SciPy 1.13.0

SciPy 1.13.0 Release Notes

SciPy 1.13.0 is the culmination of 3 months of hard work. This
out-of-band release aims to support NumPy 2.0.0, and is backwards
compatible to NumPy 1.22.4. The version of OpenBLAS used to build
the PyPI wheels has been increased to 0.3.26.dev.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• Support for NumPy 2.0.0.
• Interactive examples have been added to the documentation, allowing users
  to run the examples locally on embedded Jupyterlite notebooks in their
  browser.
• Preliminary 1D array support for the COO and DOK sparse formats.
• Several scipy.stats functions have gained support for additional
  axis, nan_policy, and keepdims arguments. scipy.stats also
  has several performance and accuracy improvements.

New features

scipy.integrate improvements

• The terminal attribute of scipy.integrate.solve_ivp events
  callables now additionally accepts integer values to specify a number
  of occurrences required for termination, rather than the previous restriction
  of only accepting a bool value to terminate on the first registered
  event.

scipy.io improvements

• scipy.io.wavfile.write has improved dtype input validation.

scipy.interpolate improvements

• The Modified Akima Interpolation has been added to
  interpolate.Akima1DInterpolator, available via the new method
  argument.
• New method BSpline.insert_knot inserts a knot into a BSpline instance.
  This routine is similar to the module-level scipy.interpolate.insert
  function, and works with the BSpline objects instead of tck tuples.
• RegularGridInterpolator gained the functionality to compute derivatives
  in place. For instance, RegularGridInterolator((x, y), values, method="cubic")(xi, nu=(1, 1)) evaluates the mixed second derivative,
  :math:\partial^2 / \partial x \partial y at xi.
• Performance characteristics of tensor-product spline methods of
  RegularGridInterpolator have been changed: evaluations should be
  significantly faster, while construction might be slower. If you experience
  issues with construction times, you may need to experiment with optional
  keyword arguments solver and solver_args. Previous behavior (fast
  construction, slow evaluations) can be obtained via "*_legacy" methods:
  method="cubic_legacy" is exactly equivalent to method="cubic" in
  previous releases. See gh-19633 for details.

scipy.signal improvements

• Many filter design functions now have improved input validation for the
  sampling frequency (fs).

scipy.sparse improvements

• coo_array now supports 1D shapes, and has additional 1D support for
  min, max, argmin, and argmax. The DOK format now has
  preliminary 1D support as well, though only supports simple integer indices
  at the time of writing.
• Experimental support has been added for pydata/sparse array inputs to
  scipy.sparse.csgraph.
• dok_array and dok_matrix now have proper implementations of
  fromkeys.
• csr and csc formats now have improved setdiag performance.

scipy.spatial improvements

• voronoi_plot_2d now draws Voronoi edges to infinity more clearly
  when the aspect ratio is skewed.

scipy.special improvements

• All Fortran code, namely, AMOS, specfun, and cdflib libraries
  that the majority of special functions depend on, is ported to Cython/C.
• The function factorialk now also supports faster, approximate
  calculation using exact=False.

scipy.stats improvements

• scipy.stats.rankdata and scipy.stats.wilcoxon have been vectorized,
  improving their performance and the performance of hypothesis tests that
  depend on them.
• stats.mannwhitneyu should now be faster due to a vectorized statistic
  calculation, improved caching, improved exploitation of symmetry, and a
  memory reduction. PermutationMethod support was also added.
• scipy.stats.mood now has nan_policy and keepdims support.
• scipy.stats.brunnermunzel now has axis and keepdims support.
• scipy.stats.friedmanchisquare, scipy.stats.shapiro,
  scipy.stats.normaltest, scipy.stats.skewtest,
  scipy.stats.kurtosistest, scipy.stats.f_oneway,
  scipy.stats.alexandergovern, scipy.stats.combine_pvalues, and
  scipy.stats.kstest have gained axis, nan_policy and
  keepdims support.
• scipy.stats.boxcox_normmax has gained a ymax parameter to allow user
  specification of the maximum value of the transformed data.
• scipy.stats.vonmises pdf method has been extended to support
  kappa=0. The fit method is also more performant due to the use of
  non-trivial bounds to solve for kappa.
• High order moment calculations for scipy.stats.powerlaw are now more
  accurate.
• The fit methods of scipy.stats.gamma (with method='mm') and
  scipy.stats.loglaplace are faster and more reliable.
• scipy.stats.goodness_of_fit now supports the use of a custom statistic
  provided by the user.
• scipy.stats.wilcoxon now supports PermutationMethod, enabling
  calculation of accurate p-values in the presence of ties and zeros.
• scipy.stats.monte_carlo_test now has improved robustness in the face of
  numerical noise.
• scipy.stats.wasserstein_distance_nd was introduced to compute the
  Wasserstein-1 distance between two N-D discrete distributions.

Deprecated features

• Complex dtypes in PchipInterpolator and Akima1DInterpolator have
  been deprecated and will raise an error in SciPy 1.15.0. If you are trying
  to use the real components of the passed array, use np.real on y.
• Non-integer values of n together with exact=True are deprecated for
  scipy.special.factorial.

Expired Deprecations

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

• scipy.signal.{lsim2,impulse2,step2} have been removed in favour of
  scipy.signal.{lsim,impulse,step}.
• Window functions can no longer be imported from the scipy.signal namespace and
  instead should be accessed through either scipy.signal.windows or
  scipy.signal.get_window.
• scipy.sparse no longer supports multi-Ellipsis indexing
• scipy.signal.{bspline,quadratic,cubic} have been removed in favour of alternatives
  in scipy.interpolate.
• scipy.linalg.tri{,u,l} have been removed in favour of numpy.tri{,u,l}.
• Non-integer arrays in scipy.special.factorial with exact=True now raise an
  error.
• Functions from NumPy's main namespace which were exposed in SciPy's main
  namespace, such as numpy.histogram exposed by scipy.histogram, have
  been removed from SciPy's main namespace. Please use the functions directly
  from numpy. This was originally performed for SciPy 1.12.0 however was missed from
  the release notes so is included here for completeness.

