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) (implementation को सीधे call करना हो तो 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), registry से लेना हो तो opsphoton.get("dtof_cube_simulate"))
SPAD array द्वारा बनाया जाने वाला (H, W, T) photon histogram cube synthesise करता है।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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 (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_dtof_ranging — py -3.11 examples/poc_dtof_ranging.py
histcube को input के रूप में लेते हैं)dtof)*Provenance: photoncount.py — PHOTON operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.