Update dependency scipy to v1.10.0 - autoclosed

[mend-for-github-com]

This PR contains the following updates:

Package	Update	Change
scipy (source)	minor	==1.1.0 -> ==1.10.0

By merging this PR, the below issues will be automatically resolved and closed:

Severity	CVSS Score	CVE	GitHub Issue
Critical	9.8	CVE-2023-29824	#586
Medium	5.5	CVE-2023-25399	#587

---

Release Notes

scipy/scipy

v1.10.0: SciPy 1.10.0

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SciPy 1.10.0 Release Notes

SciPy 1.10.0 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
1.10.x branch, and on adding new features on the main branch.

This release requires Python 3.8+ and NumPy 1.19.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• A new dedicated datasets submodule (scipy.datasets) has been added, and is
  now preferred over usage of scipy.misc for dataset retrieval.
• A new scipy.interpolate.make_smoothing_spline function was added. This
  function constructs a smoothing cubic spline from noisy data, using the
  generalized cross-validation (GCV) criterion to find the tradeoff between
  smoothness and proximity to data points.
• scipy.stats has three new distributions, two new hypothesis tests, three
  new sample statistics, a class for greater control over calculations
  involving covariance matrices, and many other enhancements.

New features

scipy.datasets introduction

• A new dedicated datasets submodule has been added. The submodules
  is meant for datasets that are relevant to other SciPy submodules ands
  content (tutorials, examples, tests), as well as contain a curated
  set of datasets that are of wider interest. As of this release, all
  the datasets from scipy.misc have been added to scipy.datasets
  (and deprecated in scipy.misc).

• The submodule is based on Pooch
  (a new optional dependency for SciPy), a Python package to simplify fetching
  data files. This move will, in a subsequent release, facilitate SciPy
  to trim down the sdist/wheel sizes, by decoupling the data files and
  moving them out of the SciPy repository, hosting them externally and
  downloading them when requested. After downloading the datasets once,
  the files are cached to avoid network dependence and repeated usage.

• Added datasets from scipy.misc: scipy.datasets.face,
  scipy.datasets.ascent, scipy.datasets.electrocardiogram

• Added download and caching functionality:

  • scipy.datasets.download_all: a function to download all the scipy.datasets
    associated files at once.
  • scipy.datasets.clear_cache: a simple utility function to clear cached dataset
    files from the file system.
  • scipy/datasets/_download_all.py can be run as a standalone script for
    packaging purposes to avoid any external dependency at build or test time.
    This can be used by SciPy packagers (e.g., for Linux distros) which may
    have to adhere to rules that forbid downloading sources from external
    repositories at package build time.

scipy.integrate improvements

• Added parameter complex_func to scipy.integrate.quad, which can be set
  True to integrate a complex integrand.

scipy.interpolate improvements

• scipy.interpolate.interpn now supports tensor-product interpolation methods
  (slinear, cubic, quintic and pchip)
• Tensor-product interpolation methods (slinear, cubic, quintic and
  pchip) in scipy.interpolate.interpn and
  scipy.interpolate.RegularGridInterpolator now allow values with trailing
  dimensions.
• scipy.interpolate.RegularGridInterpolator has a new fast path for
  method="linear" with 2D data, and RegularGridInterpolator is now
  easier to subclass
• scipy.interpolate.interp1d now can take a single value for non-spline
  methods.
• A new extrapolate argument is available to scipy.interpolate.BSpline.design_matrix,
  allowing extrapolation based on the first and last intervals.
• A new function scipy.interpolate.make_smoothing_spline has been added. It is an
  implementation of the generalized cross-validation spline smoothing
  algorithm. The lam=None (default) mode of this function is a clean-room
  reimplementation of the classic gcvspl.f Fortran algorithm for
  constructing GCV splines.
• A new method="pchip" mode was aded to
  scipy.interpolate.RegularGridInterpolator. This mode constructs an
  interpolator using tensor products of C1-continuous monotone splines
  (essentially, a scipy.interpolate.PchipInterpolator instance per
  dimension).

scipy.sparse.linalg improvements

• The spectral 2-norm is now available in scipy.sparse.linalg.norm.

• The performance of scipy.sparse.linalg.norm for the default case (Frobenius
  norm) has been improved.

• LAPACK wrappers were added for trexc and trsen.

• The scipy.sparse.linalg.lobpcg algorithm was rewritten, yielding
  the following improvements:

  • a simple tunable restart potentially increases the attainable
    accuracy for edge cases,
  • internal postprocessing runs one final exact Rayleigh-Ritz method
    giving more accurate and orthonormal eigenvectors,
  • output the computed iterate with the smallest max norm of the residual
    and drop the history of subsequent iterations,
  • remove the check for LinearOperator format input and thus allow
    a simple function handle of a callable object as an input,
  • better handling of common user errors with input data, rather
    than letting the algorithm fail.

scipy.linalg improvements

• scipy.linalg.lu_factor now accepts rectangular arrays instead of being restricted
  to square arrays.

