chore(deps): update dependency scipy to v1.18.0

[eagleoflqj]

ℹ️ Note

This PR body was truncated due to platform limits.

This PR contains the following updates:

Package	Change	Age	Confidence
scipy	==1.9.3 → ==1.18.0

---

Release Notes

scipy/scipy (scipy)

v1.18.0: SciPy 1.18.0

Compare Source

SciPy 1.18.0 Release Notes

SciPy 1.18.0 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
1.18.x branch, and on adding new features on the main branch.

This release requires Python 3.12-3.14 and NumPy 2.0.0 or greater.

Highlights of this release

• SciPy now supports three different build modes for BLAS and LAPACK
  LP64/ILP64 support, and machinery is provided for downstream cython_lapack
  consumers to gracefully handle LP64/ILP64 backend builds. ILP64 support
  has been substantially improved across the SciPy library.
• Remaining Fortran to C translations have been completed---an experimental
  Fortran-free build option is now available to developers for testing
  purposes. Developer feedback is welcome on Fortran-free builds.
• scipy.signal.whittaker_henderson now provides access to Whittaker-Henderson
  smoothing of a discrete signal.
• A large number of scipy.stats functions now support lazy arrays and JAX
  JIT. Array API support has been improved substantially in SciPy, with at least
  21 functions gaining new support in this release. 16 scipy.stats functions
  have also gained support for MArray input.

New features

scipy.fft improvements

• SciPy's internal FFT backend has switched from pocketfft to its
  successor package ducc0.fft, which features several incremental
  improvements. The most significant of those from SciPy's perspective is
  probably that storage requirements for internally cached plans have been
  significantly reduced for most long 1D transforms. Plans that require more
  storage than 1MB will no longer be cached; this mainly affects huge 1D
  transforms of prime and near-prime sizes.

scipy.interpolate improvements

• Users may now increase the QHull simplex assignment tolerance via the new
  simplex_tolerance argument to the _call__ methods of
  LinearNDInterpolator and CloughTocher2dInterpolator. This can
  help users avoid holes in certain interpolation problems.
• The FITPACK Fortran code has been ported to C.

scipy.differentiate improvements

• scipy.differentiate.derivative now supports passing kwargs to the
  function whose derivative is desired.

scipy.linalg improvements

• We now support three different build modes for BLAS and LAPACK: LP64-only,
  ILP64-only, and ILP64 for everyting except cython_blas/cython_lapack/
  linalg.blas/linalg.lapack (support for Accelerate and MKL).
• Machinery is now provided for downstream cython_lapack users to gracefully
  handle LP64/ILP64 backend builds. Worked examples, including build system
  details, have been included in this release.
• An overwrite_b keyword argument was added to eigvals, for consistency
  with other similar linalg functions.
• linalg.cholesky now leverages symmetry properties for performance
  improvements, especially for real matrices. The batching loop of cholesky
  has now also been moved to a C implementation.
• scipy.linalg.lu and scipy.linalg.det have been rewritten in C++ with
  batching support in the compiled code.
• Added ILP64 support to scipy.linalg.expm and scipy.linalg.sqrtm.
• The batching loops of scipy.linalg.qr, scipy.linalg.eig,
  scipy.linalg.lstsq, and scipy.linalg.svd have been moved to C,
  providing a substantial speedup for batched input.
• The performance of scipy.linalg.expm has been improved.
• The performance for scipy.linalg.solve has improved for batched inputs.
• scipy.linalg.bandwidth now supports batching for greater than or equal to
  2 dimensional input.

scipy.optimize improvements

• The trust_constr method for minimize was adjusted so that if the x
  array would result in infeasible constraints, and those constraints were
  marked as keep_feasible, then the objective function is not called with
  that x array.
• The COBYQA method for minimize now supports being called
  concurrently by multiple threads. Previously, multiple threads calling this
  function would only run one at a time.
• scipy.optimize.nnls, and minimize methods SLSQP and L-BFGS-B
  now have support for ILP64 LAPACK, when available.
• Functions in scipy.optimize.elementwise now support passing kwargs
  to the callable function.

scipy.signal improvements

• The new ~scipy.signal.whittaker_henderson implements Whittaker-Henderson smoothing
  of a discrete signal. It offers different penalties to control the smoothness as well
  as automatic selection of the penalty strength via optimization of the restricted
  maximum likelihood (REML) criterion.
  It is a valuable alternative for the Savitzky-Golay filter
  ~scipy.signal.savgol_filter.
  In econometrics, Whittaker-Henderson graduation of penalty order 2 is also known as
  Hodrick-Prescott filter.
• lfilter_zi was refactored for improved numerical stability and
  efficiency. It now raises a ValueError if parameter a has leading
  zeros, i.e., a[0] == 0, since lfilter and filtfilt do not support
  that as well. Furthermore, a ValueError instead of a LinAlgError is
  raised if the filter is unstable due to having a pole at z = 1.

scipy.sparse improvements

• In scipy.sparse.csgraph the computation of strongly connected components
  for directed graphs is now 2x faster with better cache locality, using
  algorithmic improvements described in the recent survey by Tarjan and Zwick.
• Added ILP64 BLAS/LAPACK support to SuperLU and PROPACK extensions.
• All sparse array/matrix formats now support matrix_transpose/.mT.
• Support for n-dimensional linear operators has been added to
  scipy.sparse.linalg.LinearOperator, and LinearOperator now has
  a new rdot method.
• scipy.sparse.linalg.minres now supports complex hermitian matrices.

scipy.integrate improvements

• ILP64 support was added for ODEPACK
• scipy.integrate.tanhsinh and scipy.integrate.nsum now support passing
  kwargs to the function to be integrated.

scipy.spatial improvements

• 3D area calculations are now faster in scipy.spatial.SphericalVoronoi.
• N-dimensional input is now supported for scipy.spatial.distance.minkowski,
  scipy.spatial.distance.euclidean, and scipy.spatial.distance.sqeuclidean.
• It is now possible to return sparse arrays rather than matrices from
  KDTree.sparse_distance_matrix.
• It is now possible to compose Rotation and RigidTransform directly,
  by automatically promoting Rotation when the two are composed via
  a multiplication operator.

