# SciPy 1.7.0 Release Notes

**Note**: Scipy `1.7.0`

is not released yet!

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

other NumFOCUS packages like NumPy.

- 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.

## Backwards incompatible changes

## 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 `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.