v1.7.3

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SciPy 1.7.3 Release Notes

SciPy 1.7.3 is a bug-fix release that provides binary wheels
for MacOS arm64 with Python 3.8, 3.9, and 3.10. The MacOS arm64 wheels
are only available for MacOS version 12.0 and greater, as explained
in Issue 14688.

Authors

• Anirudh Dagar
• Ralf Gommers
• Tyler Reddy
• Pamphile Roy
• Olivier Grisel
• Isuru Fernando

A total of 6 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.

v1.7.2

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SciPy 1.7.2 Release Notes

SciPy 1.7.2 is a bug-fix release with no new features
compared to 1.7.1. Notably, the release includes wheels
for Python 3.10, and wheels are now built with a newer
version of OpenBLAS, 0.3.17. Python 3.10 wheels are provided
for MacOS x86_64 (thin, not universal2 or arm64 at this time),
and Windows/Linux 64-bit. Many wheels are now built with newer
versions of manylinux, which may require newer versions of pip.

Authors

• Peter Bell
• da-woods +
• Isuru Fernando
• Ralf Gommers
• Matt Haberland
• Nicholas McKibben
• Ilhan Polat
• Judah Rand +
• Tyler Reddy
• Pamphile Roy
• Charles Harris
• Matti Picus
• Hugo van Kemenade
• Jacob Vanderplas

A total of 14 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.

v1.7.1

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SciPy 1.7.1 Release Notes

SciPy 1.7.1 is a bug-fix release with no new features
compared to 1.7.0.

Authors

• Peter Bell
• Evgeni Burovski
• Justin Charlong +
• Ralf Gommers
• Matti Picus
• Tyler Reddy
• Pamphile Roy
• Sebastian Wallkötter
• Arthur Volant

A total of 9 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.

v1.7.0

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SciPy 1.7.0 Release Notes

SciPy 1.7.0 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
1.7.x branch, and on adding new features on the master branch.

This release requires Python 3.7+ and NumPy 1.16.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• A new submodule for quasi-Monte Carlo, scipy.stats.qmc, was added
• The documentation design was updated to use the same PyData-Sphinx theme as
  NumPy and other ecosystem libraries.
• We now vendor and leverage the Boost C++ library to enable numerous
  improvements for long-standing weaknesses in scipy.stats
• scipy.stats has six new distributions, eight new (or overhauled)
  hypothesis tests, a new function for bootstrapping, a class that enables
  fast random variate sampling and percentile point function evaluation,
  and many other enhancements.
• cdist and pdist distance calculations are faster for several metrics,
  especially weighted cases, thanks to a rewrite to a new C++ backend framework
• A new class for radial basis function interpolation, RBFInterpolator, was
  added to address issues with the Rbf class.

We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of the improvements to
scipy.stats.

New features

scipy.cluster improvements

An optional argument, seed, has been added to kmeans and kmeans2 to
set the random generator and random state.

scipy.interpolate improvements

Improved input validation and error messages for fitpack.bispev and
fitpack.parder for scenarios that previously caused substantial confusion
for users.

The class RBFInterpolator was added to supersede the Rbf class. The new
class has usage that more closely follows other interpolator classes, corrects
sign errors that caused unexpected smoothing behavior, includes polynomial
terms in the interpolant (which are necessary for some RBF choices), and
supports interpolation using only the k-nearest neighbors for memory
efficiency.

scipy.linalg improvements

An LAPACK wrapper was added for access to the tgexc subroutine.

scipy.ndimage improvements

scipy.ndimage.affine_transform is now able to infer the output_shape from
the out array.

scipy.optimize improvements

The optional parameter bounds was added to
_minimize_neldermead to support bounds constraints
for the Nelder-Mead solver.

trustregion methods trust-krylov, dogleg and trust-ncg can now
estimate hess by finite difference using one of
["2-point", "3-point", "cs"].

halton was added as a sampling_method in scipy.optimize.shgo.
sobol was fixed and is now using scipy.stats.qmc.Sobol.

halton and sobol were added as init methods in
scipy.optimize.differential_evolution.

differential_evolution now accepts an x0 parameter to provide an
initial guess for the minimization.

least_squares has a modest performance improvement when SciPy is built
with Pythran transpiler enabled.

When linprog is used with method 'highs', 'highs-ipm', or
'highs-ds', the result object now reports the marginals (AKA shadow
prices, dual values) and residuals associated with each constraint.

scipy.signal improvements

get_window supports general_cosine and general_hamming window
functions.

scipy.signal.medfilt2d now releases the GIL where appropriate to enable
performance gains via multithreaded calculations.

scipy.sparse improvements

Addition of dia_matrix sparse matrices is now faster.

scipy.spatial improvements

distance.cdist and distance.pdist performance has greatly improved for
certain weighted metrics. Namely: minkowski, euclidean, chebyshev,
canberra, and cityblock.

Modest performance improvements for many of the unweighted cdist and
pdist metrics noted above.

The parameter seed was added to scipy.spatial.vq.kmeans and
scipy.spatial.vq.kmeans2.

The parameters axis and keepdims where added to
scipy.spatial.distance.jensenshannon.

The rotation methods from_rotvec and as_rotvec now accept a
degrees argument to specify usage of degrees instead of radians.

scipy.special improvements

Wright's generalized Bessel function for positive arguments was added as
scipy.special.wright_bessel.

