SciPy 1.12.0 is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with python -Wd and check for DeprecationWarning s).
Our development attention will now shift to bug-fix releases on the
1.12.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.
Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.
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Bumps scipy from 1.11.4 to 1.12.0.
Release notes
Sourced from scipy's releases.
... (truncated)
Commits
4edfcaa
REL: SciPy 1.12.0 release commit [wheel build]e3cff26
Merge pull request #19922 from tylerjereddy/treddy_1_12_0_final_prepbf02582
DOC: PR 19922 revisions [wheel build]41ed3d6
Revert "ENH: stats.wasserstein_distance: multivariate Wasserstein distance/EM...db2cb8c
DOC: update 1.12.0 relnotesc76bedf
BLD: ensure the name of the installedscipy
package is lower-caseaedbb2b
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 unreleasedDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
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