Backwards incompatible changes

Other changes

• The second argument of scipy.stats.moment has been renamed to order
  while maintaining backward compatibility.

Authors

• Name (commits)
• h-vetinari (50)
• acceptacross (1) +
• Petteri Aimonen (1) +
• Francis Allanah (2) +
• Jonas Kock am Brink (1) +
• anupriyakkumari (12) +
• Aman Atman (2) +
• Aaditya Bansal (1) +
• Christoph Baumgarten (2)
• Sebastian Berg (4)
• Nicolas Bloyet (2) +
• Matt Borland (1)
• Jonas Bosse (1) +
• Jake Bowhay (25)
• Matthew Brett (1)
• Dietrich Brunn (7)
• Evgeni Burovski (65)
• Matthias Bussonnier (4)
• Tim Butters (1) +
• Cale (1) +
• CJ Carey (5)
• Thomas A Caswell (1)
• Sean Cheah (44) +
• Lucas Colley (97)
• com3dian (1)
• Gianluca Detommaso (1) +
• Thomas Duvernay (1)
• DWesl (2)
• f380cedric (1) +
• fancidev (13) +
• Daniel Garcia (1) +
• Lukas Geiger (3)
• Ralf Gommers (147)
• Matt Haberland (81)
• Tessa van der Heiden (2) +
• Shawn Hsu (1) +
• inky (3) +
• Jannes Münchmeyer (2) +
• Aditya Vidyadhar Kamath (2) +
• Agriya Khetarpal (1) +
• Andrew Landau (1) +
• Eric Larson (7)
• Zhen-Qi Liu (1) +
• Christian Lorentzen (2)
• Adam Lugowski (4)
• m-maggi (6) +
• Chethin Manage (1) +
• Ben Mares (1)
• Chris Markiewicz (1) +
• Mateusz Sokół (3)
• Daniel McCloy (1) +
• Melissa Weber Mendonça (6)
• Josue Melka (1)
• Michał Górny (4)
• Juan Montesinos (1) +
• Juan F. Montesinos (1) +
• Takumasa Nakamura (1)
• Andrew Nelson (27)
• Praveer Nidamaluri (1)
• Yagiz Olmez (5) +
• Dimitri Papadopoulos Orfanos (1)
• Drew Parsons (1) +
• Tirth Patel (7)
• Pearu Peterson (1)
• Matti Picus (3)
• Rambaud Pierrick (1) +
• Ilhan Polat (30)
• Quentin Barthélemy (1)
• Tyler Reddy (117)
• Pamphile Roy (10)
• Atsushi Sakai (8)
• Daniel Schmitz (10)
• Dan Schult (17)
• Eli Schwartz (4)
• Stefanie Senger (1) +
• Scott Shambaugh (2)
• Kevin Sheppard (2)
• sidsrinivasan (4) +
• Samuel St-Jean (1)
• Albert Steppi (31)
• Adam J. Stewart (4)
• Kai Striega (3)
• Ruikang Sun (1) +
• Mike Taves (1)
• Nicolas Tessore (3)
• Benedict T Thekkel (1) +
• Will Tirone (4)
• Jacob Vanderplas (2)
• Christian Veenhuis (1)
• Isaac Virshup (2)
• Ben Wallace (1) +
• Xuefeng Xu (3)
• Xiao Yuan (5)
• Irwin Zaid (8)
• Elmar Zander (1) +
• Mathias Zechmeister (1) +

A total of 96 p...

SciPy 1.13.0rc1

SciPy 1.13.0 Release Notes

Note: SciPy 1.13.0 is not released yet!

SciPy 1.13.0 is the culmination of 3 months of hard work. This
out-of-band release aims to support NumPy 2.0.0, and is backwards
compatible to NumPy 1.22.4. The version of OpenBLAS used to build
the PyPI wheels has been increased to 0.3.26.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• Support for NumPy 2.0.0.
• Interactive examples have been added to the documentation, allowing users
  to run the examples locally on embedded Jupyterlite notebooks in their
  browser.
• Preliminary 1D array support for the COO and DOK sparse formats.
• Several scipy.stats functions have gained support for additional
  axis, nan_policy, and keepdims arguments. scipy.stats also
  has several performance and accuracy improvements.

New features

scipy.integrate improvements

• The terminal attribute of scipy.integrate.solve_ivp events
  callables now additionally accepts integer values to specify a number
  of occurrences required for termination, rather than the previous restriction
  of only accepting a bool value to terminate on the first registered
  event.

scipy.io improvements

• scipy.io.wavfile.write has improved dtype input validation.

scipy.interpolate improvements

• The Modified Akima Interpolation has been added to
  interpolate.Akima1DInterpolator, available via the new method
  argument.
• RegularGridInterpolator gained the functionality to compute derivatives
  in place. For instance, RegularGridInterolator((x, y), values, method="cubic")(xi, nu=(1, 1)) evaluates the mixed second derivative,
  :math:\partial^2 / \partial x \partial y at xi.
• Performance characteristics of tensor-product spline methods of
  RegularGridInterpolator have been changed: evaluations should be
  significantly faster, while construction might be slower. If you experience
  issues with construction times, you may need to experiment with optional
  keyword arguments solver and solver_args. Previous behavior (fast
  construction, slow evaluations) can be obtained via "*_legacy" methods:
  method="cubic_legacy" is exactly equivalent to method="cubic" in
  previous releases. See gh-19633 for details.

scipy.signal improvements

• Many filter design functions now have improved input validation for the
  sampling frequency (fs).

scipy.sparse improvements

• coo_array now supports 1D shapes, and has additional 1D support for
  min, max, argmin, and argmax. The DOK format now has
  preliminary 1D support as well, though only supports simple integer indices
  at the time of writing.
• Experimental support has been added for pydata/sparse array inputs to
  scipy.sparse.csgraph.
• dok_array and dok_matrix now have proper implementations of
  fromkeys.
• csr and csc formats now have improved setdiag performance.

scipy.spatial improvements

• voronoi_plot_2d now draws Voronoi edges to infinity more clearly
  when the aspect ratio is skewed.

