Update dependency scipy to v1.17.0

[renovate]

Note: This PR body was truncated due to platform limits.

This PR contains the following updates:

Package	Change	Age	Confidence
scipy	1.9.3 → 1.17.0

---

Release Notes

scipy/scipy (scipy)

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)
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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.

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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.

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• 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.stats.Binomial, was added.
• An equal_var keyword was added to scipy.stats.tukey_hsd (enables the
  Games-Howell test) and scipy.stats.f_oneway (enables Welch ANOVA).
• The moment calculation for scipy.stats.gennorm was improved.
• The scipy.stats.mode implementation was vectorized, for faster batch
  calculation.
• Support for axis, nan_policy, and keepdims keywords was added to
  scipy.stats.power_divergence, scipy.stats.chisquare,
  scipy.stats.pointbiserialr, scipy.stats.kendalltau,
  scipy.stats.weightedtau, scipy.stats.theilslopes,
  scipy.stats.siegelslopes, scipy.stats.boxcox_llf, and
  scipy.stats.linregress.
• Support for keepdims and nan_policy keywords was added to
  scipy.stats.gstd.
• The performance of scipy.stats.special_ortho_group and scipy.stats.pearsonr
  was improved.
• Support for an rng keyword argument was added to the logcdf and
  cdf methods of multivariate_normal_gen and multivariate_normal_frozen.

Array API Standard Support

Experimental support for array libraries other than NumPy has been added to
multiple submodules in recent versions of SciPy. Please consider testing
these features by setting the environment variable SCIPY_ARRAY_API=1 and
providing PyTorch, JAX, CuPy or Dask arrays as array arguments.

Many functions in scipy.stats, scipy.special, scipy.optimize, and
scipy.constants now provide tables documenting compatible array and device
types as well as support for lazy arrays and JIT compilation. New features with
support and old features with support added for SciPy 1.16.0 include:

• Most of the scipy.signal functionality
• scipy.ndimage.vectorized_filter
• scipy.special.stdtrit
• scipy.special.softmax
• scipy.special.log_softmax
• scipy.stats.quantile
• scipy.stats.gstd
• scipy.stats.rankdata

Features with extended array API support (generally, improved support
for JAX and Dask) in SciPy 1.16.0 include:

• many of the scipy.cluster.hierarchy functions
• many functions in scipy.special
• many of the trimmed statistics functions in scipy.stats

SciPy now has a CI job that exercises GPU (CUDA) support, and as a result
using PyTorch, CuPy or JAX arrays on GPU with SciPy is now more reliable.

Deprecated features

• The unused atol argument of scipy.optimize.nnls is deprecated and will
  be removed in SciPy 1.18.0.
• The disp argument of scipy.linalg.signm, scipy.linalg.logm, and
  scipy.linalg.sqrtm will be removed in SciPy 1.18.0.
• scipy.stats.multinomial now emits a FutureWarning if the rows of p
  do not sum to 1.0. This condition will produce NaNs beginning in SciPy
  1.18.0.
• The disp and iprint arguments of the l-bfgs-b solver of scipy.optimize
  have been deprecated, and will be removed in SciPy 1.18.0.

Expired Deprecations

• scipy.sparse.conjtransp has been removed. Use .T.conj() instead.
• The quadrature='trapz' option has been removed from
  scipy.integrate.quad_vec, and scipy.stats.trapz has been removed. Use
  trapezoid in both instances instead.
• scipy.special.comb and scipy.special.perm now raise when exact=True
  and arguments are non-integral.
• Support for inference of the two sets of measurements from the single
  argument x has been removed from scipy.stats.linregress. The data
  must be specified separately as x and y.
• Support for NumPy masked arrays has been removed from
  scipy.stats.power_divergence and scipy.stats.chisquare.
• A significant number of functions from non-public namespaces
  (e.g., scipy.sparse.base, scipy.interpolate.dfitpack) were cleaned
  up. They were previously already emitting deprecation warnings.

