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.

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.

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_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
  incompatib...

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.

• 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_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 rai...

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 :ref:migration guide<migration_to_sparray> 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...

SciPy 1.14.1 Release Notes

SciPy 1.14.1 adds support for Python 3.13, including binary
wheels on PyPI. Apart from that, it is a bug-fix release with
no new features compared to 1.14.0.

Authors

• Name (commits)
• h-vetinari (1)
• Evgeni Burovski (1)
• CJ Carey (2)
• Lucas Colley (3)
• Ralf Gommers (3)
• Melissa Weber Mendonça (1)
• Andrew Nelson (3)
• Nick ODell (1)
• Tyler Reddy (36)
• Daniel Schmitz (1)
• Dan Schult (4)
• Albert Steppi (2)
• Ewout ter Hoeven (1)
• Tibor Völcker (2) +
• Adam Turner (1) +
• Warren Weckesser (2)
• ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (1)

A total of 17 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.

SciPy 1.14.0 Release Notes

SciPy 1.14.0 is the culmination of 3 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
1.14.x branch, and on adding new features on the main branch.

This release requires Python 3.10+ and NumPy 1.23.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• SciPy now supports the new Accelerate library introduced in macOS 13.3, and
  has wheels built against Accelerate for macOS >=14 resulting in significant
  performance improvements for many linear algebra operations.
• A new method, cobyqa, has been added to scipy.optimize.minimize - this
  is an interface for COBYQA (Constrained Optimization BY Quadratic
  Approximations), a derivative-free optimization solver, designed to
  supersede COBYLA, developed by the Department of Applied Mathematics, The
  Hong Kong Polytechnic University.
• scipy.sparse.linalg.spsolve_triangular is now more than an order of
  magnitude faster in many cases.

New features

scipy.fft improvements

• A new function, scipy.fft.prev_fast_len, has been added. This function
  finds the largest composite of FFT radices that is less than the target
  length. It is useful for discarding a minimal number of samples before FFT.

scipy.io improvements

• wavfile now supports reading and writing of wav files in the RF64
  format, allowing files greater than 4 GB in size to be handled.

scipy.constants improvements

• Experimental support for the array API standard has been added.

scipy.interpolate improvements

• scipy.interpolate.Akima1DInterpolator now supports extrapolation via the
  extrapolate argument.

scipy.optimize improvements

• scipy.optimize.HessianUpdateStrategy now also accepts square arrays for
  init_scale.
• A new method, cobyqa, has been added to scipy.optimize.minimize - this
  is an interface for COBYQA (Constrained Optimization BY Quadratic
  Approximations), a derivative-free optimization solver, designed to
  supersede COBYLA, developed by the Department of Applied Mathematics, The
  Hong Kong Polytechnic University.
• There are some performance improvements in
  scipy.optimize.differential_evolution.
• scipy.optimize.approx_fprime now has linear space complexity.

scipy.signal improvements

• scipy.signal.minimum_phase has a new argument half, allowing the
  provision of a filter of the same length as the linear-phase FIR filter
  coefficients and with the same magnitude spectrum.

scipy.sparse improvements

• Sparse arrays now support 1D shapes in COO, DOK and CSR formats.
  These are all the formats we currently intend to support 1D shapes.
  Other sparse array formats raise an exception for 1D input.
• Sparse array methods min/nanmin/argmin and max analogs now return 1D arrays.
  Results are still COO format sparse arrays for min/nanmin and
  dense np.ndarray for argmin.
• Iterating over csr_array or csc_array yields 1D (CSC) arrays.
• Sparse matrix and array objects improve their repr and str output.
• A special case has been added to handle multiplying a dia_array by a
  scalar, which avoids a potentially costly conversion to CSR format.
• scipy.sparse.csgraph.yen has been added, allowing usage of Yen's K-Shortest
  Paths algorithm on a directed on undirected graph.
• Addition between DIA-format sparse arrays and matrices is now faster.
• scipy.sparse.linalg.spsolve_triangular is now more than an order of
  magnitude faster in many cases.

scipy.spatial improvements

• Rotation supports an alternative "scalar-first" convention of quaternion
  component ordering. It is available via the keyword argument scalar_first
  of from_quat and as_quat methods.
• Some minor performance improvements for inverting of Rotation objects.