scipy.ndimage improvements

• The new scipy.ndimage.value_indices function provides a time-efficient method to
  search for the locations of individual values with an array of image data.
• A new radius argument is supported by scipy.ndimage.gaussian_filter1d and
  scipy.ndimage.gaussian_filter for adjusting the kernel size of the filter.

scipy.optimize improvements

• scipy.optimize.brute now coerces non-iterable/single-value args into a
  tuple.
• scipy.optimize.least_squares and scipy.optimize.curve_fit now accept
  scipy.optimize.Bounds for bounds constraints.
• Added a tutorial for scipy.optimize.milp.
• Improved the pretty-printing of scipy.optimize.OptimizeResult objects.
• Additional options (parallel, threads, mip_rel_gap) can now
  be passed to scipy.optimize.linprog with method='highs'.

scipy.signal improvements

• The new window function scipy.signal.windows.lanczos was added to compute a
  Lanczos window, also known as a sinc window.

scipy.sparse.csgraph improvements

• the performance of scipy.sparse.csgraph.dijkstra has been improved, and
  star graphs in particular see a marked performance improvement

scipy.special improvements

• The new function scipy.special.powm1, a ufunc with signature
  powm1(x, y), computes x**y - 1. The function avoids the loss of
  precision that can result when y is close to 0 or when x is close to
  1.
• scipy.special.erfinv is now more accurate as it leverages the Boost equivalent under
  the hood.

scipy.stats improvements

• Added scipy.stats.goodness_of_fit, a generalized goodness-of-fit test for
  use with any univariate distribution, any combination of known and unknown
  parameters, and several choices of test statistic (Kolmogorov-Smirnov,
  Cramer-von Mises, and Anderson-Darling).

• Improved scipy.stats.bootstrap: Default method 'BCa' now supports
  multi-sample statistics. Also, the bootstrap distribution is returned in the
  result object, and the result object can be passed into the function as
  parameter bootstrap_result to add additional resamples or change the
  confidence interval level and type.

• Added maximum spacing estimation to scipy.stats.fit.

• Added the Poisson means test ("E-test") as scipy.stats.poisson_means_test.

• Added new sample statistics.

  • Added scipy.stats.contingency.odds_ratio to compute both the conditional
    and unconditional odds ratios and corresponding confidence intervals for
    2x2 contingency tables.
  • Added scipy.stats.directional_stats to compute sample statistics of
    n-dimensional directional data.
  • Added scipy.stats.expectile, which generalizes the expected value in the
    same way as quantiles are a generalization of the median.

• Added new statistical distributions.

  • Added scipy.stats.uniform_direction, a multivariate distribution to
    sample uniformly from the surface of a hypersphere.
  • Added scipy.stats.random_table, a multivariate distribution to sample
    uniformly from m x n contingency tables with provided marginals.
  • Added scipy.stats.truncpareto, the truncated Pareto distribution.

• Improved the fit method of several distributions.

  • scipy.stats.skewnorm and scipy.stats.weibull_min now use an analytical
    solution when method='mm', which also serves a starting guess to
    improve the performance of method='mle'.
  • scipy.stats.gumbel_r and scipy.stats.gumbel_l: analytical maximum
    likelihood estimates have been extended to the cases in which location or
    scale are fixed by the user.
  • Analytical maximum likelihood estimates have been added for
    scipy.stats.powerlaw.

• Improved random variate sampling of several distributions.

  • Drawing multiple samples from scipy.stats.matrix_normal,
    scipy.stats.ortho_group, scipy.stats.special_ortho_group, and
    scipy.stats.unitary_group is faster.
  • The rvs method of scipy.stats.vonmises now wraps to the interval
    [-np.pi, np.pi].
  • Improved the reliability of scipy.stats.loggamma rvs method for small
    values of the shape parameter.

• Improved the speed and/or accuracy of functions of several statistical
  distributions.

  • Added scipy.stats.Covariance for better speed, accuracy, and user control
    in multivariate normal calculations.
  • scipy.stats.skewnorm methods cdf, sf, ppf, and isf
    methods now use the implementations from Boost, improving speed while
    maintaining accuracy. The calculation of higher-order moments is also
    faster and more accurate.
  • scipy.stats.invgauss methods ppf and isf methods now use the
    implementations from Boost, improving speed and accuracy.
  • scipy.stats.invweibull methods sf and isf are more accurate for
    small probability masses.
  • scipy.stats.nct and scipy.stats.ncx2 now rely on the implementations
    from Boost, improving speed and accuracy.
  • Implemented the logpdf method of scipy.stats.vonmises for reliability
    in extreme tails.
  • Implemented the isf method of scipy.stats.levy for speed and
    accuracy.
  • Improved the robustness of scipy.stats.studentized_range for large df
    by adding an infinite degree-of-freedom approximation.
  • Added a parameter lower_limit to scipy.stats.multivariate_normal,
    allowing the user to change the integration limit from -inf to a desired
    value.
  • Improved the robustness of entropy of scipy.stats.vonmises for large
    concentration values.

• Enhanced scipy.stats.gaussian_kde.

