On Monday, September 23, 2013, Lorenzo Di Gregorio wrote:
Hello,
numpy.divide(0.0,0.0) raises a warning which doesn't appear to get caught
by numpy.seterr(divide='ignore')
This example:
import numpy
numpy.divide(0.0,0.0)
numpy.divide(1.0,0.0)
numpy.seterr(divide='ignore')
numpy.divide(1.0,0.0)
numpy.divide(0.0,0.0)
produces this output:
Warning (from warnings module):
File C:\Users\ldigrego\Desktop\tst.py, line 2
numpy.divide(0.0,0.0)
RuntimeWarning: invalid value encountered in divide
Warning (from warnings module):
File C:\Users\ldigrego\Desktop\tst.py, line 3
numpy.divide(1.0,0.0)
RuntimeWarning: divide by zero encountered in divide
Warning (from warnings module):
File C:\Users\ldigrego\Desktop\tst.py, line 6
numpy.divide(0.0,0.0)
RuntimeWarning: invalid value encountered in divide
Strangely, on Ipython the numpy.seterr(divide='ignore') seems to catch the
warning:
import numpy
numpy.divide(0.0,0.0)
Out[1]: nan
C:\eclipse\plugins\org.python.pydev_2.8.1.2013072611\pysrc\pydevconsole.py:1:
RuntimeWarning: invalid value encountered in divide
from __future__ import nested_scopes #Jython 2.1 support
numpy.divide(1.0,0.0)
Out[1]: inf
C:\eclipse\plugins\org.python.pydev_2.8.1.2013072611\pysrc\pydevconsole.py:1:
RuntimeWarning: divide by zero encountered in divide
from __future__ import nested_scopes #Jython 2.1 support
numpy.seterr(divide='ignore')
Out[1]: {'divide': 'warn', 'invalid': 'warn', 'over': 'warn', 'under':
'ignore'}
numpy.divide(1.0,0.0)
Out[1]: inf
numpy.divide(0.0,0.0)
Out[1]: nan
I could not find information on Google: is this a known problem? Is there
a way to suppress this warning
This is not a division warning. It is warning you that a NaN (an invalid
value) was generated. It can be controlled using invalid='ignore'. The
reason that it looks like you suppressed it in the IPython example is that
you triggered the warning earlier. The behavior of warnings is that they
only get issued once for each code location. So the second time you
attempted it after setting divide='ignore' it wasn't going to show up
anyways.
I'm working on a 64b Win7 machine employing
numpy-MKL-1.7.0.win-amd64-py2.7.exe.
Best Regards,
Lorenzo
--
Robert Kern
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