fitting a linear surface with numpy least squares

Question

So I want to solve the equation z= a + b*y +c*x,. getting a,b,c. ie: making a (plane) surface fit to a load of scatter points in 3D space.

Answer 1

I think you're on the right track. You could still try following the example of the scipy.linalg documentation, in particular the Solving least-squares...` section

A = np.column_stack((np.ones(x.size), x, y))
c, resid,rank,sigma = np.linalg.lstsq(A,zi)

(we added a column of 1 for the constant).