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
1.4.x branch, and on adding new features on the master branch.
This release requires Python 3.5+ and NumPy `>=1.13.3` (for Python 3.5, 3.6),
`>=1.14.5` (for Python 3.7), `>= 1.17.3` (for Python 3.8)
For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.
Highlights of this release
---------------------------
- a new submodule, `scipy.fft`, now supersedes `scipy.fftpack`; this
means support for ``long double`` transforms, faster multi-dimensional
transforms, improved algorithm time complexity, release of the global
intepreter lock, and control over threading behavior
- support for ``pydata/sparse`` arrays in `scipy.sparse.linalg`
- substantial improvement to the documentation and functionality of
several `scipy.special` functions, and some new additions
- the generalized inverse Gaussian distribution has been added to
`scipy.stats`
- an implementation of the Edmonds-Karp algorithm in
`scipy.sparse.csgraph.maximum_flow`
- `scipy.spatial.SphericalVoronoi` now supports n-dimensional input,
has linear memory complexity, improved performance, and
supports single-hemisphere generators
New features
============
Infrastructure
----------------
Documentation can now be built with ``runtests.py --doc``
A ``Dockerfile`` is now available in the ``scipy/scipy-dev`` repository to
facilitate getting started with SciPy development.
`scipy.constants` improvements
--------------------------------
`scipy.constants` has been updated with the CODATA 2018 constants.
`scipy.fft` added
-------------------
`scipy.fft` is a new submodule that supersedes the `scipy.fftpack` submodule.
For the most part, this is a drop-in replacement for ``numpy.fft`` and
`scipy.fftpack` alike. With some important differences, `scipy.fft`:
- uses NumPy's conventions for real transforms (``rfft``). This means the
return value is a complex array, half the size of the full ``fft`` output.
This is different from the output of ``fftpack`` which returned a real array
representing complex components packed together.
- the inverse real to real transforms (``idct`` and ``idst``) are normalized
for ``norm=None`` in thesame way as ``ifft``. This means the identity
``idct(dct(x)) == x`` is now ``True`` for all norm modes.
- does not include the convolutions or pseudo-differential operators
from ``fftpack``.
This submodule is based on the ``pypocketfft`` library, developed by the
author of ``pocketfft`` which was recently adopted by NumPy as well.
``pypocketfft`` offers a number of advantages over fortran ``FFTPACK``:
- support for long double (``np.longfloat``) precision transforms.
- faster multi-dimensional transforms using vectorisation
- Bluestein’s algorithm removes the worst-case ``O(n^2)`` complexity of
``FFTPACK``
- the global interpreter lock (``GIL``) is released during transforms
- optional multithreading of multi-dimensional transforms via the ``workers``
argument
Note that `scipy.fftpack` has not been deprecated and will continue to be
maintained but is now considered legacy. New code is recommended to use
`scipy.fft` instead, where possible.
`scipy.fftpack` improvements
--------------------------------
`scipy.fftpack` now uses pypocketfft to perform its FFTs, offering the same
speed and accuracy benefits listed for scipy.fft above but without the
improved API.
`scipy.integrate` improvements
--------------------------------
The function `scipy.integrate.solve_ivp` now has an ``args`` argument.
This allows the user-defined functions passed to the function to have
additional parameters without having to create wrapper functions or
lambda expressions for them.
`scipy.integrate.solve_ivp` can now return a ``y_events`` attribute
representing the solution of the ODE at event times
New ``OdeSolver`` is implemented --- ``DOP853``. This is a high-order explicit
Runge-Kutta method originally implemented in Fortran. Now we provide a pure
Python implementation usable through ``solve_ivp`` with all its features.
`scipy.integrate.quad` provides better user feedback when break points are
specified with a weighted integrand.
`scipy.integrate.quad_vec` is now available for general purpose integration
of vector-valued functions
`scipy.interpolate` improvements
----------------------------------
`scipy.interpolate.pade` now handles complex input data gracefully
`scipy.interpolate.Rbf` can now interpolate multi-dimensional functions
`scipy.io` improvements
-------------------------
`scipy.io.wavfile.read` can now read data from a `WAV` file that has a
malformed header, similar to other modern `WAV` file parsers
`scipy.io.FortranFile` now has an expanded set of available ``Exception``
classes for handling poorly-formatted files
`scipy.linalg` improvements
-----------------------------
The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
results for complex-valued matrices. Before this, the function only returned
correct values for real-valued matrices.
