On Dec 11, 10:02 am, Jeremy Bailin <astroco...@gmail.com> wrote:
> On Dec 10, 10:21 am, Elkunn <Wasit.Weat...@gmail.com> wrote:
>
>
>
>
>
>> On Dec 10, 7:54 am, Jeremy Bailin <astroco...@gmail.com> wrote:
>
>>> On Dec 10, 12:12 am, Elkunn <Wasit.Weat...@gmail.com> wrote:
>
>>>> Hello,
>>>> I have a data array like this. The 1st value a[0] is somewhat
>>>> contaminated and need to be removed.
>
>>>> a=[0.0382000, 0.3919000, 0.3843000, 0.3880000, 0.3720000, 0.4221000,
>>>> 0.5966000, 0.8063000,0.7955000
>>>> 0.8022000,0.7941000,0.8149000,0.8170000,0.7212000,0.7299000, 0.7644000,0.7738000,0.7574000
>>>> 0.6756000, 0.6122000,0.5646000,0.5595000,0.5151000]
>
>>>> I want to remove 1st pixel and replace it by a predicted value from
>>>> curve fitting and smooth the overll data, return them back into
>>>> spatial domain. I do not have error values. Is there any simpler
>>>> method to do that?
>
>>>> Thanks a lot!
>
>>>> Elkuun
>
>>> And do you expect whatever the curve you fit to have compact support
>>> (probably a good idea for most applications, but I have no clue about
>>> in your case) or to depend on all of the other data?
>
>>> -Jeremy.- Hide quoted text -
>
>>> - Show quoted text -
>
>> Thanks for your reply!
>> This is NDVI data of one pixel over one year. I think Gussian curve
>> fit works for that. Some pixels has no clouds, then I just need to
>> smooth the curve, but one for like this, I want to remove the cloud
>> pixel, then predict its value from least-square fitting, then smooth
>> the whole curve.
>
>> Thank you!
>
> Hmmm. Well, you can certainly fit a Gaussian to that, if you have good
> reason to think that it should be a good parameterization... but it
> doesn't *look* that good to me (of course, I have no idea what the
> errors on those points are). It would look something like this:
>
> na = n_elements(a)
> gfit = gaussfit(lindgen(na-1)+1, a[1:*], gparms, nterms=3)
> ; replace a[0] with value from Gaussian. x=0 at this point, which
> makes it simpler.
> a[0] = gparms[0] * exp(-0.5*(gparms[1]/gparms[2])^2)
>
> If you want to add more terms, just increase nterms and add gparms[3]
> at the end. For example, to add a linear term (possibly a good idea,
> but I don't know what you'd theoretically expect):
>
> gfit = gaussfit(lindgen(na-1)+1, a[1:*], gparms, nterms=5)
> a[0] = gparms[0] * exp(-0.5*(gparms[1]/gparms[2])^2) + gparms[3]
>
> -Jeremy.- Hide quoted text -
>
> - Show quoted text -
Thank you Jeremy,
Indeed, my data for this point is not work well with Gaussian Fit.
But, this helps me to understand the "fit" procedure. I probabely use
poly_fit, or SDVFIT with user supplied functions.
Cheers!
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