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
`1.11.x` branch, and on adding new features on the main branch.
This release requires Python `3.9+` and NumPy `1.21.6` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
Highlights of this release
====================
- Several `scipy.sparse` array API improvements, including a new public base
class distinct from the older matrix class, proper 64-bit index support,
and numerous deprecations paving the way to a modern sparse array experience.
- Added three new statistical distributions, and wide-ranging performance and
precision improvements to several other statistical distributions.
- A new function was added for quasi-Monte Carlo integration, and linear
algebra functions ``det`` and ``lu`` now accept nD-arrays.
- An ``axes`` argument was added broadly to ``ndimage`` functions, facilitating
analysis of stacked image data.
New features
===========
`scipy.integrate` improvements
==============================
- Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration.
- For an even number of points, `scipy.integrate.simpson` now calculates
a parabolic segment over the last three points which gives improved
accuracy over the previous implementation.
`scipy.cluster` improvements
============================
- ``disjoint_set`` has a new method ``subset_size`` for providing the size
of a particular subset.
`scipy.constants` improvements
================================
- The ``quetta``, ``ronna``, ``ronto``, and ``quecto`` SI prefixes were added.
`scipy.linalg` improvements
===========================
- `scipy.linalg.det` is improved and now accepts nD-arrays.
- `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new
``p_indices`` switch the output permutation argument can be 1D ``(n,)``
permutation index instead of the full ``(n, n)`` array.
`scipy.ndimage` improvements
============================
- ``axes`` argument was added to ``rank_filter``, ``percentile_filter``,
``median_filter``, ``uniform_filter``, ``minimum_filter``,
``maximum_filter``, and ``gaussian_filter``, which can be useful for
processing stacks of image data.
`scipy.optimize` improvements
=============================
- `scipy.optimize.linprog` now passes unrecognized options directly to HiGHS.
- `scipy.optimize.root_scalar` now uses Newton's method to be used without
providing ``fprime`` and the ``secant`` method to be used without a second
guess.
- `scipy.optimize.lsq_linear` now accepts ``bounds`` arguments of type
`scipy.optimize.Bounds`.
- `scipy.optimize.minimize` ``method='cobyla'`` now supports simple bound
constraints.
- Users can opt into a new callback interface for most methods of
`scipy.optimize.minimize`: If the provided callback callable accepts
a single keyword argument, ``intermediate_result``, `scipy.optimize.minimize`
now passes both the current solution and the optimal value of the objective
function to the callback as an instance of `scipy.optimize.OptimizeResult`.
It also allows the user to terminate optimization by raising a
``StopIteration`` exception from the callback function.
`scipy.optimize.minimize` will return normally, and the latest solution
information is provided in the result object.
- `scipy.optimize.curve_fit` now supports an optional ``nan_policy`` argument.
- `scipy.optimize.shgo` now has parallelization with the ``workers`` argument,
symmetry arguments that can improve performance, class-based design to
improve usability, and generally improved performance.
`scipy.signal` improvements
===========================
- ``istft`` has an improved warning message when the NOLA condition fails.
`scipy.sparse` improvements
===========================
- `scipy.sparse` array (not matrix) classes now return a sparse array instead
of a dense array when divided by a dense array.
- A new public base class `scipy.sparse.sparray` was introduced, allowing
`isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes,
while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse
matrix types.
- The behavior of `scipy.sparse.isspmatrix()` was updated to return True for
only the sparse matrix types. If you want to check for either sparse arrays
or sparse matrices, use `scipy.sparse.issparse()` instead. (Previously,
these had identical behavior.)
- Sparse arrays constructed with 64-bit indices will no longer automatically
downcast to 32-bit.
- A new `scipy.sparse.diags_array` function was added, which behaves like the
existing `scipy.sparse.diags` function except that it returns a sparse
array instead of a sparse matrix.
- ``argmin`` and ``argmax`` methods now return the correct result when no
implicit zeros are present.
`scipy.sparse.linalg` improvements
==================================
- dividing ``LinearOperator`` by a number now returns a
``_ScaledLinearOperator``
- ``LinearOperator`` now supports right multiplication by arrays
- ``lobpcg`` should be more efficient following removal of an extraneous
QR decomposition.
`scipy.spatial` improvements
============================
- Usage of new C++ backend for additional distance metrics, the majority of
which will see substantial performance improvements, though a few minor
regressions are known. These are focused on distances between boolean
arrays.