New boolean keyword argument ``check_finite`` for `scipy.linalg.norm`; whether
to check that the input matrix contains only finite numbers. Disabling may
give a performance gain, but may result in problems (crashes, non-termination)
if the inputs do contain infinities or NaNs.
`scipy.linalg.solve_triangular` has improved performance for a C-ordered
triangular matrix
``LAPACK`` wrappers have been added for ``?geequ``, ``?geequb``, ``?syequb``,
and ``?heequb``
Some performance improvements may be observed due to an internal optimization
in operations involving LAPACK routines via ``_compute_lwork``. This is
particularly true for operations on small arrays.
Block ``QR`` wrappers are now available in `scipy.linalg.lapack`
`scipy.ndimage` improvements
------------------------------
`scipy.optimize` improvements
--------------------------------
It is now possible to use linear and non-linear constraints with
`scipy.optimize.differential_evolution`.
`scipy.optimize.linear_sum_assignment` has been re-written in C++ to improve
performance, and now allows input costs to be infinite.
A ``ScalarFunction.fun_and_grad`` method was added for convenient simultaneous
retrieval of a function and gradient evaluation
`scipy.optimize.minimize` ``BFGS`` method has improved performance by avoiding
duplicate evaluations in some cases
Better user feedback is provided when an objective function returns an array
instead of a scalar.
`scipy.signal` improvements
-----------------------------
Added a new function to calculate convolution using the overlap-add method,
named `scipy.signal.oaconvolve`. Like `scipy.signal.fftconvolve`, this
function supports specifying dimensions along which to do the convolution.
`scipy.signal.cwt` now supports complex wavelets.
The implementation of ``choose_conv_method`` has been updated to reflect the
new FFT implementation. In addition, the performance has been significantly
improved (with rather drastic improvements in edge cases).
The function ``upfirdn`` now has a ``mode`` keyword argument that can be used
to select the signal extension mode used at the signal boundaries. These modes
are also available for use in ``resample_poly`` via a newly added ``padtype``
argument.
`scipy.signal.sosfilt` now benefits from Cython code for improved performance
`scipy.signal.resample` should be more efficient by leveraging ``rfft`` when
possible
`scipy.sparse` improvements
-------------------------------
It is now possible to use the LOBPCG method in `scipy.sparse.linalg.svds`.
`scipy.sparse.linalg.LinearOperator` now supports the operation ``rmatmat``
for adjoint matrix-matrix multiplication, in addition to ``rmatvec``.
Multiple stability updates enable float32 support in the LOBPCG eigenvalue
solver for symmetric and Hermitian eigenvalues problems in
``scipy.sparse.linalg.lobpcg``.
A solver for the maximum flow problem has been added as
`scipy.sparse.csgraph.maximum_flow`.
`scipy.sparse.csgraph.maximum_bipartite_matching` now allows non-square inputs,
no longer requires a perfect matching to exist, and has improved performance.
`scipy.sparse.lil_matrix` conversions now perform better in some scenarios
Basic support is available for ``pydata/sparse`` arrays in
`scipy.sparse.linalg`
`scipy.sparse.linalg.spsolve_triangular` now supports the ``unit_diagonal``
argument to improve call signature similarity with its dense counterpart,
`scipy.linalg.solve_triangular`
``assertAlmostEqual`` may now be used with sparse matrices, which have added
support for ``__round__``
`scipy.spatial` improvements
------------------------------
The bundled Qhull library was upgraded to version 2019.1, fixing several
issues. Scipy-specific patches are no longer applied to it.
`scipy.spatial.SphericalVoronoi` now has linear memory complexity, improved
performance, and supports single-hemisphere generators. Support has also been
added for handling generators that lie on a great circle arc (geodesic input)
and for generators in n-dimensions.
`scipy.spatial.transform.Rotation` now includes functions for calculation of a
mean rotation, generation of the 3D rotation groups, and reduction of rotations
with rotational symmetries.
`scipy.spatial.transform.Slerp` is now callable with a scalar argument
`scipy.spatial.voronoi_plot_2d` now supports furthest site Voronoi diagrams
`scipy.spatial.Delaunay` and `scipy.spatial.Voronoi` now have attributes
for tracking whether they are furthest site diagrams
`scipy.special` improvements
------------------------------
The Voigt profile has been added as `scipy.special.voigt_profile`.
A real dispatch has been added for the Wright Omega function
(`scipy.special.wrightomega`).
