Update scipy from 1.11.3 to 1.12.0.

Changelog

### 1.12.0 ``` 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``

ericmjl / Network-Analysis-Made-Simple

Scheduled weekly dependency update for week 06 #388

Update ipykernel from 6.26.0 to 6.29.2.

Update matplotlib from 3.8.1 to 3.8.2.

Update mkdocs-material from 9.4.8 to 9.5.9.

Update nbconvert from 7.11.0 to 7.16.0.

Update numpy from 1.26.1 to 1.26.4.

Update pandas from 2.1.2 to 2.2.0.

Update pymdown-extensions from 10.3.1 to 10.7.

Update scipy from 1.11.3 to 1.12.0.