[SciPy-User] fast spline interpolation of multiple equal length waveforms

[Hjalmar Turesson]

Thanks for the reply.

Both x and y values are different, but they have the same length.
I'll try your simple piecewise polynomial interpolation over the weekend,
and report back when I know how well it works.

Thanks,
Hjalmar


On Fri, Sep 23, 2011 at 9:54 AM, Jonathan Stickel <jjstickel at vcn.com> wrote:

> On 9/22/11 20:36 , scipy-user-request at scipy.org wrote:
>
>> Date: Thu, 22 Sep 2011 21:59:59 -0400
>> From: Hjalmar Turesson<hturesson at gmail.com>
>> Subject: [SciPy-User] fast spline interpolation of multiple equal
>>        length  waveforms
>> To:scipy-user at scipy.org
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>>
>> Hi,
>> I got a data set with hundreds of thousands for 40 point long waveforms. I
>> want to use cubic splines to interpolate these at intermediate time
>> points.
>> However, the points are different all waveforms, only the number of points
>> is the same. In other words, I want to interpolate a large number of
>> equally
>> short waveforms, each to its own grid of x-values/time points, and I want
>> to
>> do this as FAST as possible.
>>
>> Are there any functions that can take a whole array for waveforms and a
>> size
>> matched array of new x-values, and interpolate each waveform at a matched
>> row (or column) of x-values?
>>
>> What I've found, this far, appear to require a loop to one by one go
>> through
>> the waveforms and corresponding grid of x-values. I fear that a long loop
>> will be significantly slower than a direct evaluation of the entire array.
>>
>> Thanks,
>> Hjalmar
>>
>
> For each data set (x,y), are the x-values the same and the y-values
> different?  If so, you may find this code useful:
>
> http://scipy-central.org/item/**21/1/simple-piecewise-**
> polynomial-interpolation<http://scipy-central.org/item/21/1/simple-piecewise-polynomial-interpolation>
>
> It is not splines, but nonetheless provides good quality interpolation and
> is very fast.  For given x and x_interp, it can create an interpolation
> matrix P.  Then y_interp = P*y.  If you have all your y-data in Y, then
> Y_interp = P*Y.
>
> HTH,
> Jonathan
>
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