[AstroPy] time-series tools in Python

Matteo matteo at matteobachetti.it
Sat Mar 5 14:12:57 EST 2016

Hi Hang,

If you just mean orbital (regular) gaps,  and you're looking for short-term
variability, MaLTPyNT should work.

Otherwise L-S, that you have used previously. Take also a look at CARMA,
it's very powerful for long - term variability.




On 15:53, Sat, Mar 5, 2016 Brigitta Sipocz <sic at elte.hu> wrote:

> Hi Hang,
> The "Python users in astronomy" facebook group [0] may be a better place
> to ask these kind of general questions as there may be more people reading
> and answering it there.
> The answer may depend on what kind of periodic signals you are looking
> for. I have more experience with the following two options below, but there
> are several more approaches, too e.g. using wavelets, or gaussian process,
> etc.
> One of the fastest/simplest method would be to run Lomb-Scargle [1], but
> it has some serious limitations if your signal is far from being
> sinusoidal, or you are going after quasi periodicity with changing signal
> shape, etc. There are quite a few python implementation, e.g in scipy or
> gatspy.
> Another method to search for a period is to use the autocorrelation
> function. Since your data is unevenly sampled you should use a *discrete
> correlation function *instead. Python implementations can be found in the
> time_series module of the astroML package.
>  Chapter 10 in the book of Statistics, Data Mining, and Machine Learning
> in Astronomy by Ivezić et al, is also an excellent guide on the subject.
> Hope it helped.
> Cheers,
>  Brigitta
> [0] - https://www.facebook.com/groups/astropython/
> [1] - https://jakevdp.github.io/blog/2015/06/13/lomb-scargle-in-python/
> On 5 March 2016 at 14:20, gonghang.naoc <ghang.naoc at gmail.com> wrote:
>> Hi all,
>> This may not be a good question posted here, but possibly there should be
>> pythoners who work in this field.
>> I wonder anybody knows an algorithm for the detection of periodic signals
>> in gapped data?
>> I searched ADS, but it seems most of the data people deal with are evenly
>> sampled.
>> Thank you.
>> Regards,
>> Hang
>> _______________________________________________
>> AstroPy mailing list
>> AstroPy at scipy.org
>> https://mail.scipy.org/mailman/listinfo/astropy
> _______________________________________________
> AstroPy mailing list
> AstroPy at scipy.org
> https://mail.scipy.org/mailman/listinfo/astropy
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/astropy/attachments/20160305/14662eb9/attachment.html>

More information about the AstroPy mailing list