chore(deps): update dependency scipy to ~1.8 #3

renovate-bot · 2022-02-23T21:09:05Z

renovate-bot commented

2022-02-23 21:09:05 +00:00

This PR contains the following updates:

Package	Type	Update	Change
scipy (source)	dependencies	minor	`~1.5` -> `~1.8`

Release Notes

scipy/scipy

`v1.8.0`

Compare Source

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.

`v1.7.3`

Compare Source

SciPy 1.7.3 Release Notes

SciPy 1.7.3 is a bug-fix release that provides binary wheels
for MacOS arm64 with Python 3.8, 3.9, and 3.10. The MacOS arm64 wheels
are only available for MacOS version 12.0 and greater, as explained
in Issue 14688.

Authors

Anirudh Dagar
Ralf Gommers
Tyler Reddy
Pamphile Roy
Olivier Grisel
Isuru Fernando

A total of 6 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.

`v1.7.2`

Compare Source

SciPy 1.7.2 Release Notes

SciPy 1.7.2 is a bug-fix release with no new features
compared to 1.7.1. Notably, the release includes wheels
for Python 3.10, and wheels are now built with a newer
version of OpenBLAS, 0.3.17. Python 3.10 wheels are provided
for MacOS x86_64 (thin, not universal2 or arm64 at this time),
and Windows/Linux 64-bit. Many wheels are now built with newer
versions of manylinux, which may require newer versions of pip.

Authors

Peter Bell
da-woods +
Isuru Fernando
Ralf Gommers
Matt Haberland
Nicholas McKibben
Ilhan Polat
Judah Rand +
Tyler Reddy
Pamphile Roy
Charles Harris
Matti Picus
Hugo van Kemenade
Jacob Vanderplas

A total of 14 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.

`v1.7.1`

Compare Source

SciPy 1.7.1 Release Notes

SciPy 1.7.1 is a bug-fix release with no new features
compared to 1.7.0.

Authors

Peter Bell
Evgeni Burovski
Justin Charlong +
Ralf Gommers
Matti Picus
Tyler Reddy
Pamphile Roy
Sebastian Wallkötter
Arthur Volant

A total of 9 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.

`v1.7.0`

Compare Source

SciPy 1.7.0 Release Notes

SciPy 1.7.0 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
1.7.x branch, and on adding new features on the master branch.

This release requires Python 3.7+ and NumPy 1.16.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

A new submodule for quasi-Monte Carlo, scipy.stats.qmc, was added
The documentation design was updated to use the same PyData-Sphinx theme as
NumPy and other ecosystem libraries.
We now vendor and leverage the Boost C++ library to enable numerous
improvements for long-standing weaknesses in scipy.stats
scipy.stats has six new distributions, eight new (or overhauled)
hypothesis tests, a new function for bootstrapping, a class that enables
fast random variate sampling and percentile point function evaluation,
and many other enhancements.
cdist and pdist distance calculations are faster for several metrics,
especially weighted cases, thanks to a rewrite to a new C++ backend framework
A new class for radial basis function interpolation, RBFInterpolator, was
added to address issues with the Rbf class.

We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of the improvements to
scipy.stats.

New features

`scipy.cluster` improvements

An optional argument, seed, has been added to kmeans and kmeans2 to
set the random generator and random state.

`scipy.interpolate` improvements

Improved input validation and error messages for fitpack.bispev and
fitpack.parder for scenarios that previously caused substantial confusion
for users.

The class RBFInterpolator was added to supersede the Rbf class. The new
class has usage that more closely follows other interpolator classes, corrects
sign errors that caused unexpected smoothing behavior, includes polynomial
terms in the interpolant (which are necessary for some RBF choices), and
supports interpolation using only the k-nearest neighbors for memory
efficiency.

`scipy.linalg` improvements

An LAPACK wrapper was added for access to the tgexc subroutine.

`scipy.ndimage` improvements

scipy.ndimage.affine_transform is now able to infer the output_shape from
the out array.

`scipy.optimize` improvements

The optional parameter bounds was added to
_minimize_neldermead to support bounds constraints
for the Nelder-Mead solver.

trustregion methods trust-krylov, dogleg and trust-ncg can now
estimate hess by finite difference using one of
["2-point", "3-point", "cs"].

halton was added as a sampling_method in scipy.optimize.shgo.
sobol was fixed and is now using scipy.stats.qmc.Sobol.

halton and sobol were added as init methods in
scipy.optimize.differential_evolution.

differential_evolution now accepts an x0 parameter to provide an
initial guess for the minimization.

least_squares has a modest performance improvement when SciPy is built
with Pythran transpiler enabled.

When linprog is used with method 'highs', 'highs-ipm', or
'highs-ds', the result object now reports the marginals (AKA shadow
prices, dual values) and residuals associated with each constraint.

`scipy.signal` improvements

get_window supports general_cosine and general_hamming window
functions.

scipy.signal.medfilt2d now releases the GIL where appropriate to enable
performance gains via multithreaded calculations.

`scipy.sparse` improvements

Addition of dia_matrix sparse matrices is now faster.

`scipy.spatial` improvements

distance.cdist and distance.pdist performance has greatly improved for
certain weighted metrics. Namely: minkowski, euclidean, chebyshev,
canberra, and cityblock.

Modest performance improvements for many of the unweighted cdist and
pdist metrics noted above.

The parameter seed was added to scipy.spatial.vq.kmeans and
scipy.spatial.vq.kmeans2.

The parameters axis and keepdims where added to
scipy.spatial.distance.jensenshannon.

The rotation methods from_rotvec and as_rotvec now accept a
degrees argument to specify usage of degrees instead of radians.

`scipy.special` improvements

Wright's generalized Bessel function for positive arguments was added as
scipy.special.wright_bessel.

An implementation of the inverse of the Log CDF of the Normal Distribution is
now available via scipy.special.ndtri_exp.

`scipy.stats` improvements

Hypothesis Tests

The Mann-Whitney-Wilcoxon test, mannwhitneyu, has been rewritten. It now
supports n-dimensional input, an exact test method when there are no ties,
and improved documentation. Please see "Other changes" for adjustments to
default behavior.

The new function scipy.stats.binomtest replaces scipy.stats.binom_test. The
new function returns an object that calculates a confidence intervals of the
proportion parameter. Also, performance was improved from O(n) to O(log(n)) by
using binary search.

The two-sample version of the Cramer-von Mises test is implemented in
scipy.stats.cramervonmises_2samp.

The Alexander-Govern test is implemented in the new function
scipy.stats.alexandergovern.

The new functions scipy.stats.barnard_exact and scipy.stats. boschloo_exact
respectively perform Barnard's exact test and Boschloo's exact test
for 2x2 contingency tables.

The new function scipy.stats.page_trend_test performs Page's test for ordered
alternatives.

The new function scipy.stats.somersd performs Somers' D test for ordinal
association between two variables.

An option, permutations, has been added in scipy.stats.ttest_ind to
perform permutation t-tests. A trim option was also added to perform
a trimmed (Yuen's) t-test.

The alternative parameter was added to the skewtest, kurtosistest,
ranksums, mood, ansari, linregress, and spearmanr functions
to allow one-sided hypothesis testing.

Sample statistics

The new function scipy.stats.differential_entropy estimates the differential
entropy of a continuous distribution from a sample.

The boxcox and boxcox_normmax now allow the user to control the
optimizer used to minimize the negative log-likelihood function.

A new function scipy.stats.contingency.relative_risk calculates the
relative risk, or risk ratio, of a 2x2 contingency table. The object
returned has a method to compute the confidence interval of the relative risk.

Performance improvements in the skew and kurtosis functions achieved
by removal of repeated/redundant calculations.

Substantial performance improvements in scipy.stats.mstats.hdquantiles_sd.

The new function scipy.stats.contingency.association computes several
measures of association for a contingency table: Pearsons contingency
coefficient, Cramer's V, and Tschuprow's T.

The parameter nan_policy was added to scipy.stats.zmap to provide options
for handling the occurrence of nan in the input data.

The parameter ddof was added to scipy.stats.variation and
scipy.stats.mstats.variation.