scipy.special improvements

• All Fortran code, namely, AMOS, specfun, and cdflib libraries
  that the majority of special functions depend on, is ported to Cython/C.
• The function factorialk now also supports faster, approximate
  calculation using exact=False.

scipy.stats improvements

• scipy.stats.rankdata and scipy.stats.wilcoxon have been vectorized,
  improving their performance and the performance of hypothesis tests that
  depend on them.
• stats.mannwhitneyu should now be faster due to a vectorized statistic
  calculation, improved caching, improved exploitation of symmetry, and a
  memory reduction. PermutationMethod support was also added.
• scipy.stats.mood now has nan_policy and keepdims support.
• scipy.stats.brunnermunzel now has axis and keepdims support.
• scipy.stats.friedmanchisquare, scipy.stats.shapiro,
  scipy.stats.normaltest, scipy.stats.skewtest,
  scipy.stats.kurtosistest, scipy.stats.f_oneway,
  scipy.stats.alexandergovern, scipy.stats.combine_pvalues, and
  scipy.stats.kstest have gained axis, nan_policy and
  keepdims support.
• scipy.stats.boxcox_normmax has gained a ymax parameter to allow user
  specification of the maximum value of the transformed data.
• scipy.stats.vonmises pdf method has been extended to support
  kappa=0. The fit method is also more performant due to the use of
  non-trivial bounds to solve for kappa.
• High order moment calculations for scipy.stats.powerlaw are now more
  accurate.
• The fit methods of scipy.stats.gamma (with method='mm') and
  scipy.stats.loglaplace are faster and more reliable.
• scipy.stats.goodness_of_fit now supports the use of a custom statistic
  provided by the user.
• scipy.stats.wilcoxon now supports PermutationMethod, enabling
  calculation of accurate p-values in the presence of ties and zeros.
• scipy.stats.monte_carlo_test now has improved robustness in the face of
  numerical noise.
• scipy.stats.wasserstein_distance_nd was introduced to compute the
  Wasserstein-1 distance between two N-D discrete distributions.

Deprecated features

• Complex dtypes in PchipInterpolator and Akima1DInterpolator have
  been deprecated and will raise an error in SciPy 1.15.0. If you are trying
  to use the real components of the passed array, use np.real on y.

Backwards incompatible changes

Other changes

• The second argument of scipy.stats.moment has been renamed to order
  while maintaining backward compatibility.

Authors

• Name (commits)
• h-vetinari (50)
• acceptacross (1) +
• Petteri Aimonen (1) +
• Francis Allanah (2) +
• Jonas Kock am Brink (1) +
• anupriyakkumari (12) +
• Aman Atman (2) +
• Aaditya Bansal (1) +
• Christoph Baumgarten (2)
• Sebastian Berg (4)
• Nicolas Bloyet (2) +
• Matt Borland (1)
• Jonas Bosse (1) +
• Jake Bowhay (25)
• Matthew Brett (1)
• Dietrich Brunn (7)
• Evgeni Burovski (48)
• Matthias Bussonnier (4)
• Cale (1) +
• CJ Carey (4)
• Thomas A Caswell (1)
• Sean Cheah (44) +
• Lucas Colley (97)
• com3dian (1)
• Gianluca Detommaso (1) +
• Thomas Duvernay (1)
• DWesl (2)
• f380cedric (1) +
• fancidev (13) +
• Daniel Garcia (1) +
• Lukas Geiger (3)
• Ralf Gommers (139)
• Matt Haberland (79)
• Tessa van der Heiden (2) +
• inky (3) +
• Jannes Münchmeyer (2) +
• Aditya Vidyadhar Kamath (2) +
• Agriya Khetarpal (1) +
• Andrew Landau (1) +
• Eric Larson (7)
• Zhen-Qi Liu (1) +
• Adam Lugowski (4)
• m-maggi (6) +
• Chethin Manage (1) +
• Ben Mares (1)
• Chris Markiewicz (1) +
• Mateusz Sokół (3)
• Daniel McCloy (1) +
• Melissa Weber Mendonça (6)
• Josue Melka (1)
• Michał Górny (4)
• Juan Montesinos (1) +
• Juan F. Montesinos (1) +
• Takumasa Nakamura (1)
• Andrew Nelson (26)
• Praveer Nidamaluri (1)
• Yagiz Olmez (5) +
• Dimitri Papadopoulos Orfanos (1)
• Drew Parsons (1) +
• Tirth Patel (7)
• Matti Picus (3)
• Rambaud Pierrick (1) +
• Ilhan Polat (30)
• Quentin Barthélemy (1)
• Tyler Reddy (81)
• Pamphile Roy (10)
• Atsushi Sakai (4)
• Daniel Schmitz (10)
• Dan Schult (16)
• Eli Schwartz (4)
• Stefanie Senger (1) +
• Scott Shambaugh (2)
• Kevin Sheppard (2)
• sidsrinivasan (4) +
• Samuel St-Jean (1)
• Albert Steppi (30)
• Adam J. Stewart (4)
• Kai Striega (3)
• Ruikang Sun (1) +
• Mike Taves (1)
• Nicolas Tessore (3)
• Benedict T Thekkel (1) +
• Will Tirone (4)
• Jacob Vanderplas (2)
• Christian Veenhuis (1)
• Isaac Virshup (2)
• Ben Wallace (1) +
• Xuefeng Xu (3)
• Xiao Yuan (5)
• Irwin Zaid (6)
• Mathias Zechmeister (1) +

A total of 91 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 1.12.0

SciPy 1.12.0 Release Notes

SciPy 1.12.0 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
1.12.x branch, and on adding new features on the main branch.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• Experimental support for the array API standard has been added to part of
  scipy.special, and to all of scipy.fft and scipy.cluster. There are
  likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
  tensors, and other array API compatible libraries is appreciated. Use the
  SCIPY_ARRAY_API environment variable for testing.
• A new class, ShortTimeFFT, provides a more versatile implementation of the
  short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
  spectrogram. It utilizes an improved algorithm for calculating the ISTFT.
• Several new constructors have been added for sparse arrays, and many operations
  now additionally support sparse arrays, further facilitating the migration
  from sparse matrices.
• A large portion of the scipy.stats API now has improved support for handling
  NaN values, masked arrays, and more fine-grained shape-handling. The
  accuracy and performance of a number of stats methods have been improved,
  and a number of new statistical tests and distributions have been added.

