Update dependency scipy to v1.8.1

This MR contains the following updates:

Package	Type	Update	Change
scipy (source)	ironbank-pypi	minor	1.4.1 -> 1.8.1
scipy (source)		minor	==1.4.1 -> ==1.8.1

---

Release Notes

scipy/scipy

v1.8.1

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SciPy 1.8.1 Release Notes

SciPy 1.8.1 is a bug-fix release with no new features compared to 1.8.0. Notably, usage of Pythran has been restored for Windows builds/binaries.

Authors

• Henry Schreiner
• Maximilian Nöthe
• Sebastian Berg (1)
• Sameer Deshmukh (1) +
• Niels Doucet (1) +
• DWesl (4)
• Isuru Fernando (1)
• Ralf Gommers (4)
• Matt Haberland (1)
• Andrew Nelson (1)
• Dimitri Papadopoulos Orfanos (1) +
• Tirth Patel (3)
• Tyler Reddy (46)
• Pamphile Roy (7)
• Niyas Sait (1) +
• H. Vetinari (2)
• Warren Weckesser (1)

A total of 17 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.

v1.8.0

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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 MROPACK is now vendored with SciPy, and an interface is exposed via scipy.sparse.svds with solver='MROPACK'. 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_MROPACK=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 MROPACK is now vendored with SciPy, and an interface is exposed via scipy.sparse.svds with solver='MROPACK'. For some problems, this may be faster and/or more accurate than the default, ARPACK. MROPACK functionality is currently opt-in--you must specify USE_MROPACK=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 #&#8203;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 #&#8203;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., MR 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

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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

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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

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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

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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

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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

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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

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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

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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 (MR 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 (MR 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.

v1.5.4

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SciPy 1.5.4 Release Notes

SciPy 1.5.4 is a bug-fix release with no new features compared to 1.5.3. Importantly, wheels are now available for Python 3.9 and a more complete fix has been applied for issues building with XCode 12.

Authors

• Peter Bell
• CJ Carey
• Andrew McCluskey +
• Andrew Nelson
• Tyler Reddy
• Eli Rykoff +
• Ian Thomas +

A total of 7 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.

v1.5.3

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SciPy 1.5.3 Release Notes

SciPy 1.5.3 is a bug-fix release with no new features compared to 1.5.2. In particular, Linux ARM64 wheels are now available and a compatibility issue with XCode 12 has been fixed.

Authors

• Peter Bell
• CJ Carey
• Thomas Duvernay +
• Gregory Lee
• Eric Moore
• odidev
• Dima Pasechnik
• Tyler Reddy
• Simon Segerblom Rex +
• Daniel B. Smith
• Will Tirone +
• Warren Weckesser

A total of 12 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.

v1.5.2

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SciPy 1.5.2 Release Notes

SciPy 1.5.2 is a bug-fix release with no new features compared to 1.5.1.

Authors

• Peter Bell
• Tobias Biester +
• Evgeni Burovski
• Thomas A Caswell
• Ralf Gommers
• Sturla Molden
• Andrew Nelson
• ofirr +
• Sambit Panda
• Ilhan Polat
• Tyler Reddy
• Atsushi Sakai
• Pauli Virtanen

A total of 13 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.

v1.5.1

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SciPy 1.5.1 Release Notes

SciPy 1.5.1 is a bug-fix release with no new features compared to 1.5.0. In particular, an issue where DLL loading can fail for SciPy wheels on Windows with Python 3.6 has been fixed.

Authors

• Peter Bell
• Loïc Estève
• Philipp Thölke +
• Tyler Reddy
• Paul van Mulbregt
• Pauli Virtanen
• Warren Weckesser

A total of 7 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.

v1.5.0

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SciPy 1.5.0 Release Notes

SciPy 1.5.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.5.x branch, and on adding new features on the master branch.

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

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

Highlights of this release

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

New features

scipy.cluster improvements

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

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

scipy.fft improvements

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

scipy.integrate improvements

scipy.interpolate improvements

scipy.io improvements

scipy.io.wavfile error messages are more explicit about what's wrong, and extraneous bytes at the ends of files are ignored instead of raising an error when the data has successfully been read.

scipy.io.loadmat gained a simplify_cells parameter, which if set to True simplifies the structure of the return value if the .mat file contains cell arrays.

