https://groups.google.com/forum/?fromgroups#!topic... implies that it's doable, but a periodigram is intended to find a single frequency, is it not?

TTFN
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"as the amplitude of the Periodogram seems to go down proportionally with the amount of significant frequency components.
"

is that not what you'd expect?


Here's what i'd do

1 synthesise some white noise with equal time sampling

1a analyse it with fft

1b analyse it with your LSSA code

1c do 1a and 1b agree?

2 extract interpolated signals from signal 1 at your uneven sampling intervals

2a analyse it with your LSSA code


2b do 2a and 1b agree well enough?


3 analyse your actual data with your LSSA code

Cheers

Greg Locock


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hello

@IRstuff: as of my understanding and of the documentation I have available and is providing examples, a periodogram is able to identify more than 1 frequency component.
@GregLockck: what you indicate is the procedure I followed in order to compare and try to understand the LombScargle "animal".. and I find my self at the point of asking the community :)

What I understood is that
1) LombScargle is a "Power Spectrum" tool (spectrum in "units²"), whereas FFT is an "amplitude" (spectrum in "units") tool.

2) the higher the amount of spectral components (for a given constant time window and sampling), the LombScargle spectrum decreases, whereas I would expect that the amplitudes are also estimated. Therefore I understand that the spectrum restituted by the LombScargle is assessing the product spectral componenent amplitude*probability. But for this I did not find any confirmation or statement.
eg.
1 frequency component with amplitude 1-> 100% probability* amplitude 1
2 frequency components each with amplitude 1-> 50% probability* ampitude 1
3 frequency components each with amplitude 1-> 33% probability* ampitude 1
4 frequency components each with amplitude 1-> 25% probability* amplitude 1

3) What is also unclear to me is if LombScargle is a Power Spectral Density (units²/Hz) or it it "just" a Power Spectrum (units²). But in all documentation I have gone through, I don't find a clear statement ...

thanks in advance for feedbacks,
Edo

But, you claim you're looking at white noise...

TTFN
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yep. Correct, IRstuff.
and white noise is generated as a train of frequency components by creating the spectrum and then backtransforming it into the time domain and resampling it.
as stated also by GregLocock..

I don't read that from Greg's posting. You cannot get a "train" of frequency components, because there isn't any. Random noise is RANDOM; at any given instance, you get random parts of the PSD that randomly sum up to the random time domain signal. Pick a different window in time, and you get a different answer. You will NEVER see the PSD from any single periodogram or FFT, unless it's infinitely long. The PSD will only be as a consequence of collecting gobs of data and superimposing each individual measured spectrum and enveloping that aggregate.

TTFN
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