scipy.special improvements

• The accuracy of the following functions was improved: scipy.special.bdtrik,
  scipy.special.bdtrin, scipy.special.nbdtrik, scipy.special.nbdtrin.
• The numerical behavior for scipy.special.eval_jacobi has been improved
  for several parameter combinations.
• The Bessel functions scipy.special.j0 and scipy.special.y0
  have improved accuracy for large arguments.

scipy.stats improvements

• The accuracy of scipy.stats.pmean with tiny, nonzero p has been
  improved.
• The performance of scipy.stats.halfgennorm has been improved.
• zstatistic has been added to the result object of
  scipy.stats.mannwhitneyu.
• A large number of stats functions now support lazy arrays and JAX
  JIT (see Python Array API support section below).
• Support for the nan_policy keyword argument has been added to:
  scipy.stats.obrientransform, scipy.stats.boxcox,
  scipy.stats.boxcox_normmax, scipy.stats.yeojohnson,
  scipy.stats.yeojohnson_normmax, scipy.stats.sigmaclip, and
  scipy.stats.expectile.
• scipy.stats.ContinuousDistribution.lmoment has been added for computing
  population L-moments.
• The new function scipy.stats.estimated_cdf has been added. It reproduces
  much of the functionality of stats.mstats.plotting_positions,
  stats.percentileofscore, stats.ecdf.cdf, and stats.cumfreq, but
  is also vectorized.
• scipy.stats.ansari accepts a new method argument.
• scipy.stats.bws_test, scipy.stats.expectile, and
  scipy.stats.quantile_test now accept an axis argument.
• scipy.stats.expectile and scipy.stats.quantile_test accept a new
  keepdims argument.
• scipy.stats.binomtest now supports batching of k, n, and p.

Python Array API Standard Support

• Support has been added for CuPy delegation for: interpolate.PPoly,
  interpolate.BPoly, and interpolate.BSpline.
• CuPy support has been added for scipy.stats.rankdata.
• Array API support has been added for method and trim usage
  in scipy.stats.ttest_ind.
• Support for MArrays has been added to: scipy.stats.cramervonmises,
  scipy.stats.ks_1samp, scipy.stats.ks_2samp, scipy.stats.mode,
  scipy.stats.rankdata, scipy.stats.kruskal, scipy.stats.brunnermunzel,
  scipy.stats.spearmanrho, scipy.stats.friedmanchisquare,
  scipy.stats.cramervonmises_2samp, scipy.stats.mannwhitneyu,
  scipy.stats.wilcoxon, scipy.stats.fligner, scipy.stats.linregress,
  scipy.stats.alexandergovern, and scipy.stats.levene.
• Array API support has been added to: scipy.stats.quantile_test,
  scipy.stats.kendalltau (via NumPy conversion), scipy.stats.kstest,
  scipy.sparse.linalg.LinearOperator, scipy.stats.cumfreq,
  scipy.stats.relfreq, scipy.stats.ks_2samp, scipy.stats.theilslopes,
  scipy.stats.siegelslopes, scipy.stats.obrientransform (including marray),
  scipy.stats.binomtest, scipy.integrate.fixed_quad, scipy.signal.square,
  scipy.stats.expectile, scipy.stats.shapiro, scipy.stats.pointbiserialr,
  scipy.stats.bws_test, scipy.stats.estimated_cdf (new function),
  scipy.stats.linregress, scipy.integrate.simpson, and
  scipy.signal.sawtooth.
• The torch support for scipy.signal.fftconvolve now correctly
  handles the float32 dtype.
• JAX JIT support has been added for: scipy.stats.binomtest
  (except for method='two-sided'), scipy.stats.mannwhitneyu
  (except for method='auto'), scipy.stats.lmoment, scipy.stats.moment,
  scipy.stats.ansari (related to new method argument),
  scipy.stats.yeojohnson_llf, scipy.stats.epps_singleton_2samp,
  scipy.stats.wilcoxon (except for method='exact' and method='auto'),
  scipy.stats.rankdata (via delegation), scipy.signal.oaconvolve,
  scipy.signal.hilbert, and scipy.signal.hilbert2.

Deprecated features and future changes

• passing lwork parameter to scipy.linalg.qr has been deprecated. The
  functionality was rarely used; the function computes the optimal size of the
  work arrays automatically, therefore users should simply remove their uses
  of the lwork parameter.
• The sparse construction functions kron, kronsum and block_diag
  choose return type sparray or spmatrix depending on the type of the
  sparse input arrays. When no inputs are sparse, the output is chosen to be
  spmatrix. That has been deprecated. The return type when no inputs are
  sparse will be changing to sparray. You can control the output type by
  ensuring that at least one input array is sparse. If any are sparray,
  the output will be sparray. If all sparse inputs are spmatrix,
  the output will be spmatrix.
• A FutureWarning is now issued for calling {r}matvec on column vectors
  with LinearOperator. Identical behavior can be achieved (and extended to
  batch dimensions) via {r}matmat.
• scipy.linalg functions are now stricter--using non-LAPACK dtypes is
  deprecated. When the deprecations expire, this will effectively limit the
  dtypes allowed in linear algebra functions to: integers (upcast to float),
  and single/double precision float/complex dtypes.
• scipy.spatial.minkowsi_distance, scipy.spatial.minkowsi_distance_p,
  and scipy.spatial.distance_matrix have been deprecated in favor of
  other superior functions.
• scipy.spatial.tsearch has been deprecated because it duplicates functionality
  more conveniently provided within the Delaunay class proper.
• The following functions have been deprecated because they were deemed
  not practically useful: scipy.interpolate.pade, scipy.interpolate.lagrange,
  and scipy.interpolate.approximate_taylor_polynomial.
• Setting spmatrix=True for the scipy.io readers mmio, FFM, hb,
  and matlab/_mio is now deprecated, including when set as the default
  value.
• The unintentionally public scipy.cluster.vq.py_vq has been deprecated.

