scipy/scipy: SciPy 1.9.0

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) +
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• Christoph Baumgarten (3)
• Nickolai Belakovski (1)
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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.