Changelog
### 1.13.1
```
compared to `1.13.0`. The version of OpenBLAS shipped with
the PyPI binaries has been increased to `0.3.27`.
Authors
=======
* Name (commits)
* h-vetinari (1)
* Jake Bowhay (2)
* Evgeni Burovski (6)
* Sean Cheah (2)
* Lucas Colley (2)
* DWesl (2)
* Ralf Gommers (7)
* Ben Greiner (1) +
* Matt Haberland (2)
* Gregory R. Lee (1)
* Philip Loche (1) +
* Sijo Valayakkad Manikandan (1) +
* Matti Picus (1)
* Tyler Reddy (62)
* Atsushi Sakai (1)
* Daniel Schmitz (2)
* Dan Schult (3)
* Scott Shambaugh (2)
* Edgar Andrés Margffoy Tuay (1)
A total of 19 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.
```
### 1.13.0
```
out-of-band release aims to support NumPy ``2.0.0``, and is backwards
compatible to NumPy ``1.22.4``. The version of OpenBLAS used to build
the PyPI wheels has been increased to ``0.3.26``.
This release requires Python 3.9+ and NumPy 1.22.4 or greater.
For running on PyPy, PyPy3 6.0+ is required.
Highlights of this release
===================
- Support for NumPy ``2.0.0``.
- Interactive examples have been added to the documentation, allowing users
to run the examples locally on embedded Jupyterlite notebooks in their
browser.
- Preliminary 1D array support for the COO and DOK sparse formats.
- Several `scipy.stats` functions have gained support for additional
``axis``, ``nan_policy``, and ``keepdims`` arguments. `scipy.stats` also
has several performance and accuracy improvements.
New features
==========
`scipy.integrate` improvements
==============================
- The ``terminal`` attribute of `scipy.integrate.solve_ivp` ``events``
callables now additionally accepts integer values to specify a number
of occurrences required for termination, rather than the previous restriction
of only accepting a ``bool`` value to terminate on the first registered
event.
`scipy.io` improvements
=======================
- `scipy.io.wavfile.write` has improved ``dtype`` input validation.
`scipy.interpolate` improvements
================================
- The Modified Akima Interpolation has been added to
``interpolate.Akima1DInterpolator``, available via the new ``method``
argument.
- ``RegularGridInterpolator`` gained the functionality to compute derivatives
in place. For instance, ``RegularGridInterolator((x, y), values,
method="cubic")(xi, nu=(1, 1))`` evaluates the mixed second derivative,
:math:`\partial^2 / \partial x \partial y` at ``xi``.
- Performance characteristics of tensor-product spline methods of
``RegularGridInterpolator`` have been changed: evaluations should be
significantly faster, while construction might be slower. If you experience
issues with construction times, you may need to experiment with optional
keyword arguments ``solver`` and ``solver_args``. Previous behavior (fast
construction, slow evaluations) can be obtained via `"*_legacy"` methods:
``method="cubic_legacy"`` is exactly equivalent to ``method="cubic"`` in
previous releases. See ``gh-19633`` for details.
`scipy.signal` improvements
===========================
- Many filter design functions now have improved input validation for the
sampling frequency (``fs``).
`scipy.sparse` improvements
===========================
- ``coo_array`` now supports 1D shapes, and has additional 1D support for
``min``, ``max``, ``argmin``, and ``argmax``. The DOK format now has
preliminary 1D support as well, though only supports simple integer indices
at the time of writing.
- Experimental support has been added for ``pydata/sparse`` array inputs to
`scipy.sparse.csgraph`.
- ``dok_array`` and ``dok_matrix`` now have proper implementations of
``fromkeys``.
- ``csr`` and ``csc`` formats now have improved ``setdiag`` performance.
`scipy.spatial` improvements
============================
- ``voronoi_plot_2d`` now draws Voronoi edges to infinity more clearly
when the aspect ratio is skewed.
`scipy.special` improvements
============================
- All Fortran code, namely, ``AMOS``, ``specfun``, and ``cdflib`` libraries
that the majority of special functions depend on, is ported to Cython/C.
- The function ``factorialk`` now also supports faster, approximate
calculation using ``exact=False``.
`scipy.stats` improvements
==========================
- `scipy.stats.rankdata` and `scipy.stats.wilcoxon` have been vectorized,
improving their performance and the performance of hypothesis tests that
depend on them.
- ``stats.mannwhitneyu`` should now be faster due to a vectorized statistic
calculation, improved caching, improved exploitation of symmetry, and a
memory reduction. ``PermutationMethod`` support was also added.
- `scipy.stats.mood` now has ``nan_policy`` and ``keepdims`` support.
- `scipy.stats.brunnermunzel` now has ``axis`` and ``keepdims`` support.
- `scipy.stats.friedmanchisquare`, `scipy.stats.shapiro`,
`scipy.stats.normaltest`, `scipy.stats.skewtest`,
`scipy.stats.kurtosistest`, `scipy.stats.f_oneway`,
`scipy.stats.alexandergovern`, `scipy.stats.combine_pvalues`, and
`scipy.stats.kstest` have gained ``axis``, ``nan_policy`` and
``keepdims`` support.
- `scipy.stats.boxcox_normmax` has gained a ``ymax`` parameter to allow user
specification of the maximum value of the transformed data.
- `scipy.stats.vonmises` ``pdf`` method has been extended to support
``kappa=0``. The ``fit`` method is also more performant due to the use of
non-trivial bounds to solve for ``kappa``.
- High order ``moment`` calculations for `scipy.stats.powerlaw` are now more
accurate.
- The ``fit`` methods of `scipy.stats.gamma` (with ``method='mm'``) and
`scipy.stats.loglaplace` are faster and more reliable.
- `scipy.stats.goodness_of_fit` now supports the use of a custom ``statistic``
provided by the user.
- `scipy.stats.wilcoxon` now supports ``PermutationMethod``, enabling
calculation of accurate p-values in the presence of ties and zeros.
- `scipy.stats.monte_carlo_test` now has improved robustness in the face of
numerical noise.
- `scipy.stats.wasserstein_distance_nd` was introduced to compute the
Wasserstein-1 distance between two N-D discrete distributions.
Deprecated features
=================
- Complex dtypes in ``PchipInterpolator`` and ``Akima1DInterpolator`` have
been deprecated and will raise an error in SciPy 1.15.0. If you are trying
to use the real components of the passed array, use ``np.real`` on ``y``.
Backwards incompatible changes
=========================
Other changes
===========
- The second argument of `scipy.stats.moment` has been renamed to ``order``
while maintaining backward compatibility.
Authors
======
* Name (commits)
* h-vetinari (50)
* acceptacross (1) +
* Petteri Aimonen (1) +
* Francis Allanah (2) +
* Jonas Kock am Brink (1) +
* anupriyakkumari (12) +
* Aman Atman (2) +
* Aaditya Bansal (1) +
* Christoph Baumgarten (2)
* Sebastian Berg (4)
* Nicolas Bloyet (2) +
* Matt Borland (1)
* Jonas Bosse (1) +
* Jake Bowhay (25)
* Matthew Brett (1)
* Dietrich Brunn (7)
* Evgeni Burovski (48)
* Matthias Bussonnier (4)
* Cale (1) +
* CJ Carey (4)
* Thomas A Caswell (1)
* Sean Cheah (44) +
* Lucas Colley (97)
* com3dian (1)
* Gianluca Detommaso (1) +
* Thomas Duvernay (1)
* DWesl (2)
* f380cedric (1) +
* fancidev (13) +
* Daniel Garcia (1) +
* Lukas Geiger (3)
* Ralf Gommers (139)
* Matt Haberland (79)
* Tessa van der Heiden (2) +
* inky (3) +
* Jannes Münchmeyer (2) +
* Aditya Vidyadhar Kamath (2) +
* Agriya Khetarpal (1) +
* Andrew Landau (1) +
* Eric Larson (7)
* Zhen-Qi Liu (1) +
* Adam Lugowski (4)
* m-maggi (6) +
* Chethin Manage (1) +
* Ben Mares (1)
* Chris Markiewicz (1) +
* Mateusz Sokół (3)
* Daniel McCloy (1) +
* Melissa Weber Mendonça (6)
* Josue Melka (1)
* Michał Górny (4)
* Juan Montesinos (1) +
* Juan F. Montesinos (1) +
* Takumasa Nakamura (1)
* Andrew Nelson (26)
* Praveer Nidamaluri (1)
* Yagiz Olmez (5) +
* Dimitri Papadopoulos Orfanos (1)
* Drew Parsons (1) +
* Tirth Patel (7)
* Matti Picus (3)
* Rambaud Pierrick (1) +
* Ilhan Polat (30)
* Quentin Barthélemy (1)
* Tyler Reddy (81)
* Pamphile Roy (10)
* Atsushi Sakai (4)
* Daniel Schmitz (10)
* Dan Schult (16)
* Eli Schwartz (4)
* Stefanie Senger (1) +
* Scott Shambaugh (2)
* Kevin Sheppard (2)
* sidsrinivasan (4) +
* Samuel St-Jean (1)
* Albert Steppi (30)
* Adam J. Stewart (4)
* Kai Striega (3)
* Ruikang Sun (1) +
* Mike Taves (1)
* Nicolas Tessore (3)
* Benedict T Thekkel (1) +
* Will Tirone (4)
* Jacob Vanderplas (2)
* Christian Veenhuis (1)
* Isaac Virshup (2)
* Ben Wallace (1) +
* Xuefeng Xu (3)
* Xiao Yuan (5)
* Irwin Zaid (6)
* Mathias Zechmeister (1) +
A total of 91 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.
```
### 1.12.0
```
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
`1.12.x` branch, and on adding new features on the main branch.
This release requires Python `3.9+` and NumPy `1.22.4` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
Highlights of this release
==================
- Experimental support for the array API standard has been added to part of
`scipy.special`, and to all of `scipy.fft` and `scipy.cluster`. There are
likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
tensors, and other array API compatible libraries is appreciated. Use the
``SCIPY_ARRAY_API`` environment variable for testing.
- A new class, ``ShortTimeFFT``, provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT.
- Several new constructors have been added for sparse arrays, and many operations
now additionally support sparse arrays, further facilitating the migration
from sparse matrices.
- A large portion of the `scipy.stats` API now has improved support for handling
``NaN`` values, masked arrays, and more fine-grained shape-handling. The
accuracy and performance of a number of ``stats`` methods have been improved,
and a number of new statistical tests and distributions have been added.
New features
==========
`scipy.cluster` improvements
======================
- Experimental support added for the array API standard; PyTorch tensors,
CuPy arrays and array API compatible array libraries are now accepted
(GPU support is limited to functions with pure Python implementations).
CPU arrays which can be converted to and from NumPy are supported
module-wide and returned arrays will match the input type.
This behaviour is enabled by setting the ``SCIPY_ARRAY_API`` environment
variable before importing ``scipy``. This experimental support is still
under development and likely to contain bugs - testing is very welcome.
`scipy.fft` improvements
===================
- Experimental support added for the array API standard; functions which are
part of the ``fft`` array API standard extension module, as well as the
Fast Hankel Transforms and the basic FFTs which are not in the extension
module, now accept PyTorch tensors, CuPy arrays and array API compatible
array libraries. CPU arrays which can be converted to and from NumPy arrays
are supported module-wide and returned arrays will match the input type.
This behaviour is enabled by setting the ``SCIPY_ARRAY_API`` environment
variable before importing ``scipy``. This experimental support is still under
development and likely to contain bugs - testing is very welcome.
`scipy.integrate` improvements
========================
- Added `scipy.integrate.cumulative_simpson` for cumulative quadrature
from sampled data using Simpson's 1/3 rule.
`scipy.interpolate` improvements
=========================
- New class ``NdBSpline`` represents tensor-product splines in N dimensions.
This class only knows how to evaluate a tensor product given coefficients
and knot vectors. This way it generalizes ``BSpline`` for 1D data to N-D, and
parallels ``NdPPoly`` (which represents N-D tensor product polynomials).
Evaluations exploit the localized nature of b-splines.
- ``NearestNDInterpolator.__call__`` accepts ``**query_options``, which are
passed through to the ``KDTree.query`` call to find nearest neighbors. This
allows, for instance, to limit the neighbor search distance and parallelize
the query using the ``workers`` keyword.
- ``BarycentricInterpolator`` now allows computing the derivatives.
- It is now possible to change interpolation values in an existing
``CloughTocher2DInterpolator`` instance, while also saving the barycentric
coordinates of interpolation points.
`scipy.linalg` improvements
=====================
- Access to new low-level LAPACK functions is provided via ``dtgsyl`` and
``stgsyl``.
`scipy.ndimage` improvements
=======================
`scipy.optimize` improvements
=======================
- `scipy.optimize.nnls` is rewritten in Python and now implements the so-called
fnnls or fast nnls.
- The result object of `scipy.optimize.root` and `scipy.optimize.root_scalar`
now reports the method used.
- The ``callback`` method of `scipy.optimize.differential_evolution` can now be
passed more detailed information via the ``intermediate_results`` keyword
parameter. Also, the evolution ``strategy`` now accepts a callable for
additional customization. The performance of ``differential_evolution`` has
also been improved.
- ``minimize`` method ``Newton-CG`` has been made slightly more efficient.
- ``minimize`` method ``BFGS`` now accepts an initial estimate for the inverse
of the Hessian, which allows for more efficient workflows in some
circumstances. The new parameter is ``hess_inv0``.
- ``minimize`` methods ``CG``, ``Newton-CG``, and ``BFGS`` now accept parameters
``c1`` and ``c2``, allowing specification of the Armijo and curvature rule
parameters, respectively.
- ``curve_fit`` performance has improved due to more efficient memoization
of the callable function.
- ``isotonic_regression`` has been added to allow nonparametric isotonic
regression.
`scipy.signal` improvements
=====================
- ``freqz``, ``freqz_zpk``, and ``group_delay`` are now more accurate
when ``fs`` has a default value.
- The new class ``ShortTimeFFT`` provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
dual windows and provides more fine-grained control of the parametrization especially
in regard to scaling and phase-shift. Functionality was implemented to ease
working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
providing algorithmic details. The functions ``stft``, ``istft`` and ``spectrogram``
have been marked as legacy.
`scipy.sparse` improvements
======================
- ``sparse.linalg`` iterative solvers ``sparse.linalg.cg``,
``sparse.linalg.cgs``, ``sparse.linalg.bicg``, ``sparse.linalg.bicgstab``,
``sparse.linalg.gmres``, and ``sparse.linalg.qmr`` are rewritten in Python.
- Updated vendored SuperLU version to ``6.0.1``, along with a few additional
fixes.
- Sparse arrays have gained additional constructors: ``eye_array``,
``random_array``, ``block_array``, and ``identity``. ``kron`` and ``kronsum``
have been adjusted to additionally support operation on sparse arrays.
- Sparse matrices now support a transpose with ``axes=(1, 0)``, to mirror
the ``.T`` method.
