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)
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)