[SciPy-User] Spline Interpolation with non continuous data

[Eric Carlson]

Hello,
pchip sometimes does wonders ("wonders"=="oscillation-free") on data 
with piecewise continuous derivatives. Spline fits of degree 1 should 
also work. OTOH, sometimes pchip makes little difference from regular 
cubic splines, and in those cases the filtering may be your only option


import scipy.interpolate
from numpy import linspace
xdata = ...
ydata = ...
f_approx = scipy.interpolate.pchip(xdata,ydata)

####Now that you have continuous function, use it many ways,
###   for example:
xeval = linspace(x_low, x_high, 201) #set evaluation points
yeval = f_approx(xeval)


Cheers,
Eric


On 6/22/2013 9:14 AM, Franz Engel wrote:
> Hi,
>
> I try to interpolated a spline throw a dataset (the record of a robot
> motion path). Usually it works really good, always when the robot drives
> without stops. But if the robot stops he moves a little bit backwards.
> If this happens I can’t use my “normal” method with the
> interpolate.UnivariateSpline-function(http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.UnivariateSpline.html),
> because the robot motion is not longer continuously. Does somebody has
> an idea which function could solve my problem? Or is there a good filter
> to reduce the redundant robot path. (the backwards path is only a little
> bit displaced relative to path without stops)
>
> Regards,
>
>       Franz
>
>
>
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