html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/pull/2778#issuecomment-470240931,https://api.github.com/repos/pydata/xarray/issues/2778,470240931,MDEyOklzc3VlQ29tbWVudDQ3MDI0MDkzMQ==,6628425,2019-03-06T19:22:54Z,2019-03-06T19:23:27Z,MEMBER,"Oh, I should have been a little clearer!
> For now I've held off on making these changes dask-compatible (I could do it, but I'm not sure it is worth the extra complexity)
This comment only applies to the changes regarding `duck_array_ops.mean`, which is used by default on the coordinates involved in `coarsen`. Since indexes are always loaded into memory, i.e. backed by NumPy arrays, we don't really need to worry about dask-compatibility there. In other words with this PR a DataArray can hold dask array data indexed by a cftime time coordinate, and `coarsen` will work just fine:
```
In [1]: import xarray as xr
In [2]: import numpy as np
In [3]: data = np.random.random((10, 5))
In [4]: times = xr.cftime_range('2000', periods=10)
In [5]: da = xr.DataArray(data, coords={'time': times}, dims=['time', 'x'])
In [6]: da = da.chunk({'time': 1, 'x': 1})
In [7]: da
Out[7]:
<xarray.DataArray (time: 10, x: 5)>
dask.array<shape=(10, 5), dtype=float64, chunksize=(1, 1)>
Coordinates:
* time (time) object 2000-01-01 00:00:00 ... 2000-01-10 00:00:00
Dimensions without coordinates: x
In [8]: da.coarsen(time=2).mean()
Out[8]:
<xarray.DataArray (time: 5, x: 5)>
dask.array<shape=(5, 5), dtype=float64, chunksize=(1, 1)>
Coordinates:
* time (time) object 2000-01-01 12:00:00 ... 2000-01-09 12:00:00
Dimensions without coordinates: x
```
This would only come up as a possible issue if you tried to lazily take the mean of a DataArray of cftime objects, e.g.:
```
In [9]: da = xr.DataArray(times, dims=['t']).chunk()
In [10]: da
Out[10]:
<xarray.DataArray (t: 10)>
dask.array<shape=(10,), dtype=object, chunksize=(10,)>
Coordinates:
* t (t) object 2000-01-01 00:00:00 ... 2000-01-10 00:00:00
In [11]: da.mean()
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-19-c02402258881> in <module>
----> 1 da.mean()
~/xarray-dev/xarray/xarray/core/common.py in wrapped_func(self, dim, axis, skipna, **kwargs)
23 **kwargs):
24 return self.reduce(func, dim, axis,
---> 25 skipna=skipna, allow_lazy=True, **kwargs)
26 else:
27 def wrapped_func(self, dim=None, axis=None, # type: ignore
~/xarray-dev/xarray/xarray/core/dataarray.py in reduce(self, func, dim, axis, keep_attrs, **kwargs)
1603 """"""
1604
-> 1605 var = self.variable.reduce(func, dim, axis, keep_attrs, **kwargs)
1606 return self._replace_maybe_drop_dims(var)
1607
~/xarray-dev/xarray/xarray/core/variable.py in reduce(self, func, dim, axis, keep_attrs, allow_lazy, **kwargs)
1366 data = func(input_data, axis=axis, **kwargs)
1367 else:
-> 1368 data = func(input_data, **kwargs)
1369
1370 if getattr(data, 'shape', ()) == self.shape:
~/xarray-dev/xarray/xarray/core/duck_array_ops.py in mean(array, axis, skipna, **kwargs)
348 if isinstance(array, dask_array_type):
349 raise NotImplementedError(
--> 350 'Computing the mean of an array containing '
351 'cftime.datetime objects is not yet implemented on '
352 'dask arrays.')
NotImplementedError: Computing the mean of an array containing cftime.datetime objects is not yet implemented on dask arrays.
```
but I think that's a pretty rare use case, hence why I've held off on adding that support for now.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,412078232