I guess the speed up, if any, will be machine dependent, but you can
give a try at:
import numpy as np
import time
N = 3000
m_0 = np.arange(N)
t = time.time()
a = np.ones(N)
m_1 = np.outer(m_0, a).ravel()
m_2 = np.outer(a, m_0).ravel()
t = time.time() - t
On 2021-01-09 20:07, klark--k...@yandex.ru wrote:
> Hello. There is a random 1D array m_0 with size 3000, for example:
>
> m_0 = np.array([0, 1, 2])
>
> I need to generate two 1D arrays:
>
> m_1 = np.array([0, 1, 2, 0, 1, 2, 0, 1, 2])
> m_2 = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
>
> Is there faster way to do it than this one:
>
> import numpy as np
> import time
> N = 3
> m_0 = np.arange(N)
>
> t = time.time()
> m_1 = np.tile(m_0, N)
> m_2 = np.repeat(m_0, N)
> t = time.time() - t
>
> I tried other ways but they are slower or have the same time. Other NumPy
> operations in my code 10-100 times faster. Why the repeating an array is so
> slow? I need 10 times speed up. Thank you for your attantion to my problem.
>
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