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.powerlognorm logpdf, logsf, sf, and isf
  • scipy.stats.powernorm sf and isf
  • scipy.stats.t entropy, logpdf, and pdf
  • scipy.stats.truncexpon sf, and isf
  • scipy.stats.truncnorm entropy
  • scipy.stats.truncpareto fit
  • scipy.stats.vonmises fit

• scipy.stats.multivariate_t now has cdf and entropy methods.

• scipy.stats.multivariate_normal, scipy.stats.matrix_normal, and
  scipy.stats.invwishart now have an entropy method.

Other Improvements

• scipy.stats.monte_carlo_test now supports multi-sample statistics.
• scipy.stats.bootstrap can now produce one-sided confidence intervals.
• scipy.stats.rankdata performance was improved for method=ordinal and
  method=dense.
• scipy.stats.moment now supports non-central moment calculation.
• scipy.stats.anderson now supports the weibull_min distribution.
• scipy.stats.sem and scipy.stats.iqr now support axis, nan_policy,
  and masked array input.

Deprecated features

• Multi-Ellipsis sparse matrix indexing has been deprecated and will
  be removed in SciPy 1.13.
• Several methods were deprecated for sparse arrays: asfptype, getrow,
  getcol, get_shape, getmaxprint, set_shape,
  getnnz, and getformat. Additionally, the .A and .H
  attributes were deprecated. Sparse matrix types are not affected.
• The scipy.linalg functions tri, triu & tril are deprecated and
  will be removed in SciPy 1.13. Users are recommended to use the NumPy
  versions of these functions with identical names.
• The scipy.signal functions bspline, quadratic & cubic are
  deprecated and will be removed in SciPy 1.13. Users are recommended to use
  scipy.interpolate.BSpline instead.
• The even keyword of scipy.integrate.simpson is deprecated and will be
  removed in SciPy 1.13.0. Users should leave this as the default as this
  gives improved accuracy compared to the other methods.
• Using exact=True when passing integers in a float array to factorial
  is deprecated and will be removed in SciPy 1.13.0.
• float128 and object dtypes are deprecated for scipy.signal.medfilt and
  scipy.signal.order_filter
• The functions scipy.signal.{lsim2, impulse2, step2} had long been
  deprecated in documentation only. They now raise a DeprecationWarning and
  will be removed in SciPy 1.13.0.
• Importing window functions directly from scipy.window has been soft
  deprecated since SciPy 1.1.0. They now raise a DeprecationWarning and
  will be removed in SciPy 1.13.0. Users should instead import them from
  scipy.signal.window or use the convenience function
  scipy.signal.get_window.

Backwards incompatible changes

• The default for the legacy keyword of scipy.special.comb has changed
  from True to False, as announced since its introduction.

Expired Deprecations

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

• The n keyword has been removed from scipy.stats.moment.
• The alpha keyword has been removed from scipy.stats.interval.
• The misspelt gilbrat distribution has been removed (use
  scipy.stats.gibrat).
• The deprecated spelling of the kulsinski distance metric has been
  removed (use scipy.spatial.distance.kulczynski1).
• The vertices keyword of scipy.spatial.Delauney.qhull has been removed
  (use simplices).
• The residual property of scipy.sparse.csgraph.maximum_flow has been
  removed (use flow).
• The extradoc keyword of scipy.stats.rv_continuous,
  scipy.stats.rv_discrete and scipy.stats.rv_sample has been removed.
• The sym_pos keyword of scipy.linalg.solve has been removed.
• The scipy.optimize.minimize function now raises an error for x0 with
  x0.ndim > 1.
• In scipy.stats.mode, the default value of keepdims is now False,
  and support for non-numeric input has been removed.
• The function scipy.signal.lsim does not support non-uniform time steps
  anymore.

Other changes

• Rewrote the source build docs and restructured the contributor guide.
• Improved support for cross-compiling with meson build system.
• MyST-NB notebook infrastructure has been added to our documentation.

