Monthly Archives: June 2017

MATLAB vs Python Computational Speed

Fundamentals, ,

Windows Edition
Windows 8.1 Pro

System
Processor Intel(R) Core(TM) i7-5500U CPU @ 2.4GHz
Installed Memory 8.00 GB
System Type 64 Bit Operating System, x64 Based Processor

Integrated Development Environment (IDE)
Enthought Canopy
Version 2.1.3.3542 (32 bit)

Operation Time in sec (MATLAB) Time in sec (PYTHON)
10 million uniform random variable generation 0.10 0.15
10 million normal random variable generation 0.13 0.40
for loop counting up to 100 million 0.40 11.60
Comparing two vectors of length 10 million each 0.39 0.55
Plotting a histogram of 10 million values 0.89 0.76
Plotting a scatter plot of 1 million values 0.30 0.23
Bit error rate calculation of BPSK for 10 values of SNR 2.49 4.51

Although Python is a bit slower than MATLAB for most of the cases but the real difference is in implementation of “for loop” where the speed of MATLAB is 29x that of Python. Another surprising result was that the plot functions for Python were somewhat faster than MATLAB.

BPSK Bit Error Rate Calculation Using Python

BER Performance, Fundamentals, ,

Have you ever thought about how life would be without MATLAB. As it turns out there are free and open source options such as Python. We have so far restricted ourselves to MATLAB in this blog but now we venture out to find out what are the other options. Given below is a most basic Python code that calculates the Bit Error Rate of Binary Phase Shift Keying (BPSK). Compare this to our MATLAB implementation earlier [BPSK BER].

There are various IDEs available for writing your code but I have used Enthought Canopy Editor (32 bit) which is free to download and is also quite easy to use [download here]. So as it turns out that there is life beyond MATLAB. In fact there are several advantages of using Python over MATLAB which we will discuss later in another post. Lastly please note the indentation in the code below as there is no “end” statement in a “for loop” in Python.

from numpy import sqrt
from numpy.random import rand, randn
import matplotlib.pyplot as plt
  
N = 5000000
EbNodB_range = range(0,11)
itr = len(EbNodB_range)
ber = [None]*itr

for n in range (0, itr): 
 
    EbNodB = EbNodB_range[n]   
    EbNo=10.0**(EbNodB/10.0)
    x = 2 * (rand(N) >= 0.5) - 1
    noise_std = 1/sqrt(2*EbNo)
    y = x + noise_std * randn(N)
    y_d = 2 * (y >= 0) - 1
    errors = (x != y_d).sum()
    ber[n] = 1.0 * errors / N
    
    print "EbNodB:", EbNodB
    print "Error bits:", errors
    print "Error probability:", ber[n] 
        
plt.plot(EbNodB_range, ber, 'bo', EbNodB_range, ber, 'k')
plt.axis([0, 10, 1e-6, 0.1])
plt.xscale('linear')
plt.yscale('log')
plt.xlabel('EbNo(dB)')
plt.ylabel('BER')
plt.grid(True)
plt.title('BPSK Modulation')
plt.show()
BPSK Bit Error Rate
BPSK Bit Error Rate

MATLAB vs PYTHON A COMPARISON