On Mon, Apr 9, 2012 at 12:22 PM, Benjamin Root <ben.r...@ou.edu> wrote:
> > > On Mon, Apr 9, 2012 at 12:14 PM, Jonathan T. Niehof <jnie...@lanl.gov>wrote: > >> On 04/06/2012 06:54 AM, Benjamin Root wrote: >> >> > Take a peek at how np.gradient() does it. It creates a list of None with >> > a length equal to the number of dimensions, and then inserts a slice >> > object in the appropriate spot in the list. >> >> List of slice(None), correct? At least that's what I see in the source, >> and: >> >> >>> a = numpy.array([[1,2],[3,4]]) >> >>> operator.getitem(a, (None, slice(1, 2))) >> array([[[3, 4]]]) >> >>> operator.getitem(a, (slice(None), slice(1, 2))) >> array([[2], >> [4]]) >> >> > Correct, sorry, I was working from memory. > > Ben Root > > I guess I wasn't reading very carefully and assumed that you meant a list of `slice(None)` instead of a list of `None`. In any case, both your solution and Matthew's solution work (and both are more readable than my original implementation). After I got everything cleaned up (and wrote documentation and tests), I found out that numpy already has a function to do *exactly* what I wanted in the first place: `np.split` (the slicing was just one component of this). I was initially misled by the docstring<https://github.com/numpy/numpy/pull/249>, but with a list of indices, you can split an array into subarrays of variable length (I wanted to use this to save and load ragged arrays). Well, I guess it was a learning experience, at least. In case anyone is wondering about the original question, `np.split` (and `np.array_split`) uses `np.swapaxes` to specify the slicing axis. Thanks for all your help. -Tony
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