Hi all,
On behalf of the SciPy development team, I'm pleased to announce the
release of SciPy 1.12.0.
Sources and binary wheels can be found at:
https://pypi.org/project/scipy/
and at: https://github.com/scipy/scipy/releases/tag/v1.12.0
One of a few ways to install this release with pip:
pip install scipy==1.12.0
==========================
SciPy 1.12.0 Release Notes
==========================
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.

`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.isotonic_regression` has been added to allow
nonparametric isotonic
regression.
- `scipy.optimize.nnls` is rewritten in Python and now implements the
so-called
fnnls or fast nnls, making it more efficient for high-dimensional
problems.
- 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.
- `scipy.optimize.minimize` method ``Newton-CG`` now supports functions that
return sparse Hessian matrices/arrays for the ``hess`` parameter and is
slightly
more efficient.
- `scipy.optimize.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``.
- `scipy.optimize.minimize` methods ``CG``, ``Newton-CG``, and ``BFGS`` now
accept
parameters ``c1`` and ``c2``, allowing specification of the Armijo and
curvature rule
parameters, respectively.
- `scipy.optimize.curve_fit` performance has improved due to more efficient
memoization
of the callable function.
`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.

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