Changelog
### 1.4.0
```
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.
```
### 1.3.3
```
compared to `1.3.2`. In particular, a test suite issue
involving multiprocessing was fixed for Windows and
Python `3.8` on macOS.
Wheels were also updated to place `msvcp140.dll` at the
appropriate location, which was previously causing issues.
Authors
=======
Ilhan Polat
Tyler Reddy
Ralf Gommers
```
### 1.3.2
```
SciPy `1.3.2` is a bug-fix and maintenance release that adds support for Python `3.8`.
Authors
=====
* CJ Carey
* Dany Vohl
* Martin Gauch +
* Ralf Gommers
* Matt Haberland
* Eric Larson
* Nikolay Mayorov
* Sam McCormack +
* Andrew Nelson
* Tyler Reddy
* Pauli Virtanen
* Huize Wang +
* Warren Weckesser
* Joseph Weston +
A total of 14 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.
```
### 1.3.1
```
SciPy `1.3.1` is a bug-fix release with no new features compared to `1.3.0`.
Authors
=======
* Matt Haberland
* Geordie McBain
* Yu Feng
* Evgeni Burovski
* Sturla Molden
* Tapasweni Pathak
* Eric Larson
* Peter Bell
* Carlos Ramos Carreño +
* Ralf Gommers
* David Hagen
* Antony Lee
* Ayappan P
* Tyler Reddy
* Pauli Virtanen
A total of 15 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.
```
### 1.3.0
```
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been some 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
1.3.x branch, and on adding new features on the master branch.
This release requires Python 3.5+ and NumPy 1.13.3 or greater.
For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.
Highlights of this release
--------------------------
- Three new ``stats`` functions, a rewrite of ``pearsonr``, and an exact
computation of the Kolmogorov-Smirnov two-sample test
- A new Cython API for bounded scalar-function root-finders in `scipy.optimize`
- Substantial ``CSR`` and ``CSC`` sparse matrix indexing performance
improvements
- Added support for interpolation of rotations with continuous angular
rate and acceleration in ``RotationSpline``
New features
============
`scipy.interpolate` improvements
--------------------------------
A new class ``CubicHermiteSpline`` is introduced. It is a piecewise-cubic
interpolator which matches observed values and first derivatives. Existing
cubic interpolators ``CubicSpline``, ``PchipInterpolator`` and
``Akima1DInterpolator`` were made subclasses of ``CubicHermiteSpline``.
`scipy.io` improvements
-----------------------
For the Attribute-Relation File Format (ARFF) `scipy.io.arff.loadarff`
now supports relational attributes.
`scipy.io.mmread` can now parse Matrix Market format files with empty lines.
`scipy.linalg` improvements
---------------------------
Added wrappers for ``?syconv`` routines, which convert a symmetric matrix
given by a triangular matrix factorization into two matrices and vice versa.
`scipy.linalg.clarkson_woodruff_transform` now uses an algorithm that leverages
sparsity. This may provide a 60-90 percent speedup for dense input matrices.
Truly sparse input matrices should also benefit from the improved sketch
algorithm, which now correctly runs in ``O(nnz(A))`` time.
Added new functions to calculate symmetric Fiedler matrices and
Fiedler companion matrices, named `scipy.linalg.fiedler` and
`scipy.linalg.fiedler_companion`, respectively. These may be used
for root finding.
`scipy.ndimage` improvements
----------------------------
Gaussian filter performances may improve by an order of magnitude in
some cases, thanks to removal of a dependence on ``np.polynomial``. This
may impact `scipy.ndimage.gaussian_filter` for example.
`scipy.optimize` improvements
-----------------------------
The `scipy.optimize.brute` minimizer obtained a new keyword ``workers``, which
can be used to parallelize computation.
A Cython API for bounded scalar-function root-finders in `scipy.optimize`
is available in a new module `scipy.optimize.cython_optimize` via ``cimport``.
This API may be used with ``nogil`` and ``prange`` to loop
over an array of function arguments to solve for an array of roots more
quickly than with pure Python.
``'interior-point'`` is now the default method for ``linprog``, and
``'interior-point'`` now uses SuiteSparse for sparse problems when the
required scikits (scikit-umfpack and scikit-sparse) are available.
On benchmark problems (gh-10026), execution time reductions by factors of 2-3
were typical. Also, a new ``method='revised simplex'`` has been added.
It is not as fast or robust as ``method='interior-point'``, but it is a faster,
more robust, and equally accurate substitute for the legacy
``method='simplex'``.
``differential_evolution`` can now use a ``Bounds`` class to specify the
bounds for the optimizing argument of a function.
`scipy.optimize.dual_annealing` performance improvements related to
vectorisation of some internal code.
`scipy.signal` improvements
---------------------------
Two additional methods of discretization are now supported by
`scipy.signal.cont2discrete`: ``impulse`` and ``foh``.
`scipy.signal.firls` now uses faster solvers
`scipy.signal.detrend` now has a lower physical memory footprint in some
cases, which may be leveraged using the new ``overwrite_data`` keyword argument
`scipy.signal.firwin` ``pass_zero`` argument now accepts new string arguments
that allow specification of the desired filter type: ``'bandpass'``,
``'lowpass'``, ``'highpass'``, and ``'bandstop'``
`scipy.signal.sosfilt` may have improved performance due to lower retention
of the global interpreter lock (GIL) in algorithm
`scipy.sparse` improvements
---------------------------
A new keyword was added to ``csgraph.dijsktra`` that
allows users to query the shortest path to ANY of the passed in indices,
as opposed to the shortest path to EVERY passed index.
`scipy.sparse.linalg.lsmr` performance has been improved by roughly 10 percent
on large problems
Improved performance and reduced physical memory footprint of the algorithm
used by `scipy.sparse.linalg.lobpcg`
``CSR`` and ``CSC`` sparse matrix fancy indexing performance has been
improved substantially
`scipy.spatial` improvements
----------------------------
`scipy.spatial.ConvexHull` now has a ``good`` attribute that can be used
alongsize the ``QGn`` Qhull options to determine which external facets of a
convex hull are visible from an external query point.
`scipy.spatial.cKDTree.query_ball_point` has been modernized to use some newer
Cython features, including GIL handling and exception translation. An issue
with ``return_sorted=True`` and scalar queries was fixed, and a new mode named
``return_length`` was added. ``return_length`` only computes the length of the
returned indices list instead of allocating the array every time.
`scipy.spatial.transform.RotationSpline` has been added to enable interpolation
of rotations with continuous angular rates and acceleration
`scipy.stats` improvements
--------------------------
Added a new function to compute the Epps-Singleton test statistic,
`scipy.stats.epps_singleton_2samp`, which can be applied to continuous and
discrete distributions.
New functions `scipy.stats.median_absolute_deviation` and `scipy.stats.gstd`
(geometric standard deviation) were added. The `scipy.stats.combine_pvalues`
method now supports ``pearson``, ``tippett`` and ``mudholkar_george`` pvalue
combination methods.
The `scipy.stats.ortho_group` and `scipy.stats.special_ortho_group`
``rvs(dim)`` functions' algorithms were updated from a ``O(dim^4)``
implementation to a ``O(dim^3)`` which gives large speed improvements
for ``dim>100``.
A rewrite of `scipy.stats.pearsonr` to use a more robust algorithm,
provide meaningful exceptions and warnings on potentially pathological input,
and fix at least five separate reported issues in the original implementation.
Improved the precision of ``hypergeom.logcdf`` and ``hypergeom.logsf``.
Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing
the previously approximate computation for the two-sided test `stats.ks_2samp`.
Also added a one-sided, two-sample KS test, and a keyword ``alternative`` to
`stats.ks_2samp`.
Backwards incompatible changes
==============================
`scipy.interpolate` changes
---------------------------
Functions from ``scipy.interpolate`` (``spleval``, ``spline``, ``splmake``,
and ``spltopp``) and functions from ``scipy.misc`` (``bytescale``,
``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,
``imsave``, ``imshow``, ``toimage``) have been removed. The former set has
been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.
Similarly, aliases from ``scipy.misc`` (``comb``, ``factorial``,
``factorial2``, ``factorialk``, ``logsumexp``, ``pade``, ``info``, ``source``,
``who``) which have been deprecated since v1.0.0 are removed.
`SciPy documentation for
```
### 1.2.2
```
================
SciPy `1.2.2` is a bug-fix release with no new features compared to `1.2.1`.
Importantly, the SciPy 1.2.2 wheels are built with OpenBLAS `0.3.7.dev` to
alleviate issues with SkylakeX AVX512 kernels.
Authors
=======
* CJ Carey
* Tyler Dawson +
* Ralf Gommers
* Kai Striega
* Andrew Nelson
* Tyler Reddy
* Kevin Sheppard +
A total of 7 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.
```
### 1.2.1
```
==========================
SciPy `1.2.1` is a bug-fix release with no new features compared to `1.2.0`.
Most importantly, it solves the issue that `1.2.0` cannot be installed
from source on Python `2.7` because of non-ASCII character issues.
It is also notable that SciPy `1.2.1` wheels were built with OpenBLAS
```
### 1.2.0
```
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
1.2.x branch, and on adding new features on the master branch.
This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.
**Note**: This will be the last SciPy release to support Python 2.7.
Consequently, the 1.2.x series will be a long term support (LTS)
release; we will backport bug fixes until 1 Jan 2020.
For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.
Highlights of this release
--------------------------
- 1-D root finding improvements with a new solver, ``toms748``, and a new
unified interface, ``root_scalar``
- New ``dual_annealing`` optimization method that combines stochastic and
local deterministic searching
- A new optimization algorithm, ``shgo`` (simplicial homology
global optimization) for derivative free optimization problems
- A new category of quaternion-based transformations are available in
`scipy.spatial.transform`
New features
============
`scipy.ndimage` improvements
--------------------------------
Proper spline coefficient calculations have been added for the ``mirror``,
``wrap``, and ``reflect`` modes of `scipy.ndimage.rotate`
`scipy.fftpack` improvements
--------------------------------
DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in
`scipy.fftpack`.
