SciPy Changelog

What's new in SciPy 0.16.1

Jan 29, 2016

Bug fixes

New in SciPy 0.16.0 (Jan 29, 2016)

New in SciPy 1.15.1 (Jan 19, 2015)

New in SciPy 0.15.0 Beta 1 (Nov 25, 2014)

NEW FEATURES:
LINEAR PROGRAMMING INTERFACE:
The new function ``scipy.optimize.linprog`` provides a generic linear programming similar to the way ``scipy.optimize.minimize`` provides a generic interface to nonlinear programming optimizers. Currently the only method supported is *simplex* which provides a two-phase, dense-matrix-based simplex algorithm. Callbacks functions are supported,allowing the user to monitor the progress of the algorithm.
DIFFERENTIAL_EVOLUTION, A GLOBAL OPTIMIZER:
A new ``differential_evolution`` function is available in the ``scipy.optimize`` module. Differential Evolution is an algorithm used for finding the global minimum of multivariate functions. It is stochastic in nature (does not use gradient methods), and can search large areas of candidate space, but often requires larger numbers of function evaluations than conventional gradient based techniques.
``SCIPY.SIGNAL`` IMPROVEMENTS:
The function ``max_len_seq`` was added, which computes a Maximum Length Sequence (MLS) signal.
``SCIPY.INTEGRATE`` IMPROVEMENTS:
It is now possible to use ``scipy.integrate`` routines to integrate multivariate ctypes functions, thus avoiding callbacks to Python and providing better performance.
``SCIPY.LINALG`` IMPROVEMENTS:
Add function ``orthogonal_procrustes`` for solving the procrustes linear algebra problem.
``SCIPY.SPARSE`` IMPROVEMENTS:
``scipy.sparse.linalg.svds`` can now take a ``LinearOperator`` as its main input.
``SCIPY.SPECIAL`` IMPROVEMENTS:
Values of ellipsoidal harmonic (i.e. Lame) functions and associated normalization constants can be now computed using ``ellip_harm``, ``ellip_harm_2``, and ``ellip_normal``.
New convenience functions ``entr``, ``rel_entr`` ``kl_div``, ``huber``, and ``pseudo_huber`` were added.
``scipy.sparse.csgraph`` improvements:
Routines ``reverse_cuthill_mckee`` and ``maximum_bipartite_matching`` for computing reorderings of sparse graphs were added.
``SCIPY.STATS`` IMPROVEMENTS:
Added a Dirichlet distribution as multivariate distribution.
The new function ``scipy.stats.median_test`` computes Mood's median test.
The new function ``scipy.stats.combine_pvalues`` implements Fisher's and Stouffer's methods for combining p-values.
`scipy.stats.describe`` returns a namedtuple rather than a tuple, allowing users to access results by index or by name.
DEPRECATED FEATURES:
The ``scipy.weave`` module is deprecated. It was the only module never ported to Python 3.x, and is not recommended to be used for new code - use Cython instead. In order to support existing code, ``scipy.weave`` has been packaged
separately: `https://github.com/scipy/weave`_. It is a pure Python package, and can easily be installed with ``pip install weave``.
``scipy.special.bessel_diff_formula`` is deprecated. It is a private function, and therefore will be removed from the public API in a following release.
BACKWARDS INCOMPATIBLE CHANGES:
scipy.ndimage:
The functions ``scipy.ndimage.minimum_positions``, ``scipy.ndimage.maximum_positions`` and ``scipy.ndimage.extrema`` return positions as ints instead of floats.
scipy.integrate:
The format of banded Jacobians in ``scipy.integrate.ode`` solvers is changed. Note that the previous documentation of this feature was erroneous.