The parameter weights was added to scipy.stats.gmean.

Statistical Distributions

We now vendor and leverage the Boost C++ library to address a number of
previously reported issues in stats. Notably, beta, binom,
nbinom now have Boost backends, and it is straightforward to leverage
the backend for additional functions.

The skew Cauchy probability distribution has been implemented as
scipy.stats.skewcauchy.

The Zipfian probability distribution has been implemented as
scipy.stats.zipfian.

The new distributions nchypergeom_fisher and nchypergeom_wallenius
implement the Fisher and Wallenius versions of the noncentral hypergeometric
distribution, respectively.

The generalized hyperbolic distribution was added in
scipy.stats.genhyperbolic.

The studentized range distribution was added in scipy.stats.studentized_range.

scipy.stats.argus now has improved handling for small parameter values.

Better argument handling/preparation has resulted in performance improvements
for many distributions.

The cosine distribution has added ufuncs for ppf, cdf, sf, and
isf methods including numerical precision improvements at the edges of the
support of the distribution.

An option to fit the distribution to data by the method of moments has been
added to the fit method of the univariate continuous distributions.

Other

scipy.stats.bootstrap has been added to allow estimation of the confidence
interval and standard error of a statistic.

The new function scipy.stats.contingency.crosstab computes a contingency
table (i.e. a table of counts of unique entries) for the given data.

scipy.stats.NumericalInverseHermite enables fast random variate sampling
and percentile point function evaluation of an arbitrary univariate statistical
distribution.

New `scipy.stats.qmc` module

This new module provides Quasi-Monte Carlo (QMC) generators and associated
helper functions.

It provides a generic class scipy.stats.qmc.QMCEngine which defines a QMC
engine/sampler. An engine is state aware: it can be continued, advanced and
reset. 3 base samplers are available:

scipy.stats.qmc.Sobol the well known Sobol low discrepancy sequence.
Several warnings have been added to guide the user into properly using this
sampler. The sequence is scrambled by default.
scipy.stats.qmc.Halton: Halton low discrepancy sequence. The sequence is
scrambled by default.
scipy.stats.qmc.LatinHypercube: plain LHS design.

And 2 special samplers are available:

scipy.stats.qmc.MultinomialQMC: sampling from a multinomial distribution
using any of the base scipy.stats.qmc.QMCEngine.
scipy.stats.qmc.MultivariateNormalQMC: sampling from a multivariate Normal
using any of the base scipy.stats.qmc.QMCEngine.

The module also provide the following helpers:

scipy.stats.qmc.discrepancy: assess the quality of a set of points in terms
of space coverage.
scipy.stats.qmc.update_discrepancy: can be used in an optimization loop to
construct a good set of points.
scipy.stats.qmc.scale: easily scale a set of points from (to) the unit
interval to (from) a given range.

Deprecated features

`scipy.linalg` deprecations

scipy.linalg.pinv2 is deprecated and its functionality is completely
subsumed into scipy.linalg.pinv
Both rcond, cond keywords of scipy.linalg.pinv and
scipy.linalg.pinvh were not working and now are deprecated. They are now
replaced with functioning atol and rtol keywords with clear usage.

`scipy.spatial` deprecations

scipy.spatial.distance metrics expect 1d input vectors but will call
np.squeeze on their inputs to accept any extra length-1 dimensions. That
behaviour is now deprecated.

Other changes

We now accept and leverage performance improvements from the ahead-of-time
Python-to-C++ transpiler, Pythran, which can be optionally disabled (via
export SCIPY_USE_PYTHRAN=0) but is enabled by default at build time.

There are two changes to the default behavior of scipy.stats.mannwhitenyu:

For years, use of the default alternative=None was deprecated; explicit
alternative specification was required. Use of the new default value of
alternative, "two-sided", is now permitted.
Previously, all p-values were based on an asymptotic approximation. Now, for
small samples without ties, the p-values returned are exact by default.

Support has been added for PEP 621 (project metadata in pyproject.toml)

We now support a Gitpod environment to reduce the barrier to entry for SciPy
development; for more details see :ref:quickstart-gitpod.

Authors

@endolith
Jelle Aalbers +
Adam +
Tania Allard +
Sven Baars +
Max Balandat +
baumgarc +
Christoph Baumgarten
Peter Bell
Lilian Besson
Robinson Besson +
Max Bolingbroke
Blair Bonnett +
Jordão Bragantini
Harm Buisman +
Evgeni Burovski
Matthias Bussonnier
Dominic C
CJ Carey
Ramón Casero +
Chachay +
charlotte12l +
Benjamin Curtice Corbett +
Falcon Dai +
Ian Dall +
Terry Davis
droussea2001 +
DWesl +
dwight200 +
Thomas J. Fan +
Joseph Fox-Rabinovitz
Max Frei +
Laura Gutierrez Funderburk +
gbonomib +
Matthias Geier +
Pradipta Ghosh +
Ralf Gommers
Evan H +
h-vetinari
Matt Haberland
Anselm Hahn +
Alex Henrie
Piet Hessenius +
Trever Hines +
Elisha Hollander +
Stephan Hoyer
Tom Hu +
Kei Ishikawa +
Julien Jerphanion
Robert Kern
Shashank KS +
Peter Mahler Larsen
Eric Larson
Cheng H. Lee +
Gregory R. Lee
Jean-Benoist Leger +
lgfunderburk +
liam-o-marsh +
Xingyu Liu +
Alex Loftus +
Christian Lorentzen +
Cong Ma
Marc +
MarkPundurs +
Markus Löning +
Liam Marsh +
Nicholas McKibben
melissawm +
Jamie Morton
Andrew Nelson
Nikola Forró
Tor Nordam +
Olivier Gauthé +
Rohit Pandey +
Avanindra Kumar Pandeya +
Tirth Patel
paugier +
Alex H. Wagner, PhD +
Jeff Plourde +
Ilhan Polat
pranavrajpal +
Vladyslav Rachek
Bharat Raghunathan
Recursing +
Tyler Reddy
Lucas Roberts
Gregor Robinson +
Pamphile Roy +
Atsushi Sakai
Benjamin Santos
Martin K. Scherer +
Thomas Schmelzer +
Daniel Scott +
Sebastian Wallkötter +
serge-sans-paille +
Namami Shanker +
Masashi Shibata +
Alexandre de Siqueira +
Albert Steppi +
Adam J. Stewart +
Kai Striega
Diana Sukhoverkhova
Søren Fuglede Jørgensen
Mike Taves
Dan Temkin +
Nicolas Tessore +
tsubota20 +
Robert Uhl
christos val +
Bas van Beek +
Ashutosh Varma +
Jose Vazquez +
Sebastiano Vigna
Aditya Vijaykumar
VNMabus
Arthur Volant +
Samuel Wallan
Stefan van der Walt
Warren Weckesser
Anreas Weh
Josh Wilson
Rory Yorke
Egor Zemlyanoy
Marc Zoeller +
zoj613 +
秋纫 +

A total of 126 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.

`v1.6.3`

Compare Source

SciPy 1.6.3 Release Notes

SciPy 1.6.3 is a bug-fix release with no new features
compared to 1.6.2.

Authors

Peter Bell
Ralf Gommers
Matt Haberland
Peter Mahler Larsen
Tirth Patel
Tyler Reddy
Pamphile ROY +
Xingyu Liu +

A total of 8 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.

`v1.6.2`

Compare Source

SciPy 1.6.2 Release Notes

SciPy 1.6.2 is a bug-fix release with no new features
compared to 1.6.1. This is also the first SciPy release
to place upper bounds on some dependencies to improve
the long-term repeatability of source builds.

Authors

Pradipta Ghosh +
Tyler Reddy
Ralf Gommers
Martin K. Scherer +
Robert Uhl
Warren Weckesser

A total of 6 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.

`v1.6.1`

Compare Source

SciPy 1.6.1 Release Notes

SciPy 1.6.1 is a bug-fix release with no new features
compared to 1.6.0.

Please note that for SciPy wheels to correctly install with pip on
macOS 11, pip >= 20.3.3 is needed.