`scipy.interpolate` improvements
--------------------------------
`scipy.interpolate.pade` now accepts a new argument for the order of the
numerator
`scipy.cluster` improvements
----------------------------
`scipy.cluster.vq.kmeans2` gained a new initialization method, kmeans++.
`scipy.special` improvements
----------------------------
The function ``softmax`` was added to `scipy.special`.
`scipy.optimize` improvements
-----------------------------
The one-dimensional nonlinear solvers have been given a unified interface
`scipy.optimize.root_scalar`, similar to the `scipy.optimize.root` interface
for multi-dimensional solvers. ``scipy.optimize.root_scalar(f, bracket=[a ,b],
method="brenth")`` is equivalent to ``scipy.optimize.brenth(f, a ,b)``. If no
``method`` is specified, an appropriate one will be selected based upon the
bracket and the number of derivatives available.
The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within
an enclosing interval has been added as `scipy.optimize.toms748`. This provides
guaranteed convergence to a root with convergence rate per function evaluation
of approximately 1.65 (for sufficiently well-behaved functions.)
``differential_evolution`` now has the ``updating`` and ``workers`` keywords.
The first chooses between continuous updating of the best solution vector (the
default), or once per generation. Continuous updating can lead to faster
convergence. The ``workers`` keyword accepts an ``int`` or map-like callable,
and parallelises the solver (having the side effect of updating once per
generation). Supplying an ``int`` evaluates the trial solutions in N parallel
parts. Supplying a map-like callable allows other parallelisation approaches
(such as ``mpi4py``, or ``joblib``) to be used.
``dual_annealing`` (and ``shgo`` below) is a powerful new general purpose
global optizimation (GO) algorithm. ``dual_annealing`` uses two annealing
processes to accelerate the convergence towards the global minimum of an
objective mathematical function. The first annealing process controls the
stochastic Markov chain searching and the second annealing process controls the
deterministic minimization. So, dual annealing is a hybrid method that takes
advantage of stochastic and local deterministic searching in an efficient way.
``shgo`` (simplicial homology global optimization) is a similar algorithm
appropriate for solving black box and derivative free optimization (DFO)
problems. The algorithm generally converges to the global solution in finite
time. The convergence holds for non-linear inequality and
equality constraints. In addition to returning a global minimum, the
algorithm also returns any other global and local minima found after every
iteration. This makes it useful for exploring the solutions in a domain.
`scipy.optimize.newton` can now accept a scalar or an array
``MINPACK`` usage is now thread-safe, such that ``MINPACK`` + callbacks may
be used on multiple threads.
`scipy.signal` improvements
---------------------------
Digital filter design functions now include a parameter to specify the sampling
rate. Previously, digital filters could only be specified using normalized
frequency, but different functions used different scales (e.g. 0 to 1 for
``butter`` vs 0 to π for ``freqz``), leading to errors and confusion. With
the ``fs`` parameter, ordinary frequencies can now be entered directly into
functions, with the normalization handled internally.
``find_peaks`` and related functions no longer raise an exception if the
properties of a peak have unexpected values (e.g. a prominence of 0). A
``PeakPropertyWarning`` is given instead.
The new keyword argument ``plateau_size`` was added to ``find_peaks``.
``plateau_size`` may be used to select peaks based on the length of the
flat top of a peak.
``welch()`` and ``csd()`` methods in `scipy.signal` now support calculation
of a median average PSD, using ``average='mean'`` keyword
`scipy.sparse` improvements
---------------------------
The `scipy.sparse.bsr_matrix.tocsr` method is now implemented directly instead
of converting via COO format, and the `scipy.sparse.bsr_matrix.tocsc` method
is now also routed via CSR conversion instead of COO. The efficiency of both
conversions is now improved.
The issue where SuperLU or UMFPACK solvers crashed on matrices with
non-canonical format in `scipy.sparse.linalg` was fixed. The solver wrapper
canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK
solver.
The ``largest`` option of `scipy.sparse.linalg.lobpcg()` was fixed to have
a correct (and expected) behavior. The order of the eigenvalues was made
consistent with the ARPACK solver (``eigs()``), i.e. ascending for the
smallest eigenvalues, and descending for the largest eigenvalues.
The `scipy.sparse.random` function is now faster and also supports integer and
complex values by passing the appropriate value to the ``dtype`` argument.
`scipy.spatial` improvements
----------------------------
The function `scipy.spatial.distance.jaccard` was modified to return 0 instead
of ``np.nan`` when two all-zero vectors are compared.
Support for the Jensen Shannon distance, the square-root of the divergence, has
been added under `scipy.spatial.distance.jensenshannon`
An optional keyword was added to the function
`scipy.spatial.cKDTree.query_ball_point()` to sort or not sort the returned
indices. Not sorting the indices can speed up calls.
A new category of quaternion-based transformations are available in
`scipy.spatial.transform`, including spherical linear interpolation of
rotations (``Slerp``), conversions to and from quaternions, Euler angles,
and general rotation and inversion capabilities
(`spatial.transform.Rotation`), and uniform random sampling of 3D
rotations (`spatial.transform.Rotation.random`).
`scipy.stats` improvements
--------------------------
The Yeo-Johnson power transformation is now supported (``yeojohnson``,
``yeojohnson_llf``, ``yeojohnson_normmax``, ``yeojohnson_normplot``). Unlike
the Box-Cox transformation, the Yeo-Johnson transformation can accept negative
values.
Added a general method to sample random variates based on the density only, in
the new function ``rvs_ratio_uniforms``.
The Yule-Simon distribution (``yulesimon``) was added -- this is a new
discrete probability distribution.
``stats`` and ``mstats`` now have access to a new regression method,
``siegelslopes``, a robust linear regression algorithm
`scipy.stats.gaussian_kde` now has the ability to deal with weighted samples,
and should have a modest improvement in performance
Levy Stable Parameter Estimation, PDF, and CDF calculations are now supported
for `scipy.stats.levy_stable`.
The Brunner-Munzel test is now available as ``brunnermunzel`` in ``stats``
and ``mstats``
`scipy.linalg` improvements
--------------------------
`scipy.linalg.lapack` now exposes the LAPACK routines using the Rectangular
Full Packed storage (RFP) for upper triangular, lower triangular, symmetric,
or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition
routines are now available as well.
Deprecated features
===================
The functions ``hyp2f0``, ``hyp1f2`` and ``hyp3f0`` in ``scipy.special`` have
been deprecated.
Backwards incompatible changes
==============================
LAPACK version 3.4.0 or later is now required. Building with
Apple Accelerate is no longer supported.
The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
results for all angles. Before this, the function only returned
correct values for those angles which were greater than pi/4.
Support for the Bento build system has been removed. Bento has not been
maintained for several years, and did not have good Python 3 or wheel support,
hence it was time to remove it.
The required signature of `scipy.optimize.lingprog` ``method=simplex``
callback function has changed. Before iteration begins, the simplex solver
first converts the problem into a standard form that does not, in general,
have the same variables or constraints
as the problem defined by the user. Previously, the simplex solver would pass a
user-specified callback function several separate arguments, such as the
current solution vector ``xk``, corresponding to this standard form problem.
Unfortunately, the relationship between the standard form problem and the
user-defined problem was not documented, limiting the utility of the
information passed to the callback function.
In addition to numerous bug fix changes, the simplex solver now passes a
user-specified callback function a single ``OptimizeResult`` object containing
information that corresponds directly to the user-defined problem. In future
releases, this ``OptimizeResult`` object may be expanded to include additional
information, such as variables corresponding to the standard-form problem and
information concerning the relationship between the standard-form and
user-defined problems.
The implementation of `scipy.sparse.random` has changed, and this affects the
numerical values returned for both ``sparse.random`` and ``sparse.rand`` for
some matrix shapes and a given seed.
`scipy.optimize.newton` will no longer use Halley's method in cases where it
negatively impacts convergence
Other changes
=============
Authors
=======
* endolith
* luzpaz
* Hameer Abbasi +
* akahard2dj +
* Anton Akhmerov
* Joseph Albert
* alexthomas93 +
* ashish +
* atpage +
* Blair Azzopardi +
* Yoshiki Vázquez Baeza
* Bence Bagi +
* Christoph Baumgarten
* Lucas Bellomo +
* BH4 +
* Aditya Bharti
* Max Bolingbroke
* François Boulogne
* Ward Bradt +
* Matthew Brett
* Evgeni Burovski
* Rafał Byczek +
* Alfredo Canziani +
* CJ Carey
* Lucía Cheung +
* Poom Chiarawongse +
* Jeanne Choo +
* Robert Cimrman
* Graham Clenaghan +
* cynthia-rempel +
* Johannes Damp +
* Jaime Fernandez del Rio
* Dowon +
* emmi474 +
* Stefan Endres +
* Thomas Etherington +
* Alex Fikl +
* fo40225 +
* Joseph Fox-Rabinovitz
* Lars G
* Abhinav Gautam +
* Stiaan Gerber +
* C.A.M. Gerlach +
* Ralf Gommers
* Todd Goodall
* Lars Grueter +
* Sylvain Gubian +
* Matt Haberland
* David Hagen
* Will Handley +
* Charles Harris
* Ian Henriksen
* Thomas Hisch +
* Theodore Hu
* Michael Hudson-Doyle +
* Nicolas Hug +
* jakirkham +
* Jakob Jakobson +
* James +
* Jan Schlüter
* jeanpauphilet +
* josephmernst +
* Kai +
* Kai-Striega +
* kalash04 +
* Toshiki Kataoka +
* Konrad0 +
* Tom Krauss +
* Johannes Kulick
* Lars Grüter +
* Eric Larson
* Denis Laxalde
* Will Lee +
* Katrin Leinweber +
* Yin Li +
* P. L. Lim +
* Jesse Livezey +
* Duncan Macleod +
* MatthewFlamm +
* Nikolay Mayorov
* Mike McClurg +
* Christian Meyer +
* Mark Mikofski
* Naoto Mizuno +
* mohmmadd +
* Nathan Musoke
* Anju Geetha Nair +
* Andrew Nelson
* Ayappan P +
* Nick Papior
* Haesun Park +
* Ronny Pfannschmidt +
* pijyoi +
* Ilhan Polat
* Anthony Polloreno +
* Ted Pudlik
* puenka
* Eric Quintero
* Pradeep Reddy Raamana +
* Vyas Ramasubramani +
* Ramon Viñas +
* Tyler Reddy
* Joscha Reimer
* Antonio H Ribeiro
* richardjgowers +
* Rob +
* robbystk +
* Lucas Roberts +
* rohan +
* Joaquin Derrac Rus +
* Josua Sassen +
* Bruce Sharpe +
* Max Shinn +
* Scott Sievert
* Sourav Singh
* Strahinja Lukić +
* Kai Striega +
* Shinya SUZUKI +
* Mike Toews +
* Piotr Uchwat
* Miguel de Val-Borro +
* Nicky van Foreest
* Paul van Mulbregt
* Gael Varoquaux
* Pauli Virtanen
* Stefan van der Walt
* Warren Weckesser
* Joshua Wharton +
* Bernhard M. Wiedemann +
* Eric Wieser
* Josh Wilson
* Tony Xiang +
* Roman Yurchak +
* Roy Zywina +
A total of 137 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.
