RFI Identification

New in version 0.5.

Module for computing spectral kurtosis both for instantaneous PSDs and spectrometer output. This module also provides functions to estimate the spectral kurtosis limits for a given confidence interval in sigma.

This module is based on:

• Nita & Gary (2010, PASP 155, 595)
• Nita & Gary (2010, MNRAS 406, L60)

lsl.statistics.kurtosis.mean(M, N=1)

Return the expected mean spectral kurtosis value for M points each composed of N measurements.

lsl.statistics.kurtosis.std(M, N=1)

Return the expected standard deviation of the spectral kurtosis for M points each composed of N measurements.

lsl.statistics.kurtosis.var(M, N=1)

Return the expected variance (second central moment) of the spectral kurtosis for M points each composed of N measurements.

lsl.statistics.kurtosis.skew(M, N=1)

Return the expected skewness (third central moment) of the spectral kurtosis for M points each composed of N measurements.

lsl.statistics.kurtosis.getLimits(sigma, M, N=1)

Return the limits on the spectral kurtosis value to exclude the specified confidence interval in sigma using a Pearson Type VI distribution (betaprime in scipy.stats world). The return value is a two-element tuple of lower limit, upper limit.

Note

This corresponds to Section 3.1 in Nita & Gary (2010, MNRAS 406, L60)

lsl.statistics.kurtosis.spectralFFT(x)

Compute the spectral kurtosis for a set of unaveraged FFT measurements. For a distribution consistent with Gaussian noise, this value should be ~1.

lsl.statistics.kurtosis.spectralPower(x, N=1)

Compute the spectral kurtosis for a set of power measurements averaged over N FFT windows. For a distribution consistent with Gaussian noise, this value should be ~1.