numpy-discussion

[Numpy-discussion] numpy.where behavior

[<val...@bl...>]

Using numpy 1.0, why does



>>> a = numpy.array([0.0,1.0,2.0],'d')

>>> numpy.where(a
== 0.0,1,1/a)



give the correct result, but with the warning "Warning: divide
by zero encountered in divide"?



? I thought that the point of where was
that the second expression is never used for the elements where the condition
evaluates true. 



If this is the desired behavior, is there a way to suppress
the warning?



Thanks!



Michele

Re: [Numpy-discussion] numpy.where behavior

[Robert K. <rob...@gm...>]

val...@bl... wrote:
> ? I thought that the point of where was
> that the second expression is never used for the elements where the condition
> evaluates true. 

It is not used, but the expression still gets evaluated. There's really no way
around that.

> If this is the desired behavior, is there a way to suppress
> the warning?

In [1]: from numpy import *

In [2]: a = zeros(3)

In [3]: 1/a
Warning: divide by zero encountered in divide
Warning: invalid value encountered in double_scalars
Out[3]: array([              inf,               inf,               inf])

In [4]: seterr(divide='ignore', invalid='ignore')
Out[4]: {'divide': 'print', 'invalid': 'print', 'over': 'print', 'under': 'ignore'}

In [5]: 1/a
Out[5]: array([              inf,               inf,               inf])

In [6]: seterr?
Type:           function
Base Class:     <type 'function'>
Namespace:      Interactive
File:
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy-1.0.1.dev3432-py2.5-macosx-10.4-i386.egg
/numpy/core/numeric.py
Definition:     seterr(all=None, divide=None, over=None, under=None, invalid=None)
Docstring:
    Set how floating-point errors are handled.

    Valid values for each type of error are the strings
    "ignore", "warn", "raise", and "call". Returns the old settings.
    If 'all' is specified, values that are not otherwise specified
    will be set to 'all', otherwise they will retain their old
    values.

    Note that operations on integer scalar types (such as int16) are
    handled like floating point, and are affected by these settings.

    Example:

    >>> seterr(over='raise')
    {'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'}
    >>> seterr(all='warn', over='raise')
    {'over': 'raise', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'}
    >>> int16(32000) * int16(3)
    Traceback (most recent call last):
          File "<stdin>", line 1, in ?
    FloatingPointError: overflow encountered in short_scalars
    >>> seterr(all='ignore')
    {'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'}


-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
 that is made terrible by our own mad attempt to interpret it as though it had
 an underlying truth."
  -- Umberto Eco

Re: [Numpy-discussion] numpy.where behavior

[Tim H. <tim...@ie...>]

val...@bl... wrote:
> Using numpy 1.0, why does
>
>
>
>   
>>>> a = numpy.array([0.0,1.0,2.0],'d')
>>>>         
>
>   
>>>> numpy.where(a
>>>>         
> == 0.0,1,1/a)
>
>
>
> give the correct result, but with the warning "Warning: divide
> by zero encountered in divide"?
>
>
>
> ? I thought that the point of where was
> that the second expression is never used for the elements where the condition
> evaluates true. 
>
>
>
> If this is the desired behavior, is there a way to suppress
> the warning?
>   
Robert Kern has already pointed you to seterr. If you are using Python 
2.5, you also have the option using the with statement, which is more 
convenient if you want to temporarily change the error state. You'll 
need a "from __future__ import with_statement" at the top of your file. 
Then you can temporarily disable errors as shown:

     >>> a = zeros([3])
     >>> b = 1/a  # This will warn
    Warning: divide by zero encountered in divide
     >>> with errstate(divide='ignore'): # But this will not
    ...     c = 1/a
    ...
     >>> d = 1/a # And this will warn again since the error state is
    restored when we exit the block
    Warning: divide by zero encountered in divide


Another little tidbit: this is not as general as where, and could 
probably be considered a little too clever to be clear, but:

        b = 1 / (a + (a==0.0))

is faster than using where in this particular case and sidesteps the 
divide by zero issue altogether.

-tim

Re: [Numpy-discussion] numpy.where behavior

[Paul D. <pfd...@gm...>]

Unfortunately, where does not have the behavior of not evaluating the second
argument where the first one is true. That would be nice (if the speed were
ok) but it isn't possible unless where is built into the language, since
where doesn't even get called until the arguments have all been calculated.
It was intended as having a different use than avoiding zero-divide. The ma
package can calculate 1/a without problem, resulting in masked results where
a is 0.0.

I put where into numeric after it had proved invaluable in Basis, even
though it has this limitation; it takes care of doing both merge and
compress.

On 13 Nov 2006 20:02:31 -0800, Tim Hochberg <tim...@ie...> wrote:
>
> val...@bl... wrote:
> > Using numpy 1.0, why does
> >
> >
> >
> >
> >>>> a = numpy.array([0.0,1.0,2.0],'d')
> >>>>
> >
> >
> >>>> numpy.where(a
> >>>>
> > == 0.0,1,1/a)
> >
> >
> >
> > give the correct result, but with the warning "Warning: divide
> > by zero encountered in divide"?
> >
> >
> >
> > ? I thought that the point of where was
> > that the second expression is never used for the elements where the
> condition
> > evaluates true.
> >
> >
> >
> > If this is the desired behavior, is there a way to suppress
> > the warning?
> >
> Robert Kern has already pointed you to seterr. If you are using Python
> 2.5, you also have the option using the with statement, which is more
> convenient if you want to temporarily change the error state. You'll
> need a "from __future__ import with_statement" at the top of your file.
> Then you can temporarily disable errors as shown:
>
>      >>> a = zeros([3])
>      >>> b = 1/a  # This will warn
>     Warning: divide by zero encountered in divide
>      >>> with errstate(divide='ignore'): # But this will not
>     ...     c = 1/a
>     ...
>      >>> d = 1/a # And this will warn again since the error state is
>     restored when we exit the block
>     Warning: divide by zero encountered in divide
>
>
> Another little tidbit: this is not as general as where, and could
> probably be considered a little too clever to be clear, but:
>
>         b = 1 / (a + (a==0.0))
>
> is faster than using where in this particular case and sidesteps the
> divide by zero issue altogether.
>
> -tim
>
>
>
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Re: [Numpy-discussion] numpy.where behavior

[Robert K. <rob...@gm...>]

Tim Hochberg wrote:
> Another little tidbit: this is not as general as where, and could 
> probably be considered a little too clever to be clear, but:
> 
>         b = 1 / (a + (a==0.0))
> 
> is faster than using where in this particular case and sidesteps the 
> divide by zero issue altogether.

A less clever approach that does much the same thing:

  b = 1.0 / where(a==0, 1.0, a)

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
 that is made terrible by our own mad attempt to interpret it as though it had
 an underlying truth."
  -- Umberto Eco