An implementation of the inverse of the Log CDF of the Normal Distribution is
now available via scipy.special.ndtri_exp.

scipy.stats improvements

Hypothesis Tests

The Mann-Whitney-Wilcoxon test, mannwhitneyu, has been rewritten. It now
supports n-dimensional input, an exact test method when there are no ties,
and improved documentation. Please see "Other changes" for adjustments to
default behavior.

The new function scipy.stats.binomtest replaces scipy.stats.binom_test. The
new function returns an object that calculates a confidence intervals of the
proportion parameter. Also, performance was improved from O(n) to O(log(n)) by
using binary search.

The two-sample version of the Cramer-von Mises test is implemented in
scipy.stats.cramervonmises_2samp.

The Alexander-Govern test is implemented in the new function
scipy.stats.alexandergovern.

The new functions scipy.stats.barnard_exact and scipy.stats. boschloo_exact
respectively perform Barnard's exact test and Boschloo's exact test
for 2x2 contingency tables.

The new function scipy.stats.page_trend_test performs Page's test for ordered
alternatives.

The new function scipy.stats.somersd performs Somers' D test for ordinal
association between two variables.

An option, permutations, has been added in scipy.stats.ttest_ind to
perform permutation t-tests. A trim option was also added to perform
a trimmed (Yuen's) t-test.

The alternative parameter was added to the skewtest, kurtosistest,
ranksums, mood, ansari, linregress, and spearmanr functions
to allow one-sided hypothesis testing.

Sample statistics

The new function scipy.stats.differential_entropy estimates the differential
entropy of a continuous distribution from a sample.

The boxcox and boxcox_normmax now allow the user to control the
optimizer used to minimize the negative log-likelihood function.

A new function scipy.stats.contingency.relative_risk calculates the
relative risk, or risk ratio, of a 2x2 contingency table. The object
returned has a method to compute the confidence interval of the relative risk.

Performance improvements in the skew and kurtosis functions achieved
by removal of repeated/redundant calculations.

Substantial performance improvements in scipy.stats.mstats.hdquantiles_sd.

The new function scipy.stats.contingency.association computes several
measures of association for a contingency table: Pearsons contingency
coefficient, Cramer's V, and Tschuprow's T.

The parameter nan_policy was added to scipy.stats.zmap to provide options
for handling the occurrence of nan in the input data.

The parameter ddof was added to scipy.stats.variation and
scipy.stats.mstats.variation.

The parameter weights was added to scipy.stats.gmean.

Statistical Distributions

We now vendor and leverage the Boost C++ library to address a number of
previously reported issues in stats. Notably, beta, binom,
nbinom now have Boost backends, and it is straightforward to leverage
the backend for additional functions.

The skew Cauchy probability distribution has been implemented as
scipy.stats.skewcauchy.

The Zipfian probability distribution has been implemented as
scipy.stats.zipfian.

The new distributions nchypergeom_fisher and nchypergeom_wallenius
implement the Fisher and Wallenius versions of the noncentral hypergeometric
distribution, respectively.

The generalized hyperbolic distribution was added in
scipy.stats.genhyperbolic.

The studentized range distribution was added in scipy.stats.studentized_range.

scipy.stats.argus now has improved handling for small parameter values.

Better argument handling/preparation has resulted in performance improvements
for many distributions.

The cosine distribution has added ufuncs for ppf, cdf, sf, and
isf methods including numerical precision improvements at the edges of the
support of the distribution.

An option to fit the distribution to data by the method of moments has been
added to the fit method of the univariate continuous distributions.

Other

scipy.stats.bootstrap has been added to allow estimation of the confidence
interval and standard error of a statistic.

The new function scipy.stats.contingency.crosstab computes a contingency
table (i.e. a table of counts of unique entries) for the given data.

scipy.stats.NumericalInverseHermite enables fast random variate sampling
and percentile point function evaluation of an arbitrary univariate statistical
distribution.

New scipy.stats.qmc module

This new module provides Quasi-Monte Carlo (QMC) generators and associated
helper functions.

It provides a generic class scipy.stats.qmc.QMCEngine which defines a QMC
engine/sampler. An engine is state aware: it can be continued, advanced and
reset. 3 base samplers are available:

• scipy.stats.qmc.Sobol the well known Sobol low discrepancy sequence.
  Several warnings have been added to guide the user into properly using this
  sampler. The sequence is scrambled by default.
• scipy.stats.qmc.Halton: Halton low discrepancy sequence. The sequence is
  scrambled by default.
• scipy.stats.qmc.LatinHypercube: plain LHS design.

And 2 special samplers are available:

• scipy.stats.qmc.MultinomialQMC: sampling from a multinomial distribution
  using any of the base scipy.stats.qmc.QMCEngine.
• scipy.stats.qmc.MultivariateNormalQMC: sampling from a multivariate Normal
  using any of the base scipy.stats.qmc.QMCEngine.

The module also provide the following helpers:

• scipy.stats.qmc.discrepancy: assess the quality of a set of points in terms
  of space coverage.
• scipy.stats.qmc.update_discrepancy: can be used in an optimization loop to
  construct a good set of points.
• scipy.stats.qmc.scale: easily scale a set of points from (to) the unit
  interval to (from) a given range.