Backwards incompatible changes

• The output of scipy.stats.rankdata is now always of a floating point
  dtype -- the result dtype of the input and a Python float.
• The behavior of the residuals returned by scipy.linalg.lstsq has been
  changed. For lapack_driver == "gelsy" or the system being either
  underdetermined or square, empty residuals are still returned. For
  lapack_driver == "gesld"/"gelss" in combination with an overdetermined
  system a non-empty residual is always returned. However, in the case where a
  slice is not full column rank, the corresponding residual is set to NaN.
• The 2nd output object of scipy.stats.contingency.crosstab when kwarg
  sparse=True is now a sparse array holding the counts instead of a sparse
  matrix. This allows it to be nD, so can accept more than 2 sequences as
  inputs, but it is a different class. Most operations work the same for
  sparse arrays and matrixes with notable differences for matrix: * means
  matmul and always-2D. For more info see migration_to_sparray.
• scipy.stats.obrientransform now returns a tuple of arrays instead of
  a single ndarray.
• scipy.stats.multinomial now returns NaNs when the category probability
  (p) rows/arrays do not sum to unity. This is an expiration of the deprecated
  behavior of adjusting the final element in the p array to compensate.
  Note that multinomial.rvs will now raise an error in such cases, since it
  has an integral return type.
• The iprint and disp parameters of scipy.optimize.fmin_l_bfgs_b
  have been removed, following the expiry of their deprecation.
• For scipy.linalg.{sqrtm, logm, signm}, disp (and sqrtm
blocksize) parameters were removed (expired deprecations).
• The deprecated atol argument of scipy.optimize.nnls has been
  removed.
• For 2D input, the return type of scipy.linalg.bandwidth has changed from
  (int, int) to (np.int64, np.int64).
• The second return type of scipy.linalg.cho_factor changed from bool
  to NDArray[np.bool].
• The second return type of scipy.interpolate.splint changed from a 1D
  float64 array to None when full_output=True.
• The types of the k and n attributes of the BinomTestResult
  object returned by scipy.stats.binomtest have changed from int to
  np.float64.

Other changes

• The vendored Boost.Math was updated from 1.89.0 to 1.91.0.
• SciPy now has a Pixi package definition, allowing developers to easily build
  SciPy from source inside Pixi workspaces.
• Developers may be interested in the private build option _without-fortran,
  which allows building SciPy from source in the absence of a Fortran compiler.
  This is an early prototype of the planned capability of a Fortran-free
  SciPy.
• The private scipy.interpolate._regrid function may be of experimental
  interest. It provides an interface for 2-D smoothing B-spline fitting via
  separable 1-D FITPACK kernels. It is under consideraton for public exposure
  in some form in the future.
• Python 3.13t (3.13 free threading) wheels are not provided on PyPI for this
  and subsequent releases because 3.13t was deprecated by manylinux and
  dropped by cibuildwheel in favor of 3.14t.
• Previously, return values of scipy.linalg.eig were always Fortran-ordered, and in
  SciPy 1.18.0 they may or may not be. If the ordering is important (for example,
  when interfacing with compiled code which expects specific array strides), users
  should ensure the desired ordering manually.

Authors

• Name (commits)
• h-vetinari (1)
• Joseph Adams (1) +
• Adrián Raso González (1) +
• Virgile Andreani (1)
• AshwathElang0 (1) +
• Mart-Mihkel Aun (1) +
• BarnikRB (2) +
• Richie Bendall (1) +
• J Berg (7) +
• Florian Bourgey (50)
• Jake Bowhay (99)
• Jonathan Brodrick (1) +
• Dietrich Brunn (36)
• Evgeni Burovski (200)
• Matthias Bussonnier (6)
• CJ Carey (9)
• Christine P. Chai (2)
• Lucas Colley (90)
• Dan (3) +
• devdanzin (2) +
• Martin Diehl (4)
• Sam Dolat (2) +
• dphipps-qnx (1) +
• DWesl (2)
• efrat99 (3) +
• fbrandt (1) +
• August Femtehjell (2) +
• Matthew H Flamm (1)
• Juan Flores (1) +
• foreverallama (1) +
• fumoboy007 (4) +
• John Patrick Gallagher (1) +
• Wei Bo Gao (1) +
• Christoph Gohlke (1)
• Nathan Goldbaum (20)
• Ludmila Golomozin (11) +
• Ralf Gommers (173)
• Mathieu Guay-Paquet (1) +
• Matt Haberland (147)
• Joren Hammudoglu (30)
• Jacob Hass (4)
• Maya Horii (1) +
• Guido Imperiale (1)
• Jan Möseritz-Schmidt (2)
• Leo Ji (4) +
• JOD (2) +
• Aditya Kamath (2) +
• Mukunda Rao Katta (1) +
• Robert Kern (1)
• Ria Khatoniar (1) +
• Matthias Koeppe (1)
• krishneetRAJ (1) +
• Iason Krommydas (1) +
• Eric Larson (1)
• Basil Liekens (31) +
• lnzwz (2) +
• Christian Lorentzen (15)
• Alex Lubbock (1) +
• Echedey Luis (2) +
• Lunyxis (1) +
• Zhang Maiyun (1) +
• Diego Medina Medina (1) +
• Elle Musoke (11) +
• Andrew Nelson (103)
• Nick ODell (29)
• Dimitri Papadopoulos Orfanos (1)
• partev (1)
• Matti Picus (7)
• Ilhan Polat (190)
• Pradyot Ranjan (2) +
• Adrian Raso (3)
• Aditya Rawat (1) +
• Tyler Reddy (99)
• Martin Reinecke (1)
• Lucas Roberts (6)
• Pamphile Roy (1)
• Daniel Schmitz (26)
• Martin Schuck (4)
• Dan Schult (47)
• Scott Shambaugh (16)
• Sabaa Siddique (1) +
• Nicholas Smith (1) +
• Johannes F. Sommerfeldt (1) +
• SpookyYomo (2) +
• Albert Steppi (80)
• Charalampos Stratakis (16) +
• Taylor (1) +
• thecaptain789 (1) +
• Adam Turner (1)
• Jacob Vanderplas (1)
• Christian Veenhuis (2)
• Sebastiano Vigna (1)
• Rivka Walles (14) +
• Warren Weckesser (11)
• Soeren Wolfers (1) +
• wongaokay (1) +
• Xuefeng Xu (1)
• Aniket Singh Yadav (2) +
• yaochengchen (2) +
• Fadi Younes (2) +
• Isaiah Zimmerman (1) +
• Simon Zwieback (1) +
• ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (19)

A total of 103 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.

Note that the source and binary assets associated with this release were published to PyPI using trusted publishing, and so the trusted assets and their hashes are made available more securely at https://pypi.org/project/scipy/1.18.0/ rather than providing them here in a less secure manner.

v1.17.1: SciPy 1.17.1

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SciPy 1.17.1 Release Notes

SciPy 1.17.1 is a bug-fix release with no new features compared to 1.17.0.