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

scipy.linalg improvements

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

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

Wrappers for the following LAPACK routines have been added to scipy.linalg.lapack:

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

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

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

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

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

scipy.ndimage improvements

scipy.optimize improvements

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

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

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

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

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

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

scipy.signal improvements

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

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

scipy.sparse improvements

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

scipy.sparse.lil.tocsr is faster

Fixed/improved comparisons between pydata sparse arrays and sparse matrices

BSR format sparse multiplication performance has been improved.

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

scipy.spatial improvements

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

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

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

scipy.special improvements

The following functions now have Cython interfaces in cython_special:

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

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

scipy.stats improvements

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

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

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

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

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

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

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

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

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

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

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

The new function median_abs_deviation replaces the deprecated median_absolute_deviation.

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

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

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

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

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

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

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

Deprecated features

scipy deprecations

scipy.special changes

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

scipy.stats changes

The function median_absolute_deviation is deprecated. Use median_abs_deviation instead.

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

Backwards incompatible changes

scipy.interpolate changes

scipy.linalg changes

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

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

scipy.signal changes

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

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

scipy.stats changes

Other changes

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

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

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

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

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

Authors

• @​endolith
• Hameer Abbasi
• ADmitri +
• Wesley Alves +
• Berkay Antmen +
• Sylwester Arabas +
• Arne Küderle +
• Christoph Baumgarten
• Peter Bell
• Felix Berkenkamp
• Jordão Bragantini +
• Clemens Brunner +
• Evgeni Burovski
• Matthias Bussonnier +
• CJ Carey
• Derrick Chambers +
• Leander Claes +
• Christian Clauss
• Luigi F. Cruz +
• dankleeman
• Andras Deak
• Milad Sadeghi DM +
• jeremie du boisberranger +
• Stefan Endres
• Malte Esders +
• Leo Fang +
• felixhekhorn +
• Isuru Fernando
• Andrew Fowlie
• Lakshay Garg +
• Gaurav Gijare +
• Ralf Gommers
• Emmanuelle Gouillart +
• Kevin Green +
• Martin Grignard +
• Maja Gwozdz
• Sturla Molden
• gyu-don +
• Matt Haberland
• hakeemo +
• Charles Harris
• Alex Henrie
• Santi Hernandez +
• William Hickman +
• Till Hoffmann +
• Joseph T. Iosue +
• Anany Shrey Jain
• Jakob Jakobson
• Charles Jekel +
• Julien Jerphanion +
• Jiacheng-Liu +
• Christoph Kecht +
• Paul Kienzle +
• Reidar Kind +
• Dmitry E. Kislov +
• Konrad +
• Konrad0
• Takuya KOUMURA +
• Krzysztof Pióro
• Peter Mahler Larsen
• Eric Larson
• Antony Lee
• Gregory Lee +
• Gregory R. Lee
• Chelsea Liu
• Cong Ma +
• Kevin Mader +
• Maja Gwóźdź +
• Alex Marvin +
• Matthias Kümmerer
• Nikolay Mayorov
• Mazay0 +
• G. D. McBain
• Nicholas McKibben +
• Sabrina J. Mielke +
• Sebastian J. Mielke +
• Miloš Komarčević +
• Shubham Mishra +
• Santiago M. Mola +
• Grzegorz Mrukwa +
• Peyton Murray
• Andrew Nelson
• Nico Schlömer
• nwjenkins +
• odidev +
• Sambit Panda
• Vikas Pandey +
• Rick Paris +
• Harshal Prakash Patankar +
• Balint Pato +
• Matti Picus
• Ilhan Polat
• poom +
• Siddhesh Poyarekar
• Vladyslav Rachek +
• Bharat Raghunathan
• Manu Rajput +
• Tyler Reddy
• Andrew Reed +
• Lucas Roberts
• Ariel Rokem
• Heshy Roskes
• Matt Ruffalo
• Atsushi Sakai +
• Benjamin Santos +
• Christoph Schock +
• Lisa Schwetlick +
• Chris Simpson +
• Leo Singer
• Kai Striega
• Søren Fuglede Jørgensen
• Kale-ab Tessera +
• Seth Troisi +
• Robert Uhl +
• Paul van Mulbregt
• Vasiliy +
• Isaac Virshup +
• Pauli Virtanen
• Shakthi Visagan +
• Jan Vleeshouwers +
• Sam Wallan +
• Lijun Wang +
• Warren Weckesser
• Richard Weiss +
• wenhui-prudencemed +
• Eric Wieser
• Josh Wilson
• James Wright +
• Ruslan Yevdokymov +
• Ziyao Zhang +

A total of 129 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.

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