Question

I checked the available interpolation method in scipy, but could not get the proper solution for my case.
assume i have 100 points whose coordinates are random,
e.g., their x and y positions are:

x=np.random.rand(100)*100
y=np.random.rand(100)*100
z = f(x,y) #the point value calculated by certain function    

now i want to get the point value z of a new evenly sampled coordinates (xnew and y new)

xnew = range(100)
ynew = range(100)

how should i do this using bilinear sampling?
i know it is possible to do it point by point, e.g., find the 4 nearest random points, and do the interpolation, but there got to be some easier existing functions to do this

thanks alot!

Answer 1

Use scipy.interpolate.griddata. It does the exact thing you need

# griddata expects an ndarray for the interpolant coordinates
interpolants = numpy.array([xnew, ynew])

# defaults to linear interpolation
znew = scipy.interpolate.griddata((x, y), z, interpolants) 

http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html#scipy.interpolate.griddata