Backwards incompatible changes

• Several of the scipy.linalg functions for solving a linear system (e.g.
  scipy.linalg.solve) documented that the RHS argument must be either 1-D or
  2-D but did not always raise an error when the RHS argument had more the
  two dimensions. Now, many-dimensional right hand sides are treated according
  to the rules specified in linalg_batch.
• scipy.stats.bootstrap now explicitly broadcasts elements of data to the
  same shape (ignoring axis) before performing the calculation.
• Several submodule names are no longer available via from scipy.signal import *,
  but may still be imported directly, as detailed at scipy/scipy-stubs#549.

Build and packaging related changes

• The minimum supported version of Clang was bumped from 12.0 to 15.0.
• The lowest supported macOS version for wheels on PyPI is now 10.14 instead of
  10.13.
• The sdist contents were optimized, resulting in a size reduction of about 50%,
  from 60 MB to 30 MB.
• For Cython>=3.1.0, SciPy now uses the new cython --generate-shared
  functionality, which reduces the total size of SciPy's wheels and on-disk
  installations significantly.
• SciPy no longer contains an internal shared library that requires RPATH support,
  after sf_error_state was removed from scipy.special.
• A new build option -Duse-system-libraries has been added. It allows
  opting in to using system libraries instead of using vendored sources.
  Currently Boost.Math and Qhull are supported as system build
  dependencies.

Other changes

• A new accompanying release of scipy-stubs (v1.16.0.0) is
  available at https://github.com/scipy/scipy-stubs/releases/tag/v1.16.0.0
• The internal dependency of scipy._lib on scipy.sparse was removed,
  which reduces the import time of a number of other SciPy submodules.
• Support for free-threaded CPython was improved: the last known thread-safety
  issues in scipy.special were fixed, and pytest-run-parallel is now used
  in a CI job to guard against regressions.
• Support for spin as a developer
  CLI was added, including support for editable installs. The SciPy-specific
  python dev.py CLI will be removed in the next release cycle in favor of
  spin.
• The vendored Qhull library was upgraded from version 2019.1 to 2020.2.
• A large amount of the C++ code in scipy.special was moved to the new
  header-only xsf library. That library was
  included back in the SciPy source tree as a git submodule.
• The namedtuple-like bunch objects returned by some SciPy functions
  now have improved compatibility with the polars library.
• The output of the rvs method of scipy.stats.wrapcauchy is now mapped to
  the unit circle between 0 and 2 * pi.
• The lm method of scipy.optimize.least_squares now has a different behavior
  for the maximum number of function evaluations, max_nfev. The default for
  the lm method is changed to 100 * n, for both a callable and a
  numerically estimated jacobian. This limit on function evaluations excludes
  those used for any numerical estimation of the Jacobian. Previously the
  default when using an estimated jacobian was 100 * n * (n + 1), because
  the method included evaluations used in the estimation. In addition, for the
  lm method the number of function calls used in Jacobian approximation
  is no longer included in OptimizeResult.nfev. This brings the behavior
  of lm, trf, and dogbox into line.

