ANN: SciPy 1.15.0rc1 – please test

[tylerjereddy]

Hi all,

On behalf of the SciPy development team, I’m pleased to announce the pre-release SciPy 1.15.0rc1 – please help us test this version.

Sources and binary wheels can be found at:
https://github.com/scipy/scipy/releases/tag/v1.15.0rc1
and at:
https://pypi.org/project/scipy/1.15.0rc1/

One of a few ways install this release with pip:
pip install scipy==1.15.0rc1

SciPy 1.15.0 Release Notes

Note: SciPy 1.15.0 is not released yet!

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.
• scipy.differentiate is a new top-level submodule for accurate
  estimation of derivatives of black box functions.
• scipy.optimize.elementwise provides vectorized root-finding and
  minimization of univariate functions, and it supports the array API
  as do new integrate functions tanhsinh, nsum, and cubature.
• scipy.interpolate.AAA adds the AAA algorithm for barycentric rational
  approximation of real or complex functions.

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

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_minimum
  for root-finding and minimization of univariate functions. 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). Compared to existing functions (e.g.
  scipy.optimize.root_scalar and scipy.optimize.minimize_scalar),
  these functions can offer speedups of over 100x when used with NumPy arrays,
  and even greater gains are possible with other Array API Standard compatible
  array libraries (e.g. CuPy).
• scipy.optimize.differential_evolution now supports more general use of
  workers, such as passing a map-like callable.
• scipy.optimize.nnls was rewritten in Cython.
• HessianUpdateStrategy now supports __matmul__.

scipy.signal improvements

• Add functionality of complex-valued waveforms to signal.chirp().
• scipy.signal.lombscargle has two new arguments, weights and
  floating_mean, enabling sample weighting and removal of an unknown
  y-offset independently for each frequency. Additionally, the normalize
  argument includes a new option to return the complex representation of the
  amplitude and phase.
• New function scipy.signal.envelope for computation of the envelope of a
  real or complex valued signal.

scipy.sparse improvements

• A migration guide is now available for
  moving from sparse.matrix to sparse.array in your code/library.
• Sparse arrays now support indexing for 1-D and 2-D arrays. So, sparse
  arrays are now fully functional for 1-D and 2D.
• n-D sparse arrays in COO format can now be constructed, reshaped and used
  for basic arithmetic.
• New functions sparse.linalg.is_sptriangular and
  sparse.linalg.spbandwidth mimic the existing dense tools
  linalg.is_triangular and linalg.bandwidth.
• sparse.linalg and sparse.csgraph now work with sparse arrays. Be
  careful that your index arrays are 32-bit. We are working on 64bit support.
• The vendored ARPACK library has been upgraded to version 3.9.1.
• COO, CSR, CSC and LIL formats now support the axis argument for
  count_nonzero.
• Sparse arrays and matrices may now raise errors when initialized with
  incompatible data types, such as float16.
• min, max, argmin, and argmax now support computation
  over nonzero elements only via the new explicit argument.
• New functions get_index_dtype and safely_cast_index_arrays are
  available to facilitate index array casting in sparse.

scipy.spatial improvements

• Rotation.concatenate now accepts a bare Rotation object, and will
  return a copy of it.

scipy.special improvements

• The factorial functions special.{factorial,factorial2,factorialk} now
  offer an extension to the complex domain by passing the kwarg
  extend='complex'. This is opt-in because it changes the values for
  negative inputs (which by default return 0), as well as for some integers
  (in the case of factorial2 and factorialk; for more details,
  check the respective docstrings).

• scipy.special.zeta now defines the Riemann zeta function on the complex
  plane.

• scipy.special.softplus computes the softplus function

• The spherical Bessel functions (scipy.special.spherical_jn,
  scipy.special.spherical_yn, scipy.special.spherical_in, and
  scipy.special.spherical_kn) now support negative arguments with real dtype.

• scipy.special.logsumexp now preserves precision when one element of the
  sum has magnitude much bigger than the rest.

• The accuracy of several functions has been improved:

  • scipy.special.ncfdtr and scipy.special.nctdtr have been improved
    throughout the domain.
  • scipy.special.hyperu is improved for the case of b=1, small x,
    and small a.
  • scipy.special.logit is improved near the argument p=0.5.
  • scipy.special.rel_entr is improved when x/y overflows, underflows,
    or is close to 1.

• scipy.special.gdtrib may now be used in a CuPy ElementwiseKernel on
  GPUs.

