SciPy 1.8.0 Release Notes

SciPy 1.8.0 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
1.8.x branch, and on adding new features on the master branch.

This release requires Python 3.8+ and NumPy 1.17.3 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• A sparse array API has been added for early testing and feedback; this
  work is ongoing, and users should expect minor API refinements over
  the next few releases.
• The sparse SVD library PROPACK is now vendored with SciPy, and an interface
  is exposed via scipy.sparse.svds with solver='PROPACK'. It is currently
  default-off due to potential issues on Windows that we aim to
  resolve in the next release, but can be optionally enabled at runtime for
  friendly testing with an environment variable setting of USE_PROPACK=1.
• A new scipy.stats.sampling submodule that leverages the UNU.RAN C
  library to sample from arbitrary univariate non-uniform continuous and
  discrete distributions
• All namespaces that were private but happened to miss underscores in
  their names have been deprecated.

New features

scipy.fft improvements

Added an orthogonalize=None parameter to the real transforms in scipy.fft
which controls whether the modified definition of DCT/DST is used without
changing the overall scaling.

scipy.fft backend registration is now smoother, operating with a single
registration call and no longer requiring a context manager.

scipy.integrate improvements

scipy.integrate.quad_vec introduces a new optional keyword-only argument,
args. args takes in a tuple of extra arguments if any (default is
args=()), which is then internally used to pass into the callable function
(needing these extra arguments) which we wish to integrate.

scipy.interpolate improvements

scipy.interpolate.BSpline has a new method, design_matrix, which
constructs a design matrix of b-splines in the sparse CSR format.

A new method from_cubic in BSpline class allows to convert a
CubicSpline object to BSpline object.

scipy.linalg improvements

scipy.linalg gained three new public array structure investigation functions.
scipy.linalg.bandwidth returns information about the bandedness of an array
and can be used to test for triangular structure discovery, while
scipy.linalg.issymmetric and scipy.linalg.ishermitian test the array for
exact and approximate symmetric/Hermitian structure.

scipy.optimize improvements

scipy.optimize.check_grad introduces two new optional keyword only arguments,
direction and seed. direction can take values, 'all' (default),
in which case all the one hot direction vectors will be used for verifying
the input analytical gradient function and 'random', in which case a
random direction vector will be used for the same purpose. seed
(default is None) can be used for reproducing the return value of
check_grad function. It will be used only when direction='random'.

The scipy.optimize.minimize TNC method has been rewritten to use Cython
bindings. This also fixes an issue with the callback altering the state of the
optimization.

Added optional parameters target_accept_rate and stepwise_factor for
adapative step size adjustment in basinhopping.

The epsilon argument to approx_fprime is now optional so that it may
have a default value consistent with most other functions in scipy.optimize.

scipy.signal improvements

Add analog argument, default False, to zpk2sos, and add new pairing
option 'minimal' to construct analog and minimal discrete SOS arrays.
tf2sos uses zpk2sos; add analog argument here as well, and pass it on
to zpk2sos.

savgol_coeffs and savgol_filter now work for even window lengths.

Added the Chirp Z-transform and Zoom FFT available as scipy.signal.CZT and
scipy.signal.ZoomFFT.

scipy.sparse improvements

An array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases. Please refer to the scipy.sparse
docstring for more information.

maximum_flow introduces optional keyword only argument, method
which accepts either, 'edmonds-karp' (Edmonds Karp algorithm) or
'dinic' (Dinic's algorithm). Moreover, 'dinic' is used as default
value for method which means that Dinic's algorithm is used for computing
maximum flow unless specified. See, the comparison between the supported
algorithms in
this comment <https://github.com/scipy/scipy/pull/14358#issue-684212523>_.

Parameters atol, btol now default to 1e-6 in
scipy.sparse.linalg.lsmr to match with default values in
scipy.sparse.linalg.lsqr.

Add the Transpose-Free Quasi-Minimal Residual algorithm (TFQMR) for general
nonsingular non-Hermitian linear systems in scipy.sparse.linalg.tfqmr.

The sparse SVD library PROPACK is now vendored with SciPy, and an interface is
exposed via scipy.sparse.svds with solver='PROPACK'. For some problems,
this may be faster and/or more accurate than the default, ARPACK. PROPACK
functionality is currently opt-in--you must specify USE_PROPACK=1 at
runtime to use it due to potential issues on Windows
that we aim to resolve in the next release.

sparse.linalg iterative solvers now have a nonzero initial guess option,
which may be specified as x0 = 'Mb'.

The trace method has been added for sparse matrices.

scipy.spatial improvements

scipy.spatial.transform.Rotation now supports item assignment and has a new
concatenate method.

