typed op• Data kinds: counts → counts
• Call: fullseye.apply(img, "tb_tcspc_irf_convolve", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_tcspc_irf_convolve: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_tcspc_irf_convolve: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_tcspc_irf_convolve: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_tcspc_irf_convolve: other inputs (docs site)
Blur an arrival-time histogram by the instrument response (timing jitter).
The temporal analogue of a PSF convolution: a detector's timing uncertainty
(SPAD jitter + TDC quantisation + laser pulse width) smears every arrival
time by the instrument response function, here a Gaussian of full width at
half maximum *irf_fwhm_ps*. The kernel is the exact bin integral of that
Gaussian (erf differences), normalised to sum 1, truncated at
`+-truncate*sigma` and forced to odd length so the convolution is centred.
Ground truth: convolving a unit spike in the middle of a 256-bin window with
`irf_fwhm_ps = 500 at bin_ps = 50` leaves the centroid exactly
where it was (measured shift 0.0 ps — the kernel is symmetric) and gives a
profile whose measured FWHM is 501.22 ps. That 0.24% excess over 500 is the
*measurement*, not the kernel: :func:tcspc_stats finds the half-maximum
crossings by linear interpolation between bins, which slightly overestimates
the width of a Gaussian.
Total counts are preserved *except* at the window edges, where
`mode='same'` discards the tail that falls outside — measured loss exactly
0 for that centred spike, but a genuine loss for a pulse within a few sigma
of either end.
Returns a float64 1-D histogram of the same length as *hist*.
Raises `ValueError`: negative, non-finite or non-1-D *hist*, a
non-positive *bin_ps* / *irf_fwhm_ps* / *truncate*, an IRF sigma below
1e-3 bins (the kernel would be a delta and the op a no-op — say so instead
of pretending to blur), and a kernel that would be longer than the
:data:MAX_BINS cap.
Typed bridge of the photon op `tcspc_irf_convolve into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives bin_ps (default 100) and b drives irf_fwhm_ps` (default 200).
• 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 program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
img_to_counts 0.50 0.50 tb_tcspc_irf_convolve 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op tcspc_irf_convolve. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
counts as input)identity · tb_spad_deadtime_apply · tb_spad_deadtime_correct · tb_tcspc_coates_correct · tb_tcspc_background_subtract · tb_dtof_depth · tb_countrate_to_counts · tb_counts_to_countrate
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
*Provenance: ops.py — 2D 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.