Deprecated features

scipy.linalg deprecations

• scipy.linalg.pinv2 is deprecated and its functionality is completely
  subsumed into scipy.linalg.pinv
• Both rcond, cond keywords of scipy.linalg.pinv and
  scipy.linalg.pinvh were not working and now are deprecated. They are now
  replaced with functioning atol and rtol keywords with clear usage.

scipy.spatial deprecations

• scipy.spatial.distance metrics expect 1d input vectors but will call
  np.squeeze on their inputs to accept any extra length-1 dimensions. That
  behaviour is now deprecated.

Other changes

We now accept and leverage performance improvements from the ahead-of-time
Python-to-C++ transpiler, Pythran, which can be optionally disabled (via
export SCIPY_USE_PYTHRAN=0) but is enabled by default at build time.

There are two changes to the default behavior of scipy.stats.mannwhitenyu:

• For years, use of the default alternative=None was deprecated; explicit
  alternative specification was required. Use of the new default value of
  alternative, "two-sided", is now permitted.
• Previously, all p-values were based on an asymptotic approximation. Now, for
  small samples without ties, the p-values returned are exact by default.

Support has been added for PEP 621 (project metadata in pyproject.toml)

We now support a Gitpod environment to reduce the barrier to entry for SciPy
development; for more details see :ref:quickstart-gitpod.

Authors

• @​endolith
• Jelle Aalbers +
• Adam +
• Tania Allard +
• Sven Baars +
• Max Balandat +
• baumgarc +
• Christoph Baumgarten
• Peter Bell
• Lilian Besson
• Robinson Besson +
• Max Bolingbroke
• Blair Bonnett +
• Jordão Bragantini
• Harm Buisman +
• Evgeni Burovski
• Matthias Bussonnier
• Dominic C
• CJ Carey
• Ramón Casero +
• Chachay +
• charlotte12l +
• Benjamin Curtice Corbett +
• Falcon Dai +
• Ian Dall +
• Terry Davis
• droussea2001 +
• DWesl +
• dwight200 +
• Thomas J. Fan +
• Joseph Fox-Rabinovitz
• Max Frei +
• Laura Gutierrez Funderburk +
• gbonomib +
• Matthias Geier +
• Pradipta Ghosh +
• Ralf Gommers
• Evan H +
• h-vetinari
• Matt Haberland
• Anselm Hahn +
• Alex Henrie
• Piet Hessenius +
• Trever Hines +
• Elisha Hollander +
• Stephan Hoyer
• Tom Hu +
• Kei Ishikawa +
• Julien Jerphanion
• Robert Kern
• Shashank KS +
• Peter Mahler Larsen
• Eric Larson
• Cheng H. Lee +
• Gregory R. Lee
• Jean-Benoist Leger +
• lgfunderburk +
• liam-o-marsh +
• Xingyu Liu +
• Alex Loftus +
• Christian Lorentzen +
• Cong Ma
• Marc +
• MarkPundurs +
• Markus Löning +
• Liam Marsh +
• Nicholas McKibben
• melissawm +
• Jamie Morton
• Andrew Nelson
• Nikola Forró
• Tor Nordam +
• Olivier Gauthé +
• Rohit Pandey +
• Avanindra Kumar Pandeya +
• Tirth Patel
• paugier +
• Alex H. Wagner, PhD +
• Jeff Plourde +
• Ilhan Polat
• pranavrajpal +
• Vladyslav Rachek
• Bharat Raghunathan
• Recursing +
• Tyler Reddy
• Lucas Roberts
• Gregor Robinson +
• Pamphile Roy +
• Atsushi Sakai
• Benjamin Santos
• Martin K. Scherer +
• Thomas Schmelzer +
• Daniel Scott +
• Sebastian Wallkötter +
• serge-sans-paille +
• Namami Shanker +
• Masashi Shibata +
• Alexandre de Siqueira +
• Albert Steppi +
• Adam J. Stewart +
• Kai Striega
• Diana Sukhoverkhova
• Søren Fuglede Jørgensen
• Mike Taves
• Dan Temkin +
• Nicolas Tessore +
• tsubota20 +
• Robert Uhl
• christos val +
• Bas van Beek +
• Ashutosh Varma +
• Jose Vazquez +
• Sebastiano Vigna
• Aditya Vijaykumar
• VNMabus
• Arthur Volant +
• Samuel Wallan
• Stefan van der Walt
• Warren Weckesser
• Anreas Weh
• Josh Wilson
• Rory Yorke
• Egor Zemlyanoy
• Marc Zoeller +
• zoj613 +
• 秋纫 +

A total of 126 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.

v1.6.3

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SciPy 1.6.3 Release Notes

SciPy 1.6.3 is a bug-fix release with no new features
compared to 1.6.2.

Authors

• Peter Bell
• Ralf Gommers
• Matt Haberland
• Peter Mahler Larsen
• Tirth Patel
• Tyler Reddy
• Pamphile ROY +
• Xingyu Liu +

A total of 8 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.

v1.6.2

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SciPy 1.6.2 Release Notes

SciPy 1.6.2 is a bug-fix release with no new features
compared to 1.6.1. This is also the first SciPy release
to place upper bounds on some dependencies to improve
the long-term repeatability of source builds.

