Vectorization a code to make it faster than this

Question

I have a little bit code which I’ll have to vectorizate it to make it faster. I’m not very attached into python and thinking that the for loop is not so efficient.

Is there any way to reduce the time?

import numpy as np
import time

start = time.time()

N = 10000000 #9 seconds
#N = 100000000 #93 seconds

alpha = np.linspace(0.00000000000001, np.pi/2, N)

tmp = 2.47*np.sin(alpha)
for i in range(N):
    if (abs(tmp[i])>1.0):
        tmp[i]=1.0*np.sign(tmp[i])
beta = np.arcsin(tmp)

end = time.time()
print("Executed time: ",round(end-start,1),"Seconds")

I read about some numpy functions but I don’t have a solution for this.

Asked By: nico.wagner

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Source

Answer 1

Instead of using loop with condition, you can access the values by computing a mask. Here is example:

N = 10000000

alpha = np.linspace(0.00000000000001, np.pi/2, N)

tmp = 2.47*np.sin(alpha)
indices = np.abs(tmp) > 1.0
tmp[indices] = np.sign(tmp[indices])
beta = np.arcsin(tmp)

Results on my setup:

before: 5.66 s ± 30.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each),
after: 182 ms ± 877 µs per loop (mean ± std. dev. of 7 runs, 10 loops each).

Answered By: maciek97x

Answer 2

Clip the array:

tmp = np.clip(2.47 * np.sin(alpha), -1.0, 1.0)

Answered By: Mad Physicist

Vectorization a code to make it faster than this

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