On Mon, Apr 11, 2011 at 2:36 PM, Sergio Pascual <sergio.pa...@gmail.com> wrote:
> Hi list.
>
> For mi application, I would like to implement some new statistics
> functions over numpy arrays, such as truncated mean. Ideally this new
> function should have the same arguments
> than numpy.mean: axis, dtype and out. Is there a way of writing this
> function that doesn't imply writing it in C from scratch?
>
> I have read the documentation, but as far a I see ufuncs convert a N
> dimensional array into another and generalized ufuncs require fixed
> dimensions. numpy mean converts a N dimensional array either in a
> number or a N - 1 dimensional array.
Here's a slow, brute force method:
>> a = np.arange(9).reshape(3,3)
>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>> idx = a > 6
>> b = a. copy()
>> b[idx] = 0
>> b
array([[0, 1, 2],
[3, 4, 5],
[6, 0, 0]])
>> 1.0 * b.sum(axis=0) / (~idx).sum(axis=0)
array([ 3. , 2.5, 3.5])
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