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SciPy 1.5.0

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@tylerjereddy tylerjereddy released this 21 Jun 17:30
· 107 commits to maintenance/1.5.x since this release

SciPy 1.5.0 Release Notes

SciPy 1.5.0 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. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with python -Wd and check for DeprecationWarning s).
Our development attention will now shift to bug-fix releases on the
1.5.x branch, and on adding new features on the master branch.

This release requires Python 3.6+ and NumPy 1.14.5 or greater.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release

  • wrappers for more than a dozen new LAPACK routines are now available
    in scipy.linalg.lapack
  • Improved support for leveraging 64-bit integer size from linear algebra
  • addition of the probability distribution for two-sided one-sample
    Kolmogorov-Smirnov tests

New features

scipy.cluster improvements

Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.

scipy.cluster.hierarchy.dendrogram now respects the matplotlib color

scipy.fft improvements

A new keyword-only argument plan is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW and mkl-fft), and it is
currently not used in SciPy.

scipy.integrate improvements

scipy.interpolate improvements improvements error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read. gained a simplify_cells parameter, which if set to
True simplifies the structure of the return value if the .mat file
contains cell arrays.

pathlib.Path objects are now supported in Matrix Market I/O

scipy.linalg improvements

scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value keyword. Another keyword subset_by_index is introduced.
Keywords turbo and eigvals are deprecated.

Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
?<sy/he>evx are added and existing ones now have full _lwork

Wrappers for the following LAPACK routines have been added to

  • ?getc2: computes the LU factorization of a general matrix with complete
  • ?gesc2: solves a linear system given an LU factorization from ?getc2
  • ?gejsv: computes the singular value decomposition of a general matrix
    with higher accuracy calculation of tiny singular values and their
    corresponding singular vectors
  • ?geqrfp: computes the QR factorization of a general matrix with
    non-negative elements on the diagonal of R
  • ?gtsvx: solves a linear system with general tridiagonal matrix
  • ?gttrf: computes the LU factorization of a tridiagonal matrix
  • ?gttrs: solves a linear system given an LU factorization from ?gttrf
  • ?ptsvx: solves a linear system with symmetric positive definite
    tridiagonal matrix
  • ?pttrf: computes the LU factorization of a symmetric positive definite
    tridiagonal matrix
  • ?pttrs: solves a linear system given an LU factorization from ?pttrf
  • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
    tridiagonal matrix
  • ?tbtrs: solves a linear system with a triangular banded matrix
  • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary

Generalized QR factorization routines (?geqrf) now have full _lwork

scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been

The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
was added.

The new function scipy.linalg.convolution_matrix constructs the Toeplitz
matrix representing one-dimensional convolution.

scipy.ndimage improvements

scipy.optimize improvements

The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:

  • 2-point, 3-point, or complex step finite differences can be used. Previously
    only a 2-step finite difference was available.
  • There is now the possibility to use a relative step size, previously only an
    absolute step size was available.
  • If the minimize method uses bounds the numerical differentiation strictly
    obeys those limits.
  • The numerical differentiation machinery now makes use of a simple cache,
    which in some cases can reduce the number of function evaluations.
  • minimize's method= 'powell' now supports simple bound constraints

There have been several improvements to scipy.optimize.linprog:

  • The linprog benchmark suite has been expanded considerably.
  • linprog's dense pivot-based redundancy removal routine and sparse
    presolve are faster
  • When scikit-sparse is available, solving sparse problems with
    method='interior-point' is faster

The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation such as BFGS.

differential_evolution can now use the modern np.random.Generator as
well as the legacy np.random.RandomState as a seed.

scipy.signal improvements

A new optional argument include_nyquist is added to freqz functions in
this module. It is used for including the last frequency (Nyquist frequency).

scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
size of the window used to calculate the noise floor.

scipy.sparse improvements

Outer indexing is now faster when using a 2d column vector to select column

scipy.sparse.lil.tocsr is faster

Fixed/improved comparisons between pydata sparse arrays and sparse matrices

BSR format sparse multiplication performance has been improved.