Authors

Peter Bell
Evgeni Burovski
CJ Carey
Ralf Gommers
Peter Mahler Larsen
Cheng H. Lee +
Cong Ma
Nicholas McKibben
Nikola Forró
Tyler Reddy
Warren Weckesser

A total of 11 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.

`v1.6.0`

Compare Source

SciPy 1.6.0 Release Notes

SciPy 1.6.0 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
1.6.x branch, and on adding new features on the master branch.

This release requires Python 3.7+ and NumPy 1.16.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

scipy.ndimage improvements: Fixes and ehancements to boundary extension
modes for interpolation functions. Support for complex-valued inputs in many
filtering and interpolation functions. New grid_mode option for
scipy.ndimage.zoom to enable results consistent with scikit-image's
rescale.
scipy.optimize.linprog has fast, new methods for large, sparse problems
from the HiGHS library.
scipy.stats improvements including new distributions, a new test, and
enhancements to existing distributions and tests

New features

`scipy.special` improvements

scipy.special now has improved support for 64-bit LAPACK backend

`scipy.odr` improvements

scipy.odr now has support for 64-bit integer BLAS

scipy.odr.ODR has gained an optional overwrite argument so that existing
files may be overwritten.

`scipy.integrate` improvements

Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:

integrate.simps was renamed to integrate.simpson
integrate.trapz was renamed to integrate.trapezoid
integrate.cumtrapz was renamed to integrate.cumulative_trapezoid

`scipy.cluster` improvements

scipy.cluster.hierarchy.DisjointSet has been added for incremental
connectivity queries.

scipy.cluster.hierarchy.dendrogram return value now also includes leaf color
information in leaves_color_list.

`scipy.interpolate` improvements

scipy.interpolate.interp1d has a new method nearest-up, similar to the
existing method nearest but rounds half-integers up instead of down.

`scipy.io` improvements

Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.

`scipy.linalg` improvements

The new function scipy.linalg.matmul_toeplitz uses the FFT to compute the
product of a Toeplitz matrix with another matrix.

scipy.linalg.sqrtm and scipy.linalg.logm have performance improvements
thanks to additional Cython code.

Python LAPACK wrappers have been added for pptrf, pptrs, ppsv,
pptri, and ppcon.

scipy.linalg.norm and the svd family of functions will now use 64-bit
integer backends when available.

`scipy.ndimage` improvements

scipy.ndimage.convolve, scipy.ndimage.correlate and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. gaussian_filter, uniform_filter, etc.).

Multiple fixes and enhancements to boundary handling were introduced to
scipy.ndimage interpolation functions (i.e. affine_transform,
geometric_transform, map_coordinates, rotate, shift, zoom).

A new boundary mode, grid-wrap was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing wrap mode which uses a period that is one sample smaller
than the original signal extent along each dimension.

A long-standing bug in the reflect boundary condition has been fixed and
the mode grid-mirror was introduced as a synonym for reflect.

A new boundary mode, grid-constant is now available. This is similar to
the existing ndimage constant mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
grid-constant mode is consistent with OpenCV's BORDER_CONSTANT mode
and scikit-image's constant mode.

Spline pre-filtering (used internally by ndimage interpolation functions
when order >= 2), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
spline_filter and spline_filter1d have analytical boundary conditions
that match modes mirror, grid-wrap and reflect.

scipy.ndimage interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.

The ndimage tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.

scipy.ndimage.zoom now has a grid_mode option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
resize and rescale functions (and OpenCV's cv2.resize).

`scipy.optimize` improvements

scipy.optimize.linprog has fast, new methods for large, sparse problems from
the HiGHS C++ library. method='highs-ds' uses a high performance dual
revised simplex implementation (HSOL), method='highs-ipm' uses an
interior-point method with crossover, and method='highs' chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other linprog methods, so we recommend explicitly
specifying one of these three method values when using linprog.

scipy.optimize.quadratic_assignment has been added for approximate solution
of the quadratic assignment problem.

scipy.optimize.linear_sum_assignment now has a substantially reduced overhead
for small cost matrix sizes

scipy.optimize.least_squares has improved performance when the user provides
the jacobian as a sparse jacobian already in csr_matrix format

scipy.optimize.linprog now has an rr_method argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.

`scipy.signal` improvements

scipy.signal.gammatone has been added to design FIR or IIR filters that
model the human auditory system.

scipy.signal.iircomb has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.

scipy.signal.sosfilt performance has been improved to avoid some previously-
observed slowdowns

scipy.signal.windows.taylor has been added--the Taylor window function is
commonly used in radar digital signal processing

scipy.signal.gauss_spline now supports list type input for consistency
with other related SciPy functions

scipy.signal.correlation_lags has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.

`scipy.sparse` improvements

A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
scipy.sparse.csgraph.min_weight_full_bipartite_matching. In particular, this
provides functionality analogous to that of
scipy.optimize.linear_sum_assignment, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.

The time complexity of scipy.sparse.block_diag has been improved dramatically
from quadratic to linear.

`scipy.sparse.linalg` improvements

The vendored version of SuperLU has been updated

`scipy.fft` improvements

The vendored pocketfft library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.

`scipy.spatial` improvements

The python implementation of KDTree has been dropped and KDTree is now
implemented in terms of cKDTree. You can now expect cKDTree-like
performance by default. This also means sys.setrecursionlimit no longer
needs to be increased for querying large trees.

transform.Rotation has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).

scipy.spatial.transform.Rotation has been partially cythonized, with some
performance improvements observed

scipy.spatial.distance.cdist has improved performance with the minkowski
metric, especially for p-norm values of 1 or 2.

`scipy.stats` improvements

New distributions have been added to scipy.stats:

The asymmetric Laplace continuous distribution has been added as
scipy.stats.laplace_asymmetric.
The negative hypergeometric distribution has been added as scipy.stats.nhypergeom.
The multivariate t distribution has been added as scipy.stats.multivariate_t.
The multivariate hypergeometric distribution has been added as scipy.stats.multivariate_hypergeom.

The fit method has been overridden for several distributions (laplace,
pareto, rayleigh, invgauss, logistic, gumbel_l,
gumbel_r); they now use analytical, distribution-specific maximum
likelihood estimation results for greater speed and accuracy than the generic
(numerical optimization) implementation.

The one-sample Cramér-von Mises test has been added as
scipy.stats.cramervonmises.

An option to compute one-sided p-values was added to scipy.stats.ttest_1samp,
scipy.stats.ttest_ind_from_stats, scipy.stats.ttest_ind and
scipy.stats.ttest_rel.

The function scipy.stats.kendalltau now has an option to compute Kendall's
tau-c (also known as Stuart's tau-c), and support has been added for exact
p-value calculations for sample sizes > 171.

stats.trapz was renamed to stats.trapezoid, with the former name retained
as an alias for backwards compatibility reasons.

The function scipy.stats.linregress now includes the standard error of the
intercept in its return value.

The _logpdf, _sf, and _isf methods have been added to
scipy.stats.nakagami; _sf and _isf methods also added to
scipy.stats.gumbel_r

The sf method has been added to scipy.stats.levy and scipy.stats.levy_l
for improved precision.

scipy.stats.binned_statistic_dd performance improvements for the following
computed statistics: max, min, median, and std.

We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of these improvements to
scipy.stats.

Deprecated features

`scipy.spatial` changes

Calling KDTree.query with k=None to find all neighbours is deprecated.
Use KDTree.query_ball_point instead.

distance.wminkowski was deprecated; use distance.minkowski and supply
weights with the w keyword instead.

Backwards incompatible changes

`scipy` changes

Using scipy.fft as a function aliasing numpy.fft.fft was removed after
being deprecated in SciPy 1.4.0. As a result, the scipy.fft submodule
must be explicitly imported now, in line with other SciPy subpackages.

`scipy.signal` changes

The output of decimate, lfilter_zi, lfiltic, sos2tf, and
sosfilt_zi have been changed to match numpy.result_type of their inputs.

The window function slepian was removed. It had been deprecated since SciPy
1.1.

`scipy.spatial` changes

cKDTree.query now returns 64-bit rather than 32-bit integers on Windows,
making behaviour consistent between platforms (PR gh-12673).