`scipy.special` improvements
============================
- The factorial functions ``factorial``, ``factorial2`` and ``factorialk``
were made consistent in their behavior (in terms of dimensionality,
errors etc.). Additionally, ``factorial2`` can now handle arrays with
``exact=True``, and ``factorialk`` can handle arrays.
`scipy.stats` improvements
==========================
New Features
------------
- `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices.
- `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple
experimental groups against the mean of a control group.
- `scipy.stats.ecdf` for computing the empirical CDF and complementary
CDF (survival function / SF) from uncensored or right-censored data. This
function is also useful for survival analysis / Kaplain-Meier estimation.
- `scipy.stats.logrank` to compare survival functions underlying samples.
- `scipy.stats.false_discovery_control` for adjusting p-values to control the
false discovery rate of multiple hypothesis tests using the
Benjamini-Hochberg or Benjamini-Yekutieli procedures.
- `scipy.stats.CensoredData` to represent censored data. It can be used as
input to the ``fit`` method of univariate distributions and to the new
``ecdf`` function.
- Filliben's goodness of fit test as ``method='Filliben'`` of
`scipy.stats.goodness_of_fit`.
- `scipy.stats.ttest_ind` has a new method, ``confidence_interval`` for
computing confidence intervals.
- `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and
`scipy.stats.BootstrapMethod` are new classes to configure resampling and/or
Monte Carlo versions of hypothesis tests. They can currently be used with
`scipy.stats.pearsonr`.
Statistical Distributions
-------------------------
- Added the von-Mises Fisher distribution as `scipy.stats.vonmises_fisher`.
This distribution is the most common analogue of the normal distribution
on the unit sphere.
- Added the relativistic Breit-Wigner distribution as
`scipy.stats.rel_breitwigner`.
It is used in high energy physics to model resonances.
- Added the Dirichlet multinomial distribution as
`scipy.stats.dirichlet_multinomial`.
- Improved the speed and precision of several univariate statistical
distributions.
- `scipy.stats.anglit` ``sf``
- `scipy.stats.beta` ``entropy``
- `scipy.stats.betaprime` ``cdf``, ``sf``, ``ppf``
- `scipy.stats.chi` ``entropy``
- `scipy.stats.chi2` ``entropy``
- `scipy.stats.dgamma` ``entropy``, ``cdf``, ``sf``, ``ppf``, and ``isf``
- `scipy.stats.dweibull` ``entropy``, ``sf``, and ``isf``
- `scipy.stats.exponweib` ``sf`` and ``isf``
- `scipy.stats.f` ``entropy``
- `scipy.stats.foldcauchy` ``sf``
- `scipy.stats.foldnorm` ``cdf`` and ``sf``
- `scipy.stats.gamma` ``entropy``
- `scipy.stats.genexpon` ``ppf``, ``isf``, ``rvs``
- `scipy.stats.gengamma` ``entropy``
- `scipy.stats.geom` ``entropy``
- `scipy.stats.genlogistic` ``entropy``, ``logcdf``, ``sf``, ``ppf``,
and ``isf``
- `scipy.stats.genhyperbolic` ``cdf`` and ``sf``
- `scipy.stats.gibrat` ``sf`` and ``isf``
- `scipy.stats.gompertz` ``entropy``, ``sf``. and ``isf``
- `scipy.stats.halflogistic` ``sf``, and ``isf``
- `scipy.stats.halfcauchy` ``sf`` and ``isf``
- `scipy.stats.halfnorm` ``cdf``, ``sf``, and ``isf``
- `scipy.stats.invgamma` ``entropy``
- `scipy.stats.invgauss` ``entropy``
- `scipy.stats.johnsonsb` ``pdf``, ``cdf``, ``sf``, ``ppf``, and ``isf``
- `scipy.stats.johnsonsu` ``pdf``, ``sf``, ``isf``, and ``stats``
- `scipy.stats.lognorm` ``fit``
- `scipy.stats.loguniform` ``entropy``, ``logpdf``, ``pdf``, ``cdf``, ``ppf``,
and ``stats``
- `scipy.stats.maxwell` ``sf`` and ``isf``
- `scipy.stats.nakagami` ``entropy``
- `scipy.stats.powerlaw` ``sf``
- `scipy.stats.powerlognorm` ``logpdf``, ``logsf``, ``sf``, and ``isf``
- `scipy.stats.powernorm` ``sf`` and ``isf``
- `scipy.stats.t` ``entropy``, ``logpdf``, and ``pdf``
- `scipy.stats.truncexpon` ``sf``, and ``isf``
- `scipy.stats.truncnorm` ``entropy``
- `scipy.stats.truncpareto` ``fit``
- `scipy.stats.vonmises` ``fit``
- `scipy.stats.multivariate_t` now has ``cdf`` and ``entropy`` methods.
