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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