• scipy.special.ndtr is now more efficient.

scipy.stats improvements

• A new probability distribution infrastructure has been added for the
  implementation of univariate, continuous distributions with speed,
  accuracy, and memory advantages:

  • scipy.stats.Normal represents the normal distribution with the new
    interface. In typical cases, its methods are faster and more accurate than
    those of scipy.stats.norm.
  • Use scipy.stats.make_distribution to treat an existing continuous
    distribution (e.g. scipy.stats.norm) with the new infrastructure.
    This can improve the speed and accuracy of existing distributions,
    especially for methods not overridden with custom formulas in the
    implementation.

• scipy.stats.Mixture has been added to represent mixture distributions.

• Instances of scipy.stats.Normal and the classes returned by
  scipy.stats.make_distribution are supported by several new mathematical
  transformations.

  • scipy.stats.truncate for truncation of the support.
  • scipy.stats.order_statistic for the order statistics of a given number
    of IID random variables.
  • scipy.stats.abs, scipy.stats.exp, and scipy.stats.log. For example,
    scipy.stats.abs(Normal()) is distributed according to the folded normal
    and scipy.stats.exp(Normal()) is lognormally distributed.

• The new scipy.stats.lmoment calculates sample l-moments and l-moment
  ratios. Notably, these sample estimators are unbiased.

• scipy.stats.chatterjeexi computes the Xi correlation coefficient, which
  can detect nonlinear dependence. The function also performs a hypothesis
  test of independence between samples.

• scipy.stats.wilcoxon has improved method resolution logic for the default
  method='auto'. Other values of method provided by the user are now
  respected in all cases, and the method argument approx has been
  renamed to asymptotic for consistency with similar functions. (Use of
  approx is still allowed for backward compatibility.)

• There are several new probability distributions:

  • scipy.stats.dpareto_lognorm represents the double Pareto lognormal
    distribution.
  • scipy.stats.landau represents the Landau distribution.
  • scipy.stats.normal_inverse_gamma represents the normal-inverse-gamma
    distribution.
  • scipy.stats.poisson_binom represents the Poisson binomial distribution.

• Batch calculation with scipy.stats.alexandergovern and
  scipy.stats.combine_pvalues is faster.

• scipy.stats.chisquare added an argument sum_check. By default, the
  function raises an error when the sum of expected and obseved frequencies
  are not equal; setting sum_check=False disables this check to
  facilitate hypothesis tests other than Pearson’s chi-squared test.

• The accuracy of several distribution methods has been improved, including:

  • scipy.stats.nct method pdf
  • scipy.stats.crystalball method sf
  • scipy.stats.geom method rvs
  • scipy.stats.cauchy methods logpdf, pdf, ppf and isf
  • The logcdf and/or logsf methods of distributions that do not
    override the generic implementation of these methods, including
    scipy.stats.beta, scipy.stats.betaprime, scipy.stats.cauchy,
    scipy.stats.chi, scipy.stats.chi2, scipy.stats.exponweib,
    scipy.stats.gamma, scipy.stats.gompertz, scipy.stats.halflogistic,
    scipy.stats.hypsecant, scipy.stats.invgamma, scipy.stats.laplace,
    scipy.stats.levy, scipy.stats.loggamma, scipy.stats.maxwell,
    scipy.stats.nakagami, and scipy.stats.t.

• scipy.stats.qmc.PoissonDisk now accepts lower and upper bounds
  parameters l_bounds and u_bounds.

• scipy.stats.fisher_exact now supports two-dimensional tables with shapes
  other than (2, 2).

Preliminary Support for Free-Threaded CPython 3.13

SciPy 1.15 has preliminary support for the free-threaded build of CPython
3.13. This allows SciPy functionality to execute in parallel with Python
threads (see the threading stdlib module). This support was enabled by fixing a
significant number of thread-safety issues in both pure Python and
C/C++/Cython/Fortran extension modules. Wheels are provided on PyPI for this
release; NumPy >=2.1.3 is required at runtime. Note that building for a
free-threaded interpreter requires a recent pre-release or nightly for Cython
3.1.0.

Support for free-threaded Python does not mean that SciPy is fully thread-safe.
Please see scipy_thread_safety for more details.

If you are interested in free-threaded Python, for example because you have a
multiprocessing-based workflow that you are interested in running with Python
threads, we encourage testing and experimentation. If you run into problems
that you suspect are because of SciPy, please open an issue, checking first if
the bug also occurs in the “regular” non-free-threaded CPython 3.13 build.
Many threading bugs can also occur in code that releases the GIL; disabling
the GIL only makes it easier to hit threading bugs.

