Vectorization Demonstration using Numpy

Tanvi_VectorizationUsingNumpy.py

import numpy as np
from math import sin as sn
import matplotlib.pyplot as plt
import time

# Number of test points
N_point  = 1000

# Define a custom function with some if-else loops
def myfunc(x,y):
    if (x>0.5*y and y<0.3):
        return (sn(x-y))
    elif (x<0.5*y):
        return 0
    elif (x>0.2*y):
        return (2*sn(x+2*y))
    else:
        return (sn(y+x))

# List of stored elements, generated from a Normal distribution
lst_x = np.random.randn(N_point)
lst_y = np.random.randn(N_point)
lst_result = []

# Optional plots of the data
plt.hist(lst_x,bins=20)
plt.show()
plt.hist(lst_y,bins=20)
plt.show()

# First, plain vanilla for-loop
t1=time.time()
for i in range(len(lst_x)):
    x = lst_x[i]
    y= lst_y[i]
    if (x>0.5*y and y<0.3):
        lst_result.append(sn(x-y))
    elif (x<0.5*y):
        lst_result.append(0)
    elif (x>0.2*y):
        lst_result.append(2*sn(x+2*y))
    else:
        lst_result.append(sn(y+x))
t2=time.time()

print("
Time taken by the plain vanilla for-loop
———————————————-
{} us".format(1000000*(t2-t1)))

# List comprehension
print("
Time taken by list comprehension and zip
"+'-'*40)
%timeit lst_result = [myfunc(x,y) for x,y in zip(lst_x,lst_y)]

# Map() function
print("
Time taken by map function
"+'-'*40)
%timeit list(map(myfunc,lst_x,lst_y))

# Numpy.vectorize method
print("
Time taken by numpy.vectorize method
"+'-'*40)
vectfunc = np.vectorize(myfunc,otypes=[np.float],cache=False)
%timeit list(vectfunc(lst_x,lst_y))

# Results
Time taken by the plain vanilla for-loop
———————————————-
2000.0934600830078 us

Time taken by list comprehension and zip
—————————————-
1000 loops, best of 3: 810 µs per loop

Time taken by map function
—————————————-
1000 loops, best of 3: 726 µs per loop

Timetaken by numpy.vectorize method
—————————————-
1000 loops, best of 3: 516 µs per loop