Authors

• Name (commits)
• h-vetinari (4)
• aiudirog (1) +
• Anton Akhmerov (2)
• Thorsten Alteholz (1) +
• Gabriel Augusto (1) +
• Backfisch263 (1) +
• Nickolai Belakovski (5)
• Peter Bell (1)
• Benoît W. (1) +
• Evandro Bernardes (1)
• Gauthier Berthomieu (1) +
• Maxwell Bileschi (1) +
• Sam Birch (1) +
• Florian Bourgey (3) +
• Charles Bousseau (2) +
• Richard Strong Bowen (2) +
• Jake Bowhay (127)
• Matthew Brett (1)
• Dietrich Brunn (53)
• Evgeni Burovski (254)
• Christine P. Chai (12) +
• Gayatri Chakkithara (1) +
• Saransh Chopra (2) +
• Omer Cohen (1) +
• Lucas Colley (91)
• Yahya Darman (3) +
• Benjamin Eisele (1) +
• Donnie Erb (1)
• Sagi Ezri (58) +
• Alexander Fabisch (2) +
• Matthew H Flamm (1)
• Karthik Viswanath Ganti (1) +
• Neil Girdhar (1)
• Ralf Gommers (162)
• Rohit Goswami (4)
• Saarthak Gupta (4) +
• Matt Haberland (326)
• Sasha Hafner (1) +
• Joren Hammudoglu (11)
• Chengyu Han (1) +
• Charles Harris (1)
• Kim Hsieh (4) +
• Yongcai Huang (2) +
• Lukas Huber (1) +
• Yuji Ikeda (2) +
• Guido Imperiale (105) +
• Robert Kern (2)
• Harin Khakhi (2) +
• Agriya Khetarpal (4)
• Daniil Kiktenko (1) +
• Kirill R. (2) +
• Tetsuo Koyama (1)
• Jigyasu Krishnan (1) +
• Abhishek Kumar (2) +
• Pratham Kumar (3) +
• David Kun (1) +
• Eric Larson (3)
• lciti (1)
• Antony Lee (1)
• Kieran Leschinski (1) +
• Thomas Li (2) +
• Yuxi Long (2) +
• Christian Lorentzen (2)
• Loïc Estève (4)
• Panos Mavrogiorgos (1) +
• Nikolay Mayorov (2)
• Melissa Weber Mendonça (10)
• Michał Górny (1)
• Miguel Cárdenas (2) +
• Swastik Mishra (1) +
• Sturla Molden (2)
• Andreas Nazlidis (1) +
• Andrew Nelson (209)
• Parth Nobel (1) +
• Nick ODell (9)
• Giacomo Petrillo (1)
• Victor PM (10) +
• pmav99 (1) +
• Ilhan Polat (74)
• Tyler Reddy (128)
• Érico Nogueira Rolim (1) +
• Pamphile Roy (10)
• Mikhail Ryazanov (6)
• Atsushi Sakai (9)
• Marco Salathe (1) +
• sanvi (1) +
• Neil Schemenauer (2) +
• Daniel Schmitz (20)
• Martin Schuck (1) +
• Dan Schult (33)
• Tomer Sery (19)
• Adrian Seyboldt (1) +
• Scott Shambaugh (4)
• ShannonS00 (1) +
• sildater (3) +
• Param Singh (1) +
• G Sreeja (7) +
• Albert Steppi (133)
• Kai Striega (3)
• Anushka Suyal (2)
• Julia Tatz (1) +
• Tearyt (1) +
• Elia Tomasi (1) +
• Jamie Townsend (2) +
• Edgar Andrés Margffoy Tuay (4)
• Matthias Urlichs (1) +
• Mark van Rossum (1) +
• Jacob Vanderplas (2)
• David Varela (2) +
• Christian Veenhuis (3)
• vfdev (1)
• Stefan van der Walt (2)
• Warren Weckesser (5)
• Jason N. White (1) +
• windows-server-2003 (5)
• Zhiqing Xiao (1)
• Pavadol Yamsiri (1)
• Rory Yorke (3)
• Irwin Zaid (4)
• Austin Zhang (1) +
• William Zijie Zhang (1) +
• Zaikun Zhang (1) +
• Zhenyu Zhu (1) +
• Eric Zitong Zhou (11) +
• Case Zumbrum (2) +
• ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (45)

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.

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

v1.15.3: SciPy 1.15.3

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SciPy 1.15.3 Release Notes

SciPy 1.15.3 is a bug-fix release with no new features
compared to 1.15.2.

For the complete issue and PR lists see the raw release notes.