scipy.special improvements

• Added scipy.special.log_wright_bessel, for calculation of the logarithm of
  Wright's Bessel function.
• The relative error in scipy.special.hyp2f1 calculations has improved
  substantially.
• Improved behavior of boxcox, inv_boxcox, boxcox1p, and
  inv_boxcox1p by preventing premature overflow.

scipy.stats improvements

• A new function scipy.stats.power can be used for simulating the power
  of a hypothesis test with respect to a specified alternative.
• The Irwin-Hall (AKA Uniform Sum) distribution has been added as
  scipy.stats.irwinhall.
• Exact p-value calculations of scipy.stats.mannwhitneyu are much faster
  and use less memory.
• scipy.stats.pearsonr now accepts n-D arrays and computes the statistic
  along a specified axis.
• scipy.stats.kstat, scipy.stats.kstatvar, and scipy.stats.bartlett
  are faster at performing calculations along an axis of a large n-D array.

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, or CuPy arrays as array arguments.

As of 1.14.0, there is support for

• scipy.cluster

• scipy.fft

• scipy.constants

• scipy.special: (select functions)

  • scipy.special.log_ndtr
  • scipy.special.ndtr
  • scipy.special.ndtri
  • scipy.special.erf
  • scipy.special.erfc
  • scipy.special.i0
  • scipy.special.i0e
  • scipy.special.i1
  • scipy.special.i1e
  • scipy.special.gammaln
  • scipy.special.gammainc
  • scipy.special.gammaincc
  • scipy.special.logit
  • scipy.special.expit
  • scipy.special.entr
  • scipy.special.rel_entr
  • scipy.special.xlogy
  • scipy.special.chdtrc

• scipy.stats: (select functions)

  • scipy.stats.describe
  • scipy.stats.moment
  • scipy.stats.skew
  • scipy.stats.kurtosis
  • scipy.stats.kstat
  • scipy.stats.kstatvar
  • scipy.stats.circmean
  • scipy.stats.circvar
  • scipy.stats.circstd
  • scipy.stats.entropy
  • scipy.stats.variation
  • scipy.stats.sem
  • scipy.stats.ttest_1samp
  • scipy.stats.pearsonr
  • scipy.stats.chisquare
  • scipy.stats.skewtest
  • scipy.stats.kurtosistest
  • scipy.stats.normaltest
  • scipy.stats.jarque_bera
  • scipy.stats.bartlett
  • scipy.stats.power_divergence
  • scipy.stats.monte_carlo_test

Deprecated features

• scipy.stats.gstd, scipy.stats.chisquare, and
  scipy.stats.power_divergence have deprecated support for masked array
  input.
• scipy.stats.linregress has deprecated support for specifying both samples
  in one argument; x and y are to be provided as separate arguments.
• The conjtransp method for scipy.sparse.dok_array and
  scipy.sparse.dok_matrix has been deprecated and will be removed in SciPy
  1.16.0.
• The option quadrature="trapz" in scipy.integrate.quad_vec has been
  deprecated in favour of quadrature="trapezoid" and will be removed in
  SciPy 1.16.0.
• scipy.special.{comb,perm} have deprecated support for use of exact=True in
  conjunction with non-integral N and/or k.

Backwards incompatible changes

• Many scipy.stats functions now produce a standardized warning message when
  an input sample is too small (e.g. zero size). Previously, these functions
  may have raised an error, emitted one or more less informative warnings, or
  emitted no warnings. In most cases, returned results are unchanged; in almost
  all cases the correct result is NaN.

Expired deprecations

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

• Several previously deprecated methods for sparse arrays were removed:
  asfptype, getrow, getcol, get_shape, getmaxprint,
  set_shape, getnnz, and getformat. Additionally, the .A and
  .H attributes were removed.
• scipy.integrate.{simps,trapz,cumtrapz} have been removed in favour of
  simpson, trapezoid, and cumulative_trapezoid.
• The tol argument of scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk, mres,lgmres,minres,qmr,tfqmr} has been removed in favour of rtol.
  Furthermore, the default value of atol for these functions has changed
  to 0.0.
• The restrt argument of scipy.sparse.linalg.gmres has been removed in
  favour of restart.
• The initial_lexsort argument of scipy.stats.kendalltau has been
  removed.
• The cond and rcond arguments of scipy.linalg.pinv have been
  removed.
• The even argument of scipy.integrate.simpson has been removed.
• The turbo and eigvals arguments from scipy.linalg.{eigh,eigvalsh}
  have been removed.
• The legacy argument of scipy.special.comb has been rem...

