issue_comments

Note that with the apply_ufunc implementation we're only reshaping dims-sized ndarrays, not (necessarily) the whole DataArray, so maybe it's not too bad? It might be better to first sort dims to be in the same order as self.dims. i.e. dims = [dim_ for dim_ in self.dims if dim_ in dims]. But I'm just speculating.

A few minor tweaks needed: ``` In [20]: import bottleneck

In [21]: xr.apply_ufunc( ...: lambda x: bottleneck.rankdata(x).reshape(x.shape), ...: d, ...: input_core_dims=[['xyz', 'abc']], ...: output_core_dims=[['xyz', 'abc']], ...: vectorize=True ...: ).transpose(*d.dims)
Out[21]: <xarray.DataArray (abc: 4, xyz: 3)> array([[ 1., 2., 3.], [ 4., 5., 6.], [ 7., 8., 9.], [10., 11., 12.]]) Dimensions without coordinates: abc, xyz ```

Despite what the docs say, bottleneck.{nan}rankdata(a) returns a 1-dimensional ndarray, not an array with the same shape as a.

What's wrong with the following? (Still need to deal with pct and keep_attrs.) apply_ufunc( bottleneck.{nan}rankdata, self, input_core_dims=[dims], output_core_dims=[dims], vectorize=True )

Per https://kwgoodman.github.io/bottleneck-doc/reference.html#bottleneck.rankdata, "The default (axis=None) is to rank the elements of the flattened array."

Assuming dims is a non-empty list of dimensions, the following code seems to work: temp_dim = '__temp_dim__' return da.stack(**{temp_dim: dims}).\ rank(temp_dim, pct=pct, keep_attrs=keep_attrs).\ unstack(temp_dim).transpose(*da.dims).\ drop_vars([dim_ for dim_ in dims if dim_ not in da.coords])