Update dependency scipy to v1.12.0

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
scipy (source)	==1.10.0 -> ==1.12.0

---

Release Notes

scipy/scipy (scipy)

v1.12.0: SciPy 1.12.0

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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.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, and scipy.special.expit now accept PyTorch tensors
  and CuPy arrays. These features are still under development and likely to
  contain bugs, so they are disabled by default; enable them by setting a
  SCIPY_ARRAY_API environment variable to 1 before importing scipy.
  Testing is appreciated!

scipy.stats improvements

• Added scipy.stats.quantile_test, a nonparametric test of whether a
  hypothesized value is the quantile associated with a specified probability.
  The confidence_interval method of the result object gives a confidence
  interval of the quantile.
• scipy.stats.sampling.FastGeneratorInversion provides a convenient
  interface to fast random sampling via numerical inversion of distribution
  CDFs.
• scipy.stats.geometric_discrepancy adds geometric/topological discrepancy
  metrics for random samples.
• scipy.stats.multivariate_normal now has a fit method for fitting
  distribution parameters to data via maximum likelihood estimation.
• scipy.stats.bws_test performs the Baumgartner-Weiss-Schindler test of
  whether two-samples were drawn from the same distribution.
• scipy.stats.jf_skew_t implements the Jones and Faddy skew-t distribution.
• scipy.stats.anderson_ksamp now supports a permutation version of the test
  using the method parameter.
• The fit methods of scipy.stats.halfcauchy, scipy.stats.halflogistic, and
  scipy.stats.halfnorm are faster and more accurate.
• scipy.stats.beta entropy accuracy has been improved for extreme values of
  distribution parameters.
• The accuracy of sf and/or isf methods have been improved for
  several distributions: scipy.stats.burr, scipy.stats.hypsecant,
  scipy.stats.kappa3, scipy.stats.loglaplace, scipy.stats.lognorm,
  scipy.stats.lomax, scipy.stats.pearson3, scipy.stats.rdist, and
  scipy.stats.pareto.
• The following functions now support parameters axis, nan_policy, and
  keep_dims: scipy.stats.entropy, scipy.stats.differential_entropy,
  scipy.stats.variation, scipy.stats.ansari, scipy.stats.bartlett,
  scipy.stats.levene, scipy.stats.fligner, scipy.stats.circmean,
  scipy.stats.circvar, scipy.stats.circstd, scipy.stats.tmean,
  scipy.stats.tvar, scipy.stats.tstd, scipy.stats.tmin, scipy.stats.tmax,
  and scipy.stats.tsem.
• The logpdf and fit methods of scipy.stats.skewnorm have been improved.
• The beta negative binomial distribution is implemented as scipy.stats.betanbinom.
• Improved performance of scipy.stats.invwishart rvs and logpdf.
• A source of intermediate overflow in scipy.stats.boxcox_normmax with
  method='mle' has been eliminated, and the returned value of lmbda is
  constrained such that the transformed data will not overflow.
• scipy.stats.nakagami stats is more accurate and reliable.
• A source of intermediate overflow in scipy.norminvgauss.pdf has been eliminated.
• Added support for masked arrays to scipy.stats.circmean, scipy.stats.circvar,
  scipy.stats.circstd, and scipy.stats.entropy.
• scipy.stats.dirichlet has gained a new covariance (cov) method.
• Improved accuracy of entropy method of scipy.stats.multivariate_t for large
  degrees of freedom.
• scipy.stats.loggamma has an improved entropy method.

Deprecated features

• Error messages have been made clearer for objects that don't exist in the
  public namespace and warnings sharpened for private attributes that are not
  supposed to be imported at all.

• scipy.signal.cmplx_sort has been deprecated and will be removed in
  SciPy 1.15. A replacement you can use is provided in the deprecation message.

• Values the the argument initial of scipy.integrate.cumulative_trapezoid
  other than 0 and None are now deprecated.

• scipy.stats.rvs_ratio_uniforms is deprecated in favour of
  scipy.stats.sampling.RatioUniforms

• scipy.integrate.quadrature and scipy.integrate.romberg have been
  deprecated due to accuracy issues and interface shortcomings. They will
  be removed in SciPy 1.15. Please use scipy.integrate.quad instead.

• Coinciding with upcoming changes to function signatures (e.g. removal of a
  deprecated keyword), we are deprecating positional use of keyword arguments
  for the affected functions, which will raise an error starting with
  SciPy 1.14. In some cases, this has delayed the originally announced
  removal date, to give time to respond to the second part of the deprecation.
  Affected functions are:

  • linalg.{eigh, eigvalsh, pinv}
  • integrate.simpson
  • signal.{firls, firwin, firwin2, remez}
  • sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}
  • special.comb
  • stats.kendalltau

• All wavelet functions have been deprecated, as PyWavelets provides suitable
  implementations; affected functions are: signal.{daub, qmf, cascade, morlet, morlet2, ricker, cwt}

• scipy.integrate.trapz, scipy.integrate.cumtrapz, and scipy.integrate.simps have
  been deprecated in favour of scipy.integrate.trapezoid, scipy.integrate.cumulative_trapezoid,
  and scipy.integrate.simpson respectively and will be removed in SciPy 1.14.

• The tol argument of scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk,gmres,lgmres,minres,qmr,tfqmr}
  is now deprecated in favour of rtol and will be removed in SciPy 1.14.
  Furthermore, the default value of atol for these functions is due
  to change to 0.0 in SciPy 1.14.

Expired Deprecations

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

• The centered keyword of scipy.stats.qmc.LatinHypercube has been removed.
  Use scrambled=False instead of centered=True.
• scipy.stats.binom_test has been removed in favour of scipy.stats.binomtest.
• In scipy.stats.iqr, the use of scale='raw' has been removed in favour
  of scale=1.

Backwards incompatible changes

Other changes

• The arguments used to compile and link SciPy are now available via
  show_config.

Authors

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A total of 163 people contributed to this release.
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v1.11.4: SciPy 1.11.4

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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

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• Nicolas Vetsch (1) +

A total of 9 people contributed to this release.
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v1.11.3: SciPy 1.11.3

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SciPy 1.11.3 Release Notes

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

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v1.11.2: SciPy 1.11.2

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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.

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v1.11.1: SciPy 1.11.1

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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.

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v1.11.0: SciPy 1.11.0

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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.

v1.10.1: SciPy 1.10.1

Compare Source

SciPy 1.10.1 Release Notes

SciPy 1.10.1 is a bug-fix release with no new features
compared to 1.10.0.

Authors

• Name (commits)
• alice (1) +
• Matt Borland (2) +
• Evgeni Burovski (2)
• CJ Carey (1)
• Ralf Gommers (9)
• Brett Graham (1) +
• Matt Haberland (5)
• Alex Herbert (1) +
• Ganesh Kathiresan (2) +
• Rishi Kulkarni (1) +
• Loïc Estève (1)
• Michał Górny (1) +
• Jarrod Millman (1)
• Andrew Nelson (4)
• Tyler Reddy (50)
• Pamphile Roy (2)
• Eli Schwartz (2)
• Tomer Sery (1) +
• Kai Striega (1)
• Jacopo Tissino (1) +
• windows-server-2003 (1)

A total of 21 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.

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