As you have shown, you can write this as a system of six first-order odes:
x' = x2 y' = y2 z' = z2 x2' = -mu / np.sqrt(x ** 2 + y ** 2 + z ** 2) * x y2' = -mu / np.sqrt(x ** 2 + y ** 2 + z ** 2) * y z2' = -mu / np.sqrt(x ** 2 + y ** 2 + z ** 2) * z
You can save this as a vector:
u = (x, y, z, x2, y2, z2)
and thereby create a function that returns its derivative:
def deriv(u, t): n = -mu / np.sqrt(u[0]**2 + u[1]**2 + u[2]**2) return [u[3],
Given the initial state u0 = (x0, y0, z0, x20, y20, z20) and the variable for time t , this can be passed to scipy.integrate.odeint as such:
u = odeint(deriv, u0, t)
where u will be a list as above. Or you can unzip u from the beginning and ignore the values ββfor x2 , y2 and z2 (you must first transfer the output with .T )
x, y, z, _, _, _ = odeint(deriv, u0, t).T