New features

scipy.cluster improvements

• Experimental support added for the array API standard; PyTorch tensors,
  CuPy arrays and array API compatible array libraries are now accepted
  (GPU support is limited to functions with pure Python implementations).
  CPU arrays which can be converted to and from NumPy are supported
  module-wide and returned arrays will match the input type.
  This behaviour is enabled by setting the SCIPY_ARRAY_API environment
  variable before importing scipy. This experimental support is still
  under development and likely to contain bugs - testing is very welcome.

scipy.fft improvements

• Experimental support added for the array API standard; functions which are
  part of the fft array API standard extension module, as well as the
  Fast Hankel Transforms and the basic FFTs which are not in the extension
  module, now accept PyTorch tensors, CuPy arrays and array API compatible
  array libraries. CPU arrays which can be converted to and from NumPy arrays
  are supported module-wide and returned arrays will match the input type.
  This behaviour is enabled by setting the SCIPY_ARRAY_API environment
  variable before importing scipy. This experimental support is still under
  development and likely to contain bugs - testing is very welcome.

scipy.integrate improvements

• Added scipy.integrate.cumulative_simpson for cumulative quadrature
  from sampled data using Simpson's 1/3 rule.

scipy.interpolate improvements

• New class NdBSpline represents tensor-product splines in N dimensions.
  This class only knows how to evaluate a tensor product given coefficients
  and knot vectors. This way it generalizes BSpline for 1D data to N-D, and
  parallels NdPPoly (which represents N-D tensor product polynomials).
  Evaluations exploit the localized nature of b-splines.
• NearestNDInterpolator.__call__ accepts **query_options, which are
  passed through to the KDTree.query call to find nearest neighbors. This
  allows, for instance, to limit the neighbor search distance and parallelize
  the query using the workers keyword.
• BarycentricInterpolator now allows computing the derivatives.
• It is now possible to change interpolation values in an existing
  CloughTocher2DInterpolator instance, while also saving the barycentric
  coordinates of interpolation points.

scipy.linalg improvements

• Access to new low-level LAPACK functions is provided via dtgsyl and
  stgsyl.

scipy.optimize improvements

• scipy.optimize.isotonic_regression has been added to allow nonparametric isotonic
  regression.
• scipy.optimize.nnls is rewritten in Python and now implements the so-called
  fnnls or fast nnls, making it more efficient for high-dimensional problems.
• The result object of scipy.optimize.root and scipy.optimize.root_scalar
  now reports the method used.
• The callback method of scipy.optimize.differential_evolution can now be
  passed more detailed information via the intermediate_results keyword
  parameter. Also, the evolution strategy now accepts a callable for
  additional customization. The performance of differential_evolution has
  also been improved.
• scipy.optimize.minimize method Newton-CG now supports functions that
  return sparse Hessian matrices/arrays for the hess parameter and is slightly
  more efficient.
• scipy.optimize.minimize method BFGS now accepts an initial estimate for the
  inverse of the Hessian, which allows for more efficient workflows in some
  circumstances. The new parameter is hess_inv0.
• scipy.optimize.minimize methods CG, Newton-CG, and BFGS now accept
  parameters c1 and c2, allowing specification of the Armijo and curvature rule
  parameters, respectively.
• scipy.optimize.curve_fit performance has improved due to more efficient memoization
  of the callable function.

scipy.signal improvements

• freqz, freqz_zpk, and group_delay are now more accurate
  when fs has a default value.
• The new class ShortTimeFFT provides a more versatile implementation of the
  short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
  spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
  dual windows and provides more fine-grained control of the parametrization especially
  in regard to scaling and phase-shift. Functionality was implemented to ease
  working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
  providing algorithmic details. The functions stft, istft and spectrogram
  have been marked as legacy.

scipy.sparse improvements

• sparse.linalg iterative solvers sparse.linalg.cg,
  sparse.linalg.cgs, sparse.linalg.bicg, sparse.linalg.bicgstab,
  sparse.linalg.gmres, and sparse.linalg.qmr are rewritten in Python.
• Updated vendored SuperLU version to 6.0.1, along with a few additional
  fixes.
• Sparse arrays have gained additional constructors: eye_array,
  random_array, block_array, and identity. kron and kronsum
  have been adjusted to additionally support operation on sparse arrays.
• Sparse matrices now support a transpose with axes=(1, 0), to mirror
  the .T method.
• LaplacianNd now allows selection of the largest subset of eigenvalues,
  and additionally now supports retrieval of the corresponding eigenvectors.
  The performance of LaplacianNd has also been improved.
• The performance of dok_matrix and dok_array has been improved,
  and their inheritance behavior should be more robust.
• hstack, vstack, and block_diag now work with sparse arrays, and
  preserve the input sparse type.
• A new function, scipy.sparse.linalg.matrix_power, has been added, allowing
  for exponentiation of sparse arrays.

scipy.spatial improvements

• Two new methods were implemented for spatial.transform.Rotation:
  __pow__ to raise a rotation to integer or fractional power and
  approx_equal to check if two rotations are approximately equal.
• The method Rotation.align_vectors was extended to solve a constrained
  alignment problem where two vectors are required to be aligned precisely.
  Also when given a single pair of vectors, the algorithm now returns the
  rotation with minimal magnitude, which can be considered as a minor
  backward incompatible change.
• A new representation for spatial.transform.Rotation called Davenport
  angles is available through from_davenport and as_davenport methods.
• Performance improvements have been added to distance.hamming and
  distance.correlation.
• Improved performance of SphericalVoronoi sort_vertices_of_regions
  and two dimensional area calculations.

scipy.special improvements

• Added scipy.special.stirling2 for computation of Stirling numbers of the
  second kind. Both exact calculation and an asymptotic approximation
  (the default) are supported via exact=True and exact=False (the
  default) respectively.
• Added scipy.special.betaincc for computation of the complementary
  incomplete Beta function and scipy.special.betainccinv for computation of
  its inverse.
• Improved precision of scipy.special.betainc and scipy.special.betaincinv.
• Experimental support added for alternative backends: functions
  scipy.special.log_ndtr, scipy.special.ndtr, scipy.special.ndtri,
  scipy.special.erf, `scipy.speci...