```
### 1.1.0
```
=========================
**Note: Scipy 1.1.0 is not released yet!**
SciPy 1.1.0 is the culmination of 7 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 1.1.x branch, and on adding new features on the master branch.
This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.
This release has improved but not necessarily 100% compatibility with the [PyPy](https://pypy.org/) Python implementation. For running on PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required.
New features
------------
scipy.integrate improvements
The argument `tfirst` has been added to the function scipy.integrate.odeint. This allows odeint to use the same user functions as scipy.integrate.solve\_ivp and scipy.integrate.ode without the need for wrapping them in a function that swaps the first two arguments.
Error messages from `quad()` are now clearer.
scipy.linalg improvements
The function scipy.linalg.ldl has been added for factorization of indefinite symmetric/hermitian matrices into triangular and block diagonal matrices.
Python wrappers for LAPACK `sygst`, `hegst` added in scipy.linalg.lapack.
Added scipy.linalg.null\_space, scipy.linalg.cdf2rdf, scipy.linalg.rsf2csf.
scipy.misc improvements
An electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram.
scipy.ndimage improvements
The routines scipy.ndimage.binary\_opening, and scipy.ndimage.binary\_closing now support masks and different border values.
scipy.optimize improvements
The method `trust-constr` has been added to scipy.optimize.minimize. The method switches between two implementations depending on the problem definition. For equality constrained problems it is an implementation of a trust-region sequential quadratic programming solver and, when inequality constraints are imposed, it switches to a trust-region interior point method. Both methods are appropriate for large scale
problems. Quasi-Newton options BFGS and SR1 were implemented and can be used to approximate second order derivatives for this new method. Also, finite-differences can be used to approximate either first-order or
second-order derivatives.
Random-to-Best/1/bin and Random-to-Best/1/exp mutation strategies were added to scipy.optimize.differential\_evolution as `randtobest1bin` and `randtobest1exp`, respectively. Note: These names were already in use but implemented a different mutation strategy. See [Backwards incompatible changes](backwards-incompatible-changes), below. The `init` keyword for the scipy.optimize.differential\_evolution function can now accept an array. This array allows the user to specify the
entire population.
Add an `adaptive` option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem.
Minor improvements in scipy.optimize.basinhopping.
scipy.signal improvements
Three new functions for peak finding in one-dimensional arrays were added. scipy.signal.find\_peaks searches for peaks (local maxima) based on simple value comparison of neighbouring samples and returns those
peaks whose properties match optionally specified conditions for their height, prominence, width, threshold and distance to each other. scipy.signal.peak\_prominences and scipy.signal.peak\_widths can directly calculate the prominences or widths of known peaks.
Added ZPK versions of frequency transformations: scipy.signal.bilinear\_zpk, scipy.signal.lp2bp\_zpk, scipy.signal.lp2bs\_zpk, scipy.signal.lp2hp\_zpk, scipy.signal.lp2lp\_zpk.
Added scipy.signal.windows.dpss, scipy.signal.windows.general\_cosine and scipy.signal.windows.general\_hamming.
scipy.sparse improvements
An in-place `resize` method has been added to all sparse matrix formats, which was only available for scipy.sparse.dok\_matrix in previous releases.
scipy.special improvements
Added Owen's T function as scipy.special.owens\_t.
Accuracy improvements in `chndtr`, `digamma`, `gammaincinv`, `lambertw`, `zetac`.
scipy.stats improvements
The Moyal distribution has been added as scipy.stats.moyal.
Added the normal inverse Gaussian distribution as scipy.stats.norminvgauss.
Deprecated features
-------------------
The iterative linear equation solvers in scipy.sparse.linalg had a sub-optimal way of how absolute tolerance is considered. The default behavior will be changed in a future Scipy release to a more standard and less surprising one. To silence deprecation warnings, set the `atol=` parameter explicitly.
scipy.signal.windows.slepian is deprecated, replaced by scipy.signal.windows.dpss.
The window functions in scipy.signal are now available in scipy.signal.windows. They will remain also available in the old location in the scipy.signal namespace in future Scipy versions. However, importing them from scipy.signal.windows is preferred, and new window functions will be added only there.
Indexing sparse matrices with floating-point numbers instead of integers is deprecated.
The function scipy.stats.itemfreq is deprecated.
Backwards incompatible changes
------------------------------
Previously, scipy.linalg.orth used a singular value cutoff value appropriate for double precision numbers also for single-precision input. The cutoff value is now tunable, and the default has been changed to depend on the input data precision.
In previous versions of Scipy, the `randtobest1bin` and `randtobest1exp` mutation strategies in scipy.optimize.differential\_evolution were actually implemented using the Current-to-Best/1/bin and Current-to-Best/1/exp strategies, respectively. These strategies were renamed to `currenttobest1bin` and `currenttobest1exp` and the implementations of `randtobest1bin` and `randtobest1exp` strategies were corrected.
Functions in the ndimage module now always return their output array. Before this most functions only returned the output array if it had been allocated by the function, and would return `None` if it had been provided by the user.
Distance metrics in scipy.spatial.distance now require non-negative weights.
scipy.special.loggamma returns now real-valued result when the input is real-valued.
Other changes
-------------
When building on Linux with GNU compilers, the `.so` Python extension files now hide all symbols except those required by Python, which can avoid problems when embedding the Python interpreter.
Authors
-------
- Saurabh Agarwal +
- Diogo Aguiam +
- Joseph Albert +
- Gerrit Ansmann +
- Astrofysicus +
- Jean-François B +
- Vahan Babayan +
- Alessandro Pietro Bardelli
- Christoph Baumgarten +
- Felix Berkenkamp
- Lilian Besson +
- Aditya Bharti +
- Matthew Brett
- Evgeni Burovski
- CJ Carey
- Martin Ø. Christensen +
- Robert Cimrman
- Vicky Close +
- Peter Cock +
- Philip DeBoer
- Jaime Fernandez del Rio
- Dieter Werthmüller +
- Tom Donoghue +
- Matt Dzugan +
- Lars G +
- Jacques Gaudin +
- Andriy Gelman +
- Sean Gillies +
- Dezmond Goff
- Christoph Gohlke
- Ralf Gommers
- Uri Goren +
- Deepak Kumar Gouda +
- Douglas Lessa Graciosa +
- Matt Haberland
- David Hagen
- Charles Harris
- Jordan Heemskerk +
- Danny Hermes +
- Stephan Hoyer +
- Theodore Hu +
- Jean-François B. +
- Mads Jensen +
- Jon Haitz Legarreta Gorroño +
- Ben Jude +
- Noel Kippers +
- Julius Bier Kirkegaard +
- Maria Knorps +
- Mikkel Kristensen +
- Eric Larson
- Kasper Primdal Lauritzen +
- Denis Laxalde
- KangWon Lee +
- Jan Lehky +
- Jackie Leng +
- P.L. Lim +
- Nikolay Mayorov
- Mihai Capotă +
- Max Mikhaylov +
- Mark Mikofski +
- Jarrod Millman
- Raden Muhammad +
- Paul Nation
- Andrew Nelson
- Nico Schlömer
- Joel Nothman
- Kyle Oman +
- Egor Panfilov +
- Nick Papior
- Anubhav Patel +
- Oleksandr Pavlyk
- Ilhan Polat
- Robert Pollak +
- Anant Prakash +
- Aman Pratik
- Sean Quinn +
- Giftlin Rajaiah +
- Tyler Reddy
- Joscha Reimer
- Antonio H Ribeiro +
- Antonio Horta Ribeiro
- Benjamin Rose +
- Fabian Rost
- Divakar Roy +
- Scott Sievert
- Leo Singer
- Sourav Singh
- Martino Sorbaro +
- Eric Stansifer +
- Martin Thoma
- Phil Tooley +
- Piotr Uchwat +
- Paul van Mulbregt
- Pauli Virtanen
- Stefan van der Walt
- Warren Weckesser
- Florian Weimer +
- Eric Wieser
- Josh Wilson
- Ted Ying +
- Evgeny Zhurko
- Zé Vinícius
- awakenting +
- endolith
- FormerPhysicist +
- gaulinmp +
- hugovk
- ksemb +
- kshitij12345 +
- luzpaz +
- NKrvavica +
- rafalalgo +
- samyak0210 +
- soluwalana +
- sudheerachary +
- Tokixix +
- tttthomasssss +
- vkk800 +
- xoviat
- ziejcow +
A total of 122 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.
```
### 1.0.1
```
SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0.
Probably the most important change is a fix for an incompatibility between
SciPy 1.0.0 and ``numpy.f2py`` in the NumPy master branch.