The analytic continuation of the Riemann zeta function has been added. (The
Riemann zeta function is the one-argument variant of `scipy.special.zeta`.)
The complete elliptic integral of the first kind (`scipy.special.ellipk`) is
now available in `scipy.special.cython_special`.
The accuracy of `scipy.special.hyp1f1` for real arguments has been improved.
The documentation of many functions has been improved.
`scipy.stats` improvements
----------------------------
`scipy.stats.multiscale_graphcorr` added as an independence test that
operates on high dimensional and nonlinear data sets. It has higher statistical
power than other `scipy.stats` tests while being the only one that operates on
multivariate data.
The generalized inverse Gaussian distribution (`scipy.stats.geninvgauss`) has
been added.
It is now possible to efficiently reuse `scipy.stats.binned_statistic_dd`
with new values by providing the result of a previous call to the function.
`scipy.stats.hmean` now handles input with zeros more gracefully.
The beta-binomial distribution is now available in `scipy.stats.betabinom`.
`scipy.stats.zscore`, `scipy.stats.circmean`, `scipy.stats.circstd`, and
`scipy.stats.circvar` now support the ``nan_policy`` argument for enhanced
handling of ``NaN`` values
`scipy.stats.entropy` now accepts an ``axis`` argument
`scipy.stats.gaussian_kde.resample` now accepts a ``seed`` argument to empower
reproducibility
`scipy.stats.multiscale_graphcorr` has been added for calculation of the
multiscale graph correlation (MGC) test statistic
`scipy.stats.kendalltau` performance has improved, especially for large inputs,
due to improved cache usage
`scipy.stats.truncnorm` distribution has been rewritten to support much wider
tails
Deprecated features
===================
`scipy` deprecations
-----------------------
Support for NumPy functions exposed via the root SciPy namespace is deprecated
and will be removed in 2.0.0. For example, if you use ``scipy.rand`` or
``scipy.diag``, you should change your code to directly use
``numpy.random.default_rng`` or ``numpy.diag``, respectively.
They remain available in the currently continuing Scipy 1.x release series.
The exception to this rule is using ``scipy.fft`` as a function --
:mod:`scipy.fft` is now meant to be used only as a module, so the ability to
call ``scipy.fft(...)`` will be removed in SciPy 1.5.0.
In `scipy.spatial.Rotation` methods ``from_dcm``, ``as_dcm`` were renamed to
``from_matrix``, ``as_matrix`` respectively. The old names will be removed in
SciPy 1.6.0.
Backwards incompatible changes
==============================
`scipy.special` changes
-----------------------------
The deprecated functions ``hyp2f0``, ``hyp1f2``, and ``hyp3f0`` have been
removed.
The deprecated function ``bessel_diff_formula`` has been removed.
The function ``i0`` is no longer registered with ``numpy.dual``, so that
``numpy.dual.i0`` will unconditionally refer to the NumPy version regardless
of whether `scipy.special` is imported.
The function ``expn`` has been changed to return ``nan`` outside of its
domain of definition (``x, n < 0``) instead of ``inf``.
`scipy.sparse` changes
-----------------------------
Sparse matrix reshape now raises an error if shape is not two-dimensional,
rather than guessing what was meant. The behavior is now the same as before
SciPy 1.1.0.
`scipy.spatial` changes
--------------------------
The default behavior of the ``match_vectors`` method of
`scipy.spatial.transform.Rotation` was changed for input vectors
that are not normalized and not of equal lengths.
Previously, such vectors would be normalized within the method.
Now, the calculated rotation takes the vector length into account, longer
vectors will have a larger weight. For more details, see
https://github.com/scipy/scipy/issues/10968.
`scipy.signal` changes
-------------------------
`scipy.signal.resample` behavior for length-1 signal inputs has been
fixed to output a constant (DC) value rather than an impulse, consistent with
the assumption of signal periodicity in the FFT method.
`scipy.signal.cwt` now performs complex conjugation and time-reversal of
wavelet data, which is a backwards-incompatible bugfix for
time-asymmetric wavelets.
`scipy.stats` changes
------------------------
`scipy.stats.loguniform` added with better documentation as (an alias for
``scipy.stats.reciprocal``). ``loguniform`` generates random variables
that are equally likely in the log space; e.g., ``1``, ``10`` and ``100``
are all equally likely if ``loguniform(10 ** 0, 10 ** 2).rvs()`` is used.