Authors

• Name (commits)
• Evgeni Burovski (5)
• Lucas Colley (1)
• Christoph Gohlke (1)
• Ralf Gommers (6)
• Matt Haberland (5)
• Matthias Koeppe (1)
• Nick ODell (1)
• Ilhan Polat (10)
• Tyler Reddy (44)
• Martin Schuck (3)
• Dan Schult (3)
• stratakis (1) +
• ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (1)

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.

Complete issue list, PR list, and release asset hashes are available in the associated README.txt.

v1.17.0: SciPy 1.17.0

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SciPy 1.17.0 Release Notes

SciPy 1.17.0 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
1.17.x branch, and on adding new features on the main branch.

This release requires Python 3.11-3.14 and NumPy 1.26.4 or greater.

Highlights of this release

• Many SciPy functions have gained native support for batching of N-dimensional
  array input and additional support for the array API standard. An overall
  summary of the latter is now available in a set of tables.
• In scipy.sparse, coo_array now supports indexing. This includes integers,
  slices, arrays, np.newaxis, Ellipsis, in 1D, 2D and the relatively
  new nD. In scipy.sparse.linalg, ARPACK and PROPACK rewrites from Fortran77
  to C now empower the use of external pseudorandom number generators, e.g.
  from numpy.
• In scipy.spatial, transform.Rotation and transform.RigidTransform
  have been extended to support N-D arrays. geometric_slerp now has support
  for extrapolation.
• scipy.stats has gained the matrix t and logistic distributions and many
  performance and accuracy improvements.
• Initial support for 64-bit integer (ILP64) BLAS and LAPACK libraries has
  been added, including for MKL and Apple Accelerate. Please report any issues with
  ILP64 you encounter.

New features

scipy.integrate improvements

• The integration routines dopri5, dopri853, LSODA, vode, and
  zvode have been ported from Fortran77 to C.
• scipy.integrate.quad now has a fast path for returning 0 when the integration
  interval is empty.
• The BDF, DOP853, RK23, RK45, OdeSolver, DenseOutput,
  ode, and complex_ode classes now support subscription, making them
  generic types, for compatibility with scipy-stubs.

scipy.cluster improvements

• scipy.cluster.hierarchy.is_isomorphic has improved performance and array
  API support.

scipy.interpolate improvements

• A new bc_type argument has been added to scipy.interpolate.make_splrep,
  scipy.interpolate.make_splprep, and scipy.interpolate.generate_knots to
  control the boundary conditions for spline fitting. Allowed values are
  "not-a-knot" (default) and "periodic".
• A new derivative method has been added to the
  scipy.interpolate.NdBSpline class, to construct a new spline representing a
  partial derivative of the given spline. This method is similar to the
  BSpline.derivative method of 1-D spline objects. In addition, the
  NdBSpline mutable instance attribute .c was changed into a read-only
  @property.
• Performance of "cubic" and "quintic" modes of
  scipy.interpolate.RegularGridInterpolator has been improved. Furthermore,
  the (mutable) instance attributes .grid and .values were changed into
  (read-only) properties.
• Numerical stability of scipy.interpolate.AAA has been improved and it has
  gained a new axis parameter.
• scipy.interpolate.FloaterHormannInterpolator added support for
  multidimensional, batched inputs and gained a new axis parameter to
  select the interpolation axis.
• RBFInterpolator has gained an array API standard compatible backend, with an
  improved support for GPU arrays.
• The AAA, *Interpolator, *Poly, and *Spline classes now
  support subscription, making them generic types, for compatibility with
  scipy-stubs.

scipy.linalg improvements

• scipy.linalg.inv routine has been improved:

  • it now attempts to detect the structure of its argument and selects an
    appropriate low-level matrix inversion routine. A new assume_a keyword
    allows to bypass the structure detection if the structure is known. For
    batched inputs, the detection is run for each 2D slice, unless an explicit
    value for assume_a is provided (in which case, the structure is
    assumed to be the same for all 2-D slices of the batch);
  • the new lower={True,False} keyword argument has been added to help
    select the upper or lower triangle of the input matrix for symmetric
    inputs; refer to the docstring of scipy.linalg.inv for details;
  • the routine emits a LinAlgWarning if it detects an ill-conditioned
    input;
  • performance for batched inputs has been improved.

• scipy.linalg.fiedler has gained native support for batched inputs.

• performance has improved for scipy.linalg.solve with batched inputs
  for certain matrix structures.

scipy.optimize improvements

• optimize.minimize(method="trust-exact") now accepts a
  solver-specific "subproblem_maxiter" option. This option can be used to
  assure that the algorithm converges for functions with an ill-conditioned
  Hessian.
• Callback functions used by optimize.minimize(method="slsqp") can
  opt into the new callback interface by accepting a single keyword argument
  intermediate_result.
• The BroydenFirst, *Jacobian, and Bounds classes now support
  subscription, making them generic types, for compatibility with
  scipy-stubs.

scipy.signal improvements

• scipy.signal.abcd_normalize gained more informative error messages and the
  documentation was improved.
• scipy.signal.get_window now accepts the suffixes '_periodic' and
  '_symmetric' to distinguish between periodic and symmetric windows
  (overriding the fftbin parameter). This benefits the functions
  coherence, csd, periodogram, welch, spectrogram,
  stft, istft, resample, resample_poly, firwin,
  firwin2, firwin_2d, check_COLA and check_NOLA, which utilize
  get_window but do not expose the fftbin parameter.
• scipy.signal.hilbert2 gained the new keyword axes for specifying the
  axes along which the two-dimensional analytic signal should be calculated.
  Furthermore, the documentation of scipy.signal.hilbert and
  scipy.signal.hilbert2 was significantly improved.
• The ShortTimeFFT and LinearTimeInvariant classes now support
  subscription, making them generic types, for compatibility with
  scipy-stubs.

scipy.sparse improvements

• coo_array now supports indexing. This includes slices, arrays,
  np.newaxis, Ellipsis, in 1D, 2D and the new nD. So COO format now
  has full support for nD and COO now allows indexing without converting
  formats.
• Additional sparse construction functions include expand_dims,
  swapaxes, permute_dims, and nD support for the kron function.
• ARPACK Fortran77 library is ported to C. Among many changes, it is now
  possible to use external random generators including NumPy PRNGs for
  reproducible runs. Previously this was not the case due to internal seeding
  behavior of the original ARPACK code.
• Similarly, PROPACK Fortran77 library is also ported to C with the same PRNG
  enhancements and other improvements.
• scipy.sparse.dok_array now supports an update method which can be
  used to update the sparse array using a dict, dict.items()-like iterable,
  or another dok_array matrix. It performs additional validation that keys
  are valid index tuples.
• scipy.sparse.dia_array.tocsr is approximately three times faster and
  some unnecessary copy operations have been removed from sparse format
  interconversions more broadly.
• Added scipy.sparse.linalg.funm_multiply_krylov, a restarted Krylov method
  for evaluating y = f(tA) b.
• In sparse.linalg, the LinearOperator, LaplacianNd, and SuperLU
  classes now support subscription, making them generic types, for
  compatibility with scipy-stubs.
• In sparse.linalg the eigs and eigsh functions now accept a new
  rng parameter.

scipy.spatial improvements

• The spatial.transform module has gained an array API standard compatible
  backend.