```
### 1.0.0
```
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. Moreover, our development attention
will now shift to bug-fix releases on the 1.0.x branch, and on adding
new features on the master branch.
Some of the highlights of this release are:
- Major build improvements. Windows wheels are available on PyPI for the
first time, and continuous integration has been set up on Windows and OS X
in addition to Linux.
- A set of new ODE solvers and a unified interface to them
(`scipy.integrate.solve_ivp`).
- Two new trust region optimizers and a new linear programming method, with
improved performance compared to what `scipy.optimize` offered previously.
- Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now
complete.
This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.
This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the
lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate
providing the LAPACK 3.2.1 API. We have decided that it's time to either drop
Accelerate or, if there is enough interest, provide shims for functions added
in more recent LAPACK versions so it can still be used.
New features
============
`scipy.cluster` improvements
----------------------------
`scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a
linkage matrix to minimize distances between adjacent leaves, was added.
`scipy.fftpack` improvements
----------------------------
N-dimensional versions of the discrete sine and cosine transforms and their
inverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``.
`scipy.integrate` improvements
------------------------------
A set of new ODE solvers have been added to `scipy.integrate`. The convenience
function `scipy.integrate.solve_ivp` allows uniform access to all solvers.
The individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and ``LSODA``)
can also be used directly.
`scipy.linalg` improvements
----------------------------
The BLAS wrappers in `scipy.linalg.blas` have been completed. Added functions
are ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``, ``*spr``,
``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``,
``*spr2``,
Wrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``, ``*hetrd``,
``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``,
``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added.
The function `scipy.linalg.subspace_angles` has been added to compute the
subspace angles between two matrices.
The function `scipy.linalg.clarkson_woodruff_transform` has been added.
It finds low-rank matrix approximation via the Clarkson-Woodruff Transform.
The functions `scipy.linalg.eigh_tridiagonal` and
`scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and
eigenvectors of tridiagonal hermitian/symmetric matrices, were added.
`scipy.ndimage` improvements
----------------------------
Support for homogeneous coordinate transforms has been added to
`scipy.ndimage.affine_transform`.
The ``ndimage`` C code underwent a significant refactoring, and is now
a lot easier to understand and maintain.
`scipy.optimize` improvements
-----------------------------
The methods ``trust-region-exact`` and ``trust-krylov`` have been added to the
function `scipy.optimize.minimize`. These new trust-region methods solve the
subproblem with higher accuracy at the cost of more Hessian factorizations
(compared to dogleg) or more matrix vector products (compared to ncg) but
usually require less nonlinear iterations and are able to deal with indefinite
Hessians. They seem very competitive against the other Newton methods
implemented in scipy.
`scipy.optimize.linprog` gained an interior point method. Its performance is
superior (both in accuracy and speed) to the older simplex method.
`scipy.signal` improvements
---------------------------
An argument ``fs`` (sampling frequency) was added to the following functions:
``firwin``, ``firwin2``, ``firls``, and ``remez``. This makes these functions
consistent with many other functions in `scipy.signal` in which the sampling
frequency can be specified.
`scipy.signal.freqz` has been sped up significantly for FIR filters.
`scipy.sparse` improvements
---------------------------
Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%.
The ``tocsr`` method of COO matrices is now several times faster.
The ``diagonal`` method of sparse matrices now takes a parameter, indicating
which diagonal to return.
`scipy.sparse.linalg` improvements
----------------------------------
A new iterative solver for large-scale nonsymmetric sparse linear systems,
`scipy.sparse.linalg.gcrotmk`, was added. It implements ``GCROT(m,k)``, a
flexible variant of ``GCROT``.
`scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding potentially
faster convergence.
SuperLU was updated to version 5.2.1.
`scipy.spatial` improvements
----------------------------
Many distance metrics in `scipy.spatial.distance` gained support for weights.
The signatures of `scipy.spatial.distance.pdist` and
`scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order to
support a wider range of metrics (e.g. string-based metrics that need extra
keywords). Also, an optional ``out`` parameter was added to ``pdist`` and
``cdist`` allowing the user to specify where the resulting distance matrix is
to be stored
`scipy.stats` improvements
--------------------------
The methods ``cdf`` and ``logcdf`` were added to
`scipy.stats.multivariate_normal`, providing the cumulative distribution
function of the multivariate normal distribution.
New statistical distance functions were added, namely
`scipy.stats.wasserstein_distance` for the first Wasserstein distance and
`scipy.stats.energy_distance` for the energy distance.
Deprecated features
===================
The following functions in `scipy.misc` are deprecated: ``bytescale``,
``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,
``imsave``, ``imshow`` and ``toimage``. Most of those functions have unexpected
behavior (like rescaling and type casting image data without the user asking
for that). Other functions simply have better alternatives.
``scipy.interpolate.interpolate_wrapper`` and all functions in that submodule
are deprecated. This was a never finished set of wrapper functions which is
not relevant anymore.
The ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the
dtypes of the input arrays in the future and checked that it is a scalar or
an array with a single element.
Backwards incompatible changes
==============================
The following deprecated functions have been removed from `scipy.stats`:
``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``,
``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and
``threshold``.
The following deprecated functions have been removed from `scipy.stats.mstats`:
``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``.
The deprecated ``a`` and ``reta`` keywords have been removed from
`scipy.stats.shapiro`.
The deprecated functions ``sparse.csgraph.cs_graph_components`` and
``sparse.linalg.symeig`` have been removed from `scipy.sparse`.
The following deprecated keywords have been removed in `scipy.sparse.linalg`:
``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``, ``cg``,
``cgs``, ``gmres``, ``qmr`` and ``minres``.
The deprecated functions ``expm2`` and ``expm3`` have been removed from
`scipy.linalg`. The deprecated keyword ``q`` was removed from
`scipy.linalg.expm`. And the deprecated submodule ``linalg.calc_lwork`` was
removed.
The deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and
``K2F`` have been removed from `scipy.constants`.
The deprecated ``ppform`` class was removed from `scipy.interpolate`.
The deprecated keyword ``iprint`` was removed from `scipy.optimize.fmin_cobyla`.
The default value for the ``zero_phase`` keyword of `scipy.signal.decimate`
has been changed to True.
The ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the
method used for random initialization, so using a fixed random seed will
not necessarily produce the same results as in previous versions.
`scipy.special.gammaln` does not accept complex arguments anymore.
The deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``,
``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use
the functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and
``spherical_kn``. Be aware that the new functions have different
signatures.
The cross-class properties of `scipy.signal.lti` systems have been removed.
The following properties/setters have been removed:
Name - (accessing/setting has been removed) - (setting has been removed)
* StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``)
* TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``, ``poles``)
* ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - ()
``signal.freqz(b, a)`` with ``b`` or ``a`` >1-D raises a ``ValueError``. This
was a corner case for which it was unclear that the behavior was well-defined.
The method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather than
an ndarray when the length of alpha is 1.
Other changes
=============
SciPy now has a formal governance structure. It consists of a BDFL (Pauli
Virtanen) and a Steering Committee. See `the governance document
<https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst>`_
for details.
It is now possible to build SciPy on Windows with MSVC + gfortran! Continuous
integration has been set up for this build configuration on Appveyor, building
against OpenBLAS.
Continuous integration for OS X has been set up on TravisCI.
The SciPy test suite has been migrated from ``nose`` to ``pytest``.
``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to
add custom code that needs to run when importing SciPy (e.g. checks for
hardware, DLL search paths, etc.).
Support for PEP 518 (specifying build system requirements) was added - see
``pyproject.toml`` in the root of the SciPy repository.
In order to have consistent function names, the function
``scipy.linalg.solve_lyapunov`` is renamed to
`scipy.linalg.solve_continuous_lyapunov`. The old name is kept for
backwards-compatibility.
Authors
=======
* arcady +
* xoviat +
* Anton Akhmerov
* Dominic Antonacci +
* Alessandro Pietro Bardelli
* Ved Basu +
* Michael James Bedford +
* Ray Bell +
* Juan M. Bello-Rivas +
* Sebastian Berg
* Felix Berkenkamp
* Jyotirmoy Bhattacharya +
* Matthew Brett
* Jonathan Bright
* Bruno Jiménez +
* Evgeni Burovski
* Patrick Callier
* Mark Campanelli +
* CJ Carey
* Adam Cox +
* Michael Danilov +
* David Haberthür +
* Andras Deak +
* Philip DeBoer
* Anne-Sylvie Deutsch
* Cathy Douglass +
* Dominic Else +
* Guo Fei +
* Roman Feldbauer +
* Yu Feng
* Jaime Fernandez del Rio
* Orestis Floros +
* David Freese +
* Adam Geitgey +
* James Gerity +
* Dezmond Goff +
* Christoph Gohlke
* Ralf Gommers
* Dirk Gorissen +
* Matt Haberland +
* David Hagen +
* Charles Harris
* Lam Yuen Hei +
* Jean Helie +
* Gaute Hope +
* Guillaume Horel +
* Franziska Horn +
* Yevhenii Hyzyla +
* Vladislav Iakovlev +
* Marvin Kastner +
* Mher Kazandjian
* Thomas Keck
* Adam Kurkiewicz +
* Ronan Lamy +
* J.L. Lanfranchi +
* Eric Larson
* Denis Laxalde
* Gregory R. Lee
* Felix Lenders +
* Evan Limanto
* Julian Lukwata +
* François Magimel
* Syrtis Major +
* Charles Masson +
* Nikolay Mayorov
* Tobias Megies
* Markus Meister +
* Roman Mirochnik +
* Jordi Montes +
* Nathan Musoke +
* Andrew Nelson
* M.J. Nichol
* Nico Schlömer +
* Juan Nunez-Iglesias
* Arno Onken +
* Dima Pasechnik +
* Ashwin Pathak +
* Stefan Peterson
* Ilhan Polat
* Andrey Portnoy +
* Ravi Kumar Prasad +
* Aman Pratik
* Eric Quintero
* Vedant Rathore +
* Tyler Reddy
* Joscha Reimer
* Philipp Rentzsch +
* Antonio Horta Ribeiro
* Ned Richards +
* Kevin Rose +
* Benoit Rostykus +
* Matt Ruffalo +
* Eli Sadoff +
* Pim Schellart
* Klaus Sembritzki +
* Nikolay Shebanov +
* Jonathan Tammo Siebert
* Scott Sievert
* Max Silbiger +
* Mandeep Singh +
* Michael Stewart +
* Jonathan Sutton +
* Deep Tavker +
* Martin Thoma
* James Tocknell +
* Aleksandar Trifunovic +
* Paul van Mulbregt +
* Jacob Vanderplas
* Aditya Vijaykumar
* Pauli Virtanen
* James Webber
* Warren Weckesser
* Eric Wieser +
* Josh Wilson
* Zhiqing Xiao +
* Evgeny Zhurko
* Nikolay Zinov +
* Zé Vinícius +
A total of 118 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This
This PR pins scipy to the latest release 1.4.0.
