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.

Authors

• Name (commits)
• h-vetinari (10)
• Jelle Aalbers (1)
• Oriol Abril-Pla (1) +
• Alan-Hung (1) +
• Tania Allard (7)
• Oren Amsalem (1) +
• Sven Baars (10)
• Balthasar (1) +
• Ross Barnowski (1)
• Christoph Baumgarten (2)
• Peter Bell (2)
• Sebastian Berg (1)
• Aaron Berk (1) +
• boatwrong (1) +
• boeleman (1) +
• Jake Bowhay (50)
• Matthew Brett (4)
• Evgeni Burovski (93)
• Matthias Bussonnier (6)
• Dominic C (2)
• Mingbo Cai (1) +
• James Campbell (2) +
• CJ Carey (4)
• cesaregarza (1) +
• charlie0389 (1) +
• Hood Chatham (5)
• Andrew Chin (1) +
• Daniel Ching (1) +
• Leo Chow (1) +
• chris (3) +
• John Clow (1) +
• cm7S (1) +
• cmgodwin (1) +
• Christopher Cowden (2) +
• Henry Cuzco (2) +
• Anirudh Dagar (12)
• Hans Dembinski (2) +
• Jaiden di Lanzo (24) +
• Felipe Dias (1) +
• Dieter Werthmüller (1)
• Giuseppe Dilillo (1) +
• dpoerio (1) +
• drpeteb (1) +
• Christopher Dupuis (1) +
• Jordan Edmunds (1) +
• Pieter Eendebak (1) +
• Jérome Eertmans (1) +
• Fabian Egli (2) +
• Sebastian Ehlert (2) +
• Kian Eliasi (1) +
• Tomohiro Endo (1) +
• Stefan Endres (1)
• Zeb Engberg (4) +
• Jonas Eschle (1) +
• Thomas J. Fan (9)
• fiveseven (1) +
• Neil Flood (1) +
• Franz Forstmayr (1)
• Sara Fridovich-Keil (1)
• David Gilbertson (1) +
• Ralf Gommers (251)
• Marco Gorelli (2) +
• Matt Haberland (387)
• Andrew Hawryluk (2) +
• Christoph Hohnerlein (2) +
• Loïc Houpert (2) +
• Shamus Husheer (1) +
• ideasrule (1) +
• imoiwm (1) +
• Lakshaya Inani (1) +
• Joseph T. Iosue (1)
• iwbc-mzk (1) +
• Nathan Jacobi (3) +
• Julien Jerphanion (5)
• He Jia (1)
• jmkuebler (1) +
• Johannes Müller (1) +
• Vedant Jolly (1) +
• Juan Luis Cano Rodríguez (2)
• Justin (1) +
• jvavrek (1) +
• jyuv (2)
• Kai Mühlbauer (1) +
• Nikita Karetnikov (3) +
• Reinert Huseby Karlsen (1) +
• kaspar (2) +
• Toshiki Kataoka (1)
• Robert Kern (3)
• Joshua Klein (1) +
• Andrew Knyazev (7)
• Jozsef Kutas (16) +
• Eric Larson (4)
• Lechnio (1) +
• Antony Lee (2)
• Aditya Limaye (1) +
• Xingyu Liu (2)
• Christian Lorentzen (4)
• Loïc Estève (2)
• Thibaut Lunet (2) +
• Peter Lysakovski (1)
• marianasalamoni (2) +
• mariprudencio (1) +
• Paige Martin (1) +
• Arno Marty (1) +
• matthewborish (3) +
• Damon McDougall (1)
• Nicholas McKibben (22)
• McLP (1) +
• mdmahendri (1) +
• Melissa Weber Mendonça (9)
• Jarrod Millman (1)
• Naoto Mizuno (2)
• Shashaank N (1)
• Pablo S Naharro (1) +
• nboudrie (2) +
• Andrew Nelson (52)
• Nico Schlömer (1)
• NiMlr (1) +
• o-alexandre-felipe (1) +
• Maureen Ononiwu (1) +
• Dimitri Papadopoulos (2) +
• partev (1) +
• Tirth Patel (10)
• Paulius Šarka (1) +
• Josef Perktold (1)
• Giacomo Petrillo (3) +
• Matti Picus (1)
• Rafael Pinto (1) +
• PKNaveen (1) +
• Ilhan Polat (6)
• Akshita Prasanth (2) +
• Sean Quinn (1)
• Tyler Reddy (155)
• Martin Reinecke (1)
• Ned Richards (1)
• Marie Roald (1) +
• Sam Rosen (4) +
• Pamphile Roy (105)
• sabonerune (2) +
• Atsushi Sakai (94)
• Daniel Schmitz (27)
• Anna Scholtz (1) +
• Eli Schwartz (11)
• serge-sans-paille (2)
• JEEVANSHI SHARMA (1) +
• ehsan shirvanian (2) +
• siddhantwahal (2)
• Mathieu Dutour Sikiric (1) +
• Sourav Singh (1)
• Alexander Soare (1) +
• Bjørge Solli (2) +
• Scott Staniewicz (1)
• Ethan Steinberg (3) +
• Albert Steppi (3)
• Thomas Stoeger (1) +
• Kai Striega (4)
• Tartopohm (1) +
• Mamoru TASAKA (2) +
• Ewout ter Hoeven (5)
• TianyiQ (1) +
• Tiger (1) +
• Will Tirone (1)
• Ajay Shanker Tripathi (1) +
• Edgar Andrés Margffoy Tuay (1) +
• Dmitry Ulyumdzhiev (1) +
• Hari Vamsi (1) +
• VitalyChait (1) +
• Rik Voorhaar (1) +
• Samuel Wallan (4)
• Stefan van der Walt (2)
• Warren Weckesser (145)
• wei2222 (1) +
• windows-server-2003 (3) +
• Marek Wojciechowski (2) +
• Niels Wouda (1) +
• WRKampi (1) +
• Yeonjoo Yoo (1) +
• Rory Yorke (1)
• Xiao Yuan (2) +
• Meekail Zain (2) +
• Fabio Zanini (1) +
• Steffen Zeile (1) +
• Egor Zemlyanoy (19)
• Gavin Zhang (3) +