- `scipy.stats.multivariate_normal`, `scipy.stats.matrix_normal`, and
`scipy.stats.invwishart` now have an ``entropy`` method.
Other Improvements
------------------
- `scipy.stats.monte_carlo_test` now supports multi-sample statistics.
- `scipy.stats.bootstrap` can now produce one-sided confidence intervals.
- `scipy.stats.rankdata` performance was improved for ``method=ordinal`` and
``method=dense``.
- `scipy.stats.moment` now supports non-central moment calculation.
- `scipy.stats.anderson` now supports the ``weibull_min`` distribution.
- `scipy.stats.sem` and `scipy.stats.iqr` now support ``axis``, ``nan_policy``,
and masked array input.
Deprecated features
=================
- Multi-Ellipsis sparse matrix indexing has been deprecated and will
be removed in SciPy 1.13.
- Several methods were deprecated for sparse arrays: ``asfptype``, ``getrow``,
``getcol``, ``get_shape``, ``getmaxprint``, ``set_shape``,
``getnnz``, and ``getformat``. Additionally, the ``.A`` and ``.H``
attributes were deprecated. Sparse matrix types are not affected.
- The `scipy.linalg` functions ``tri``, ``triu`` & ``tril`` are deprecated and
will be removed in SciPy 1.13. Users are recommended to use the NumPy
versions of these functions with identical names.
- The `scipy.signal` functions ``bspline``, ``quadratic`` & ``cubic`` are
deprecated and will be removed in SciPy 1.13. Users are recommended to use
`scipy.interpolate.BSpline` instead.
- The ``even`` keyword of `scipy.integrate.simpson` is deprecated and will be
removed in SciPy 1.13.0. Users should leave this as the default as this
gives improved accuracy compared to the other methods.
- Using ``exact=True`` when passing integers in a float array to ``factorial``
is deprecated and will be removed in SciPy 1.13.0.
- float128 and object dtypes are deprecated for `scipy.signal.medfilt` and
`scipy.signal.order_filter`
- The functions ``scipy.signal.{lsim2, impulse2, step2}`` had long been
deprecated in documentation only. They now raise a DeprecationWarning and
will be removed in SciPy 1.13.0.
- Importing window functions directly from `scipy.window` has been soft
deprecated since SciPy 1.1.0. They now raise a ``DeprecationWarning`` and
will be removed in SciPy 1.13.0. Users should instead import them from
`scipy.signal.window` or use the convenience function
`scipy.signal.get_window`.
Backwards incompatible changes
============================
- The default for the ``legacy`` keyword of `scipy.special.comb` has changed
from ``True`` to ``False``, as announced since its introduction.
Expired Deprecations
==================
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
- The ``n`` keyword has been removed from `scipy.stats.moment`.
- The ``alpha`` keyword has been removed from `scipy.stats.interval`.
- The misspelt ``gilbrat`` distribution has been removed (use
`scipy.stats.gibrat`).
- The deprecated spelling of the ``kulsinski`` distance metric has been
removed (use `scipy.spatial.distance.kulczynski1`).
- The ``vertices`` keyword of `scipy.spatial.Delauney.qhull` has been removed
(use simplices).
- The ``residual`` property of `scipy.sparse.csgraph.maximum_flow` has been
removed (use ``flow``).
- The ``extradoc`` keyword of `scipy.stats.rv_continuous`,
`scipy.stats.rv_discrete` and `scipy.stats.rv_sample` has been removed.
- The ``sym_pos`` keyword of `scipy.linalg.solve` has been removed.
- The `scipy.optimize.minimize` function now raises an error for ``x0`` with
``x0.ndim > 1``.
- In `scipy.stats.mode`, the default value of ``keepdims`` is now ``False``,
and support for non-numeric input has been removed.
- The function `scipy.signal.lsim` does not support non-uniform time steps
anymore.
Other changes
============
- Rewrote the source build docs and restructured the contributor guide.
- Improved support for cross-compiling with meson build system.
- MyST-NB notebook infrastructure has been added to our documentation.
