Tips for combining two numpy ndarrays together without the concatenate function

Question

Tips for combining two numpy ndarrays together without the concatenate function

I'm currently coding with Numba to JIT compile my Python code. My function takes two arrays of the same length as input, randomly selects a slicing point, and then returns a tuple containing two "Frankenstein" arrays created from parts of the two input strings. Unfortunately, Numba does not yet support the numpy.concatenate function (and it's unclear if it ever will). Since I am not willing to part ways with Numpy, I am in search of a performant solution for concatenating two Numpy arrays without using the concatenate function.

def randomSlice(str1, str2):
    lenstr = len(str1)
    rnd = np.random.randint(1, lenstr)
    return (np.concatenate((str1[:rnd], str2[rnd:])), np.concatenate((str2[:rnd], str1[rnd:])))

python numpy numba

Answer 1

Answer №1

Perhaps this solution could be useful for your situation:

import numpy as np
import numba as nb

@nb.jit(nopython=True)
def randomSlice_nb(str1, str2):
    lenstr = len(str1)
    rnd = np.random.randint(1, lenstr)

    out1 = np.empty_like(str1)
    out2 = np.empty_like(str1)

    out1[:rnd] = str1[:rnd]
    out1[rnd:] = str2[rnd:]

    out2[:rnd] = str2[:rnd]
    out2[rnd:] = str1[rnd:]
    return (out1, out2)

In my testing environment, utilizing Numba 0.27 and measuring performance with the timeit module to ensure accurate timings without including compilation time, the Numba implementation shows a minor yet noticeable improvement in speed when processing arrays of integers or floating-point numbers with various sizes. However, it is noteworthy that if the arrays have a data type like |S1, the Numba version performs notably slower. This can be attributed to Numba's focus on optimizing numerical computations rather than non-numeric scenarios. Since I lack precise details regarding the structure of your input arrays str1 and str2, I cannot guarantee the code will cater perfectly to your specific requirements.

Answer 2

Perhaps this solution could be useful for your situation:

import numpy as np
import numba as nb

@nb.jit(nopython=True)
def randomSlice_nb(str1, str2):
    lenstr = len(str1)
    rnd = np.random.randint(1, lenstr)

    out1 = np.empty_like(str1)
    out2 = np.empty_like(str1)

    out1[:rnd] = str1[:rnd]
    out1[rnd:] = str2[rnd:]

    out2[:rnd] = str2[:rnd]
    out2[rnd:] = str1[rnd:]
    return (out1, out2)

In my testing environment, utilizing Numba 0.27 and measuring performance with the timeit module to ensure accurate timings without including compilation time, the Numba implementation shows a minor yet noticeable improvement in speed when processing arrays of integers or floating-point numbers with various sizes. However, it is noteworthy that if the arrays have a data type like |S1, the Numba version performs notably slower. This can be attributed to Numba's focus on optimizing numerical computations rather than non-numeric scenarios. Since I lack precise details regarding the structure of your input arrays str1 and str2, I cannot guarantee the code will cater perfectly to your specific requirements.

Tips for combining two numpy ndarrays together without the concatenate function

Answer №1

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