A total of 184 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.

v1.24.1

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NumPy 1.24.1 Release Notes

NumPy 1.24.1 is a maintenance release that fixes bugs and regressions discovered after the 1.24.0 release. The Python versions supported by this release are 3.8-3.11.

Contributors

A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Andrew Nelson
• Ben Greiner +
• Charles Harris
• Clément Robert
• Matteo Raso
• Matti Picus
• Melissa Weber Mendonça
• Miles Cranmer
• Ralf Gommers
• Rohit Goswami
• Sayed Adel
• Sebastian Berg

Pull requests merged

A total of 18 pull requests were merged for this release.

• #​22820: BLD: add workaround in setup.py for newer setuptools
• #​22830: BLD: CIRRUS_TAG redux
• #​22831: DOC: fix a couple typos in 1.23 notes
• #​22832: BUG: Fix refcounting errors found using pytest-leaks
• #​22834: BUG, SIMD: Fix invalid value encountered in several ufuncs
• #​22837: TST: ignore more np.distutils.log imports
• #​22839: BUG: Do not use getdata() in np.ma.masked_invalid
• #​22847: BUG: Ensure correct behavior for rows ending in delimiter in...
• #​22848: BUG, SIMD: Fix the bitmask of the boolean comparison
• #​22857: BLD: Help raspian arm + clang 13 about __builtin_mul_overflow
• #​22858: API: Ensure a full mask is returned for masked_invalid
• #​22866: BUG: Polynomials now copy properly (#​22669)
• #​22867: BUG, SIMD: Fix memory overlap in ufunc comparison loops
• #​22868: BUG: Fortify string casts against floating point warnings
• #​22875: TST: Ignore nan-warnings in randomized out tests
• #​22883: MAINT: restore npymath implementations needed for freebsd
• #​22884: BUG: Fix integer overflow in in1d for mixed integer dtypes #​22877
• #​22887: BUG: Use whole file for encoding checks with charset_normalizer.

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v1.24.0

Compare Source

NumPy 1.24 Release Notes

The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There are also a large number of new and expired deprecations due to changes in promotion and cleanups. This might be called a deprecation release. Highlights are

• Many new deprecations, check them out.
• Many expired deprecations,
• New F2PY features and fixes.
• New "dtype" and "casting" keywords for stacking functions.

See below for the details,

This release supports Python versions 3.8-3.11.