Authors

• Pradipta Ghosh +
• Tyler Reddy
• Ralf Gommers
• Martin K. Scherer +
• Robert Uhl
• Warren Weckesser

A total of 6 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.

v1.6.1

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SciPy 1.6.1 Release Notes

SciPy 1.6.1 is a bug-fix release with no new features
compared to 1.6.0.

Please note that for SciPy wheels to correctly install with pip on
macOS 11, pip >= 20.3.3 is needed.

Authors

• Peter Bell
• Evgeni Burovski
• CJ Carey
• Ralf Gommers
• Peter Mahler Larsen
• Cheng H. Lee +
• Cong Ma
• Nicholas McKibben
• Nikola Forró
• Tyler Reddy
• Warren Weckesser

A total of 11 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.

v1.6.0

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SciPy 1.6.0 Release Notes

SciPy 1.6.0 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
1.6.x branch, and on adding new features on the master branch.

This release requires Python 3.7+ and NumPy 1.16.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• scipy.ndimage improvements: Fixes and ehancements to boundary extension
  modes for interpolation functions. Support for complex-valued inputs in many
  filtering and interpolation functions. New grid_mode option for
  scipy.ndimage.zoom to enable results consistent with scikit-image's
  rescale.
• scipy.optimize.linprog has fast, new methods for large, sparse problems
  from the HiGHS library.
• scipy.stats improvements including new distributions, a new test, and
  enhancements to existing distributions and tests

New features

scipy.special improvements

scipy.special now has improved support for 64-bit LAPACK backend

scipy.odr improvements

scipy.odr now has support for 64-bit integer BLAS

scipy.odr.ODR has gained an optional overwrite argument so that existing
files may be overwritten.

scipy.integrate improvements

Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:

• integrate.simps was renamed to integrate.simpson
• integrate.trapz was renamed to integrate.trapezoid
• integrate.cumtrapz was renamed to integrate.cumulative_trapezoid

scipy.cluster improvements

scipy.cluster.hierarchy.DisjointSet has been added for incremental
connectivity queries.

scipy.cluster.hierarchy.dendrogram return value now also includes leaf color
information in leaves_color_list.

scipy.interpolate improvements

scipy.interpolate.interp1d has a new method nearest-up, similar to the
existing method nearest but rounds half-integers up instead of down.

scipy.io improvements

Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.

scipy.linalg improvements

The new function scipy.linalg.matmul_toeplitz uses the FFT to compute the
product of a Toeplitz matrix with another matrix.

scipy.linalg.sqrtm and scipy.linalg.logm have performance improvements
thanks to additional Cython code.

Python LAPACK wrappers have been added for pptrf, pptrs, ppsv,
pptri, and ppcon.

scipy.linalg.norm and the svd family of functions will now use 64-bit
integer backends when available.

scipy.ndimage improvements

scipy.ndimage.convolve, scipy.ndimage.correlate and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. gaussian_filter, uniform_filter, etc.).

Multiple fixes and enhancements to boundary handling were introduced to
scipy.ndimage interpolation functions (i.e. affine_transform,
geometric_transform, map_coordinates, rotate, shift, zoom).

A new boundary mode, grid-wrap was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing wrap mode which uses a period that is one sample smaller
than the original signal extent along each dimension.

A long-standing bug in the reflect boundary condition has been fixed and
the mode grid-mirror was introduced as a synonym for reflect.

A new boundary mode, grid-constant is now available. This is similar to
the existing ndimage constant mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
grid-constant mode is consistent with OpenCV's BORDER_CONSTANT mode
and scikit-image's constant mode.

Spline pre-filtering (used internally by ndimage interpolation functions
when order >= 2), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
spline_filter and spline_filter1d have analytical boundary conditions
that match modes mirror, grid-wrap and reflect.

scipy.ndimage interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.

The ndimage tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.

scipy.ndimage.zoom now has a grid_mode option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
resize and rescale functions (and OpenCV's cv2.resize).

scipy.optimize improvements

scipy.optimize.linprog has fast, new methods for large, sparse problems from
the HiGHS C++ library. method='highs-ds' uses a high performance dual
revised simplex implementation (HSOL), method='highs-ipm' uses an
interior-point method with crossover, and method='highs' chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other linprog methods, so we recommend explicitly
specifying one of these three method values when using linprog.

scipy.optimize.quadratic_assignment has been added for approximate solution
of the quadratic assignment problem.

scipy.optimize.linear_sum_assignment now has a substantially reduced overhead
for small cost matrix sizes

scipy.optimize.least_squares has improved performance when the user provides
the jacobian as a sparse jacobian already in csr_matrix format

scipy.optimize.linprog now has an rr_method argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.

scipy.signal improvements

scipy.signal.gammatone has been added to design FIR or IIR filters that
model the human auditory system.

scipy.signal.iircomb has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.

scipy.signal.sosfilt performance has been improved to avoid some previously-
observed slowdowns

scipy.signal.windows.taylor has been added--the Taylor window function is
commonly used in radar digital signal processing

scipy.signal.gauss_spline now supports list type input for consistency
with other related SciPy functions

scipy.signal.correlation_lags has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.

scipy.sparse improvements

A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
scipy.sparse.csgraph.min_weight_full_bipartite_matching. In particular, this
provides functionality analogous to that of
scipy.optimize.linear_sum_assignment, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.

