I am new to programming and in my latest Python 2.7 project I encountered the following:
"RuntimeWarning: overflow encountered in long_scalars"
Could someone please elaborate what this means and what I could do to fix that.
The code runs through but I'm not sure if it is a good idea to just ignore the warning.
It happens during an append process like:
Here's an example which issues the same warning:
import numpy as np np.seterr(all='warn') A = np.array() a=A[-1] a**a
RuntimeWarning: overflow encountered in long_scalars
In the example above it happens because
a is of dtype
int32, and the maximim value storable in an
int32 is 2**31-1. Since
10**10 > 2**32-1, the exponentiation results in a number that is bigger than that which can be stored in an
Note that you can not rely on
np.seterr(all='warn') to catch all overflow
errors in numpy. For example, on 32-bit NumPy
>>> np.multiply.reduce(np.arange(21)+1) -1195114496
while on 64-bit NumPy:
>>> np.multiply.reduce(np.arange(21)+1) -4249290049419214848
Both fail without any warning, although it is also due to an overflow error. The correct answer is that 21! equals
In : import math In : math.factorial(21) Out: 51090942171709440000L
Unlike true floating point errors (where the hardware FPU sets a flag whenever it does an atomic operation that overflows), we need to implement the integer overflow detection ourselves. We do it on the scalars, but not arrays because it would be too slow to implement for every atomic operation on arrays.
So the burden is on you to choose appropriate
dtypes so that no operation overflows.