• transform.Rotation and transform.RigidTransform have been extended
  from 0D single values and 1D arrays to N-D arrays, with standard indexing and
  broadcasting rules. Both now have the following additions:

  • A shape property.
  • A shape argument to their identity() constructors, which should be
    preferred over the existing num argument. This has also been added as an
    argument for Rotation.random() (RigidTransform does not currently
    have a random constructor).
  • An axis argument to their mean() functions.

• The resulting shapes for transform.Rotation.from_euler /
  from_davenport have changed to make them consistent with broadcasting
  rules. Angle inputs to Euler angles must now strictly match the number of
  provided axes in the last dimension. The resulting Rotation has the shape
  np.atleast_1d(angles).shape[:-1]. Angle inputs to Davenport angles must
  also match the number of axes in the last dimension. The resulting Rotation
  has the shape np.broadcast_shapes(np.atleast_2d(axes).shape[:-2], np.atleast_1d(angles).shape[:-1]).

• Rotation.from_matrix has gained an assume_valid argument that allows for
  performance improvements when users can guarantee valid matrix inputs.
  from_matrix is now also faster in cases where a known orthogonal matrix
  is used.

• The scipy.spatial.geometric_slerp function can now extrapolate. When given a
  value outside the range [0, 1], geometric_slerp() will continue with
  the same rotation outside this range. For example, if spherically
  interpolating with start being a point on the equator, and end
  being a point at the north pole, then a value of t=-1 would give you a
  point at the south pole.

• Rotation.as_euler and Rotation.as_davenport methods have gained a
  suppress_warnings parameter to enable suppression of gimbal lock warnings.

• Rotation.__init__ has gained a new optional scalar_first parameter and
  there is a new Rotation.__setitem__ method.

scipy.special improvements

• The following functions for statistical applications have significantly
  improved parameter ranges and reduced error rates: btdtria, btdtrib,
  chdtriv, chndtr, chndtrix, chndtridf, chndtrinc, fdtr,
  fdtrc, fdtri, gdtria, gdtrix, pdtrik, stdtr and
  stdtrit.
• The incomplete beta functions betainc, betaincc, betaincinv and
  betainccinv are improved for extreme parameter ranges.

scipy.stats improvements

• scipy.stats.matrix_t has been added to represent the matrix t distribution.
  It supports methods pdf (and logpdf) for computing the probability
  density function and rvs for generating random variates.
• scipy.stats.Logistic was added for modeling random variables that follow a
  logistic distribution.
• scipy.stats.quantile now accepts a weights argument to specify
  frequency weights.
• scipy.stats.quantile is now faster on large arrays as it no longer uses
  stable sort internally.
• scipy.stats.quantile supports three new values of the method argument,
  'round_inward', 'round_outward', and 'round_neareast', for use in
  the context of trimming and winsorizing data.
• scipy.stats.truncpareto now accepts negative values for the exponent shape
  parameter, enabling use of truncpareto as a more general power law
  distribution.
• scipy.stats.logser now provides a distribution-specific implementation of
  the sf method, improving speed and accuracy.
• Implementations of the following function have been vectorized:
  scipy.stats.ansari, scipy.stats.cramervonmises,
  scipy.stats.cramervonmises_2samp, scipy.stats.epps_singleton_2samp,
  scipy.stats.fligner, scipy.stats.friedmanchisquare, scipy.stats.kruskal,
  scipy.stats.ks_1samp, scipy.stats.levene, and scipy.stats.mood.
  Typically, this improves performance with multidimensional (batch) input.
• The critical value tables of scipy.stats.anderson have been updated.
• A new method parameter of scipy.stats.anderson allows the user
  to compute p-values by interpolating between tabulated values or using Monte
  Carlo simulation. The method parameter must be passed explicitly
  to add a pvalue attribute to the result object and avoid a warning
  about the upcoming removal of critical_value, significance_level,
  and fit_result attributes.
• A new variant parameter of scipy.stats.anderson_ksamp allows the user
  to select between three different variants of the statistic, superseding the
  midrank parameter which allowed toggling between two. The new 'continuous'
  variant is equivalent to 'discrete' when there are no ties in the sample, but
  the calculation is faster. The variant parameter must be passed explicitly to
  avoid a warning about the deprecation of the midrank attribute and the upcoming
  removal of critical_values from the result object.
• The speed and accuracy of most scipy.stats.zipfian methods has been
  improved.
• The accuracies of the scipy.stats.Binomial methods logcdf and
  logccdf have been improved in the tails.
• The default guess of scipy.stats.trapezoid.fit has been improved.
• The accuracy and range of the cdf, sf, isf, and ppf methods
  of scipy.stats.binom and scipy.stats.nbinom has been improved.
• The Covariance, Uniform, Normal, Binomial, Mixture,
  rv_frozen, and multi_rv_frozen classes now support subscription,
  making them generic types, for compatibility with scipy-stubs.
• The multivariate_t and multivariate_normal distributions have gained
  a new marginal method.
• yeojohnson_llf gained new parameters axis, nan_policy,
  and keepdims, and now returns a numpy scalar where it would previously
  return a 0D array.
• The new spearmanrho function is an array API compatible substitute for
  spearmanr.
• The median_abs_deviation function has gained a keepdims parameter.
• The trim_mean function has gained new nan_policy and keepdims
  parameters.

