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
* 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 +
* MiloÅ¡ KomarÄević +
* 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.