In a numpy array (a list of tuples), the processing speed is slow by extending () many times. I want to make that part faster

Question

There is a numpy array that can be formed by combining an array of tuples in a for loop like "res" in this code. (Variable names and contents are simplified from the actual code.)

If you take a closer look at this, a for loop is executed for the length of arr_2, and the array extends () is executed.It turns out that the processing speed becomes extremely heavy when arr_2 becomes long.

Wouldn't it be possible to process at high speed by making array creation well?

# -*- coding: utf-8 -*-
import numpy as np

arr_1 = np.array([[0, 0, 1], [0, 0.5, -1], [-1, 0, -1], [0, -0.5, -1], [1, 0, -1]])
arr_2 = np.array([[0, 1, 2], [0, 1, 2]])

all_arr = []
for p in arr_2:
    all_arr = [
    (arr_1[0], p), (arr_1[1], p), (arr_1[2], p), 
    (arr_1[0], p), (arr_1[1], p), (arr_1[4], p),
    (arr_1[0], p), (arr_1[2], p), (arr_1[3], p), 
    (arr_1[0], p), (arr_1[3], p), (arr_1[4], p),
    (arr_1[1], p), (arr_1[2], p), (arr_1[4], p), 
    (arr_1[2], p), (arr_1[3], p), (arr_1[4], p)]
    all_arr.extend(all_arr)


vtype = [('type_a', np.float32, 3), ('type_b', np.float32, 3)]
res = np.array(all_arr, dtype=vtype)

print(res)

Answer 1

I couldn't figure out why you used this indexing for arr_1 so I just copied it

import numpy as np

arr_1 = np.array([[0, 0, 1], [0, 0.5, -1], [-1, 0, -1], [0, -0.5, -1], [1, 0, -1]])
arr_2 = np.array([[0, 1, 2], [0, 1, 2]])

weird_idx = np.array([0,1,2,0,1,4,0,2,3,0,3,4,1,2,4,2,3,4])
weird_arr1 = arr_1[weird_idx]

all_arr = [(wiered_arr1[i],arr_2[j]) for j in range(len(arr_2)) for i in range(len(wiered_arr1)) ]

vtype = [('type_a', np.float32, 3), ('type_b', np.float32, 3)]
res = np.array(all_arr, dtype=vtype)

you can also repeat the arrays

arr1_rep = np.tile(weird_arr1.T,2).T 
arr2_rep = np.repeat(arr_2,weird_arr1.shape[0],0)
res = np.empty(arr1_rep.shape[0],dtype=vtype)
res['type_a']=arr1_rep
res['type_b']=arr2_rep

Answer 2

Often with structured arrays it is faster to assign by field instead of the list of tuples approach:

In [388]: idx = [0,1,2,0,1,4,0,2,3,0,3,4,1,2,4,2,3,4] 
In [400]: res1 = np.zeros(36, dtype=vtype)                                                     
In [401]: res1['type_a'][:18] = arr_1[idx]                                                     
In [402]: res1['type_a'][18:] = arr_1[idx]                                                     
In [403]: res1['type_b'][:18] = arr_2[0]                                                       
In [404]: res1['type_b'][18:] = arr_2[1]                                                       
In [405]: np.allclose(res['type_a'], res1['type_a'])                                           
Out[405]: True
In [406]: np.allclose(res['type_b'], res1['type_b'])                                           
Out[406]: True