SciPy 1.14.0 Release Notes

Note: SciPy 1.14.0 is not released yet!

SciPy 1.14.0 is the culmination of 3 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
1.14.x branch, and on adding new features on the main branch.

This release requires Python 3.10+ and NumPy 1.23.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• SciPy now supports the new Accelerate library introduced in macOS 13.3, and
  has wheels built against Accelerate for macOS >=14 resulting in significant
  performance improvements for many linear algebra operations.
• A new method, cobyqa, has been added to scipy.optimize.minimize - this
  is an interface for COBYQA (Constrained Optimization BY Quadratic
  Approximations), a derivative-free optimization solver, designed to
  supersede COBYLA, developed by the Department of Applied Mathematics, The
  Hong Kong Polytechnic University.
• scipy.sparse.linalg.spsolve_triangular is now more than an order of
  magnitude faster in many cases.

New features

scipy.fft improvements

• A new function, scipy.fft.prev_fast_len, has been added. This function
  finds the largest composite of FFT radices that is less than the target
  length. It is useful for discarding a minimal number of samples before FFT.

scipy.io improvements

• wavfile now supports reading and writing of wav files in the RF64
  format, allowing files greater than 4 GB in size to be handled.

scipy.constants improvements

• Experimental support for the array API standard has been added.

scipy.interpolate improvements

• scipy.interpolate.Akima1DInterpolator now supports extrapolation via the
  extrapolate argument.

scipy.optimize improvements

• scipy.optimize.HessianUpdateStrategy now also accepts square arrays for
  init_scale.
• A new method, cobyqa, has been added to scipy.optimize.minimize - this
  is an interface for COBYQA (Constrained Optimization BY Quadratic
  Approximations), a derivative-free optimization solver, designed to
  supersede COBYLA, developed by the Department of Applied Mathematics, The
  Hong Kong Polytechnic University.
• There are some performance improvements in
  scipy.optimize.differential_evolution.
• scipy.optimize.approx_fprime now has linear space complexity.

scipy.signal improvements

• scipy.signal.minimum_phase has a new argument half, allowing the
  provision of a filter of the same length as the linear-phase FIR filter
  coefficients and with the same magnitude spectrum.

scipy.sparse improvements

• Sparse arrays now support 1D shapes in COO, DOK and CSR formats.
  These are all the formats we currently intend to support 1D shapes.
  Other sparse array formats raise an exception for 1D input.
• Sparse array methods min/nanmin/argmin and max analogs now return 1D arrays.
  Results are still COO format sparse arrays for min/nanmin and
  dense np.ndarray for argmin.
• Sparse matrix and array objects improve their repr and str output.
• A special case has been added to handle multiplying a dia_array by a
  scalar, which avoids a potentially costly conversion to CSR format.
• scipy.sparse.csgraph.yen has been added, allowing usage of Yen's K-Shortest
  Paths algorithm on a directed on undirected graph.
• Addition between DIA-format sparse arrays and matrices is now faster.
• scipy.sparse.linalg.spsolve_triangular is now more than an order of
  magnitude faster in many cases.

scipy.spatial improvements

• Rotation supports an alternative "scalar-first" convention of quaternion
  component ordering. It is available via the keyword argument scalar_first
  of from_quat and as_quat methods.
• Some minor performance improvements for inverting of Rotation objects.

scipy.special improvements

• Added scipy.special.log_wright_bessel, for calculation of the logarithm of
  Wright's Bessel function.
• The relative error in scipy.special.hyp2f1 calculations has improved
  substantially.
• Improved behavior of boxcox, inv_boxcox, boxcox1p, and
  inv_boxcox1p by preventing premature overflow.

scipy.stats improvements

• A new function scipy.stats.power can be used for simulating the power
  of a hypothesis test with respect to a specified alternative.
• The Irwin-Hall (AKA Uniform Sum) distribution has been added as
  scipy.stats.irwinhall.
• Exact p-value calculations of scipy.stats.mannwhitneyu are much faster
  and use less memory.
• scipy.stats.pearsonr now accepts n-D arrays and computes the statistic
  along a specified axis.
• scipy.stats.kstat, scipy.stats.kstatvar, and scipy.stats.bartlett
  are faster at performing calculations along an axis of a large n-D array.