Authors

• h-vetinari (69)
• Oriol Abril-Pla (1) +
• Tom Adamczewski (1) +
• Anton Akhmerov (13)
• Andrey Akinshin (1) +
• alice (1) +
• Oren Amsalem (1)
• Ross Barnowski (13)
• Christoph Baumgarten (2)
• Dawson Beatty (1) +
• Doron Behar (1) +
• Peter Bell (1)
• John Belmonte (1) +
• boeleman (1) +
• Jack Borchanian (1) +
• Matt Borland (3) +
• Jake Bowhay (41)
• Larry Bradley (1) +
• Sienna Brent (1) +
• Matthew Brett (1)
• Evgeni Burovski (39)
• Matthias Bussonnier (2)
• Maria Cann (1) +
• Alfredo Carella (1) +
• CJ Carey (34)
• Hood Chatham (2)
• Anirudh Dagar (3)
• Alberto Defendi (1) +
• Pol del Aguila (1) +
• Hans Dembinski (1)
• Dennis (1) +
• Vinayak Dev (1) +
• Thomas Duvernay (1)
• DWesl (4)
• Stefan Endres (66)
• Evandro (1) +
• Tom Eversdijk (2) +
• Isuru Fernando (1)
• Franz Forstmayr (4)
• Joseph Fox-Rabinovitz (1)
• Stefano Frazzetto (1) +
• Neil Girdhar (1)
• Caden Gobat (1) +
• Ralf Gommers (153)
• GonVas (1) +
• Marco Gorelli (1)
• Brett Graham (2) +
• Matt Haberland (388)
• harshvardhan2707 (1) +
• Alex Herbert (1) +
• Guillaume Horel (1)
• Geert-Jan Huizing (1) +
• Jakob Jakobson (2)
• Julien Jerphanion (10)
• jyuv (2)
• Rajarshi Karmakar (1) +
• Ganesh Kathiresan (3) +
• Robert Kern (4)
• Andrew Knyazev (4)
• Sergey Koposov (1)
• Rishi Kulkarni (2) +
• Eric Larson (1)
• Zoufiné Lauer-Bare (2) +
• Antony Lee (3)
• Gregory R. Lee (8)
• Guillaume Lemaitre (2) +
• lilinjie (2) +
• Yannis Linardos (1) +
• Christian Lorentzen (5)
• Loïc Estève (1)
• Adam Lugowski (1) +
• Charlie Marsh (2) +
• Boris Martin (1) +
• Nicholas McKibben (11)
• Melissa Weber Mendonça (58)
• Michał Górny (1) +
• Jarrod Millman (5)
• Stefanie Molin (2) +
• Mark W. Mueller (1) +
• mustafacevik (1) +
• Takumasa N (1) +
• nboudrie (1)
• Andrew Nelson (112)
• Nico Schlömer (4)
• Lysandros Nikolaou (2) +
• Kyle Oman (1)
• OmarManzoor (2) +
• Simon Ott (1) +
• Geoffrey Oxberry (1) +
• Geoffrey M. Oxberry (2) +
• Sravya papaganti (1) +
• Tirth Patel (2)
• Ilhan Polat (32)
• Quentin Barthélemy (1)
• Matteo Raso (12) +
• Tyler Reddy (143)
• Lucas Roberts (1)
• Pamphile Roy (225)
• Jordan Rupprecht (1) +
• Atsushi Sakai (11)
• Omar Salman (7) +
• Leo Sandler (1) +
• Ujjwal Sarswat (3) +
• Saumya (1) +
• Daniel Schmitz (79)
• Henry Schreiner (2) +
• Dan Schult (8) +
• Eli Schwartz (6)
• Tomer Sery (2) +
• Scott Shambaugh (10) +
• Gagandeep Singh (1)
• Ethan Steinberg (6) +
• stepeos (2) +
• Albert Steppi (3)
• Strahinja Lukić (1)
• Kai Striega (4)
• suen-bit (1) +
• Tartopohm (2)
• Logan Thomas (2) +
• Jacopo Tissino (1) +
• Matus Valo (12) +
• Jacob Vanderplas (2)
• Christian Veenhuis (1) +
• Isaac Virshup (3)
• Stefan van der Walt (14)
• Warren Weckesser (63)
• windows-server-2003 (1)
• Levi John Wolf (3)
• Nobel Wong (1) +
• Benjamin Yeh (1) +
• Rory Yorke (1)
• Younes (2) +
• Zaikun ZHANG (1) +
• Alex Zverianskii (1) +

A total of 134 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.