- ``LaplacianNd`` now allows selection of the largest subset of eigenvalues,
and additionally now supports retrieval of the corresponding eigenvectors.
The performance of ``LaplacianNd`` has also been improved.
- The performance of ``dok_matrix`` and ``dok_array`` has been improved,
and their inheritance behavior should be more robust.
- ``hstack``, ``vstack``, and ``block_diag`` now work with sparse arrays, and
preserve the input sparse type.
- A new function, `scipy.sparse.linalg.matrix_power`, has been added, allowing
for exponentiation of sparse arrays.
`scipy.spatial` improvements
======================
- Two new methods were implemented for ``spatial.transform.Rotation``:
``__pow__`` to raise a rotation to integer or fractional power and
``approx_equal`` to check if two rotations are approximately equal.
- The method ``Rotation.align_vectors`` was extended to solve a constrained
alignment problem where two vectors are required to be aligned precisely.
Also when given a single pair of vectors, the algorithm now returns the
rotation with minimal magnitude, which can be considered as a minor
backward incompatible change.
- A new representation for ``spatial.transform.Rotation`` called Davenport
angles is available through ``from_davenport`` and ``as_davenport`` methods.
- Performance improvements have been added to ``distance.hamming`` and
``distance.correlation``.
- Improved performance of ``SphericalVoronoi`` ``sort_vertices_of_regions``
and two dimensional area calculations.
`scipy.special` improvements
======================
- Added `scipy.special.stirling2` for computation of Stirling numbers of the
second kind. Both exact calculation and an asymptotic approximation
(the default) are supported via ``exact=True`` and ``exact=False`` (the
default) respectively.
- Added `scipy.special.betaincc` for computation of the complementary incomplete Beta function and `scipy.special.betainccinv` for computation of its inverse.
- Improved precision of `scipy.special.betainc` and `scipy.special.betaincinv`
- Experimental support added for alternative backends: functions
`scipy.special.log_ndtr`, `scipy.special.ndtr`, `scipy.special.ndtri`,
`scipy.special.erf`, `scipy.special.erfc`, `scipy.special.i0`,
`scipy.special.i0e`, `scipy.special.i1`, `scipy.special.i1e`,
`scipy.special.gammaln`, `scipy.special.gammainc`, `scipy.special.gammaincc`,
`scipy.special.logit`, and `scipy.special.expit` now accept PyTorch tensors
and CuPy arrays. These features are still under development and likely to
contain bugs, so they are disabled by default; enable them by setting a
``SCIPY_ARRAY_API`` environment variable to ``1`` before importing ``scipy``.
Testing is appreciated!
`scipy.stats` improvements
=====================
- Added `scipy.stats.quantile_test`, a nonparametric test of whether a
hypothesized value is the quantile associated with a specified probability.
The ``confidence_interval`` method of the result object gives a confidence
interval of the quantile.
- `scipy.stats.wasserstein_distance` now computes the Wasserstein distance
in the multidimensional case.
- `scipy.stats.sampling.FastGeneratorInversion` provides a convenient
interface to fast random sampling via numerical inversion of distribution
CDFs.
- `scipy.stats.geometric_discrepancy` adds geometric/topological discrepancy
metrics for random samples.
- `scipy.stats.multivariate_normal` now has a ``fit`` method for fitting
distribution parameters to data via maximum likelihood estimation.
- `scipy.stats.bws_test` performs the Baumgartner-Weiss-Schindler test of
whether two-samples were drawn from the same distribution.
- `scipy.stats.jf_skew_t` implements the Jones and Faddy skew-t distribution.
- `scipy.stats.anderson_ksamp` now supports a permutation version of the test
using the ``method`` parameter.
- The ``fit`` methods of `scipy.stats.halfcauchy`, `scipy.stats.halflogistic`, and
`scipy.stats.halfnorm` are faster and more accurate.
- `scipy.stats.beta` ``entropy`` accuracy has been improved for extreme values of
distribution parameters.
- The accuracy of ``sf`` and/or ``isf`` methods have been improved for
several distributions: `scipy.stats.burr`, `scipy.stats.hypsecant`,
`scipy.stats.kappa3`, `scipy.stats.loglaplace`, `scipy.stats.lognorm`,
`scipy.stats.lomax`, `scipy.stats.pearson3`, `scipy.stats.rdist`, and
`scipy.stats.pareto`.
- The following functions now support parameters ``axis``, ``nan_policy``, and ``keep_dims``: `scipy.stats.entropy`, `scipy.stats.differential_entropy`, `scipy.stats.variation`, `scipy.stats.ansari`, `scipy.stats.bartlett`, `scipy.stats.levene`, `scipy.stats.fligner`, `scipy.stats.cirmean, `scipy.stats.circvar`, `scipy.stats.circstd`, `scipy.stats.tmean`, `scipy.stats.tvar`, `scipy.stats.tstd`, `scipy.stats.tmin`, `scipy.stats.tmax`, and `scipy.stats.tsem`.
- The ``logpdf`` and ``fit`` methods of `scipy.stats.skewnorm` have been improved.
- The beta negative binomial distribution is implemented as `scipy.stats.betanbinom`.
- The speed of `scipy.stats.invwishart` ``rvs`` and ``logpdf`` have been improved.
- A source of intermediate overflow in `scipy.stats.boxcox_normmax` with ``method='mle'`` has been eliminated, and the returned value of ``lmbda`` is constrained such that the transformed data will not overflow.
- `scipy.stats.nakagami` ``stats`` is more accurate and reliable.
- A source of intermediate overflow in `scipy.norminvgauss.pdf` has been eliminated.
- Added support for masked arrays to ``stats.circmean``, ``stats.circvar``,
``stats.circstd``, and ``stats.entropy``.
- ``dirichlet`` has gained a new covariance (``cov``) method.
- Improved accuracy of ``multivariate_t`` entropy with large degrees of
freedom.
- ``loggamma`` has an improved ``entropy`` method.
Deprecated features
===============
- Error messages have been made clearer for objects that don't exist in the
public namespace and warnings sharpened for private attributes that are not
supposed to be imported at all.
- `scipy.signal.cmplx_sort` has been deprecated and will be removed in
SciPy 1.14. A replacement you can use is provided in the deprecation message.
- Values the the argument ``initial`` of `scipy.integrate.cumulative_trapezoid`
other than ``0`` and ``None`` are now deprecated.
- `scipy.stats.rvs_ratio_uniforms` is deprecated in favour of
`scipy.stats.sampling.RatioUniforms`
- `scipy.integrate.quadrature` and `scipy.integrate.romberg` have been
deprecated due to accuracy issues and interface shortcomings. They will
be removed in SciPy 1.14. Please use `scipy.integrate.quad` instead.
- Coinciding with upcoming changes to function signatures (e.g. removal of a
deprecated keyword), we are deprecating positional use of keyword arguments
for the affected functions, which will raise an error starting with
SciPy 1.14. In some cases, this has delayed the originally announced
removal date, to give time to respond to the second part of the deprecation.
Affected functions are:
- ``linalg.{eigh, eigvalsh, pinv}``
- ``integrate.simpson``
- ``signal.{firls, firwin, firwin2, remez}``
- ``sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}``
- ``special.comb``
- ``stats.kendalltau``
- All wavelet functions have been deprecated, as PyWavelets provides suitable
implementations; affected functions are: ``signal.{daub, qmf, cascade,
morlet, morlet2, ricker, cwt}``
Expired Deprecations
================
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
- The ``centered`` keyword of `stats.qmc.LatinHypercube` has been removed.
Use ``scrambled=False`` instead of ``centered=True``.
Backwards incompatible changes
=========================
Other changes
===========
- The arguments used to compile and link SciPy are now available via
``show_config``.
Authors
======
* Name (commits)
* endolith (1)
* h-vetinari (29)
* Tom Adamczewski (3) +
* Anudeep Adiraju (1) +
* akeemlh (1)
* Alex Amadori (2) +
* Raja Yashwanth Avantsa (2) +
* Seth Axen (1) +
* Ross Barnowski (1)
* Dan Barzilay (1) +
* Ashish Bastola (1) +
* Christoph Baumgarten (2)
* Ben Beasley (3) +
* Doron Behar (1)
* Peter Bell (1)
* Sebastian Berg (1)
* Ben Boeckel (1) +
* David Boetius (1) +
* Jake Bowhay (102)
* Larry Bradley (1) +
* Dietrich Brunn (5)
* Evgeni Burovski (101)
* Matthias Bussonnier (18)
* CJ Carey (6)
* Colin Carroll (1) +
* Aadya Chinubhai (1) +
* Luca Citi (1)
* Lucas Colley (140) +
* com3dian (1) +
* Anirudh Dagar (4)
* Danni (1) +
* Dieter Werthmüller (1)
* John Doe (2) +
* Philippe DONNAT (2) +
* drestebon (1) +
* Thomas Duvernay (1)
* elbarso (1) +
* emilfrost (2) +
* Paul Estano (8) +
* Evandro (2)
* Franz Király (1) +
* Nikita Furin (1) +
* gabrielthomsen (1) +
* Lukas Geiger (9) +
* Artem Glebov (22) +
* Caden Gobat (1)
* Ralf Gommers (125)
* Alexander Goscinski (2) +
* Rohit Goswami (2) +
* Olivier Grisel (1)
* Matt Haberland (243)
* Charles Harris (1)
* harshilkamdar (1) +
* Alon Hovav (2) +
* Gert-Ludwig Ingold (1)
* Romain Jacob (1) +
* jcwhitehead (1) +
* Julien Jerphanion (13)
* He Jia (1)
* JohnWT (1) +
* jokasimr (1) +
* Evan W Jones (1)
* Karen Róbertsdóttir (1) +
* Ganesh Kathiresan (1)
* Robert Kern (11)
* Andrew Knyazev (4)
* Uwe L. Korn (1) +
* Rishi Kulkarni (1)
* Kale Kundert (3) +
* Jozsef Kutas (2)
* Kyle0 (2) +
* Robert Langefeld (1) +
* Jeffrey Larson (1) +
* Jessy Lauer (1) +
* lciti (1) +
* Hoang Le (1) +
* Antony Lee (5)
* Thilo Leitzbach (4) +
* LemonBoy (2) +
* Ellie Litwack (8) +
* Thomas Loke (4) +
* Malte Londschien (1) +
* Christian Lorentzen (6)
* Adam Lugowski (9) +
* lutefiskhotdish (1)
* mainak33 (1) +
* Ben Mares (11) +
* mart-mihkel (2) +
* Mateusz Sokół (24) +
* Nikolay Mayorov (4)
* Nicholas McKibben (1)
* Melissa Weber Mendonça (7)
* Kat Mistberg (2) +
* mkiffer (1) +
* mocquin (1) +
* Nicolas Mokus (2) +
* Sturla Molden (1)
* Roberto Pastor Muela (3) +
* Bijay Nayak (1) +
* Andrew Nelson (105)
* Praveer Nidamaluri (2) +
* Lysandros Nikolaou (2)
* Dimitri Papadopoulos Orfanos (7)
* Pablo Rodríguez Pérez (1) +
* Dimitri Papadopoulos (2)
* Tirth Patel (14)
* Kyle Paterson (1) +
* Paul (4) +
* Yann Pellegrini (2) +
* Matti Picus (4)
* Ilhan Polat (36)
* Pranav (1) +
* Bharat Raghunathan (1)
* Chris Rapson (1) +
* Matteo Raso (4)
* Tyler Reddy (165)
* Martin Reinecke (1)
* Tilo Reneau-Cardoso (1) +
* resting-dove (2) +
* Simon Segerblom Rex (4)
* Lucas Roberts (2)
* Pamphile Roy (31)
* Feras Saad (3) +
* Atsushi Sakai (3)
* Masahiro Sakai (2) +
* Omar Salman (14)
* Andrej Savikin (1) +
* Daniel Schmitz (52)
* Dan Schult (19)
* Scott Shambaugh (9)
* Sheila-nk (2) +
* Mauro Silberberg (3) +
* Maciej Skorski (1) +
* Laurent Sorber (1) +
* Albert Steppi (28)
* Kai Striega (1)
* Saswat Susmoy (1) +
* Alex Szatmary (1) +
* Søren Fuglede Jørgensen (3)
* othmane tamri (3) +
* Ewout ter Hoeven (1)
* Will Tirone (1)
* TLeitzbach (1) +
* Kevin Topolski (1) +
* Edgar Andrés Margffoy Tuay (1)
* Dipansh Uikey (1) +
* Matus Valo (3)
* Christian Veenhuis (2)
* Nicolas Vetsch (1) +
* Isaac Virshup (7)
* Hielke Walinga (2) +
* Stefan van der Walt (2)
* Warren Weckesser (7)
* Bernhard M. Wiedemann (4)
* Levi John Wolf (1)
* Xuefeng Xu (4) +
* Rory Yorke (2)
* YoussefAli1 (1) +
* Irwin Zaid (4) +
* Jinzhe Zeng (1) +
* JIMMY ZHAO (1) +
A total of 161 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.
```
### 1.11.4
```
compared to `1.11.3`.
Authors
=======
* Name (commits)
* Jake Bowhay (2)
* Ralf Gommers (4)
* Julien Jerphanion (2)
* Nikolay Mayorov (2)
* Melissa Weber Mendonça (1)
* Tirth Patel (1)
* Tyler Reddy (22)
* Dan Schult (3)
* Nicolas Vetsch (1) +
A total of 9 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.
```
### 1.11.3
```
compared to `1.11.2`.
Authors
=======
* Name (commits)
* Jake Bowhay (2)
* CJ Carey (1)
* Colin Carroll (1) +
* Anirudh Dagar (2)
* drestebon (1) +
* Ralf Gommers (5)
* Matt Haberland (2)
* Julien Jerphanion (1)
* Uwe L. Korn (1) +
* Ellie Litwack (2)
* Andrew Nelson (5)
* Bharat Raghunathan (1)
* Tyler Reddy (37)
* Søren Fuglede Jørgensen (2)
* Hielke Walinga (1) +
* Warren Weckesser (1)
* Bernhard M. Wiedemann (1)
A total of 17 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.
```
### 1.11.2
```
compared to `1.11.1`. Python `3.12` and musllinux wheels
are provided with this release.
Authors
=======
* Name (commits)
* Evgeni Burovski (2)
* CJ Carey (3)
* Dieter Werthmüller (1)
* elbarso (1) +
* Ralf Gommers (2)
* Matt Haberland (1)
* jokasimr (1) +
* Thilo Leitzbach (1) +
* LemonBoy (1) +
* Ellie Litwack (2) +
* Sturla Molden (1)
* Andrew Nelson (5)
* Tyler Reddy (39)
* Daniel Schmitz (6)
* Dan Schult (2)
* Albert Steppi (1)
* Matus Valo (1)
* Stefan van der Walt (1)
A total of 18 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.
```
### 1.11.1
```
compared to `1.11.0`. In particular, a licensing issue
discovered after the release of `1.11.0` has been addressed.