The time complexity of scipy.sparse.block_diag has been improved dramatically
from quadratic to linear.

scipy.sparse.linalg improvements

The vendored version of SuperLU has been updated

scipy.fft improvements

The vendored pocketfft library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.

scipy.spatial improvements

The python implementation of KDTree has been dropped and KDTree is now
implemented in terms of cKDTree. You can now expect cKDTree-like
performance by default. This also means sys.setrecursionlimit no longer
needs to be increased for querying large trees.

transform.Rotation has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).

scipy.spatial.transform.Rotation has been partially cythonized, with some
performance improvements observed

scipy.spatial.distance.cdist has improved performance with the minkowski
metric, especially for p-norm values of 1 or 2.

scipy.stats improvements

New distributions have been added to scipy.stats:

• The asymmetric Laplace continuous distribution has been added as
  scipy.stats.laplace_asymmetric.
• The negative hypergeometric distribution has been added as scipy.stats.nhypergeom.
• The multivariate t distribution has been added as scipy.stats.multivariate_t.
• The multivariate hypergeometric distribution has been added as scipy.stats.multivariate_hypergeom.

The fit method has been overridden for several distributions (laplace,
pareto, rayleigh, invgauss, logistic, gumbel_l,
gumbel_r); they now use analytical, distribution-specific maximum
likelihood estimation results for greater speed and accuracy than the generic
(numerical optimization) implementation.

The one-sample Cramér-von Mises test has been added as
scipy.stats.cramervonmises.

An option to compute one-sided p-values was added to scipy.stats.ttest_1samp,
scipy.stats.ttest_ind_from_stats, scipy.stats.ttest_ind and
scipy.stats.ttest_rel.

The function scipy.stats.kendalltau now has an option to compute Kendall's
tau-c (also known as Stuart's tau-c), and support has been added for exact
p-value calculations for sample sizes > 171.

stats.trapz was renamed to stats.trapezoid, with the former name retained
as an alias for backwards compatibility reasons.

The function scipy.stats.linregress now includes the standard error of the
intercept in its return value.

The _logpdf, _sf, and _isf methods have been added to
scipy.stats.nakagami; _sf and _isf methods also added to
scipy.stats.gumbel_r

The sf method has been added to scipy.stats.levy and scipy.stats.levy_l
for improved precision.

scipy.stats.binned_statistic_dd performance improvements for the following
computed statistics: max, min, median, and std.

We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of these improvements to
scipy.stats.

Deprecated features

scipy.spatial changes

Calling KDTree.query with k=None to find all neighbours is deprecated.
Use KDTree.query_ball_point instead.

distance.wminkowski was deprecated; use distance.minkowski and supply
weights with the w keyword instead.

Backwards incompatible changes

scipy changes

Using scipy.fft as a function aliasing numpy.fft.fft was removed after
being deprecated in SciPy 1.4.0. As a result, the scipy.fft submodule
must be explicitly imported now, in line with other SciPy subpackages.

scipy.signal changes

The output of decimate, lfilter_zi, lfiltic, sos2tf, and
sosfilt_zi have been changed to match numpy.result_type of their inputs.

The window function slepian was removed. It had been deprecated since SciPy
1.1.

scipy.spatial changes

cKDTree.query now returns 64-bit rather than 32-bit integers on Windows,
making behaviour consistent between platforms (PR gh-12673).

scipy.stats changes

The frechet_l and frechet_r distributions were removed. They were
deprecated since SciPy 1.0.

Other changes

setup_requires was removed from setup.py. This means that users
invoking python setup.py install without having numpy already installed
will now get an error, rather than having numpy installed for them via
easy_install. This install method was always fragile and problematic, users
are encouraged to use pip when installing from source.

• Fixed a bug in scipy.optimize.dual_annealing accept_reject calculation
  that caused uphill jumps to be accepted less frequently.
• The time required for (un)pickling of scipy.stats.rv_continuous,
  scipy.stats.rv_discrete, and scipy.stats.rv_frozen has been significantly
  reduced (gh12550). Inheriting subclasses should note that __setstate__ no
  longer calls __init__ upon unpickling.