New in SciPy 0.14.0 (May 5, 2014)

NEW FEATURES:
``scipy.interpolate`` improvements:
A new wrapper function `scipy.interpolate.interpn` for interpolation on regular grids has been added. `interpn` supports linear and nearest-neighbor interpolation in arbitrary dimensions and spline interpolation in two dimensions.
Faster implementations of piecewise polynomials in power and Bernstein polynomial bases have been added as `scipy.interpolate.PPoly` and
`scipy.interpolate.BPoly`. New users should use these in favor of `scipy.interpolate.PiecewisePolynomial`.
`scipy.interpolate.interp1d` now accepts non-monotonic inputs and sorts them. If performance is critical, sorting can be turned off by using the new ``assume_sorted`` keyword.
Functionality for evaluation of bivariate spline derivatives in ``scipy.interpolate`` has been added.
The new class `scipy.interpolate.Akima1DInterpolator` implements the piecewise cubic polynomial interpolation scheme devised by H. Akima.
Functionality for fast interpolation on regular, unevenly spaced grids in arbitrary dimensions has been added as `scipy.interpolate.RegularGridInterpolator` .
``scipy.linalg`` improvements:
The new function `scipy.linalg.dft` computes the matrix of the discrete Fourier transform.
A condition number estimation function for matrix exponential, `scipy.linalg.expm_cond`, has been added.
``scipy.optimize`` improvements:
A set of benchmarks for optimize, which can be run with ``optimize.bench()``, has been added.
`scipy.optimize.curve_fit` now has more controllable error estimation via the ``absolute_sigma`` keyword.
Support for passing custom minimization methods to ``optimize.minimize()`` and ``optimize.minimize_scalar()`` has been added, currently useful especially for combining ``optimize.basinhopping()`` with custom local optimizer routines.
``scipy.stats`` improvements:
A new class `scipy.stats.multivariate_normal` with functionality for multivariate normal random variables has been added.
A lot of work on the ``scipy.stats`` distribution framework has been done. Moment calculations (skew and kurtosis mainly) are fixed and verified, all examples are now runnable, and many small accuracy and performance improvements for individual distributions were merged.
The new function `scipy.stats.anderson_ksamp` computes the k-sample Anderson-Darling test for the null hypothesis that k samples come from the same parent population.
``scipy.signal`` improvements:
``scipy.signal.iirfilter`` and related functions to design Butterworth, Chebyshev, elliptical and Bessel IIR filters now all use pole-zero ("zpk") format internally instead of using transformations to numerator/denominator format. The accuracy of the produced filters, especially high-order ones, is improved significantly as a result.
The new function `scipy.signal.vectorstrength` computes the vector strength, a measure of phase synchrony, of a set of events.
``scipy.special`` improvements:
The functions `scipy.special.boxcox` and `scipy.special.boxcox1p`, which compute the Box-Cox transformation, have been added.
``scipy.sparse`` improvements:
Significant performance improvement in CSR, CSC, and DOK indexing speed.
When using Numpy >= 1.9 (to be released in MM 2014), sparse matrices function correctly when given to arguments of ``np.dot``, ``np.multiply`` and other ufuncs. With earlier Numpy and Scipy versions, the results of such operations are undefined and usually unexpected.
Sparse matrices are no longer limited to ``2^31`` nonzero elements. They automatically switch to using 64-bit index data type for matrices containing more elements. User code written assuming the sparse matrices use int32 as the index data type will continue to work, except for such large matrices. Code dealing with larger matrices needs to accept either int32 or int64 indices.

New in SciPy 0.13.1 (Nov 18, 2013)

New in SciPy 0.13.0 (Oct 21, 2013)

New in SciPy 0.13.0 RC 1 (Oct 12, 2013)

New in SciPy 0.12.0 (Apr 8, 2013)

