typed op• Data kinds: signal → image
• Call: fullseye.apply(img, "tb_spectrogram", 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.*
*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_spectrogram: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_spectrogram: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_spectrogram: other inputs (docs site)
STFT magnitude spectrogram -> (freqs, times, S) with S shape (n_freqs, n_frames). Hann-windowed; *hop* defaults to win//2.
Same raw convention as spectrum, but a different divisor. Each column is the unnormalised |rfft(frame * hann(win))|, so it is not an amplitude either — and dividing by 2/win is *wrong* here, because the Hann window has already thrown away part of the signal. The correct one-sided amplitude conversion divides by the window's coherent gain::
w = np.hanning(win)
amp = S * (2.0 / w.sum()) # bins 1 .. win/2-1; DC / Nyquist: 1/w.sum()
Measured on a unit sine at a bin centre (rate = 16000 Hz, 1000 Hz, amplitude exactly 1.0, win = 256): the raw column peak is 63.7497786196906; * 2/win gives 0.49804514546633283 (too small by exactly the Hann coherent gain sum(w)/win = 0.498046875), while * 2/sum(w) gives 0.9999965273676957. Only the second one is the amplitude that was actually in the signal.
Peak *positions*, frame-to-frame ratios and any dB *difference* are unaffected by either factor. This function returns magnitudes only — the phase is discarded, so it cannot be inverted; use acoustics.stft / acoustics.istft for a round-trip.
Typed bridge of the 1d op spectrogram into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives rate (default 1) and b drives win (default 256).
• 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_signal 0.50 0.50 tb_spectrogram 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op spectrogram. 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).
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
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.