for loop in python is 10x slower than matlab

Question

I run python 2.7 and matlab R2010a on the same machine, doing nothing, and it gives me 10x different in speed

I looked online, and heard it should be the same order

Answer 1

This is just my opinion, but I think the process is a bit more complex. Basically Matlab is an optimized layer of C, so with the appropriate initialization of matrices and minimization of function calls (avoid "." objects-like operators in Matlab) you obtain extremely different results. Consider the simple following example of wave generator with cosine function. Matlab time = 0.15 secs in practical debug session, Python time = 25 secs in practical debug session (Spyder), thus Python becomes 166x slower. Run directly by Python 3.7.4. machine the time is = 5 secs aprox, so still be a non negligible 33x.

MATLAB:

AW(1,:) = [800 , 0    ]; % [amp frec]
AW(2,:) = [300 , 4E-07]; 
AW(3,:) = [200 , 1E-06];
AW(4,:) = [ 50 , 4E-06];
AW(5,:) = [ 30 , 9E-06];
AW(6,:) = [ 20 , 3E-05];
AW(7,:) = [ 10 , 4E-05];
AW(8,:) = [  9 , 5E-04];
AW(9,:) = [  7 , 7E-04];
AW(10,:)= [  5 , 8E-03];

phas    = 0

tini    = -2*365 *86400; % 2 years backwards in seconds
dt      = 200;        % step, 200 seconds
tfin    = 0;          % present
vec_t   = ( tini: dt: tfin)'; % vector_time

nt      = length(vec_t);
vec_t   = vec_t - phas;
wave    = zeros(nt,1);

for it = 1:nt
    suma = 0;
    t    = vec_t(it,1);
    for iW = 1:size(AW,1)
        suma = suma + AW(iW,1)*cos(AW(iW,2)*t);
    end
    wave(it,1) = suma;
end

PYTHON:

import numpy as np

AW      = np.zeros((10,2))
AW[0,:] = [800 , 0.0]
AW[1,:] = [300 , 4E-07]; # [amp frec]
AW[2,:] = [200 , 1E-06];
AW[3,:] = [ 50 , 4E-06];
AW[4,:] = [ 30 , 9E-06];
AW[5,:] = [ 20 , 3E-05];
AW[6,:] = [ 10 , 4E-05];
AW[7,:] = [  9 , 5E-04];
AW[8,:] = [  7 , 7E-04];
AW[9,:] = [  5 , 8E-03];

phas    = 0

tini    = -2*365 *86400 # 2 years backwards
dt      = 200
tfin    = 0           # present
nt      = round((tfin-tini)/dt) + 1 
vec_t   = np.linspace(tini,tfin1,nt) - phas

wave    = np.zeros((nt))

for it in range(nt):
    suma   = 0
    t      = vec_t[fil]
    for iW in range(np.size(AW,0)):
        suma = suma + AW[iW,0]*np.cos(AW[iW,1]*t)
    #endfor iW
    wave[it] = suma
#endfor it

To deal such aspects in Python I would suggest to compile into executable directly to binary the numerical parts that may compromise the project (or for example C or Fortran into executable and be called by Python afterwards). Of course, other suggestions are appreciated.