Authors
=======
* Name (commits)
* h-vetinari (1)
* Robert Kern (1)
* Ilhan Polat (4)
* Tyler Reddy (8)
A total of 4 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.
```
### 1.11.0
```
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
`1.11.x` branch, and on adding new features on the main branch.
This release requires Python `3.9+` and NumPy `1.21.6` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
Highlights of this release
====================
- Several `scipy.sparse` array API improvements, including a new public base
class distinct from the older matrix class, proper 64-bit index support,
and numerous deprecations paving the way to a modern sparse array experience.
- Added three new statistical distributions, and wide-ranging performance and
precision improvements to several other statistical distributions.
- A new function was added for quasi-Monte Carlo integration, and linear
algebra functions ``det`` and ``lu`` now accept nD-arrays.
- An ``axes`` argument was added broadly to ``ndimage`` functions, facilitating
analysis of stacked image data.
New features
===========
`scipy.integrate` improvements
==============================
- Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration.
- For an even number of points, `scipy.integrate.simpson` now calculates
a parabolic segment over the last three points which gives improved
accuracy over the previous implementation.
`scipy.cluster` improvements
============================
- ``disjoint_set`` has a new method ``subset_size`` for providing the size
of a particular subset.
`scipy.constants` improvements
================================
- The ``quetta``, ``ronna``, ``ronto``, and ``quecto`` SI prefixes were added.
`scipy.linalg` improvements
===========================
- `scipy.linalg.det` is improved and now accepts nD-arrays.
- `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new
``p_indices`` switch the output permutation argument can be 1D ``(n,)``
permutation index instead of the full ``(n, n)`` array.
`scipy.ndimage` improvements
============================
- ``axes`` argument was added to ``rank_filter``, ``percentile_filter``,
``median_filter``, ``uniform_filter``, ``minimum_filter``,
``maximum_filter``, and ``gaussian_filter``, which can be useful for
processing stacks of image data.
`scipy.optimize` improvements
=============================
- `scipy.optimize.linprog` now passes unrecognized options directly to HiGHS.
- `scipy.optimize.root_scalar` now uses Newton's method to be used without
providing ``fprime`` and the ``secant`` method to be used without a second
guess.
- `scipy.optimize.lsq_linear` now accepts ``bounds`` arguments of type
`scipy.optimize.Bounds`.
- `scipy.optimize.minimize` ``method='cobyla'`` now supports simple bound
constraints.
- Users can opt into a new callback interface for most methods of
`scipy.optimize.minimize`: If the provided callback callable accepts
a single keyword argument, ``intermediate_result``, `scipy.optimize.minimize`
now passes both the current solution and the optimal value of the objective
function to the callback as an instance of `scipy.optimize.OptimizeResult`.
It also allows the user to terminate optimization by raising a
``StopIteration`` exception from the callback function.
`scipy.optimize.minimize` will return normally, and the latest solution
information is provided in the result object.
- `scipy.optimize.curve_fit` now supports an optional ``nan_policy`` argument.
- `scipy.optimize.shgo` now has parallelization with the ``workers`` argument,
symmetry arguments that can improve performance, class-based design to
improve usability, and generally improved performance.
`scipy.signal` improvements
===========================
- ``istft`` has an improved warning message when the NOLA condition fails.
`scipy.sparse` improvements
===========================
- `scipy.sparse` array (not matrix) classes now return a sparse array instead
of a dense array when divided by a dense array.
- A new public base class `scipy.sparse.sparray` was introduced, allowing
`isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes,
while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse
matrix types.
- The behavior of `scipy.sparse.isspmatrix()` was updated to return True for
only the sparse matrix types. If you want to check for either sparse arrays
or sparse matrices, use `scipy.sparse.issparse()` instead. (Previously,
these had identical behavior.)
- Sparse arrays constructed with 64-bit indices will no longer automatically
downcast to 32-bit.
- A new `scipy.sparse.diags_array` function was added, which behaves like the
existing `scipy.sparse.diags` function except that it returns a sparse
array instead of a sparse matrix.
- ``argmin`` and ``argmax`` methods now return the correct result when no
implicit zeros are present.
`scipy.sparse.linalg` improvements
==================================
- dividing ``LinearOperator`` by a number now returns a
``_ScaledLinearOperator``
- ``LinearOperator`` now supports right multiplication by arrays
- ``lobpcg`` should be more efficient following removal of an extraneous
QR decomposition.
`scipy.spatial` improvements
============================
- Usage of new C++ backend for additional distance metrics, the majority of
which will see substantial performance improvements, though a few minor
regressions are known. These are focused on distances between boolean
arrays.
`scipy.special` improvements
============================
- The factorial functions ``factorial``, ``factorial2`` and ``factorialk``
were made consistent in their behavior (in terms of dimensionality,
errors etc.). Additionally, ``factorial2`` can now handle arrays with
``exact=True``, and ``factorialk`` can handle arrays.
`scipy.stats` improvements
==========================
New Features
------------
- `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices.
- `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple
experimental groups against the mean of a control group.
- `scipy.stats.ecdf` for computing the empirical CDF and complementary
CDF (survival function / SF) from uncensored or right-censored data. This
function is also useful for survival analysis / Kaplain-Meier estimation.
- `scipy.stats.logrank` to compare survival functions underlying samples.
- `scipy.stats.false_discovery_control` for adjusting p-values to control the
false discovery rate of multiple hypothesis tests using the
Benjamini-Hochberg or Benjamini-Yekutieli procedures.
- `scipy.stats.CensoredData` to represent censored data. It can be used as
input to the ``fit`` method of univariate distributions and to the new
``ecdf`` function.
- Filliben's goodness of fit test as ``method='Filliben'`` of
`scipy.stats.goodness_of_fit`.
- `scipy.stats.ttest_ind` has a new method, ``confidence_interval`` for
computing confidence intervals.
- `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and
`scipy.stats.BootstrapMethod` are new classes to configure resampling and/or
Monte Carlo versions of hypothesis tests. They can currently be used with
`scipy.stats.pearsonr`.
Statistical Distributions
-------------------------
- Added the von-Mises Fisher distribution as `scipy.stats.vonmises_fisher`.
This distribution is the most common analogue of the normal distribution
on the unit sphere.
- Added the relativistic Breit-Wigner distribution as
`scipy.stats.rel_breitwigner`.
It is used in high energy physics to model resonances.
- Added the Dirichlet multinomial distribution as
`scipy.stats.dirichlet_multinomial`.
- Improved the speed and precision of several univariate statistical
distributions.
- `scipy.stats.anglit` ``sf``
- `scipy.stats.beta` ``entropy``
- `scipy.stats.betaprime` ``cdf``, ``sf``, ``ppf``
- `scipy.stats.chi` ``entropy``
- `scipy.stats.chi2` ``entropy``
- `scipy.stats.dgamma` ``entropy``, ``cdf``, ``sf``, ``ppf``, and ``isf``
- `scipy.stats.dweibull` ``entropy``, ``sf``, and ``isf``
- `scipy.stats.exponweib` ``sf`` and ``isf``
- `scipy.stats.f` ``entropy``
- `scipy.stats.foldcauchy` ``sf``
- `scipy.stats.foldnorm` ``cdf`` and ``sf``
- `scipy.stats.gamma` ``entropy``
- `scipy.stats.genexpon` ``ppf``, ``isf``, ``rvs``
- `scipy.stats.gengamma` ``entropy``
- `scipy.stats.geom` ``entropy``
- `scipy.stats.genlogistic` ``entropy``, ``logcdf``, ``sf``, ``ppf``,
and ``isf``
- `scipy.stats.genhyperbolic` ``cdf`` and ``sf``
- `scipy.stats.gibrat` ``sf`` and ``isf``
- `scipy.stats.gompertz` ``entropy``, ``sf``. and ``isf``
- `scipy.stats.halflogistic` ``sf``, and ``isf``
- `scipy.stats.halfcauchy` ``sf`` and ``isf``
- `scipy.stats.halfnorm` ``cdf``, ``sf``, and ``isf``
- `scipy.stats.invgamma` ``entropy``
- `scipy.stats.invgauss` ``entropy``
- `scipy.stats.johnsonsb` ``pdf``, ``cdf``, ``sf``, ``ppf``, and ``isf``
- `scipy.stats.johnsonsu` ``pdf``, ``sf``, ``isf``, and ``stats``
- `scipy.stats.lognorm` ``fit``
- `scipy.stats.loguniform` ``entropy``, ``logpdf``, ``pdf``, ``cdf``, ``ppf``,
and ``stats``
- `scipy.stats.maxwell` ``sf`` and ``isf``
- `scipy.stats.nakagami` ``entropy``
- `scipy.stats.powerlaw` ``sf``
- `scipy.stats.powerlognorm` ``logpdf``, ``logsf``, ``sf``, and ``isf``
- `scipy.stats.powernorm` ``sf`` and ``isf``
- `scipy.stats.t` ``entropy``, ``logpdf``, and ``pdf``
- `scipy.stats.truncexpon` ``sf``, and ``isf``
- `scipy.stats.truncnorm` ``entropy``
- `scipy.stats.truncpareto` ``fit``
- `scipy.stats.vonmises` ``fit``
- `scipy.stats.multivariate_t` now has ``cdf`` and ``entropy`` methods.
- `scipy.stats.multivariate_normal`, `scipy.stats.matrix_normal`, and
`scipy.stats.invwishart` now have an ``entropy`` method.
Other Improvements
------------------
- `scipy.stats.monte_carlo_test` now supports multi-sample statistics.
- `scipy.stats.bootstrap` can now produce one-sided confidence intervals.
- `scipy.stats.rankdata` performance was improved for ``method=ordinal`` and
``method=dense``.
- `scipy.stats.moment` now supports non-central moment calculation.
- `scipy.stats.anderson` now supports the ``weibull_min`` distribution.
- `scipy.stats.sem` and `scipy.stats.iqr` now support ``axis``, ``nan_policy``,
and masked array input.
Deprecated features
=================
- Multi-Ellipsis sparse matrix indexing has been deprecated and will
be removed in SciPy 1.13.
- Several methods were deprecated for sparse arrays: ``asfptype``, ``getrow``,
``getcol``, ``get_shape``, ``getmaxprint``, ``set_shape``,
``getnnz``, and ``getformat``. Additionally, the ``.A`` and ``.H``
attributes were deprecated. Sparse matrix types are not affected.
- The `scipy.linalg` functions ``tri``, ``triu`` & ``tril`` are deprecated and
will be removed in SciPy 1.13. Users are recommended to use the NumPy
versions of these functions with identical names.
- The `scipy.signal` functions ``bspline``, ``quadratic`` & ``cubic`` are
deprecated and will be removed in SciPy 1.13. Users are recommended to use
`scipy.interpolate.BSpline` instead.
- The ``even`` keyword of `scipy.integrate.simpson` is deprecated and will be
removed in SciPy 1.13.0. Users should leave this as the default as this
gives improved accuracy compared to the other methods.
- Using ``exact=True`` when passing integers in a float array to ``factorial``
is deprecated and will be removed in SciPy 1.13.0.
- float128 and object dtypes are deprecated for `scipy.signal.medfilt` and
`scipy.signal.order_filter`
- The functions ``scipy.signal.{lsim2, impulse2, step2}`` had long been
deprecated in documentation only. They now raise a DeprecationWarning and
will be removed in SciPy 1.13.0.
- Importing window functions directly from `scipy.window` has been soft
deprecated since SciPy 1.1.0. They now raise a ``DeprecationWarning`` and
will be removed in SciPy 1.13.0. Users should instead import them from
`scipy.signal.window` or use the convenience function
`scipy.signal.get_window`.
Backwards incompatible changes
============================
- The default for the ``legacy`` keyword of `scipy.special.comb` has changed
from ``True`` to ``False``, as announced since its introduction.
Expired Deprecations
==================
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
- The ``n`` keyword has been removed from `scipy.stats.moment`.
- The ``alpha`` keyword has been removed from `scipy.stats.interval`.
- The misspelt ``gilbrat`` distribution has been removed (use
`scipy.stats.gibrat`).
- The deprecated spelling of the ``kulsinski`` distance metric has been
removed (use `scipy.spatial.distance.kulczynski1`).
- The ``vertices`` keyword of `scipy.spatial.Delauney.qhull` has been removed
(use simplices).
- The ``residual`` property of `scipy.sparse.csgraph.maximum_flow` has been
removed (use ``flow``).
- The ``extradoc`` keyword of `scipy.stats.rv_continuous`,
`scipy.stats.rv_discrete` and `scipy.stats.rv_sample` has been removed.
- The ``sym_pos`` keyword of `scipy.linalg.solve` has been removed.
- The `scipy.optimize.minimize` function now raises an error for ``x0`` with
``x0.ndim > 1``.
- In `scipy.stats.mode`, the default value of ``keepdims`` is now ``False``,
and support for non-numeric input has been removed.
- The function `scipy.signal.lsim` does not support non-uniform time steps
anymore.
Other changes
============
- Rewrote the source build docs and restructured the contributor guide.
- Improved support for cross-compiling with meson build system.
- MyST-NB notebook infrastructure has been added to our documentation.
