[AstroPy] LinearNDInterpolator

[Erik Tollerud]

I'm not clear on what you're asking about LinearNDInterpolator in your
second paragraph... what linear interpolator does is take in a list of
coordinates and the corresponding values at those points (they can be
gridded or not), and then can be used to give you the data value at
any point inside the point values (although it does *not* extrapolate
beyond the input points).  Is that what you want? If not, please
clarify, or if so, see the example below for a sample usage - it makes
a grid of points that represent a 3d gaussian and then uses the
interpolator to do (I think) what you want.




from numpy import mgrid,exp,array
from scipy.interpolate import LinearNDInterpolator

#x,y,and z are 11x11x11 arrays with the x,y, and z values for a 3d box
from -1 to 1
x,y,z = mgrid[-1:1:11j,-1:1:11j,-1:1:11j]

#s is a gaussian evaluated at the grid points - replace this with your
grid values
s = exp(-(x**2+y**2+z**2)/2)

#this reshapes the points into the kind of input LinearNDInterpolator
wants: Npts x Ndims
pts = array((x.ravel(),y.ravel(),z.ravel())).T

#and the values should be a 1d array that matches the first dimension of pts
sr = s.ravel()

i = LinearNDInterpolator(pts,sr)


Now to get values at whatever points you want, you call the
interpolator with an array of dimensions nyourpoints x ndims.  e.g.

>>> i([[0,0,0]])
array([ 1.])
>>> i([[0,0,0],[0,1,0],[1,1,0],[0,0,.5]])
array([ 1.        ,  0.60653066,  0.36787944,  0.87919328])

Although note that if you go off the grid, you get NaN back:
>>> i([[0,0,2]])
array([ nan])





On Fri, Apr 22, 2011 at 10:27 AM, Jonathan Slavin
<jslavin at cfa.harvard.edu> wrote:
> Hi all,
>
> I'm wondering if anyone has used the routine
> scipy.interpolate.LinearNDInterpolator
> >From what I can tell, it should do fast multidimensional linear
> interpolations, which is something I could use.  My problem is that it
> isn't well documented.
>
> What I have is a 3-D grid of data values and what I want is values at a
> specific group of non-gridded points.  scipy.interpolate.griddata seems
> to do the opposite to what I want -- given a set of (non-gridded) points
> at which you know the data value, interpolate onto a regular grid.  It
> seemed like LinearNDInterpolator was what I wanted, but it seems to want
> the same input as griddata -- points at which the data value is known.
> That is, it takes two inputs points and values where the number of
> points must match the no. of values.  What I need is something more like
> linearinterp(gridded_data, coords) -- that is, more like what
> scipy.ndimage.map_coordinates does, but much faster and just linear
> (rather than spline) interpolation.  I wrote a routine that does this,
> and reasonably quickly, but it's pure python and not as fast as I'd
> like.
>
> Jon
>
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-- 
Erik Tollerud