issue_comments

There could be some display options exposed to manage this - for instance I personally would not like a flat array - but see how it could make sense.

Additionally / alternatively, the repr I'm talking (small slice of values laid out with coordinate labels) could called something other than __repr__ - something like pandas .head() although may be a better name to use here.

@shoyer - I agree on 3) that it might too much to pack in to xr.apply. As one possibility, here's a half-implemented (probably buggy!) wrapper that would allow this:

``` python @xarray_gufunc @numba.guvectorize(['void(f8[:], f8[:])'], '(n)->()') def std_gufunc(arr, out): out[0] = np.std(arr)

std_gufunc(arr, dims=('x',)) ```

https://gist.github.com/chris-b1/d28c6b8e78bf65ef7eb97e1095bc87f2

A few pieces of feedback trying this out. I'm basically learning xarray as I go (I ran into this right away), so weight appropriately.

Usecase - I have a numba gufunc I want to apply to DataArray, e.g. a reduction like this (std is just for sake of example)

``` @numba.guvectorize(['void(f8[:], f8[:])'], '(n)->()') def std_gufunc(arr, out): out[0] = np.std(arr)

arr = xr.DataArray(np.random.randn(100, 100, 100), dims=('x', 'y', 'z')) ```

1) The "obvious" thing doesn't work - maybe catch and show a nicer error message here

xr.apply(std_gufunc, arr) ValueError: dimensions ('x', 'y', 'z') must have the same length as the number of data dimensions, ndim=2

2) I personally found the non-string version of signature really difficult to wrap my mind around (the below took several tries to get right). I don't have a concrete suggestion though, maybe only the string form is meant to really be the public api?

xr.apply(std_gufunc, arr, signature=([('x',)], [()])) xr.apply(std_gufunc, arr, signature='(x)->()')

3) It would be nice to take advantage of the existing gufunc signature in some way. Maybe this is a wrapper built on top or xr.apply, or expand the api to allow something like this;

xr.apply(std_gufunc, arr, dims=('x',)) xr.apply(std_gufunc, arr, dims={'n':'x'})