[Numpy-discussion] loadtxt ndmin option
ben.root at ou.edu
Wed May 4 23:33:28 EDT 2011
On Wed, May 4, 2011 at 7:54 PM, Derek Homeier <
derek at 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...
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).
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