[SciPy-Dev] Including Lomb-Scargle code in scipy.signal (sub)module

[Bruce Southey]

On 02/09/2011 08:04 AM, Gael Varoquaux wrote:
> LSSA is incomprehensible to me. Acronymes are bad for communication,
> IMHO. I suggest 'spectral'.
>
> Gael
>
> PS: and Aubergine for the bikeshed, of course.
>
> On Wed, Feb 09, 2011 at 03:01:16PM +0100, Pim Schellart wrote:
>> Dear SciPy developers,
>> I have recently submitted code to calculate the Lomb-Scargle periodogram (see ticket http://projects.scipy.org/scipy/ticket/1352 for more information).
>> After some iterations of review, recoding, adding documentation and adding unit tests the code (according to Ralf Gommers) is ready to go in.
>> Ralf has nicely integrated the code into SciPy trunk in his git branch (https://github.com/rgommers/scipy/tree/lomb-scargle) and you may pull from there.
>> Now the time has come to discuss where to put it and Ralf has suggested to have this discussion here:
>> "I've played with your code a bit, changed a few things, and added it to scipy.signal in a github branch:https://github.com/rgommers/scipy/tree/lomb-scargle. I think it looks good, whether it really should go into signal should be discussed on the mailing list (but seemed like the logical place for it).
>> ...
>> The best name for the module I could think of was spectral_analysis, maybe there's a better one? It allows to add similar methods later.
>> "
>> I would like to suggest the module name "lssa" for "Least-squares spectral analysis". So the full path would be scipy.signal.lssa. This has several advantages.
>> 1. It is a short name without underscores (personal preference)
>> 2. It is a category name that can comprise several similar algorithms as desired.
>> 3. It is immediately obvious from this name that this is different than FFT and therefore people will not be confused looking for FFT functions in this sub-module.
>> 4. The name corresponds to the Wikipedia entry for this category of algorithms :) (but on a serious note this may help for people that want to find more information, or for people browsing wikipedia that are looking for an implementation).
>> Please let me know what you think.
>> Kind regards,
>> Pim Schellart
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I do agree with Gael about acronyms and initialisms. If the people know 
what lssa is in this context, then they should certainly know words like 
'Lomb-Scargle', 'periodogram' and signal density! Rather the audience 
you need are the ones that need help to go from signal density to 
periodogram to Lomb-Scargle to '[l]east-squares spectral analysis' to 
lssa. So using lssa is not much of a help to anyone except people who 
can not enter more than a few letters.

It is not my area but Wikipedia's entry 
(http://en.wikipedia.org/wiki/Periodogram) implies that there is more to 
this particular area than this very specific implementation. So you need 
to provide a general framework of how every relates to signal processing 
with an object-orientated view. This allows not only people to use your 
code but also to contribute in a unified manner. Also I probably 
incorrectly presume that 'spectral' is rather redundant when talking 
about signals in this context.

Given that '[t]he periodogram is an estimate of the spectral density of 
a signal' (from the Wikipedia periodogram entry) so 
'scipy.signal.density' would seem appropriate location.

Is there another way of doing 'spectral analysis' that does not involve 
least-squares? If so then you need to allow for these approaches.
Also, Wikipedia entry 
(http://en.wikipedia.org/wiki/Least-squares_spectral_analysis) gives 
multiple methods so technically the design should allow for these if 
someone wanted to add them in the future. (Of course this entry talks 
about 'estimating a frequency spectrum' not a density as per the 
periodogram entry - I do not consider frequency to be always equivalent 
to a density but could be true here.)

Anyhow, I am sure you know the differences, so I would suggest that 
there is either a single general periodogram/density/fit/estimation 
class/function (that permits different approaches) or set of functions 
that are similar numpy's polynomials.

Bruce








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