Add scipy.spatial.distance.kulczynski1 in favour of
scipy.spatial.distance.kulsinski which will be deprecated in the next
release.

scipy.spatial.distance.minkowski now also supports 0<p<1.

scipy.special improvements

The new function scipy.special.log_expit computes the logarithm of the
logistic sigmoid function. The function is formulated to provide accurate
results for large positive and negative inputs, so it avoids the problems
that would occur in the naive implementation log(expit(x)).

A suite of five new functions for elliptic integrals:
scipy.special.ellipr{c,d,f,g,j}. These are the
Carlson symmetric elliptic integrals <https://dlmf.nist.gov/19.16>_, which
have computational advantages over the classical Legendre integrals. Previous
versions included some elliptic integrals from the Cephes library
(scipy.special.ellip{k,km1,kinc,e,einc}) but was missing the integral of
third kind (Legendre's Pi), which can be evaluated using the new Carlson
functions. The new Carlson elliptic integral functions can be evaluated in the
complex plane, whereas the Cephes library's functions are only defined for
real inputs.

Several defects in scipy.special.hyp2f1 have been corrected. Approximately
correct values are now returned for z near exp(+-i*pi/3), fixing
#8054 <https://github.com/scipy/scipy/issues/8054>. Evaluation for such z
is now calculated through a series derived by
López and Temme (2013) <https://arxiv.org/abs/1306.2046> that converges in
these regions. In addition, degenerate cases with one or more of a, b,
and/or c a non-positive integer are now handled in a manner consistent with
mpmath's hyp2f1 implementation <https://mpmath.org/doc/current/functions/hypergeometric.html>,
which fixes #7340 <https://github.com/scipy/scipy/issues/7340>. These fixes
were made as part of an effort to rewrite the Fortran 77 implementation of
hyp2f1 in Cython piece by piece. This rewriting is now roughly 50% complete.

scipy.stats improvements

scipy.stats.qmc.LatinHypercube introduces two new optional keyword-only
arguments, optimization and strength. optimization is either
None or random-cd. In the latter, random permutations are performed to
improve the centered discrepancy. strength is either 1 or 2. 1 corresponds
to the classical LHS while 2 has better sub-projection properties. This
construction is referred to as an orthogonal array based LHS of strength 2.
In both cases, the output is still a LHS.

scipy.stats.qmc.Halton is faster as the underlying Van der Corput sequence
was ported to Cython.

The alternative parameter was added to the kendalltau and somersd
functions to allow one-sided hypothesis testing. Similarly, the masked
versions of skewtest, kurtosistest, ttest_1samp, ttest_ind,
and ttest_rel now also have an alternative parameter.

Add scipy.stats.gzscore to calculate the geometrical z score.

Random variate generators to sample from arbitrary univariate non-uniform
continuous and discrete distributions have been added to the new
scipy.stats.sampling submodule. Implementations of a C library
UNU.RAN <http://statmath.wu.ac.at/software/unuran/>_ are used for
performance. The generators added are:

• TransformedDensityRejection
• DiscreteAliasUrn
• NumericalInversePolynomial
• DiscreteGuideTable
• SimpleRatioUniforms

The binned_statistic set of functions now have improved performance for
the std, min, max, and median statistic calculations.

somersd and _tau_b now have faster Pythran-based implementations.

Some general efficiency improvements to handling of nan values in
several stats functions.

Added the Tukey-Kramer test as scipy.stats.tukey_hsd.

Improved performance of scipy.stats.argus rvs method.

Added the parameter keepdims to scipy.stats.variation and prevent the
undesirable return of a masked array from the function in some cases.

permutation_test performs an exact or randomized permutation test of a
given statistic on provided data.

Deprecated features

Clear split between public and private API

SciPy has always documented what its public API consisted of in
:ref:its API reference docs <scipy-api>,
however there never was a clear split between public and
private namespaces in the code base. In this release, all namespaces that were
private but happened to miss underscores in their names have been deprecated.
These include (as examples, there are many more):

• scipy.signal.spline
• scipy.ndimage.filters
• scipy.ndimage.fourier
• scipy.ndimage.measurements
• scipy.ndimage.morphology
• scipy.ndimage.interpolation
• scipy.sparse.linalg.solve
• scipy.sparse.linalg.eigen
• scipy.sparse.linalg.isolve

All functions and other objects in these namespaces that were meant to be
public are accessible from their respective public namespace (e.g.
scipy.signal). The design principle is that any public object must be
accessible from a single namespace only; there are a few exceptions, mostly for
historical reasons (e.g., stats and stats.distributions overlap).
For other libraries aiming to provide a SciPy-compatible API, it is now
unambiguous what namespace structure to follow. See
gh-14360 <https://github.com/scipy/scipy/issues/14360>_ for more details.

