Estimation of noise in Doppler spectra using the Hildebrand Sekhon method.
import numpy as np
[docs]def estimate_noise_hs74(spectrum, navg=1, nnoise_min=1):
Estimate noise parameters of a Doppler spectrum.
Use the method of estimating the noise level in Doppler spectra outlined
by Hildebrand and Sehkon, 1974.
spectrum : array like
Doppler spectrum in linear units.
navg : int, optional
The number of spectral bins over which a moving average has been
taken. Corresponds to the **p** variable from equation 9 of the
article. The default value of 1 is appropriate when no moving
average has been applied to the spectrum.
nnoise_min : int, optional
Minimum number of noise samples to consider the estimation valid.
mean : float-like
Mean of points in the spectrum identified as noise.
threshold : float-like
Threshold separating noise from signal. The point in the spectrum with
this value or below should be considered as noise, above this value
signal. It is possible that all points in the spectrum are identified
as noise. If a peak is required for moment calculation then the point
with this value should be considered as signal.
var : float-like
Variance of the points in the spectrum identified as noise.
nnoise : int
Number of noise points in the spectrum.
P. H. Hildebrand and R. S. Sekhon, Objective Determination of the Noise
Level in Doppler Spectra. Journal of Applied Meteorology, 1974, 13,
sorted_spectrum = np.sort(spectrum)
nnoise = len(spectrum) # default to all points in the spectrum as noise
rtest = 1+1/navg
sum1 = 0.
sum2 = 0.
for i, pwr in enumerate(sorted_spectrum):
npts = i+1
sum1 += pwr
sum2 += pwr*pwr
if npts < nnoise_min:
if npts*sum2 < sum1*sum1*rtest:
nnoise = npts
# partial spectrum no longer has characteristics of white noise.
sum1 -= pwr
sum2 -= pwr*pwr
mean = sum1/nnoise
var = sum2/nnoise-mean*mean
threshold = sorted_spectrum[nnoise-1]
return mean, threshold, var, nnoise