Array API Standard Support

Experimental support for array libraries other than NumPy has been added to
existing sub-packages in recent versions of SciPy. Please consider testing
these features by setting an environment variable SCIPY_ARRAY_API=1 and
providing PyTorch, JAX, ndonnx, or CuPy arrays as array arguments. Features
with support added for SciPy 1.15.0 include:

• All functions in scipy.differentiate (new sub-package)
• All functions in scipy.optimize.elementwise (new namespace)
• scipy.optimize.rosen, scipy.optimize.rosen_der, and
  scipy.optimize.rosen_hess
• scipy.special.logsumexp
• scipy.integrate.trapezoid
• scipy.integrate.tanhsinh (newly public function)
• scipy.integrate.cubature (new function)
• scipy.integrate.nsum (new function)
• scipy.special.chdtr, scipy.special.betainc, and scipy.special.betaincc
• scipy.stats.boxcox_llf
• scipy.stats.differential_entropy
• scipy.stats.zmap, scipy.stats.zscore, and scipy.stats.gzscore
• scipy.stats.tmean, scipy.stats.tvar, scipy.stats.tstd,
  scipy.stats.tsem, scipy.stats.tmin, and scipy.stats.tmax
• scipy.stats.gmean, scipy.stats.hmean and scipy.stats.pmean
• scipy.stats.combine_pvalues
• scipy.stats.ttest_ind, scipy.stats.ttest_rel
• scipy.stats.directional_stats
• scipy.ndimage functions will now delegate to cupyx.scipy.ndimage,
  and for other backends will transit via NumPy arrays on the host.

Deprecated features

• Functions scipy.linalg.interpolative.rand and
  scipy.linalg.interpolative.seed have been deprecated and will be removed
  in SciPy 1.17.0.
• Complex inputs to scipy.spatial.distance.cosine and
  scipy.spatial.distance.correlation have been deprecated and will raise
  an error in SciPy 1.17.0.
• scipy.spatial.distance.kulczynski1 and
  scipy.spatial.distance.sokalmichener were deprecated and will be removed
  in SciPy 1.17.0.
• scipy.stats.find_repeats is deprecated as of SciPy 1.15.0 and will be
  removed in SciPy 1.17.0. Please use
  numpy.unique/numpy.unique_counts instead.
• scipy.linalg.kron is deprecated in favour of numpy.kron.
• Using object arrays and longdouble arrays in scipy.signal
  convolution/correlation functions (scipy.signal.correlate,
  scipy.signal.convolve and scipy.signal.choose_conv_method) and
  filtering functions (scipy.signal.lfilter, scipy.signal.sosfilt) has
  been deprecated as of SciPy 1.15.0 and will be removed in SciPy
  1.17.0.
• scipy.stats.linregress has deprecated one-argument use; the two
  variables must be specified as separate arguments.
• scipy.stats.trapz is deprecated in favor of scipy.stats.trapezoid.
• scipy.special.lpn is deprecated in favor of scipy.special.legendre_p_all.
• scipy.special.lpmn and scipy.special.clpmn are deprecated in favor of
  scipy.special.assoc_legendre_p_all.
• The raveling of multi-dimensional input by scipy.linalg.toeplitz has
  been deprecated. It will support batching in SciPy 1.17.0.
• The random_state and permutations arguments of
  scipy.stats.ttest_ind are deprecated. Use method to perform a
  permutation test, instead.

Expired Deprecations

• The wavelet functions in scipy.signal have been removed. This includes
  daub, qmf, cascade, morlet, morlet2, ricker,
  and cwt. Users should use pywavelets instead.
• scipy.signal.cmplx_sort has been removed.
• scipy.integrate.quadrature and scipy.integrate.romberg have been
  removed in favour of scipy.integrate.quad.
• scipy.stats.rvs_ratio_uniforms has been removed in favor of
  scipy.stats.sampling.RatioUniforms.
• scipy.special.factorial now raises an error for non-integer scalars when
  exact=True.
• scipy.integrate.cumulative_trapezoid now raises an error for values of
  initial other than 0 and None.
• Complex dtypes now raise an error in scipy.interpolate.Akima1DInterpolator
  and scipy.interpolate.PchipInterpolator
• special.btdtr and special.btdtri have been removed.
• The default of the exact= kwarg in special.factorialk has changed
  from True to False.
• All functions in the scipy.misc submodule have been removed.

Backwards incompatible changes

• interpolate.BSpline.integrate output is now always a numpy array.
  Previously, for 1D splines the output was a python float or a 0D array
  depending on the value of the extrapolate argument.
• scipy.stats.wilcoxon now respects the method argument provided by the
  user. Previously, even if method='exact' was specified, the function
  would resort to method='approx' in some cases.

Other changes

• A separate accompanying type stubs package, scipy-stubs, will be made
  available with the 1.15.0 release. Installation instructions are available <https://github.com/jorenham/scipy-stubs?tab=readme-ov-file#installation>_.

