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,
Spherical Voronoi diagrams,
Support for discrete-time linear systems,
A solver of two-point boundary value problems for ODE systems has been
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.
Cubic spline interpolation is now available via
This class represents a piecewise cubic polynomial passing through given points
and C2 continuous. It is represented in the standard polynomial basis on each
A representation of n-dimensional tensor product piecewise polynomials is
available as the
Univariate piecewise polynomial classes, PPoly and Bpoly, can now be
evaluated on periodic domains. Use
argument for this.
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
(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.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.
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
Sparse matrices now have a count_nonzero method, which counts the number of
nonzero elements in the matrix. Unlike getnnz() and
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.
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.
Trapezoidal distribution has been implemented as
Skew normal distribution has been implemented as
Burr type XII distribution has been implemented as
Three- and four-parameter kappa distributions have been implemented as
scipy.stats.iqr function computes the interquartile region of a
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.svd gained a new keyword argument,
gesdd (default) and
scipy.linalg.lapack.ilaver returns the version of the LAPACK library SciPy
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.
scipy.spatial.SphericalVoronoi constructs Voronoi diagrams on the
surface of a sphere. See pull request gh-5232 for details.
A new clustering algorithm, the nearest neighbor chain algorithm, has been
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.
The new function
scipy.special.loggamma computes the principal branch of the
logarithm of the Gamma function. For real input,
loggamma is compatible
scipy.special.gammaln. For complex input, it has more consistent
behavior in the complex plane and should be preferred over
Vectorized forms of spherical Bessel functions have been implemented as
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.
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_inkn have been deprecated in favor of
The following functions in
scipy.constants are deprecated:
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.
The convergence criterion for
works the same as
The offset in
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.
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:
A bug in the
rvs() method of the distributions in
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
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.
rvs() method also accepted some arguments that it should not have.
There is a potential for backwards incompatibility in cases where
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
scipy.io.netcdf masking now gives precedence to 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
missing_value are no longer
treated as missing values.
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.
changes the interpretation of the maxfun option in L-BFGS-B based routines
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
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.