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copied!<p>I've had a similar problem using the scipy.interpolate.interp2d function. My understanding is that the issue arises because the interp1d/interp2d and related functions use an older wrapping of FITPACK for the underlying calculations. I was able to get a problem similar to yours to work using the spline functions, which rely on a newer wrapping of FITPACK. The spline functions can be identified because they seem to all have capital letters in their names here <a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html" rel="nofollow">http://docs.scipy.org/doc/scipy/reference/interpolate.html</a>. Within the scipy installation, these newer functions appear to be located in scipy/interpolate/fitpack2.py, while the functions using the older wrappings are in fitpack.py. </p> <p>For your purposes, RectBivariateSpline is what I believe you want. Here is some sample code for implementing RectBivariateSpline:</p> <pre><code>import numpy as np from scipy import interpolate # Generate unevenly spaced x/y data for axes npoints = 25 maxaxis = 100 x = (np.random.rand(npoints)*maxaxis) - maxaxis/2. y = (np.random.rand(npoints)*maxaxis) - maxaxis/2. xsort = np.sort(x) ysort = np.sort(y) # Generate the z-data, which first requires converting # x/y data into grids xg, yg = np.meshgrid(xsort,ysort) z = xg**2 - yg**2 # Generate the interpolated, evenly spaced data # Note that the min/max of x/y isn't necessarily 0 and 100 since # randomly chosen points were used. If we want to avoid extrapolation, # the explicit min/max must be found interppoints = 100 xinterp = np.linspace(xsort[0],xsort[-1],interppoints) yinterp = np.linspace(ysort[0],ysort[-1],interppoints) # Generate the kernel that will be used for interpolation # Note that the default version uses three coefficients for # interpolation (i.e. parabolic, a*x**2 + b*x +c). Higher order # interpolation can be used by setting kx and ky to larger # integers, i.e. interpolate.RectBivariateSpline(xsort,ysort,z,kx=5,ky=5) kernel = interpolate.RectBivariateSpline(xsort,ysort,z) # Now calculate the linear, interpolated data zinterp = kernel(xinterp, yinterp) </code></pre>

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