  • Added scipy.stats.gaussian_kde.marginal, which returns the desired
    marginal distribution of the original kernel density estimate distribution.
  • The cdf method of scipy.stats.gaussian_kde now accepts a
    lower_limit parameter for integrating the PDF over a rectangular region.
  • Moved calculations for scipy.stats.gaussian_kde.logpdf to Cython,
    improving speed.
  • The global interpreter lock is released by the pdf method of
    scipy.stats.gaussian_kde for improved multithreading performance.
  • Replaced explicit matrix inversion with Cholesky decomposition for speed
    and accuracy.

• Enhanced the result objects returned by many scipy.stats functions

  • Added a confidence_interval method to the result object returned by
    scipy.stats.ttest_1samp and scipy.stats.ttest_rel.
  • The scipy.stats functions combine_pvalues, fisher_exact,
    chi2_contingency, median_test and mood now return
    bunch objects rather than plain tuples, allowing attributes to be
    accessed by name.
  • Attributes of the result objects returned by multiscale_graphcorr,
    anderson_ksamp, binomtest, crosstab, pointbiserialr,
    spearmanr, kendalltau, and weightedtau have been renamed to
    statistic and pvalue for consistency throughout scipy.stats.
    Old attribute names are still allowed for backward compatibility.
  • scipy.stats.anderson now returns the parameters of the fitted
    distribution in a scipy.stats._result_classes.FitResult object.
  • The plot method of scipy.stats._result_classes.FitResult now accepts
    a plot_type parameter; the options are 'hist' (histogram, default),
    'qq' (Q-Q plot), 'pp' (P-P plot), and 'cdf' (empirical CDF
    plot).
  • Kolmogorov-Smirnov tests (e.g. scipy.stats.kstest) now return the
    location (argmax) at which the statistic is calculated and the variant
    of the statistic used.

• Improved the performance of several scipy.stats functions.

  • Improved the performance of scipy.stats.cramervonmises_2samp and
    scipy.stats.ks_2samp with method='exact'.
  • Improved the performance of scipy.stats.siegelslopes.
  • Improved the performance of scipy.stats.mstats.hdquantile_sd.
  • Improved the performance of scipy.stats.binned_statistic_dd for several
    NumPy statistics, and binned statistics methods now support complex data.

• Added the scramble optional argument to scipy.stats.qmc.LatinHypercube.
  It replaces centered, which is now deprecated.

• Added a parameter optimization to all scipy.stats.qmc.QMCEngine
  subclasses to improve characteristics of the quasi-random variates.

• Added tie correction to scipy.stats.mood.

• Added tutorials for resampling methods in scipy.stats.

• scipy.stats.bootstrap, scipy.stats.permutation_test, and
  scipy.stats.monte_carlo_test now automatically detect whether the provided
  statistic is vectorized, so passing the vectorized argument
  explicitly is no longer required to take advantage of vectorized statistics.

• Improved the speed of scipy.stats.permutation_test for permutation types
  'samples' and 'pairings'.

• Added axis, nan_policy, and masked array support to
  scipy.stats.jarque_bera.

• Added the nan_policy optional argument to scipy.stats.rankdata.

Deprecated features

• scipy.misc module and all the methods in misc are deprecated in v1.10
  and will be completely removed in SciPy v2.0.0. Users are suggested to
  utilize the scipy.datasets module instead for the dataset methods.
• scipy.stats.qmc.LatinHypercube parameter centered has been deprecated.
  It is replaced by the scramble argument for more consistency with other
  QMC engines.
• scipy.interpolate.interp2d class has been deprecated. The docstring of the
  deprecated routine lists recommended replacements.

Expired Deprecations

• There is an ongoing effort to follow through on long-standing deprecations.

• The following previously deprecated features are affected:

  • Removed cond & rcond kwargs in linalg.pinv
  • Removed wrappers scipy.linalg.blas.{clapack, flapack}
  • Removed scipy.stats.NumericalInverseHermite and removed tol & max_intervals kwargs from scipy.stats.sampling.NumericalInverseHermite
  • Removed local_search_options kwarg frrom scipy.optimize.dual_annealing.

Other changes

• scipy.stats.bootstrap, scipy.stats.permutation_test, and
  scipy.stats.monte_carlo_test now automatically detect whether the provided
  statistic is vectorized by looking for an axis parameter in the
  signature of statistic. If an axis parameter is present in
  statistic but should not be relied on for vectorized calls, users must
  pass option vectorized==False explicitly.
• scipy.stats.multivariate_normal will now raise a ValueError when the
  covariance matrix is not positive semidefinite, regardless of which method
  is called.

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v1.9.3: SciPy 1.9.3

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SciPy 1.9.3 Release Notes

SciPy 1.9.3 is a bug-fix release with no new features
compared to 1.9.2.

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v1.9.2: SciPy 1.9.2

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SciPy 1.9.2 Release Notes

SciPy 1.9.2 is a bug-fix release with no new features
compared to 1.9.1. It also provides wheels for Python 3.11
on several platforms.

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v1.9.1: SciPy 1.9.1

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SciPy 1.9.1 Release Notes

SciPy 1.9.1 is a bug-fix release with no new features
compared to 1.9.0. Notably, some important meson build
fixes are included.

