arrays中如何按特定顺序赋值

Question

I try to assign a value from the second matrix to each value from the first matrix according to the procedure. I assign a value to each value in the first row, then to each value in the last column. Then I assign a value in the second row, just like in the penultimate column, but the first and last values from the second matrix are omitted. That is, and until each value from the first matrix is assigned a value from the second matrix.

It is better to see this in the example below. example

Input

a = np.array([[11,12,13],
              [21,22,23],
              [31,32,33],
              [41,42,43],
              [51,52,53]])

b= np.array([100,200,300,400,500,600,700])

required output

11 100
12 200
13 300
23 400
33 500
43 600
53 700
21 200
22 300
32 400
42 500
52 600

31 300
41 400
51 500

It is possible?

Answer 1

I was able to reproduce your required output for the given input with the following code:

import numpy as np

def matrix_assing(a, b):
    output = []
    
    for i in range(len(a[0])):
        horizontal_values_to_assign = a[i][: len(a[i]) - i]
        vertical_values_to_assing = a.T[-(i+1)][i+1:]
        
        values_to_assign = np.append(horizontal_values_to_assign, vertical_values_to_assing)
        
        output += list(zip(values_to_assign, b[i:len(b) - i]))
        
    return output

a = np.array([[11,12,13],
              [21,22,23],
              [31,32,33],
              [41,42,43],
              [51,52,53]])

b = np.array([100,200,300,400,500,600,700])

matrix_assing(a, b)

Could you please be more specific what should happen when the length of b does not fit around the sides of the matrix a ( len(b).= len(a[0]) + len(aT[0]) - 1 ).