[Numpy-discussion] ragged array implimentation
Mon Mar 7 11:41:47 CST 2011
I need to work with ragged arrays too. Are object arrays of 1d numpy arrays
slower than lists of 1d numpy arrays?
I'd be interested in hearing if you come up with any better solutions.
On Mon, Mar 7, 2011 at 9:37 AM, Jeff Whitaker <email@example.com> wrote:
> On 3/7/11 10:28 AM, Christopher Barker wrote:
> > Hi folks,
> > I'm setting out to write some code to access and work with ragged arrays
> > stored in netcdf files. It dawned on me that ragged arrays are not all
> > that uncommon, so I'm wondering if any of you have any code you've
> > developed that I could learn-from borrow from, etc.
> > note that when I say a "ragged array", I mean a set of data where the
> > each row could be a different arbitrary length:
> > 1, 2, 3, 4
> > 5, 6
> > 7, 8, 9, 10, 11, 12
> > 13, 14, 15
> > ...
> > In my case, these will only be 2-d, though I suppose one could have a
> > n-d version where the last dimension was ragged (or any dimension, I
> > suppose, though I'm having trouble wrapping my brain around what that
> > would look like...
> > I'm not getting more specific about what I think the API should look
> > like -- that is part of what I'm looking for suggestions, previous
> > implementations, etc for.
> > Is there any "standard" way to work with such data?
> > -Chris
> Chris: The netcdf4-python modules reads netcdf vlen arrays and returns
> numpy object arrays, where the elements of the object arrays are
> themselves 1d numpy arrays. I don't think there is any other way to do
> it. In your example, the 'ragged' array would be a 1d numpy array with
> dtype='O', and the individual elements would be 1d numpy arrays with
> dtype=int. Of course, these arrays are very awkward to deal with and
> operations will be slow.
> Jeffrey S. Whitaker Phone : (303)497-6313
> Meteorologist FAX : (303)497-6449
> NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@noaa.gov
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