Authors

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v1.9.0: SciPy 1.9.0

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SciPy 1.9.0 Release Notes

SciPy 1.9.0 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
1.9.x branch, and on adding new features on the main branch.

This release requires Python 3.8-3.11 and NumPy 1.18.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• We have modernized our build system to use meson, substantially improving
  our build performance, and providing better build-time configuration and
  cross-compilation support,
• Added scipy.optimize.milp, new function for mixed-integer linear
  programming,
• Added scipy.stats.fit for fitting discrete and continuous distributions
  to data,
• Tensor-product spline interpolation modes were added to
  scipy.interpolate.RegularGridInterpolator,
• A new global optimizer (DIviding RECTangles algorithm)
  scipy.optimize.direct.

New features

scipy.interpolate improvements

• Speed up the RBFInterpolator evaluation with high dimensional
  interpolants.
• Added new spline based interpolation methods for
  scipy.interpolate.RegularGridInterpolator and its tutorial.
• scipy.interpolate.RegularGridInterpolator and scipy.interpolate.interpn
  now accept descending ordered points.
• RegularGridInterpolator now handles length-1 grid axes.
• The BivariateSpline subclasses have a new method partial_derivative
  which constructs a new spline object representing a derivative of an
  original spline. This mirrors the corresponding functionality for univariate
  splines, splder and BSpline.derivative, and can substantially speed
  up repeated evaluation of derivatives.

scipy.linalg improvements

• scipy.linalg.expm now accepts nD arrays. Its speed is also improved.
• Minimum required LAPACK version is bumped to 3.7.1.

scipy.fft improvements

• Added uarray multimethods for scipy.fft.fht and scipy.fft.ifht
  to allow provision of third party backend implementations such as those
  recently added to CuPy.

scipy.optimize improvements

• A new global optimizer, scipy.optimize.direct (DIviding RECTangles algorithm)
  was added. For problems with inexpensive function evaluations, like the ones
  in the SciPy benchmark suite, direct is competitive with the best other
  solvers in SciPy (dual_annealing and differential_evolution) in terms
  of execution time. See
  gh-14300 <https://github.com/scipy/scipy/pull/14300>__ for more details.

• Add a full_output parameter to scipy.optimize.curve_fit to output
  additional solution information.

• Add a integrality parameter to scipy.optimize.differential_evolution,
  enabling integer constraints on parameters.

• Add a vectorized parameter to call a vectorized objective function only
  once per iteration. This can improve minimization speed by reducing
  interpreter overhead from the multiple objective function calls.

• The default method of scipy.optimize.linprog is now 'highs'.

• Added scipy.optimize.milp, new function for mixed-integer linear
  programming.

• Added Newton-TFQMR method to newton_krylov.

• Added support for the Bounds class in shgo and dual_annealing for
  a more uniform API across scipy.optimize.

• Added the vectorized keyword to differential_evolution.

• approx_fprime now works with vector-valued functions.

scipy.signal improvements

• The new window function scipy.signal.windows.kaiser_bessel_derived was
  added to compute the Kaiser-Bessel derived window.
• Single-precision hilbert operations are now faster as a result of more
  consistent dtype handling.

scipy.sparse improvements

• Add a copy parameter to scipy.sparce.csgraph.laplacian. Using inplace
  computation with copy=False reduces the memory footprint.
• Add a dtype parameter to scipy.sparce.csgraph.laplacian for type casting.
• Add a symmetrized parameter to scipy.sparce.csgraph.laplacian to produce
  symmetric Laplacian for directed graphs.
• Add a form parameter to scipy.sparce.csgraph.laplacian taking one of the
  three values: array, or function, or lo determining the format of
  the output Laplacian:
  • array is a numpy array (backward compatible default);
  • function is a pointer to a lambda-function evaluating the
    Laplacian-vector or Laplacian-matrix product;
  • lo results in the format of the LinearOperator.

scipy.sparse.linalg improvements

• lobpcg performance improvements for small input cases.

scipy.spatial improvements

• Add an order parameter to scipy.spatial.transform.Rotation.from_quat
  and scipy.spatial.transform.Rotation.as_quat to specify quaternion format.

scipy.stats improvements

• scipy.stats.monte_carlo_test performs one-sample Monte Carlo hypothesis
  tests to assess whether a sample was drawn from a given distribution. Besides
  reproducing the results of hypothesis tests like scipy.stats.ks_1samp,
  scipy.stats.normaltest, and scipy.stats.cramervonmises without small sample
  size limitations, it makes it possible to perform similar tests using arbitrary
  statistics and distributions.

• Several scipy.stats functions support new axis (integer or tuple of
  integers) and nan_policy ('raise', 'omit', or 'propagate'), and
  keepdims arguments.
  These functions also support masked arrays as inputs, even if they do not have
  a scipy.stats.mstats counterpart. Edge cases for multidimensional arrays,
  such as when axis-slices have no unmasked elements or entire inputs are of
  size zero, are handled consistently.

• Add a weight parameter to scipy.stats.hmean.