Other changes
=============
The ``LSODA`` method of `scipy.integrate.solve_ivp` now correctly detects stiff
problems.
`scipy.spatial.cKDTree` now accepts and correctly handles empty input data
`scipy.stats.binned_statistic_dd` now calculates the standard deviation
statistic in a numerically stable way.
`scipy.stats.binned_statistic_dd` now throws an error if the input data
contains either ``np.nan`` or ``np.inf``. Similarly, in `scipy.stats` now all
continuous distributions' ``.fit()`` methods throw an error if the input data
contain any instance of either ``np.nan`` or ``np.inf``.
Authors
=======
* endolith
* Abhinav +
* Anne Archibald
* ashwinpathak20nov1996 +
* Danilo Augusto +
* Nelson Auner +
* aypiggott +
* Christoph Baumgarten
* Peter Bell
* Sebastian Berg
* Arman Bilge +
* Benedikt Boecking +
* Christoph Boeddeker +
* Daniel Bunting
* Evgeni Burovski
* Angeline Burrell +
* Angeline G. Burrell +
* CJ Carey
* Carlos Ramos Carreño +
* Mak Sze Chun +
* Malayaja Chutani +
* Christian Clauss +
* Jonathan Conroy +
* Stephen P Cook +
* Dylan Cutler +
* Anirudh Dagar +
* Aidan Dang +
* dankleeman +
* Brandon David +
* Tyler Dawson +
* Dieter Werthmüller
* Joe Driscoll +
* Jakub Dyczek +
* Dávid Bodnár
* Fletcher Easton +
* Stefan Endres
* etienne +
* Johann Faouzi
* Yu Feng
* Isuru Fernando +
* Matthew H Flamm
* Martin Gauch +
* Gabriel Gerlero +
* Ralf Gommers
* Chris Gorgolewski +
* Domen Gorjup +
* Edouard Goudenhoofdt +
* Jan Gwinner +
* Maja Gwozdz +
* Matt Haberland
* hadshirt +
* Pierre Haessig +
* David Hagen
* Charles Harris
* Gina Helfrich +
* Alex Henrie +
* Francisco J. Hernandez Heras +
* Andreas Hilboll
* Lindsey Hiltner
* Thomas Hisch
* Min ho Kim +
* Gert-Ludwig Ingold
* jakobjakobson13 +
* Todd Jennings
* He Jia
* Muhammad Firmansyah Kasim +
* Andrew Knyazev +
* Holger Kohr +
* Mateusz Konieczny +
* Krzysztof Pióro +
* Philipp Lang +
* Peter Mahler Larsen +
* Eric Larson
* Antony Lee
* Gregory R. Lee
* Chelsea Liu +
* Jesse Livezey
* Peter Lysakovski +
* Jason Manley +
* Michael Marien +
* Nikolay Mayorov
* G. D. McBain +
* Sam McCormack +
* Melissa Weber Mendonça +
* Kevin Michel +
* mikeWShef +
* Sturla Molden
* Eric Moore
* Peyton Murray +
* Andrew Nelson
* Clement Ng +
* Juan Nunez-Iglesias
* Renee Otten +
* Kellie Ottoboni +
* Ayappan P
* Sambit Panda +
* Tapasweni Pathak +
* Oleksandr Pavlyk
* Fabian Pedregosa
* Petar Mlinarić
* Matti Picus
* Marcel Plch +
* Christoph Pohl +
* Ilhan Polat
* Siddhesh Poyarekar +
* Ioannis Prapas +
* James Alan Preiss +
* Yisheng Qiu +
* Eric Quintero
* Bharat Raghunathan +
* Tyler Reddy
* Joscha Reimer
* Antonio Horta Ribeiro
* Lucas Roberts
* rtshort +
* Josua Sassen
* Kevin Sheppard
* Scott Sievert
* Leo Singer
* Kai Striega
* Søren Fuglede Jørgensen
* tborisow +
* Étienne Tremblay +
* tuxcell +
* Miguel de Val-Borro
* Andrew Valentine +
* Hugo van Kemenade
* Paul van Mulbregt
* Sebastiano Vigna
* Pauli Virtanen
* Dany Vohl +
* Ben Walsh +
* Huize Wang +
* Warren Weckesser
* Anreas Weh +
* Joseph Weston +
* Adrian Wijaya +
* Timothy Willard +
* Josh Wilson
* Kentaro Yamamoto +
* Dave Zbarsky +
A total of 141 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.