Array API Standard Support

• An overall summary table for our array API standard support/coverage is
  now available.
• The overhead associated with array namespace determination has been reduced,
  providing improved performance in dispatching to different backends.
• scipy.cluster.hierarchy.is_isomorphic has gained support.
• scipy.interpolate.make_lsq_spline, scipy.interpolate.make_smoothing_spline,
  scipy.interpolate.make_splrep, scipy.interpolate.make_splprep,
  scipy.interpolate.generate_knots, and scipy.interpolate.make_interp_spline
  have gained support.
• scipy.signal.bilinear, scipy.signal.iircomb, scipy.signal.iirdesign,
  scipy.signal.iirfilter, scipy.signal.iirpeak, scipy.signal.iirnotch,
  scipy.signal.gammatone, and scipy.signal.group_delay have gained support.
• scipy.signal.butter, scipy.signal.buttap, scipy.signal.buttord,
  scipy.signal.cheby1, scipy.signal.cheb1ap, scipy.signal.cheb1ord,
  scipy.signal.cheby2, scipy.signal.cheb2ap, scipy.signal.cheb2ord,
  scipy.signal.bessel, scipy.signal.besselap, scipy.signal.ellip,
  scipy.signal.ellipap, and scipy.signal.ellipord have gained support.
• scipy.signal.savgol_filter, scipy.signal.savgol_coeffs, and
  scipy.signal.abcd_normalize have gained support.
• spatial.transform has gained support.
• scipy.integrate.qmc_quad, scipy.integrate.cumulative_simpson,
  scipy.integrate.cumulative_trapezoid, and scipy.integrate.romb have
  gained support.
• scipy.linalg.block_diag, scipy.linalg.fiedler, and
  scipy.linalg.orthogonal_procrustes have gained support.
• scipy.interpolate.BSpline, scipy.interpolate.NdBSpline,
  scipy.interpolate.RegularGridInterpolator, and
  scipy.interpolate.RBFInterpolator gained support.
• Support added for scipy.stats.alexandergovern, scipy.stats.bootstrap,
  scipy.stats.brunnermunzel, scipy.stats.chatterjeexi,
  scipy.stats.cramervonmises, scipy.stats.cramervonmises_2samp,
  scipy.stats.epps_singleton_2samp, scipy.stats.false_discovery_control,
  scipy.stats.fligner, scipy.stats.friedmanchisquare, scipy.stats.iqr,
  scipy.stats.kruskal, scipy.stats.ks_1samp, scipy.stats.levene,
  scipy.stats.lmoment, scipy.stats.mannwhitneyu,
  scipy.stats.median_abs_deviation, scipy.stats.mode, scipy.stats.mood,
  scipy.stats.ansari,
  scipy.stats.power, scipy.stats.permutation_test, scipy.stats.sigmaclip,
  scipy.stats.wilcoxon, and scipy.stats.yeojohnson_llf.
• scipy.stats.pearsonr has gained support for JAX and Dask backends.
• scipy.stats.variation has gained support for the Dask backend.
• marray support was added for stats.gtstd, stats.directional_stats,
  stats.bartlett, stats.variation, stats.pearsonr, and
  stats.entropy.

Deprecated features and future changes

• The scipy.odr module is deprecated in v1.17.0 and will be completely
  removed in v1.19.0. Users are suggested to use the odrpack package instead.
• The default dype behavior of scipy.sparse.diags and
  scipy.sparse.diags_array will change in v1.19.0.
• In v1.19.0, scipy.linalg.hankel will no longer ravel multidimensional
  inputs and instead will treat them as a batch.
• The precenter argument of scipy.signal.lombscargle is deprecated and
  will be removed in v1.19.0. Furthermore, some arguments will become keyword
  only.
• For scipy.stats.anderson, the tuple-unpacking behavior of the return object
  and attributes critical_values, significance_level, and
  fit_result are deprecated. Use the new method parameter to avoid the
  deprecation warning. Beginning in SciPy 1.19.0, these features will
  no longer be available, and the object returned will have attributes
  statistic and pvalue.
• For scipy.stats.anderson_ksamp, the midrank parameter is deprecated
  and the new variant parameter should be preferred. This also means that
  the presence of the critical_values return array is deprecated.

Expired deprecations

• scipy.stats.find_repeats has been removed. Please use
  numpy.unique/numpy.unique_counts instead.
• scipy.linalg functions for Toeplitz matrices no longer ravel n-d input
  arguments; instead, multidimensional input is treated as a batch.
• The seed and rand functions from scipy.linalg.interpolative have
  been removed. Use the rng argument instead.
• Complex inputs to scipy.spatial.distance.cosine and
  scipy.spatial.distance.correlation now raise an error.
• Support for object arrays and longdoubles has been removed from
  scipy.signal.correlate, scipy.signal.convolve, scipy.signal.lfilter,
  and scipy.signal.sosfilt.
• kulczynski1 and sokalmichener have been removed from
  scipy.spatial.distance.
• kron has been removed from scipy.linalg. Please use numpy.kron.
• Accidentally exposed functions have been removed from
  scipy.interpolate.interpnd.
• The random_state and permutation arguments of
  scipy.stats.ttest_ind have been removed.
• sph_harm, clpmn, lpn, and lpmn have been removed from
  scipy.special.

Backwards incompatible changes

• The resulting shapes for transform.Rotation.from_euler /
  from_davenport have changed to make them consistent with broadcasting
  rules. Angle inputs to Euler angles must now strictly match the number of
  provided axes in the last dimension. The resulting Rotation has the shape
  np.atleast_1d(angles).shape[:-1]. Angle inputs to Davenport angles must
  also match the number of axes in the last dimension. The resulting Rotation
  has the shape np.broadcast_shapes(np.atleast_2d(axes).shape[:-2], np.atleast_1d(angles).shape[:-1]).

Other changes

• The version of the Boost Math library leveraged by SciPy has been
  increased from 1.88.0 to 1.89.0.

• On POSIX operating systems, SciPy will now use the 'forkserver'
  multiprocessing context on Python 3.13 and older for workers=<an-int>
  calls if the user hasn't configured a default method themselves. This follows
  the default behavior on Python 3.14.