New features:
``scipy.spatial`` improvements
cKDTree feature-complete
Cython version of KDTree, cKDTree, is now feature-complete.Most operations
(construction, query, query_ball_point, query_pairs, count_neighbors and
sparse_distance_matrix) are between 200 and 1000 times faster in cKDTree than
in KDTree.With very minor caveats, cKDTree has exactly the same interface as
KDTree, and can be used as a drop-in replacement.
Voronoi diagrams and convex hulls:
`scipy.spatial` now contains functionality for computing Voronoi
diagrams and convex hulls using the Qhull library. (Delaunay
triangulation was available since Scipy 0.9.0.)
Delaunay improvements:
It's now possible to pass in custom Qhull options in Delaunay
triangulation. Coplanar points are now also recorded, if present.
Incremental construction of Delaunay triangulations is now also
possible.
Spectral estimators (``scipy.signal``):
The functions ``scipy.signal.periodogram`` and ``scipy.signal.welch`` were
added, providing DFT-based spectral estimators.
``scipy.optimize`` improvements:
Callback functions in L-BFGS-B and TNC
A callback mechanism was added to L-BFGS-B and TNC minimization solvers.
Basin hopping global optimization (``scipy.optimize.basinhopping``)
^^A new global optimization algorithm.Basinhopping is designed to efficiently
find the global minimum of a smooth function.
``scipy.special`` improvements:
Revised complex error functions
The computation of special functions related to the error function now uses a
new `Faddeeva library from MIT `__ which
increases their numerical precision. The scaled and imaginary error functions
``erfcx`` and ``erfi`` were also added, and the Dawson integral ``dawsn`` can
now be evaluated for a complex argument.
Faster orthogonal polynomials
Evaluation of orthogonal polynomials (the ``eval_*`` routines) in now
faster in ``scipy.special``, and their ``out=`` argument functions
properly.
``scipy.sparse.linalg`` features
In ``scipy.sparse.linalg.spsolve``, the ``b`` argument can now be either
a vector or a matrix.
``scipy.sparse.linalg.inv`` was added.This uses ``spsolve`` to compute
a sparse matrix inverse.
``scipy.sparse.linalg.expm`` was added.This computes the exponential of
a sparse matrix using a similar algorithm to the existing dense array
implementation in ``scipy.linalg.expm``.
Listing Matlab(R) file contents in ``scipy.io``
A new function ``whosmat`` is available in ``scipy.io`` for inspecting contents
of MAT files without reading them to memory.
Documented BLAS and LAPACK low-level interfaces (``scipy.linalg``)
The modules `scipy.linalg.blas` and `scipy.linalg.lapack` can be used
to access low-level BLAS and LAPACK functions.
Polynomial interpolation improvements (``scipy.interpolate``)
The barycentric, Krogh, piecewise and pchip polynomial interpolators in
``scipy.interpolate`` accept now an ``axis`` argument.

New in SciPy 0.12.0 RC 1 (Apr 1, 2013)

New in SciPy 0.11.0 (Sep 26, 2012)

New features:
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 Floyd-Warshall algorithm for shortest path
func:`breadth_first_order` - compute a breadth-first order of nodes
func:`depth_first_order` - compute a depth-first order of nodes
func:`breadth_first_tree` - construct the breadth-first tree from a given node
func:`depth_first_tree` - construct a depth-first 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 clean-up, 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 L-BFGS-B 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.

New in SciPy 0.11.0 RC 2 (Aug 14, 2012)

New in SciPy 0.11.0 RC 1 (Jul 20, 2012)

New in SciPy 0.11.0 Beta 1 (Jun 14, 2012)

New in SciPy 0.10.1 (Feb 28, 2012)

New in SciPy 0.10.1 RC 2 (Feb 20, 2012)

New in SciPy 0.10.1 RC 1 (Feb 13, 2012)

New in SciPy 0.10.0 (Nov 14, 2011)

New in SciPy 0.10.0 Beta 1 (Sep 13, 2011)

New in SciPy 0.9.0 (Jul 26, 2011)

New in SciPy 0.8.0 (Jul 27, 2010)