Authors
=======
* h-vetinari (69)
* Oriol Abril-Pla (1) +
* Anton Akhmerov (13)
* Andrey Akinshin (1) +
* alice (1) +
* Oren Amsalem (1)
* Ross Barnowski (11)
* Christoph Baumgarten (2)
* Dawson Beatty (1) +
* Doron Behar (1) +
* Peter Bell (1)
* John Belmonte (1) +
* boeleman (1) +
* Jack Borchanian (1) +
* Matt Borland (3) +
* Jake Bowhay (40)
* Sienna Brent (1) +
* Matthew Brett (1)
* Evgeni Burovski (38)
* Matthias Bussonnier (2)
* Maria Cann (1) +
* Alfredo Carella (1) +
* CJ Carey (18)
* Hood Chatham (2)
* Anirudh Dagar (3)
* Alberto Defendi (1) +
* Pol del Aguila (1) +
* Hans Dembinski (1)
* Dennis (1) +
* Vinayak Dev (1) +
* Thomas Duvernay (1)
* DWesl (4)
* Stefan Endres (66)
* Evandro (1) +
* Tom Eversdijk (2) +
* Isuru Fernando (1)
* Franz Forstmayr (4)
* Joseph Fox-Rabinovitz (1)
* Stefano Frazzetto (1) +
* Neil Girdhar (1)
* Caden Gobat (1) +
* Ralf Gommers (146)
* GonVas (1) +
* Marco Gorelli (1)
* Brett Graham (2) +
* Matt Haberland (385)
* harshvardhan2707 (1) +
* Alex Herbert (1) +
* Guillaume Horel (1)
* Geert-Jan Huizing (1) +
* Jakob Jakobson (2)
* Julien Jerphanion (5)
* jyuv (2)
* Rajarshi Karmakar (1) +
* Ganesh Kathiresan (3) +
* Robert Kern (4)
* Andrew Knyazev (3)
* Sergey Koposov (1)
* Rishi Kulkarni (2) +
* Eric Larson (1)
* Zoufiné Lauer-Bare (2) +
* Antony Lee (3)
* Gregory R. Lee (8)
* Guillaume Lemaitre (1) +
* lilinjie (2) +
* Yannis Linardos (1) +
* Christian Lorentzen (5)
* Loïc Estève (1)
* Charlie Marsh (2) +
* Boris Martin (1) +
* Nicholas McKibben (10)
* Melissa Weber Mendonça (57)
* Michał Górny (1) +
* Jarrod Millman (2)
* Stefanie Molin (2) +
* Mark W. Mueller (1) +
* mustafacevik (1) +
* Takumasa N (1) +
* nboudrie (1)
* Andrew Nelson (111)
* Nico Schlömer (4)
* Lysandros Nikolaou (2) +
* Kyle Oman (1)
* OmarManzoor (2) +
* Simon Ott (1) +
* Geoffrey Oxberry (1) +
* Geoffrey M. Oxberry (2) +
* Sravya papaganti (1) +
* Tirth Patel (2)
* Ilhan Polat (32)
* Quentin Barthélemy (1)
* Matteo Raso (12) +
* Tyler Reddy (97)
* Lucas Roberts (1)
* Pamphile Roy (224)
* Jordan Rupprecht (1) +
* Atsushi Sakai (11)
* Omar Salman (7) +
* Leo Sandler (1) +
* Ujjwal Sarswat (3) +
* Saumya (1) +
* Daniel Schmitz (79)
* Henry Schreiner (2) +
* Dan Schult (3) +
* Eli Schwartz (6)
* Tomer Sery (2) +
* Scott Shambaugh (4) +
* Gagandeep Singh (1)
* Ethan Steinberg (6) +
* stepeos (2) +
* Albert Steppi (3)
* Strahinja Lukić (1)
* Kai Striega (4)
* suen-bit (1) +
* Tartopohm (2)
* Logan Thomas (2) +
* Jacopo Tissino (1) +
* Matus Valo (10) +
* Jacob Vanderplas (2)
* Christian Veenhuis (1) +
* Isaac Virshup (1)
* Stefan van der Walt (14)
* Warren Weckesser (63)
* windows-server-2003 (1)
* Levi John Wolf (3)
* Nobel Wong (1) +
* Benjamin Yeh (1) +
* Rory Yorke (1)
* Younes (2) +
* Zaikun ZHANG (1) +
* Alex Zverianskii (1) +
A total of 131 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.
```
### 1.10.1
```
compared to `1.10.0`.
Authors
=======
* Name (commits)
* alice (1) +
* Matt Borland (2) +
* Evgeni Burovski (2)
* CJ Carey (1)
* Ralf Gommers (9)
* Brett Graham (1) +
* Matt Haberland (5)
* Alex Herbert (1) +
* Ganesh Kathiresan (2) +
* Rishi Kulkarni (1) +
* Loïc Estève (1)
* Michał Górny (1) +
* Jarrod Millman (1)
* Andrew Nelson (4)
* Tyler Reddy (50)
* Pamphile Roy (2)
* Eli Schwartz (2)
* Tomer Sery (1) +
* Kai Striega (1)
* Jacopo Tissino (1) +
* windows-server-2003 (1)
A total of 21 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.
```
### 1.10.0
```
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
1.10.x branch, and on adding new features on the main branch.
This release requires Python `3.8+` and NumPy `1.19.5` or greater.
For running on PyPy, PyPy3 `6.0+` is required.
Highlights of this release
====================
- A new dedicated datasets submodule (`scipy.datasets`) has been added, and is
now preferred over usage of `scipy.misc` for dataset retrieval.
- A new `scipy.interpolate.make_smoothing_spline` function was added. This
function constructs a smoothing cubic spline from noisy data, using the
generalized cross-validation (GCV) criterion to find the tradeoff between
smoothness and proximity to data points.
- `scipy.stats` has three new distributions, two new hypothesis tests, three
new sample statistics, a class for greater control over calculations
involving covariance matrices, and many other enhancements.
New features
===========
`scipy.datasets` introduction
========================
- A new dedicated ``datasets`` submodule has been added. The submodules
is meant for datasets that are relevant to other SciPy submodules ands
content (tutorials, examples, tests), as well as contain a curated
set of datasets that are of wider interest. As of this release, all
the datasets from `scipy.misc` have been added to `scipy.datasets`
(and deprecated in `scipy.misc`).
- The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/)
(a new optional dependency for SciPy), a Python package to simplify fetching
data files. This move will, in a subsequent release, facilitate SciPy
to trim down the sdist/wheel sizes, by decoupling the data files and
moving them out of the SciPy repository, hosting them externally and
downloading them when requested. After downloading the datasets once,
the files are cached to avoid network dependence and repeated usage.
- Added datasets from ``scipy.misc``: `scipy.datasets.face`,
`scipy.datasets.ascent`, `scipy.datasets.electrocardiogram`
- Added download and caching functionality:
- `scipy.datasets.download_all`: a function to download all the `scipy.datasets`
associated files at once.
- `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset
files from the file system.
- ``scipy/datasets/_download_all.py`` can be run as a standalone script for
packaging purposes to avoid any external dependency at build or test time.
This can be used by SciPy packagers (e.g., for Linux distros) which may
have to adhere to rules that forbid downloading sources from external
repositories at package build time.
`scipy.integrate` improvements
==============================
- Added `scipy.integrate.qmc_quad`, which performs quadrature using Quasi-Monte
Carlo points.
- Added parameter ``complex_func`` to `scipy.integrate.quad`, which can be set
``True`` to integrate a complex integrand.
`scipy.interpolate` improvements
================================
- `scipy.interpolate.interpn` now supports tensor-product interpolation methods
(``slinear``, ``cubic``, ``quintic`` and ``pchip``)
- Tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and
``pchip``) in `scipy.interpolate.interpn` and
`scipy.interpolate.RegularGridInterpolator` now allow values with trailing
dimensions.
- `scipy.interpolate.RegularGridInterpolator` has a new fast path for
``method="linear"`` with 2D data, and ``RegularGridInterpolator`` is now
easier to subclass
- `scipy.interpolate.interp1d` now can take a single value for non-spline
methods.
- A new ``extrapolate`` argument is available to `scipy.interpolate.BSpline.design_matrix`,
allowing extrapolation based on the first and last intervals.
- A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an
implementation of the generalized cross-validation spline smoothing
algorithm. The ``lam=None`` (default) mode of this function is a clean-room
reimplementation of the classic ``gcvspl.f`` Fortran algorithm for
constructing GCV splines.
- A new ``method="pchip"`` mode was aded to
`scipy.interpolate.RegularGridInterpolator`. This mode constructs an
interpolator using tensor products of C1-continuous monotone splines
(essentially, a `scipy.interpolate.PchipInterpolator` instance per
dimension).
`scipy.sparse.linalg` improvements
==================================
- The spectral 2-norm is now available in `scipy.sparse.linalg.norm`.
- The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius
norm) has been improved.
- LAPACK wrappers were added for ``trexc`` and ``trsen``.
- The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding
the following improvements:
- a simple tunable restart potentially increases the attainable
accuracy for edge cases,
- internal postprocessing runs one final exact Rayleigh-Ritz method
giving more accurate and orthonormal eigenvectors,
- output the computed iterate with the smallest max norm of the residual
and drop the history of subsequent iterations,
- remove the check for ``LinearOperator`` format input and thus allow
a simple function handle of a callable object as an input,
- better handling of common user errors with input data, rather
than letting the algorithm fail.
`scipy.linalg` improvements
===========================
- `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted
to square arrays.
`scipy.ndimage` improvements
============================
- The new `scipy.ndimage.value_indices` function provides a time-efficient method to
search for the locations of individual values with an array of image data.
- A new ``radius`` argument is supported by `scipy.ndimage.gaussian_filter1d` and
`scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter.
`scipy.optimize` improvements
=============================
- `scipy.optimize.brute` now coerces non-iterable/single-value ``args`` into a
tuple.
- `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept
`scipy.optimize.Bounds` for bounds constraints.
- Added a tutorial for `scipy.optimize.milp`.
- Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects.
- Additional options (``parallel``, ``threads``, ``mip_rel_gap``) can now
be passed to `scipy.optimize.linprog` with ``method='highs'``.
`scipy.signal` improvements
===========================
- The new window function `scipy.signal.windows.lanczos` was added to compute a
Lanczos window, also known as a sinc window.
`scipy.sparse.csgraph` improvements
===================================
- the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and
star graphs in particular see a marked performance improvement
`scipy.special` improvements
============================
- The new function `scipy.special.powm1`, a ufunc with signature
``powm1(x, y)``, computes ``x**y - 1``. The function avoids the loss of
precision that can result when ``y`` is close to 0 or when ``x`` is close to
1.
- `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under
the hood.
`scipy.stats` improvements
==========================
- Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for
use with any univariate distribution, any combination of known and unknown
parameters, and several choices of test statistic (Kolmogorov-Smirnov,
Cramer-von Mises, and Anderson-Darling).
- Improved `scipy.stats.bootstrap`: Default method ``'BCa'`` now supports
multi-sample statistics. Also, the bootstrap distribution is returned in the
result object, and the result object can be passed into the function as
parameter ``bootstrap_result`` to add additional resamples or change the
confidence interval level and type.
- Added maximum spacing estimation to `scipy.stats.fit`.
- Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`.
- Added new sample statistics.
- Added `scipy.stats.contingency.odds_ratio` to compute both the conditional
and unconditional odds ratios and corresponding confidence intervals for
2x2 contingency tables.
- Added `scipy.stats.directional_stats` to compute sample statistics of
n-dimensional directional data.
- Added `scipy.stats.expectile`, which generalizes the expected value in the
same way as quantiles are a generalization of the median.
- Added new statistical distributions.
- Added `scipy.stats.uniform_direction`, a multivariate distribution to
sample uniformly from the surface of a hypersphere.
- Added `scipy.stats.random_table`, a multivariate distribution to sample
uniformly from m x n contingency tables with provided marginals.
- Added `scipy.stats.truncpareto`, the truncated Pareto distribution.
- Improved the ``fit`` method of several distributions.
- `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical
solution when ``method='mm'``, which also serves a starting guess to
improve the performance of ``method='mle'``.
- `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum
likelihood estimates have been extended to the cases in which location or
scale are fixed by the user.
- Analytical maximum likelihood estimates have been added for
`scipy.stats.powerlaw`.
- Improved random variate sampling of several distributions.
- Drawing multiple samples from `scipy.stats.matrix_normal`,
`scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and
`scipy.stats.unitary_group` is faster.
- The ``rvs`` method of `scipy.stats.vonmises` now wraps to the interval
``[-np.pi, np.pi]``.
- Improved the reliability of `scipy.stats.loggamma` ``rvs`` method for small
values of the shape parameter.
- Improved the speed and/or accuracy of functions of several statistical
distributions.
- Added `scipy.stats.Covariance` for better speed, accuracy, and user control
in multivariate normal calculations.
- `scipy.stats.skewnorm` methods ``cdf``, ``sf``, ``ppf``, and ``isf``
methods now use the implementations from Boost, improving speed while
maintaining accuracy. The calculation of higher-order moments is also
faster and more accurate.
- `scipy.stats.invgauss` methods ``ppf`` and ``isf`` methods now use the
implementations from Boost, improving speed and accuracy.
- `scipy.stats.invweibull` methods ``sf`` and ``isf`` are more accurate for
small probability masses.
- `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations
from Boost, improving speed and accuracy.
- Implemented the ``logpdf`` method of `scipy.stats.vonmises` for reliability
in extreme tails.
- Implemented the ``isf`` method of `scipy.stats.levy` for speed and
accuracy.
- Improved the robustness of `scipy.stats.studentized_range` for large ``df``
by adding an infinite degree-of-freedom approximation.
- Added a parameter ``lower_limit`` to `scipy.stats.multivariate_normal`,
allowing the user to change the integration limit from -inf to a desired
value.
- Improved the robustness of ``entropy`` of `scipy.stats.vonmises` for large
concentration values.
- Enhanced `scipy.stats.gaussian_kde`.
- Added `scipy.stats.gaussian_kde.marginal`, which returns the desired
marginal distribution of the original kernel density estimate distribution.
- The ``cdf`` method of `scipy.stats.gaussian_kde` now accepts a
``lower_limit`` parameter for integrating the PDF over a rectangular region.
- Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython,
improving speed.
- The global interpreter lock is released by the ``pdf`` method of
`scipy.stats.gaussian_kde` for improved multithreading performance.
- Replaced explicit matrix inversion with Cholesky decomposition for speed
and accuracy.
- Enhanced the result objects returned by many `scipy.stats` functions
- Added a ``confidence_interval`` method to the result object returned by
`scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`.
- The `scipy.stats` functions ``combine_pvalues``, ``fisher_exact``,
``chi2_contingency``, ``median_test`` and ``mood`` now return
bunch objects rather than plain tuples, allowing attributes to be
accessed by name.
- Attributes of the result objects returned by ``multiscale_graphcorr``,
``anderson_ksamp``, ``binomtest``, ``crosstab``, ``pointbiserialr``,
``spearmanr``, ``kendalltau``, and ``weightedtau`` have been renamed to
``statistic`` and ``pvalue`` for consistency throughout `scipy.stats`.
Old attribute names are still allowed for backward compatibility.
- `scipy.stats.anderson` now returns the parameters of the fitted
distribution in a `scipy.stats._result_classes.FitResult` object.
- The ``plot`` method of `scipy.stats._result_classes.FitResult` now accepts
a ``plot_type`` parameter; the options are ``'hist'`` (histogram, default),
``'qq'`` (Q-Q plot), ``'pp'`` (P-P plot), and ``'cdf'`` (empirical CDF
plot).
- Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the
location (argmax) at which the statistic is calculated and the variant
of the statistic used.
- Improved the performance of several `scipy.stats` functions.
- Improved the performance of `scipy.stats.cramervonmises_2samp` and
`scipy.stats.ks_2samp` with ``method='exact'``.
- Improved the performance of `scipy.stats.siegelslopes`.
- Improved the performance of `scipy.stats.mstats.hdquantile_sd`.
- Improved the performance of `scipy.stats.binned_statistic_dd` for several
NumPy statistics, and binned statistics methods now support complex data.
- Added the ``scramble`` optional argument to `scipy.stats.qmc.LatinHypercube`.
It replaces ``centered``, which is now deprecated.
- Added a parameter ``optimization`` to all `scipy.stats.qmc.QMCEngine`
subclasses to improve characteristics of the quasi-random variates.
- Added tie correction to `scipy.stats.mood`.
- Added tutorials for resampling methods in `scipy.stats`.
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
`scipy.stats.monte_carlo_test` now automatically detect whether the provided
``statistic`` is vectorized, so passing the ``vectorized`` argument
explicitly is no longer required to take advantage of vectorized statistics.
