On Fri, Oct 17, 2014 at 10:56 PM, Stephan Hoyer <sho...@gmail.com> wrote:
> Yesterday I created a GitHub issue proposing adding an axis argument to > numpy's gufuncs: > https://github.com/numpy/numpy/issues/5197 > > I was told I should repost this on the mailing list, so here's the recap: > > I would like to write generalized ufuncs (probably using numba), to create > fast functions such as nanmean (signature '(n)->()') or rolling_mean > (signature '(n),()->(n)') that take the axis along which to aggregate as a > keyword argument, e.g., nanmean(x, axis=0) or rolling_mean(x, window=5, > axis=0). > > Of course, I could write my own wrapper for this that reorders dimensions > using swapaxes or transpose. But I also think that an "axis" argument to > allow for specifying the core dimensions of gufuncs would be more generally > useful, and we should consider adding it to numpy. > > Nathaniel and Jaime added some good points, noting that such an axis > argument should cleanly handle multiple input and output arguments and have > a plan for handling optional dimensions (e.g., (m?,n),(n,p?)->(m?,p?) for > the new dot). > > Here are my initial thoughts on the syntax: > > (1) Generally speaking, I think the "nested tuple" syntax (e.g., axis=[(0, > 1), (2, 3)]) would be most congruous with the axis arguments numpy already > supports. > > (2) For gufuncs with simpler signatures, we should support supplying an > integer or an unnested tuple, e.g., > - axis=0 for (n)->() > - axis=(0, 1) for (n)(m)->() or (n,m)->() > - axis=[(0, 1), 2] for (n,m),(o)->(). > > (3) If we require a full axis specification for core dimensions, we could > use the axis argument for unambiguous control of optional core dimensions: > e.g., axis=(0, 1) would indicate that you want the "vectorized inner > product" version of the new dot operator, rather than matrix > multiplication, and axis=[(-2, -1), -1] would mean that you want the > "vectorized matrix-vector product". This seems relatively tidy, although I > admit I am not convinced that optional core dimensions are necessary. > > (4) We can either include the output axis as part of the signature, or add > another argument "axis_out" or "out_axis". I think prefer the separate > argument, particularly if we require "axis" to specify all core dimensions, > which may be a good idea even if we don't use "axis" for controlling > optional core dimensions. > > Might want to contact continuum analytics also. They recently created a gufunc <https://github.com/ContinuumIO/blaze> repository. Chuck
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