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, or CuPy arrays as array arguments.

As of 1.14.0, there is support for

• scipy.cluster

• scipy.fft

• scipy.constants

• scipy.special: (select functions)

  • scipy.special.log_ndtr
  • scipy.special.ndtr
  • scipy.special.ndtri
  • scipy.special.erf
  • scipy.special.erfc
  • scipy.special.i0
  • scipy.special.i0e
  • scipy.special.i1
  • scipy.special.i1e
  • scipy.special.gammaln
  • scipy.special.gammainc
  • scipy.special.gammaincc
  • scipy.special.logit
  • scipy.special.expit
  • scipy.special.entr
  • scipy.special.rel_entr
  • scipy.special.xlogy
  • scipy.special.chdtrc

• scipy.stats: (select functions)

  • scipy.stats.describe
  • scipy.stats.moment
  • scipy.stats.skew
  • scipy.stats.kurtosis
  • scipy.stats.kstat
  • scipy.stats.kstatvar
  • scipy.stats.circmean
  • scipy.stats.circvar
  • scipy.stats.circstd
  • scipy.stats.entropy
  • scipy.stats.variation
  • scipy.stats.sem
  • scipy.stats.ttest_1samp
  • scipy.stats.pearsonr
  • scipy.stats.chisquare
  • scipy.stats.skewtest
  • scipy.stats.kurtosistest
  • scipy.stats.normaltest
  • scipy.stats.jarque_bera
  • scipy.stats.bartlett
  • scipy.stats.power_divergence
  • scipy.stats.monte_carlo_test

Deprecated features

• scipy.stats.gstd, scipy.stats.chisquare, and
  scipy.stats.power_divergence have deprecated support for masked array
  input.
• scipy.stats.linregress has deprecated support for specifying both samples
  in one argument; x and y are to be provided as separate arguments.
• The conjtransp method for scipy.sparse.dok_array and
  scipy.sparse.dok_matrix has been deprecated and will be removed in SciPy
  1.16.0.
• The option quadrature="trapz" in scipy.integrate.quad_vec has been
  deprecated in favour of quadrature="trapezoid" and will be removed in
  SciPy 1.16.0.
• scipy.special.comb has deprecated support for use of exact=True in
  conjunction with non-integral N and/or k.

Backwards incompatible changes

• Many scipy.stats functions now produce a standardized warning message when
  an input sample is too small (e.g. zero size). Previously, these functions
  may have raised an error, emitted one or more less informative warnings, or
  emitted no warnings. In most cases, returned results are unchanged; in almost
  all cases the correct result is NaN.

Expired deprecations

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

• Several previously deprecated methods for sparse arrays were removed:
  asfptype, getrow, getcol, get_shape, getmaxprint,
  set_shape, getnnz, and getformat. Additionally, the .A and
  .H attributes were removed.
• scipy.integrate.{simps,trapz,cumtrapz} have been removed in favour of
  simpson, trapezoid, and cumulative_trapezoid.
• The tol argument of scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk, mres,lgmres,minres,qmr,tfqmr} has been removed in favour of rtol.
  Furthermore, the default value of atol for these functions has changed
  to 0.0.
• The restrt argument of scipy.sparse.linalg.gmres has been removed in
  favour of restart.
• The initial_lexsort argument of scipy.stats.kendalltau has been
  removed.
• The cond and rcond arguments of scipy.linalg.pinv have been
  removed.
• The even argument of scipy.integrate.simpson has been removed.
• The turbo and eigvals arguments from scipy.linalg.{eigh,eigvalsh}
  have been removed.
• The legacy argument of scipy.special.comb has been removed.
• The hz/nyq argum...

SciPy 1.14.0 Release Notes

Note: SciPy 1.14.0 is not released yet!

SciPy 1.14.0 is the culmination of 3 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
1.14.x branch, and on adding new features on the main branch.

