SciPy 1.12.0 Release Notes

Note: SciPy 1.12.0 is not released yet!

SciPy 1.12.0 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 1.12.x branch, and on adding new features on the main branch.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• Experimental support for the array API standard has been added to part of scipy.special, and to all of scipy.fft and scipy.cluster. There are likely to be bugs and early feedback for usage with CuPy arrays, PyTorch tensors, and other array API compatible libraries is appreciated. Use the SCIPY_ARRAY_API environment variable for testing.
• A new class, ShortTimeFFT, provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT.
• Several new constructors have been added for sparse arrays, and many operations now additionally support sparse arrays, further facilitating the migration from sparse matrices.
• A large portion of the scipy.stats API now has improved support for handling NaN values, masked arrays, and more fine-grained shape-handling. The accuracy and performance of a number of stats methods have been improved, and a number of new statistical tests and distributions have been added.

New features

scipy.cluster improvements

• Experimental support added for the array API standard; PyTorch tensors, CuPy arrays and array API compatible array libraries are now accepted (GPU support is limited to functions with pure Python implementations). CPU arrays which can be converted to and from NumPy are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the SCIPY_ARRAY_API environment variable before importing scipy. This experimental support is still under development and likely to contain bugs - testing is very welcome.

scipy.fft improvements

• Experimental support added for the array API standard; functions which are part of the fft array API standard extension module, as well as the Fast Hankel Transforms and the basic FFTs which are not in the extension module, now accept PyTorch tensors, CuPy arrays and array API compatible array libraries. CPU arrays which can be converted to and from NumPy arrays are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the SCIPY_ARRAY_API environment variable before importing scipy. This experimental support is still under development and likely to contain bugs - testing is very welcome.

scipy.integrate improvements

• Added scipy.integrate.cumulative_simpson for cumulative quadrature from sampled data using Simpson's 1/3 rule.

scipy.interpolate improvements

• New class NdBSpline represents tensor-product splines in N dimensions. This class only knows how to evaluate a tensor product given coefficients and knot vectors. This way it generalizes BSpline for 1D data to N-D, and parallels NdPPoly (which represents N-D tensor product polynomials). Evaluations exploit the localized nature of b-splines.
• NearestNDInterpolator.__call__ accepts **query_options, which are passed through to the KDTree.query call to find nearest neighbors. This allows, for instance, to limit the neighbor search distance and parallelize the query using the workers keyword.
• BarycentricInterpolator now allows computing the derivatives.
• It is now possible to change interpolation values in an existing CloughTocher2DInterpolator instance, while also saving the barycentric coordinates of interpolation points.

scipy.linalg improvements

• Access to new low-level LAPACK functions is provided via dtgsyl and stgsyl.

scipy.ndimage improvements

scipy.optimize improvements

• scipy.optimize.nnls is rewritten in Python and now implements the so-called fnnls or fast nnls.
• The result object of scipy.optimize.root and scipy.optimize.root_scalar now reports the method used.
• The callback method of scipy.optimize.differential_evolution can now be passed more detailed information via the intermediate_results keyword parameter. Also, the evolution strategy now accepts a callable for additional customization. The performance of differential_evolution has also been improved.
• minimize method Newton-CG has been made slightly more efficient.
• minimize method BFGS now accepts an initial estimate for the inverse of the Hessian, which allows for more efficient workflows in some circumstances. The new parameter is hess_inv0.
• minimize methods CG, Newton-CG, and BFGS now accept parameters c1 and c2, allowing specification of the Armijo and curvature rule parameters, respectively.
• curve_fit performance has improved due to more efficient memoization of the callable function.
• isotonic_regression has been added to allow nonparametric isotonic regression.

scipy.signal improvements

• freqz, freqz_zpk, and group_delay are now more accurate when fs has a default value.
• The new class ShortTimeFFT provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on dual windows and provides more fine-grained control of the parametrization especially in regard to scaling and phase-shift. Functionality was implemented to ease working with signal and STFT chunks. A section has been added to the "SciPy User Guide" providing algorithmic details. The functions stft, istft and spectrogram have been marked as legacy.