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

The numpy.fastCopyAndTranspose function has been deprecated. Use the corresponding copy and transpose methods directly:

arr.T.copy()

The underlying C function PyArray_CopyAndTranspose has also been deprecated from the NumPy C-API.

(gh-22313)

Conversion of out-of-bound Python integers

Attempting a conversion from a Python integer to a NumPy value will now always check whether the result can be represented by NumPy. This means the following examples will fail in the future and give a DeprecationWarning now:

np.uint8(-1)
np.array([3000], dtype=np.int8)

Many of these did succeed before. Such code was mainly useful for unsigned integers with negative values such as np.uint8(-1) giving np.iinfo(np.uint8).max.

Note that conversion between NumPy integers is unaffected, so that np.array(-1).astype(np.uint8) continues to work and use C integer overflow logic. For negative values, it will also work to view the array: np.array(-1, dtype=np.int8).view(np.uint8). In some cases, using np.iinfo(np.uint8).max or val % 2**8 may also work well.

In rare cases input data may mix both negative values and very large unsigned values (i.e. -1 and 2**63). There it is unfortunately necessary to use % on the Python value or use signed or unsigned conversion depending on whether negative values are expected.

(gh-22385)

Deprecate msort

The numpy.msort function is deprecated. Use np.sort(a, axis=0) instead.

(gh-22456)

np.str0 and similar are now deprecated

The scalar type aliases ending in a 0 bit size: np.object0, np.str0, np.bytes0, np.void0, np.int0, np.uint0 as well as np.bool8 are now deprecated and will eventually be removed.

(gh-22607)

Expired deprecations

• The normed keyword argument has been removed from [np.histogram]{.title-ref}, [np.histogram2d]{.title-ref}, and [np.histogramdd]{.title-ref}. Use density instead. If normed was passed by position, density is now used.

  (gh-21645)

• Ragged array creation will now always raise a ValueError unless dtype=object is passed. This includes very deeply nested sequences.

  (gh-22004)

• Support for Visual Studio 2015 and earlier has been removed.

• Support for the Windows Interix POSIX interop layer has been removed.

  (gh-22139)

• Support for Cygwin < 3.3 has been removed.

  (gh-22159)

• The mini() method of np.ma.MaskedArray has been removed. Use either np.ma.MaskedArray.min() or np.ma.minimum.reduce().

• The single-argument form of np.ma.minimum and np.ma.maximum has been removed. Use np.ma.minimum.reduce() or np.ma.maximum.reduce() instead.

  (gh-22228)

• Passing dtype instances other than the canonical (mainly native byte-order) ones to dtype= or signature= in ufuncs will now raise a TypeError. We recommend passing the strings "int8" or scalar types np.int8 since the byte-order, datetime/timedelta unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.)

  (gh-22540)

• The dtype= argument to comparison ufuncs is now applied correctly. That means that only bool and object are valid values and dtype=object is enforced.

  (gh-22541)

• The deprecation for the aliases np.object, np.bool, np.float, np.complex, np.str, and np.int is expired (introduces NumPy 1.20). Some of these will now give a FutureWarning in addition to raising an error since they will be mapped to the NumPy scalars in the future.

  (gh-22607)

Compatibility notes

array.fill(scalar) may behave slightly different

numpy.ndarray.fill may in some cases behave slightly different now due to the fact that the logic is aligned with item assignment:

arr = np.array([1])  # with any dtype/value
arr.fill(scalar)

is now identical to:

arr[0] = scalar

Previously casting may have produced slightly different answers when using values that could not be represented in the target dtype or when the target had object dtype.

(gh-20924)

Subarray to object cast now copies

Casting a dtype that includes a subarray to an object will now ensure a copy of the subarray. Previously an unsafe view was returned:

arr = np.ones(3, dtype=[("f", "i", 3)])
subarray_fields = arr.astype(object)[0]
subarray = subarray_fields[0]  # "f" field

np.may_share_memory(subarray, arr)

Is now always false. While previously it was true for the specific cast.

(gh-21925)

Returned arrays respect uniqueness of dtype kwarg objects

When the dtype keyword argument is used with :pynp.array(){.interpreted-text role="func"} or :pyasarray(){.interpreted-text role="func"}, the dtype of the returned array now always exactly matches the dtype provided by the caller.

