Re: very fast spline interp function for heavy oversampling?

[Martin Schultz <martin.schultz(at)dkrz.de>]

"R.G. Stockwell" wrote:
> 
> Greetings,
> 
> I have a situation where I take a time series, and need to interpolate the
> function to many more samples.
> i.e. original time index  = [1,2,3,4....10]
> and I need samples at new time index =
> [1.000,1.001,1.002.......9.999,10.000].
> 
> The canned IDL routine spline() works great, but is slow. Unfortunately, I
> don't have time to rewrite the interpolation to something more efficient.
> 
> I don't want to use any linear scheme to interpolate, since I want a smooth
> function (i.e. smooth "derivatives") around the data points.  I would guess
> that
> it would be easy to efficiently calculate this interp with spline, perhaps
> some vectorization could be put into the function.  Or perhaps an
>  "upsample" function would work, but modifications would be needed as the
> time series is not evenly sampled.
> 
> Are there any user routines out there that can do this interpolation
> efficiently?
> 
> Cheers,
> bob

Well, if you don't have time to solve the problem, then you must have
enough
time to wait for the results ;-)  But, seriously, as far as I
understand, IDL
uses the Numeical Recipes SPLINE functions, and there are two related
functions which operate in two steps:
SPL_INIT and SPL_INTERP
Since SPL_INIT is executed only once with your coarse data set as input,
this might be
what you are looking for. See the online help for more info.

Martin

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[[ Dr. Martin Schultz   Max-Planck-Institut fuer Meteorologie    [[
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