Inverting a Matrix of variables

Question

I have already solved the Lorenz transformation in terms of x and t, by Cramers rule on paper. I was wondering if there is a way to compute matrix operations in terms of variables, such as the inverse of some matrix. If I could take the inverse of M above and dot it with k (the solution matrix). I could solve for x and t. I have tried computing inverses of matrices of variables with no luck on python. Any help would be appreciated!

Summary: I need help computing inverses of matrices containing variables. Here is one of my attempts.

 import numpy as np 
 from IPython.display import display 
 import sympy as sp
 sp.init_printing()  # LaTeX like pretty printing for IPython 

 γ, xp, tp, x, t, v, c  = sp.symbols('γ, xp, tp, x, t, v, c')

 k = sp.Matrix( [ xp, tp ] ) 

 M = sp.Matrix([ [ γ        , -γ*v],
                 [-γ*v/c**2 , γ   ] ])

 Minv = np.linalg.inv(M)

Answer 1

NumPy only operates on numeric types, not symbols. Try the sympy method directly. Either M.inv() if you don't have a specific method in mind, or M.inverse_{METHOD} if you do have a specific method in mind.