In some cases this change means that a view rather than the input array is returned. The following is an example for this on 64bit Linux where long and longlong are the same precision but different dtypes:

>>> arr = np.array([1, 2, 3], dtype="long")
>>> new_dtype = np.dtype("longlong")
>>> new = np.asarray(arr, dtype=new_dtype)
>>> new.dtype is new_dtype
True
>>> new is arr
False

Before the change, the dtype did not match because new is arr was True.

(gh-21995)

DLPack export raises BufferError

When an array buffer cannot be exported via DLPack a BufferError is now always raised where previously TypeError or RuntimeError was raised. This allows falling back to the buffer protocol or __array_interface__ when DLPack was tried first.

(gh-22542)

NumPy builds are no longer tested on GCC-6

Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available on Ubuntu 20.04, so builds using that compiler are no longer tested. We still test builds using GCC-7 and GCC-8.

(gh-22598)

New Features

New attribute symbol added to polynomial classes

The polynomial classes in the numpy.polynomial package have a new symbol attribute which is used to represent the indeterminate of the polynomial. This can be used to change the value of the variable when printing:

>>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
>>> print(P_y)
1.0 + 0.0·y¹ - 1.0·y²

Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate:

>>> P = np.polynomial.Polynomial([1, -1])  # default symbol is "x"
>>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
>>> P * P_z
Traceback (most recent call last)
   ...
ValueError: Polynomial symbols differ

The symbol can be any valid Python identifier. The default is symbol=x, consistent with existing behavior.

(gh-16154)

F2PY support for Fortran character strings

F2PY now supports wrapping Fortran functions with:

• character (e.g. character x)
• character array (e.g. character, dimension(n) :: x)
• character string (e.g. character(len=10) x)
• and character string array (e.g. character(len=10), dimension(n, m) :: x)

arguments, including passing Python unicode strings as Fortran character string arguments.

(gh-19388)

New function np.show_runtime

A new function numpy.show_runtime has been added to display the runtime information of the machine in addition to numpy.show_config which displays the build-related information.

(gh-21468)

strict option for testing.assert_array_equal

The strict option is now available for testing.assert_array_equal. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-21595)

New parameter equal_nan added to np.unique

np.unique was changed in 1.21 to treat all NaN values as equal and return a single NaN. Setting equal_nan=False will restore pre-1.21 behavior to treat NaNs as unique. Defaults to True.

(gh-21623)

casting and dtype keyword arguments for numpy.stack

The casting and dtype keyword arguments are now available for numpy.stack. To use them, write np.stack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.vstack

The casting and dtype keyword arguments are now available for numpy.vstack. To use them, write np.vstack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.hstack

The casting and dtype keyword arguments are now available for numpy.hstack. To use them, write np.hstack(..., dtype=None, casting='same_kind').

(gh-21627)

The bit generator underlying the singleton RandomState can be changed

The singleton RandomState instance exposed in the numpy.random module is initialized at startup with the MT19937 bit generator. The new function set_bit_generator allows the default bit generator to be replaced with a user-provided bit generator. This function has been introduced to provide a method allowing seamless integration of a high-quality, modern bit generator in new code with existing code that makes use of the singleton-provided random variate generating functions. The companion function get_bit_generator returns the current bit generator being used by the singleton RandomState. This is provided to simplify restoring the original source of randomness if required.

The preferred method to generate reproducible random numbers is to use a modern bit generator in an instance of Generator. The function default_rng simplifies instantiation:

>>> rg = np.random.default_rng(3728973198)
>>> rg.random()

The same bit generator can then be shared with the singleton instance so that calling functions in the random module will use the same bit generator:

>>> orig_bit_gen = np.random.get_bit_generator()
>>> np.random.set_bit_generator(rg.bit_generator)
>>> np.random.normal()

The swap is permanent (until reversed) and so any call to functions in the random module will use the new bit generator. The original can be restored if required for code to run correctly:

>>> np.random.set_bit_generator(orig_bit_gen)

(gh-21976)

np.void now has a dtype argument

NumPy now allows constructing structured void scalars directly by passing the dtype argument to np.void.

(gh-22316)

Improvements

F2PY Improvements

• The generated extension modules don't use the deprecated NumPy-C API anymore
• Improved f2py generated exception messages
• Numerous bug and flake8 warning fixes
• various CPP macros that one can use within C-expressions of signature files are prefixed with f2py_. For example, one should use f2py_len(x) instead of len(x)
• A new construct character(f2py_len=...) is introduced to support returning assumed length character strings (e.g. character(len=*)) from wrapper functions

A hook to support rewriting f2py internal data structures after reading all its input files is introduced. This is required, for instance, for BC of SciPy support where character arguments are treated as character strings arguments in C expressions.