SciPy 1.12.0rc2

SciPy 1.12.0 Release Notes

Note: SciPy 1.12.0 is not released yet!

SciPy 1.12.0 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
1.12.x branch, and on adding new features on the main branch.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• Experimental support for the array API standard has been added to part of
  scipy.special, and to all of scipy.fft and scipy.cluster. There are
  likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
  tensors, and other array API compatible libraries is appreciated. Use the
  SCIPY_ARRAY_API environment variable for testing.
• A new class, ShortTimeFFT, provides a more versatile implementation of the
  short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
  spectrogram. It utilizes an improved algorithm for calculating the ISTFT.
• Several new constructors have been added for sparse arrays, and many operations
  now additionally support sparse arrays, further facilitating the migration
  from sparse matrices.
• A large portion of the scipy.stats API now has improved support for handling
  NaN values, masked arrays, and more fine-grained shape-handling. The
  accuracy and performance of a number of stats methods have been improved,
  and a number of new statistical tests and distributions have been added.

New features

scipy.cluster improvements

• Experimental support added for the array API standard; PyTorch tensors,
  CuPy arrays and array API compatible array libraries are now accepted
  (GPU support is limited to functions with pure Python implementations).
  CPU arrays which can be converted to and from NumPy are supported
  module-wide and returned arrays will match the input type.
  This behaviour is enabled by setting the SCIPY_ARRAY_API environment
  variable before importing scipy. This experimental support is still
  under development and likely to contain bugs - testing is very welcome.

scipy.fft improvements

• Experimental support added for the array API standard; functions which are
  part of the fft array API standard extension module, as well as the
  Fast Hankel Transforms and the basic FFTs which are not in the extension
  module, now accept PyTorch tensors, CuPy arrays and array API compatible
  array libraries. CPU arrays which can be converted to and from NumPy arrays
  are supported module-wide and returned arrays will match the input type.
  This behaviour is enabled by setting the SCIPY_ARRAY_API environment
  variable before importing scipy. This experimental support is still under
  development and likely to contain bugs - testing is very welcome.

scipy.integrate improvements

• Added scipy.integrate.cumulative_simpson for cumulative quadrature
  from sampled data using Simpson's 1/3 rule.

scipy.interpolate improvements

• New class NdBSpline represents tensor-product splines in N dimensions.
  This class only knows how to evaluate a tensor product given coefficients
  and knot vectors. This way it generalizes BSpline for 1D data to N-D, and
  parallels NdPPoly (which represents N-D tensor product polynomials).
  Evaluations exploit the localized nature of b-splines.
• NearestNDInterpolator.__call__ accepts **query_options, which are
  passed through to the KDTree.query call to find nearest neighbors. This
  allows, for instance, to limit the neighbor search distance and parallelize
  the query using the workers keyword.
• BarycentricInterpolator now allows computing the derivatives.
• It is now possible to change interpolation values in an existing
  CloughTocher2DInterpolator instance, while also saving the barycentric
  coordinates of interpolation points.

scipy.linalg improvements

• Access to new low-level LAPACK functions is provided via dtgsyl and
  stgsyl.

scipy.ndimage improvements

scipy.optimize improvements

• scipy.optimize.nnls is rewritten in Python and now implements the so-called
  fnnls or fast nnls.
• The result object of scipy.optimize.root and scipy.optimize.root_scalar
  now reports the method used.
• The callback method of scipy.optimize.differential_evolution can now be
  passed more detailed information via the intermediate_results keyword
  parameter. Also, the evolution strategy now accepts a callable for
  additional customization. The performance of differential_evolution has
  also been improved.
• minimize method Newton-CG has been made slightly more efficient.
• minimize method BFGS now accepts an initial estimate for the inverse
  of the Hessian, which allows for more efficient workflows in some
  circumstances. The new parameter is hess_inv0.
• minimize methods CG, Newton-CG, and BFGS now accept parameters
  c1 and c2, allowing specification of the Armijo and curvature rule
  parameters, respectively.
• curve_fit performance has improved due to more efficient memoization
  of the callable function.
• isotonic_regression has been added to allow nonparametric isotonic
  regression.

scipy.signal improvements

• freqz, freqz_zpk, and group_delay are now more accurate
  when fs has a default value.
• The new class ShortTimeFFT provides a more versatile implementation of the
  short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
  spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
  dual windows and provides more fine-grained control of the parametrization especially
  in regard to scaling and phase-shift. Functionality was implemented to ease
  working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
  providing algorithmic details. The functions stft, istft and spectrogram
  have been marked as legacy.

scipy.sparse improvements

• sparse.linalg iterative solvers sparse.linalg.cg,
  sparse.linalg.cgs, sparse.linalg.bicg, sparse.linalg.bicgstab,
  sparse.linalg.gmres, and sparse.linalg.qmr are rewritten in Python.
• Updated vendored SuperLU version to 6.0.1, along with a few additional
  fixes.
• Sparse arrays have gained additional constructors: eye_array,
  random_array, block_array, and identity. kron and kronsum
  have been adjusted to additionally support operation on sparse arrays.
• Sparse matrices now support a transpose with axes=(1, 0), to mirror
  the .T method.
• LaplacianNd now allows selection of the largest subset of eigenvalues,
  and additionally now supports retrieval of the corresponding eigenvectors.
  The performance of LaplacianNd has also been improved.
• The performance of dok_matrix and dok_array has been improved,
  and their inheritance behavior should be more robust.
• hstack, vstack, and block_diag now work with sparse arrays, and
  preserve the input sparse type.
• A new function, scipy.sparse.linalg.matrix_power, has been added, allowing
  for exponentiation of sparse arrays.

