Bump scipy from 1.17.1 to 1.18.0 in /tools

[dependabot]

Bumps scipy from 1.17.1 to 1.18.0.

Release notes

Sourced from scipy's releases.

SciPy 1.18.0 Release Notes

SciPy 1.18.0 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 1.18.x branch, and on adding new features on the main branch.

This release requires Python 3.12-3.14 and NumPy 2.0.0 or greater.

Highlights of this release

• SciPy now supports three different build modes for BLAS and LAPACK LP64/ILP64 support, and machinery is provided for downstream cython_lapack consumers to gracefully handle LP64/ILP64 backend builds. ILP64 support has been substantially improved across the SciPy library.
• Remaining Fortran to C translations have been completed---an experimental Fortran-free build option is now available to developers for testing purposes. Developer feedback is welcome on Fortran-free builds.
• scipy.signal.whittaker_henderson now provides access to Whittaker-Henderson smoothing of a discrete signal.
• A large number of scipy.stats functions now support lazy arrays and JAX JIT. Array API support has been improved substantially in SciPy, with at least 21 functions gaining new support in this release. 16 scipy.stats functions have also gained support for MArray input.

New features

scipy.fft improvements

• SciPy's internal FFT backend has switched from pocketfft to its successor package ducc0.fft, which features several incremental improvements. The most significant of those from SciPy's perspective is probably that storage requirements for internally cached plans have been significantly reduced for most long 1D transforms. Plans that require more storage than 1MB will no longer be cached; this mainly affects huge 1D transforms of prime and near-prime sizes.

scipy.interpolate improvements

... (truncated)

Commits

• 54ef542 REL: 1.18.0 release commit
• a45e463 Merge pull request #25395 from tylerjereddy/treddy_prep_1.18.0_final
• ee2442e TYP, MAINT, TST: PR 25395 revisions
• 3225154 DOC: PR 25395 revisions
• 14c7bd7 TYP: Fix mypy errors with pytest==9.1.0 (#25410)
• 71522e9 DOC: revise SciPy 1.18.0 release notes
• 92f8482 MAINT: fix errors from pytest 9.1 (#25378)
• 7b27bf4 TST, MAINT: modernize spatial tests for pytest 10 (#25376)
• d05b640 Merge pull request #25347 from tylerjereddy/treddy_version_bump_1.18.0rc3
• e60fe8b REL: set to 1.18.0rc3 unreleased
• Additional commits viewable in compare view

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