ellipse_fit_sample.py

def ellipse_fit(x, y):
    def ellipse_err(p, x, y):
        return (x-p[0])**2/p[2]**2 + (y-p[1])**2/p[3]**2 - 1

    p = [0]*4
    p[0] = np.mean(x)
    p[1] = np.mean(y)
    p[2] = np.max(np.max(x)-p[0], p[0]-np.min(x))
    p[3] = np.max(np.max(y)-p[1], p[1]-np.min(y))

    p_opt, success = optimize.leastsq(ellipse_err, p, args=(x,y))

    return p_opt