Bump scipy from 1.11.4 to 1.12.0
Bumps scipy from 1.11.4 to 1.12.0.
Release notes
Sourced from scipy's releases.
SciPy 1.12.0 Release Notes
SciPy
1.12.0
is the culmination of6
months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code withpython -Wd
and check forDeprecationWarning
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 NumPy1.22.4
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 ofscipy.fft
andscipy.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 theSCIPY_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 handlingNaN
values, masked arrays, and more fine-grained shape-handling. The accuracy and performance of a number ofstats
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 importingscipy
. This experimental support is still under development and likely to contain bugs - testing is very welcome.
... (truncated)
Commits
-
4edfcaa
REL: SciPy 1.12.0 release commit [wheel build] -
e3cff26
Merge pull request #19922 from tylerjereddy/treddy_1_12_0_final_prep -
bf02582
DOC: PR 19922 revisions [wheel build] -
41ed3d6
Revert "ENH: stats.wasserstein_distance: multivariate Wasserstein distance/EM... -
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DOC: update 1.12.0 relnotes -
c76bedf
BLD: ensure the name of the installedscipy
package is lower-case -
aedbb2b
DEP: reflect extended deprecations also in release notes (#19903) -
0b4c8dc
DEP: extend some announced deprecations due to out-of-band 1.13 release (#19892) -
82dddc5
DOC: 1.12 release notes tweaks (#19877) -
e22a5ff
REL: 1.12.0 final unreleased - Additional commits viewable in compare view