Authors
=======
* h-vetinari (69)
* Oriol Abril-Pla (1) +
* Anton Akhmerov (13)
* Andrey Akinshin (1) +
* alice (1) +
* Oren Amsalem (1)
* Ross Barnowski (11)
* Christoph Baumgarten (2)
* Dawson Beatty (1) +
* Doron Behar (1) +
* Peter Bell (1)
* John Belmonte (1) +
* boeleman (1) +
* Jack Borchanian (1) +
* Matt Borland (3) +
* Jake Bowhay (40)
* Sienna Brent (1) +
* Matthew Brett (1)
* Evgeni Burovski (38)
* Matthias Bussonnier (2)
* Maria Cann (1) +
* Alfredo Carella (1) +
* CJ Carey (18)
* Hood Chatham (2)
* Anirudh Dagar (3)
* Alberto Defendi (1) +
* Pol del Aguila (1) +
* Hans Dembinski (1)
* Dennis (1) +
* Vinayak Dev (1) +
* Thomas Duvernay (1)
* DWesl (4)
* Stefan Endres (66)
* Evandro (1) +
* Tom Eversdijk (2) +
* Isuru Fernando (1)
* Franz Forstmayr (4)
* Joseph Fox-Rabinovitz (1)
* Stefano Frazzetto (1) +
* Neil Girdhar (1)
* Caden Gobat (1) +
* Ralf Gommers (146)
* GonVas (1) +
* Marco Gorelli (1)
* Brett Graham (2) +
* Matt Haberland (385)
* harshvardhan2707 (1) +
* Alex Herbert (1) +
* Guillaume Horel (1)
* Geert-Jan Huizing (1) +
* Jakob Jakobson (2)
* Julien Jerphanion (5)
* jyuv (2)
* Rajarshi Karmakar (1) +
* Ganesh Kathiresan (3) +
* Robert Kern (4)
* Andrew Knyazev (3)
* Sergey Koposov (1)
* Rishi Kulkarni (2) +
* Eric Larson (1)
* Zoufiné Lauer-Bare (2) +
* Antony Lee (3)
* Gregory R. Lee (8)
* Guillaume Lemaitre (1) +
* lilinjie (2) +
* Yannis Linardos (1) +
* Christian Lorentzen (5)
* Loïc Estève (1)
* Charlie Marsh (2) +
* Boris Martin (1) +
* Nicholas McKibben (10)
* Melissa Weber Mendonça (57)
* Michał Górny (1) +
* Jarrod Millman (2)
* Stefanie Molin (2) +
* Mark W. Mueller (1) +
* mustafacevik (1) +
* Takumasa N (1) +
* nboudrie (1)
* Andrew Nelson (111)
* Nico Schlömer (4)
* Lysandros Nikolaou (2) +
* Kyle Oman (1)
* OmarManzoor (2) +
* Simon Ott (1) +
* Geoffrey Oxberry (1) +
* Geoffrey M. Oxberry (2) +
* Sravya papaganti (1) +
* Tirth Patel (2)
* Ilhan Polat (32)
* Quentin Barthélemy (1)
* Matteo Raso (12) +
* Tyler Reddy (97)
* Lucas Roberts (1)
* Pamphile Roy (224)
* Jordan Rupprecht (1) +
* Atsushi Sakai (11)
* Omar Salman (7) +
* Leo Sandler (1) +
* Ujjwal Sarswat (3) +
* Saumya (1) +
* Daniel Schmitz (79)
* Henry Schreiner (2) +
* Dan Schult (3) +
* Eli Schwartz (6)
* Tomer Sery (2) +
* Scott Shambaugh (4) +
* Gagandeep Singh (1)
* Ethan Steinberg (6) +
* stepeos (2) +
* Albert Steppi (3)
* Strahinja Lukić (1)
* Kai Striega (4)
* suen-bit (1) +
* Tartopohm (2)
* Logan Thomas (2) +
* Jacopo Tissino (1) +
* Matus Valo (10) +
* Jacob Vanderplas (2)
* Christian Veenhuis (1) +
* Isaac Virshup (1)
* Stefan van der Walt (14)
* Warren Weckesser (63)
* windows-server-2003 (1)
* Levi John Wolf (3)
* Nobel Wong (1) +
* Benjamin Yeh (1) +
* Rory Yorke (1)
* Younes (2) +
* Zaikun ZHANG (1) +
* Alex Zverianskii (1) +
A total of 131 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.