• Several improvements have been made to scipy.stats.levy_stable. Substantial
  improvement has been made for numerical evaluation of the pdf and cdf,
  resolving #​12658 and
  #​14944. The improvement is
  particularly dramatic for stability parameter alpha close to or equal to 1
  and for alpha below but approaching its maximum value of 2. The alternative
  fast Fourier transform based method for pdf calculation has also been updated
  to use the approach of Wang and Zhang from their 2008 conference paper
  Simpson’s rule based FFT method to compute densities of stable distribution,
  making this method more competitive with the default method. In addition,
  users now have the option to change the parametrization of the Levy Stable
  distribution to Nolan's "S0" parametrization which is used internally by
  SciPy's pdf and cdf implementations. The "S0" parametrization is described in
  Nolan's paper Numerical calculation of stable densities and distribution
  functions upon which SciPy's
  implementation is based. "S0" has the advantage that delta and gamma
  are proper location and scale parameters. With delta and gamma fixed,
  the location and scale of the resulting distribution remain unchanged as
  alpha and beta change. This is not the case for the default "S1"
  parametrization. Finally, more options have been exposed to allow users to
  trade off between runtime and accuracy for both the default and FFT methods of
  pdf and cdf calculation. More information can be found in the documentation
  here (to be linked).

• Added scipy.stats.fit for fitting discrete and continuous distributions to
  data.

• The methods "pearson" and "tippet" from scipy.stats.combine_pvalues
  have been fixed to return the correct p-values, resolving
  #​15373. In addition, the
  documentation for scipy.stats.combine_pvalues has been expanded and improved.

• Unlike other reduction functions, stats.mode didn't consume the axis
  being operated on and failed for negative axis inputs. Both the bugs have been
  fixed. Note that stats.mode will now consume the input axis and return an
  ndarray with the axis dimension removed.

• Replaced implementation of scipy.stats.ncf with the implementation from
  Boost for improved reliability.

• Add a bits parameter to scipy.stats.qmc.Sobol. It allows to use from 0
  to 64 bits to compute the sequence. Default is None which corresponds to
  30 for backward compatibility. Using a higher value allow to sample more
  points. Note: bits does not affect the output dtype.

• Add a integers method to scipy.stats.qmc.QMCEngine. It allows sampling
  integers using any QMC sampler.

• Improved the fit speed and accuracy of stats.pareto.

• Added qrvs method to NumericalInversePolynomial to match the
  situation for NumericalInverseHermite.

• Faster random variate generation for gennorm and nakagami.

• lloyd_centroidal_voronoi_tessellation has been added to allow improved
  sample distributions via iterative application of Voronoi diagrams and
  centering operations

• Add scipy.stats.qmc.PoissonDisk to sample using the Poisson disk sampling
  method. It guarantees that samples are separated from each other by a
  given radius.

• Add scipy.stats.pmean to calculate the weighted power mean also called
  generalized mean.

Deprecated features

• Due to collision with the shape parameter n of several distributions,
  use of the distribution moment method with keyword argument n is
  deprecated. Keyword n is replaced with keyword order.
• Similarly, use of the distribution interval method with keyword arguments
  alpha is deprecated. Keyword alpha is replaced with keyword
  confidence.
• The 'simplex', 'revised simplex', and 'interior-point' methods
  of scipy.optimize.linprog are deprecated. Methods highs, highs-ds,
  or highs-ipm should be used in new code.
• Support for non-numeric arrays has been deprecated from stats.mode.
  pandas.DataFrame.mode can be used instead.
• The function spatial.distance.kulsinski has been deprecated in favor
  of spatial.distance.kulczynski1.
• The maxiter keyword of the truncated Newton (TNC) algorithm has been
  deprecated in favour of maxfun.
• The vertices keyword of Delauney.qhull now raises a
  DeprecationWarning, after having been deprecated in documentation only
  for a long time.
• The extradoc keyword of rv_continuous, rv_discrete and
  rv_sample now raises a DeprecationWarning, after having been deprecated in
  documentation only for a long time.

Expired Deprecations

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

• Object arrays in sparse matrices now raise an error.
• Inexact indices into sparse matrices now raise an error.
• Passing radius=None to scipy.spatial.SphericalVoronoi now raises an
  error (not adding radius defaults to 1, as before).
• Several BSpline methods now raise an error if inputs have ndim > 1.
• The _rvs method of statistical distributions now requires a size
  parameter.
• Passing a fillvalue that cannot be cast to the output type in
  scipy.signal.convolve2d now raises an error.
• scipy.spatial.distance now enforces that the input vectors are
  one-dimensional.
• Removed stats.itemfreq.
• Removed stats.median_absolute_deviation.
• Removed n_jobs keyword argument and use of k=None from
  kdtree.query.
• Removed right keyword from interpolate.PPoly.extend.
• Removed debug keyword from scipy.linalg.solve_*.
• Removed class _ppform scipy.interpolate.
• Removed BSR methods matvec and matmat.
• Removed mlab truncation mode from cluster.dendrogram.
• Removed cluster.vq.py_vq2.
• Removed keyword arguments ftol and xtol from
  optimize.minimize(method='Nelder-Mead').
• Removed signal.windows.hanning.
• Removed LAPACK gegv functions from linalg; this raises the minimally
  required LAPACK version to 3.7.1.
• Removed spatial.distance.matching.
• Removed the alias scipy.random for numpy.random.
• Removed docstring related functions from scipy.misc (docformat,
  inherit_docstring_from, extend_notes_in_docstring,
  replace_notes_in_docstring, indentcount_lines, filldoc,
  unindent_dict, unindent_string).
• Removed linalg.pinv2.