scipy.sparse improvements

• sparse.linalg iterative solvers sparse.linalg.cg, sparse.linalg.cgs, sparse.linalg.bicg, sparse.linalg.bicgstab, sparse.linalg.gmres, and sparse.linalg.qmr are rewritten in Python.
• Updated vendored SuperLU version to 6.0.1, along with a few additional fixes.
• Sparse arrays have gained additional constructors: eye_array, random_array, block_array, and identity. kron and kronsum have been adjusted to additionally support operation on sparse arrays.
• Sparse matrices now support a transpose with axes=(1, 0), to mirror the .T method.
• LaplacianNd now allows selection of the largest subset of eigenvalues, and additionally now supports retrieval of the corresponding eigenvectors. The performance of LaplacianNd has also been improved.
• The performance of dok_matrix and dok_array has been improved, and their inheritance behavior should be more robust.
• hstack, vstack, and block_diag now work with sparse arrays, and preserve the input sparse type.
• A new function, scipy.sparse.linalg.matrix_power, has been added, allowing for exponentiation of sparse arrays.

scipy.spatial improvements

• Two new methods were implemented for spatial.transform.Rotation: __pow__ to raise a rotation to integer or fractional power and approx_equal to check if two rotations are approximately equal.
• The method Rotation.align_vectors was extended to solve a constrained alignment problem where two vectors are required to be aligned precisely. Also when given a single pair of vectors, the algorithm now returns the rotation with minimal magnitude, which can be considered as a minor backward incompatible change.
• A new representation for spatial.transform.Rotation called Davenport angles is available through from_davenport and as_davenport methods.
• Performance improvements have been added to distance.hamming and distance.correlation.
• Improved performance of SphericalVoronoi sort_vertices_of_regions and two dimensional area calculations.

scipy.special improvements

• Added scipy.special.stirling2 for computation of Stirling numbers of the second kind. Both exact calculation and an asymptotic approximation (the default) are supported via exact=True and exact=False (the default) respectively.
• Added scipy.special.betaincc for computation of the complementary incomplete Beta function and scipy.special.betainccinv for computation of its inverse.
• Improved precision of scipy.special.betainc and scipy.special.betaincinv
• Experimental support added for alternative backends: functions scipy.special.log_ndtr, scipy.special.ndtr, scipy.special.ndtri, scipy.special.erf, scipy.special.erfc, scipy.special.i0, scipy.special.i0e, scipy.special.i1, scipy.special.i1e, scipy.special.gammaln, scipy.special.gammainc, scipy.special.gammaincc, scipy.special.logit, and scipy.special.expit now accept PyTorch tensors and CuPy arrays. These features are still under development and likely to contain bugs, so they are disabled by default; enable them by setting a SCIPY_ARRAY_API environment variable to 1 before importing scipy. Testing is appreciated!

scipy.stats improvements

• Added scipy.stats.quantile_test, a nonparametric test of whether a hypothesized value is the quantile associated with a specified probability. The confidence_interval method of the result object gives a confidence interval of the quantile.
• scipy.stats.wasserstein_distance now computes the Wasserstein distance in the multidimensional case.
• scipy.stats.sampling.FastGeneratorInversion provides a convenient interface to fast random sampling via numerical inversion of distribution CDFs.
• scipy.stats.geometric_discrepancy adds geometric/topological discrepancy metrics for random samples.
• scipy.stats.multivariate_normal now has a fit method for fitting distribution parameters to data via maximum likelihood estimation.
• scipy.stats.bws_test performs the Baumgartner-Weiss-Schindler test of whether two-samples were drawn from the same distribution.
• scipy.stats.jf_skew_t implements the Jones and Faddy skew-t distribution.
• scipy.stats.anderson_ksamp now supports a permutation version of the test using the method parameter.
• The fit methods of scipy.stats.halfcauchy, scipy.stats.halflogistic, and scipy.stats.halfnorm are faster and more accurate.
• scipy.stats.beta entropy accuracy has been improved for extreme values of distribution parameters.
• The accuracy of sf and/or isf methods have been improved for several distributions: scipy.stats.burr, scipy.stats.hypsecant, scipy.stats.kappa3, scipy.stats.loglaplace, scipy.stats.lognorm, scipy.stats.lomax, scipy.stats.pearson3, scipy.stats.rdist, and scipy.stats.pareto.
• The following functions now support parameters axis, nan_policy, and keep_dims: scipy.stats.entropy, scipy.stats.differential_entropy, scipy.stats.variation, scipy.stats.ansari, scipy.stats.bartlett, scipy.stats.levene, scipy.stats.fligner, scipy.stats.cirmean, scipy.stats.circvar, scipy.stats.circstd, scipy.stats.tmean, scipy.stats.tvar, scipy.stats.tstd, scipy.stats.tmin, scipy.stats.tmax, and scipy.stats.tsem`.
• The logpdf and fit methods of scipy.stats.skewnorm have been improved.
• The beta negative binomial distribution is implemented as scipy.stats.betanbinom.
• The speed of scipy.stats.invwishart rvs and logpdf have been improved.
• A source of intermediate overflow in scipy.stats.boxcox_normmax with method='mle' has been eliminated, and the returned value of lmbda is constrained such that the transformed data will not overflow.
• scipy.stats.nakagami stats is more accurate and reliable.
• A source of intermediate overflow in scipy.norminvgauss.pdf has been eliminated.
• Added support for masked arrays to stats.circmean, stats.circvar, stats.circstd, and stats.entropy.
• dirichlet has gained a new covariance (cov) method.
• Improved accuracy of multivariate_t entropy with large degrees of freedom.
• loggamma has an improved entropy method.

