dtof op• Data kinds: depth → histcube
• Call: import fullseye as fs; fs.ledger.dtof_cube_simulate(depth, bins=256, bin_ps=100.0, reflectivity=None, signal_photons=20.0, ambient_photons=5.0, irf_fwhm_ps=200.0, seed=0, noise=True) (to call the implementation directly, import photoncount; photoncount.dtof_cube_simulate(depth, bins=256, bin_ps=100.0, reflectivity=None, signal_photons=20.0, ambient_photons=5.0, irf_fwhm_ps=200.0, seed=0, noise=True); from the registry, opsphoton.get("dtof_cube_simulate"))
Synthesise the (H, W, T) photon histogram cube a SPAD array produces.
The per-pixel version of tcspc_simulate: every pixel of the *depth* map (metres, one-way distance) gets a Gaussian return at its own round-trip time 2d/c, scaled by *signal_photons* times that pixel's *reflectivity*, on a uniform ambient pedestal of ambient_photons/bins per bin, Poisson sampled with numpy.random.default_rng(seed).
The output is the cube that dtof_cube_depth inverts, and the axis order is (H, W, T) with time LAST — the same layout a SPAD array streams. That is not the (D, H, W) of a volops voxel volume; the two are both 3-D float arrays and swapping them silently produces a plausible-wrong depth map, which is why dtof_cube_depth checks and says so.
noise=False returns the exact expectation cube (no sampling).
Ground truth: with noise=False the per-pixel centroid of the cube returns the input depth map to an RMS error of 3.2e-16 m (pinned in the tests) — the pulse integral is analytic, so the only error is float round-off.
Raises ValueError: a non-2-D, non-finite or non-positive *depth*, a *reflectivity* that is negative or not the same shape as *depth*, a *bins* outside [2, MAX_BINS], non-positive *bin_ps* / *irf_fwhm_ps*, negative photon budgets, a non-integer *seed*, a cube over MAX_CUBE_ELEMENTS (H*W*bins grows fast — 512x512x256 is 8x the cap), and any depth whose round-trip time falls outside the time window (which a real sensor would alias into a short distance).
• photon_timeresolved family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_dtof_ranging — py -3.11 examples/poc_dtof_ranging.py
histcube as input)dtof)*Provenance: photoncount.py — PHOTON operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.