Name

• noise_corrected_PW - returns a noise corrected power spectrum and the factors a and b which were used to subtract the function f(x)=exp( a*x*x+b ) from the original power spectrum

Usage

• freq,pw_ns,b_factor,norm = noise_corrected_PW(pw,lo_limit,hi_limit,abs_limit)

Input

ps

a list containing the values of a power spectrum

lo_limit

lower frequency threshold for minima search (value between 0.0 and 0.5)

hi_limit

upper frequency threshold for minima search (value between 0.0 and 0.5)

abs_limit

upper limit of the power spectrum, no usable information is contained above this threshold

Output

freq

list of corresponding frequency values

pw_ns

noise corrected power spectrum

b_factor

exponential factor

norm

normalization factor

Description

• This function returns a noise corrected power spectrum, a list of corresponding frequency values and the factors a and b which were used to subtract the function f(x)=exp( a*x*x+b ) from the original power spectrum pw

Method

• Fist the minima of the power spectrum (in the specified freq. range) are searched, then a regression (linreg) fits f(x) as described above. This function is subtracted from the original power spectrum to yield an estimate of the noise corrected spectrum. Minima are searched again. This leads to a more accurate fit of f(x) and thereby to an accurate estimation of the noise background. After subtraction a noise corrected power spectrum is returned.

Suggested frequencies

• noise_corrected_PW(pw,0.06,0.3,0.35) worked out well for the power spectra investigated so far.

Author / Maintainer

• Jan-Christian Schuette

Keywords

category 1

STATISTICS

category 2

SPECTRUM

category 3

NOISE

Files

statistics.py

Maturity

stable

works for most people, has been tested; test cases/examples available.