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)
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lsl.statistics.kurtosis.
get_limits
(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)
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lsl.statistics.kurtosis.
mean
(M, N=1)¶ Return the expected mean spectral kurtosis value for M points each composed of N measurements.
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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.
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lsl.statistics.kurtosis.
spectral_fft
(x, axis=None)¶ Compute the spectral kurtosis for a set of unaveraged FFT measurements. For a distribution consistent with Gaussian noise, this value should be ~1.
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lsl.statistics.kurtosis.
spectral_power
(x, N=1, axis=None)¶ 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.
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lsl.statistics.kurtosis.
std
(M, N=1)¶ Return the expected standard deviation of the spectral kurtosis for M points each composed of N measurements.
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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.