On Wed, May 4, 2011 at 11:08 PM, Paul Anton Letnes < paul.anton.let...@gmail.com> wrote:
> > On 4. mai 2011, at 20.33, Benjamin Root wrote: > > > On Wed, May 4, 2011 at 7:54 PM, Derek Homeier < > de...@astro.physik.uni-goettingen.de> wrote: > > On 05.05.2011, at 2:40AM, Paul Anton Letnes wrote: > > > > > But: Isn't the numpy.atleast_2d and numpy.atleast_1d functions written > for this? Shouldn't we reuse them? Perhaps it's overkill, and perhaps it > will reintroduce the 'transposed' problem? > > > > Yes, good point, one could replace the > > X.shape = (X.size, ) with X = np.atleast_1d(X), > > but for the ndmin=2 case, we'd need to replace > > X.shape = (X.size, 1) with X = np.atleast_2d(X).T - > > not sure which solution is more efficient in terms of memory access > etc... > > > > Cheers, > > Derek > > > > > > I can confirm that the current behavior is not sufficient for all of the > original corner cases that ndmin was supposed to address. Keep in mind that > np.loadtxt takes a one-column data file and a one-row data file down to the > same shape. I don't see how the current code is able to produce the correct > array shape when ndmin=2. Do we have some sort of counter in loadtxt for > counting the number of rows and columns read? Could we use those to help > guide the ndmin=2 case? > > > > I think that using atleast_1d(X) might be a bit overkill, but it would be > very clear as to the code's intent. I don't think we have to worry about > memory usage if we limit its use to only situations where ndmin is greater > than the number of dimensions of the array. In those cases, the array is > either an empty result, a scalar value (in which memory access is trivial), > or 1-d (in which a transpose is cheap). > > What if one does things the other way around - avoid calling squeeze until > _after_ doing the atleast_Nd() magic? That way the row/column information > should be conserved, right? Also, we avoid transposing, memory use, ... > > Oh, and someone could conceivably have a _looong_ 1D file, but would want > it read as a 2D array. > > Paul > > > @Derek, good catch with noticing the error in the tests. We do still need to handle the case I mentioned, however. I have attached an example script to demonstrate the issue. In this script, I would expect the second-to-last array to be a shape of (1, 5). I believe that the single-row, multi-column case would actually be the more common type of edge-case encountered by users than the others. Therefore, I believe that this ndmin fix is not adequate until this is addressed. @Paul, we can't call squeeze after doing the atleast_Nd() magic. That would just undo whatever we had just done. Also, wrt the transpose, a (1, 100000) array looks the same in memory as a (100000, 1) array, right? Ben Root
loadtest.py
Description: Binary data
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
