I am trying to write a code to conduct a double summation (see pic)

in which; M is the subjects, N is the Trials, Yijt is the measured wave form data (3d array)

so far I have; Given Y is the data arranged as Y[subjects, trials, time]

# ranges:
I = len(Y)
J = len(Y[0])Y_i_vals = 0for i in range(M):for j in range(N):Y_i_vals = Y_i_vals +Y[i][j]
Yt = (1.0/(M*N)) * Y_i_vals

this doesnt seem the most effective way to do this, nor am i certain it is giving the correct result.

If you're using numpy just do

np.mean(Y)

Also, it's good to add sample input and expected output data to your question.

If you want means for each t you can do np.mean(np.mean(a, axis=0), axis=0) , or as noted by @ophion you can shorten this to np.mean(a, axis=(0, 1)) in newer (1.71 and on) versions of NumPy.