This release requires Python 3.10+ and NumPy 1.23.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• SciPy now supports the new Accelerate library introduced in macOS 13.3, and
  has wheels built against Accelerate for macOS >=14 resulting in significant
  performance improvements for many linear algebra operations.
• A new method, cobyqa, has been added to scipy.optimize.minimize - this
  is an interface for COBYQA (Constrained Optimization BY Quadratic
  Approximations), a derivative-free optimization solver, designed to
  supersede COBYLA, developed by the Department of Applied Mathematics, The
  Hong Kong Polytechnic University.
• scipy.sparse.linalg.spsolve_triangular is now more than an order of
  magnitude faster in many cases.

New features

scipy.fft improvements

• A new function, scipy.fft.prev_fast_len, has been added. This function
  finds the largest composite of FFT radices that is less than the target
  length. It is useful for discarding a minimal number of samples before FFT.

scipy.io improvements

• wavfile now supports reading and writing of wav files in the RF64
  format, allowing files greater than 4 GB in size to be handled.

scipy.constants improvements

• Experimental support for the array API standard has been added.

scipy.interpolate improvements

• scipy.interpolate.Akima1DInterpolator now supports extrapolation via the
  extrapolate argument.

scipy.optimize improvements

• scipy.optimize.HessianUpdateStrategy now also accepts square arrays for
  init_scale.
• A new method, cobyqa, has been added to scipy.optimize.minimize - this
  is an interface for COBYQA (Constrained Optimization BY Quadratic
  Approximations), a derivative-free optimization solver, designed to
  supersede COBYLA, developed by the Department of Applied Mathematics, The
  Hong Kong Polytechnic University.
• There are some performance improvements in
  scipy.optimize.differential_evolution.
• scipy.optimize.approx_fprime now has linear space complexity.

scipy.signal improvements

• scipy.signal.minimum_phase has a new argument half, allowing the
  provision of a filter of the same length as the linear-phase FIR filter
  coefficients and with the same magnitude spectrum.

scipy.sparse improvements

• A special case has been added to handle multiplying a dia_array by a
  scalar, which avoids a potentially costly conversion to CSR format.
• scipy.sparse.csgraph.yen has been added, allowing usage of Yen's K-Shortest
  Paths algorithm on a directed on undirected graph.
• Addition between DIA-format sparse arrays and matrices is now faster.
• scipy.sparse.linalg.spsolve_triangular is now more than an order of
  magnitude faster in many cases.

scipy.spatial improvements

• Rotation supports an alternative "scalar-first" convention of quaternion
  component ordering. It is available via the keyword argument scalar_first
  of from_quat and as_quat methods.
• Some minor performance improvements for inverting of Rotation objects.

scipy.special improvements

• Added scipy.special.log_wright_bessel, for calculation of the logarithm of
  Wright's Bessel function.
• The relative error in scipy.special.hyp2f1 calculations has improved
  substantially.
• Improved behavior of boxcox, inv_boxcox, boxcox1p, and
  inv_boxcox1p by preventing premature overflow.

scipy.stats improvements

• A new function scipy.stats.power can be used for simulating the power
  of a hypothesis test with respect to a specified alternative.
• The Irwin-Hall (AKA Uniform Sum) distribution has been added as
  scipy.stats.irwinhall.
• Exact p-value calculations of scipy.stats.mannwhitneyu are much faster
  and use less memory.
• scipy.stats.pearsonr now accepts n-D arrays and computes the statistic
  along a specified axis.
• scipy.stats.kstat, scipy.stats.kstatvar, and scipy.stats.bartlett
  are faster at performing calculations along an axis of a large n-D array.

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, or CuPy arrays as array arguments.

As of 1.14.0, there is support for

• scipy.cluster

• scipy.fft

• scipy.constants

• scipy.special: (select functions)

  • scipy.special.log_ndtr
  • scipy.special.ndtr
  • scipy.special.ndtri
  • scipy.special.erf
  • scipy.special.erfc
  • scipy.special.i0
  • scipy.special.i0e
  • scipy.special.i1
  • scipy.special.i1e
  • scipy.special.gammaln
  • scipy.special.gammainc
  • scipy.special.gammaincc
  • scipy.special.logit
  • scipy.special.expit
  • scipy.special.entr
  • scipy.special.rel_entr
  • scipy.special.xlogy
  • scipy.special.chdtrc