Authors

• @​endolith
• @​vkk800
• aditya +
• George Bateman +
• Christoph Baumgarten
• Peter Bell
• Tobias Biester +
• Keaton J. Burns +
• Evgeni Burovski
• Rüdiger Busche +
• Matthias Bussonnier
• Dominic C +
• Corallus Caninus +
• CJ Carey
• Thomas A Caswell
• chapochn +
• Lucía Cheung
• Zach Colbert +
• Coloquinte +
• Yannick Copin +
• Devin Crowley +
• Terry Davis +
• Michaël Defferrard +
• devonwp +
• Didier +
• divenex +
• Thomas Duvernay +
• Eoghan O'Connell +
• Gökçen Eraslan
• Kristian Eschenburg +
• Ralf Gommers
• Thomas Grainger +
• GreatV +
• Gregory Gundersen +
• h-vetinari +
• Matt Haberland
• Mark Harfouche +
• He He +
• Alex Henrie
• Chun-Ming Huang +
• Martin James McHugh III +
• Alex Izvorski +
• Joey +
• ST John +
• Jonas Jonker +
• Julius Bier Kirkegaard
• Marcin Konowalczyk +
• Konrad0
• Sam Van Kooten +
• Sergey Koposov +
• Peter Mahler Larsen
• Eric Larson
• Antony Lee
• Gregory R. Lee
• Loïc Estève
• Jean-Luc Margot +
• MarkusKoebis +
• Nikolay Mayorov
• G. D. McBain
• Andrew McCluskey +
• Nicholas McKibben
• Sturla Molden
• Denali Molitor +
• Eric Moore
• Shashaank N +
• Prashanth Nadukandi +
• nbelakovski +
• Andrew Nelson
• Nick +
• Nikola Forró +
• odidev
• ofirr +
• Sambit Panda
• Dima Pasechnik
• Tirth Patel +
• Matti Picus
• Paweł Redzyński +
• Vladimir Philipenko +
• Philipp Thölke +
• Ilhan Polat
• Eugene Prilepin +
• Vladyslav Rachek
• Ram Rachum +
• Tyler Reddy
• Martin Reinecke +
• Simon Segerblom Rex +
• Lucas Roberts
• Benjamin Rowell +
• Eli Rykoff +
• Atsushi Sakai
• Moritz Schulte +
• Daniel B. Smith
• Steve Smith +
• Jan Soedingrekso +
• Victor Stinner +
• Jose Storopoli +
• Diana Sukhoverkhova +
• Søren Fuglede Jørgensen
• taoky +
• Mike Taves +
• Ian Thomas +
• Will Tirone +
• Frank Torres +
• Seth Troisi
• Ronald van Elburg +
• Hugo van Kemenade
• Paul van Mulbregt
• Saul Ivan Rivas Vega +
• Pauli Virtanen
• Jan Vleeshouwers
• Samuel Wallan
• Warren Weckesser
• Ben West +
• Eric Wieser
• WillTirone +
• Levi John Wolf +
• Zhiqing Xiao
• Rory Yorke +
• Yun Wang (Maigo) +
• Egor Zemlyanoy +
• ZhihuiChen0903 +
• Jacob Zhong +

A total of 122 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.

v1.5.4

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SciPy 1.5.4 Release Notes

SciPy 1.5.4 is a bug-fix release with no new features
compared to 1.5.3. Importantly, wheels are now available
for Python 3.9 and a more complete fix has been applied for
issues building with XCode 12.

Authors

• Peter Bell
• CJ Carey
• Andrew McCluskey +
• Andrew Nelson
• Tyler Reddy
• Eli Rykoff +
• Ian Thomas +

A total of 7 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.

v1.5.3

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SciPy 1.5.3 Release Notes

SciPy 1.5.3 is a bug-fix release with no new features
compared to 1.5.2. In particular, Linux ARM64 wheels are now
available and a compatibility issue with XCode 12 has
been fixed.

Authors

• Peter Bell
• CJ Carey
• Thomas Duvernay +
• Gregory Lee
• Eric Moore
• odidev
• Dima Pasechnik
• Tyler Reddy
• Simon Segerblom Rex +
• Daniel B. Smith
• Will Tirone +
• Warren Weckesser

A total of 12 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.

v1.5.2

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SciPy 1.5.2 Release Notes

SciPy 1.5.2 is a bug-fix release with no new features
compared to 1.5.1.

Authors

• Peter Bell
• Tobias Biester +
• Evgeni Burovski
• Thomas A Caswell
• Ralf Gommers
• Sturla Molden
• Andrew Nelson
• ofirr +
• Sambit Panda
• Ilhan Polat
• Tyler Reddy
• Atsushi Sakai
• Pauli Virtanen

A total of 13 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.

v1.5.1

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SciPy 1.5.1 Release Notes

SciPy 1.5.1 is a bug-fix release with no new features
compared to 1.5.0. In particular, an issue where DLL loading
can fail for SciPy wheels on Windows with Python 3.6 has been
fixed.

Authors

• Peter Bell
• Loïc Estève
• Philipp Thölke +
• Tyler Reddy
• Paul van Mulbregt
• Pauli Virtanen
• Warren Weckesser

A total of 7 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.

v1.5.0

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SciPy 1.5.0 Release Notes

SciPy 1.5.0 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
1.5.x branch, and on adding new features on the master branch.

This release requires Python 3.6+ and NumPy 1.14.5 or greater.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release

• wrappers for more than a dozen new LAPACK routines are now available
  in scipy.linalg.lapack
• Improved support for leveraging 64-bit integer size from linear algebra
  backends
• addition of the probability distribution for two-sided one-sample
  Kolmogorov-Smirnov tests

New features

scipy.cluster improvements

Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.

scipy.cluster.hierarchy.dendrogram now respects the matplotlib color
palette

scipy.fft improvements

A new keyword-only argument plan is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW and mkl-fft), and it is
currently not used in SciPy.

scipy.integrate improvements

scipy.interpolate improvements

scipy.io improvements

scipy.io.wavfile error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.

scipy.io.loadmat gained a simplify_cells parameter, which if set to
True simplifies the structure of the return value if the .mat file
contains cell arrays.

pathlib.Path objects are now supported in scipy.io Matrix Market I/O
functions

scipy.linalg improvements

scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value keyword. Another keyword subset_by_index is introduced.
Keywords turbo and eigvals are deprecated.

Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
?<sy/he>evx are added and existing ones now have full _lwork
counterparts.

Wrappers for the following LAPACK routines have been added to
scipy.linalg.lapack:

• ?getc2: computes the LU factorization of a general matrix with complete
  pivoting
• ?gesc2: solves a linear system given an LU factorization from ?getc2
• ?gejsv: computes the singular value decomposition of a general matrix
  with higher accuracy calculation of tiny singular values and their
  corresponding singular vectors
• ?geqrfp: computes the QR factorization of a general matrix with
  non-negative elements on the diagonal of R
• ?gtsvx: solves a linear system with general tridiagonal matrix
• ?gttrf: computes the LU factorization of a tridiagonal matrix
• ?gttrs: solves a linear system given an LU factorization from ?gttrf
• ?ptsvx: solves a linear system with symmetric positive definite
  tridiagonal matrix
• ?pttrf: computes the LU factorization of a symmetric positive definite
  tridiagonal matrix
• ?pttrs: solves a linear system given an LU factorization from ?pttrf
• ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
  tridiagonal matrix
• ?tbtrs: solves a linear system with a triangular banded matrix
• ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary
  matrix

Generalized QR factorization routines (?geqrf) now have full _lwork
counterparts.

scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
added.

The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
was added.

The new function scipy.linalg.convolution_matrix constructs the Toeplitz
matrix representing one-dimensional convolution.

scipy.ndimage improvements

scipy.optimize improvements

The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:

• 2-point, 3-point, or complex step finite differences can be used. Previously
  only a 2-step finite difference was available.
• There is now the possibility to use a relative step size, previously only an
  absolute step size was available.
• If the minimize method uses bounds the numerical differentiation strictly
  obeys those limits.
• The numerical differentiation machinery now makes use of a simple cache,
  which in some cases can reduce the number of function evaluations.
• minimize's method= 'powell' now supports simple bound constraints

There have been several improvements to scipy.optimize.linprog:

• The linprog benchmark suite has been expanded considerably.
• linprog's dense pivot-based redundancy removal routine and sparse
  presolve are faster
• When scikit-sparse is available, solving sparse problems with
  method='interior-point' is faster

The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation such as BFGS.

differential_evolution can now use the modern np.random.Generator as
well as the legacy np.random.RandomState as a seed.

scipy.signal improvements

A new optional argument include_nyquist is added to freqz functions in
this module. It is used for including the last frequency (Nyquist frequency).

scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
size of the window used to calculate the noise floor.

scipy.sparse improvements

Outer indexing is now faster when using a 2d column vector to select column
indices.

scipy.sparse.lil.tocsr is faster

Fixed/improved comparisons between pydata sparse arrays and sparse matrices

BSR format sparse multiplication performance has been improved.

scipy.sparse.linalg.LinearOperator has gained the new ndim class
attribute

scipy.spatial improvements

scipy.spatial.geometric_slerp has been added to enable geometric
spherical linear interpolation on an n-sphere

scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
and 3D cases

The tree building algorithm used by cKDTree has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.

scipy.special improvements

The following functions now have Cython interfaces in cython_special:

• scipy.special.erfinv
• scipy.special.erfcinv
• scipy.special.spherical_jn
• scipy.special.spherical_yn
• scipy.special.spherical_in
• scipy.special.spherical_kn

scipy.special.log_softmax has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x)) for
inputs that make softmax saturate.

scipy.stats improvements

The function for generating random samples in scipy.stats.dlaplace has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).

scipy.stats functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator as well as the legacy
np.random.RandomState as a seed.

The axis parameter was added to scipy.stats.rankdata. This allows slices
of an array along the given axis to be ranked independently.

The axis parameter was added to scipy.stats.f_oneway, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway was also improved for some cases.

The PDF and CDF methods for stats.geninvgauss are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
LowLevelCallable.

Moments of the normal distribution (scipy.stats.norm) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy

Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy

Methods of the truncated normal distribution (scipy.stats.truncnorm),
especially _rvs, are significantly faster after a complete rewrite.

The fit method of the Laplace distribution, scipy.stats.laplace, now uses
the analytical formulas for the maximum likelihood estimates of the parameters.

Generation of random variates is now thread safe for all SciPy distributions.
3rd-party distributions may need to modify the signature of the _rvs()
method to conform to _rvs(self, ..., size=None, random_state=None). (A
one-time VisibleDeprecationWarning is emitted when using non-conformant
distributions.)

The Kolmogorov-Smirnov two-sided test statistic distribution
(scipy.stats.kstwo) was added. Calculates the distribution of the K-S
two-sided statistic D_n for a sample of size n, using a mixture of exact
and asymptotic algorithms.

The new function median_abs_deviation replaces the deprecated
median_absolute_deviation.

The wilcoxon function now computes the p-value for Wilcoxon's signed rank
test using the exact distribution for inputs up to length 25. The function has
a new mode parameter to specify how the p-value is to be computed. The
default is "auto", which uses the exact distribution for inputs up to length
25 and the normal approximation for larger inputs.

Added a new Cython-based implementation to evaluate guassian kernel estimates,
which should improve the performance of gaussian_kde

The winsorize function now has a nan_policy argument for refined
handling of nan input values.