Deprecated features

• Error messages have been made clearer for objects that don't exist in the public namespace and warnings sharpened for private attributes that are not supposed to be imported at all.

• scipy.signal.cmplx_sort has been deprecated and will be removed in SciPy 1.14. A replacement you can use is provided in the deprecation message.

• Values the the argument initial of scipy.integrate.cumulative_trapezoid other than 0 and None are now deprecated.

• scipy.stats.rvs_ratio_uniforms is deprecated in favour of scipy.stats.sampling.RatioUniforms

• scipy.integrate.quadrature and scipy.integrate.romberg have been deprecated due to accuracy issues and interface shortcomings. They will be removed in SciPy 1.14. Please use scipy.integrate.quad instead.

• Coinciding with upcoming changes to function signatures (e.g. removal of a deprecated keyword), we are deprecating positional use of keyword arguments for the affected functions, which will raise an error starting with SciPy 1.14. In some cases, this has delayed the originally announced removal date, to give time to respond to the second part of the deprecation. Affected functions are:

  • linalg.{eigh, eigvalsh, pinv}
  • integrate.simpson
  • signal.{firls, firwin, firwin2, remez}
  • sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}
  • special.comb
  • stats.kendalltau

• All wavelet functions have been deprecated, as PyWavelets provides suitable implementations; affected functions are: signal.{daub, qmf, cascade, morlet, morlet2, ricker, cwt}

• scipy.integrate.trapz, scipy.integrate.cumtrapz, and scipy.integrate.simps have been deprecated in favour of scipy.integrate.trapezoid, scipy.integrate.cumulative_trapezoid, and scipy.integrate.simpson respectively and will be removed in SciPy 1.14.

• The tol argument of scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk,gmres,lgmres,minres,qmr,tfqmr} is now deprecated in favour of rtol and will be removed in SciPy 1.14. Furthermore, the default value of atol for these functions is due to change to 0.0 in SciPy 1.14.

Expired Deprecations

There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected:

• The centered keyword of scipy.stats.qmc.LatinHypercube has been removed. Use scrambled=False instead of centered=True.
• scipy.stats.binom_test has been removed in favour of scipy.stats.binomtest.
• In scipy.stats.iqr, the use of scale='raw' has been removed in favour of scale=1.

Backwards incompatible changes

Other changes

• The arguments used to compile and link SciPy are now available via show_config.

