipy.signal)
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----------------------------------------------------------
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* The functions `impulse2` and `step2` were added to `scipy.signal`.
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They use the function `scipy.signal.lsim2` to compute the impulse and
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step response of a system, respectively.
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* The function `scipy.signal.lsim2` was changed to pass any additional
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keyword arguments to the ODE solver.
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Improved waveform generators (scipy.signal)
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-------------------------------------------
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Several improvements to the `chirp` function in `scipy.signal` were made:
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* The waveform generated when `method="logarithmic"` was corrected; it
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now generates a waveform that is also known as an "exponential" or
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"geometric" chirp. (See http://en.wikipedia.org/wiki/Chirp.)
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* A new `chirp` method, "hyperbolic", was added.
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* Instead of the keyword `qshape`, `chirp` now uses the keyword
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`vertex_zero`, a boolean.
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* `chirp` no longer handles an arbitrary polynomial. This functionality
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has been moved to a new function, `sweep_poly`.
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A new function, `sweep_poly`, was added.
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New functions and other changes in scipy.linalg
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-----------------------------------------------
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The functions `cho_solve_banded`, `circulant`, `companion`, `hadamard` and
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`leslie` were added to `scipy.linalg`.
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The function `block_diag` was enhanced to accept scalar and 1D arguments,
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along with the usual 2D arguments.
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New function and changes in scipy.optimize
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------------------------------------------
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The `curve_fit` function has been added; it takes a function and uses
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non-linear least squares to fit that to the provided data.
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The `leastsq` and `fsolve` functions now return an array of size one instead of
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a scalar when solving for a single parameter.
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New sparse least squares solver
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-------------------------------
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The `lsqr` function was added to `scipy.sparse`. `This routine
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<http://www.stanford.edu/group/SOL/software/lsqr.html>`_ finds a
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least-squares solution to a large, sparse, linear system of equations.
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ARPACK-based sparse SVD
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-----------------------
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A naive implementation of SVD for sparse matrices is available in
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scipy.sparse.linalg.eigen.arpack. It is based on using an symmetric solver on
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<A, A>, and as such may not be very precise.
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Alternative behavior available for `scipy.constants.find`
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---------------------------------------------------------
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The keyword argument `disp` was added to the function `scipy.constants.find`,
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with the default value `True`. When `disp` is `True`, the behavior is the
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same as in Scipy version 0.7. When `False`, the function returns the list of
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keys instead of printing them. (In SciPy version 0.9, the default will be
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reversed.)
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Incomplete sparse LU decompositions
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-----------------------------------
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Scipy now wraps SuperLU version 4.0, which supports incomplete sparse LU
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decompositions. These can be accessed via `scipy.sparse.linalg.spilu`.
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Upgrade to SuperLU 4.0 also fixes some known bugs.
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Faster matlab file reader and default behavior change
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------------------------------------------------------
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We've rewritten the matlab file reader in Cython and it should now read
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matlab files at around the same speed that Matlab does.
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The reader reads matlab named and anonymous functions, but it can't
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write them.
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Until scipy 0.8.0 we have returned arrays of matlab structs as numpy
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object arrays, where the objects have attributes named for the struct
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fields. As of 0.8.0, we return matlab structs as numpy structured
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arrays. You can get the older behavior by using the optional
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``struct_as_record=False`` keyword argument to `scipy.io.loadmat` and
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friends.
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There is an inconsistency in the matlab file writer, in that it writes
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numpy 1D arrays as column vectors in matlab 5 files, and row vectors in
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matlab 4 files. We will change this in the next version, so both write
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row vectors. There is a `FutureWarning` when calling the writer to warn
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of this change; for now we suggest using the ``oned_as='row'`` keyword
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argument to `scipy.io.savemat` and friends.
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Faster evaluation of orthogonal polynomials
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-------------------------------------------
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Values of orthogonal polynomials can be evaluated with new vectorized functions
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in `scipy.special`: `eval_legendre`, `eval_chebyt`, `eval_chebyu`,
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`eval_chebyc`, `eval_chebys`, `eval_jacobi`, `eval_laguerre`,
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`eval_genlaguerre`, `eval_hermite`, `eval_hermitenorm`,
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`eval_gegenbauer`, `eval_sh_legendre`, `eval_sh_chebyt`,
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`eval_sh_chebyu`, `eval_sh_jacobi`. This is faster than constructing the
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full coefficient representation of the polynomials, which was previously the
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only available way.
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Note that the previous orthogonal polynomial routines will now also invoke this
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feature, when possible.
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Lambert W function
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------------------
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`scipy.special.lambertw` can now be used for evaluating the Lambert W
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function.
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Improved hypergeometric 2F1 function
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------------------------------------
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Implementation of `scipy.special.hyp2f1` for real parameters was revised.
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The new version should produce accurate values for all real parameters.
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More flexible interface for Radial basis function interpolation
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---------------------------------------------------------------
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The `scipy.interpolate.Rbf` class now accepts a callable as input for the
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"function" argument, in addition to the built-in radial basis functions which
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can be selected with a string argument.
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Removed features
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================
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scipy.stsci: the package was removed
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The module `scipy.misc.limits` was removed.
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scipy.io
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--------
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The IO code in both NumPy and SciPy is being extensively
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reworked. NumPy will be where basic code for reading and writing NumPy
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arrays is located, while SciPy will house file readers and writers for
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various data formats (data, audio, video, images, matlab, etc.).
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Several functions in `scipy.io` are removed in the 0.8.0 release including:
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`npfile`, `save`, `load`, `create_module`, `create_shelf`,
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`objload`, `objsave`, `fopen`, `read_array`, `write_array`,
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`fread`, `fwrite`, `bswap`, `packbits`, `unpackbits`, and
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`convert_objectarray`. Some of these functions have been replaced by NumPy's
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raw reading and writing capabilities, memory-mapping capabilities, or array
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methods. Others have been moved from SciPy to NumPy, since basic array reading
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and writing capability is now handled by NumPy.
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