Backwards incompatible changes

• Several scipy.stats functions now convert np.matrix to np.ndarrays
  before the calculation is performed. In this case, the output will be a scalar
  or np.ndarray of appropriate shape rather than a 2D np.matrix.
  Similarly, while masked elements of masked arrays are still ignored, the
  output will be a scalar or np.ndarray rather than a masked array with
  mask=False.
• The default method of scipy.optimize.linprog is now 'highs', not
  'interior-point' (which is now deprecated), so callback functions and
  some options are no longer supported with the default method. With the
  default method, the x attribute of the returned OptimizeResult is
  now None (instead of a non-optimal array) when an optimal solution
  cannot be found (e.g. infeasible problem).
• For scipy.stats.combine_pvalues, the sign of the test statistic returned
  for the method "pearson" has been flipped so that higher values of the
  statistic now correspond to lower p-values, making the statistic more
  consistent with those of the other methods and with the majority of the
  literature.
• scipy.linalg.expm due to historical reasons was using the sparse
  implementation and thus was accepting sparse arrays. Now it only works with
  nDarrays. For sparse usage, scipy.sparse.linalg.expm needs to be used
  explicitly.
• The definition of scipy.stats.circvar has reverted to the one that is
  standard in the literature; note that this is not the same as the square of
  scipy.stats.circstd.
• Remove inheritance to QMCEngine in MultinomialQMC and
  MultivariateNormalQMC. It removes the methods fast_forward and reset.
• Init of MultinomialQMC now require the number of trials with n_trials.
  Hence, MultinomialQMC.random output has now the correct shape (n, pvals).
• Several function-specific warnings (F_onewayConstantInputWarning,
  F_onewayBadInputSizesWarning, PearsonRConstantInputWarning,
  PearsonRNearConstantInputWarning, SpearmanRConstantInputWarning, and
  BootstrapDegenerateDistributionWarning) have been replaced with more
  general warnings.

Other changes

• A draft developer CLI is available for SciPy, leveraging the doit,
  click and rich-click tools. For more details, see
  gh-15959.

• The SciPy contributor guide has been reorganized and updated
  (see #​15947 for details).

• QUADPACK Fortran routines in scipy.integrate, which power
  scipy.integrate.quad, have been marked as recursive. This should fix rare
  issues in multivariate integration (nquad and friends) and obviate the need
  for compiler-specific compile flags (/recursive for ifort etc). Please file
  an issue if this change turns out problematic for you. This is also true for
  FITPACK routines in scipy.interpolate, which power splrep,
  splev etc., and *UnivariateSpline and *BivariateSpline classes.

• the USE_PROPACK environment variable has been renamed to
  SCIPY_USE_PROPACK; setting to a non-zero value will enable
  the usage of the PROPACK library as before

• Building SciPy on windows with MSVC now requires at least the vc142
  toolset (available in Visual Studio 2019 and higher).

Lazy access to subpackages

Before this release, all subpackages of SciPy (cluster, fft, ndimage,
etc.) had to be explicitly imported. Now, these subpackages are lazily loaded
as soon as they are accessed, so that the following is possible (if desired
for interactive use, it's not actually recommended for code,
see :ref:scipy-api):
import scipy as sp; sp.fft.dct([1, 2, 3]). Advantages include: making it
easier to navigate SciPy in interactive terminals, reducing subpackage import
conflicts (which before required
import networkx.linalg as nla; import scipy.linalg as sla),
and avoiding repeatedly having to update imports during teaching &
experimentation. Also see
the related community specification document.

SciPy switched to Meson as its build system

This is the first release that ships with Meson as
the build system. When installing with pip or pypa/build, Meson will be
used (invoked via the meson-python build hook). This change brings
significant benefits - most importantly much faster build times, but also
better support for cross-compilation and cleaner build logs.

Note:

This release still ships with support for numpy.distutils-based builds
as well. Those can be invoked through the setup.py command-line
interface (e.g., python setup.py install). It is planned to remove
numpy.distutils support before the 1.10.0 release.

When building from source, a number of things have changed compared to building
with numpy.distutils:

• New build dependencies: meson, ninja, and pkg-config.
  setuptools and wheel are no longer needed.
• BLAS and LAPACK libraries that are supported haven't changed, however the
  discovery mechanism has: that is now using pkg-config instead of hardcoded
  paths or a site.cfg file.
• The build defaults to using OpenBLAS. See :ref:blas-lapack-selection for
  details.

The two CLIs that can be used to build wheels are pip and build. In
addition, the SciPy repo contains a python dev.py CLI for any kind of
development task (see its --help for details). For a comparison between old
(distutils) and new (meson) build commands, see :ref:meson-faq.

For more information on the introduction of Meson support in SciPy, see
gh-13615 <https://github.com/scipy/scipy/issues/13615>__ and
this blog post <https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson/>__.