- Improved the speed of `scipy.stats.permutation_test` for permutation types
``'samples'`` and ``'pairings'``.
- Added ``axis``, ``nan_policy``, and masked array support to
`scipy.stats.jarque_bera`.
- Added the ``nan_policy`` optional argument to `scipy.stats.rankdata`.
Deprecated features
=================
- `scipy.misc` module and all the methods in ``misc`` are deprecated in v1.10
and will be completely removed in SciPy v2.0.0. Users are suggested to
utilize the `scipy.datasets` module instead for the dataset methods.
- `scipy.stats.qmc.LatinHypercube` parameter ``centered`` has been deprecated.
It is replaced by the ``scramble`` argument for more consistency with other
QMC engines.
- `scipy.interpolate.interp2d` class has been deprecated. The docstring of the
deprecated routine lists recommended replacements.
Expired Deprecations
==================
- There is an ongoing effort to follow through on long-standing deprecations.
- The following previously deprecated features are affected:
- Removed ``cond`` & ``rcond`` kwargs in ``linalg.pinv``
- Removed wrappers ``scipy.linalg.blas.{clapack, flapack}``
- Removed ``scipy.stats.NumericalInverseHermite`` and removed ``tol`` & ``max_intervals`` kwargs from ``scipy.stats.sampling.NumericalInverseHermite``
- Removed ``local_search_options`` kwarg frrom ``scipy.optimize.dual_annealing``.
Other changes
============
- `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
`scipy.stats.monte_carlo_test` now automatically detect whether the provided
``statistic`` is vectorized by looking for an ``axis`` parameter in the
signature of ``statistic``. If an ``axis`` parameter is present in
``statistic`` but
This PR updates scipy from 1.5.4 to 1.13.1.
Changelog
### 1.13.1 ``` compared to `1.13.0`. The version of OpenBLAS shipped with the PyPI binaries has been increased to `0.3.27`. Authors ======= * Name (commits) * h-vetinari (1) * Jake Bowhay (2) * Evgeni Burovski (6) * Sean Cheah (2) * Lucas Colley (2) * DWesl (2) * Ralf Gommers (7) * Ben Greiner (1) + * Matt Haberland (2) * Gregory R. Lee (1) * Philip Loche (1) + * Sijo Valayakkad Manikandan (1) + * Matti Picus (1) * Tyler Reddy (62) * Atsushi Sakai (1) * Daniel Schmitz (2) * Dan Schult (3) * Scott Shambaugh (2) * Edgar Andrés Margffoy Tuay (1) A total of 19 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. ``` ### 1.13.0 ``` out-of-band release aims to support NumPy ``2.0.0``, and is backwards compatible to NumPy ``1.22.4``. The version of OpenBLAS used to build the PyPI wheels has been increased to ``0.3.26``. This release requires Python 3.9+ and NumPy 1.22.4 or greater. For running on PyPy, PyPy3 6.0+ is required. Highlights of this release =================== - Support for NumPy ``2.0.0``. - Interactive examples have been added to the documentation, allowing users to run the examples locally on embedded Jupyterlite notebooks in their browser. - Preliminary 1D array support for the COO and DOK sparse formats. - Several `scipy.stats` functions have gained support for additional ``axis``, ``nan_policy``, and ``keepdims`` arguments. `scipy.stats` also has several performance and accuracy improvements. New features ========== `scipy.integrate` improvements ============================== - The ``terminal`` attribute of `scipy.integrate.solve_ivp` ``events`` callables now additionally accepts integer values to specify a number of occurrences required for termination, rather than the previous restriction of only accepting a ``bool`` value to terminate on the first registered event. `scipy.io` improvements ======================= - `scipy.io.wavfile.write` has improved ``dtype`` input validation. `scipy.interpolate` improvements ================================ - The Modified Akima Interpolation has been added to ``interpolate.Akima1DInterpolator``, available via the new ``method`` argument. - ``RegularGridInterpolator`` gained the functionality to compute derivatives in place. For instance, ``RegularGridInterolator((x, y), values, method="cubic")(xi, nu=(1, 1))`` evaluates the mixed second derivative, :math:`\partial^2 / \partial x \partial y` at ``xi``. - Performance characteristics of tensor-product spline methods of ``RegularGridInterpolator`` have been changed: evaluations should be significantly faster, while construction might be slower. If you experience issues with construction times, you may need to experiment with optional keyword arguments ``solver`` and ``solver_args``. Previous behavior (fast construction, slow evaluations) can be obtained via `"*_legacy"` methods: ``method="cubic_legacy"`` is exactly equivalent to ``method="cubic"`` in previous releases. See ``gh-19633`` for details. `scipy.signal` improvements =========================== - Many filter design functions now have improved input validation for the sampling frequency (``fs``). `scipy.sparse` improvements =========================== - ``coo_array`` now supports 1D shapes, and has additional 1D support for ``min``, ``max``, ``argmin``, and ``argmax``. The DOK format now has preliminary 1D support as well, though only supports simple integer indices at the time of writing. - Experimental support has been added for ``pydata/sparse`` array inputs to `scipy.sparse.csgraph`. - ``dok_array`` and ``dok_matrix`` now have proper implementations of ``fromkeys``. - ``csr`` and ``csc`` formats now have improved ``setdiag`` performance. `scipy.spatial` improvements ============================ - ``voronoi_plot_2d`` now draws Voronoi edges to infinity more clearly when the aspect ratio is skewed. `scipy.special` improvements ============================ - All Fortran code, namely, ``AMOS``, ``specfun``, and ``cdflib`` libraries that the majority of special functions depend on, is ported to Cython/C. - The function ``factorialk`` now also supports faster, approximate calculation using ``exact=False``. `scipy.stats` improvements ========================== - `scipy.stats.rankdata` and `scipy.stats.wilcoxon` have been vectorized, improving their performance and the performance of hypothesis tests that depend on them. - ``stats.mannwhitneyu`` should now be faster due to a vectorized statistic calculation, improved caching, improved exploitation of symmetry, and a memory reduction. ``PermutationMethod`` support was also added. - `scipy.stats.mood` now has ``nan_policy`` and ``keepdims`` support. - `scipy.stats.brunnermunzel` now has ``axis`` and ``keepdims`` support. - `scipy.stats.friedmanchisquare`, `scipy.stats.shapiro`, `scipy.stats.normaltest`, `scipy.stats.skewtest`, `scipy.stats.kurtosistest`, `scipy.stats.f_oneway`, `scipy.stats.alexandergovern`, `scipy.stats.combine_pvalues`, and `scipy.stats.kstest` have gained ``axis``, ``nan_policy`` and ``keepdims`` support. - `scipy.stats.boxcox_normmax` has gained a ``ymax`` parameter to allow user specification of the maximum value of the transformed data. - `scipy.stats.vonmises` ``pdf`` method has been extended to support ``kappa=0``. The ``fit`` method is also more performant due to the use of non-trivial bounds to solve for ``kappa``. - High order ``moment`` calculations for `scipy.stats.powerlaw` are now more accurate. - The ``fit`` methods of `scipy.stats.gamma` (with ``method='mm'``) and `scipy.stats.loglaplace` are faster and more reliable. - `scipy.stats.goodness_of_fit` now supports the use of a custom ``statistic`` provided by the user. - `scipy.stats.wilcoxon` now supports ``PermutationMethod``, enabling calculation of accurate p-values in the presence of ties and zeros. - `scipy.stats.monte_carlo_test` now has improved robustness in the face of numerical noise. - `scipy.stats.wasserstein_distance_nd` was introduced to compute the Wasserstein-1 distance between two N-D discrete distributions. Deprecated features ================= - Complex dtypes in ``PchipInterpolator`` and ``Akima1DInterpolator`` have been deprecated and will raise an error in SciPy 1.15.0. If you are trying to use the real components of the passed array, use ``np.real`` on ``y``. Backwards incompatible changes ========================= Other changes =========== - The second argument of `scipy.stats.moment` has been renamed to ``order`` while maintaining backward compatibility. Authors ====== * Name (commits) * h-vetinari (50) * acceptacross (1) + * Petteri Aimonen (1) + * Francis Allanah (2) + * Jonas Kock am Brink (1) + * anupriyakkumari (12) + * Aman Atman (2) + * Aaditya Bansal (1) + * Christoph Baumgarten (2) * Sebastian Berg (4) * Nicolas Bloyet (2) + * Matt Borland (1) * Jonas Bosse (1) + * Jake Bowhay (25) * Matthew Brett (1) * Dietrich Brunn (7) * Evgeni Burovski (48) * Matthias Bussonnier (4) * Cale (1) + * CJ Carey (4) * Thomas A Caswell (1) * Sean Cheah (44) + * Lucas Colley (97) * com3dian (1) * Gianluca Detommaso (1) + * Thomas Duvernay (1) * DWesl (2) * f380cedric (1) + * fancidev (13) + * Daniel Garcia (1) + * Lukas Geiger (3) * Ralf Gommers (139) * Matt Haberland (79) * Tessa van der Heiden (2) + * inky (3) + * Jannes Münchmeyer (2) + * Aditya Vidyadhar Kamath (2) + * Agriya Khetarpal (1) + * Andrew Landau (1) + * Eric Larson (7) * Zhen-Qi Liu (1) + * Adam Lugowski (4) * m-maggi (6) + * Chethin Manage (1) + * Ben Mares (1) * Chris Markiewicz (1) + * Mateusz Sokół (3) * Daniel McCloy (1) + * Melissa Weber Mendonça (6) * Josue Melka (1) * Michał Górny (4) * Juan Montesinos (1) + * Juan F. Montesinos (1) + * Takumasa Nakamura (1) * Andrew Nelson (26) * Praveer Nidamaluri (1) * Yagiz Olmez (5) + * Dimitri Papadopoulos Orfanos (1) * Drew Parsons (1) + * Tirth Patel (7) * Matti Picus (3) * Rambaud Pierrick (1) + * Ilhan Polat (30) * Quentin Barthélemy (1) * Tyler Reddy (81) * Pamphile Roy (10) * Atsushi Sakai (4) * Daniel Schmitz (10) * Dan Schult (16) * Eli Schwartz (4) * Stefanie Senger (1) + * Scott Shambaugh (2) * Kevin Sheppard (2) * sidsrinivasan (4) + * Samuel St-Jean (1) * Albert Steppi (30) * Adam J. Stewart (4) * Kai Striega (3) * Ruikang Sun (1) + * Mike Taves (1) * Nicolas Tessore (3) * Benedict T Thekkel (1) + * Will Tirone (4) * Jacob Vanderplas (2) * Christian Veenhuis (1) * Isaac Virshup (2) * Ben Wallace (1) + * Xuefeng Xu (3) * Xiao Yuan (5) * Irwin Zaid (6) * Mathias Zechmeister (1) + A total of 91 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. ``` ### 1.12.0 ``` 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 `1.12.x` branch, and on adding new features on the main branch. This release requires Python `3.9+` and NumPy `1.22.4` or greater. For running on PyPy, PyPy3 `6.0+` is required. Highlights of this release ================== - Experimental support for the array API standard has been added to part of `scipy.special`, and to all of `scipy.fft` and `scipy.cluster`. There are likely to be bugs and early feedback for usage with CuPy arrays, PyTorch tensors, and other array API compatible libraries is appreciated. Use the ``SCIPY_ARRAY_API`` environment variable for testing. - A new class, ``ShortTimeFFT``, provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT. - Several new constructors have been added for sparse arrays, and many operations now additionally support sparse arrays, further facilitating the migration from sparse matrices. - A large portion of the `scipy.stats` API now has improved support for handling ``NaN`` values, masked arrays, and more fine-grained shape-handling. The accuracy and performance of a number of ``stats`` methods have been improved, and a number of new statistical tests and distributions have been added. New features ========== `scipy.cluster` improvements ====================== - Experimental support added for the array API standard; PyTorch tensors, CuPy arrays and array API compatible array libraries are now accepted (GPU support is limited to functions with pure Python implementations). CPU arrays which can be converted to and from NumPy are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the ``SCIPY_ARRAY_API`` environment variable before importing ``scipy``. This experimental support is still under development and likely to contain bugs - testing is very welcome. `scipy.fft` improvements =================== - Experimental support added for the array API standard; functions which are part of the ``fft`` array API standard extension module, as well as the Fast Hankel Transforms and the basic FFTs which are not in the extension module, now accept PyTorch tensors, CuPy arrays and array API compatible array libraries. CPU arrays which can be converted to and from NumPy arrays are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the ``SCIPY_ARRAY_API`` environment variable before importing ``scipy``. This experimental support is still under development and likely to contain bugs - testing is very welcome. `scipy.integrate` improvements ======================== - Added `scipy.integrate.cumulative_simpson` for cumulative quadrature from sampled data using Simpson's 1/3 rule. `scipy.interpolate` improvements ========================= - New class ``NdBSpline`` represents tensor-product splines in N dimensions. This class only knows how to evaluate a tensor product given coefficients and knot vectors. This way it generalizes ``BSpline`` for 1D data to N-D, and parallels ``NdPPoly`` (which represents N-D tensor product polynomials). Evaluations exploit the localized nature of b-splines. - ``NearestNDInterpolator.