I'm trying to learn more about noise, power spectral density (PSD) and statistical variances. With regard this I'm trying to compute the power spectral density of white noise. However, when I do I get a very odd symmetry. My spectrum seems to be symmetric around the central frequency value, which is obviously incorrect. I'm new to using autocorrelations and power spectral densities, so I'd appreciate if someone could help nudge me in the direction of the error.

Code to calculate PSD:

import numpy as np 
from math import sin, pi, log10
from allan_noise import white,pink,brown, violet
import acor
import numpy as np

def psd1(time_dats):
    #auto_corr = acor.function(time_dats)
    auto_corr = np.correlate(time_dats,time_dats,mode="same")

    #To check autocorrelation
    for i in range(len(auto_corr)):
        print i*.0000001 ,auto_corr[i]

    return fft(auto_corr) 

t = .0001       #simulation time
N = 100000  #number of data points 
dt = t/N    #time interval between data points

sig = white(N)
df = 1.0/(t)
freq_axis = np.arange(0,N/t,df)

spec = psd1(sig)

f = open('data/psdtest_f','w')
g = open('data/psdtest_t','w')

for i in range(N):
    f.write('%g %g\n' %(freq_axis[i],log10(abs(spec[i]))))
    g.write('%g %g\n' %(i*dt, sig[i]))

Using this code I produce the following graphs:

Temporal Profile of the noise before calculations Autocorrelation function calculated from the temporal profile (I'm aware the x axis scale is wrong but that doesn't contribute to the code any where else Power Spectral Denisty, beautifully symmetric though not supposed to be

Any help anyone could provide as to what's causing this symmetry would be most helpful. I've tested the code with a simple sine wave signal, and I get expected results: a single peak (with no symmetry). I know I should expect a flat (non-symmetric) power spectral density from white noise.


Your sampling rate is 1 Ghz. The Niquist criterion https://en.wikipedia.org/wiki/Nyquist_rate gives rise to aliassing and your observed symmetry around $N_s/2=.5$ GHz

Hope it helps,

Kind greetings


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