spectral_noise_proxy — n4m.diagnostics.spectral_noise_proxy¶
Namespace: n4m.diagnostics · Fully-qualified: n4m.diagnostics.spectral_noise_proxy · Catalog id: diagnostics.spectral_noise_proxy
API surface¶
C ABI: no standalone exported symbol is declared for this method.
Python (verified public re-export): from n4m.roles import SpectralNoiseProxy
Signature: SpectralNoiseProxy(*, valid_mask = ())
R (source-verified): n4m_spectral_noise_proxy(valid_mask = c()).
library(n4m)
result <- n4m_spectral_noise_proxy()
MATLAB / Octave: no current source-verified entry point was found for this catalog method.
Parameters¶
Name |
Type |
Default |
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Explanations¶
Bibliographic source¶
A declared noise proxy under an approximately smooth signal, using normal-scaled MAD. SciPy documents the median absolute deviation definition used by the independent oracle: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.median_abs_deviation.html.
Mathematical principle¶
For each row, median absolute deviation of index-space second differences is multiplied by 1.4826/sqrt(6). The sqrt(6) removes variance inflation for independent equal-variance white noise under differencing twice. Native summaries expose mean absolute amplitude, amplitude/noise and 20log10 of that positive finite ratio.
Appropriate uses¶
Describing high-frequency variation on an explicitly declared index-grid protocol and comparing that same protocol on identical populations and masks.
Limits and validation¶
Smooth signal structure, correlated/heteroscedastic noise and nonuniform physical sampling can invalidate an instrumental interpretation. This is not instrument SNR and is distinct from the existing SG residual/noise-gain estimator. Zero estimated noise does not create an infinite or arbitrarily large finite ratio.
Implementation¶
One mask-aware native procedure with per-row sparse availability and native global median/noise ratio. Independent NumPy reductions validate the normalization and dB; no MAD or percentile is recomputed by a binding.
Sources and provenance¶
https://github.com/GBeurier/nirs4all-methods/blob/main/cpp/src/core/estimator/intrinsic_diagnostics.cpp; https://github.com/GBeurier/nirs4all-methods/blob/main/bindings/python/tests/test_intrinsic_diagnostics.py; https://github.com/GBeurier/nirs4all-methods/blob/main/docs/intrinsic_diagnostics_contract.v1.json
Catalog note¶
Versioned descriptive protocol with native aggregates and explicit per-row availability; see docs/intrinsic_diagnostics_contract.v1.json.
See also: methods index.