Embed linkcell from Python

The Python module wraps the C waist with DLPack (dlpk). It does not take numpy.ndarray as the ABI. Any object with __dlpack__() is valid input. The return value implements __dlpack__(); consume it with numpy.from_dlpack or torch.from_dlpack.

import numpy as np
import linkcell

xyz = np.ascontiguousarray([[0.2, 0.0, 0.0], [9.4, 0.0, 0.0]], dtype=np.float64)
cell = np.ascontiguousarray([10.0, 10.0, 10.0], dtype=np.float64)
nn, d2 = linkcell.knearest(xyz, cell, k=1)
nn = np.from_dlpack(nn)
d2 = np.from_dlpack(d2)
assert int(nn[0, 0]) == 1
assert abs(float(d2[0, 0]) - 0.64) < 1e-12

xyz is float64 (n, 3) or (n_frames, n, 3). cell is float64 (3,) (ortho lengths), (3, 3) lattice rows (vesin / lc_cell order), or (4, 3) rows plus origin, on any DLPack device. Optional mask is length n. The GIL is detached for the rayon CPU walk.

The return is (indices, dist2). Indices are int32 (-1 unused). dist2 is float64 (NaN unused). A batched xyz yields shape (n_frames, n, k).

CUDA xyz (kDLCUDA, including a torch.Tensor on GPU) is passed by device pointer into lc_gpu_*. A CUDA cell is inverted on device; the host only reads the four launch ints (nx, ny, nz, nC). The result capsules are on the same CUDA device; torch.from_dlpack takes them without a host bounce. linkcell.gpu_available() is true when the driver and nvrtc load (gpulite, no CUDA SDK at build time).

Wheels:

• abi3-py312: one wheel per platform, GIL CPython 3.12+
• abi3t-py315: one wheel per platform, CPython 3.15+ GIL and free-threaded (PEP 803). Build with maturin 1.14+ and --features abi3t.