scipy.spatial improvements

• Two new methods were implemented for spatial.transform.Rotation:
  __pow__ to raise a rotation to integer or fractional power and
  approx_equal to check if two rotations are approximately equal.
• The method Rotation.align_vectors was extended to solve a constrained
  alignment problem where two vectors are required to be aligned precisely.
  Also when given a single pair of vectors, the algorithm now returns the
  rotation with minimal magnitude, which can be considered as a minor
  backward incompatible change.
• A new representation for spatial.transform.Rotation called Davenport
  angles is available through from_davenport and as_davenport methods.
• Performance improvements have been added to distance.hamming and
  distance.correlation.
• Improved performance of SphericalVoronoi sort_vertices_of_regions
  and two dimensional area calculations.

scipy.special improvements

• Added scipy.special.stirling2 for computation of Stirling numbers of the
  second kind. Both exact calculation and an asymptotic approximation
  (the default) are supported via exact=True and exact=False (the
  default) respectively.
• Added scipy.special.betaincc for computation of the complementary incomplete Beta function and scipy.special.betainccinv for computation of its inverse.
• Improved precision of scipy.special.betainc and scipy.special.betaincinv
• Experimental support added for alternative backends: 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.g...

SciPy 1.12.0rc1

SciPy 1.12.0 Release Notes

Note: SciPy 1.12.0 is not released yet!

SciPy 1.12.0 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
1.12.x branch, and on adding new features on the main branch.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• Experimental support for the array API standard has been added to part of
  scipy.special, and to all of scipy.fft and scipy.cluster. There are
  likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
  tensors, and other array API compatible libraries is appreciated. Use the
  SCIPY_ARRAY_API environment variable for testing.
• A new class, ShortTimeFFT, provides a more versatile implementation of the
  short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
  spectrogram. It utilizes an improved algorithm for calculating the ISTFT.
• Several new constructors have been added for sparse arrays, and many operations
  now additionally support sparse arrays, further facilitating the migration
  from sparse matrices.
• A large portion of the scipy.stats API now has improved support for handling
  NaN values, masked arrays, and more fine-grained shape-handling. The
  accuracy and performance of a number of stats methods have been improved,
  and a number of new statistical tests and distributions have been added.

New features

scipy.cluster improvements

• Experimental support added for the array API standard; PyTorch tensors,
  CuPy arrays and array API compatible array libraries are now accepted
  (GPU support is limited to functions with pure Python implementations).
  CPU arrays which can be converted to and from NumPy are supported
  module-wide and returned arrays will match the input type.
  This behaviour is enabled by setting the SCIPY_ARRAY_API environment
  variable before importing scipy. This experimental support is still
  under development and likely to contain bugs - testing is very welcome.

scipy.fft improvements

• Experimental support added for the array API standard; functions which are
  part of the fft array API standard extension module, as well as the
  Fast Hankel Transforms and the basic FFTs which are not in the extension
  module, now accept PyTorch tensors, CuPy arrays and array API compatible
  array libraries. CPU arrays which can be converted to and from NumPy arrays
  are supported module-wide and returned arrays will match the input type.
  This behaviour is enabled by setting the SCIPY_ARRAY_API environment
  variable before importing scipy. This experimental support is still under
  development and likely to contain bugs - testing is very welcome.

scipy.integrate improvements

• Added scipy.integrate.cumulative_simpson for cumulative quadrature
  from sampled data using Simpson's 1/3 rule.

scipy.interpolate improvements

• New class NdBSpline represents tensor-product splines in N dimensions.
  This class only knows how to evaluate a tensor product given coefficients
  and knot vectors. This way it generalizes BSpline for 1D data to N-D, and
  parallels NdPPoly (which represents N-D tensor product polynomials).
  Evaluations exploit the localized nature of b-splines.
• NearestNDInterpolator.__call__ accepts **query_options, which are
  passed through to the KDTree.query call to find nearest neighbors. This
  allows, for instance, to limit the neighbor search distance and parallelize
  the query using the workers keyword.
• BarycentricInterpolator now allows computing the derivatives.
• It is now possible to change interpolation values in an existing
  CloughTocher2DInterpolator instance, while also saving the barycentric
  coordinates of interpolation points.

scipy.linalg improvements

• Access to new low-level LAPACK functions is provided via dtgsyl and
  stgsyl.

scipy.ndimage improvements

scipy.optimize improvements

• scipy.optimize.nnls is rewritten in Python and now implements the so-called
  fnnls or fast nnls.
• The result object of scipy.optimize.root and scipy.optimize.root_scalar
  now reports the method used.
• The callback method of scipy.optimize.differential_evolution can now be
  passed more detailed information via the intermediate_results keyword
  parameter. Also, the evolution strategy now accepts a callable for
  additional customization. The performance of differential_evolution has
  also been improved.
• minimize method Newton-CG has been made slightly more efficient.
• minimize method BFGS now accepts an initial estimate for the inverse
  of the Hessian, which allows for more efficient workflows in some
  circumstances. The new parameter is hess_inv0.
• minimize methods CG, Newton-CG, and BFGS now accept parameters
  c1 and c2, allowing specification of the Armijo and curvature rule
  parameters, respectively.
• curve_fit performance has improved due to more efficient memoization
  of the callable function.
• isotonic_regression has been added to allow nonparametric isotonic
  regression.

scipy.signal improvements

• freqz, freqz_zpk, and group_delay are now more accurate
  when fs has a default value.
• The new class ShortTimeFFT provides a more versatile implementation of the
  short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
  spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
  dual windows and provides more fine-grained control of the parametrization especially
  in regard to scaling and phase-shift. Functionality was implemented to ease
  working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
  providing algorithmic details. The functions stft, istft and spectrogram
  have been marked as legacy.