Authors

• endolith (12)
• h-vetinari (11)
• Caio Agiani (2) +
• Emmy Albert (1) +
• Joseph Albert (1)
• Tania Allard (3)
• Carsten Allefeld (1) +
• Kartik Anand (1) +
• Virgile Andreani (2) +
• Weh Andreas (1) +
• Francesco Andreuzzi (5) +
• Kian-Meng Ang (2) +
• Gerrit Ansmann (1)
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• Shehan Atukorala (1) +
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• Ross Barnowski (9)
• Christoph Baumgarten (3)
• Nickolai Belakovski (1)
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• Bharath (1) +
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• Luigi Cruz (1) +
• Anirudh Dagar (5)
• Brandon David (1)
• deepakdinesh1123 (1) +
• Denton DeLoss (1) +
• derbuihan (2) +
• Sameer Deshmukh (13) +
• Niels Doucet (1) +
• DWesl (8)
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• Joseph Fox-Rabinovitz (1)
• Ryan Gibson (4) +
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A total of 154 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.

v1.8.1: SciPy 1.8.1

Compare Source

SciPy 1.8.1 Release Notes

SciPy 1.8.1 is a bug-fix release with no new features
compared to 1.8.0. Notably, usage of Pythran has been
restored for Windows builds/binaries.

Authors

• Henry Schreiner
• Maximilian Nöthe
• Sebastian Berg (1)
• Sameer Deshmukh (1) +
• Niels Doucet (1) +
• DWesl (4)
• Isuru Fernando (1)
• Ralf Gommers (4)
• Matt Haberland (1)
• Andrew Nelson (1)
• Dimitri Papadopoulos Orfanos (1) +
• Tirth Patel (3)
• Tyler Reddy (46)
• Pamphile Roy (7)
• Niyas Sait (1) +
• H. Vetinari (2)
• Warren Weckesser (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.

v1.8.0: SciPy 1.8.0

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SciPy 1.8.0 Release Notes

SciPy 1.8.0 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
1.8.x branch, and on adding new features on the master branch.

This release requires Python 3.8+ and NumPy 1.17.3 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• A sparse array API has been added for early testing and feedback; this
  work is ongoing, and users should expect minor API refinements over
  the next few releases.
• The sparse SVD library PROPACK is now vendored with SciPy, and an interface
  is exposed via scipy.sparse.svds with solver='PROPACK'. It is currently
  default-off due to potential issues on Windows that we aim to
  resolve in the next release, but can be optionally enabled at runtime for
  friendly testing with an environment variable setting of USE_PROPACK=1.
• A new scipy.stats.sampling submodule that leverages the UNU.RAN C
  library to sample from arbitrary univariate non-uniform continuous and
  discrete distributions
• All namespaces that were private but happened to miss underscores in
  their names have been deprecated.

New features

scipy.fft improvements

Added an orthogonalize=None parameter to the real transforms in scipy.fft
which controls whether the modified definition of DCT/DST is used without
changing the overall scaling.

scipy.fft backend registration is now smoother, operating with a single
registration call and no longer requiring a context manager.

scipy.integrate improvements

scipy.integrate.quad_vec introduces a new optional keyword-only argument,
args. args takes in a tuple of extra arguments if any (default is
args=()), which is then internally used to pass into the callable function
(needing these extra arguments) which we wish to integrate.

scipy.interpolate improvements

scipy.interpolate.BSpline has a new method, design_matrix, which
constructs a design matrix of b-splines in the sparse CSR format.

A new method from_cubic in BSpline class allows to convert a
CubicSpline object to BSpline object.

scipy.linalg improvements

scipy.linalg gained three new public array structure investigation functions.
scipy.linalg.bandwidth returns information about the bandedness of an array
and can be used to test for triangular structure discovery, while
scipy.linalg.issymmetric and scipy.linalg.ishermitian test the array for
exact and approximate symmetric/Hermitian structure.

scipy.optimize improvements

scipy.optimize.check_grad introduces two new optional keyword only arguments,
direction and seed. direction can take values, 'all' (default),
in which case all the one hot direction vectors will be used for verifying
the input analytical gradient function and 'random', in which case a
random direction vector will be used for the same purpose. seed
(default is None) can be used for reproducing the return value of
check_grad function. It will be used only when direction='random'.

The scipy.optimize.minimize TNC method has been rewritten to use Cython
bindings. This also fixes an issue with the callback altering the state of the
optimization.

Added optional parameters target_accept_rate and stepwise_factor for
adapative step size adjustment in basinhopping.

The epsilon argument to approx_fprime is now optional so that it may
have a default value consistent with most other functions in scipy.optimize.

scipy.signal improvements

Add analog argument, default False, to zpk2sos, and add new pairing
option 'minimal' to construct analog and minimal discrete SOS arrays.
tf2sos uses zpk2sos; add analog argument here as well, and pass it on
to zpk2sos.

savgol_coeffs and savgol_filter now work for even window lengths.

