issue_comments

@shoyer My 2 cents for how this might work after 0.8+ (auto-align during concat, merge and auto_combine goes a long to solving this already) is that the compat option of merge etc could have a 4th option 'nonnull_equals' (or better named...), with compatibility tested by e.g.

``` python import xarray.ufuncs as xrufuncs

def nonnull_compatible(first, second): """ Check whether two (aligned) datasets have any conflicting non-null values. """

# mask for where both objects are not null
both_not_null = xrufuncs.logical_not(first.isnull() | second.isnull())

# check remaining values are equal
return first.where(both_not_null).equals(second.where(both_not_null))

```

And then fillna to combine variables. Looking now I think this is very similar to what you are suggesting in #835.

Woops - I actually meant to put

python ds['var'].loc[{...}]

in there as the one that works ... my understanding is that this is supported as long as the specified coordinates are 'nice' (according to pandas) slices/scalars.

And yes, default values for DataArray/Dataset would definitely fill the "create_all_missing" need.

Yes following a similar line of thought to you I recently wrote an 'all missing' dataset constructor (rather than 'empty' which I think of as no variables):

python def all_missing_ds(coords, var_names, var_dims, var_types): """ Make a dataset whose data is all missing. """ # Empty dataset with appropirate coordinates ds = xr.Dataset(coords=coords) for v_name, v_dims, v_type in zip(var_names, var_dims, var_types): shape = tuple(ds[d].size for d in v_dims) if v_type == int or v_type == float: # Warn about up-casting int to float? nodata = np.tile(np.nan, shape) elif v_type == complex: # astype(complex) produces (nan + 0.0j) nodata = np.tile(np.nan + np.nan*1.0j, shape) else: nodata = np.tile(np.nan, shape).astype(object) ds[v_name] = (v_dims, nodata) return ds

To go with this (and this might be separate issue), a set_value method would be helpful --- just so that one does not have to remember which particular combination of

python ds.sel(...).var = new_values ds.sel(...)['var'] = new_values ds.var.sel(...) = new_values ds['var'].sel(...) = new_values

guarantees assigning a new value, (currently only the last syntax I believe).

Just a comment that the appearance of object types is likely due to the fact that numpy's NaNs are inherently 'floats' - so this will be an issue for any method with an intermediate `missing data' stage if non-floats are being used.

I still use use the align and fillna method since I mostly deal with floats/complex numbers, although @shoyer 's suggestion of a partial align and then concat could definitely be cleaner when the added coordinates are all 'new'.