I have a dummy example of an iterator container below (the real one reads a file too large to fit in memory):

class DummyIterator:def __init__(self, max_value):self.max_value = max_valuedef __iter__(self):for i in range(self.max_value):yield idef regular_dummy_iterator(max_value):for i in range(max_value):yield i

This allows me to iterate over the value more than once so that I can implement something like this:

def normalise(data):total = sum(i for i in data)for val in data:yield val / total# this works when I call next()
normalise(DummyIterator(100))# this doesn't work when I call next()
normalise(regular_dummy_iterator(100))

How do I check in the normalise function that I am being passed an iterator container rather than a normal generator?

First of all: There is no such thing as a iterator container. You have an iterable.

An iterable produces an iterator. Any iterator is also an iterable, but produces itself as the iterator:

>>> list_iter = iter([])
>>> iter(list_iter) is list_iter
True

You don't have an iterator if the iter(ob) is ob test is false.