Consider the following brief numpy session showcasing uint64 data type
import numpy as np
a = np.zeros(1,np.uint64)
a
# array([0], dtype=uint64)
a[0] -= 1
a
# array([18446744073709551615], dtype=uint64)
# this is 0xffff ffff ffff ffff, as expected
a[0] -= 1
a
# array([0], dtype=uint64)
# what the heck?
I'm utterly confused by this last output.
I would expect 0xFFFF'FFFF'FFFF'FFFE.
What exactly is going on here?
My setup:
>>> sys.platform
'linux'
>>> sys.version
'3.10.5 (main, Jul 20 2022, 08:58:47) [GCC 7.5.0]'
>>> np.version.version
'1.23.1'
By default, NumPy converts Python int objects to numpy.int_, a signed integer dtype corresponding to C long. (This decision was made back in the early days when Python int also corresponded to C long.)
There is no integer dtype big enough to hold all values of numpy.uint64 dtype and numpy.int_ dtype, so operations between numpy.uint64 scalars and Python int objects produce float64 results instead of integer results. (Operations between uint64 arrays and Python ints may behave differently, as the int is converted to a dtype based on its value in such operations, but a[0] is a scalar.)
Your first subtraction produces a float64 with value -1, and your second subtraction produces a float64 with value 2**64 (since float64 doesn't have enough precision to perform the subtraction exactly). Both of these values are out of range for uint64 dtype, so converting back to uint64 for the assignment to a[0] produces undefined behavior (inherited from C - NumPy just uses a C cast).
On your machine, this happened to produce wraparound behavior, so -1 wrapped around to 18446744073709551615 and 2**64 wrapped around to 0, but that's not a guarantee. You might see different behavior on other setups. People in the comments did see different behavior.