[Numpy-discussion] add axis to results of reduction (mean, min, ...)
Thu Aug 6 11:21:26 CDT 2009
On Thu, Aug 6, 2009 at 12:07 PM, Robert Kern<email@example.com> wrote:
> On Thu, Aug 6, 2009 at 11:03, Keith Goodman<firstname.lastname@example.org> wrote:
>> On Thu, Aug 6, 2009 at 8:55 AM, <email@example.com> wrote:
>>> What's the best way of getting back the correct shape to be able to
>>> broadcast, mean, min,.. to the original array, that works for
>>> arbitrary dimension and axis?
>>> I thought I have seen some helper functions, but I don't find them anymore?
>>> array([[1, 2, 3, 3, 0],
>>> [2, 2, 3, 2, 1]])
>>> array([[-1, 0, 0, 0, -1],
>>> [ 0, 0, 0, -1, 0]])
>>> Traceback (most recent call last):
>>> File "<pyshell#135>", line 1, in <module>
>>> ValueError: shape mismatch: objects cannot be broadcast to a single shape
>>> array([[-2, -1, 0, 0, -3],
>>> [-1, -1, 0, -1, -2]])
>> Would this do it?
>> Type: function
>> Base Class: <type 'function'>
>> String Form: <function demean at 0x3c5c050>
>> Namespace: Interactive
>> File: /usr/lib/python2.6/dist-packages/matplotlib/mlab.py
>> Definition: pylab.demean(x, axis=0)
>> def demean(x, axis=0):
>> "Return x minus its mean along the specified axis"
>> x = np.asarray(x)
>> if axis:
>> ind = [slice(None)] * axis
>> return x - x.mean(axis)[ind]
>> return x - x.mean(axis)
> Ouch! That doesn't handle axis=-1.
> if axis != 0:
> ind = [slice(None)] * x.ndim
> ind[axis] = np.newaxis
Thanks, that's it.
I have seen implementation of helper functions similar to this in
other packages, but I thought there is already something in numpy. I
think this should be a simple helper function in numpy to avoid
mistakes and complicated implementation like the one in stats.nanstd
even if it's only a few lines.
> 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
> NumPy-Discussion mailing list
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