`scipy.stats` changes

The frechet_l and frechet_r distributions were removed. They were
deprecated since SciPy 1.0.

Other changes

setup_requires was removed from setup.py. This means that users
invoking python setup.py install without having numpy already installed
will now get an error, rather than having numpy installed for them via
easy_install. This install method was always fragile and problematic, users
are encouraged to use pip when installing from source.

Fixed a bug in scipy.optimize.dual_annealing accept_reject calculation
that caused uphill jumps to be accepted less frequently.
The time required for (un)pickling of scipy.stats.rv_continuous,
scipy.stats.rv_discrete, and scipy.stats.rv_frozen has been significantly
reduced (gh12550). Inheriting subclasses should note that __setstate__ no
longer calls __init__ upon unpickling.

Authors

@endolith
@vkk800
aditya +
George Bateman +
Christoph Baumgarten
Peter Bell
Tobias Biester +
Keaton J. Burns +
Evgeni Burovski
Rüdiger Busche +
Matthias Bussonnier
Dominic C +
Corallus Caninus +
CJ Carey
Thomas A Caswell
chapochn +
Lucía Cheung
Zach Colbert +
Coloquinte +
Yannick Copin +
Devin Crowley +
Terry Davis +
Michaël Defferrard +
devonwp +
Didier +
divenex +
Thomas Duvernay +
Eoghan O'Connell +
Gökçen Eraslan
Kristian Eschenburg +
Ralf Gommers
Thomas Grainger +
GreatV +
Gregory Gundersen +
h-vetinari +
Matt Haberland
Mark Harfouche +
He He +
Alex Henrie
Chun-Ming Huang +
Martin James McHugh III +
Alex Izvorski +
Joey +
ST John +
Jonas Jonker +
Julius Bier Kirkegaard
Marcin Konowalczyk +
Konrad0
Sam Van Kooten +
Sergey Koposov +
Peter Mahler Larsen
Eric Larson
Antony Lee
Gregory R. Lee
Loïc Estève
Jean-Luc Margot +
MarkusKoebis +
Nikolay Mayorov
G. D. McBain
Andrew McCluskey +
Nicholas McKibben
Sturla Molden
Denali Molitor +
Eric Moore
Shashaank N +
Prashanth Nadukandi +
nbelakovski +
Andrew Nelson
Nick +
Nikola Forró +
odidev
ofirr +
Sambit Panda
Dima Pasechnik
Tirth Patel +
Matti Picus
Paweł Redzyński +
Vladimir Philipenko +
Philipp Thölke +
Ilhan Polat
Eugene Prilepin +
Vladyslav Rachek
Ram Rachum +
Tyler Reddy
Martin Reinecke +
Simon Segerblom Rex +
Lucas Roberts
Benjamin Rowell +
Eli Rykoff +
Atsushi Sakai
Moritz Schulte +
Daniel B. Smith
Steve Smith +
Jan Soedingrekso +
Victor Stinner +
Jose Storopoli +
Diana Sukhoverkhova +
Søren Fuglede Jørgensen
taoky +
Mike Taves +
Ian Thomas +
Will Tirone +
Frank Torres +
Seth Troisi
Ronald van Elburg +
Hugo van Kemenade
Paul van Mulbregt
Saul Ivan Rivas Vega +
Pauli Virtanen
Jan Vleeshouwers
Samuel Wallan
Warren Weckesser
Ben West +
Eric Wieser
WillTirone +
Levi John Wolf +
Zhiqing Xiao
Rory Yorke +
Yun Wang (Maigo) +
Egor Zemlyanoy +
ZhihuiChen0903 +
Jacob Zhong +

A total of 122 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.

Configuration

📅 Schedule: At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

If you want to rebase/retry this PR, click this checkbox.

This PR has been generated by Renovate Bot.