Several improvements to the `chirp` function in `scipy.signal` were made:
The waveform generated when `method="logarithmic"` was corrected; it
now generates a waveform that is also known as an "exponential" or
"geometric" chirp. (See http://en.wikipedia.org/wiki/Chirp.)
A new `chirp` method, "hyperbolic", was added.
Instead of the keyword `qshape`, `chirp` now uses the keyword
`vertex_zero`, a boolean.
`chirp` no longer handles an arbitrary polynomial. This functionality
has been moved to a new function, `sweep_poly`.
A new function, `sweep_poly`, was added.
New functions and other changes in scipy.linalg
The functions `cho_solve_banded`, `circulant`, `companion`, `hadamard` and
`leslie` were added to `scipy.linalg`.
The function `block_diag` was enhanced to accept scalar and 1D arguments,
along with the usual 2D arguments.
New function and changes in scipy.optimize
The `curve_fit` function has been added; it takes a function and uses
non-linear least squares to fit that to the provided data.
The `leastsq` and `fsolve` functions now return an array of size one instead of
a scalar when solving for a single parameter.
New sparse least squares solver
The `lsqr` function was added to `scipy.sparse`. `This routine
`_ finds a
least-squares solution to a large, sparse, linear system of equations.
ARPACK-based sparse SVD
A naive implementation of SVD for sparse matrices is available in
scipy.sparse.linalg.eigen.arpack. It is based on using an symmetric solver on
, and as such may not be very precise.
Alternative behavior available for `scipy.constants.find`
The keyword argument `disp` was added to the function `scipy.constants.find`,
with the default value `True`. When `disp` is `True`, the behavior is the
same as in Scipy version 0.7. When `False`, the function returns the list of
keys instead of printing them. (In SciPy version 0.9, the default will be
reversed.)
Incomplete sparse LU decompositions
Scipy now wraps SuperLU version 4.0, which supports incomplete sparse LU
decompositions. These can be accessed via `scipy.sparse.linalg.spilu`.
Upgrade to SuperLU 4.0 also fixes some known bugs.
Faster matlab file reader and default behavior change
We've rewritten the matlab file reader in Cython and it should now read
matlab files at around the same speed that Matlab does.
The reader reads matlab named and anonymous functions, but it can't
write them.
Until scipy 0.8.0 we have returned arrays of matlab structs as numpy
object arrays, where the objects have attributes named for the struct
fields. As of 0.8.0, we return matlab structs as numpy structured
arrays. You can get the older behavior by using the optional
``struct_as_record=False`` keyword argument to `scipy.io.loadmat` and
friends.
There is an inconsistency in the matlab file writer, in that it writes
numpy 1D arrays as column vectors in matlab 5 files, and row vectors in
matlab 4 files. We will change this in the next version, so both write
row vectors. There is a `FutureWarning` when calling the writer to warn
of this change; for now we suggest using the ``oned_as='row'`` keyword
argument to `scipy.io.savemat` and friends.
Faster evaluation of orthogonal polynomials
Values of orthogonal polynomials can be evaluated with new vectorized functions
in `scipy.special`: `eval_legendre`, `eval_chebyt`, `eval_chebyu`,
`eval_chebyc`, `eval_chebys`, `eval_jacobi`, `eval_laguerre`,
`eval_genlaguerre`, `eval_hermite`, `eval_hermitenorm`,
`eval_gegenbauer`, `eval_sh_legendre`, `eval_sh_chebyt`,
`eval_sh_chebyu`, `eval_sh_jacobi`. This is faster than constructing the
full coefficient representation of the polynomials, which was previously the
only available way.
Note that the previous orthogonal polynomial routines will now also invoke this
feature, when possible.
Lambert W function
`scipy.special.lambertw` can now be used for evaluating the Lambert W
function.
Improved hypergeometric 2F1 function
Implementation of `scipy.special.hyp2f1` for real parameters was revised.
The new version should produce accurate values for all real parameters.
More flexible interface for Radial basis function interpolation
The `scipy.interpolate.Rbf` class now accepts a callable as input for the
"function" argument, in addition to the built-in radial basis functions which
can be selected with a string argument.
Removed features
scipy.stsci: the package was removed
The module `scipy.misc.limits` was removed.

