id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
257079041,MDU6SXNzdWUyNTcwNzkwNDE=,1571,to_netcdf fails for engine=h5netcdf when using dask-backed arrays,8982598,closed,0,,,2,2017-09-12T15:08:27Z,2019-02-12T05:39:19Z,2019-02-12T05:39:19Z,CONTRIBUTOR,,,,"When using dask-backed datasets/arrays it does not seem possible to use the 'h5netcdf' engine to write to disk:

```python
import xarray as xr
ds = xr.Dataset({'a': ('x', [1, 2])}, {'x': [3, 4]}).chunk()
ds.to_netcdf(""test.h5"", engine='h5netcdf')
```

results in the error:

```bash
...

h5py/h5a.pyx in h5py.h5a.open()

KeyError: ""Can't open attribute (can't locate attribute: 'dask')""
```

Not sure if this is a xarray or h5netcdf issue - or some inherent limitation in which case apologies!","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1571/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
326834855,MDExOlB1bGxSZXF1ZXN0MTkwNzk4NTYw,2189,docs: add xyzpy to projects,8982598,closed,0,,,1,2018-05-27T17:38:44Z,2018-05-27T20:46:07Z,2018-05-27T20:45:38Z,CONTRIBUTOR,,0,pydata/xarray/pulls/2189,Justs adds [xyzpy](http://xyzpy.readthedocs.io) to the list of projects utilizing ``xarray``.,"{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2189/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
130753818,MDU6SXNzdWUxMzA3NTM4MTg=,742,merge and align DataArrays/Datasets on different domains,8982598,closed,0,,,11,2016-02-02T17:27:17Z,2017-01-23T22:42:18Z,2017-01-23T22:42:18Z,CONTRIBUTOR,,,,"Firstly, I think `xarray` is great and for the type of physics simulations I run n-dimensional labelled arrays is _exactly_ what I need. But, and I may be missing something, is there a way to merge (or concatenate/update) DataArrays with _different_ domains on the same coordinates? 

For example consider this setup:

``` python
import xarray as xr

x1 = [100]
y1 = [1, 2, 3, 4, 5]
dat1 = [[101, 102, 103, 104, 105]]

x2 = [200]
y2 = [3, 4, 5, 6]  # different size and domain
dat2 = [[203, 204, 205, 206]]

da1 = xr.DataArray(dat1, dims=['x', 'y'], coords={'x': x1, 'y': y1})
da2 = xr.DataArray(dat2, dims=['x', 'y'], coords={'x': x2, 'y': y2})
```

I would like to aggregate such DataArrays into a new, single DataArray with `nan` padding such that:

``` python
>>> merge(da1, da2, align=True)  # made up syntax
<xarray.DataArray (x: 2, y: 6)>
array([[ 101.,  102.,  103.,  104.,  105.,   nan],
       [  nan,   nan,  203.,  204.,  205.,  206.]])
Coordinates:
  * x        (x) int64 100 200
  * y        (y) int64 1 2 3 4 5 6
```

Here is a quick function I wrote to do such but I would worried about the performance of 'expanding' the new data to the old data's size every iteration (i.e. supposing that the first argument is a large DataArray that you are adding to but doesn't necessarily contain the dimensions already).

``` python
def xrmerge(*das, accept_new=True):
    da = das[0]
    for new_da in das[1:]:
        # Expand both to have same dimensions, padding with NaN
        da, new_da = xr.align(da, new_da, join='outer')
        # Fill NaNs one way or the other re. accept_new
        da = new_da.fillna(da) if accept_new else da.fillna(new_da)
    return da
```

Might this be (or is this already!) possible in simpler form in `xarray`? I know _Datasets_ have `merge` and `update` methods but I couldn't make them work as above.
I also notice there are possible plans ( #417 ) to introduce a `merge` function for DataArrays.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/742/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
174404136,MDExOlB1bGxSZXF1ZXN0ODM1NDgwNzU=,996,add 'no_conflicts' as compat option for merging non-conflicting data,8982598,closed,0,,,8,2016-08-31T23:38:00Z,2016-09-15T16:07:16Z,2016-09-15T16:07:16Z,CONTRIBUTOR,,0,pydata/xarray/pulls/996,"This solves #742 and partially #835 (does not address a combine_first option yet). It essentially adds a notnull_equals method to Variable (and currently DataArray/set) that merge can use to compare objects, subsequently combining them with 'fillna'. Used as such:

``` python
>>> import xarray as xr
>>> ds1 = xr.Dataset(data_vars={'x': ('a', [1, 2])})
>>> ds2 = xr.Dataset(data_vars={'x': ('a', [2, 3])}, coords={'a': [1, 2]})
>>> xr.merge([ds1, ds2], compat='notnull_equals')
<xarray.Dataset>
Dimensions:  (a: 3)
Coordinates:
  * a        (a) int64 0 1 2
Data variables:
    x        (a) float64 1.0 2.0 3.0
```

Should be very easy to add other merging options such as overwriting left to write and vice versa. 
## TODO
- docs
- tests for various type combinations
- some of the error excepting is mirrored from `equals` and might not be necessary (e.g. for numpy structured arrays).
## ISSUES
- It seemed natural to add notnull_equals as a method to Dataset, but it might be unnecessary/not useful. Current version is conservative in that it still requires aligned Datasets with all the same variables.
- Due to the float nature of NaN, type is sometimes not preserved, e.g. merging two int arrays yields a float array, even when the final array has no NaN values itself.
- float rounding errors can cause unintended failures, so a notnull_allclose option might be nice.

Currently all tests passing (6 new), and no flake8 errors on the diff...
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