Other deprecations

NumericalInverseHermite has been deprecated from scipy.stats and moved
to the scipy.stats.sampling submodule. It now uses the C implementation of
the UNU.RAN library so the result of methods like ppf may vary slightly.
Parameter tol has been deprecated and renamed to u_resolution. The
parameter max_intervals has also been deprecated and will be removed in a
future release of SciPy.

Backwards incompatible changes

• SciPy has raised the minimum compiler versions to GCC 6.3 on linux and
  VS2019 on windows. In particular, this means that SciPy may now use C99 and
  C++14 features. For more details see
  here <https://docs.scipy.org/doc/scipy/reference/dev/toolchain.html>_.
• The result for empty bins for scipy.stats.binned_statistic with the builtin
  'std' metric is now nan, for consistency with np.std.
• The function scipy.spatial.distance.wminkowski has been removed. To achieve
  the same results as before, please use the minkowski distance function
  with the (optional) w= keyword-argument for the given weight.

Other changes

Some Fortran 77 code was modernized to be compatible with NAG's nagfor Fortran
compiler (see, e.g., PR 13229 <https://github.com/scipy/scipy/pull/13229>_).

threadpoolctl may now be used by our test suite to substantially improve
the efficiency of parallel test suite runs.

Authors

• @endolith
• adamadanandy +
• akeemlh +
• Anton Akhmerov
• Marvin Albert +
• alegresor +
• Andrew Annex +
• Pantelis Antonoudiou +
• Ross Barnowski +
• Christoph Baumgarten
• Stephen Becker +
• Nickolai Belakovski
• Peter Bell
• berberto +
• Georgii Bocharov +
• Evgeni Burovski
• Matthias Bussonnier
• CJ Carey
• Justin Charlong +
• Hood Chatham +
• Dennis Collaris +
• David Cottrell +
• cruyffturn +
• da-woods +
• Anirudh Dagar
• Tiger Du +
• Thomas Duvernay
• Dani El-Ayyass +
• Castedo Ellerman +
• Donnie Erb +
• Andreas Esders-Kopecky +
• Livio F +
• Isuru Fernando
• Evelyn Fitzgerald +
• Sara Fridovich-Keil +
• Mark E Fuller +
• Ralf Gommers
• Kevin Richard Green +
• guiweber +
• Nitish Gupta +
• h-vetinari
• Matt Haberland
• J. Hariharan +
• Charles Harris
• Jonathan Helgert +
• Trever Hines
• Nadav Horesh
• Ian Hunt-Isaak +
• ich +
• Itrimel +
• Jan-Hendrik Müller +
• Jebby993 +
• Yikun Jiang +
• Evan W Jones +
• Nathaniel Jones +
• Jeffrey Kelling +
• Malik Idrees Hasan Khan +
• Paul Kienzle
• Sergey B Kirpichev
• Kadatatlu Kishore +
• Andrew Knyazev
• Ravin Kumar +
• Peter Mahler Larsen
• Eric Larson
• Antony Lee
• Gregory R. Lee
• Tim Leslie
• lezcano +
• Xingyu Liu
• Christian Lorentzen
• Lorenzo +
• Smit Lunagariya +
• Lv101Magikarp +
• Yair M +
• Cong Ma
• Lorenzo Maffioli +
• majiang +
• Brian McFee +
• Nicholas McKibben
• John Speed Meyers +
• millivolt9 +
• Jarrod Millman
• Harsh Mishra +
• Boaz Mohar +
• naelsondouglas +
• Andrew Nelson
• Nico Schlömer
• Thomas Nowotny +
• nullptr +
• Teddy Ort +
• Nick Papior
• ParticularMiner +
• Dima Pasechnik
• Tirth Patel
• Matti Picus
• Ilhan Polat
• Adrian Price-Whelan +
• Quentin Barthélemy +
• Sundar R +
• Judah Rand +
• Tyler Reddy
• Renal-Of-Loon +
• Frederic Renner +
• Pamphile Roy
• Bharath Saiguhan +
• Atsushi Sakai
• Eric Schanet +
• Sebastian Wallkötter
• serge-sans-paille
• Reshama Shaikh +
• Namami Shanker
• siddhantwahal +
• Walter Simson +
• Gagandeep Singh +
• Leo C. Stein +
• Albert Steppi
• Kai Striega
• Diana Sukhoverkhova
• Søren Fuglede Jørgensen
• Masayuki Takagi +
• Mike Taves
• Ben Thompson +
• Bas van Beek
• Jacob Vanderplas
• Dhruv Vats +
• H. Vetinari +
• Thomas Viehmann +
• Pauli Virtanen
• Vlad +
• Arthur Volant
• Samuel Wallan
• Stefan van der Walt
• Warren Weckesser
• Josh Wilson
• Haoyin Xu +
• Rory Yorke
• Egor Zemlyanoy
• Gang Zhao +
• 赵丰 (Zhao Feng) +

A total of 139 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.