• scipy.stats.bootstrap now emits a FutureWarning if the shapes of the
  input arrays do not agree. Broadcast the arrays to the same batch shape
  (i.e. for all dimensions except those specified by the axis argument)
  to avoid the warning. Broadcasting will be performed automatically in the
  future.

• SciPy endorsed SPEC-7, which proposes a rng argument to control pseudorandom number generation
  (PRNG) in a standard way, replacing legacy arguments like seed and
  random_sate. In many cases, use of rng will change the behavior of
  the function unless the argument is already an instance of
  numpy.random.Generator.

  • Effective in SciPy 1.15.0:

    • The rng argument has been added to the following functions:
      scipy.cluster.vq.kmeans, scipy.cluster.vq.kmeans2,
      scipy.interpolate.BarycentricInterpolator,
      scipy.interpolate.barycentric_interpolate,
      scipy.linalg.clarkson_woodruff_transform,
      scipy.optimize.basinhopping,
      scipy.optimize.differential_evolution, scipy.optimize.dual_annealing,
      scipy.optimize.check_grad, scipy.optimize.quadratic_assignment,
      scipy.sparse.random, scipy.sparse.random_array, scipy.sparse.rand,
      scipy.sparse.linalg.svds, scipy.spatial.transform.Rotation.random,
      scipy.spatial.distance.directed_hausdorff,
      scipy.stats.goodness_of_fit, scipy.stats.BootstrapMethod,
      scipy.stats.PermutationMethod, scipy.stats.bootstrap,
      scipy.stats.permutation_test, scipy.stats.dunnett, all
      scipy.stats.qmc classes that consume random numbers, and
      scipy.stats.sobol_indices.
    • When passed by keyword, the rng argument will follow the SPEC 7
      standard behavior: the argument will be normalized with
      np.random.default_rng before being used.
    • When passed by position or legacy keyword, the behavior of the argument
      will remain unchanged (for now).

  • It is planned that in 1.17.0 the legacy argument will start emitting
    warnings, and that in 1.19.0 the default behavior will change.

  • In all cases, users can avoid future disruption by proactively passing
    an instance of np.random.Generator by keyword rng. For details,
    see SPEC-7.

• The SciPy build no longer adds -std=legacy for Fortran code,
  except when using Gfortran. This avoids problems with the new Flang and
  AMD Fortran compilers. It may make new build warnings appear for other
  compilers - if so, please file an issue.

• scipy.signal.sosfreqz has been renamed to scipy.signal.freqz_sos.
  New code should use the new name. The old name is maintained as an alias for
  backwards compatibility.

• Testing thread-safety improvements related to Python 3.13t have been
  made in: scipy.special, scipy.spatial, scipy.sparse,
  scipy.interpolate.

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• Stephan Rave (1) +
• Tyler Reddy (131)
• redha2404 (2) +
• Ritvik1sharma (1) +
• Érico Nogueira Rolim (1) +
• Heshy Roskes (1)
• Pamphile Roy (34)
• Mikhail Ryazanov (1) +
• Sina Saber (1) +
• Atsushi Sakai (1)
• Clemens Schmid (1) +
• Daniel Schmitz (15)
• Moritz Schreiber (1) +
• Dan Schult (88)
• Searchingdays (1) +
• Matias Senger (1) +
• Scott Shambaugh (1)
• Zhida Shang (1) +
• Sheila-nk (4)
• Romain Simon (2) +
• Gagandeep Singh (31)
• Albert Steppi (36)
• Kai Striega (1)
• Anushka Suyal (143) +
• Alex Szatmary (1)
• Svetlin Tassev (1) +
• Ewout ter Hoeven (1)
• Tibor Völcker (4) +
• Kanishk Tiwari (1) +
• Yusuke Toyama (1) +
• Edgar Andrés Margffoy Tuay (124)
• Adam Turner (2) +
• Nicole Vadot (1) +
• Andrew Valentine (1)
• Christian Veenhuis (2)
• vfdev (2) +
• Pauli Virtanen (2)
• Simon Waldherr (1) +
• Stefan van der Walt (2)
• Warren Weckesser (23)
• Anreas Weh (1)
• Benoît Wygas (2) +
• Pavadol Yamsiri (3) +
• ysard (1) +
• Xiao Yuan (2)
• Irwin Zaid (12)
• Gang Zhao (1)
• ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (10)

A total of 148 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.

Because of the Discourse character limit, the remainder of the release notes are not included but are available at: https://github.com/scipy/scipy/releases/download/v1.15.0rc1/README.txt