Changelog
### 1.4.0 ``` 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. ``` ### 1.3.3 ``` compared to `1.3.2`. In particular, a test suite issue involving multiprocessing was fixed for Windows and Python `3.8` on macOS. Wheels were also updated to place `msvcp140.dll` at the appropriate location, which was previously causing issues. Authors ======= Ilhan Polat Tyler Reddy Ralf Gommers ``` ### 1.3.2 ``` SciPy `1.3.2` is a bug-fix and maintenance release that adds support for Python `3.8`. Authors ===== * CJ Carey * Dany Vohl * Martin Gauch + * Ralf Gommers * Matt Haberland * Eric Larson * Nikolay Mayorov * Sam McCormack + * Andrew Nelson * Tyler Reddy * Pauli Virtanen * Huize Wang + * Warren Weckesser * Joseph Weston + A total of 14 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. ``` ### 1.3.1 ``` SciPy `1.3.1` is a bug-fix release with no new features compared to `1.3.0`. Authors ======= * Matt Haberland * Geordie McBain * Yu Feng * Evgeni Burovski * Sturla Molden * Tapasweni Pathak * Eric Larson * Peter Bell * Carlos Ramos Carreño + * Ralf Gommers * David Hagen * Antony Lee * Ayappan P * Tyler Reddy * Pauli Virtanen A total of 15 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. ``` ### 1.3.0 ``` many new features, numerous bug-fixes, improved test coverage and better documentation. There have been some 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 1.3.x branch, and on adding new features on the master branch. This release requires Python 3.5+ and NumPy 1.13.3 or greater. For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required. Highlights of this release -------------------------- - Three new ``stats`` functions, a rewrite of ``pearsonr``, and an exact computation of the Kolmogorov-Smirnov two-sample test - A new Cython API for bounded scalar-function root-finders in `scipy.optimize` - Substantial ``CSR`` and ``CSC`` sparse matrix indexing performance improvements - Added support for interpolation of rotations with continuous angular rate and acceleration in ``RotationSpline`` New features ============ `scipy.interpolate` improvements -------------------------------- A new class ``CubicHermiteSpline`` is introduced. It is a piecewise-cubic interpolator which matches observed values and first derivatives. Existing cubic interpolators ``CubicSpline``, ``PchipInterpolator`` and ``Akima1DInterpolator`` were made subclasses of ``CubicHermiteSpline``. `scipy.io` improvements ----------------------- For the Attribute-Relation File Format (ARFF) `scipy.io.arff.loadarff` now supports relational attributes. `scipy.io.mmread` can now parse Matrix Market format files with empty lines. `scipy.linalg` improvements --------------------------- Added wrappers for ``?syconv`` routines, which convert a symmetric matrix given by a triangular matrix factorization into two matrices and vice versa. `scipy.linalg.clarkson_woodruff_transform` now uses an algorithm that leverages sparsity. This may provide a 60-90 percent speedup for dense input matrices. Truly sparse input matrices should also benefit from the improved sketch algorithm, which now correctly runs in ``O(nnz(A))`` time. Added new functions to calculate symmetric Fiedler matrices and Fiedler companion matrices, named `scipy.linalg.fiedler` and `scipy.linalg.fiedler_companion`, respectively. These may be used for root finding. `scipy.ndimage` improvements ---------------------------- Gaussian filter performances may improve by an order of magnitude in some cases, thanks to removal of a dependence on ``np.polynomial``. This may impact `scipy.ndimage.gaussian_filter` for example. `scipy.optimize` improvements ----------------------------- The `scipy.optimize.brute` minimizer obtained a new keyword ``workers``, which can be used to parallelize computation. A Cython API for bounded scalar-function root-finders in `scipy.optimize` is available in a new module `scipy.optimize.cython_optimize` via ``cimport``. This API may be used with ``nogil`` and ``prange`` to loop over an array of function arguments to solve for an array of roots more quickly than with pure Python. ``'interior-point'`` is now the default method for ``linprog``, and ``'interior-point'`` now uses SuiteSparse for sparse problems when the required scikits (scikit-umfpack and scikit-sparse) are available. On benchmark problems (gh-10026), execution time reductions by factors of 2-3 were typical. Also, a new ``method='revised simplex'`` has been added. It is not as fast or robust as ``method='interior-point'``, but it is a faster, more robust, and equally accurate substitute for the legacy ``method='simplex'``. ``differential_evolution`` can now use a ``Bounds`` class to specify the bounds for the optimizing argument of a function. `scipy.optimize.dual_annealing` performance improvements related to vectorisation of some internal code. `scipy.signal` improvements --------------------------- Two additional methods of discretization are now supported by `scipy.signal.cont2discrete`: ``impulse`` and ``foh``. `scipy.signal.firls` now uses faster solvers `scipy.signal.detrend` now has a lower physical memory footprint in some cases, which may be leveraged using the new ``overwrite_data`` keyword argument `scipy.signal.firwin` ``pass_zero`` argument now accepts new string arguments that allow specification of the desired filter type: ``'bandpass'``, ``'lowpass'``, ``'highpass'``, and ``'bandstop'`` `scipy.signal.sosfilt` may have improved performance due to lower retention of the global interpreter lock (GIL) in algorithm `scipy.sparse` improvements --------------------------- A new keyword was added to ``csgraph.dijsktra`` that allows users to query the shortest path to ANY of the passed in indices, as opposed to the shortest path to EVERY passed index. `scipy.sparse.linalg.lsmr` performance has been improved by roughly 10 percent on large problems Improved performance and reduced physical memory footprint of the algorithm used by `scipy.sparse.linalg.lobpcg` ``CSR`` and ``CSC`` sparse matrix fancy indexing performance has been improved substantially `scipy.spatial` improvements ---------------------------- `scipy.spatial.ConvexHull` now has a ``good`` attribute that can be used alongsize the ``QGn`` Qhull options to determine which external facets of a convex hull are visible from an external query point. `scipy.spatial.cKDTree.query_ball_point` has been modernized to use some newer Cython features, including GIL handling and exception translation. An issue with ``return_sorted=True`` and scalar queries was fixed, and a new mode named ``return_length`` was added. ``return_length`` only computes the length of the returned indices list instead of allocating the array every time. `scipy.spatial.transform.RotationSpline` has been added to enable interpolation of rotations with continuous angular rates and acceleration `scipy.stats` improvements -------------------------- Added a new function to compute the Epps-Singleton test statistic, `scipy.stats.epps_singleton_2samp`, which can be applied to continuous and discrete distributions. New functions `scipy.stats.median_absolute_deviation` and `scipy.stats.gstd` (geometric standard deviation) were added. The `scipy.stats.combine_pvalues` method now supports ``pearson``, ``tippett`` and ``mudholkar_george`` pvalue combination methods. The `scipy.stats.ortho_group` and `scipy.stats.special_ortho_group` ``rvs(dim)`` functions' algorithms were updated from a ``O(dim^4)`` implementation to a ``O(dim^3)`` which gives large speed improvements for ``dim>100``. A rewrite of `scipy.stats.pearsonr` to use a more robust algorithm, provide meaningful exceptions and warnings on potentially pathological input, and fix at least five separate reported issues in the original implementation. Improved the precision of ``hypergeom.logcdf`` and ``hypergeom.logsf``. Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing the previously approximate computation for the two-sided test `stats.ks_2samp`. Also added a one-sided, two-sample KS test, and a keyword ``alternative`` to `stats.ks_2samp`. Backwards incompatible changes ============================== `scipy.interpolate` changes --------------------------- Functions from ``scipy.interpolate`` (``spleval``, ``spline``, ``splmake``, and ``spltopp``) and functions from ``scipy.misc`` (``bytescale``, ``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``, ``imsave``, ``imshow``, ``toimage``) have been removed. The former set has been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0. Similarly, aliases from ``scipy.misc`` (``comb``, ``factorial``, ``factorial2``, ``factorialk``, ``logsumexp``, ``pade``, ``info``, ``source``, ``who``) which have been deprecated since v1.0.0 are removed. `SciPy documentation for ``` ### 1.2.2 ``` ================ SciPy `1.2.2` is a bug-fix release with no new features compared to `1.2.1`. Importantly, the SciPy 1.2.2 wheels are built with OpenBLAS `0.3.7.dev` to alleviate issues with SkylakeX AVX512 kernels. Authors ======= * CJ Carey * Tyler Dawson + * Ralf Gommers * Kai Striega * Andrew Nelson * Tyler Reddy * Kevin Sheppard + A total of 7 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. ``` ### 1.2.1 ``` ========================== SciPy `1.2.1` is a bug-fix release with no new features compared to `1.2.0`. Most importantly, it solves the issue that `1.2.0` cannot be installed from source on Python `2.7` because of non-ASCII character issues. It is also notable that SciPy `1.2.1` wheels were built with OpenBLAS ``` ### 1.2.0 ``` 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 1.2.