"There's very little to be gained by reading pure Fortran code, especially if you don't know the mathematical tricks/optimisations used beforehand. The Python c

"There's very little to be gained by reading pure Fortran code, especially if you don't know the mathematical tricks/optimisations used beforehand. The Python code, on the other hand, stays (relatively) readable." Depends. His first Python code was: x = np.asarray(x, dtype=float) N = x.shape[0] n = np.arange(N) k = n.reshape((N, 1)) M = np.exp(-2j * np.pi * k * n / N) return np.dot(M, x) I can write more or less the same in Fortran: n = reshape([(i, i=0,len-1)], shape=[1,len]) k = transpose(n) M = exp(-2 * j * pi * matmul(k,n) / len) res = matmul(M, x) But of course Fortran is a typed language, so I need to explicitly write the types for everything, so my whole function gets longer pure function dft_slow(x, len) result(res) complex, intent(in) :: x(len) integer, intent(in) :: len complex :: n(1,len), k(len,1), M(len, len), res(len) integer :: i n = reshape([(i, i=0,len-1)], shape=[1,len]) k = transpose(n) M = exp(-2 * j * pi * matmul(k,n) / len) res = matmul(M, x) end function dft_slow and so yes, maybe less readable. P.S. One must also define somewhere global constants complex, parameter :: j = (0,1) ! imaginary unit real, parameter :: pi = acos(-1.) for the above function to work. Also everything is by default in single precision here.