oineus.diff.cube_filtration¶
- oineus.diff.cube_filtration(data, negate=False, wrap=False, max_dim=None, values_on='vertices', n_threads=1)[source]¶
Differentiable cubical filtration from tensor-valued grid data.
Mirrors oineus.cube_filtration, but returns a DiffFiltration whose filtration values are gathered back onto the autograd-tracked input.
- Parameters:
data – 1D/2D/3D tensor-like (PyTorch/JAX/NumPy) of grid values.
negate (bool) – Compute upper-star (superlevel) instead of lower-star.
wrap (bool) – Not supported; raises if True.
max_dim (int | None) – Maximal cube dimension; defaults to
data.ndim.values_on (str) –
"vertices"(default) or"cells". Critical indices are flat indices into the original data array in either case.n_threads (int) – Threads for C++ filtration construction.
- Return type: