typed op• Data kinds: signal → signal
• Call: fullseye.apply(img, "tb_weighting_response", a=0.5, b=0.5) (2-D model = एक image + दो scalar knobs a,b∈[0,1])
*Figure synthetic 128×128 input पर सच में चलाकर मिला output है। बाईं ओर input, दाईं ओर output। Point clouds ऊपर से देखे गए scatter के रूप में (brightness = z), 1-D series line के रूप में, volumes z दिशा में maximum-intensity projection के रूप में, videos बीच के frame के रूप में, complex images amplitude के रूप में, और जो return values चित्र नहीं बन सकते वे स्वयं values के रूप में दिखाए जाते हैं।*
*Knob a output नहीं बदलता (मापा गया: 0.1 / 0.5 / 0.9 पर एक जैसा)।*
*Knob b output नहीं बदलता (मापा गया: 0.1 / 0.5 / 0.9 पर एक जैसा)।*
Stages (पहले आने वाले ops → यह op, बाएँ से क्रम में):
▸ tb_weighting_response: stages का figure (docs site)
दूसरी images पर भी (synthetic scene / photo / coins। ऊपर की row में inputs, नीचे की row में उनके outputs। Knobs default पर):
▸ tb_weighting_response: दूसरी images पर output (docs site)
दी गई frequencies पर A / C / Z frequency-weighting curve, dB में।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
Computed from the four pole frequencies that *define* the networks and normalised so that the response at 1 kHz is exactly 0 dB by construction — the curve is divided by its own value at 1 kHz rather than having a published offset constant added to it. That is why the tests can assert equality at 1 kHz to 0.0 rather than to a tolerance, and why no standard's table of attenuations appears anywhere in this repository.
The response depends on f only through f**2, so it is an even function and negative frequencies are evaluated at |f| — that is the definition, not a repair. f = 0 has zero response (both curves have a zero at DC) and is reported as floor_db rather than -inf.
Measured (computed, then printed — these are outputs, not transcriptions):
======== ========= =========
f (Hz) A (dB) C (dB)
======== ========= =========
10 -70.4304 -14.3300
31.5 -39.5250 -3.0305
100 -19.1428 -0.2996
1000 0.0000 0.0000
4000 0.9633 -0.8260
10000 -2.4918 -4.4055
20000 -9.3469 -11.2786
======== ========= =========
A(1000) and C(1000) are exactly 0.0 — the Python float, not a rounding — because of the construction. The low-frequency asymptote is a closed form and is asserted in the tests: A falls at exactly 80 dB/decade as f -> 0 (f**4 over three constants) and C at exactly 40 dB/decade (f**2). Measured between 0.001 and 0.01 Hz with the floor lowered out of the way: 79.999998 and 39.999998 dB/decade.
That last caveat is real and is why the floor is an argument: with the default floor_db = -200 the A curve reaches the floor below about 0.35 Hz (unfloored, A(0.1) = -228.55 dB), so the asymptote measured against the default floor comes out as 0.0 dB/decade between 0.01 and 0.1 Hz — a clamp, correctly reported, that would look like a bug if the floor were not visible.
Returns a float64 array the same shape as *freqs*.
Raises ValueError: a non-1-D / non-finite / complex / masked freqs, an unknown kind.
Typed bridge of the acoustics op weighting_response into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. This op has no tunable parameter; a and b are unused.
• 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।
नीचे का program सच में चलता है, यह जाँचा गया है (figure वाला ही input)। Studio की help में यह block buttons बन जाता है, जिनसे इसे वहीं load करके चलाया जा सकता है।
img_to_signal 0.50 0.50 tb_weighting_response 0.50 0.50
▸ यह pipeline load करें · Load करके चलाएँ
नीचे के उदाहरण मूल ledger op weighting_response को call करते हैं। यह bridge op वही implementation है, बस fn(v, a, b) convention के अनुसार ढाला गया है, इसलिए व्यवहार वैसा ही लागू होता है (केवल call का रूप अलग है)।
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
signal को input के रूप में लेते हैं)identity · tb_create_funct_1d_array · tb_smooth_funct_1d_gauss · tb_smooth_funct_1d_mean · tb_derivate_funct_1d · tb_integrate_funct_1d · tb_zero_crossings_funct_1d · tb_abs_funct_1d
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. 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.