Comparing NumPy arange and custom range function for producing ranges with decimal increments

Question

Here\'s a custom function that allows stepping through decimal increments:

def my_range(start, stop, step):
    i = start
    while i < stop:
        yiel         


        

Answer 1

Aside from the different representation of lists and arrays NumPys arange works by multiplying instead of repeated adding. It's more like:

def my_range2(start, stop, step):
    i = 0
    while start+(i*step) < stop:
        yield start+(i*step)
        i += 1

Then the output is completely equal:

>>> np.arange(0, 1, 0.1).tolist() == list(my_range2(0, 1, 0.1))
True

With repeated addition you would "accumulate" floating point rounding errors. The multiplication is still affected by rounding but the error doesn't accumulate.

---

As pointed out in the comments it's not really what is happening. As far as I see it it's more like:

def my_range2(start, stop, step):
    length = math.ceil((stop-start)/step)
    # The next two lines are mostly so the function really behaves like NumPy does
    # Remove them to get better accuracy...
    next = start + step
    step = next - start
    for i in range(length):
        yield start+(i*step)

But not sure if that's exactly right either because there's a lot more going on in NumPy.