timkado - 3 months ago 30

Python Question

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:

`SomeList.append(VeryLongFormula)`

Thanks for the comments

Best

T

Answer

Here's an example which issues the same warning:

```
import numpy as np
np.seterr(all='warn')
A = np.array([10])
a=A[-1]
a**a
```

yields

```
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 `int32`

.

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 [47]: import math
In [48]: math.factorial(21)
Out[50]: 51090942171709440000L
```

According to numpy developer, Robert Kern,

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.