# 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 % for`

`pdf()`

`

`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.