Authors

• Name (commits)
• aiudirog (1) +
• Nickolai Belakovski (1)
• Florian Bourgey (1) +
• Richard Strong Bowen (2) +
• Jake Bowhay (1)
• Dietrich Brunn (2)
• Evgeni Burovski (1)
• Lucas Colley (1)
• Ralf Gommers (1)
• Saarthak Gupta (1) +
• Matt Haberland (4)
• Chengyu Han (1) +
• Lukas Huber (1) +
• Nick ODell (2)
• Ilhan Polat (4)
• Tyler Reddy (52)
• Neil Schemenauer (1) +
• Dan Schult (1)
• sildater (1) +
• Gagandeep Singh (4)
• Albert Steppi (2)
• Matthias Urlichs (1) +
• David Varela (1) +
• ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (3)

A total of 24 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.

v1.15.2: SciPy 1.15.2

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SciPy 1.15.2 Release Notes

SciPy 1.15.2 is a bug-fix release with no new features
compared to 1.15.1. Free-threaded Python 3.13 wheels
for Linux ARM platform are available on PyPI starting with
this release.

Authors

• Name (commits)
• Peter Bell (1)
• Charles Bousseau (1) +
• Jake Bowhay (3)
• Matthew Brett (1)
• Ralf Gommers (3)
• Rohit Goswami (1)
• Matt Haberland (4)
• Parth Nobel (1) +
• Tyler Reddy (33)
• Daniel Schmitz (2)
• Dan Schult (5)
• Scott Shambaugh (2)
• Edgar Andrés Margffoy Tuay (1)
• Warren Weckesser (4)

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.

v1.15.1: SciPy 1.15.1

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SciPy 1.15.1 Release Notes

SciPy 1.15.1 is a bug-fix release with no new features
compared to 1.15.0. Importantly, an issue with the
import of scipy.optimize breaking other packages
has been fixed.

Authors

• Name (commits)
• Ralf Gommers (3)
• Rohit Goswami (1)
• Matt Haberland (2)
• Tyler Reddy (7)
• Daniel Schmitz (1)

A total of 5 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.

v1.15.0: SciPy 1.15.0

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SciPy 1.15.0 Release Notes

SciPy 1.15.0 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
1.15.x branch, and on adding new features on the main branch.

This release requires Python 3.10-3.13 and NumPy 1.23.5 or greater.

Highlights of this release

• Sparse arrays are now fully functional for 1-D and 2-D arrays. We recommend
  that all new code use sparse arrays instead of sparse matrices and that
  developers start to migrate their existing code from sparse matrix to sparse
  array: migration_to_sparray. Both sparse.linalg and sparse.csgraph
  work with either sparse matrix or sparse array and work internally with
  sparse array.

• Sparse arrays now provide basic support for n-D arrays in the COO format
  including add, subtract, reshape, transpose, matmul,
  dot, tensordot and others. More functionality is coming in future
  releases.

• Preliminary support for free-threaded Python 3.13.

• New probability distribution features in scipy.stats can be used to improve
  the speed and accuracy of existing continuous distributions and perform new
  probability calculations.

• Several new features support vectorized calculations with Python Array API
  Standard compatible input (see "Array API Standard Support" below):

  • scipy.differentiate is a new top-level submodule for accurate
    estimation of derivatives of black box functions.
  • scipy.optimize.elementwise contains new functions for root-finding and
    minimization of univariate functions.
  • scipy.integrate offers new functions cubature, tanhsinh, and
    nsum for multivariate integration, univariate integration, and
    univariate series summation, respectively.

• scipy.interpolate.AAA adds the AAA algorithm for barycentric rational
  approximation of real or complex functions.

• scipy.special adds new functions offering improved Legendre function
  implementations with a more consistent interface.

New features

scipy.differentiate introduction

The new scipy.differentiate sub-package contains functions for accurate
estimation of derivatives of black box functions.

