Python Fit Polynomial to 3d Data

Question

Have a set of data points (x,y,z), and trying to fit a generic quadratic to them using scipy optimize.curve_fit.

I have tried a couple different methods, but can't seem to make it work. Any guidance on why I would be getting this error, or a recommended different method?

Error is "ValueError: operands could not be broadcast together with shapes (2,) (12,)"

 import scipy.optimize as optimize XY = [(11, 70), (11, 75), (11, 80), (11, 85), (12, 70), (12, 75), (12, 80), (12, 85), (13, 70), (13, 75), (13, 80), (13, 85)] Z = [203.84, 208, 218.4, 235.872, 228.30080000000004, 232.96000000000004, 244.60800000000006, 264.1766400000001, 254.8, 260, 273, 294.84000000000003] guess = (1,1,1,1,1,1) def fit(X, a, b, c, d, f, g): return a + (b*X[0])+(c*X[1])+(d*X[0]**2)+(f*X[1]**2)+(g*X[0]*X[1]) params, cov = optimize.curve_fit(fit, XY, Z, guess)

jodag · Accepted Answer · 2019-09-24 19:53:24Z

According to the docs you need XY to be size (k,M) where k is the dimension and M is the number of samples. In your case you've defined XY to be size (M,k).

Try the following

import numpy as np ... params, cov = optimize.curve_fit(fit, np.transpose(XY), Z, guess)

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