SciPy 1.17.0 Release Notes

Note: SciPy 1.17.0 is not released yet!

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 has full support for indexing across
  dimensions without needing to convert between sparse formats. ARPACK
  and PROPACK rewrites from Fortran77 to C now empower the use of external
  pseudorandom number generators.
• 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, Apple Accelerate and OpenBLAS. 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.

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
  and scipy.interpolate.make_splprep 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.
• Performance of "cubic" and "quintic" modes of
  scipy.interpolate.RegularGridInterpolator has been improved.
• Numerical stability of scipy.interpolate.AAA has been improved.
• scipy.interpolate.FloaterHormannInterpolator added support for
  multidimensional, batched inputs and gained a new axis parameter to
  select the interpolation axis.

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.

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.

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 unneccesary 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.

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.

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.
• 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.

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.

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, Apple Accelerate and OpenBLAS
  through the scipy-openblas64 package. 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 (1)
• Jake Bowhay (102)
• Matteo Brivio (1) +
• Dietrich Brunn (34)
• Johannes Buchner (2) +
• Evgeni Burovski (288)
• Nicholas Carlini (1) +
• Luca Cerina (1) +
• Christine P. Chai (35)
• Saransh Chopra (1)
• Lucas Colley (117)
• 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 (121)
• Nicolas Guidotti (1) +
• Geoffrey Gunter (1) +
• Matt Haberland (177)
• Joren Hammudoglu (56)
• Jacob Hass (2) +
• Nick Hodgskin (1) +
• Stephen Huan (1) +
• Guido Imperiale (41)
• Gert-Ludwig Ingold (1)
• Jaime Rodríguez-Guerra (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)
• Joshua Markovic (1) +
• Gabryel Mason-Williams (1) +
• mcdigman (1) +
• Rafael Menezes (1) +
• Stefano Miccoli (1) +
• Michał Górny (2)
• Jost Migenda (7) +
• Suriyaa MM (1) +
• Andrew Nelson (72)
• newyork_loki (2) +
• Nick ODell (33)
• Dimitri Papadopoulos Orfanos (2)
• Drew Parsons (1)
• Gilles Peiffer (3) +
• Matti Picus (1)
• Jonas Pleyer (2) +
• Ilhan Polat (116)
• Akshay Priyadarshi (2) +
• Mohammed Abdul Rahman (1) +
• Daniele Raimondi (2) +
• Ritesh Rana (1) +
• Adrian Raso (1) +
• Dan Raviv (1) +
• Tyler Reddy (116)
• Lucas Roberts (4)
• Bernard Roesler (1) +
• Mikhail Ryazanov (27)
• Jaro Schmidt (1) +
• Daniel Schmitz (25)
• Martin Schuck (25)
• Dan Schult (29)
• Mugunthan Selvanayagam (1) +
• Scott Shambaugh (14)
• Rodrigo Silva (1) +
• Samaresh Kumar Singh (8) +
• Kartik Sirohi (1) +
• Albert Steppi (178)
• Matthias Straka (1) +
• Theo Teske (1) +
• Noam Teyssier (1) +
• tommie979 (1) +
• Christian Veenhuis (1)
• Pierre Veron (1) +
• 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.

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