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 :func: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.