Authors

• Name (commits)
• endolith (1)
• h-vetinari (32)
• Tom Adamczewski (3) +
• Anudeep Adiraju (1) +
• akeemlh (1)
• Alex Amadori (2) +
• Raja Yashwanth Avantsa (2) +
• Seth Axen (1) +
• Ross Barnowski (1)
• Dan Barzilay (1) +
• Ashish Bastola (1) +
• Christoph Baumgarten (2)
• Ben Beasley (3) +
• Doron Behar (1)
• Peter Bell (1)
• Sebastian Berg (1)
• Ben Boeckel (1) +
• David Boetius (1) +
• Matt Borland (1)
• Jake Bowhay (103)
• Larry Bradley (1) +
• Dietrich Brunn (5)
• Evgeni Burovski (102)
• Matthias Bussonnier (18)
• CJ Carey (6)
• Colin Carroll (1) +
• Aadya Chinubhai (1) +
• Luca Citi (1)
• Lucas Colley (141) +
• com3dian (1) +
• Anirudh Dagar (4)
• Danni (1) +
• Dieter Werthmüller (1)
• John Doe (2) +
• Philippe DONNAT (2) +
• drestebon (1) +
• Thomas Duvernay (1)
• elbarso (1) +
• emilfrost (2) +
• Paul Estano (8) +
• Evandro (2)
• Franz Király (1) +
• Nikita Furin (1) +
• gabrielthomsen (1) +
• Lukas Geiger (9) +
• Artem Glebov (22) +
• Caden Gobat (1)
• Ralf Gommers (126)
• Alexander Goscinski (2) +
• Rohit Goswami (2) +
• Olivier Grisel (1)
• Matt Haberland (243)
• Charles Harris (1)
• harshilkamdar (1) +
• Alon Hovav (2) +
• Gert-Ludwig Ingold (1)
• Romain Jacob (1) +
• jcwhitehead (1) +
• Julien Jerphanion (13)
• He Jia (1)
• JohnWT (1) +
• jokasimr (1) +
• Evan W Jones (1)
• Karen Róbertsdóttir (1) +
• Ganesh Kathiresan (1)
• Robert Kern (11)
• Andrew Knyazev (4)
• Uwe L. Korn (1) +
• Rishi Kulkarni (1)
• Kale Kundert (3) +
• Jozsef Kutas (2)
• Kyle0 (2) +
• Robert Langefeld (1) +
• Jeffrey Larson (1) +
• Jessy Lauer (1) +
• lciti (1) +
• Hoang Le (1) +
• Antony Lee (5)
• Thilo Leitzbach (4) +
• LemonBoy (2) +
• Ellie Litwack (8) +
• Thomas Loke (4) +
• Malte Londschien (1) +
• Christian Lorentzen (6)
• Adam Lugowski (10) +
• lutefiskhotdish (1)
• mainak33 (1) +
• Ben Mares (11) +
• mart-mihkel (2) +
• Mateusz Sokół (24) +
• Nikolay Mayorov (4)
• Nicholas McKibben (1)
• Melissa Weber Mendonça (7)
• Michał Górny (1)
• Kat Mistberg (2) +
• mkiffer (1) +
• mocquin (1) +
• Nicolas Mokus (2) +
• Sturla Molden (1)
• Roberto Pastor Muela (3) +
• Bijay Nayak (1) +
• Andrew Nelson (105)
• Praveer Nidamaluri (3) +
• Lysandros Nikolaou (2)
• Dimitri Papadopoulos Orfanos (7)
• Pablo Rodríguez Pérez (1) +
• Dimitri Papadopoulos (2)
• Tirth Patel (14)
• Kyle Paterson (1) +
• Paul (4) +
• Yann Pellegrini (2) +
• Matti Picus (4)
• Ilhan Polat (36)
• Pranav (1) +
• Bharat Raghunathan (1)
• Chris Rapson (1) +
• Matteo Raso (4)
• Tyler Reddy (201)
• Martin Reinecke (1)
• Tilo Reneau-Cardoso (1) +
• resting-dove (2) +
• Simon Segerblom Rex (4)
• Lucas Roberts (2)
• Pamphile Roy (31)
• Feras Saad (3) +
• Atsushi Sakai (3)
• Masahiro Sakai (2) +
• Omar Salman (14)
• Andrej Savikin (1) +
• Daniel Schmitz (54)
• Dan Schult (19)
• Scott Shambaugh (9)
• Sheila-nk (2) +
• Mauro Silberberg (3) +
• Maciej Skorski (1) +
• Laurent Sorber (1) +
• Albert Steppi (28)
• Kai Striega (1)
• Saswat Susmoy (1) +
• Alex Szatmary (1) +
• Søren Fuglede Jørgensen (3)
• othmane tamri (3) +
• Ewout ter Hoeven (1)
• Will Tirone (1)
• TLeitzbach (1) +
• Kevin Topolski (1) +
• Edgar Andrés Margffoy Tuay (1)
• Dipansh Uikey (1) +
• Matus Valo (3)
• Christian Veenhuis (2)
• Nicolas Vetsch (1) +
• Isaac Virshup (7)
• Hielke Walinga (2) +
• Stefan van der Walt (2)
• Warren Weckesser (7)
• Bernhard M. Wiedemann (4)
• Levi John Wolf (1)
• Xuefeng Xu (4) +
• Rory Yorke (2)
• YoussefAli1 (1) +
• Irwin Zaid (4) +
• Jinzhe Zeng (1) +
• JIMMY ZHAO (1) +

A total of 163 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.