import numpy as np
import random as rd

# The idea is to use a list of [v1, v2, v3] faces to query the vertices in groups of 3, for easy use in geometrical ops
# The result of the fancy slicing must be an array of len-3 arrays of vertices (also len-3 arrays)
# In other words, a big array containing 3-by-3 arrays of coordinates for each face
vertices = np.zeros((1000,3))  # Apparently, the size of the array being queried does not matter
face = lambda : [rd.randint(0, 999), rd.randint(0, 999), rd.randint(0, 999)]

i = 2  # If trying to slice with a 1-by-3 list, this returns a single 3-by-3 array, even given the higher dimension of the list (expected result would be a 1-by-3-by-3 array)
       # Yet this is an "understandable" behaviour, even if wrong and misleading

while True:
    faces_list = [face() for f in range(i)]
    assert vertices[np.array(faces_list)].shape == (i, 3, 3) # This works as expected and will not throw any exception

    try:
        my_vertices = vertices[faces_list]  # This behaves as some sort of multi-dimensional slicing instead, up to a certain limit of dimensions, then it starts behaving as expected
        assert my_vertices.shape == (i, 3, 3)  # Does not fail if the line above worked as intended
        break
    except:  # The block above throws "IndexError: too many indices for array"
        print("numpy will not handle a list with {} lists for fancy slicing :^/".format(i))
        i += 1

print("numpy can handle a list with {} or more lists :)".format(i))
exit(0)