CODEWITHSUNDEEP
pythonpython-3.xlistperformancefor-loop
Saha_1994
Saha_1994

Reputation: 111

How to make the code run faster, possibly replacing the for loops in his code? (Python)

import numpy as np

list_shot = list(np.arange(0, 100, 50)) 
list_lens = list(np.arange(10))
list_total = []

for shot in list_shot:
    print("-", shot)
    list_out = []

    for lens in list_lens:
        val = shot * lens
        list_out.append(val)
    print(list_out)

    list_total.append(list_out)

Basically, the above code is just a simple representation of this code below that needs to be optimised for it to run faster.

CN_list_shot = []
counter = 0

for shot in bec[1:1000:500]: ### reads from time snapshots
    CN_list = []
    for length in length_list: ### all the lenghts for the same snapshot 
        i = neighbor_list("i", shot, {('Ge', 'Ge'): length}, self_interaction= False)
        coord = np.bincount(i)
        CN_list.append(np.sum(coord) / np.count_nonzero(coord))
            
        print("CN_list: ", CN_list) ### value added to the list for the snapshot    
    
    CN_list_shot.append(CN_List) ### the list added for each snapshots

Upvotes: 1

Views: 281

Answers (1)

Gabe Morris
Gabe Morris

Reputation: 862

Yes there is a simpler way to achieve this using linear algebra methods. When using numpy try to avoid transforming a numpy array back to a python list. This defeats the purpose.

Essentially, what you are doing here is a simple matrix multiplication. All you have to do is reshape the numpy arrays in order to achieve what you want.

import numpy as np

list_shot = np.arange(0, 100, 50).reshape((2, 1))
list_lens = np.arange(10).reshape((1, 10))
print(list_shot)
print(list_lens)
print(np.matmul(list_shot, list_lens))

Output:

[[ 0]
 [50]]
[[0 1 2 3 4 5 6 7 8 9]]
[[  0   0   0   0   0   0   0   0   0   0]
 [  0  50 100 150 200 250 300 350 400 450]]

Upvotes: 3

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