v1.3.3

1.3.3 - 2026-04-23

Fixed

• Kernel numerical consistency and speed. The CUDA kernels in diffct/differentiable.py are now consistently float32 end to end — literal constants (0.0, 0.5, 1.0, -inf, ...) and integer-to-float casts inside the Siddon and SF kernels are routed through typed _DTYPE helpers so numba no longer implicitly promotes intermediates to float64. This removes a silent precision mismatch that was slowing down every projector/backprojector kernel; the library runs substantially faster with no change in public-API behavior.
• Thread indexing and grid configuration in the 3D cone Siddon forward / backward kernels and the 2D fan SF backward kernel. The cuda.grid(...) unpack order and the matching _grid_2d / _grid_3d launch arguments are aligned so the warp-adjacent axis matches the stride-1 axis of the output buffer: (iv, iu, iview) for 3D cone (d_sino[view, u, v] / d_vol[ix, iy, iz]) and (ix, iy) for 2D fan SF. Fixes mismatched launches between ConeProjectorFunction, ConeBackprojectorFunction, FanProjectorFunction, and FanBackprojectorFunction and their kernels.
• Walnut rotation-axis correction in the shipped preprocessing and sample data. examples/data/preprocess_walnut.py now applies the Zenodo record's recommended 5-raw-pixel left shift before binning and cropping, records the correction in the .npz metadata, and the shipped examples/data/walnut_cone.npz / walnut_reco.png are regenerated accordingly. examples/realdata_walnut_fdk.py and examples/data/NOTICE are updated to surface the correction and the richer walnut metadata.

Changed

• Examples now require CUDA explicitly. examples/fbp_parallel.py, fbp_fan.py, fdk_cone.py, iterative_reco_*.py, realdata_fbp_*.py, realdata_fdk_cone.py, and realdata_walnut_fdk.py assert torch.cuda.is_available() and initialize the CUDA device up front instead of silently falling back to CPU (which the numba-cuda kernels never supported anyway).

Full Changelog: v1.3.2...v1.3.3