SciPy 0.18.0 Release Notes#
SciPy 0.18.0 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 0.19.x branch, and on adding new features on the master branch.
This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater.
Highlights of this release include:
A new ODE solver for two-point boundary value problems, scipy.optimize.solve_bvp.
A new class, CubicSpline, for cubic spline interpolation of data.
N-dimensional tensor product polynomials,
scipy.interpolate.NdPPoly.Spherical Voronoi diagrams,
scipy.spatial.SphericalVoronoi.Support for discrete-time linear systems,
scipy.signal.dlti.
New features#
scipy.integrate improvements#
A solver of two-point boundary value problems for ODE systems has been
implemented in scipy.integrate.solve_bvp. The solver allows for non-separated
boundary conditions, unknown parameters and certain singular terms. It finds
a C1 continious solution using a fourth-order collocation algorithm.
scipy.interpolate improvements#
Cubic spline interpolation is now available via scipy.interpolate.CubicSpline.
This class represents a piecewise cubic polynomial passing through given points
and C2 continuous. It is represented in the standard polynomial basis on each
segment.
A representation of n-dimensional tensor product piecewise polynomials is
available as the scipy.interpolate.NdPPoly class.
Univariate piecewise polynomial classes, PPoly and Bpoly, can now be
evaluated on periodic domains. Use extrapolate="periodic" keyword
argument for this.
scipy.fftpack improvements#
scipy.fftpack.next_fast_len function computes the next “regular” number for
FFTPACK. Padding the input to this length can give significant performance
increase for scipy.fftpack.fft.
scipy.signal improvements#
Resampling using polyphase filtering has been implemented in the function
scipy.signal.resample_poly. This method upsamples a signal, applies a
zero-phase low-pass FIR filter, and downsamples using scipy.signal.upfirdn
(which is also new in 0.18.0). This method can be faster than FFT-based
filtering provided by scipy.signal.resample for some signals.
scipy.signal.firls, which constructs FIR filters using least-squares error
minimization, was added.
scipy.signal.sosfiltfilt, which does forward-backward filtering like
scipy.signal.filtfilt but for second-order sections, was added.
Discrete-time linear systems#
scipy.signal.dlti provides an implementation of discrete-time linear systems.
Accordingly, the StateSpace, TransferFunction and ZerosPolesGain classes
have learned a the new keyword, dt, which can be used to create discrete-time
instances of the corresponding system representation.
scipy.sparse improvements#
The functions sum, max, mean, min, transpose, and reshape in
scipy.sparse have had their signatures augmented with additional arguments
and functionality so as to improve compatibility with analogously defined
functions in numpy.
Sparse matrices now have a count_nonzero method, which counts the number of
nonzero elements in the matrix. Unlike getnnz() and nnz property,
which return the number of stored entries (the length of the data attribute),
this method counts the actual number of non-zero entries in data.
scipy.optimize improvements#
The implementation of Nelder-Mead minimization, scipy.minimize(…, method=”Nelder-Mead”), obtained a new keyword, initial_simplex, which can be used to specify the initial simplex for the optimization process.
Initial step size selection in CG and BFGS minimizers has been improved. We expect that this change will improve numeric stability of optimization in some cases. See pull request gh-5536 for details.
Handling of infinite bounds in SLSQP optimization has been improved. We expect that this change will improve numeric stability of optimization in the some cases. See pull request gh-6024 for details.
A large suite of global optimization benchmarks has been added to
scipy/benchmarks/go_benchmark_functions. See pull request gh-4191 for details.
Nelder-Mead and Powell minimization will now only set defaults for maximum iterations or function evaluations if neither limit is set by the caller. In some cases with a slow converging function and only 1 limit set, the minimization may continue for longer than with previous versions and so is more likely to reach convergence. See issue gh-5966.
scipy.stats improvements#
Trapezoidal distribution has been implemented as scipy.stats.trapz.
Skew normal distribution has been implemented as scipy.stats.skewnorm.
Burr type XII distribution has been implemented as scipy.stats.burr12.
Three- and four-parameter kappa distributions have been implemented as
scipy.stats.kappa3 and scipy.stats.kappa4, respectively.
New scipy.stats.iqr function computes the interquartile region of a
distribution.
Random matrices#
scipy.stats.special_ortho_group and scipy.stats.ortho_group provide
generators of random matrices in the SO(N) and O(N) groups, respectively. They
generate matrices in the Haar distribution, the only uniform distribution on
these group manifolds.
scipy.stats.random_correlation provides a generator for random
correlation matrices, given specified eigenvalues.
scipy.linalg improvements#
scipy.linalg.svd gained a new keyword argument, lapack_driver. Available
drivers are gesdd (default) and gesvd.
scipy.linalg.lapack.ilaver returns the version of the LAPACK library SciPy
links to.
scipy.spatial improvements#
Boolean distances, scipy.spatial.pdist, have been sped up. Improvements vary by the function and the input size. In many cases, one can expect a speed-up of x2–x10.
New class scipy.spatial.SphericalVoronoi constructs Voronoi diagrams on the
surface of a sphere. See pull request gh-5232 for details.
scipy.cluster improvements#
A new clustering algorithm, the nearest neighbor chain algorithm, has been
implemented for scipy.cluster.hierarchy.linkage. As a result, one can expect
a significant algorithmic improvement (\(O(N^2)\) instead of \(O(N^3)\))
for several linkage methods.
scipy.special improvements#
The new function scipy.special.loggamma computes the principal branch of the
logarithm of the Gamma function. For real input, loggamma is compatible
with scipy.special.gammaln. For complex input, it has more consistent
behavior in the complex plane and should be preferred over gammaln.
Vectorized forms of spherical Bessel functions have been implemented as
scipy.special.spherical_jn, scipy.special.spherical_kn,
scipy.special.spherical_in and scipy.special.spherical_yn.
They are recommended for use over sph_* functions, which are now deprecated.
Several special functions have been extended to the complex domain and/or have seen domain/stability improvements. This includes spence, digamma, log1p and several others.
Deprecated features#
The cross-class properties of lti systems have been deprecated. The following properties/setters will raise a DeprecationWarning:
Name - (accessing/setting raises warning) - (setting raises warning) * StateSpace - (num, den, gain) - (zeros, poles) * TransferFunction (A, B, C, D, gain) - (zeros, poles) * ZerosPolesGain (A, B, C, D, num, den) - ()
Spherical Bessel functions, sph_in, sph_jn, sph_kn, sph_yn,
sph_jnyn and sph_inkn have been deprecated in favor of
scipy.special.spherical_jn and spherical_kn, spherical_yn,
spherical_in.
The following functions in scipy.constants are deprecated: C2K, K2C,
C2F, F2C, F2K and K2F. They are superceded by a new function
scipy.constants.convert_temperature that can perform all those conversions
plus to/from the Rankine temperature scale.
Backwards incompatible changes#
scipy.optimize#
The convergence criterion for optimize.bisect,
optimize.brentq, optimize.brenth, and optimize.ridder now
works the same as numpy.allclose.
scipy.ndimage#
The offset in ndimage.iterpolation.affine_transform
is now consistently added after the matrix is applied,
independent of if the matrix is specified using a one-dimensional
or a two-dimensional array.
scipy.stats#
stats.ks_2samp used to return nonsensical values if the input was
not real or contained nans. It now raises an exception for such inputs.
Several deprecated methods of scipy.stats distributions have been removed:
est_loc_scale, vecfunc, veccdf and vec_generic_moment.
Deprecated functions nanmean, nanstd and nanmedian have been removed
from scipy.stats. These functions were deprecated in scipy 0.15.0 in favor
of their numpy equivalents.
A bug in the rvs() method of the distributions in scipy.stats has
been fixed. When arguments to rvs() were given that were shaped for
broadcasting, in many cases the returned random samples were not random.
A simple example of the problem is stats.norm.rvs(loc=np.zeros(10)).
Because of the bug, that call would return 10 identical values. The bug
only affected code that relied on the broadcasting of the shape, location
and scale parameters.
The rvs() method also accepted some arguments that it should not have.
There is a potential for backwards incompatibility in cases where rvs()
accepted arguments that are not, in fact, compatible with broadcasting.
An example is
stats.gamma.rvs([2, 5, 10, 15], size=(2,2))
The shape of the first argument is not compatible with the requested size,
but the function still returned an array with shape (2, 2). In scipy 0.18,
that call generates a ValueError.
scipy.io#
scipy.io.netcdf masking now gives precedence to the _FillValue attribute
over the missing_value attribute, if both are given. Also, data are only
treated as missing if they match one of these attributes exactly: values that
differ by roundoff from _FillValue or missing_value are no longer
treated as missing values.
scipy.interpolate#
scipy.interpolate.PiecewisePolynomial class has been removed. It has been
deprecated in scipy 0.14.0, and scipy.interpolate.BPoly.from_derivatives serves
as a drop-in replacement.
Other changes#
Scipy now uses setuptools for its builds instead of plain distutils. This
fixes usage of install_requires='scipy' in the setup.py files of
projects that depend on Scipy (see Numpy issue gh-6551 for details). It
potentially affects the way that build/install methods for Scipy itself behave
though. Please report any unexpected behavior on the Scipy issue tracker.
PR #6240
changes the interpretation of the maxfun option in L-BFGS-B based routines
in the scipy.optimize module.
An L-BFGS-B search consists of multiple iterations,
with each iteration consisting of one or more function evaluations.
Whereas the old search strategy terminated immediately upon reaching maxfun
function evaluations, the new strategy allows the current iteration
to finish despite reaching maxfun.
The bundled copy of Qhull in the scipy.spatial subpackage has been upgraded to
version 2015.2.
The bundled copy of ARPACK in the scipy.sparse.linalg subpackage has been
upgraded to arpack-ng 3.3.0.
The bundled copy of SuperLU in the scipy.sparse subpackage has been upgraded
to version 5.1.1.