__call__`` accepts ``**query_options``, which are passed through to the ``KDTree.query`` call to find nearest neighbors. This allows, for instance, to limit the neighbor search distance and parallelize the query using the ``workers`` keyword. - ``BarycentricInterpolator`` now allows computing the derivatives. - It is now possible to change interpolation values in an existing ``CloughTocher2DInterpolator`` instance, while also saving the barycentric coordinates of interpolation points. `scipy.linalg` improvements ===================== - Access to new low-level LAPACK functions is provided via ``dtgsyl`` and ``stgsyl``. `scipy.ndimage` improvements ======================= `scipy.optimize` improvements ======================= - `scipy.optimize.nnls` is rewritten in Python and now implements the so-called fnnls or fast nnls. - The result object of `scipy.optimize.root` and `scipy.optimize.root_scalar` now reports the method used. - The ``callback`` method of `scipy.optimize.differential_evolution` can now be passed more detailed information via the ``intermediate_results`` keyword parameter. Also, the evolution ``strategy`` now accepts a callable for additional customization. The performance of ``differential_evolution`` has also been improved. - ``minimize`` method ``Newton-CG`` has been made slightly more efficient. - ``minimize`` method ``BFGS`` now accepts an initial estimate for the inverse of the Hessian, which allows for more efficient workflows in some circumstances. The new parameter is ``hess_inv0``. - ``minimize`` methods ``CG``, ``Newton-CG``, and ``BFGS`` now accept parameters ``c1`` and ``c2``, allowing specification of the Armijo and curvature rule parameters, respectively. - ``curve_fit`` performance has improved due to more efficient memoization of the callable function. - ``isotonic_regression`` has been added to allow nonparametric isotonic regression. `scipy.signal` improvements ===================== - ``freqz``, ``freqz_zpk``, and ``group_delay`` are now more accurate when ``fs`` has a default value. - The new class ``ShortTimeFFT`` provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on dual windows and provides more fine-grained control of the parametrization especially in regard to scaling and phase-shift. Functionality was implemented to ease working with signal and STFT chunks. A section has been added to the "SciPy User Guide" providing algorithmic details. The functions ``stft``, ``istft`` and ``spectrogram`` have been marked as legacy. `scipy.sparse` improvements ====================== - ``sparse.linalg`` iterative solvers ``sparse.linalg.cg``, ``sparse.linalg.cgs``, ``sparse.linalg.bicg``, ``sparse.linalg.bicgstab``, ``sparse.linalg.gmres``, and ``sparse.linalg.qmr`` are rewritten in Python. - Updated vendored SuperLU version to ``6.0.1``, along with a few additional fixes. - Sparse arrays have gained additional constructors: ``eye_array``, ``random_array``, ``block_array``, and ``identity``. ``kron`` and ``kronsum`` have been adjusted to additionally support operation on sparse arrays. - Sparse matrices now support a transpose with ``axes=(1, 0)``, to mirror the ``.T`` method. - ``LaplacianNd`` now allows selection of the largest subset of eigenvalues, and additionally now supports retrieval of the corresponding eigenvectors. The performance of ``LaplacianNd`` has also been improved. - The performance of ``dok_matrix`` and ``dok_array`` has been improved, and their inheritance behavior should be more robust. - ``hstack``, ``vstack``, and ``block_diag`` now work with sparse arrays, and preserve the input sparse type. - A new function, `scipy.sparse.linalg.matrix_power`, has been added, allowing for exponentiation of sparse arrays. `scipy.spatial` improvements ====================== - Two new methods were implemented for ``spatial.transform.Rotation``: ``__pow__`` to raise a rotation to integer or fractional power and ``approx_equal`` to check if two rotations are approximately equal. - The method ``Rotation.align_vectors`` was extended to solve a constrained alignment problem where two vectors are required to be aligned precisely. Also when given a single pair of vectors, the algorithm now returns the rotation with minimal magnitude, which can be considered as a minor backward incompatible change. - A new representation for ``spatial.transform.Rotation`` called Davenport angles is available through ``from_davenport`` and ``as_davenport`` methods. - Performance improvements have been added to ``distance.hamming`` and ``distance.correlation``. - Improved performance of ``SphericalVoronoi`` ``sort_vertices_of_regions`` and two dimensional area calculations. `scipy.special` improvements ====================== - Added `scipy.special.stirling2` for computation of Stirling numbers of the second kind. Both exact calculation and an asymptotic approximation (the default) are supported via ``exact=True`` and ``exact=False`` (the default) respectively. - Added `scipy.special.betaincc` for computation of the complementary incomplete Beta function and `scipy.special.betainccinv` for computation of its inverse. - Improved precision of `scipy.special.betainc` and `scipy.special.betaincinv` - Experimental support added for alternative backends: functions `scipy.special.log_ndtr`, `scipy.special.ndtr`, `scipy.special.ndtri`, `scipy.special.erf`, `scipy.special.erfc`, `scipy.special.i0`, `scipy.special.i0e`, `scipy.special.i1`, `scipy.special.i1e`, `scipy.special.gammaln`, `scipy.special.gammainc`, `scipy.special.gammaincc`, `scipy.special.logit`, and `scipy.special.expit` now accept PyTorch tensors and CuPy arrays. These features are still under development and likely to contain bugs, so they are disabled by default; enable them by setting a ``SCIPY_ARRAY_API`` environment variable to ``1`` before importing ``scipy``. Testing is appreciated! `scipy.stats` improvements ===================== - Added `scipy.stats.quantile_test`, a nonparametric test of whether a hypothesized value is the quantile associated with a specified probability. The ``confidence_interval`` method of the result object gives a confidence interval of the quantile. - `scipy.stats.wasserstein_distance` now computes the Wasserstein distance in the multidimensional case. - `scipy.stats.sampling.FastGeneratorInversion` provides a convenient interface to fast random sampling via numerical inversion of distribution CDFs. - `scipy.stats.geometric_discrepancy` adds geometric/topological discrepancy metrics for random samples. - `scipy.stats.multivariate_normal` now has a ``fit`` method for fitting distribution parameters to data via maximum likelihood estimation. - `scipy.stats.bws_test` performs the Baumgartner-Weiss-Schindler test of whether two-samples were drawn from the same distribution. - `scipy.stats.jf_skew_t` implements the Jones and Faddy skew-t distribution. - `scipy.stats.anderson_ksamp` now supports a permutation version of the test using the ``method`` parameter. - The ``fit`` methods of `scipy.stats.halfcauchy`, `scipy.stats.halflogistic`, and `scipy.stats.halfnorm` are faster and more accurate. - `scipy.stats.beta` ``entropy`` accuracy has been improved for extreme values of distribution parameters. - The accuracy of ``sf`` and/or ``isf`` methods have been improved for several distributions: `scipy.stats.burr`, `scipy.stats.hypsecant`, `scipy.stats.kappa3`, `scipy.stats.loglaplace`, `scipy.stats.lognorm`, `scipy.stats.lomax`, `scipy.stats.pearson3`, `scipy.stats.rdist`, and `scipy.stats.pareto`. - The following functions now support parameters ``axis``, ``nan_policy``, and ``keep_dims``: `scipy.stats.entropy`, `scipy.stats.differential_entropy`, `scipy.stats.variation`, `scipy.stats.ansari`, `scipy.stats.bartlett`, `scipy.stats.levene`, `scipy.stats.fligner`, `scipy.stats.cirmean, `scipy.stats.circvar`, `scipy.stats.circstd`, `scipy.stats.tmean`, `scipy.stats.tvar`, `scipy.stats.tstd`, `scipy.stats.tmin`, `scipy.stats.tmax`, and `scipy.stats.tsem`. - The ``logpdf`` and ``fit`` methods of `scipy.stats.skewnorm` have been improved. - The beta negative binomial distribution is implemented as `scipy.stats.betanbinom`. - The speed of `scipy.stats.invwishart` ``rvs`` and ``logpdf`` have been improved. - A source of intermediate overflow in `scipy.stats.boxcox_normmax` with ``method='mle'`` has been eliminated, and the returned value of ``lmbda`` is constrained such that the transformed data will not overflow. - `scipy.stats.nakagami` ``stats`` is more accurate and reliable. - A source of intermediate overflow in `scipy.norminvgauss.pdf` has been eliminated. - Added support for masked arrays to ``stats.circmean``, ``stats.circvar``, ``stats.circstd``, and ``stats.entropy``. - ``dirichlet`` has gained a new covariance (``cov``) method. - Improved accuracy of ``multivariate_t`` entropy with large degrees of freedom. - ``loggamma`` has an improved ``entropy`` method. Deprecated features =============== - Error messages have been made clearer for objects that don't exist in the public namespace and warnings sharpened for private attributes that are not supposed to be imported at all. - `scipy.signal.cmplx_sort` has been deprecated and will be removed in SciPy 1.14. A replacement you can use is provided in the deprecation message. - Values the the argument ``initial`` of `scipy.integrate.cumulative_trapezoid` other than ``0`` and ``None`` are now deprecated. - `scipy.stats.rvs_ratio_uniforms` is deprecated in favour of `scipy.stats.sampling.RatioUniforms` - `scipy.integrate.quadrature` and `scipy.integrate.romberg` have been deprecated due to accuracy issues and interface shortcomings. They will be removed in SciPy 1.14. Please use `scipy.integrate.quad` instead. - Coinciding with upcoming changes to function signatures (e.g. removal of a deprecated keyword), we are deprecating positional use of keyword arguments for the affected functions, which will raise an error starting with SciPy 1.14. In some cases, this has delayed the originally announced removal date, to give time to respond to the second part of the deprecation. Affected functions are: - ``linalg.{eigh, eigvalsh, pinv}`` - ``integrate.simpson`` - ``signal.{firls, firwin, firwin2, remez}`` - ``sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}`` - ``special.comb`` - ``stats.kendalltau`` - All wavelet functions have been deprecated, as PyWavelets provides suitable implementations; affected functions are: ``signal.{daub, qmf, cascade, morlet, morlet2, ricker, cwt}`` Expired Deprecations ================ There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - The ``centered`` keyword of `stats.qmc.LatinHypercube` has been removed. Use ``scrambled=False`` instead of ``centered=True``. Backwards incompatible changes ========================= Other changes =========== - The arguments used to compile and link SciPy are now available via ``show_config``. Authors ====== * Name (commits) * endolith (1) * h-vetinari (29) * Tom Adamczewski (3) + * Anudeep Adiraju (1) + * akeemlh (1) * Alex Amadori (2) + * Raja Yashwanth Avantsa (2) + * Seth Axen (1) + * Ross Barnowski (1) * Dan Barzilay (1) + * Ashish Bastola (1) + * Christoph Baumgarten (2) * Ben Beasley (3) + * Doron Behar (1) * Peter Bell (1) * Sebastian Berg (1) * Ben Boeckel (1) + * David Boetius (1) + * Jake Bowhay (102) * Larry Bradley (1) + * Dietrich Brunn (5) * Evgeni Burovski (101) * Matthias Bussonnier (18) * CJ Carey (6) * Colin Carroll (1) + * Aadya Chinubhai (1) + * Luca Citi (1) * Lucas Colley (140) + * com3dian (1) + * Anirudh Dagar (4) * Danni (1) + * Dieter Werthmüller (1) * John Doe (2) + * Philippe DONNAT (2) + * drestebon (1) + * Thomas Duvernay (1) * elbarso (1) + * emilfrost (2) + * Paul Estano (8) + * Evandro (2) * Franz Király (1) + * Nikita Furin (1) + * gabrielthomsen (1) + * Lukas Geiger (9) + * Artem Glebov (22) + * Caden Gobat (1) * Ralf Gommers (125) * Alexander Goscinski (2) + * Rohit Goswami (2) + * Olivier Grisel (1) * Matt Haberland (243) * Charles Harris (1) * harshilkamdar (1) + * Alon Hovav (2) + * Gert-Ludwig Ingold (1) * Romain Jacob (1) + * jcwhitehead (1) + * Julien Jerphanion (13) * He Jia (1) * JohnWT (1) + * jokasimr (1) + * Evan W Jones (1) * Karen Róbertsdóttir (1) + * Ganesh Kathiresan (1) * Robert Kern (11) * Andrew Knyazev (4) * Uwe L. Korn (1) + * Rishi Kulkarni (1) * Kale Kundert (3) + * Jozsef Kutas (2) * Kyle0 (2) + * Robert Langefeld (1) + * Jeffrey Larson (1) + * Jessy Lauer (1) + * lciti (1) + * Hoang Le (1) + * Antony Lee (5) * Thilo Leitzbach (4) + * LemonBoy (2) + * Ellie Litwack (8) + * Thomas Loke (4) + * Malte Londschien (1) + * Christian Lorentzen (6) * Adam Lugowski (9) + * lutefiskhotdish (1) * mainak33 (1) + * Ben Mares (11) + * mart-mihkel (2) + * Mateusz Sokół (24) + * Nikolay Mayorov (4) * Nicholas McKibben (1) * Melissa Weber Mendonça (7) * Kat Mistberg (2) + * mkiffer (1) + * mocquin (1) + * Nicolas Mokus (2) + * Sturla Molden (1) * Roberto Pastor Muela (3) + * Bijay Nayak (1) + * Andrew Nelson (105) * Praveer Nidamaluri (2) + * Lysandros Nikolaou (2) * Dimitri Papadopoulos Orfanos (7) * Pablo Rodríguez Pérez (1) + * Dimitri Papadopoulos (2) * Tirth Patel (14) * Kyle Paterson (1) + * Paul (4) + * Yann Pellegrini (2) + * Matti Picus (4) * Ilhan Polat (36) * Pranav (1) + * Bharat Raghunathan (1) * Chris Rapson (1) + * Matteo Raso (4) * Tyler Reddy (165) * Martin Reinecke (1) * Tilo Reneau-Cardoso (1) + * resting-dove (2) + * Simon Segerblom Rex (4) * Lucas Roberts (2) * Pamphile Roy (31) * Feras Saad (3) + * Atsushi Sakai (3) * Masahiro Sakai (2) + * Omar Salman (14) * Andrej Savikin (1) + * Daniel Schmitz (52) * Dan Schult (19) * Scott Shambaugh (9) * Sheila-nk (2) + * Mauro Silberberg (3) + * Maciej Skorski (1) + * Laurent Sorber (1) + * Albert Steppi (28) * Kai Striega (1) * Saswat Susmoy (1) + * Alex Szatmary (1) + * Søren Fuglede Jørgensen (3) * othmane tamri (3) + * Ewout ter Hoeven (1) * Will Tirone (1) * TLeitzbach (1) + * Kevin Topolski (1) + * Edgar Andrés Margffoy Tuay (1) * Dipansh Uikey (1) + * Matus Valo (3) * Christian Veenhuis (2) * Nicolas Vetsch (1) + * Isaac Virshup (7) * Hielke Walinga (2) + * Stefan van der Walt (2) * Warren Weckesser (7) * Bernhard M. Wiedemann (4) * Levi John Wolf (1) * Xuefeng Xu (4) + * Rory Yorke (2) * YoussefAli1 (1) + * Irwin Zaid (4) + * Jinzhe Zeng (1) + * JIMMY ZHAO (1) + A total of 161 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. ``` ### 1.11.4 ``` compared to `1.11.3`. Authors ======= * Name (commits) * Jake Bowhay (2) * Ralf Gommers (4) * Julien Jerphanion (2) * Nikolay Mayorov (2) * Melissa Weber Mendonça (1) * Tirth Patel (1) * Tyler Reddy (22) * Dan Schult (3) * Nicolas Vetsch (1) + A total of 9 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. ``` ### 1.11.3 ``` compared to `1.11.2`. Authors ======= * Name (commits) * Jake Bowhay (2) * CJ Carey (1) * Colin Carroll (1) + * Anirudh Dagar (2) * drestebon (1) + * Ralf Gommers (5) * Matt Haberland (2) * Julien Jerphanion (1) * Uwe L. Korn (1) + * Ellie Litwack (2) * Andrew Nelson (5) * Bharat Raghunathan (1) * Tyler Reddy (37) * Søren Fuglede Jørgensen (2) * Hielke Walinga (1) + * Warren Weckesser (1) * Bernhard M. Wiedemann (1) A total of 17 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. ``` ### 1.11.2 ``` compared to `1.11.1`. Python `3.12` and musllinux wheels are provided with this release. Authors ======= * Name (commits) * Evgeni Burovski (2) * CJ Carey (3) * Dieter Werthmüller (1) * elbarso (1) + * Ralf Gommers (2) * Matt Haberland (1) * jokasimr (1) + * Thilo Leitzbach (1) + * LemonBoy (1) + * Ellie Litwack (2) + * Sturla Molden (1) * Andrew Nelson (5) * Tyler Reddy (39) * Daniel Schmitz (6) * Dan Schult (2) * Albert Steppi (1) * Matus Valo (1) * Stefan van der Walt (1) A total of 18 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. ``` ### 1.11.1 ``` compared to `1.11.0`. In particular, a licensing issue discovered after the release of `1.11.0` has been addressed. Authors ======= * Name (commits) * h-vetinari (1) * Robert Kern (1) * Ilhan Polat (4) * Tyler Reddy (8) A total of 4 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. ``` ### 1.11.0 ``` 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 `1.11.x` branch, and on adding new features on the main branch. This release requires Python `3.9+` and NumPy `1.21.6` or greater. For running on PyPy, PyPy3 `6.0+` is required. Highlights of this release ==================== - Several `scipy.sparse` array API improvements, including a new public base class distinct from the older matrix class, proper 64-bit index support, and numerous deprecations paving the way to a modern sparse array experience. - Added three new statistical distributions, and wide-ranging performance and precision improvements to several other statistical distributions. - A new function was added for quasi-Monte Carlo integration, and linear algebra functions ``det`` and ``lu`` now accept nD-arrays. - An ``axes`` argument was added broadly to ``ndimage`` functions, facilitating analysis of stacked image data. New features =========== `scipy.integrate` improvements ============================== - Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration. - For