• scipy.stats: (select functions)

  • scipy.stats.moment
  • scipy.stats.skew
  • scipy.stats.kurtosis
  • scipy.stats.kstat
  • scipy.stats.kstatvar
  • scipy.stats.circmean
  • scipy.stats.circvar
  • scipy.stats.circstd
  • scipy.stats.entropy
  • scipy.stats.variation
  • scipy.stats.sem
  • scipy.stats.ttest_1samp
  • scipy.stats.pearsonr
  • scipy.stats.chisquare
  • scipy.stats.skewtest
  • scipy.stats.kurtosistest
  • scipy.stats.normaltest
  • scipy.stats.jarque_bera
  • scipy.stats.bartlett
  • scipy.stats.power_divergence
  • scipy.stats.monte_carlo_test

Deprecated features

• scipy.stats.gstd, scipy.stats.chisquare, and
  scipy.stats.power_divergence have deprecated support for masked array
  input.
• scipy.stats.linregress has deprecated support for specifying both samples
  in one argument; x and y are to be provided as separate arguments.
• The conjtransp method for scipy.sparse.dok_array and
  scipy.sparse.dok_matrix has been deprecated and will be removed in SciPy
  1.16.0.
• The option quadrature="trapz" in scipy.integrate.quad_vec has been
  deprecated in favour of quadrature="trapezoid" and will be removed in
  SciPy 1.16.0.
• scipy.special.comb has deprecated support for use of exact=True in
  conjunction with non-integral N and/or k.

Backwards incompatible changes

• Many scipy.stats functions now produce a standardized warning message when
  an input sample is too small (e.g. zero size). Previously, these functions
  may have raised an error, emitted one or more less informative warnings, or
  emitted no warnings. In most cases, returned results are unchanged; in almost
  all cases the correct result is NaN.

Expired deprecations

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

• Several previously deprecated methods for sparse arrays were removed:
  asfptype, getrow, getcol, get_shape, getmaxprint,
  set_shape, getnnz, and getformat. Additionally, the .A and
  .H attributes were removed.
• scipy.integrate.{simps,trapz,cumtrapz} have been removed in favour of
  simpson, trapezoid, and cumulative_trapezoid.
• The tol argument of scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk, mres,lgmres,minres,qmr,tfqmr} has been removed in favour of rtol.
  Furthermore, the default value of atol for these functions has changed
  to 0.0.
• The restrt argument of scipy.sparse.linalg.gmres has been removed in
  favour of restart.
• The initial_lexsort argument of scipy.stats.kendalltau has been
  removed.
• The cond and rcond arguments of scipy.linalg.pinv have been
  removed.
• The even argument of scipy.integrate.simpson has been removed.
• The turbo and eigvals arguments from scipy.linalg.{eigh,eigvalsh}
  have been removed.
• The legacy argument of scipy.special.comb has been removed.
• The hz/nyq argument of signal.{firls, firwin, firwin2, remez} has
  been removed.
• Objects that weren't part of the public interface but were accessible through
  deprecated submodules have been removed.
• float128, float96, and object arrays now raise an error in
  scipy.signal.medfilt and scipy.signal.order_filter.
• scipy.interpolate.interp2d has been replaced by an empty stub (to be
  removed completely in the future).
• Coinciding with changes to function signatures (e.g. remov...

SciPy 1.13.1 Release Notes

SciPy 1.13.1 is a bug-fix release with no new features
compared to 1.13.0. The version of OpenBLAS shipped with
the PyPI binaries has been increased to 0.3.27.

Authors

• Name (commits)
• h-vetinari (1)
• Jake Bowhay (2)
• Evgeni Burovski (6)
• Sean Cheah (2)
• Lucas Colley (2)
• DWesl (2)
• Ralf Gommers (7)
• Ben Greiner (1) +
• Matt Haberland (2)
• Gregory R. Lee (1)
• Philip Loche (1) +
• Sijo Valayakkad Manikandan (1) +
• Matti Picus (1)
• Tyler Reddy (62)
• Atsushi Sakai (1)
• Daniel Schmitz (2)
• Dan Schult (3)
• Scott Shambaugh (2)
• Edgar Andrés Margffoy Tuay (1)

A total of 19 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.