SciPy 1.10.0 Release Notes
Note: SciPy 1.10.0
is not released yet!
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 ofscipy.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 fromscipy.misc
have been added toscipy.datasets
(and deprecated inscipy.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 thescipy.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
scipy.integrate.qmc_quad
, which performs quadrature using Quasi-Monte
Carlo points. - Added parameter
complex_func
toscipy.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
andpchip
)- Tensor-product interpolation methods (
slinear
,cubic
,quintic
and
pchip
) inscipy.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, andRegularGridInterpolator
is now
easier to subclassscipy.interpolate.interp1d
now can take a single value for non-spline
methods.- A new
extrapolate
argument is available toscipy.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. Thelam=None
(default) mode of this function is a clean-room
reimplementation of the classicgcvspl.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, ascipy.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
andtrsen
. -
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.
- a simple tunable restart potentially increases the attainable
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 byscipy.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-valueargs
into a
tuple.scipy.optimize.least_squares
andscipy.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 toscipy.optimize.linprog
withmethod='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)
, computesx**y - 1
. The function avoids the loss of
precision that can result wheny
is close to 0 or whenx
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
parameterbootstrap_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
-
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.
- Added
-
Improved the
fit
method of several distributions.scipy.stats.skewnorm
andscipy.stats.weibull_min
now use an analytical
solution whenmethod='mm'
, which also serves a starting guess to
improve the performance ofmethod='mle'
.scipy.stats.gumbel_r
andscipy.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 ofscipy.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.
- Drawing multiple samples from
-
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
methodscdf
,sf
,ppf
, andisf
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
methodsppf
andisf
methods now use the
implementations from Boost, improving speed and accuracy.scipy.stats.invweibull
methodssf
andisf
are more accurate for
small probability masses.scipy.stats.nct
andscipy.stats.ncx2
now rely on the implementations
from Boost, improving speed and accuracy.- Implemented the
logpdf
method ofscipy.stats.vonmises
for reliability
in extreme tails. - Implemented the
isf
method ofscipy.stats.levy
for speed and
accuracy. - Improved the robustness of
scipy.stats.studentized_range
for largedf
by adding an infinite degree-of-freedom approximation. - Added a parameter
lower_limit
toscipy.stats.multivariate_normal
,
allowing the user to change the integration limit from -inf to a desired
value. - Improved the robustness of
entropy
ofscipy.stats.vonmises
for large
concentration values.
- Added
-
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 ofscipy.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.
- Added
-
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
andscipy.stats.ttest_rel
. - The
scipy.stats
functionscombine_pvalues
,fisher_exact
,
chi2_contingency
,median_test
andmood
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
, andweightedtau
have been renamed to
statistic
andpvalue
for consistency throughoutscipy.stats
.
Old attribute names are still allowed for backward compatibility. scipy.stats.anderson
now returns the parameters of the fitted
distribution in ascipy.stats._result_classes.FitResult
object.- The
plot
method ofscipy.stats._result_classes.FitResult
now accepts
aplot_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.
- Added a
-
Improved the performance of several
scipy.stats
functions.- Improved the performance of
scipy.stats.cramervonmises_2samp
and
scipy.stats.ks_2samp
withmethod='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.
- Improved the performance of
-
Added the
scramble
optional argument toscipy.stats.qmc.LatinHypercube
.
It replacescentered
, which is now deprecated. -
Added a parameter
optimization
to allscipy.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 thevectorized
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 toscipy.stats.rankdata
.
Deprecated features
scipy.misc
module and all the methods inmisc
are deprecated in v1.10
and will be completely removed in SciPy v2.0.0. Users are suggested to
utilize thescipy.datasets
module instead for the dataset methods.scipy.stats.qmc.LatinHypercube
parametercentered
has been deprecated.
It is replaced by thescramble
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 inlinalg.pinv
- Removed wrappers
scipy.linalg.blas.{clapack, flapack}
- Removed
scipy.stats.NumericalInverseHermite
and removedtol
&max_intervals
kwargs fromscipy.stats.sampling.NumericalInverseHermite
- Removed
local_search_options
kwarg frromscipy.optimize.dual_annealing
.
- Removed
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 anaxis
parameter in the
signature ofstatistic
. If anaxis
parameter is present in
statistic
but should not be relied on for vectorized calls, users must
pass optionvectorized==False
explicitly.scipy.stats.multivariate_normal
will now raise aValueError
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)
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- boatwrong (1) +
- boeleman (1) +
- Jake Bowhay (50)
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- Matthias Bussonnier (6)
- Dominic C (2)
- Mingbo Cai (1) +
- James Campbell (2) +
- CJ Carey (4)
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A total of 182 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.