This PR contains the following updates: | Package | Type | Update | Change | |---|---|---|---| | [scipy](https://www.scipy.org) ([source](https://github.com/scipy/scipy)) | dependencies | minor | `~1.5` -> `~1.8` | --- ### Release Notes <details> <summary>scipy/scipy</summary> ### [`v1.8.0`](https://github.com/scipy/scipy/releases/v1.8.0) [Compare Source](https://github.com/scipy/scipy/compare/v1.7.3...v1.8.0) # 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](https://github.com/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. ### [`v1.7.3`](https://github.com/scipy/scipy/releases/v1.7.3) [Compare Source](https://github.com/scipy/scipy/compare/v1.7.2...v1.7.3) # SciPy 1.7.3 Release Notes SciPy `1.7.3` is a bug-fix release that provides binary wheels for MacOS arm64 with Python `3.8`, `3.9`, and `3.10`. The MacOS arm64 wheels are only available for MacOS version `12.0` and greater, as explained in [Issue 14688](https://github.com/scipy/scipy/issues/14688). # Authors - Anirudh Dagar - Ralf Gommers - Tyler Reddy - Pamphile Roy - Olivier Grisel - Isuru Fernando A total of 6 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. ### [`v1.7.2`](https://github.com/scipy/scipy/releases/v1.7.2) [Compare Source](https://github.com/scipy/scipy/compare/v1.7.1...v1.7.2) # SciPy 1.7.2 Release Notes SciPy `1.7.2` is a bug-fix release with no new features compared to `1.7.1`. Notably, the release includes wheels for Python `3.10`, and wheels are now built with a newer version of OpenBLAS, `0.3.17`. Python `3.10` wheels are provided for MacOS x86\_64 (thin, not universal2 or arm64 at this time), and Windows/Linux 64-bit. Many wheels are now built with newer versions of manylinux, which may require newer versions of pip. # Authors - Peter Bell - da-woods + - Isuru Fernando - Ralf Gommers - Matt Haberland - Nicholas McKibben - Ilhan Polat - Judah Rand + - Tyler Reddy - Pamphile Roy - Charles Harris - Matti Picus - Hugo van Kemenade - Jacob Vanderplas A total of 14 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. ### [`v1.7.1`](https://github.com/scipy/scipy/releases/v1.7.1) [Compare Source](https://github.com/scipy/scipy/compare/v1.7.0...v1.7.1) # SciPy 1.7.1 Release Notes SciPy `1.7.1` is a bug-fix release with no new features compared to `1.7.0`. # Authors - Peter Bell - Evgeni Burovski - Justin Charlong + - Ralf Gommers - Matti Picus - Tyler Reddy - Pamphile Roy - Sebastian Wallkötter - Arthur Volant A total of 9 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. ### [`v1.7.0`](https://github.com/scipy/scipy/releases/v1.7.0) [Compare Source](https://github.com/scipy/scipy/compare/v1.6.3...v1.7.0) # SciPy 1.7.0 Release Notes SciPy `1.7.0` 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 1.7.x branch, and on adding new features on the master branch. This release requires Python `3.7+` and NumPy `1.16.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - A new submodule for quasi-Monte Carlo, `scipy.stats.qmc`, was added - The documentation design was updated to use the same PyData-Sphinx theme as NumPy and other ecosystem libraries. - We now vendor and leverage the Boost C++ library to enable numerous improvements for long-standing weaknesses in `scipy.stats` - `scipy.stats` has six new distributions, eight new (or overhauled) hypothesis tests, a new function for bootstrapping, a class that enables fast random variate sampling and percentile point function evaluation, and many other enhancements. - `cdist` and `pdist` distance calculations are faster for several metrics, especially weighted cases, thanks to a rewrite to a new C++ backend framework - A new class for radial basis function interpolation, `RBFInterpolator`, was added to address issues with the `Rbf` class. *We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source Software for Science program for supporting many of the improvements to* `scipy.stats`. # New features ## `scipy.cluster` improvements An optional argument, `seed`, has been added to `kmeans` and `kmeans2` to set the random generator and random state. ## `scipy.interpolate` improvements Improved input validation and error messages for `fitpack.bispev` and `fitpack.parder` for scenarios that previously caused substantial confusion for users. The class `RBFInterpolator` was added to supersede the `Rbf` class. The new class has usage that more closely follows other interpolator classes, corrects sign errors that caused unexpected smoothing behavior, includes polynomial terms in the interpolant (which are necessary for some RBF choices), and supports interpolation using only the k-nearest neighbors for memory efficiency. ## `scipy.linalg` improvements An LAPACK wrapper was added for access to the `tgexc` subroutine. ## `scipy.ndimage` improvements `scipy.ndimage.affine_transform` is now able to infer the `output_shape` from the `out` array. ## `scipy.optimize` improvements The optional parameter `bounds` was added to `_minimize_neldermead` to support bounds constraints for the Nelder-Mead solver. `trustregion` methods `trust-krylov`, `dogleg` and `trust-ncg` can now estimate `hess` by finite difference using one of `["2-point", "3-point", "cs"]`. `halton` was added as a `sampling_method` in `scipy.optimize.shgo`. `sobol` was fixed and is now using `scipy.stats.qmc.Sobol`. `halton` and `sobol` were added as `init` methods in `scipy.optimize.differential_evolution.` `differential_evolution` now accepts an `x0` parameter to provide an initial guess for the minimization. `least_squares` has a modest performance improvement when SciPy is built with Pythran transpiler enabled. When `linprog` is used with `method` `'highs'`, `'highs-ipm'`, or `'highs-ds'`, the result object now reports the marginals (AKA shadow prices, dual values) and residuals associated with each constraint. ## `scipy.signal` improvements `get_window` supports `general_cosine` and `general_hamming` window functions. `scipy.signal.medfilt2d` now releases the GIL where appropriate to enable performance gains via multithreaded calculations. ## `scipy.sparse` improvements Addition of `dia_matrix` sparse matrices is now faster. ## `scipy.spatial` improvements `distance.cdist` and `distance.pdist` performance has greatly improved for certain weighted metrics. Namely: `minkowski`, `euclidean`, `chebyshev`, `canberra`, and `cityblock`. Modest performance improvements for many of the unweighted `cdist` and `pdist` metrics noted above. The parameter `seed` was added to `scipy.spatial.vq.kmeans` and `scipy.spatial.vq.kmeans2`. The parameters `axis` and `keepdims` where added to `scipy.spatial.distance.jensenshannon`. The `rotation` methods `from_rotvec` and `as_rotvec` now accept a `degrees` argument to specify usage of degrees instead of radians. ## `scipy.special` improvements Wright's generalized Bessel function for positive arguments was added as `scipy.special.wright_bessel.` An implementation of the inverse of the Log CDF of the Normal Distribution is now available via `scipy.special.ndtri_exp`. ## `scipy.stats` improvements ## Hypothesis Tests The Mann-Whitney-Wilcoxon test, `mannwhitneyu`, has been rewritten. It now supports n-dimensional input, an exact test method when there are no ties, and improved documentation. Please see "Other changes" for adjustments to default behavior. The new function `scipy.stats.binomtest` replaces `scipy.stats.binom_test`. The new function returns an object that calculates a confidence intervals of the proportion parameter. Also, performance was improved from O(n) to O(log(n)) by using binary search. The two-sample version of the Cramer-von Mises test is implemented in `scipy.stats.cramervonmises_2samp`. The Alexander-Govern test is implemented in the new function `scipy.stats.alexandergovern`. The new functions `scipy.stats.barnard_exact` and `scipy.stats. boschloo_exact` respectively perform Barnard's exact test and Boschloo's exact test for 2x2 contingency tables. The new function `scipy.stats.page_trend_test` performs Page's test for ordered alternatives. The new function `scipy.stats.somersd` performs Somers' D test for ordinal association between two variables. An option, `permutations`, has been added in `scipy.stats.ttest_ind` to perform permutation t-tests. A `trim` option was also added to perform a trimmed (Yuen's) t-test. The `alternative` parameter was added to the `skewtest`, `kurtosistest`, `ranksums`, `mood`, `ansari`, `linregress`, and `spearmanr` functions to allow one-sided hypothesis testing. ## Sample statistics The new function `scipy.stats.differential_entropy` estimates the differential entropy of a continuous distribution from a sample. The `boxcox` and `boxcox_normmax` now allow the user to control the optimizer used to minimize the negative log-likelihood function. A new function `scipy.stats.contingency.relative_risk` calculates the relative risk, or risk ratio, of a 2x2 contingency table. The object returned has a method to compute the confidence interval of the relative risk. Performance improvements in the `skew` and `kurtosis` functions achieved by removal of repeated/redundant calculations. Substantial performance improvements in `scipy.stats.mstats.hdquantiles_sd`. The new function `scipy.stats.contingency.association` computes several measures of association for a contingency table: Pearsons contingency coefficient, Cramer's V, and Tschuprow's T. The parameter `nan_policy` was added to `scipy.stats.zmap` to provide options for handling the occurrence of `nan` in the input data. The parameter `ddof` was added to `scipy.stats.variation` and `scipy.stats.mstats.variation`. The parameter `weights` was added to `scipy.stats.gmean`. ## Statistical Distributions We now vendor and leverage the Boost C++ library to address a number of previously reported issues in `stats`. Notably, `beta`, `binom`, `nbinom` now have Boost backends, and it is straightforward to leverage the backend for additional functions. The skew Cauchy probability distribution has been implemented as `scipy.stats.skewcauchy`. The Zipfian probability distribution has been implemented as `scipy.stats.zipfian`. The new distributions `nchypergeom_fisher` and `nchypergeom_wallenius` implement the Fisher and Wallenius versions of the noncentral hypergeometric distribution, respectively. The generalized hyperbolic distribution was added in `scipy.stats.genhyperbolic`. The studentized range distribution was added in `scipy.stats.studentized_range`. `scipy.stats.argus` now has improved handling for small parameter values. Better argument handling/preparation has resulted in performance improvements for many distributions. The `cosine` distribution has added ufuncs for `ppf`, `cdf`, `sf`, and `isf` methods including numerical precision improvements at the edges of the support of the distribution. An option to fit the distribution to data by the method of moments has been added to the `fit` method of the univariate continuous distributions. ## Other `scipy.stats.bootstrap` has been added to allow estimation of the confidence interval and standard error of a statistic. The new function `scipy.stats.contingency.crosstab` computes a contingency table (i.e. a table of counts of unique entries) for the given data. `scipy.stats.NumericalInverseHermite` enables fast random variate sampling and percentile point function evaluation of an arbitrary univariate statistical distribution. ## New `scipy.stats.qmc` module This new module provides Quasi-Monte Carlo (QMC) generators and associated helper functions. It provides a generic class `scipy.stats.qmc.QMCEngine` which defines a QMC engine/sampler. An engine is state aware: it can be continued, advanced and reset. 