scipy.sparse.linalg.LinearOperator has gained the new ndim class

scipy.spatial improvements

scipy.spatial.geometric_slerp has been added to enable geometric
spherical linear interpolation on an n-sphere

scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
and 3D cases

The tree building algorithm used by cKDTree has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.

scipy.special improvements

The following functions now have Cython interfaces in cython_special:

  • scipy.special.erfinv
  • scipy.special.erfcinv
  • scipy.special.spherical_jn
  • scipy.special.spherical_yn
  • scipy.special.spherical_in
  • scipy.special.spherical_kn

scipy.special.log_softmax has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x)) for
inputs that make softmax saturate.

scipy.stats improvements

The function for generating random samples in scipy.stats.dlaplace has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).

scipy.stats functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator as well as the legacy
np.random.RandomState as a seed.

The axis parameter was added to scipy.stats.rankdata. This allows slices
of an array along the given axis to be ranked independently.

The axis parameter was added to scipy.stats.f_oneway, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway was also improved for some cases.

The PDF and CDF methods for stats.geninvgauss are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based

Moments of the normal distribution (scipy.stats.norm) are now calculated using
analytical formulas instead of numerical integration for greater speed and

Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy

Methods of the truncated normal distribution (scipy.stats.truncnorm),
especially _rvs, are significantly faster after a complete rewrite.

The fit method of the Laplace distribution, scipy.stats.laplace, now uses
the analytical formulas for the maximum likelihood estimates of the parameters.

Generation of random variates is now thread safe for all SciPy distributions.
3rd-party distributions may need to modify the signature of the _rvs()
method to conform to _rvs(self, ..., size=None, random_state=None). (A
one-time VisibleDeprecationWarning is emitted when using non-conformant

The Kolmogorov-Smirnov two-sided test statistic distribution
(scipy.stats.kstwo) was added. Calculates the distribution of the K-S
two-sided statistic D_n for a sample of size n, using a mixture of exact
and asymptotic algorithms.

The new function median_abs_deviation replaces the deprecated

The wilcoxon function now computes the p-value for Wilcoxon's signed rank
test using the exact distribution for inputs up to length 25. The function has
a new mode parameter to specify how the p-value is to be computed. The
default is "auto", which uses the exact distribution for inputs up to length
25 and the normal approximation for larger inputs.

Added a new Cython-based implementation to evaluate guassian kernel estimates,
which should improve the performance of gaussian_kde

The winsorize function now has a nan_policy argument for refined
handling of nan input values.

The binned_statistic_dd function with statistic="std" performance was
improved by ~4x.

scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
two-sample testing. The one-sample variation uses scipy.stats.ksone
(or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
the p-value. The two-sample variation, invoked if cdf is array_like, uses
an algorithm described by Hodges to compute the probability directly, only
backing off to scipy.stats.kstwo in case of overflow. The result in both
cases is more accurate p-values, especially for two-sample testing with
smaller (or quite different) sizes.

scipy.stats.maxwell performance improvements include a 20 % speed up for
`fit()and 5 % forpdf()``

scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
for greater consistency with other stats functions

Deprecated features

scipy deprecations

scipy.special changes

The bdtr, bdtrc, and bdtri functions are deprecating non-negative
non-integral n arguments.

scipy.stats changes

The function median_absolute_deviation is deprecated. Use
median_abs_deviation instead.

The use of the string "raw" with the scale parameter of iqr is
deprecated. Use scale=1 instead.

Backwards incompatible changes

scipy.interpolate changes

scipy.linalg changes

The output signatures of ?syevr, ?heevr have been changed from
w, v, info to w, v, m, isuppz, info

The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is

scipy.signal changes

The output length of scipy.signal.upfirdn has been corrected, resulting
outputs may now be shorter for some combinations of up/down ratios and input
signal and filter lengths.

scipy.signal.resample now supports a domain keyword argument for
specification of time or frequency domain input.

scipy.stats changes

Other changes

Improved support for leveraging 64-bit integer size from linear algebra backends
in several parts of the SciPy codebase.

Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
been removed.

Many warnings due to unused imports and unused assignments have been addressed.

Many usage examples were added to function docstrings, and many input
validations and intuitive exception messages have been added throughout the

Early stage adoption of type annotations in a few parts of the codebase


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A total of 129 people contributed to this release.
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