Log Message:
py-scipy: updated to 1.15.0

1.15.0

Highlights of this release

Sparse arrays are now fully functional for 1-D and 2-D arrays. We recommend that \ 
all new code use sparse arrays instead of sparse matrices and that developers \ 
start to migrate their existing code from sparse matrix to sparse array: \ 
Migration from spmatrix to sparray. Both sparse.linalg and sparse.csgraph work \ 
with either sparse matrix or sparse array and work internally with sparse array.
Sparse arrays now provide basic support for n-D arrays in the COO format \ 
including add, subtract, reshape, transpose, matmul, dot, tensordot and others. \ 
More functionality is coming in future releases.
Preliminary support for free-threaded Python 3.13.
New probability distribution features in scipy.stats can be used to improve the \ 
speed and accuracy of existing continuous distributions and perform new \ 
probability calculations.
Several new features support vectorized calculations with Python Array API \ 
Standard compatible input (see “Array API Standard Support” below):
scipy.differentiate is a new top-level submodule for accurate estimation of \ 
derivatives of black box functions.
scipy.optimize.elementwise contains new functions for root-finding and \ 
minimization of univariate functions.
scipy.integrate offers new functions cubature, tanhsinh, and nsum for \ 
multivariate integration, univariate integration, and univariate series \ 
summation, respectively.
scipy.interpolate.AAA adds the AAA algorithm for barycentric rational \ 
approximation of real or complex functions.
scipy.special adds new functions offering improved Legendre function \ 
implementations with a more consistent interface.