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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