[Numpy-discussion] numpy.ma.arg{min,max}

[Adam Oliner]

Christoper,

I see, thanks. Then the documentation should say that it returns an  
array of the _smallest_ indices of the minimum values along the given  
axis.

If the minimum value is not unique, as in my examples, is there an  
efficient way to get all of these indices? It seems that I can get  
roughly the behavior I want with 'where':

 >>> a
array([1, 1, 1, 5, 5])
 >>> numpy.ma.where(a==numpy.ma.max(a))[0]
array([3, 4])
 >>> numpy.ma.where(a==numpy.ma.min(a))[0]
array([0, 1, 2])

  - Adam J. Oliner
    oliner@cs.stanford.edu



On Apr 10, 2009, at 3:16 PM, Christopher Barker wrote:

> Adam Oliner wrote:
>> The documentation for numpy.ma.argmin says:
>> "Returns array of indices of the maximum values along the given  
>> axis."
>>
>> Aside from probably meaning to say the 'minimum' values,
>
> that's a typo...
>
>
>> it also doesn't seem to return an array:
>
> it does if you use a higher rank array, and specify an axis:
>
>>>> a
> array([[ 0.643502  ,  0.83867769,  0.97762954,  0.49686553],
>        [ 0.37798796,  0.69300396,  0.06683333,  0.3457077 ],
>        [ 0.51357262,  0.29095034,  0.42811108,  0.72644421],
>        [ 0.5977396 ,  0.64992292,  0.53068753,  0.46386281]])
>
>>>> np.argmin(a, 0)
> array([1, 2, 1, 1])
>
>>>> np.argmin(a, 1)
>
> array([3, 2, 1, 3])
>
>
> argmin(a, axis=None)
>     Return array of indices to the minimum values along the given  
> axis.
>
>     Parameters
>     ----------
>     a : {array_like}
>         Array to look in.
>     axis : {None, integer}
>         If None, the index is into the flattened array, otherwise  
> along
>         the specified axis
>
>
>
>
> -- 
> Christopher Barker, Ph.D.
> Oceanographer
>
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