[SciPy-User] scipy.stats.nanmedian

[Bruce Southey]

On 01/21/2010 10:18 PM, josef.pktd at gmail.com wrote:
> On Thu, Jan 21, 2010 at 10:01 PM, Keith Goodman<kwgoodman at gmail.com>  wrote:
>    
>> On Thu, Jan 21, 2010 at 6:41 PM, Pierre GM<pgmdevlist at gmail.com>  wrote:
>>      
>>> On Jan 21, 2010, at 9:28 PM, Keith Goodman wrote:
>>>        
>>>> That's the only was I was able to figure out how to pull 1.0 out of
>>>> np.array(1.0). Is there a better way?
>>>>          
>>>
>>> .item()
>>>        
>> Thanks. item() looks better than tolist().
>>
>> I simplified the function:
>>
>> def nanmedian(x, axis=0):
>>     x, axis = _chk_asarray(x,axis)
>>     if x.ndim == 0:
>>         return float(x.item())
>>     x = x.copy()
>>     x = np.apply_along_axis(_nanmedian,axis,x)
>>     if x.ndim == 0:
>>         x = float(x.item())
>>     return x
>>
>> and opened a ticket:
>>
>> http://projects.scipy.org/scipy/ticket/1098
>>      
>
> How about getting rid of apply_along_axis?    see attachment
>
> I don't know whether or how much faster it is, but there is a ticket
> that the current version is slow.
> No hidden bug or corner case guarantee yet.
>
>
> Josef
>    
>
>
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>    
Personally, I think using masked arrays is a far better solution than 
the various nan methods in stats.py. Is the call to _chk_asarray that 
much different than the call to ma? Both require conversion or checking 
if the input is a np array.

As stated in the documentation, _nanmedian only works on 1d arrays. So 
any 'axis' argument without changing the main function is perhaps a hack 
at best.

Is it possible to adapt Sturla's version?
http://projects.scipy.org/numpy/ticket/1213
I do not know the algorithm to suggest anything but perhaps the select 
method could be adapted to handle nan.

Bruce


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