3 base samplers are available: - `scipy.stats.qmc.Sobol` the well known Sobol low discrepancy sequence. Several warnings have been added to guide the user into properly using this sampler. The sequence is scrambled by default. - `scipy.stats.qmc.Halton`: Halton low discrepancy sequence. The sequence is scrambled by default. - `scipy.stats.qmc.LatinHypercube`: plain LHS design. And 2 special samplers are available: - `scipy.stats.qmc.MultinomialQMC`: sampling from a multinomial distribution using any of the base `scipy.stats.qmc.QMCEngine`. - `scipy.stats.qmc.MultivariateNormalQMC`: sampling from a multivariate Normal using any of the base `scipy.stats.qmc.QMCEngine`. The module also provide the following helpers: - `scipy.stats.qmc.discrepancy`: assess the quality of a set of points in terms of space coverage. - `scipy.stats.qmc.update_discrepancy`: can be used in an optimization loop to construct a good set of points. - `scipy.stats.qmc.scale`: easily scale a set of points from (to) the unit interval to (from) a given range. # Deprecated features ## `scipy.linalg` deprecations - `scipy.linalg.pinv2` is deprecated and its functionality is completely subsumed into `scipy.linalg.pinv` - Both `rcond`, `cond` keywords of `scipy.linalg.pinv` and `scipy.linalg.pinvh` were not working and now are deprecated. They are now replaced with functioning `atol` and `rtol` keywords with clear usage. ## `scipy.spatial` deprecations - `scipy.spatial.distance` metrics expect 1d input vectors but will call `np.squeeze` on their inputs to accept any extra length-1 dimensions. That behaviour is now deprecated. # Other changes We now accept and leverage performance improvements from the ahead-of-time Python-to-C++ transpiler, Pythran, which can be optionally disabled (via `export SCIPY_USE_PYTHRAN=0`) but is enabled by default at build time. There are two changes to the default behavior of `scipy.stats.mannwhitenyu`: - For years, use of the default `alternative=None` was deprecated; explicit `alternative` specification was required. Use of the new default value of `alternative`, "two-sided", is now permitted. - Previously, all p-values were based on an asymptotic approximation. Now, for small samples without ties, the p-values returned are exact by default. Support has been added for PEP 621 (project metadata in `pyproject.toml`) We now support a Gitpod environment to reduce the barrier to entry for SciPy development; for more details see :ref:`quickstart-gitpod`. # Authors - [@endolith](https://github.com/endolith) - Jelle Aalbers + - Adam + - Tania Allard + - Sven Baars + - Max Balandat + - baumgarc + - Christoph Baumgarten - Peter Bell - Lilian Besson - Robinson Besson + - Max Bolingbroke - Blair Bonnett + - Jordão Bragantini - Harm Buisman + - Evgeni Burovski - Matthias Bussonnier - Dominic C - CJ Carey - Ramón Casero + - Chachay + - charlotte12l + - Benjamin Curtice Corbett + - Falcon Dai + - Ian Dall + - Terry Davis - droussea2001 + - DWesl + - dwight200 + - Thomas J. Fan + - Joseph Fox-Rabinovitz - Max Frei + - Laura Gutierrez Funderburk + - gbonomib + - Matthias Geier + - Pradipta Ghosh + - Ralf Gommers - Evan H + - h-vetinari - Matt Haberland - Anselm Hahn + - Alex Henrie - Piet Hessenius + - Trever Hines + - Elisha Hollander + - Stephan Hoyer - Tom Hu + - Kei Ishikawa + - Julien Jerphanion - Robert Kern - Shashank KS + - Peter Mahler Larsen - Eric Larson - Cheng H. Lee + - Gregory R. Lee - Jean-Benoist Leger + - lgfunderburk + - liam-o-marsh + - Xingyu Liu + - Alex Loftus + - Christian Lorentzen + - Cong Ma - Marc + - MarkPundurs + - Markus Löning + - Liam Marsh + - Nicholas McKibben - melissawm + - Jamie Morton - Andrew Nelson - Nikola Forró - Tor Nordam + - Olivier Gauthé + - Rohit Pandey + - Avanindra Kumar Pandeya + - Tirth Patel - paugier + - Alex H. Wagner, PhD + - Jeff Plourde + - Ilhan Polat - pranavrajpal + - Vladyslav Rachek - Bharat Raghunathan - Recursing + - Tyler Reddy - Lucas Roberts - Gregor Robinson + - Pamphile Roy + - Atsushi Sakai - Benjamin Santos - Martin K. Scherer + - Thomas Schmelzer + - Daniel Scott + - Sebastian Wallkötter + - serge-sans-paille + - Namami Shanker + - Masashi Shibata + - Alexandre de Siqueira + - Albert Steppi + - Adam J. Stewart + - Kai Striega - Diana Sukhoverkhova - Søren Fuglede Jørgensen - Mike Taves - Dan Temkin + - Nicolas Tessore + - tsubota20 + - Robert Uhl - christos val + - Bas van Beek + - Ashutosh Varma + - Jose Vazquez + - Sebastiano Vigna - Aditya Vijaykumar - VNMabus - Arthur Volant + - Samuel Wallan - Stefan van der Walt - Warren Weckesser - Anreas Weh - Josh Wilson - Rory Yorke - Egor Zemlyanoy - Marc Zoeller + - zoj613 + - 秋纫 + A total of 126 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. ### [`v1.6.3`](https://github.com/scipy/scipy/releases/v1.6.3) [Compare Source](https://github.com/scipy/scipy/compare/v1.6.2...v1.6.3) # SciPy 1.6.3 Release Notes SciPy `1.6.3` is a bug-fix release with no new features compared to `1.6.2`. # Authors - Peter Bell - Ralf Gommers - Matt Haberland - Peter Mahler Larsen - Tirth Patel - Tyler Reddy - Pamphile ROY + - Xingyu Liu + A total of 8 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. ### [`v1.6.2`](https://github.com/scipy/scipy/releases/v1.6.2) [Compare Source](https://github.com/scipy/scipy/compare/v1.6.1...v1.6.2) # SciPy 1.6.2 Release Notes SciPy `1.6.2` is a bug-fix release with no new features compared to `1.6.1`. This is also the first SciPy release to place upper bounds on some dependencies to improve the long-term repeatability of source builds. # Authors - Pradipta Ghosh + - Tyler Reddy - Ralf Gommers - Martin K. Scherer + - Robert Uhl - Warren Weckesser A total of 6 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. ### [`v1.6.1`](https://github.com/scipy/scipy/releases/v1.6.1) [Compare Source](https://github.com/scipy/scipy/compare/v1.6.0...v1.6.1) # SciPy 1.6.1 Release Notes SciPy `1.6.1` is a bug-fix release with no new features compared to `1.6.0`. Please note that for SciPy wheels to correctly install with pip on macOS 11, pip `>= 20.3.3` is needed. # Authors - Peter Bell - Evgeni Burovski - CJ Carey - Ralf Gommers - Peter Mahler Larsen - Cheng H. Lee + - Cong Ma - Nicholas McKibben - Nikola Forró - Tyler Reddy - Warren Weckesser A total of 11 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. ### [`v1.6.0`](https://github.com/scipy/scipy/releases/v1.6.0) [Compare Source](https://github.com/scipy/scipy/compare/v1.5.4...v1.6.0) # SciPy 1.6.0 Release Notes SciPy `1.6.0` 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 `1.6.x` branch, and on adding new features on the master branch. This release requires Python `3.7+` and NumPy `1.16.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. ## Highlights of this release - `scipy.ndimage` improvements: Fixes and ehancements to boundary extension modes for interpolation functions. Support for complex-valued inputs in many filtering and interpolation functions. New `grid_mode` option for `scipy.ndimage.zoom` to enable results consistent with scikit-image's `rescale`. - `scipy.optimize.linprog` has fast, new methods for large, sparse problems from the `HiGHS` library. - `scipy.stats` improvements including new distributions, a new test, and enhancements to existing distributions and tests # New features ## `scipy.special` improvements `scipy.special` now has improved support for 64-bit `LAPACK` backend ## `scipy.odr` improvements `scipy.odr` now has support for 64-bit integer `BLAS` `scipy.odr.ODR` has gained an optional `overwrite` argument so that existing files may be overwritten. ## `scipy.integrate` improvements Some renames of functions with poor names were done, with the old names retained without being in the reference guide for backwards compatibility reasons: - `integrate.simps` was renamed to `integrate.simpson` - `integrate.trapz` was renamed to `integrate.trapezoid` - `integrate.cumtrapz` was renamed to `integrate.cumulative_trapezoid` ## `scipy.cluster` improvements `scipy.cluster.hierarchy.DisjointSet` has been added for incremental connectivity queries. `scipy.cluster.hierarchy.dendrogram` return value now also includes leaf color information in `leaves_color_list`. ## `scipy.interpolate` improvements `scipy.interpolate.interp1d` has a new method `nearest-up`, similar to the existing method `nearest` but rounds half-integers up instead of down. ## `scipy.io` improvements Support has been added for reading arbitrary bit depth integer PCM WAV files from 1- to 32-bit, including the commonly-requested 24-bit depth. ## `scipy.linalg` improvements The new function `scipy.linalg.matmul_toeplitz` uses the FFT to compute the product of a Toeplitz matrix with another matrix. `scipy.linalg.sqrtm` and `scipy.linalg.logm` have performance improvements thanks to additional Cython code. Python `LAPACK` wrappers have been added for `pptrf`, `pptrs`, `ppsv`, `pptri`, and `ppcon`. `scipy.linalg.norm` and the `svd` family of functions will now use 64-bit integer backends when available. ## `scipy.ndimage` improvements `scipy.ndimage.convolve`, `scipy.ndimage.correlate` and their 1d counterparts now accept both complex-valued images and/or complex-valued filter kernels. All convolution-based filters also now accept complex-valued inputs (e.g. `gaussian_filter`, `uniform_filter`, etc.). Multiple fixes and enhancements to boundary handling were introduced to `scipy.ndimage` interpolation functions (i.e. `affine_transform`, `geometric_transform`, `map_coordinates`, `rotate`, `shift`, `zoom`). A new boundary mode, `grid-wrap` was added which wraps images periodically, using a period equal to the shape of the input image grid. This is in contrast to the existing `wrap` mode which uses a period that is one sample smaller than the original signal extent along each dimension. A long-standing bug in the `reflect` boundary condition has been fixed and the mode `grid-mirror` was introduced as a synonym for `reflect`. A new boundary mode, `grid-constant` is now available. This is similar to the existing ndimage `constant` mode, but interpolation will still performed at coordinate values outside of the original image extent. This `grid-constant` mode is consistent with OpenCV's `BORDER_CONSTANT` mode and scikit-image's `constant` mode. Spline pre-filtering (used internally by `ndimage` interpolation functions when `order >= 2`), now supports all boundary modes rather than always defaulting to mirror boundary conditions. The standalone functions `spline_filter` and `spline_filter1d` have analytical boundary conditions that match modes `mirror`, `grid-wrap` and `reflect`. `scipy.ndimage` interpolation functions now accept complex-valued inputs. In this case, the interpolation is applied independently to the real and imaginary components. The `ndimage` tutorials (https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been updated with new figures to better clarify the exact behavior of all of the interpolation boundary modes. `scipy.ndimage.zoom` now has a `grid_mode` option that changes the coordinate of the center of the first pixel along an axis from 0 to 0.5. This allows resizing in a manner that is consistent with the behavior of scikit-image's `resize` and `rescale` functions (and OpenCV's `cv2.resize`). ## `scipy.optimize` improvements `scipy.optimize.linprog` has fast, new methods for large, sparse problems from the `HiGHS` C++ library. `method='highs-ds'` uses a high performance dual revised simplex implementation (HSOL), `method='highs-ipm'` uses an interior-point method with crossover, and `method='highs'` chooses between the two automatically. These methods are typically much faster and often exceed the accuracy of other `linprog` methods, so we recommend explicitly specifying one of these three method values when using `linprog`. `scipy.optimize.quadratic_assignment` has been added for approximate solution of the quadratic assignment problem. `scipy.optimize.linear_sum_assignment` now has a substantially reduced overhead for small cost matrix sizes `scipy.optimize.least_squares` has improved performance when the user provides the jacobian as a sparse jacobian already in `csr_matrix` format `scipy.optimize.linprog` now has an `rr_method` argument for specification of the method used for redundancy handling, and a new method for this purpose is available based on the interpolative decomposition approach. ## `scipy.signal` improvements `scipy.signal.gammatone` has been added to design FIR or IIR filters that model the human auditory system. `scipy.signal.iircomb` has been added to design IIR peaking/notching comb filters that can boost/attenuate a frequency from a signal. `scipy.signal.sosfilt` performance has been improved to avoid some previously- observed slowdowns `scipy.signal.windows.taylor` has been added--the Taylor window function is commonly used in radar digital signal processing `scipy.signal.gauss_spline` now supports `list` type input for consistency with other related SciPy functions `scipy.signal.correlation_lags` has been added to allow calculation of the lag/ displacement indices array for 1D cross-correlation. ## `scipy.sparse` improvements A solver for the minimum weight full matching problem for bipartite graphs, also known as the linear assignment problem, has been added in `scipy.sparse.csgraph.min_weight_full_bipartite_matching`. In particular, this provides functionality analogous to that of `scipy.optimize.linear_sum_assignment`, but with improved performance for sparse inputs, and the ability to handle inputs whose dense representations would not fit in memory. The time complexity of `scipy.sparse.block_diag` has been improved dramatically from quadratic to linear. ## `scipy.sparse.linalg` improvements The vendored version of `SuperLU` has been updated ## `scipy.fft` improvements The vendored `pocketfft` library now supports compiling with ARM neon vector extensions and has improved thread pool behavior. ## `scipy.spatial` improvements The python implementation of `KDTree` has been dropped and `KDTree` is now implemented in terms of `cKDTree`. You can now expect `cKDTree`-like performance by default. This also means `sys.setrecursionlimit` no longer needs to be increased for querying large trees. `transform.Rotation` has been updated with support for Modified Rodrigues Parameters alongside the existing rotation representations (PR [gh-12667](https://github.com/scipy/scipy/issues/12667)). `scipy.spatial.transform.Rotation` has been partially cythonized, with some performance improvements observed `scipy.spatial.distance.cdist` has improved performance with the `minkowski` metric, especially for p-norm values of 1 or 2. ## `scipy.stats` improvements New distributions have been added to `scipy.stats`: - The asymmetric Laplace continuous distribution has been added as `scipy.stats.laplace_asymmetric`. - The negative hypergeometric distribution has been added as `scipy.stats.nhypergeom`. - The multivariate t distribution has been added as `scipy.stats.multivariate_t`. - The multivariate hypergeometric distribution has been added as `scipy.stats.multivariate_hypergeom`. The `fit` method has been overridden for several distributions (`laplace`, `pareto`, `rayleigh`, `invgauss`, `logistic`, `gumbel_l`, `gumbel_r`); they now use analytical, distribution-specific maximum likelihood estimation results for greater speed and accuracy than the generic (numerical optimization) implementation. The one-sample Cramér-von Mises test has been added as `scipy.stats.cramervonmises`. An option to compute one-sided p-values was added to `scipy.stats.ttest_1samp`, `scipy.stats.ttest_ind_from_stats`, `scipy.stats.ttest_ind` and `scipy.stats.ttest_rel`. The function `scipy.stats.kendalltau` now has an option to compute Kendall's tau-c (also known as Stuart's tau-c), and support has been added for exact p-value calculations for sample sizes `> 171`. `stats.trapz` was renamed to `stats.trapezoid`, with the former name retained as an alias for backwards compatibility reasons. The function `scipy.stats.linregress` now includes the standard error of the intercept in its return value. The `_logpdf`, `_sf`, and `_isf` methods have been added to `scipy.stats.nakagami`; `_sf` and `_isf` methods also added to `scipy.stats.gumbel_r` The `sf` method has been added to `scipy.stats.levy` and `scipy.stats.levy_l` for improved precision. `scipy.stats.binned_statistic_dd` performance improvements for the following computed statistics: `max`, `min`, `median`, and `std`. We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source Software for Science program for supporting many of these improvements to `scipy.stats`. # Deprecated features ## `scipy.spatial` changes Calling `KDTree.query` with `k=None` to find all neighbours is deprecated. Use `KDTree.query_ball_point` instead. `distance.wminkowski` was deprecated; use `distance.minkowski` and supply weights with the `w` keyword instead. # Backwards incompatible changes ## `scipy` changes Using `scipy.fft` as a function aliasing `numpy.fft.fft` was removed after being deprecated in SciPy `1.4.0`. As a result, the `scipy.fft` submodule must be explicitly imported now, in line with other SciPy subpackages. ## `scipy.signal` changes The output of `decimate`, `lfilter_zi`, `lfiltic`, `sos2tf`, and `sosfilt_zi` have been changed to match `numpy.result_type` of their inputs. The window function `slepian` was removed. It had been deprecated since SciPy `1.1`. ## `scipy.spatial` changes `cKDTree.query` now returns 64-bit rather than 32-bit integers on Windows, making behaviour consistent between platforms (PR [gh-12673](https://github.com/scipy/scipy/issues/12673)). ## `scipy.stats` changes The `frechet_l` and `frechet_r` distributions were removed. They were deprecated since SciPy `1.0`. # Other changes `setup_requires` was removed from `setup.py`. This means that users invoking `python setup.py install` without having numpy already installed will now get an error, rather than having numpy installed for them via `easy_install`. This install method was always fragile and problematic, users are encouraged to use `pip` when installing from source. - Fixed a bug in `scipy.optimize.dual_annealing` `accept_reject` calculation that caused uphill jumps to be accepted less frequently. - The time required for (un)pickling of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete`, and `scipy.stats.rv_frozen` has been significantly reduced (gh12550). Inheriting subclasses should note that `__setstate__` no longer calls `__init__` upon unpickling. # Authors - [@endolith](https://github.com/endolith) - [@vkk800](https://github.com/vkk800) - aditya + - George Bateman + - Christoph Baumgarten - Peter Bell - Tobias Biester + - Keaton J. Burns + - Evgeni Burovski - Rüdiger Busche + - Matthias Bussonnier - Dominic C + - Corallus Caninus + - CJ Carey - Thomas A Caswell - chapochn + - Lucía Cheung - Zach Colbert + - Coloquinte + - Yannick Copin + - Devin Crowley + - Terry Davis + - Michaël Defferrard + - devonwp + - Didier + - divenex + - Thomas Duvernay + - Eoghan O'Connell + - Gökçen Eraslan - Kristian Eschenburg + - Ralf Gommers - Thomas Grainger + - GreatV + - Gregory Gundersen + - h-vetinari + - Matt Haberland - Mark Harfouche + - He He + - Alex Henrie - Chun-Ming Huang + - Martin James McHugh III + - Alex Izvorski + - Joey + - ST John + - Jonas Jonker + - Julius Bier Kirkegaard - Marcin Konowalczyk + - Konrad0 - Sam Van Kooten + - Sergey Koposov + - Peter Mahler Larsen - Eric Larson - Antony Lee - Gregory R. Lee - Loïc Estève - Jean-Luc Margot + - MarkusKoebis + - Nikolay Mayorov - G. D. McBain - Andrew McCluskey + - Nicholas McKibben - Sturla Molden - Denali Molitor + - Eric Moore - Shashaank N + - Prashanth Nadukandi + - nbelakovski + - Andrew Nelson - Nick + - Nikola Forró + - odidev - ofirr + - Sambit Panda - Dima Pasechnik - Tirth Patel + - Matti Picus - Paweł Redzyński + - Vladimir Philipenko + - Philipp Thölke + - Ilhan Polat - Eugene Prilepin + - Vladyslav Rachek - Ram Rachum + - Tyler Reddy - Martin Reinecke + - Simon Segerblom Rex + - Lucas Roberts - Benjamin Rowell + - Eli Rykoff + - Atsushi Sakai - Moritz Schulte + - Daniel B. Smith - Steve Smith + - Jan Soedingrekso + - Victor Stinner + - Jose Storopoli + - Diana Sukhoverkhova + - Søren Fuglede Jørgensen - taoky + - Mike Taves + - Ian Thomas + - Will Tirone + - Frank Torres + - Seth Troisi - Ronald van Elburg + - Hugo van Kemenade - Paul van Mulbregt - Saul Ivan Rivas Vega + - Pauli Virtanen - Jan Vleeshouwers - Samuel Wallan - Warren Weckesser - Ben West + - Eric Wieser - WillTirone + - Levi John Wolf + - Zhiqing Xiao - Rory Yorke + - Yun Wang (Maigo) + - Egor Zemlyanoy + - ZhihuiChen0903 + - Jacob Zhong + A total of 122 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. </details> --- ### Configuration 📅 **Schedule**: At any time (no schedule defined). 🚦 **Automerge**: Disabled by config. Please merge this manually once you are satisfied. ♻ **Rebasing**: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox. 🔕 **Ignore**: Close this PR and you won't be reminded about this update again. --- - [ ] If you want to rebase/retry this PR, click this checkbox. --- This PR has been generated by [Renovate Bot](https://github.com/renovatebot/renovate).