an even number of points, `scipy.integrate.simpson` now calculates a parabolic segment over the last three points which gives improved accuracy over the previous implementation. `scipy.cluster` improvements ============================ - ``disjoint_set`` has a new method ``subset_size`` for providing the size of a particular subset. `scipy.constants` improvements ================================ - The ``quetta``, ``ronna``, ``ronto``, and ``quecto`` SI prefixes were added. `scipy.linalg` improvements =========================== - `scipy.linalg.det` is improved and now accepts nD-arrays. - `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new ``p_indices`` switch the output permutation argument can be 1D ``(n,)`` permutation index instead of the full ``(n, n)`` array. `scipy.ndimage` improvements ============================ - ``axes`` argument was added to ``rank_filter``, ``percentile_filter``, ``median_filter``, ``uniform_filter``, ``minimum_filter``, ``maximum_filter``, and ``gaussian_filter``, which can be useful for processing stacks of image data. `scipy.optimize` improvements ============================= - `scipy.optimize.linprog` now passes unrecognized options directly to HiGHS. - `scipy.optimize.root_scalar` now uses Newton's method to be used without providing ``fprime`` and the ``secant`` method to be used without a second guess. - `scipy.optimize.lsq_linear` now accepts ``bounds`` arguments of type `scipy.optimize.Bounds`. - `scipy.optimize.minimize` ``method='cobyla'`` now supports simple bound constraints. - Users can opt into a new callback interface for most methods of `scipy.optimize.minimize`: If the provided callback callable accepts a single keyword argument, ``intermediate_result``, `scipy.optimize.minimize` now passes both the current solution and the optimal value of the objective function to the callback as an instance of `scipy.optimize.OptimizeResult`. It also allows the user to terminate optimization by raising a ``StopIteration`` exception from the callback function. `scipy.optimize.minimize` will return normally, and the latest solution information is provided in the result object. - `scipy.optimize.curve_fit` now supports an optional ``nan_policy`` argument. - `scipy.optimize.shgo` now has parallelization with the ``workers`` argument, symmetry arguments that can improve performance, class-based design to improve usability, and generally improved performance. `scipy.signal` improvements =========================== - ``istft`` has an improved warning message when the NOLA condition fails. `scipy.sparse` improvements =========================== - `scipy.sparse` array (not matrix) classes now return a sparse array instead of a dense array when divided by a dense array. - A new public base class `scipy.sparse.sparray` was introduced, allowing `isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes, while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse matrix types. - The behavior of `scipy.sparse.isspmatrix()` was updated to return True for only the sparse matrix types. If you want to check for either sparse arrays or sparse matrices, use `scipy.sparse.issparse()` instead. (Previously, these had identical behavior.) - Sparse arrays constructed with 64-bit indices will no longer automatically downcast to 32-bit. - A new `scipy.sparse.diags_array` function was added, which behaves like the existing `scipy.sparse.diags` function except that it returns a sparse array instead of a sparse matrix. - ``argmin`` and ``argmax`` methods now return the correct result when no implicit zeros are present. `scipy.sparse.linalg` improvements ================================== - dividing ``LinearOperator`` by a number now returns a ``_ScaledLinearOperator`` - ``LinearOperator`` now supports right multiplication by arrays - ``lobpcg`` should be more efficient following removal of an extraneous QR decomposition. `scipy.spatial` improvements ============================ - Usage of new C++ backend for additional distance metrics, the majority of which will see substantial performance improvements, though a few minor regressions are known. These are focused on distances between boolean arrays. `scipy.special` improvements ============================ - The factorial functions ``factorial``, ``factorial2`` and ``factorialk`` were made consistent in their behavior (in terms of dimensionality, errors etc.). Additionally, ``factorial2`` can now handle arrays with ``exact=True``, and ``factorialk`` can handle arrays. `scipy.stats` improvements ========================== New Features ------------ - `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices. - `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple experimental groups against the mean of a control group. - `scipy.stats.ecdf` for computing the empirical CDF and complementary CDF (survival function / SF) from uncensored or right-censored data. This function is also useful for survival analysis / Kaplain-Meier estimation. - `scipy.stats.logrank` to compare survival functions underlying samples. - `scipy.stats.false_discovery_control` for adjusting p-values to control the false discovery rate of multiple hypothesis tests using the Benjamini-Hochberg or Benjamini-Yekutieli procedures. - `scipy.stats.CensoredData` to represent censored data. It can be used as input to the ``fit`` method of univariate distributions and to the new ``ecdf`` function. - Filliben's goodness of fit test as ``method='Filliben'`` of `scipy.stats.goodness_of_fit`. - `scipy.stats.ttest_ind` has a new method, ``confidence_interval`` for computing confidence intervals. - `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and `scipy.stats.BootstrapMethod` are new classes to configure resampling and/or Monte Carlo versions of hypothesis tests. They can currently be used with `scipy.stats.pearsonr`. Statistical Distributions ------------------------- - Added the von-Mises Fisher distribution as `scipy.stats.vonmises_fisher`. This distribution is the most common analogue of the normal distribution on the unit sphere. - Added the relativistic Breit-Wigner distribution as `scipy.stats.rel_breitwigner`. It is used in high energy physics to model resonances. - Added the Dirichlet multinomial distribution as `scipy.stats.dirichlet_multinomial`. - Improved the speed and precision of several univariate statistical distributions. - `scipy.stats.anglit` ``sf`` - `scipy.stats.beta` ``entropy`` - `scipy.stats.betaprime` ``cdf``, ``sf``, ``ppf`` - `scipy.stats.chi` ``entropy`` - `scipy.stats.chi2` ``entropy`` - `scipy.stats.dgamma` ``entropy``, ``cdf``, ``sf``, ``ppf``, and ``isf`` - `scipy.stats.dweibull` ``entropy``, ``sf``, and ``isf`` - `scipy.stats.exponweib` ``sf`` and ``isf`` - `scipy.stats.f` ``entropy`` - `scipy.stats.foldcauchy` ``sf`` - `scipy.stats.foldnorm` ``cdf`` and ``sf`` - `scipy.stats.gamma` ``entropy`` - `scipy.stats.genexpon` ``ppf``, ``isf``, ``rvs`` - `scipy.stats.gengamma` ``entropy`` - `scipy.stats.geom` ``entropy`` - `scipy.stats.genlogistic` ``entropy``, ``logcdf``, ``sf``, ``ppf``, and ``isf`` - `scipy.stats.genhyperbolic` ``cdf`` and ``sf`` - `scipy.stats.gibrat` ``sf`` and ``isf`` - `scipy.stats.gompertz` ``entropy``, ``sf``. and ``isf`` - `scipy.stats.halflogistic` ``sf``, and ``isf`` - `scipy.stats.halfcauchy` ``sf`` and ``isf`` - `scipy.stats.halfnorm` ``cdf``, ``sf``, and ``isf`` - `scipy.stats.invgamma` ``entropy`` - `scipy.stats.invgauss` ``entropy`` - `scipy.stats.johnsonsb` ``pdf``, ``cdf``, ``sf``, ``ppf``, and ``isf`` - `scipy.stats.johnsonsu` ``pdf``, ``sf``, ``isf``, and ``stats`` - `scipy.stats.lognorm` ``fit`` - `scipy.stats.loguniform` ``entropy``, ``logpdf``, ``pdf``, ``cdf``, ``ppf``, and ``stats`` - `scipy.stats.maxwell` ``sf`` and ``isf`` - `scipy.stats.nakagami` ``entropy`` - `scipy.stats.powerlaw` ``sf`` - `scipy.stats.powerlognorm` ``logpdf``, ``logsf``, ``sf``, and ``isf`` - `scipy.stats.powernorm` ``sf`` and ``isf`` - `scipy.stats.t` ``entropy``, ``logpdf``, and ``pdf`` - `scipy.stats.truncexpon` ``sf``, and ``isf`` - `scipy.stats.truncnorm` ``entropy`` - `scipy.stats.truncpareto` ``fit`` - `scipy.stats.vonmises` ``fit`` - `scipy.stats.multivariate_t` now has ``cdf`` and ``entropy`` methods. - `scipy.stats.multivariate_normal`, `scipy.stats.matrix_normal`, and `scipy.stats.invwishart` now have an ``entropy`` method. Other Improvements ------------------ - `scipy.stats.monte_carlo_test` now supports multi-sample statistics. - `scipy.stats.bootstrap` can now produce one-sided confidence intervals. - `scipy.stats.rankdata` performance was improved for ``method=ordinal`` and ``method=dense``. - `scipy.stats.moment` now supports non-central moment calculation. - `scipy.stats.anderson` now supports the ``weibull_min`` distribution. - `scipy.stats.sem` and `scipy.stats.iqr` now support ``axis``, ``nan_policy``, and masked array input. Deprecated features ================= - Multi-Ellipsis sparse matrix indexing has been deprecated and will be removed in SciPy 1.13. - Several methods were deprecated for sparse arrays: ``asfptype``, ``getrow``, ``getcol``, ``get_shape``, ``getmaxprint``, ``set_shape``, ``getnnz``, and ``getformat``. Additionally, the ``.A`` and ``.H`` attributes were deprecated. Sparse matrix types are not affected. - The `scipy.linalg` functions ``tri``, ``triu`` & ``tril`` are deprecated and will be removed in SciPy 1.13. Users are recommended to use the NumPy versions of these functions with identical names. - The `scipy.signal` functions ``bspline``, ``quadratic`` & ``cubic`` are deprecated and will be removed in SciPy 1.13. Users are recommended to use `scipy.interpolate.BSpline` instead. - The ``even`` keyword of `scipy.integrate.simpson` is deprecated and will be removed in SciPy 1.13.0. Users should leave this as the default as this gives improved accuracy compared to the other methods. - Using ``exact=True`` when passing integers in a float array to ``factorial`` is deprecated and will be removed in SciPy 1.13.0. - float128 and object dtypes are deprecated for `scipy.signal.medfilt` and `scipy.signal.order_filter` - The functions ``scipy.signal.{lsim2, impulse2, step2}`` had long been deprecated in documentation only. They now raise a DeprecationWarning and will be removed in SciPy 1.13.0. - Importing window functions directly from `scipy.window` has been soft deprecated since SciPy 1.1.0. They now raise a ``DeprecationWarning`` and will be removed in SciPy 1.13.0. Users should instead import them from `scipy.signal.window` or use the convenience function `scipy.signal.get_window`. Backwards incompatible changes ============================ - The default for the ``legacy`` keyword of `scipy.special.comb` has changed from ``True`` to ``False``, as announced since its introduction. Expired Deprecations ================== There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - The ``n`` keyword has been removed from `scipy.stats.moment`. - The ``alpha`` keyword has been removed from `scipy.stats.interval`. - The misspelt ``gilbrat`` distribution has been removed (use `scipy.stats.gibrat`). - The deprecated spelling of the ``kulsinski`` distance metric has been removed (use `scipy.spatial.distance.kulczynski1`). - The ``vertices`` keyword of `scipy.spatial.Delauney.qhull` has been removed (use simplices). - The ``residual`` property of `scipy.sparse.csgraph.maximum_flow` has been removed (use ``flow``). - The ``extradoc`` keyword of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete` and `scipy.stats.rv_sample` has been removed. - The ``sym_pos`` keyword of `scipy.linalg.solve` has been removed. - The `scipy.optimize.minimize` function now raises an error for ``x0`` with ``x0.ndim > 1``. - In `scipy.stats.mode`, the default value of ``keepdims`` is now ``False``, and support for non-numeric input has been removed. - The function `scipy.signal.lsim` does not support non-uniform time steps anymore. Other changes ============ - Rewrote the source build docs and restructured the contributor guide. - Improved support for cross-compiling with meson build system. - MyST-NB notebook infrastructure has been added to our documentation. Authors ======= * h-vetinari (69) * Oriol Abril-Pla (1) + * Anton Akhmerov (13) * Andrey Akinshin (1) + * alice (1) + * Oren Amsalem (1) * Ross Barnowski (11) * Christoph Baumgarten (2) * Dawson Beatty (1) + * Doron Behar (1) + * Peter Bell (1) * John Belmonte (1) + * boeleman (1) + * Jack Borchanian (1) + * Matt Borland (3) + * Jake Bowhay (40) * Sienna Brent (1) + * Matthew Brett (1) * Evgeni Burovski (38) * Matthias Bussonnier (2) * Maria Cann (1) + * Alfredo Carella (1) + * CJ Carey (18) * Hood Chatham (2) * Anirudh Dagar (3) * Alberto Defendi (1) + * Pol del Aguila (1) + * Hans Dembinski (1) * Dennis (1) + * Vinayak Dev (1) + * Thomas Duvernay (1) * DWesl (4) * Stefan Endres (66) * Evandro (1) + * Tom Eversdijk (2) + * Isuru Fernando (1) * Franz Forstmayr (4) * Joseph Fox-Rabinovitz (1) * Stefano Frazzetto (1) + * Neil Girdhar (1) * Caden Gobat (1) + * Ralf Gommers (146) * GonVas (1) + * Marco Gorelli (1) * Brett Graham (2) + * Matt Haberland (385) * harshvardhan2707 (1) + * Alex Herbert (1) + * Guillaume Horel (1) * Geert-Jan Huizing (1) + * Jakob Jakobson (2) * Julien Jerphanion (5) * jyuv (2) * Rajarshi Karmakar (1) + * Ganesh Kathiresan (3) + * Robert Kern (4) * Andrew Knyazev (3) * Sergey Koposov (1) * Rishi Kulkarni (2) + * Eric Larson (1) * Zoufiné Lauer-Bare (2) + * Antony Lee (3) * Gregory R. Lee (8) * Guillaume Lemaitre (1) + * lilinjie (2) + * Yannis Linardos (1) + * Christian Lorentzen (5) * Loïc Estève (1) * Charlie Marsh (2) + * Boris Martin (1) + * Nicholas McKibben (10) * Melissa Weber Mendonça (57) * Michał Górny (1) + * Jarrod Millman (2) * Stefanie Molin (2) + * Mark W. Mueller (1) + * mustafacevik (1) + * Takumasa N (1) + * nboudrie (1) * Andrew Nelson (111) * Nico Schlömer (4) * Lysandros Nikolaou (2) + * Kyle Oman (1) * OmarManzoor (2) + * Simon Ott (1) + * Geoffrey Oxberry (1) + * Geoffrey M. Oxberry (2) + * Sravya papaganti (1) + * Tirth Patel (2) * Ilhan Polat (32) * Quentin Barthélemy (1) * Matteo Raso (12) + * Tyler Reddy (97) * Lucas Roberts (1) * Pamphile Roy (224) * Jordan Rupprecht (1) + * Atsushi Sakai (11) * Omar Salman (7) + * Leo Sandler (1) + * Ujjwal Sarswat (3) + * Saumya (1) + * Daniel Schmitz (79) * Henry Schreiner (2) + * Dan Schult (3) + * Eli Schwartz (6) * Tomer Sery (2) + * Scott Shambaugh (4) + * Gagandeep Singh (1) * Ethan Steinberg (6) + * stepeos (2) + * Albert Steppi (3) * Strahinja Lukić (1) * Kai Striega (4) * suen-bit (1) + * Tartopohm (2) * Logan Thomas (2) + * Jacopo Tissino (1) + * Matus Valo (10) + * Jacob Vanderplas (2) * Christian Veenhuis (1) + * Isaac Virshup (1) * Stefan van der Walt (14) * Warren Weckesser (63) * windows-server-2003 (1) * Levi John Wolf (3) * Nobel Wong (1) + * Benjamin Yeh (1) + * Rory Yorke (1) * Younes (2) + * Zaikun ZHANG (1) + * Alex Zverianskii (1) + A total of 131 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ``` ### 1.10.1 ``` compared to `1.10.0`. Authors ======= * Name (commits) * alice (1) + * Matt Borland (2) + * Evgeni Burovski (2) * CJ Carey (1) * Ralf Gommers (9) * Brett Graham (1) + * Matt Haberland (5) * Alex Herbert (1) + * Ganesh Kathiresan (2) + * Rishi Kulkarni (1) + * Loïc Estève (1) * Michał Górny (1) + * Jarrod Millman (1) * Andrew Nelson (4) * Tyler Reddy (50) * Pamphile Roy (2) * Eli Schwartz (2) * Tomer Sery (1) + * Kai Striega (1) * Jacopo Tissino (1) + * windows-server-2003 (1) A total of 21 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. ``` ### 1.10.0 ``` 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 1.10.x branch, and on adding new features on the main branch. This release requires Python `3.8+` and NumPy `1.19.