• Use scipy.differentiate.derivative for first-order derivatives of
  scalar-in, scalar-out functions.
• Use scipy.differentiate.jacobian for first-order partial derivatives of
  vector-in, vector-out functions.
• Use scipy.differentiate.hessian for second-order partial derivatives of
  vector-in, scalar-out functions.

All functions use high-order finite difference rules with adaptive (real)
step size. To facilitate batch computation, these functions are vectorized
and support several Array API compatible array libraries in addition to NumPy
(see "Array API Standard Support" below).

scipy.integrate improvements

• The new scipy.integrate.cubature function supports multidimensional
  integration, and has support for approximating integrals with
  one or more sets of infinite limits.
• scipy.integrate.tanhsinh is now exposed for public use, allowing
  evaluation of a convergent integral using tanh-sinh quadrature.
• scipy.integrate.nsum evaluates finite and infinite series and their
  logarithms.
• scipy.integrate.lebedev_rule computes abscissae and weights for
  integration over the surface of a sphere.
• The QUADPACK Fortran77 package has been ported to C.

scipy.interpolate improvements

• scipy.interpolate.AAA adds the AAA algorithm for barycentric rational
  approximation of real or complex functions.
• scipy.interpolate.FloaterHormannInterpolator adds barycentric rational
  interpolation.
• New functions scipy.interpolate.make_splrep and
  scipy.interpolate.make_splprep implement construction of smoothing splines.
  The algorithmic content is equivalent to FITPACK (splrep and splprep
  functions, and *UnivariateSpline classes) and the user API is consistent
  with make_interp_spline: these functions receive data arrays and return
  a scipy.interpolate.BSpline instance.
• New generator function scipy.interpolate.generate_knots implements the
  FITPACK strategy for selecting knots of a smoothing spline given the
  smoothness parameter, s. The function exposes the internal logic of knot
  selection that splrep and *UnivariateSpline was using.

scipy.linalg improvements

• scipy.linalg.interpolative Fortran77 code has been ported to Cython.
• scipy.linalg.solve supports several new values for the assume_a
  argument, enabling faster computation for diagonal, tri-diagonal, banded, and
  triangular matrices. Also, when assume_a is left unspecified, the
  function now automatically detects and exploits diagonal, tri-diagonal,
  and triangular structures.
• scipy.linalg matrix creation functions (scipy.linalg.circulant,
  scipy.linalg.companion, scipy.linalg.convolution_matrix,
  scipy.linalg.fiedler, scipy.linalg.fiedler_companion, and
  scipy.linalg.leslie) now support batch
  matrix creation.
• scipy.linalg.funm is faster.
• scipy.linalg.orthogonal_procrustes now supports complex input.
• Wrappers for the following LAPACK routines have been added in
  scipy.linalg.lapack: ?lantr, ?sytrs, ?hetrs, ?trcon,
  and ?gtcon.
• scipy.linalg.expm was rewritten in C.
• scipy.linalg.null_space now accepts the new arguments overwrite_a,
  check_finite, and lapack_driver.
• id_dist Fortran code was rewritten in Cython.

scipy.ndimage improvements

• Several additional filtering functions now support an axes argument
  that specifies which axes of the input filtering is to be performed on.
  These include correlate, convolve, generic_laplace, laplace,
  gaussian_laplace, derivative2, generic_gradient_magnitude,
  gaussian_gradient_magnitude and generic_filter.
• The binary and grayscale morphology functions now support an axes
  argument that specifies which axes of the input filtering is to be performed
  on.
• scipy.ndimage.rank_filter time complexity has improved from n to
  log(n).

scipy.optimize improvements

• The vendored HiGHS library has been upgraded from 1.4.0 to 1.8.0,
  bringing accuracy and performance improvements to solvers.
• The MINPACK Fortran77 package has been ported to C.
• The L-BFGS-B Fortran77 package has been ported to C.
• The new scipy.optimize.elementwise namespace includes functions
  bracket_root, find_root, bracket_minimum, and `find_mi

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