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. **Note**: This will be the last SciPy release to support Python 2.7. Consequently, the 1.2.x series will be a long term support (LTS) release; we will backport bug fixes until 1 Jan 2020. For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required. Highlights of this release -------------------------- - 1-D root finding improvements with a new solver, ``toms748``, and a new unified interface, ``root_scalar`` - New ``dual_annealing`` optimization method that combines stochastic and local deterministic searching - A new optimization algorithm, ``shgo`` (simplicial homology global optimization) for derivative free optimization problems - A new category of quaternion-based transformations are available in `scipy.spatial.transform` New features ============ `scipy.ndimage` improvements -------------------------------- Proper spline coefficient calculations have been added for the ``mirror``, ``wrap``, and ``reflect`` modes of `scipy.ndimage.rotate` `scipy.fftpack` improvements -------------------------------- DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in `scipy.fftpack`. `scipy.interpolate` improvements -------------------------------- `scipy.interpolate.pade` now accepts a new argument for the order of the numerator `scipy.cluster` improvements ---------------------------- `scipy.cluster.vq.kmeans2` gained a new initialization method, kmeans++. `scipy.special` improvements ---------------------------- The function ``softmax`` was added to `scipy.special`. `scipy.optimize` improvements ----------------------------- The one-dimensional nonlinear solvers have been given a unified interface `scipy.optimize.root_scalar`, similar to the `scipy.optimize.root` interface for multi-dimensional solvers. ``scipy.optimize.root_scalar(f, bracket=[a ,b], method="brenth")`` is equivalent to ``scipy.optimize.brenth(f, a ,b)``. If no ``method`` is specified, an appropriate one will be selected based upon the bracket and the number of derivatives available. The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within an enclosing interval has been added as `scipy.optimize.toms748`. This provides guaranteed convergence to a root with convergence rate per function evaluation of approximately 1.65 (for sufficiently well-behaved functions.) ``differential_evolution`` now has the ``updating`` and ``workers`` keywords. The first chooses between continuous updating of the best solution vector (the default), or once per generation. Continuous updating can lead to faster convergence. The ``workers`` keyword accepts an ``int`` or map-like callable, and parallelises the solver (having the side effect of updating once per generation). Supplying an ``int`` evaluates the trial solutions in N parallel parts. Supplying a map-like callable allows other parallelisation approaches (such as ``mpi4py``, or ``joblib``) to be used. ``dual_annealing`` (and ``shgo`` below) is a powerful new general purpose global optizimation (GO) algorithm. ``dual_annealing`` uses two annealing processes to accelerate the convergence towards the global minimum of an objective mathematical function. The first annealing process controls the stochastic Markov chain searching and the second annealing process controls the deterministic minimization. So, dual annealing is a hybrid method that takes advantage of stochastic and local deterministic searching in an efficient way. ``shgo`` (simplicial homology global optimization) is a similar algorithm appropriate for solving black box and derivative free optimization (DFO) problems. The algorithm generally converges to the global solution in finite time. The convergence holds for non-linear inequality and equality constraints. In addition to returning a global minimum, the algorithm also returns any other global and local minima found after every iteration. This makes it useful for exploring the solutions in a domain. `scipy.optimize.newton` can now accept a scalar or an array ``MINPACK`` usage is now thread-safe, such that ``MINPACK`` + callbacks may be used on multiple threads. `scipy.signal` improvements --------------------------- Digital filter design functions now include a parameter to specify the sampling rate. Previously, digital filters could only be specified using normalized frequency, but different functions used different scales (e.g. 0 to 1 for ``butter`` vs 0 to π for ``freqz``), leading to errors and confusion. With the ``fs`` parameter, ordinary frequencies can now be entered directly into functions, with the normalization handled internally. ``find_peaks`` and related functions no longer raise an exception if the properties of a peak have unexpected values (e.g. a prominence of 0). A ``PeakPropertyWarning`` is given instead. The new keyword argument ``plateau_size`` was added to ``find_peaks``. ``plateau_size`` may be used to select peaks based on the length of the flat top of a peak. ``welch()`` and ``csd()`` methods in `scipy.signal` now support calculation of a median average PSD, using ``average='mean'`` keyword `scipy.sparse` improvements --------------------------- The `scipy.sparse.bsr_matrix.tocsr` method is now implemented directly instead of converting via COO format, and the `scipy.sparse.bsr_matrix.tocsc` method is now also routed via CSR conversion instead of COO. The efficiency of both conversions is now improved. The issue where SuperLU or UMFPACK solvers crashed on matrices with non-canonical format in `scipy.sparse.linalg` was fixed. The solver wrapper canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK solver. The ``largest`` option of `scipy.sparse.linalg.lobpcg()` was fixed to have a correct (and expected) behavior. The order of the eigenvalues was made consistent with the ARPACK solver (``eigs()``), i.e. ascending for the smallest eigenvalues, and descending for the largest eigenvalues. The `scipy.sparse.random` function is now faster and also supports integer and complex values by passing the appropriate value to the ``dtype`` argument. `scipy.spatial` improvements ---------------------------- The function `scipy.spatial.distance.jaccard` was modified to return 0 instead of ``np.nan`` when two all-zero vectors are compared. Support for the Jensen Shannon distance, the square-root of the divergence, has been added under `scipy.spatial.distance.jensenshannon` An optional keyword was added to the function `scipy.spatial.cKDTree.query_ball_point()` to sort or not sort the returned indices. Not sorting the indices can speed up calls. A new category of quaternion-based transformations are available in `scipy.spatial.transform`, including spherical linear interpolation of rotations (``Slerp``), conversions to and from quaternions, Euler angles, and general rotation and inversion capabilities (`spatial.transform.Rotation`), and uniform random sampling of 3D rotations (`spatial.transform.Rotation.random`). `scipy.stats` improvements -------------------------- The Yeo-Johnson power transformation is now supported (``yeojohnson``, ``yeojohnson_llf``, ``yeojohnson_normmax``, ``yeojohnson_normplot``). Unlike the Box-Cox transformation, the Yeo-Johnson transformation can accept negative values. Added a general method to sample random variates based on the density only, in the new function ``rvs_ratio_uniforms``. The Yule-Simon distribution (``yulesimon``) was added -- this is a new discrete probability distribution. ``stats`` and ``mstats`` now have access to a new regression method, ``siegelslopes``, a robust linear regression algorithm `scipy.stats.gaussian_kde` now has the ability to deal with weighted samples, and should have a modest improvement in performance Levy Stable Parameter Estimation, PDF, and CDF calculations are now supported for `scipy.stats.levy_stable`. The Brunner-Munzel test is now available as ``brunnermunzel`` in ``stats`` and ``mstats`` `scipy.linalg` improvements -------------------------- `scipy.linalg.lapack` now exposes the LAPACK routines using the Rectangular Full Packed storage (RFP) for upper triangular, lower triangular, symmetric, or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition routines are now available as well. Deprecated features =================== The functions ``hyp2f0``, ``hyp1f2`` and ``hyp3f0`` in ``scipy.special`` have been deprecated. Backwards incompatible changes ============================== LAPACK version 3.4.0 or later is now required. Building with Apple Accelerate is no longer supported. The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct results for all angles. Before this, the function only returned correct values for those angles which were greater than pi/4. Support for the Bento build system has been removed. Bento has not been maintained for several years, and did not have good Python 3 or wheel support, hence it was time to remove it. The required signature of `scipy.optimize.lingprog` ``method=simplex`` callback function has changed. Before iteration begins, the simplex solver first converts the problem into a standard form that does not, in general, have the same variables or constraints as the problem defined by the user. Previously, the simplex solver would pass a user-specified callback function several separate arguments, such as the current solution vector ``xk``, corresponding to this standard form problem. Unfortunately, the relationship between the standard form problem and the user-defined problem was not documented, limiting the utility of the information passed to the callback function. In addition to numerous bug fix changes, the simplex solver now passes a user-specified callback function a single ``OptimizeResult`` object containing information that corresponds directly to the user-defined problem. In future releases, this ``OptimizeResult`` object may be expanded to include additional information, such as variables corresponding to the standard-form problem and information concerning the relationship between the standard-form and user-defined problems. The implementation of `scipy.sparse.random` has changed, and this affects the numerical values returned for both ``sparse.random`` and ``sparse.rand`` for some matrix shapes and a given seed. `scipy.optimize.newton` will no longer use Halley's method in cases where it negatively impacts convergence Other changes ============= Authors ======= * endolith * luzpaz * Hameer Abbasi + * akahard2dj + * Anton Akhmerov * Joseph Albert * alexthomas93 + * ashish + * atpage + * Blair Azzopardi + * Yoshiki Vázquez Baeza * Bence Bagi + * Christoph Baumgarten * Lucas Bellomo + * BH4 + * Aditya Bharti * Max Bolingbroke * François Boulogne * Ward Bradt + * Matthew Brett * Evgeni Burovski * Rafał Byczek + * Alfredo Canziani + * CJ Carey * Lucía Cheung + * Poom Chiarawongse + * Jeanne Choo + * Robert Cimrman * Graham Clenaghan + * cynthia-rempel + * Johannes Damp + * Jaime Fernandez del Rio * Dowon + * emmi474 + * Stefan Endres + * Thomas Etherington + * Alex Fikl + * fo40225 + * Joseph Fox-Rabinovitz * Lars G * Abhinav Gautam + * Stiaan Gerber + * C.A.M. Gerlach + * Ralf Gommers * Todd Goodall * Lars Grueter + * Sylvain Gubian + * Matt Haberland * David Hagen * Will Handley + * Charles Harris * Ian Henriksen * Thomas Hisch + * Theodore Hu * Michael Hudson-Doyle + * Nicolas Hug + * jakirkham + * Jakob Jakobson + * James + * Jan Schlüter * jeanpauphilet + * josephmernst + * Kai + * Kai-Striega + * kalash04 + * Toshiki Kataoka + * Konrad0 + * Tom Krauss + * Johannes Kulick * Lars Grüter + * Eric Larson * Denis Laxalde * Will Lee + * Katrin Leinweber + * Yin Li + * P. L. Lim + * Jesse Livezey + * Duncan Macleod + * MatthewFlamm + * Nikolay Mayorov * Mike McClurg + * Christian Meyer + * Mark Mikofski * Naoto Mizuno + * mohmmadd + * Nathan Musoke * Anju Geetha Nair + * Andrew Nelson * Ayappan P + * Nick Papior * Haesun Park + * Ronny Pfannschmidt + * pijyoi + * Ilhan Polat * Anthony Polloreno + * Ted Pudlik * puenka * Eric Quintero * Pradeep Reddy Raamana + * Vyas Ramasubramani + * Ramon Viñas + * Tyler Reddy * Joscha Reimer * Antonio H Ribeiro * richardjgowers + * Rob + * robbystk + * Lucas Roberts + * rohan + * Joaquin Derrac Rus + * Josua Sassen + * Bruce Sharpe + * Max Shinn + * Scott Sievert * Sourav Singh * Strahinja Lukić + * Kai Striega + * Shinya SUZUKI + * Mike Toews + * Piotr Uchwat * Miguel de Val-Borro + * Nicky van Foreest * Paul van Mulbregt * Gael Varoquaux * Pauli Virtanen * Stefan van der Walt * Warren Weckesser * Joshua Wharton + * Bernhard M. Wiedemann + * Eric Wieser * Josh Wilson * Tony Xiang + * Roman Yurchak + * Roy Zywina + A total of 137 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. ``` ### 1.1.0 ``` ========================= **Note: Scipy 1.1.0 is not released yet!