• Initial support for 64-bit integer (ILP64) BLAS and LAPACK libraries has been
  added. To enable it, build SciPy with -Duse-ilp64=true meson option, and make
  sure to have a LAPACK library which exposes both LP64 and ILP64 symbols.
  Currently supported LAPACK libraries are MKL and Apple Accelerate. Note that:

  • the ILP64 support is optional, and is in addition to the always-available
    LP64 interface;
  • at runtime, you can select the ILP64 variants via the
    get_{blas,lapack}_funcs functions:
    scipy.linalg.lapack.get_lapack_funcs(..., use_ilp64="preferred") selects
    the ILP64 variant if available and LP64 variant otherwise;
  • cython_blas and cython_lapack modules always contain the LP64
    routines for ABI compatibility.

Please report any issues with ILP64 you encounter.

Authors

• Name (commits)
• h-vetinari (3)
• Joshua Alexander (1) +
• Amit Aronovitch (1) +
• Ayush Baranwal (1) +
• Cristrian Batrin (1) +
• Marco Berzborn (1) +
• Ole Bialas (1) +
• Om Biradar (1) +
• Florian Bourgey (2)
• Jake Bowhay (103)
• Matteo Brivio (1) +
• Dietrich Brunn (34)
• Johannes Buchner (2) +
• Evgeni Burovski (292)
• Nicholas Carlini (1) +
• Luca Cerina (1) +
• Christine P. Chai (35)
• Saransh Chopra (1)
• Lucas Colley (121)
• Björn Ingvar Dahlgren (2) +
• Sumit Das (1) +
• Hans Dembinski (1)
• John M Dusel (1) +
• DWesl (4)
• Pieter Eendebak (6)
• Kian Eliasi (2)
• Rob Falck (1)
• Abdullah Fayed (3) +
• Emmanuel Ferdman (2) +
• Filipe Laíns (1) +
• Daniel Fremont (1) +
• Neil Girdhar (1)
• Ilan Gold (35)
• Nathan Goldbaum (3) +
• Ralf Gommers (124)
• Nicolas Guidotti (1) +
• Geoffrey Gunter (1) +
• Matt Haberland (183)
• Joren Hammudoglu (60)
• Jacob Hass (2) +
• Nick Hodgskin (1) +
• Stephen Huan (1) +
• Guido Imperiale (41)
• Gert-Ludwig Ingold (1)
• Jaime Rodríguez-Guerra (2) +
• Jan Möseritz-Schmidt (2) +
• JBlitzar (1) +
• Adam Jones (2)
• Dustin Kenefake (1) +
• Robert Kern (3)
• Gleb Khmyznikov (1) +
• Daniil Kiktenko (1) +
• Pascal Klein (2) +
• kleiter (1) +
• Oliver Kovacs (1) +
• Koven (1) +
• Abhishek Kumar (2) +
• Arthur Lacote (2) +
• Eric Larson (7)
• Mouad Leachouri (1) +
• Tristan Leclercq (1) +
• Antony Lee (5)
• Jesse Livezey (8)
• Philip Loche (1)
• Yuxi Long (4) +
• Christian Lorentzen (1)
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• Gabryel Mason-Williams (1) +
• mcdigman (1) +
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• newyork_loki (2) +
• Nick ODell (34)
• Dimitri Papadopoulos Orfanos (2)
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• Tyler Reddy (136)
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• Mikhail Ryazanov (27)
• Daniel Schmitz (25)
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• Dan Schult (33)
• Mugunthan Selvanayagam (1) +
• Scott Shambaugh (14)
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• Samaresh Kumar Singh (8) +
• Kartik Sirohi (1) +
• Albert Steppi (179)
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• Noam Teyssier (1) +
• tommie979 (1) +
• Christian Veenhuis (1)
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• Shuhei Watanabe (1) +
• Warren Weckesser (25)
• WhimsyHippo (7) +
• Rory Yorke (2)
• Will Zhang (1) +
• Eric Zitong Zhou (1)
• Tingwei Zhu (1) +
• Zhenyu Zhu (1) +
• ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (38)

A total of 117 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.

Complete issue list, PR list, and release asset hashes are available in the associated README.txt.

v1.16.3: SciPy 1.16.3

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SciPy 1.16.3 Release Notes

SciPy 1.16.3 is a bug-fix release with no new features compared to 1.16.2.

Authors

• Name (commits)
• ChrisAB (1) +
• Lucas Colley (1)
• Ralf Gommers (3)
• Matt Haberland (8)
• Nick ODell (2)
• Ilhan Polat (1)
• Tyler Reddy (28)
• Lucas Roberts (2)

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.

The full issue and pull request lists, and the release asset hashes are available
in the associated README.txt file.

v1.16.2: SciPy 1.16.2

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SciPy 1.16.2 Release Notes

SciPy 1.16.2 is a bug-fix release with no new features
compared to 1.16.1. This is the first stable release of
SciPy to provide Windows on ARM wheels on PyPI.

Authors

• Name (commits)
• Dietrich Brunn (1)
• Ralf Gommers (6)
• Adam Jones (1)
• Gleb Khmyznikov (1) +
• Jost Migenda (1) +
• newyork_loki (1)
• Nick ODell (3)
• Dimitri Papadopoulos Orfanos (1)
• Ilhan Polat (2)
• Tyler Reddy (26)
• Mugunthan Selvanayagam (1) +
• Shuhei Watanabe (1) +

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.

The full issue and pull request lists, and the release asset hashes are available
in the associated README.txt file.

v1.16.1: SciPy 1.16.1

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SciPy 1.16.1 Release Notes

SciPy 1.16.1 is a bug-fix release that adds support for Python 3.14.0rc1,
including PyPI wheels.

Authors

• Name (commits)
• Evgeni Burovski (1)
• Rob Falck (1)
• Ralf Gommers (7)
• Geoffrey Gunter (1) +
• Matt Haberland (2)
• Joren Hammudoglu (1)
• Andrew Nelson (2)
• newyork_loki (1) +
• Ilhan Polat (1)
• Tyler Reddy (25)
• Daniel Schmitz (1)
• Dan Schult (2)

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.

The full issue and pull request lists, and the release asset hashes are available
in the associated README.txt file.

v1.16.0: SciPy 1.16.0

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SciPy 1.16.0 Release Notes

SciPy 1.16.0 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
1.16.x branch, and on adding new features on the main branch.

This release requires Python 3.11-3.13 and NumPy 1.25.2 or greater.