(gh-19388)

IBM zSystems Vector Extension Facility (SIMD)

Added support for SIMD extensions of zSystem (z13, z14, z15), through the universal intrinsics interface. This support leads to performance improvements for all SIMD kernels implemented using the universal intrinsics, including the following operations: rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal, not_equal, greater, greater_equal, less, less_equal, maximum, minimum, fmax, fmin, argmax, argmin, add, subtract, multiply, divide.

(gh-20913)

NumPy now gives floating point errors in casts

In most cases, NumPy previously did not give floating point warnings or errors when these happened during casts. For examples, casts like:

np.array([2e300]).astype(np.float32)  # overflow for float32
np.array([np.inf]).astype(np.int64)

Should now generally give floating point warnings. These warnings should warn that floating point overflow occurred. For errors when converting floating point values to integers users should expect invalid value warnings.

Users can modify the behavior of these warnings using np.errstate.

Note that for float to int casts, the exact warnings that are given may be platform dependent. For example:

arr = np.full(100, value=1000, dtype=np.float64)
arr.astype(np.int8)

May give a result equivalent to (the intermediate cast means no warning is given):

arr.astype(np.int64).astype(np.int8)

May return an undefined result, with a warning set:

RuntimeWarning: invalid value encountered in cast

The precise behavior is subject to the C99 standard and its implementation in both software and hardware.

(gh-21437)

F2PY supports the value attribute

The Fortran standard requires that variables declared with the value attribute must be passed by value instead of reference. F2PY now supports this use pattern correctly. So integer, intent(in), value :: x in Fortran codes will have correct wrappers generated.

(gh-21807)

Added pickle support for third-party BitGenerators

The pickle format for bit generators was extended to allow each bit generator to supply its own constructor when during pickling. Previous versions of NumPy only supported unpickling Generator instances created with one of the core set of bit generators supplied with NumPy. Attempting to unpickle a Generator that used a third-party bit generators would fail since the constructor used during the unpickling was only aware of the bit generators included in NumPy.

(gh-22014)

arange() now explicitly fails with dtype=str

Previously, the np.arange(n, dtype=str) function worked for n=1 and n=2, but would raise a non-specific exception message for other values of n. Now, it raises a [TypeError]{.title-ref} informing that arange does not support string dtypes:

>>> np.arange(2, dtype=str)
Traceback (most recent call last)
   ...
TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>.

(gh-22055)

numpy.typing protocols are now runtime checkable

The protocols used in numpy.typing.ArrayLike and numpy.typing.DTypeLike are now properly marked as runtime checkable, making them easier to use for runtime type checkers.

(gh-22357)

Performance improvements and changes

Faster version of np.isin and np.in1d for integer arrays

np.in1d (used by np.isin) can now switch to a faster algorithm (up to >10x faster) when it is passed two integer arrays. This is often automatically used, but you can use kind="sort" or kind="table" to force the old or new method, respectively.

(gh-12065)

Faster comparison operators

The comparison functions (numpy.equal, numpy.not_equal, numpy.less, numpy.less_equal, numpy.greater and numpy.greater_equal) are now much faster as they are now vectorized with universal intrinsics. For a CPU with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and boolean data types, respectively (with N=50000).

(gh-21483)

Changes

Better reporting of integer division overflow

Integer division overflow of scalars and arrays used to provide a RuntimeWarning and the return value was undefined leading to crashes at rare occasions:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)

Integer division overflow now returns the input dtype's minimum value and raise the following RuntimeWarning:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: overflow encountered in floor_divide
array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
       -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
      dtype=int32)

(gh-21506)

masked_invalid now modifies the mask in-place

When used with copy=False, numpy.ma.masked_invalid now modifies the input masked array in-place. This makes it behave identically to masked_where and better matches the documentation.

(gh-22046)

nditer/NpyIter allows all allocating all operands

The NumPy iterator available through np.nditer in Python and as NpyIter in C now supports allocating all arrays. The iterator shape defaults to () in this case. The operands dtype must be provided, since a "common dtype" cannot be inferred from the other inputs.

(gh-22457)

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SHA256

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