Hi,
I want to interpolate (with quadratic splines) a stack of 2D-arrays/matrices
y1, y2, y3, ... in a third dimension (which I call x) e.g. for crossfading
images. I already have a working code which unfortunately still contains two
explicit loops over the rows and colums of the matrices. Inside these loops I
simply use 'interp1d' from scipy suitable for 1D-interpolations. Is anybody
here aware of a better, more efficient solution of my problem? Maybe somewhere
out there a compiled routine for my problem already exists in a python
library... :-)
My code:
-----============================================-----
from scipy.interpolate import interp1d
from numpy import array, empty_like, dstack
x = [0.0, 0.25, 0.5, 0.75, 1.0]
y1 = array([[1, 10, 100, 1000], [1, 10, 100, 1000]], float)
y2 = array([[2, 20, 200, 2000], [2, 20, 200, 2000]], float)
y3 = array([[3, 30, 300, 3000], [4, 40, 400, 4000]], float)
y4 = array([[4, 40, 400, 4000], [8, 80, 800, 8000]], float)
y5 = array([[5, 50, 500, 5000], [16, 160, 1600, 16000]], float)
y = dstack((y1, y2, y3, y4, y5))
y_interpol = empty_like(y[:, :, 0])
i_range, j_range = y.shape[:2]
for i in xrange(i_range):
for j in xrange(j_range):
# interpolated value for x = 0.2
y_interpol[i,j] = interp1d(x, y[i, j,:], kind='quadratic')(0.2)
print y_interpol
-----============================================-----
Cheers, Raphael
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