** SciPy 1.1.0 is the culmination of 7 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 1.1.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. This release has improved but not necessarily 100% compatibility with the [PyPy](https://pypy.org/) Python implementation. For running on PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required. New features ------------ scipy.integrate improvements The argument `tfirst` has been added to the function scipy.integrate.odeint. This allows odeint to use the same user functions as scipy.integrate.solve\_ivp and scipy.integrate.ode without the need for wrapping them in a function that swaps the first two arguments. Error messages from `quad()` are now clearer. scipy.linalg improvements The function scipy.linalg.ldl has been added for factorization of indefinite symmetric/hermitian matrices into triangular and block diagonal matrices. Python wrappers for LAPACK `sygst`, `hegst` added in scipy.linalg.lapack. Added scipy.linalg.null\_space, scipy.linalg.cdf2rdf, scipy.linalg.rsf2csf. scipy.misc improvements An electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram. scipy.ndimage improvements The routines scipy.ndimage.binary\_opening, and scipy.ndimage.binary\_closing now support masks and different border values. scipy.optimize improvements The method `trust-constr` has been added to scipy.optimize.minimize. The method switches between two implementations depending on the problem definition. For equality constrained problems it is an implementation of a trust-region sequential quadratic programming solver and, when inequality constraints are imposed, it switches to a trust-region interior point method. Both methods are appropriate for large scale problems. Quasi-Newton options BFGS and SR1 were implemented and can be used to approximate second order derivatives for this new method. Also, finite-differences can be used to approximate either first-order or second-order derivatives. Random-to-Best/1/bin and Random-to-Best/1/exp mutation strategies were added to scipy.optimize.differential\_evolution as `randtobest1bin` and `randtobest1exp`, respectively. Note: These names were already in use but implemented a different mutation strategy. See [Backwards incompatible changes](backwards-incompatible-changes), below. The `init` keyword for the scipy.optimize.differential\_evolution function can now accept an array. This array allows the user to specify the entire population. Add an `adaptive` option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem. Minor improvements in scipy.optimize.basinhopping. scipy.signal improvements Three new functions for peak finding in one-dimensional arrays were added. scipy.signal.find\_peaks searches for peaks (local maxima) based on simple value comparison of neighbouring samples and returns those peaks whose properties match optionally specified conditions for their height, prominence, width, threshold and distance to each other. scipy.signal.peak\_prominences and scipy.signal.peak\_widths can directly calculate the prominences or widths of known peaks. Added ZPK versions of frequency transformations: scipy.signal.bilinear\_zpk, scipy.signal.lp2bp\_zpk, scipy.signal.lp2bs\_zpk, scipy.signal.lp2hp\_zpk, scipy.signal.lp2lp\_zpk. Added scipy.signal.windows.dpss, scipy.signal.windows.general\_cosine and scipy.signal.windows.general\_hamming. scipy.sparse improvements An in-place `resize` method has been added to all sparse matrix formats, which was only available for scipy.sparse.dok\_matrix in previous releases. scipy.special improvements Added Owen's T function as scipy.special.owens\_t. Accuracy improvements in `chndtr`, `digamma`, `gammaincinv`, `lambertw`, `zetac`. scipy.stats improvements The Moyal distribution has been added as scipy.stats.moyal. Added the normal inverse Gaussian distribution as scipy.stats.norminvgauss. Deprecated features ------------------- The iterative linear equation solvers in scipy.sparse.linalg had a sub-optimal way of how absolute tolerance is considered. The default behavior will be changed in a future Scipy release to a more standard and less surprising one. To silence deprecation warnings, set the `atol=` parameter explicitly. scipy.signal.windows.slepian is deprecated, replaced by scipy.signal.windows.dpss. The window functions in scipy.signal are now available in scipy.signal.windows. They will remain also available in the old location in the scipy.signal namespace in future Scipy versions. However, importing them from scipy.signal.windows is preferred, and new window functions will be added only there. Indexing sparse matrices with floating-point numbers instead of integers is deprecated. The function scipy.stats.itemfreq is deprecated. Backwards incompatible changes ------------------------------ Previously, scipy.linalg.orth used a singular value cutoff value appropriate for double precision numbers also for single-precision input. The cutoff value is now tunable, and the default has been changed to depend on the input data precision. In previous versions of Scipy, the `randtobest1bin` and `randtobest1exp` mutation strategies in scipy.optimize.differential\_evolution were actually implemented using the Current-to-Best/1/bin and Current-to-Best/1/exp strategies, respectively. These strategies were renamed to `currenttobest1bin` and `currenttobest1exp` and the implementations of `randtobest1bin` and `randtobest1exp` strategies were corrected. Functions in the ndimage module now always return their output array. Before this most functions only returned the output array if it had been allocated by the function, and would return `None` if it had been provided by the user. Distance metrics in scipy.spatial.distance now require non-negative weights. scipy.special.loggamma returns now real-valued result when the input is real-valued. Other changes ------------- When building on Linux with GNU compilers, the `.so` Python extension files now hide all symbols except those required by Python, which can avoid problems when embedding the Python interpreter. Authors ------- - Saurabh Agarwal + - Diogo Aguiam + - Joseph Albert + - Gerrit Ansmann + - Astrofysicus + - Jean-François B + - Vahan Babayan + - Alessandro Pietro Bardelli - Christoph Baumgarten + - Felix Berkenkamp - Lilian Besson + - Aditya Bharti + - Matthew Brett - Evgeni Burovski - CJ Carey - Martin Ø. Christensen + - Robert Cimrman - Vicky Close + - Peter Cock + - Philip DeBoer - Jaime Fernandez del Rio - Dieter Werthmüller + - Tom Donoghue + - Matt Dzugan + - Lars G + - Jacques Gaudin + - Andriy Gelman + - Sean Gillies + - Dezmond Goff - Christoph Gohlke - Ralf Gommers - Uri Goren + - Deepak Kumar Gouda + - Douglas Lessa Graciosa + - Matt Haberland - David Hagen - Charles Harris - Jordan Heemskerk + - Danny Hermes + - Stephan Hoyer + - Theodore Hu + - Jean-François B. + - Mads Jensen + - Jon Haitz Legarreta Gorroño + - Ben Jude + - Noel Kippers + - Julius Bier Kirkegaard + - Maria Knorps + - Mikkel Kristensen + - Eric Larson - Kasper Primdal Lauritzen + - Denis Laxalde - KangWon Lee + - Jan Lehky + - Jackie Leng + - P.L. Lim + - Nikolay Mayorov - Mihai Capotă + - Max Mikhaylov + - Mark Mikofski + - Jarrod Millman - Raden Muhammad + - Paul Nation - Andrew Nelson - Nico Schlömer - Joel Nothman - Kyle Oman + - Egor Panfilov + - Nick Papior - Anubhav Patel + - Oleksandr Pavlyk - Ilhan Polat - Robert Pollak + - Anant Prakash + - Aman Pratik - Sean Quinn + - Giftlin Rajaiah + - Tyler Reddy - Joscha Reimer - Antonio H Ribeiro + - Antonio Horta Ribeiro - Benjamin Rose + - Fabian Rost - Divakar Roy + - Scott Sievert - Leo Singer - Sourav Singh - Martino Sorbaro + - Eric Stansifer + - Martin Thoma - Phil Tooley + - Piotr Uchwat + - Paul van Mulbregt - Pauli Virtanen - Stefan van der Walt - Warren Weckesser - Florian Weimer + - Eric Wieser - Josh Wilson - Ted Ying + - Evgeny Zhurko - Zé Vinícius - awakenting + - endolith - FormerPhysicist + - gaulinmp + - hugovk - ksemb + - kshitij12345 + - luzpaz + - NKrvavica + - rafalalgo + - samyak0210 + - soluwalana + - sudheerachary + - Tokixix + - tttthomasssss + - vkk800 + - xoviat - ziejcow + A total of 122 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. ``` ### 1.0.1 ``` SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0. Probably the most important change is a fix for an incompatibility between SciPy 1.0.0 and ``numpy.f2py`` in the NumPy master branch. ``` ### 1.0.0 ``` 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. Moreover, our development attention will now shift to bug-fix releases on the 1.0.x branch, and on adding new features on the master branch. Some of the highlights of this release are: - Major build improvements. Windows wheels are available on PyPI for the first time, and continuous integration has been set up on Windows and OS X in addition to Linux. - A set of new ODE solvers and a unified interface to them (`scipy.integrate.solve_ivp`). - Two new trust region optimizers and a new linear programming method, with improved performance compared to what `scipy.optimize` offered previously. - Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now complete. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate providing the LAPACK 3.2.1 API. We have decided that it's time to either drop Accelerate or, if there is enough interest, provide shims for functions added in more recent LAPACK versions so it can still be used. New features ============ `scipy.cluster` improvements ---------------------------- `scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a linkage matrix to minimize distances between adjacent leaves, was added. `scipy.fftpack` improvements ---------------------------- N-dimensional versions of the discrete sine and cosine transforms and their inverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``. `scipy.integrate` improvements ------------------------------ A set of new ODE solvers have been added to `scipy.integrate`. The convenience function `scipy.integrate.solve_ivp` allows uniform access to all solvers. The individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and ``LSODA``) can also be used directly. `scipy.linalg` improvements ---------------------------- The BLAS wrappers in `scipy.linalg.blas` have been completed. Added functions are ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``, ``*spr``, ``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``, ``*spr2``, Wrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``, ``*hetrd``, ``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``, ``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added. The function `scipy.linalg.subspace_angles` has been added to compute the subspace angles between two matrices. The function `scipy.linalg.clarkson_woodruff_transform` has been added. It finds low-rank matrix approximation via the Clarkson-Woodruff Transform. The functions `scipy.linalg.eigh_tridiagonal` and `scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and eigenvectors of tridiagonal hermitian/symmetric matrices, were added. `scipy.ndimage` improvements ---------------------------- Support for homogeneous coordinate transforms has been added to `scipy.ndimage.affine_transform`. The ``ndimage`` C code underwent a significant refactoring, and is now a lot easier to understand and maintain. `scipy.optimize` improvements ----------------------------- The methods ``trust-region-exact`` and ``trust-krylov`` have been added to the function `scipy.optimize.minimize`. These new trust-region methods solve the subproblem with higher accuracy at the cost of more Hessian factorizations (compared to dogleg) or more matrix vector products (compared to ncg) but usually require less nonlinear iterations and are able to deal with indefinite Hessians. They seem very competitive against the other Newton methods implemented in scipy. `scipy.optimize.linprog` gained an interior point method. Its performance is superior (both in accuracy and speed) to the older simplex method. `scipy.signal` improvements --------------------------- An argument ``fs`` (sampling frequency) was added to the following functions: ``firwin``, ``firwin2``, ``firls``, and ``remez``. This makes these functions consistent with many other functions in `scipy.signal` in which the sampling frequency can be specified. `scipy.signal.freqz` has been sped up significantly for FIR filters. `scipy.sparse` improvements --------------------------- Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%. The ``tocsr`` method of COO matrices is now several times faster. The ``diagonal`` method of sparse matrices now takes a parameter, indicating which diagonal to return. `scipy.sparse.linalg` improvements ---------------------------------- A new iterative solver for large-scale nonsymmetric sparse linear systems, `scipy.sparse.linalg.gcrotmk`, was added. It implements ``GCROT(m,k)``, a flexible variant of ``GCROT``. `scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding potentially faster convergence. SuperLU was updated to version 5.2.1. `scipy.spatial` improvements ---------------------------- Many distance metrics in `scipy.spatial.distance` gained support for weights. The signatures of `scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order to support a wider range of metrics (e.g. string-based metrics that need extra keywords). Also, an optional ``out`` parameter was added to ``pdist`` and ``cdist`` allowing the user to specify where the resulting distance matrix is to be stored `scipy.stats` improvements -------------------------- The methods ``cdf`` and ``logcdf`` were added to `scipy.stats.multivariate_normal`, providing the cumulative distribution function of the multivariate normal distribution. New statistical distance functions were added, namely `scipy.stats.wasserstein_distance` for the first Wasserstein distance and `scipy.stats.energy_distance` for the energy distance. Deprecated features =================== The following functions in `scipy.misc` are deprecated: ``bytescale``, ``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``, ``imsave``, ``imshow`` and ``toimage``. Most of those functions have unexpected behavior (like rescaling and type casting image data without the user asking for that). Other functions simply have better alternatives. ``scipy.interpolate.interpolate_wrapper`` and all functions in that submodule are deprecated. This was a never finished set of wrapper functions which is not relevant anymore. The ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the dtypes of the input arrays in the future and checked that it is a scalar or an array with a single element. Backwards incompatible changes ============================== The following deprecated functions have been removed from `scipy.stats`: ``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``, ``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and ``threshold``. The following deprecated functions have been removed from `scipy.stats.mstats`: ``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``. The deprecated ``a`` and ``reta`` keywords have been removed from `scipy.stats.shapiro`. The deprecated functions ``sparse.csgraph.cs_graph_components`` and ``sparse.linalg.symeig`` have been removed from `scipy.sparse`. The following deprecated keywords have been removed in `scipy.sparse.linalg`: ``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``, ``cg``, ``cgs``, ``gmres``, ``qmr`` and ``minres``. The deprecated functions ``expm2`` and ``expm3`` have been removed from `scipy.linalg`. The deprecated keyword ``q`` was removed from `scipy.linalg.expm`. And the deprecated submodule ``linalg.calc_lwork`` was removed. The deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and ``K2F`` have been removed from `scipy.constants`. The deprecated ``ppform`` class was removed from `scipy.interpolate`. The deprecated keyword ``iprint`` was removed from `scipy.optimize.fmin_cobyla`. The default value for the ``zero_phase`` keyword of `scipy.signal.decimate` has been changed to True. The ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the method used for random initialization, so using a fixed random seed will not necessarily produce the same results as in previous versions. `scipy.special.gammaln` does not accept complex arguments anymore. The deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``, ``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use the functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and ``spherical_kn``. Be aware that the new functions have different signatures. The cross-class properties of `scipy.signal.lti` systems have been removed. The following properties/setters have been removed: Name - (accessing/setting has been removed) - (setting has been removed) * StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``) * TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``, ``poles``) * ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - () ``signal.freqz(b, a)`` with ``b`` or ``a`` >1-D raises a ``ValueError``. This was a corner case for which it was unclear that the behavior was well-defined. The method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather than an ndarray when the length of alpha is 1. Other changes ============= SciPy now has a formal governance structure. It consists of a BDFL (Pauli Virtanen) and a Steering Committee. See `the governance document <https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst>`_ for details. It is now possible to build SciPy on Windows with MSVC + gfortran! Continuous integration has been set up for this build configuration on Appveyor, building against OpenBLAS. Continuous integration for OS X has been set up on TravisCI. The SciPy test suite has been migrated from ``nose`` to ``pytest``. ``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to add custom code that needs to run when importing SciPy (e.g. checks for hardware, DLL search paths, etc.). Support for PEP 518 (specifying build system requirements) was added - see ``pyproject.toml`` in the root of the SciPy repository. In order to have consistent function names, the function ``scipy.linalg.solve_lyapunov`` is renamed to `scipy.linalg.solve_continuous_lyapunov`. The old name is kept for backwards-compatibility. Authors ======= * arcady + * xoviat + * Anton Akhmerov * Dominic Antonacci + * Alessandro Pietro Bardelli * Ved Basu + * Michael James Bedford + * Ray Bell + * Juan M. Bello-Rivas + * Sebastian Berg * Felix Berkenkamp * Jyotirmoy Bhattacharya + * Matthew Brett * Jonathan Bright * Bruno Jiménez + * Evgeni Burovski * Patrick Callier * Mark Campanelli + * CJ Carey * Adam Cox + * Michael Danilov + * David Haberthür + * Andras Deak + * Philip DeBoer * Anne-Sylvie Deutsch * Cathy Douglass + * Dominic Else + * Guo Fei + * Roman Feldbauer + * Yu Feng * Jaime Fernandez del Rio * Orestis Floros + * David Freese + * Adam Geitgey + * James Gerity + * Dezmond Goff + * Christoph Gohlke * Ralf Gommers * Dirk Gorissen + * Matt Haberland + * David Hagen + * Charles Harris * Lam Yuen Hei + * Jean Helie + * Gaute Hope + * Guillaume Horel + * Franziska Horn + * Yevhenii Hyzyla + * Vladislav Iakovlev + * Marvin Kastner + * Mher Kazandjian * Thomas Keck * Adam Kurkiewicz + * Ronan Lamy + * J.L. Lanfranchi + * Eric Larson * Denis Laxalde * Gregory R. Lee * Felix Lenders + * Evan Limanto * Julian Lukwata + * François Magimel * Syrtis Major + * Charles Masson + * Nikolay Mayorov * Tobias Megies * Markus Meister + * Roman Mirochnik + * Jordi Montes + * Nathan Musoke + * Andrew Nelson * M.J. Nichol * Nico Schlömer + * Juan Nunez-Iglesias * Arno Onken + * Dima Pasechnik + * Ashwin Pathak + * Stefan Peterson * Ilhan Polat * Andrey Portnoy + * Ravi Kumar Prasad + * Aman Pratik * Eric Quintero * Vedant Rathore + * Tyler Reddy * Joscha Reimer * Philipp Rentzsch + * Antonio Horta Ribeiro * Ned Richards + * Kevin Rose + * Benoit Rostykus + * Matt Ruffalo + * Eli Sadoff + * Pim Schellart * Klaus Sembritzki + * Nikolay Shebanov + * Jonathan Tammo Siebert * Scott Sievert * Max Silbiger + * Mandeep Singh + * Michael Stewart + * Jonathan Sutton + * Deep Tavker + * Martin Thoma * James Tocknell + * Aleksandar Trifunovic + * Paul van Mulbregt + * Jacob Vanderplas * Aditya Vijaykumar * Pauli Virtanen * James Webber * Warren Weckesser * Eric Wieser + * Josh Wilson * Zhiqing Xiao + * Evgeny Zhurko * Nikolay Zinov + * Zé Vinícius + A total of 118 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This