为什么我不能抑制 numpy 警告

Question

I really want to avoid these annoying numpy warnings since I have to deal with a lot of NaNs . I know this is usually done with seterr, but for some reason here it does not work:

import numpy as np
data = np.random.random(100000).reshape(10, 100, 100) * np.nan
np.seterr(all="ignore")
np.nanmedian(data, axis=[1, 2])

It gives me a runtime warning even though I set numpy to ignore all errors...any help?

Edit (this is the warning that is recieved):

/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-p‌​ackages/numpy/lib/nanfunctions.py:612: RuntimeWarning: All-NaN slice encountered warnings.warn("All-NaN slice encountered", RuntimeWarning)

Answer 1

Warnings can often be useful and in most cases I wouldn't advise this, but you can always make use of the Warnings module to ignore all warnings with filterwarnings :

warnings.filterwarnings('ignore')

Should you want to suppress uniquely your particular error, you could specify it with:

with warnings.catch_warnings():
    warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')

Answer 2

The warnings controlled by seterr() are those issued by the numpy ufunc machinery; eg when A / B creates a NaN in the C code that implements the division, say because there was an inf/inf somewhere in those arrays. Other numpy code may issue their own warnings for other reasons. In this case, you are using one of the NaN -ignoring reduction functions, like nanmin() or the like. You are passing it an array that contains all NaN s, or at least all NaN s along an axis that you requested the reduction along. Since the usual reason one uses nanmin() is to not get another NaN out, nanmin() will issue a warning that it has no choice but to give you a NaN . This goes directly to the standard library warnings machinery and not the numpy ufunc error control machinery since it isn't a ufunc and this production of a NaN isn't the same as what seterr(invalid=...) otherwise deals with.

Answer 3

You may want to avoid suppressing the warning, because numpy raises this for a good reason. If you want to clean up your output, maybe handle it by explicitly returning a pre-defined value when your array is all nan.

def clean_nanmedian(s):
    if np.all(np.isnan(s)):
        return np.nan
    return np.nanmedian(s)

Also, keep in mind that this RuntimeWarning is raised only the first time that this happens in your run-time.