Added the Chirp Z-transform and Zoom FFT available as scipy.signal.CZT and
scipy.signal.ZoomFFT.

scipy.sparse improvements

An array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases. Please refer to the scipy.sparse
docstring for more information.

maximum_flow introduces optional keyword only argument, method
which accepts either, 'edmonds-karp' (Edmonds Karp algorithm) or
'dinic' (Dinic's algorithm). Moreover, 'dinic' is used as default
value for method which means that Dinic's algorithm is used for computing
maximum flow unless specified. See, the comparison between the supported
algorithms in
this comment <https://github.com/scipy/scipy/pull/14358#issue-684212523>_.

Parameters atol, btol now default to 1e-6 in
scipy.sparse.linalg.lsmr to match with default values in
scipy.sparse.linalg.lsqr.

Add the Transpose-Free Quasi-Minimal Residual algorithm (TFQMR) for general
nonsingular non-Hermitian linear systems in scipy.sparse.linalg.tfqmr.

The sparse SVD library PROPACK is now vendored with SciPy, and an interface is
exposed via scipy.sparse.svds with solver='PROPACK'. For some problems,
this may be faster and/or more accurate than the default, ARPACK. PROPACK
functionality is currently opt-in--you must specify USE_PROPACK=1 at
runtime to use it due to potential issues on Windows
that we aim to resolve in the next release.

sparse.linalg iterative solvers now have a nonzero initial guess option,
which may be specified as x0 = 'Mb'.

The trace method has been added for sparse matrices.

scipy.spatial improvements

scipy.spatial.transform.Rotation now supports item assignment and has a new
concatenate method.

Add scipy.spatial.distance.kulczynski1 in favour of
scipy.spatial.distance.kulsinski which will be deprecated in the next
release.

scipy.spatial.distance.minkowski now also supports 0<p<1.

scipy.special improvements

The new function scipy.special.log_expit computes the logarithm of the
logistic sigmoid function. The function is formulated to provide accurate
results for large positive and negative inputs, so it avoids the problems
that would occur in the naive implementation log(expit(x)).

A suite of five new functions for elliptic integrals:
scipy.special.ellipr{c,d,f,g,j}. These are the
Carlson symmetric elliptic integrals <https://dlmf.nist.gov/19.16>_, which
have computational advantages over the classical Legendre integrals. Previous
versions included some elliptic integrals from the Cephes library
(scipy.special.ellip{k,km1,kinc,e,einc}) but was missing the integral of
third kind (Legendre's Pi), which can be evaluated using the new Carlson
functions. The new Carlson elliptic integral functions can be evaluated in the
complex plane, whereas the Cephes library's functions are only defined for
real inputs.

Several defects in scipy.special.hyp2f1 have been corrected. Approximately
correct values are now returned for z near exp(+-i*pi/3), fixing
#&#8203;8054 <https://github.com/scipy/scipy/issues/8054>. Evaluation for such z
is now calculated through a series derived by
López and Temme (2013) <https://arxiv.org/abs/1306.2046> that converges in
these regions. In addition, degenerate cases with one or more of a, b,
and/or c a non-positive integer are now handled in a manner consistent with
mpmath's hyp2f1 implementation <https://mpmath.org/doc/current/functions/hypergeometric.html>,
which fixes #&#8203;7340 <https://github.com/scipy/scipy/issues/7340>. These fixes
were made as part of an effort to rewrite the Fortran 77 implementation of
hyp2f1 in Cython piece by piece. This rewriting is now roughly 50% complete.

scipy.stats improvements

scipy.stats.qmc.LatinHypercube introduces two new optional keyword-only
arguments, optimization and strength. optimization is either
None or random-cd. In the latter, random permutations are performed to
improve the centered discrepancy. strength is either 1 or 2. 1 corresponds
to the classical LHS while 2 has better sub-projection properties. This
construction is referred to as an orthogonal array based LHS of strength 2.
In both cases, the output is still a LHS.

scipy.stats.qmc.Halton is faster as the underlying Van der Corput sequence
was ported to Cython.

The alternative parameter was added to the kendalltau and somersd
functions to allow one-sided hypothesis testing. Similarly, the masked
versions of skewtest, kurtosistest, ttest_1samp, ttest_ind,
and ttest_rel now also have an alternative parameter.

Add scipy.stats.gzscore to calculate the geometrical z score.

Random variate generators to sample from arbitrary univariate non-uniform
continuous and discrete distributions have been added to the new
scipy.stats.sampling submodule. Implementations of a C library
UNU.RAN <http://statmath.wu.ac.at/software/unuran/>_ are used for
performance. The generators added are:

• TransformedDensityRejection
• DiscreteAliasUrn
• NumericalInversePolynomial
• DiscreteGuideTable
• SimpleRatioUniforms

The binned_statistic set of functions now have improved performance for
the std, min, max, and median statistic calculations.

somersd and _tau_b now have faster Pythran-based implementations.

Some general efficiency improvements to handling of nan values in
several stats functions.

Added the Tukey-Kramer test as scipy.stats.tukey_hsd.

Improved performance of scipy.stats.argus rvs method.

Added the parameter keepdims to scipy.stats.variation and prevent the
unde

[cortx-admin]

Can one of the admins verify this patch?