Highlights of this release

• Improved experimental support for the Python array API standard, including
  new support in scipy.signal, and additional support in scipy.stats and
  scipy.special. Improved support for JAX and Dask backends has been added,
  with notable support in scipy.cluster.hierarchy, many functions in
  scipy.special, and many of the trimmed statistics functions.
• scipy.optimize now uses the new Python implementation from the
  PRIMA package for COBYLA. The PRIMA implementation fixes many bugs
  in the old Fortran 77 implementation with a better performance on average.
• scipy.sparse.coo_array now supports n-D arrays with reshaping, arithmetic and
  reduction operations like sum/mean/min/max. No n-D indexing or
  scipy.sparse.random_array support yet.
• Updated guide and tools for migration from sparse matrices to sparse arrays.
• Nearly all functions in the scipy.linalg namespace that accept array
  arguments now support N-dimensional arrays to be processed as a batch.
• Two new scipy.signal functions, scipy.signal.firwin_2d and
  scipy.signal.closest_STFT_dual_window, for creation of a 2-D FIR filter and
  scipy.signal.ShortTimeFFT dual window calculation, respectively.
• A new class, scipy.spatial.transform.RigidTransform, provides functionality
  to convert between different representations of rigid transforms in 3-D
  space.
• A new function scipy.ndimage.vectorized_filter for generic filters that
  take advantage of a vectorized Python callable was added.

New features

scipy.io improvements

• scipy.io.savemat now provides informative warnings for invalid field names.
• scipy.io.mmread now provides a clearer error message when provided with
  a source file path that does not exist.
• scipy.io.wavfile.read can now read non-seekable files.

scipy.integrate improvements

• The error estimate of scipy.integrate.tanhsinh was improved.

scipy.interpolate improvements

• Batch support was added to scipy.interpolate.make_smoothing_spline.

scipy.linalg improvements

• Nearly all functions in the scipy.linalg namespace that accept array
  arguments now support N-dimensional arrays to be processed as a batch.
  See linalg_batch for details.
• scipy.linalg.sqrtm is rewritten in C and its performance is improved. It
  also tries harder to return real-valued results for real-valued inputs if
  possible. See the function docstring for more details. In this version the
  input argument disp and the optional output argument errest are
  deprecated and will be removed four versions later. Similarly, after
  changing the underlying algorithm to recursion, the blocksize keyword
  argument has no effect and will be removed two versions later.
• Wrappers for ?stevd, ?langb, ?sytri, ?hetri and
  ?gbcon were added to scipy.linalg.lapack.
• The default driver of scipy.linalg.eigh_tridiagonal was improved.
• scipy.linalg.solve can now estimate the reciprocal condition number and
  the matrix norm calculation is more efficient.

scipy.ndimage improvements

• A new function scipy.ndimage.vectorized_filter for generic filters that
  take advantage of a vectorized Python callable was added.
• scipy.ndimage.rotate has improved performance, especially on ARM platforms.

scipy.optimize improvements

• COBYLA was updated to use the new Python implementation from the
  PRIMApackage.
  The PRIMA implementation fixes many bugs
  in the old Fortran 77 implementation. In addition, it results in fewer function evaluations on average
  but it depends on the problem and for some
  problems it can result in more function evaluations or a less optimal
  result. For those cases the user can try modifying the initial and final
  trust region radii given by rhobeg and tol respectively. A larger
  rhobeg can help the algorithm take bigger steps initially, while a
  smaller tol can help it continue and find a better solution.
  For more information, see the PRIMA documentation.
• Several of the scipy.optimize.minimize methods, and the
  scipy.optimize.least_squares function, have been given a workers
  keyword. This allows parallelization of some calculations via a map-like
  callable, such as multiprocessing.Pool. These parallelization
  opportunities typically occur during numerical differentiation. This can
  greatly speed up minimization when the objective function is expensive to
  calculate.
• The lm method of scipy.optimize.least_squares can now accept
  3-point and cs for the jac keyword.
• The SLSQP Fortran 77 code was ported to C. When this method is used now the
  constraint multipliers are exposed to the user through the multiplier
  keyword of the returned scipy.optimize.OptimizeResult object.
• NNLS code has been corrected and rewritten in C to address the performance
  regression introduced in 1.15.x
• scipy.optimize.root now warns for invalid inner parameters when using the
  newton_krylov method
• The return value of minimization with method='L-BFGS-B' now has
  a faster hess_inv.todense() implementation. Time complexity has improved
  from cubic to quadratic.
• scipy.optimize.least_squares has a new callback argument that is applicable
  to the trf and dogbox methods. callback may be used to track
  optimization results at each step or to provide custom conditions for
  stopping.

scipy.signal improvements

• A new function scipy.signal.firwin_2d for the creation of a 2-D FIR Filter
  using the 1-D window method was added.
• scipy.signal.cspline1d_eval and scipy.signal.qspline1d_eval now provide
  an informative error on empty input rather than hitting the recursion limit.
• A new function scipy.signal.closest_STFT_dual_window to calculate the
  scipy.signal.ShortTimeFFT dual window of a given window closest to a
  desired dual window.
• A new classmethod scipy.signal.ShortTimeFFT.from_win_equals_dual to
  create a scipy.signal.ShortTimeFFT instance where the window and its dual
  are equal up to a scaling factor. It allows to create short-time Fourier
  transforms which are unitary mappings.
• The performance of scipy.signal.convolve2d was improved.

scipy.sparse improvements

• scipy.sparse.coo_array now supports n-D arrays using binary and reduction
  operations.
• Faster operations between two DIA arrays/matrices for: add, sub, multiply,
  matmul.
• scipy.sparse.csgraph.dijkstra shortest_path is more efficient.
• scipy.sparse.csgraph.yen has performance improvements.
• Support for lazy loading of sparse.csgraph and sparse.linalg was
  added.

scipy.spatial improvements

• A new class, scipy.spatial.transform.RigidTransform, provides functionality
  to convert between different representations of rigid transforms in 3-D
  space, its application to vectors and transform composition.
  It follows the same design approach as scipy.spatial.transform.Rotation.
• scipy.spatial.transform.Rotation now has an appropriate __repr__ method,
  and improved performance for its scipy.spatial.transform.Rotation.apply
  method.

scipy.stats improvements

• A new function scipy.stats.quantile, an array API compatible function for
  quantile estimation, was added.
• scipy.stats.make_distribution was extended to work with existing discrete
  distributions and to facilitate the creation of custom distributions in the
  new random variable infrastructure.
• A new distribution, `scipy.s

✂ Note

PR body was truncated to here.

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