keflavich - 1 month ago 21

Python Question

I'm encountering

`FloatingPointError: invalid value encountered in subtract`

My question: What causes the

`invalid value encountered in subtract`

DETAILS:

This MWE does

`FloatingPointError`

`>>> import numpy as np`

>>> np.__version__

'1.6.1'

>>> x = np.arange(5,dtype='float64')

>>> y = np.ones(5,dtype='float64')

>>> x[2]=np.nan

>>> x-y

# array([ -1., 0., nan, 2., 3.])

However, deep within a piece of code, I subtract two

`np.float64`

`ndarray`

`nan`

Much more disturbingly, I have a large grid of Jenkins tests running the exact same code with many versions of numpy, matplotlib, python, and scipy, and NONE of them raise this exception. I'm lost at this point - I don't know if there is a bug, or if there is, how to track it down.

In case you're morbidly curious, the code in question is pyspeckit and the test is failing on line 20 of

`test_hr2421.py`

EDIT: Follow-up - I think this little snippet:

`np.seterr(invalid='raise')`

Answer

Numpy has configurable behaviour as to how errors are treated. By default some errors are ignored, others cause a warning. For each category you can change this behaviour. Someone must have set it to raising errors, without changing it back.

You can suppress this exception by calling `numpy.seterr(invalid='warn')`

, or, alternatively, `invalid='ignore'`

. For a full list of possible errors, read through the documentation of `numpy.seterr`

.

You can also use a context-manager:

```
In [12]: x = np.arange(-5, 5,dtype='float64')
In [13]: with np.errstate(divide="raise"):
print(1/x)
....:
---------------------------------------------------------------------------
FloatingPointError Traceback (most recent call last)
<ipython-input-13-881589fdcb7a> in <module>()
1 with np.errstate(divide="raise"):
----> 2 print(1/x)
3
FloatingPointError: divide by zero encountered in true_divide
In [14]: with np.errstate(divide="warn"):
print(1/x)
....:
/home/users/gholl/venv/stable-3.5/bin/ipython3:2: RuntimeWarning: divide by zero encountered in true_divide
[-0.2 -0.25 -0.33333333 -0.5 -1. inf
1. 0.5 0.33333333 0.25 ]
In [15]: with np.errstate(divide="ignore"):
print(1/x)
....:
[-0.2 -0.25 -0.33333333 -0.5 -1. inf
1. 0.5 0.33333333 0.25 ]
```

I tend to wrap my entire code inside a `with np.errstate(all="raise")`

block, and then use a context-manager ignoring a particular condition if I am sure that the problem is not hiding a bug — it usually is, though.