根据其他数组的阈值创建新的2d numpy数组

Question

I have 3 2d-arrays, which I want to use to initialize a new 2d array. The new 2d-array should be populated with either a 0 or 1 in position (x,y) depending on the values in the (x,y) positions of the other 3 arrays.

For example, I have these 3 2d-arrays:

A = [[2, 3, 6],    B = [[5, 9, 0],    C = [[2, 7, 6],
     [9, 8, 3],         [2, 4, 3],         [2, 1, 6],
     [1, 0, 5]]         [4, 5, 1]]         [4, 6, 8]]

And a logic function:

D = (A > 4 && B < 5 && C > 5)

This should create the 2d-array:

D = [[0, 0, 1], 
     [0, 0, 0],
     [0, 0, 1]]

Now I can do this with 2 for loops, but I was wondering if there is a faster numpy way?

EDIT :

Here is a sample of my real code:

val_max = 10000
a = np.asarray(array_a)
b = np.asarray(array_b)
d = ((a >= val_max) and (b >= val_max)).astype(int)

But I get this error:

Traceback (most recent call last):
  File "analyze.py", line 70, in <module>
    d = ((a >= val_max) and (b >= val_max)).astype(int)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

EDIT2 :

I should have used & operator instead of and (similar for '|' vs. or )

Answer 1

Given A, B, and C, you just have to convert them into numpy arrays and compute for D using:

import numpy as np

A = np.asarray(A)
B = np.asarray(B)
C = np.asarray(C)

D = ((A > 4) & (B < 5) & (C > 5)).astype(int)

Answer 2

Try:

import numpy as np
A = np.asarray(A)
B = np.asarray(B)
C = np.asarray(C)
D = ((A > 4) & (B < 5) & (C > 5)).astype(int)