New in SciPy 0.8.0 RC 3 (Jul 15, 2010)

New in SciPy 0.8.0 RC 2 (Jul 12, 2010)

linalg: The function `solveh_banded` currently returns a tuple containing
the Cholesky factorization and the solution to the linear system. In
SciPy 0.9, the return value will be just the solution.
The function `constants.codata.find` will generate a DeprecationWarning.
In Scipy version 0.8.0, the keyword argument 'disp' was added to the
function, with the default value 'True'. In 0.9.0, the default will be
'False'.
The `qshape` keyword argument of `signal.chirp` is deprecated. Use
the argument `vertex_zero` instead.
Passing the coefficients of a polynomial as the argument `f0` to
`signal.chirp` is deprecated. Use the function `signal.sweep_poly`
instead.
The `io.recaster` module has been deprecated and will be removed in 0.9.0.
New features
DCT support (scipy.fftpack)
New realtransforms have been added, namely dct and idct for Discrete Cosine
Transform; type I, II and III are available.
Single precision support for fft functions (scipy.fftpack)
fft functions can now handle single precision inputs as well: fft(x) will
return a single precision array if x is single precision.
At the moment, for FFT sizes that are not composites of 2, 3, and 5, the
transform is computed internally in double precision to avoid rounding error in
FFTPACK.
Correlation functions now implement the usual definition (scipy.signal)
The outputs should now correspond to their matlab and R counterparts, and do
what most people expect if the old_behavior=False argument is passed:
correlate, convolve and their 2d counterparts do not swap their inputs
depending on their relative shape anymore;
correlation functions now conjugate their second argument while computing
the slided sum-products, which correspond to the usual definition of
correlation.
Additions and modification to LTI functions (scipy.signal)
The functions `impulse2` and `step2` were added to `scipy.signal`.
They use the function `scipy.signal.lsim2` to compute the impulse and
step response of a system, respectively.
The function `scipy.signal.lsim2` was changed to pass any additional
keyword arguments to the ODE solver.
Improved waveform generators (scipy.signal)
Several improvements to the `chirp` function in `scipy.signal` were made:
The waveform generated when `method="logarithmic"` was corrected; it
now generates a waveform that is also known as an "exponential" or
geometric" chirp. (See http://en.wikipedia.org/wiki/Chirp.)
A new `chirp` method, "hyperbolic", was added.
Instead of the keyword `qshape`, `chirp` now uses the keyword
`vertex_zero`, a boolean.
`chirp` no longer handles an arbitrary polynomial. This functionality
has been moved to a new function, `sweep_poly.

SciPy Changelog

What's new in SciPy 0.16.1

New in SciPy 0.16.0 (Jan 29, 2016)

New in SciPy 1.15.1 (Jan 19, 2015)

New in SciPy 0.15.0 Beta 1 (Nov 25, 2014)

New in SciPy 0.14.0 (May 5, 2014)

New in SciPy 0.13.1 (Nov 18, 2013)

New in SciPy 0.13.0 (Oct 21, 2013)

New in SciPy 0.13.0 RC 1 (Oct 12, 2013)

New in SciPy 0.12.0 (Apr 8, 2013)

New in SciPy 0.12.0 RC 1 (Apr 1, 2013)

New in SciPy 0.11.0 (Sep 26, 2012)

New in SciPy 0.11.0 RC 2 (Aug 14, 2012)

New in SciPy 0.11.0 RC 1 (Jul 20, 2012)

New in SciPy 0.11.0 Beta 1 (Jun 14, 2012)

New in SciPy 0.10.1 (Feb 28, 2012)

New in SciPy 0.10.1 RC 2 (Feb 20, 2012)

New in SciPy 0.10.1 RC 1 (Feb 13, 2012)

New in SciPy 0.10.0 (Nov 14, 2011)

New in SciPy 0.10.0 Beta 1 (Sep 13, 2011)

New in SciPy 0.9.0 (Jul 26, 2011)

New in SciPy 0.8.0 (Jul 27, 2010)

New in SciPy 0.8.0 RC 3 (Jul 15, 2010)

New in SciPy 0.8.0 RC 2 (Jul 12, 2010)

New in SciPy 0.8.0 RC 1 (Jul 5, 2010)

New in SciPy 0.8.0 Beta 1 (Jun 5, 2010)

New in SciPy 0.7.2 (Apr 22, 2010)

New in SciPy 0.7.2 RC3 (Apr 19, 2010)

New in SciPy 0.7.1 (Jul 13, 2009)