issue_comments

Indeed, thank you @fujiisoup !

This is (one more time) an extremely useful addition to xarray - thanks so much @fujiisoup !

I plan to merge this tomorrow unless anyone else has further suggestions.

+1, on 'exact'. I like the idea of making users be explicit about when to pad or trim edge cells.

@pydata/xarray any opinions on what the default value for the boundary argument to coarsen() should be?

Personally, I like boundary='exact', but I also work mostly with simulated data with dimensions setup to be exact powers of 2 :).

During writing the test codes, I realized that the default boundary='exact' is a little annoying. It would be rare that the length of the data is exactly a multiple of the window size; we almost always need to type bounary='trim' or the other. I personally think that boundary='pad' is also a good default, as it is a similar behaviour to rolling.

The down side of this is that it can make a uniformly spaced data an inhomogeneously spaced one. It is something what users may not expect. (probably boundary='exact is safer)

Updated. The main change is 1. API updated to python dataset.coarsen(self, dim=None, boundary='exact', side='left', coordinate_func='mean', **dim_kwargs): based on comments.

1. nanmean for DateTime was implemented. This is a kind of backward incompatible. Previously, we raised an Error if mean is applied to a datetime array.

I do think it would be nice for resample() to work with numbers and along multiple dimensions, but that's definitely a bigger project.

Perfect, thanks for pointing that out (I've generally been a bit out of the loop recently...)

@max-sixty we discussed this a little bit: https://github.com/pydata/xarray/issues/2525#issuecomment-434477550

The main difference is that resample is coordinate based, whereas this is integer position based which makes the implementation considerably simpler.

I think it's multi-dimensional resampling / rolling. resample & rolling operate one dimension at a time.

Thanks. If this is multi-dimensional resampling / rolling, could we implement this functionality within those methods, enabling multiple dimensions?

Potentially the implementations are different enough that the arguments don't have enough overlap?

@max-sixty I think it's multi-dimensional resampling / rolling. resample & rolling operate one dimension at a time.

Forgive me if this has an obvious answer: to what extent is this downsampling? Could this be done with resample?

@dcherian Thanks.

(da.time - da.time[0]).mean() + da.time[0] I would add mean for datetime-arrays.

(da.time - da.time[0]).mean() + da.time[0]

Or we could default to min() and add an loffset kwarg like resample (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html)