scipy.sparse improvements

• sparse.linalg iterative solvers sparse.linalg.cg,
  sparse.linalg.cgs, sparse.linalg.bicg, sparse.linalg.bicgstab,
  sparse.linalg.gmres, and sparse.linalg.qmr are rewritten in Python.
• Updated vendored SuperLU version to 6.0.1, along with a few additional
  fixes.
• Sparse arrays have gained additional constructors: eye_array,
  random_array, block_array, and identity. kron and kronsum
  have been adjusted to additionally support operation on sparse arrays.
• Sparse matrices now support a transpose with axes=(1, 0), to mirror
  the .T method.
• LaplacianNd now allows selection of the largest subset of eigenvalues,
  and additionally now supports retrieval of the corresponding eigenvectors.
  The performance of LaplacianNd has also been improved.
• The performance of dok_matrix and dok_array has been improved,
  and their inheritance behavior should be more robust.
• hstack, vstack, and block_diag now work with sparse arrays, and
  preserve the input sparse type.
• A new function, scipy.sparse.linalg.matrix_power, has been added, allowing
  for exponentiation of sparse arrays.

scipy.spatial improvements

• Two new methods were implemented for spatial.transform.Rotation:
  __pow__ to raise a rotation to integer or fractional power and
  approx_equal to check if two rotations are approximately equal.
• The method Rotation.align_vectors was extended to solve a constrained
  alignment problem where two vectors are required to be aligned precisely.
  Also when given a single pair of vectors, the algorithm now returns the
  rotation with minimal magnitude, which can be considered as a minor
  backward incompatible change.
• A new representation for spatial.transform.Rotation called Davenport
  angles is available through from_davenport and as_davenport methods.
• Performance improvements have been added to distance.hamming and
  distance.correlation.
• Improved performance of SphericalVoronoi sort_vertices_of_regions
  and two dimensional area calculations.

scipy.special improvements

• Added scipy.special.stirling2 for computation of Stirling numbers of the
  second kind. Both exact calculation and an asymptotic approximation
  (the default) are supported via exact=True and exact=False (the
  default) respectively.
• Added scipy.special.betaincc for computation of the complementary incomplete Beta function and scipy.special.betainccinv for computation of its inverse.
• Improved precision of scipy.special.betainc and scipy.special.betaincinv
• Experimental support added for alternative backends: 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 1.11.4

SciPy 1.11.4 Release Notes

SciPy 1.11.4 is a bug-fix release with no new features
compared to 1.11.3.

Authors

• Name (commits)
• Jake Bowhay (2)
• Ralf Gommers (4)
• Julien Jerphanion (2)
• Nikolay Mayorov (2)
• Melissa Weber Mendonça (1)
• Tirth Patel (1)
• Tyler Reddy (22)
• Dan Schult (3)
• Nicolas Vetsch (1) +

A total of 9 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 1.11.3

SciPy 1.11.3 Release Notes

SciPy 1.11.3 is a bug-fix release with no new features
compared to 1.11.2.

Authors

• Name (commits)
• Jake Bowhay (2)
• CJ Carey (1)
• Colin Carroll (1) +
• Anirudh Dagar (2)
• drestebon (1) +
• Ralf Gommers (5)
• Matt Haberland (2)
• Julien Jerphanion (1)
• Uwe L. Korn (1) +
• Ellie Litwack (2)
• Andrew Nelson (5)
• Bharat Raghunathan (1)
• Tyler Reddy (37)
• Søren Fuglede Jørgensen (2)
• Hielke Walinga (1) +
• Warren Weckesser (1)
• Bernhard M. Wiedemann (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 1.11.2

SciPy 1.11.2 Release Notes

SciPy 1.11.2 is a bug-fix release with no new features
compared to 1.11.1. Python 3.12 and musllinux wheels
are provided with this release.

Authors

• Name (commits)
• Evgeni Burovski (2)
• CJ Carey (3)
• Dieter Werthmüller (1)
• elbarso (1) +
• Ralf Gommers (2)
• Matt Haberland (1)
• jokasimr (1) +
• Thilo Leitzbach (1) +
• LemonBoy (1) +
• Ellie Litwack (2) +
• Sturla Molden (1)
• Andrew Nelson (5)
• Tyler Reddy (39)
• Daniel Schmitz (6)
• Dan Schult (2)
• Albert Steppi (1)
• Matus Valo (1)
• Stefan van der Walt (1)

A total of 18 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 1.11.1

SciPy 1.11.1 Release Notes

SciPy 1.11.1 is a bug-fix release with no new features
compared to 1.11.0. In particular, a licensing issue
discovered after the release of 1.11.0 has been addressed.

Authors

• Name (commits)
• h-vetinari (1)
• Robert Kern (1)
• Ilhan Polat (4)
• Tyler Reddy (8)

A total of 4 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 1.11.0

SciPy 1.11.0 Release Notes

SciPy 1.11.0 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
1.11.x branch, and on adding new features on the main branch.

This release requires Python 3.9+ and NumPy 1.21.6 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• Several scipy.sparse array API improvements, including sparse.sparray, a new
  public base class distinct from the older sparse.spmatrix class,
  proper 64-bit index support, and numerous deprecations paving the way to a
  modern sparse array experience.
• scipy.stats added tools for survival analysis, multiple hypothesis testing,
  sensitivity analysis, and working with censored data.
• A new function was added for quasi-Monte Carlo integration, and linear
  algebra functions det and lu now accept nD-arrays.
• An axes argument was added broadly to ndimage functions, facilitating
  analysis of stacked image data.

New features

scipy.integrate improvements

• Added scipy.integrate.qmc_quad for quasi-Monte Carlo integration.
• For an even number of points, scipy.integrate.simpson now calculates
  a parabolic segment over the last three points which gives improved
  accuracy over the previous implementation.

scipy.cluster improvements

• disjoint_set has a new method subset_size for providing the size
  of a particular subset.

scipy.constants improvements

• The quetta, ronna, ronto, and quecto SI prefixes were added.

scipy.linalg improvements

• scipy.linalg.det is improved and now accepts nD-arrays.
• scipy.linalg.lu is improved and now accepts nD-arrays. With the new
  p_indices switch the output permutation argument can be 1D (n,)
  permutation index instead of the full (n, n) array.

scipy.ndimage improvements

• axes argument was added to rank_filter, percentile_filter,
  median_filter, uniform_filter, minimum_filter,
  maximum_filter, and gaussian_filter, which can be useful for
  processing stacks of image data.