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. Highlights of this release ==================== - A new dedicated datasets submodule (`scipy.datasets`) has been added, and is now preferred over usage of `scipy.misc` for dataset retrieval. - A new `scipy.interpolate.make_smoothing_spline` function was added. This function constructs a smoothing cubic spline from noisy data, using the generalized cross-validation (GCV) criterion to find the tradeoff between smoothness and proximity to data points. - `scipy.stats` has three new distributions, two new hypothesis tests, three new sample statistics, a class for greater control over calculations involving covariance matrices, and many other enhancements. New features =========== `scipy.datasets` introduction ======================== - A new dedicated ``datasets`` submodule has been added. The submodules is meant for datasets that are relevant to other SciPy submodules ands content (tutorials, examples, tests), as well as contain a curated set of datasets that are of wider interest. As of this release, all the datasets from `scipy.misc` have been added to `scipy.datasets` (and deprecated in `scipy.misc`). - The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/) (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and downloading them when requested. After downloading the datasets once, the files are cached to avoid network dependence and repeated usage. - Added datasets from ``scipy.misc``: `scipy.datasets.face`, `scipy.datasets.ascent`, `scipy.datasets.electrocardiogram` - Added download and caching functionality: - `scipy.datasets.download_all`: a function to download all the `scipy.datasets` associated files at once. - `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset files from the file system. - ``scipy/datasets/_download_all.py`` can be run as a standalone script for packaging purposes to avoid any external dependency at build or test time. This can be used by SciPy packagers (e.g., for Linux distros) which may have to adhere to rules that forbid downloading sources from external repositories at package build time. `scipy.integrate` improvements ============================== - Added `scipy.integrate.qmc_quad`, which performs quadrature using Quasi-Monte Carlo points. - Added parameter ``complex_func`` to `scipy.integrate.quad`, which can be set ``True`` to integrate a complex integrand. `scipy.interpolate` improvements ================================ - `scipy.interpolate.interpn` now supports tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and ``pchip``) - Tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and ``pchip``) in `scipy.interpolate.interpn` and `scipy.interpolate.RegularGridInterpolator` now allow values with trailing dimensions. - `scipy.interpolate.RegularGridInterpolator` has a new fast path for ``method="linear"`` with 2D data, and ``RegularGridInterpolator`` is now easier to subclass - `scipy.interpolate.interp1d` now can take a single value for non-spline methods. - A new ``extrapolate`` argument is available to `scipy.interpolate.BSpline.design_matrix`, allowing extrapolation based on the first and last intervals. - A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an implementation of the generalized cross-validation spline smoothing algorithm. The ``lam=None`` (default) mode of this function is a clean-room reimplementation of the classic ``gcvspl.f`` Fortran algorithm for constructing GCV splines. - A new ``method="pchip"`` mode was aded to `scipy.interpolate.RegularGridInterpolator`. This mode constructs an interpolator using tensor products of C1-continuous monotone splines (essentially, a `scipy.interpolate.PchipInterpolator` instance per dimension). `scipy.sparse.linalg` improvements ================================== - The spectral 2-norm is now available in `scipy.sparse.linalg.norm`. - The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius norm) has been improved. - LAPACK wrappers were added for ``trexc`` and ``trsen``. - The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding the following improvements: - a simple tunable restart potentially increases the attainable accuracy for edge cases, - internal postprocessing runs one final exact Rayleigh-Ritz method giving more accurate and orthonormal eigenvectors, - output the computed iterate with the smallest max norm of the residual and drop the history of subsequent iterations, - remove the check for ``LinearOperator`` format input and thus allow a simple function handle of a callable object as an input, - better handling of common user errors with input data, rather than letting the algorithm fail. `scipy.linalg` improvements =========================== - `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted to square arrays. `scipy.ndimage` improvements ============================ - The new `scipy.ndimage.value_indices` function provides a time-efficient method to search for the locations of individual values with an array of image data. - A new ``radius`` argument is supported by `scipy.ndimage.gaussian_filter1d` and `scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter. `scipy.optimize` improvements ============================= - `scipy.optimize.brute` now coerces non-iterable/single-value ``args`` into a tuple. - `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept `scipy.optimize.Bounds` for bounds constraints. - Added a tutorial for `scipy.optimize.milp`. - Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects. - Additional options (``parallel``, ``threads``, ``mip_rel_gap``) can now be passed to `scipy.optimize.linprog` with ``method='highs'``. `scipy.signal` improvements =========================== - The new window function `scipy.signal.windows.lanczos` was added to compute a Lanczos window, also known as a sinc window. `scipy.sparse.csgraph` improvements =================================== - the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and star graphs in particular see a marked performance improvement `scipy.special` improvements ============================ - The new function `scipy.special.powm1`, a ufunc with signature ``powm1(x, y)``, computes ``x**y - 1``. The function avoids the loss of precision that can result when ``y`` is close to 0 or when ``x`` is close to 1. - `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under the hood. `scipy.stats` improvements ========================== - Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for use with any univariate distribution, any combination of known and unknown parameters, and several choices of test statistic (Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling). - Improved `scipy.stats.bootstrap`: Default method ``'BCa'`` now supports multi-sample statistics. Also, the bootstrap distribution is returned in the result object, and the result object can be passed into the function as parameter ``bootstrap_result`` to add additional resamples or change the confidence interval level and type. - Added maximum spacing estimation to `scipy.stats.fit`. - Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`. - Added new sample statistics. - Added `scipy.stats.contingency.odds_ratio` to compute both the conditional and unconditional odds ratios and corresponding confidence intervals for 2x2 contingency tables. - Added `scipy.stats.directional_stats` to compute sample statistics of n-dimensional directional data. - Added `scipy.stats.expectile`, which generalizes the expected value in the same way as quantiles are a generalization of the median. - Added new statistical distributions. - Added `scipy.stats.uniform_direction`, a multivariate distribution to sample uniformly from the surface of a hypersphere. - Added `scipy.stats.random_table`, a multivariate distribution to sample uniformly from m x n contingency tables with provided marginals. - Added `scipy.stats.truncpareto`, the truncated Pareto distribution. - Improved the ``fit`` method of several distributions. - `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical solution when ``method='mm'``, which also serves a starting guess to improve the performance of ``method='mle'``. - `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum likelihood estimates have been extended to the cases in which location or scale are fixed by the user. - Analytical maximum likelihood estimates have been added for `scipy.stats.powerlaw`. - Improved random variate sampling of several distributions. - Drawing multiple samples from `scipy.stats.matrix_normal`, `scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and `scipy.stats.unitary_group` is faster. - The ``rvs`` method of `scipy.stats.vonmises` now wraps to the interval ``[-np.pi, np.pi]``. - Improved the reliability of `scipy.stats.loggamma` ``rvs`` method for small values of the shape parameter. - Improved the speed and/or accuracy of functions of several statistical distributions. - Added `scipy.stats.Covariance` for better speed, accuracy, and user control in multivariate normal calculations. - `scipy.stats.skewnorm` methods ``cdf``, ``sf``, ``ppf``, and ``isf`` methods now use the implementations from Boost, improving speed while maintaining accuracy. The calculation of higher-order moments is also faster and more accurate. - `scipy.stats.invgauss` methods ``ppf`` and ``isf`` methods now use the implementations from Boost, improving speed and accuracy. - `scipy.stats.invweibull` methods ``sf`` and ``isf`` are more accurate for small probability masses. - `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations from Boost, improving speed and accuracy. - Implemented the ``logpdf`` method of `scipy.stats.vonmises` for reliability in extreme tails. - Implemented the ``isf`` method of `scipy.stats.levy` for speed and accuracy. - Improved the robustness of `scipy.stats.studentized_range` for large ``df`` by adding an infinite degree-of-freedom approximation. - Added a parameter ``lower_limit`` to `scipy.stats.multivariate_normal`, allowing the user to change the integration limit from -inf to a desired value. - Improved the robustness of ``entropy`` of `scipy.stats.vonmises` for large concentration values. - Enhanced `scipy.stats.gaussian_kde`. - Added `scipy.stats.gaussian_kde.marginal`, which returns the desired marginal distribution of the original kernel density estimate distribution. - The ``cdf`` method of `scipy.stats.gaussian_kde` now accepts a ``lower_limit`` parameter for integrating the PDF over a rectangular region. - Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython, improving speed. - The global interpreter lock is released by the ``pdf`` method of `scipy.stats.gaussian_kde` for improved multithreading performance. - Replaced explicit matrix inversion with Cholesky decomposition for speed and accuracy. - Enhanced the result objects returned by many `scipy.stats` functions - Added a ``confidence_interval`` method to the result object returned by `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`. - The `scipy.stats` functions ``combine_pvalues``, ``fisher_exact``, ``chi2_contingency``, ``median_test`` and ``mood`` now return bunch objects rather than plain tuples, allowing attributes to be accessed by name. - Attributes of the result objects returned by ``multiscale_graphcorr``, ``anderson_ksamp``, ``binomtest``, ``crosstab``, ``pointbiserialr``, ``spearmanr``, ``kendalltau``, and ``weightedtau`` have been renamed to ``statistic`` and ``pvalue`` for consistency throughout `scipy.stats`. Old attribute names are still allowed for backward compatibility. - `scipy.stats.anderson` now returns the parameters of the fitted distribution in a `scipy.stats._result_classes.FitResult` object. - The ``plot`` method of `scipy.stats._result_classes.FitResult` now accepts a ``plot_type`` parameter; the options are ``'hist'`` (histogram, default), ``'qq'`` (Q-Q plot), ``'pp'`` (P-P plot), and ``'cdf'`` (empirical CDF plot). - Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the location (argmax) at which the statistic is calculated and the variant of the statistic used. - Improved the performance of several `scipy.stats` functions. - Improved the performance of `scipy.stats.cramervonmises_2samp` and `scipy.stats.ks_2samp` with ``method='exact'``. - Improved the performance of `scipy.stats.siegelslopes`. - Improved the performance of `scipy.stats.mstats.hdquantile_sd`. - Improved the performance of `scipy.stats.binned_statistic_dd` for several NumPy statistics, and binned statistics methods now support complex data. - Added the ``scramble`` optional argument to `scipy.stats.qmc.LatinHypercube`. It replaces ``centered``, which is now deprecated. - Added a parameter ``optimization`` to all `scipy.stats.qmc.QMCEngine` subclasses to improve characteristics of the quasi-random variates. - Added tie correction to `scipy.stats.mood`. - Added tutorials for resampling methods in `scipy.stats`. - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided ``statistic`` is vectorized, so passing the ``vectorized`` argument explicitly is no longer required to take advantage of vectorized statistics. - Improved the speed of `scipy.stats.permutation_test` for permutation types ``'samples'`` and ``'pairings'``. - Added ``axis``, ``nan_policy``, and masked array support to `scipy.stats.jarque_bera`. - Added the ``nan_policy`` optional argument to `scipy.stats.rankdata`. Deprecated features ================= - `scipy.misc` module and all the methods in ``misc`` are deprecated in v1.10 and will be completely removed in SciPy v2.0.0. Users are suggested to utilize the `scipy.datasets` module instead for the dataset methods. - `scipy.stats.qmc.LatinHypercube` parameter ``centered`` has been deprecated. It is replaced by the ``scramble`` argument for more consistency with other QMC engines. - `scipy.interpolate.interp2d` class has been deprecated. The docstring of the deprecated routine lists recommended replacements. Expired Deprecations ================== - There is an ongoing effort to follow through on long-standing deprecations. - The following previously deprecated features are affected: - Removed ``cond`` & ``rcond`` kwargs in ``linalg.pinv`` - Removed wrappers ``scipy.linalg.blas.{clapack, flapack}`` - Removed ``scipy.stats.NumericalInverseHermite`` and removed ``tol`` & ``max_intervals`` kwargs from ``scipy.stats.sampling.NumericalInverseHermite`` - Removed ``local_search_options`` kwarg frrom ``scipy.optimize.dual_annealing``. Other changes ============ - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided ``statistic`` is vectorized by looking for an ``axis`` parameter in the signature of ``statistic``. If an ``axis`` parameter is present in ``statistic`` but