在Python中将存储在数组中的矩阵相乘

Question

This may seem like a silly question, but I am a bloody newby in Python (and in programming). I am running a physics simulation that involves many (~10 000) 2x2 matrices that I store in an array. I call these matrices M and the array T in the code below. Then I simply want to compute the product of all of these matrices. This is what I came up with, but it looks ugly and it would be so much work for 10000+ 2x2 matrices. Is there a simpler way or an inbuilt function that I could use?

import numpy as np
#build matrix M (dont read it, just an example, doesnt matter here)    
def M(k1 , k2 , x):
    a = (k1 + k2) * np.exp(1j * (k2-k1) * x)
    b = (k1 - k2) * np.exp(-1j * (k1 + k2) * x)
    c = (k1 - k2) * np.exp(1j * (k2 + k1) * x)
    d = (k1 + k2) * np.exp(-1j * (k2 - k1) * x)
    M = np.array([[a , b] , [c , d]])
    M *= 1. / (2. * k1)
    return M


#array of test matrices T
T = np.array([M(1,2,3), M(3,3,3), M(54,3,9), M(33,11,42) ,M(12,9,5)])
#compute the matrix product of T[0] * T[1] *... * T[4]
#I originally had this line of code, which is wrong, as pointed out in the comments
#np.dot(T[0],np.dot(T[1], np.dot(T[2], np.dot(T[2],np.dot(T[3],T[4])))))
#it should be:
np.dot(T[0], np.dot(T[1], np.dot(T[2],np.dot(T[3],T[4]))))

Answer 1

Not very NumPythonic, but you could do:

reduce(lambda x,y: np.dot(x,y), T, np.eye(2))

Or more concisely, as suggested

reduce(np.dot, T, np.eye(2))