@Jeff I need to work with ragged arrays too. Are object arrays of 1d numpy arrays slower than lists of 1d numpy arrays?
@ Christopher 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 <jsw...@fastmail.fm> 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. > > -Jeff > > -- > Jeffrey S. Whitaker Phone : (303)497-6313 > Meteorologist FAX : (303)497-6449 > NOAA/OAR/PSD R/PSD1 Email : jeffrey.s.whita...@noaa.gov > 325 Broadway Office : Skaggs Research Cntr 1D-113 > Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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