SciPy 0.14.0 is the culmination of 8 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 0.14.x branch, and on adding new features on the master branch.
This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or greater.
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
Faster implementations of piecewise polynomials in power and Bernstein
polynomial bases have been added as
scipy.interpolate.BPoly. New users should use these in favor of
scipy.interpolate.interp1d now accepts non-monotonic inputs and sorts them.
If performance is critical, sorting can be turned off by using the new
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
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.
A set of benchmarks for optimize, which can be run with
has been added.
scipy.optimize.curve_fit now has more controllable error estimation via the
Support for passing custom minimization methods to
optimize.minimize_scalar() has been added, currently useful especially
optimize.basinhopping() with custom local optimizer routines.
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.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.
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.multiplyand 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
2^31nonzero 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.
The global minimization function scipy.optimize.anneal is deprecated.
All users should use the
scipy.optimize.basinhopping function instead.
randwppf functions are deprecated. All users should use
rvs methods instead.
Probability calculation aliases
deprecated. Use instead the
sf methods of the corresponding distributions
special functions directly.
lpmn no longer accepts complex-valued arguments. A new function
clpmn with uniform complex analytic behavior has been added, and
it should be used instead.
Eigenvectors in the case of generalized eigenvalue problem are normalized to unit vectors in 2-norm, rather than following the LAPACK normalization convention.
The deprecated UMFPACK wrapper in
scipy.sparse.linalg has been removed due
to license and install issues. If available,
scikits.umfpack is still used
transparently in the
factorized functions. Otherwise,
SuperLU is used instead in these functions.
The deprecated functions
cmedian have been removed
stats.scoreatpercentile now returns an array instead of a list of
The API for computing derivatives of a monotone piecewise interpolation has
changed: if p is a
PchipInterpolator object, p.derivative(der)
returns a callable object representing the derivative of p. For in-place
derivatives use the second argument of the __call__ method:
p(0.1, der=2) evaluates the second derivative of p at x=0.1.
The method p.derivatives has been removed.