renovate-bot added 1 commit 2022-02-23 21:09:06 +00:00

chore(deps): update dependency scipy to ~1.8

gitea-physics/pdme/pipeline/head Build started...

Details

gitea-physics/pdme/pipeline/pr-master This commit looks good

Details

2a8f002361

deepak was assigned by renovate-bot

2022-02-23 21:09:06 +00:00

renovate-bot force-pushed renovate/scipy-1.x from 2a8f002361 to 6a1a4a58ab

2022-03-15 01:32:19 +00:00

Compare

deepak approved these changes 2022-03-15 01:56:36 +00:00

deepak merged commit f83a41ccdc into master

2022-03-15 01:56:45 +00:00

deepak deleted branch renovate/scipy-1.x

2022-03-15 01:56:45 +00:00

deepak referenced this issue from a commit

2022-03-15 01:56:46 +00:00

Merge pull request 'chore(deps): update dependency scipy to ~1.8' (#3) from renovate/scipy-1.x into master

Sign in to join this conversation.

2 Participants

Notifications

Due Date

No due date set.

Dependencies

No dependencies set.

Reference: physics/pdme#3

chore(deps): update dependency scipy to ~1.8 #3

Release Notes

SciPy 1.8.0 Release Notes

Highlights of this release

New features

scipy.fft improvements

scipy.integrate improvements

scipy.interpolate improvements

scipy.linalg improvements

scipy.optimize improvements

scipy.signal improvements

scipy.sparse improvements

scipy.spatial improvements

scipy.special improvements

scipy.stats improvements

Deprecated features

Clear split between public and private API

Other deprecations

Backwards incompatible changes

Other changes

Authors

SciPy 1.7.3 Release Notes

Authors

SciPy 1.7.2 Release Notes

Authors

SciPy 1.7.1 Release Notes

Authors

SciPy 1.7.0 Release Notes

Highlights of this release

New features

scipy.cluster improvements

scipy.interpolate improvements

scipy.linalg improvements

scipy.ndimage improvements

scipy.optimize improvements

scipy.signal improvements

scipy.sparse improvements

scipy.spatial improvements

scipy.special improvements

scipy.stats improvements

Hypothesis Tests

Sample statistics

Statistical Distributions

Other

New scipy.stats.qmc module

Deprecated features

scipy.linalg deprecations

scipy.spatial deprecations

Other changes

Authors

SciPy 1.6.3 Release Notes

Authors

SciPy 1.6.2 Release Notes

Authors

SciPy 1.6.1 Release Notes

Authors

SciPy 1.6.0 Release Notes

Highlights of this release

New features

scipy.special improvements

scipy.odr improvements

scipy.integrate improvements

scipy.cluster improvements

scipy.interpolate improvements

scipy.io improvements

scipy.linalg improvements

scipy.ndimage improvements

scipy.optimize improvements

scipy.signal improvements

scipy.sparse improvements

scipy.sparse.linalg improvements

scipy.fft improvements

scipy.spatial improvements

scipy.stats improvements

Deprecated features

scipy.spatial changes

Backwards incompatible changes

scipy changes

scipy.signal changes

scipy.spatial changes

`scipy.fft` improvements

`scipy.integrate` improvements

`scipy.interpolate` improvements

`scipy.linalg` improvements

`scipy.optimize` improvements

`scipy.signal` improvements

`scipy.sparse` improvements

`scipy.spatial` improvements

`scipy.special` improvements

`scipy.stats` improvements

`scipy.cluster` improvements

`scipy.interpolate` improvements

`scipy.linalg` improvements

`scipy.ndimage` improvements

`scipy.optimize` improvements

`scipy.signal` improvements

`scipy.sparse` improvements

`scipy.spatial` improvements

`scipy.special` improvements

`scipy.stats` improvements

New `scipy.stats.qmc` module

`scipy.linalg` deprecations

`scipy.spatial` deprecations

`scipy.special` improvements

`scipy.odr` improvements

`scipy.integrate` improvements

`scipy.cluster` improvements

`scipy.interpolate` improvements

`scipy.io` improvements

`scipy.linalg` improvements

`scipy.ndimage` improvements

`scipy.optimize` improvements

`scipy.signal` improvements

`scipy.sparse` improvements

`scipy.sparse.linalg` improvements

`scipy.fft` improvements

`scipy.spatial` improvements

`scipy.stats` improvements

`scipy.spatial` changes

`scipy` changes

`scipy.signal` changes

`scipy.spatial` changes

`scipy.stats` changes