What is meant by indexing a numpy array with a conditional statement based on itself?

I'm working on a large library for network analysis and have come across a perplexing line whose calling convention I'm not familiar with.

    monitors = [1,2,3,4]
    nmonitors = 7 # This value is passed arbitrarily to the function

    while len(monitors) < nmonitors:
        remaining = np.arange(len(routers)) # len(routers) here is == 5
        for i in monitors:
            remaining = remaining[remaining != i]
        monitors.append(np.random.choice(remaining))

The line in questions in inside the loop which indexes the remaining array by a conditional based on i and itself. After some debugging it seems to be doing more than just evaluating a bool and indexing the array using that boolean value?

Would anyone be familiar with this syntax/convention and able to point me to the relevant part of numpy documentation or explain? I've been searching for hours with no results still, thank you.

There no special syntax, just a combination of generating a boolean array with a conditional test, and indexing an array with a boolean.

A sample array:

In [125]: arr = np.arange(4)
In [126]: arr
Out[126]: array([0, 1, 2, 3])

Indexing with a boolean:

In [127]: arr[[True,False,True,False]]
Out[127]: array([0, 2])

Creating a boolean with a test:

In [128]: (arr%2)==0
Out[128]: array([ True, False,  True, False])
In [129]: arr[(arr%2)==0]
Out[129]: array([0, 2])

Or with a test like your example:

In [131]: arr!=2
Out[131]: array([ True,  True, False,  True])
In [132]: arr[arr!=2]
Out[132]: array([0, 1, 3])

So that inner loop is removing all elements equal to monitors from remaining, leaving only [0]? The larger loop is buggy, but that has nothing to do with the "syntax" of the boolean indexing.