scipy.optimize improvements

• scipy.optimize.linprog now passes unrecognized options directly to HiGHS.
• scipy.optimize.root_scalar now uses Newton's method to be used without
  providing fprime and the secant method to be used without a second
  guess.
• scipy.optimize.lsq_linear now accepts bounds arguments of type
  scipy.optimize.Bounds.
• scipy.optimize.minimize method='cobyla' now supports simple bound
  constraints.
• Users can opt into a new callback interface for most methods of
  scipy.optimize.minimize: If the provided callback callable accepts
  a single keyword argument, intermediate_result, scipy.optimize.minimize
  now passes both the current solution and the optimal value of the objective
  function to the callback as an instance of scipy.optimize.OptimizeResult.
  It also allows the user to terminate optimization by raising a
  StopIteration exception from the callback function.
  scipy.optimize.minimize will return normally, and the latest solution
  information is provided in the result object.
• scipy.optimize.curve_fit now supports an optional nan_policy argument.
• scipy.optimize.shgo now has parallelization with the workers argument,
  symmetry arguments that can improve performance, class-based design to
  improve usability, and generally improved performance.

scipy.signal improvements

• istft has an improved warning message when the NOLA condition fails.

scipy.sparse improvements

• A new public base class scipy.sparse.sparray was introduced, allowing further
  extension of the sparse array API (such as the support for 1-dimensional
  sparse arrays) without breaking backwards compatibility.
  isinstance(x, scipy.sparse.sparray) to select the new sparse array classes,
  while isinstance(x, scipy.sparse.spmatrix) selects only the old sparse
  matrix classes.
• Division of sparse arrays by a dense array now returns sparse arrays.
• scipy.sparse.isspmatrix now only returns True for the sparse matrices instances.
  scipy.sparse.issparse now has to be used instead to check for instances of sparse
  arrays or instances of sparse matrices.
• Sparse arrays constructed with int64 indices will no longer automatically
  downcast to int32.
• The argmin and argmax methods now return the correct result when explicit
  zeros are present.

scipy.sparse.linalg improvements

• dividing LinearOperator by a number now returns a
  _ScaledLinearOperator
• LinearOperator now supports right multiplication by arrays
• lobpcg should be more efficient following removal of an extraneous
  QR decomposition.

scipy.spatial improvements

• Usage of new C++ backend for additional distance metrics, the majority of
  which will see substantial performance improvements, though a few minor
  regressions are known. These are focused on distances between boolean
  arrays.

scipy.special improvements

• The factorial functions factorial, factorial2 and factorialk
  were made consistent in their behavior (in terms of dimensionality,
  errors etc.). Additionally, factorial2 can now handle arrays with
  exact=True, and factorialk can handle arrays.

scipy.stats improvements

New Features

• scipy.stats.sobol_indices, a method to compute Sobol' sensitivity indices.
• scipy.stats.dunnett, which performs Dunnett's test of the means of multiple
  experimental groups against the mean of a control group.
• scipy.stats.ecdf for computing the empirical CDF and complementary
  CDF (survival function / SF) from uncensored or right-censored data. This
  function is also useful for survival analysis / Kaplan-Meier estimation.
• scipy.stats.logrank to compare survival functions underlying samples.
• scipy.stats.false_discovery_control for adjusting p-values to control the
  false discovery rate of multiple hypothesis tests using the
  Benjamini-Hochberg or Benjamini-Yekutieli procedures.
• scipy.stats.CensoredData to represent censored data. It can be used as
  input to the fit method of univariate distributions and to the new
  ecdf function.
• Filliben's goodness of fit test as method='Filliben' of
  scipy.stats.goodness_of_fit.
• scipy.stats.ttest_ind has a new method, confidence_interval for
  computing a confidence interval of the difference between means.
• scipy.stats.MonteCarloMethod, scipy.stats.PermutationMethod, and
  scipy.stats.BootstrapMethod are new classes to configure resampling and/or
  Monte Carlo versions of hypothesis tests. They can currently be used with
  scipy.stats.pearsonr.

Statistical Distributions

• Added the von-Mises Fisher distribution as scipy.stats.vonmises_fisher.
  This distribution is the most common analogue of the normal distribution
  on the unit sphere.

• Added the relativistic Breit-Wigner distribution as
  scipy.stats.rel_breitwigner.
  It is used in high energy physics to model resonances.

• Added the Dirichlet multinomial distribution as
  scipy.stats.dirichlet_multinomial.

• Improved the speed and precision of several univariate statistical
  distributions.

  • scipy.stats.anglit sf
  • scipy.stats.beta entropy
  • scipy.stats.betaprime cdf, sf, ppf
  • scipy.stats.chi entropy
  • scipy.stats.chi2 entropy
  • scipy.stats.dgamma entropy, cdf, sf, ppf, and isf
  • scipy.stats.dweibull entropy, sf, and isf
  • scipy.stats.exponweib sf and isf
  • scipy.stats.f entropy
  • scipy.stats.foldcauchy sf
  • scipy.stats.foldnorm cdf and sf
  • scipy.stats.gamma entropy
  • scipy.stats.genexpon ppf, isf, rvs
  • scipy.stats.gengamma entropy
  • scipy.stats.geom entropy
  • scipy.stats.genlogistic entropy, logcdf, sf, ppf,
    and isf
  • scipy.stats.genhyperbolic cdf and sf
  • scipy.stats.gibrat sf and isf
  • scipy.stats.gompertz entropy, sf. and isf
  • scipy.stats.halflogistic sf, and isf
  • scipy.stats.halfcauchy sf and isf
  • scipy.stats.halfnorm cdf, sf, and isf
  • scipy.stats.invgamma entropy
  • scipy.stats.invgauss entropy
  • scipy.stats.johnsonsb pdf, cdf, sf, ppf, and isf
  • scipy.stats.johnsonsu pdf, sf, isf, and stats
  • scipy.stats.lognorm fit
  • scipy.stats.loguniform entropy, logpdf, pdf, cdf, ppf,
    and stats
  • scipy.stats.maxwell sf and isf
  • scipy.stats.nakagami entropy
  • scipy.stats.powerlaw sf
  • `scipy.stats.pow...