The binned_statistic_dd function with statistic="std" performance was
improved by ~4x.

scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
two-sample testing. The one-sample variation uses scipy.stats.ksone
(or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
the p-value. The two-sample variation, invoked if cdf is array_like, uses
an algorithm described by Hodges to compute the probability directly, only
backing off to scipy.stats.kstwo in case of overflow. The result in both
cases is more accurate p-values, especially for two-sample testing with
smaller (or quite different) sizes.

scipy.stats.maxwell performance improvements include a 20 % speed up for
`fit()and 5 % forpdf()``

scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
for greater consistency with other stats functions

Deprecated features

scipy deprecations

scipy.special changes

The bdtr, bdtrc, and bdtri functions are deprecating non-negative
non-integral n arguments.

scipy.stats changes

The function median_absolute_deviation is deprecated. Use
median_abs_deviation instead.

The use of the string "raw" with the scale parameter of iqr is
deprecated. Use scale=1 instead.

Backwards incompatible changes

scipy.interpolate changes

scipy.linalg changes

The output signatures of ?syevr, ?heevr have been changed from
w, v, info to w, v, m, isuppz, info

The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
swapped.

scipy.signal changes

The output length of scipy.signal.upfirdn has been corrected, resulting
outputs may now be shorter for some combinations of up/down ratios and input
signal and filter lengths.

scipy.signal.resample now supports a domain keyword argument for
specification of time or frequency domain input.

scipy.stats changes

Other changes

Improved support for leveraging 64-bit integer size from linear algebra backends
in several parts of the SciPy codebase.

Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
been removed.

Many warnings due to unused imports and unused assignments have been addressed.

Many usage examples were added to function docstrings, and many input
validations and intuitive exception messages have been added throughout the
codebase.

Early stage adoption of type annotations in a few parts of the codebase

Authors

• @​endolith
• Hameer Abbasi
• ADmitri +
• Wesley Alves +
• Berkay Antmen +
• Sylwester Arabas +
• Arne Küderle +
• Christoph Baumgarten
• Peter Bell
• Felix Berkenkamp
• Jordão Bragantini +
• Clemens Brunner +
• Evgeni Burovski
• Matthias Bussonnier +
• CJ Carey
• Derrick Chambers +
• Leander Claes +
• Christian Clauss
• Luigi F. Cruz +
• dankleeman
• Andras Deak
• Milad Sadeghi DM +
• jeremie du boisberranger +
• Stefan Endres
• Malte Esders +
• Leo Fang +
• felixhekhorn +
• Isuru Fernando
• Andrew Fowlie
• Lakshay Garg +
• Gaurav Gijare +
• Ralf Gommers
• Emmanuelle Gouillart +
• Kevin Green +
• Martin Grignard +
• Maja Gwozdz
• Sturla Molden
• gyu-don +
• Matt Haberland
• hakeemo +
• Charles Harris
• Alex Henrie
• Santi Hernandez +
• William Hickman +
• Till Hoffmann +
• Joseph T. Iosue +
• Anany Shrey Jain
• Jakob Jakobson
• Charles Jekel +
• Julien Jerphanion +
• Jiacheng-Liu +
• Christoph Kecht +
• Paul Kienzle +
• Reidar Kind +
• Dmitry E. Kislov +
• Konrad +
• Konrad0
• Takuya KOUMURA +
• Krzysztof Pióro
• Peter Mahler Larsen
• Eric Larson
• Antony Lee
• Gregory Lee +
• Gregory R. Lee
• Chelsea Liu
• Cong Ma +
• Kevin Mader +
• Maja Gwóźdź +
• Alex Marvin +
• Matthias Kümmerer
• Nikolay Mayorov
• Mazay0 +
• G. D. McBain
• Nicholas McKibben +
• Sabrina J. Mielke +
• Sebastian J. Mielke +
• Miloš Komarčević +
• Shubham Mishra +
• Santiago M. Mola +
• Grzegorz Mrukwa +
• Peyton Murray
• Andrew Nelson
• Nico Schlömer
• nwjenkins +
• odidev +
• Sambit Panda
• Vikas Pandey +
• Rick Paris +
• Harshal Prakash Patankar +
• Balint Pato +
• Matti Picus
• Ilhan Polat
• poom +
• Siddhesh Poyarekar
• Vladyslav Rachek +
• Bharat Raghunathan
• Manu Rajput +
• Tyler Reddy
• Andrew Reed +
• Lucas Roberts
• Ariel Rokem
• Heshy Roskes
• Matt Ruffalo
• Atsushi Sakai +
• Benjamin Santos +
• Christoph Schock +
• Lisa Schwetlick +
• Chris Simpson +
• Leo Singer
• Kai Striega
• Søren Fuglede Jørgensen
• Kale-ab Tessera +
• Seth Troisi +
• Robert Uhl +
• Paul van Mulbregt
• Vasiliy +
• Isaac Virshup +
• Pauli Virtanen
• Shakthi Visagan +
• Jan Vleeshouwers +
• Sam Wallan +
• Lijun Wang +
• Warren Weckesser
• Richard Weiss +
• wenhui-prudencemed +
• Eric Wieser
• Josh Wilson
• James Wright +
• Ruslan Yevdokymov +
• Ziyao Zhang +

A total of 129 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.