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/964#issuecomment-270863277,https://api.github.com/repos/pydata/xarray/issues/964,270863277,MDEyOklzc3VlQ29tbWVudDI3MDg2MzI3Nw==,5635139,2017-01-06T09:20:08Z,2017-01-06T09:20:08Z,MEMBER,FWIW the `bn.push` example still has some unanswered questions - would be interested to know if there's an easier way of doing that. Particularly if it's just a 'dim for axis' swap,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-270863083,https://api.github.com/repos/pydata/xarray/issues/964,270863083,MDEyOklzc3VlQ29tbWVudDI3MDg2MzA4Mw==,5635139,2017-01-06T09:18:47Z,2017-01-06T09:18:47Z,MEMBER,Congrats!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-268355105,https://api.github.com/repos/pydata/xarray/issues/964,268355105,MDEyOklzc3VlQ29tbWVudDI2ODM1NTEwNQ==,5635139,2016-12-20T20:46:55Z,2016-12-20T20:46:55Z,MEMBER,"Gave this a quick spin for filling. A few questions:
- Is there an easy of way of merely translating dims into axes? Maybe that already exists?
- Is there as easy way to keep a dimension? Or should it be in the signature and a `new_dim`?
```python
da=xr.DataArray(np.random.rand(10,3), dims=('x','y'))
da = da.where(da>0.5)
In [43]: da
Out[43]:
array([[ nan, 0.57243305, 0.84363016],
[ nan, 0.90788156, nan],
[ nan, 0.50739189, 0.93701278],
[ nan, nan, 0.86804167],
[ nan, 0.50883914, nan],
[ nan, nan, nan],
[ nan, 0.91547763, nan],
[ 0.72920182, nan, 0.6982745 ],
[ 0.73033449, 0.950719 , 0.73077113],
[ nan, nan, 0.72463932]])
In [44]: xr.apply(bn.push, da) . # already better than `bn.push(da)`!
Out[44]:
array([[ nan, 0.57243305, 0.84363016],
[ nan, 0.90788156, 0.90788156],
[ nan, 0.50739189, 0.93701278],
[ nan, nan, 0.86804167],
[ nan, 0.50883914, 0.50883914],
[ nan, nan, nan],
[ nan, 0.91547763, 0.91547763],
[ 0.72920182, 0.72920182, 0.6982745 ],
[ 0.73033449, 0.950719 , 0.73077113],
[ nan, nan, 0.72463932]])
# but changing the axis is verbose and transposes the array - are there existing tools for this?
In [48]: xr.apply(bn.push, da, signature='(x)->(x)', new_coords=[dict(x=da.x)])
Out[48]:
array([[ nan, nan, nan, nan, nan,
nan, nan, 0.72920182, 0.73033449, 0.73033449],
[ 0.57243305, 0.90788156, 0.50739189, 0.50739189, 0.50883914,
0.50883914, 0.91547763, 0.91547763, 0.950719 , 0.950719 ],
[ 0.84363016, 0.84363016, 0.93701278, 0.86804167, 0.86804167,
0.86804167, 0.86804167, 0.6982745 , 0.73077113, 0.72463932]])
Coordinates:
* x (x) int64 0 1 2 3 4 5 6 7 8 9
o y (y) -
```
- The triple nested signature is pretty tough to write! Two kwargs?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-263988895,https://api.github.com/repos/pydata/xarray/issues/964,263988895,MDEyOklzc3VlQ29tbWVudDI2Mzk4ODg5NQ==,5635139,2016-11-30T20:41:14Z,2016-11-30T20:41:14Z,MEMBER,"> Either way, the first step is probably to write a function backfill(values, axis) that acts on NumPy arrays.
Right.
Surprisingly, I can't actually find something like this out there. The pandas code is good but highly 1-2 dimension specific.
Let me know if I'm missing (pun intended - long day) something. Is there a library of these sorts of functions over n-dims somewhere else (even R / Julia)? Or are we really the first people in the world to be doing this? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-263942583,https://api.github.com/repos/pydata/xarray/issues/964,263942583,MDEyOklzc3VlQ29tbWVudDI2Mzk0MjU4Mw==,5635139,2016-11-30T17:45:43Z,2016-11-30T17:45:43Z,MEMBER,"I'm thinking through how difficult it would be to add back-fill method to `DataArray` (that could be an argument to `fillna` or a `bfill` method - that's a separate discussion).
Would this PR help? I'm trying to wrap my head around the options. Thanks
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-246796164,https://api.github.com/repos/pydata/xarray/issues/964,246796164,MDEyOklzc3VlQ29tbWVudDI0Njc5NjE2NA==,5635139,2016-09-13T19:28:58Z,2016-09-13T19:28:58Z,MEMBER,"Would it be possible to write something like [np.einsum](http://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html) with xarray named dimensions?
I _think_ it's possible, by supplying the dimensions to sum over, and broadcasting the others. Similar to the `inner_product` example, but taking `*dims` rather than `dims`. Is that right?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-239556426,https://api.github.com/repos/pydata/xarray/issues/964,239556426,MDEyOklzc3VlQ29tbWVudDIzOTU1NjQyNg==,5635139,2016-08-12T20:50:24Z,2016-08-12T20:50:32Z,MEMBER,"Thanks for thinking through these
> This suggests maybe `ds[bool_array] -> da.where(bool_array, drop=True).`
I think that makes sense. `drop=False` would be too confusing
> Maybe something like: `left_join(da, inner_join(bool_array, other))`?
The way I was thinking about it: both `other` and `bool_array` need a value for every value in `da`. So they both need to be subsets. So something like:
``` python
assert set(other.dims) =< set(da.dims)
assert set(bool_array.dims) =< set(da.dims)
other, _ = xr.broadcast(other, da)
bool_array, _ = xr.broadcast(bool_array, da)
da.where(bool_array, other)
```
Is that consistent with the joins you were thinking of?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-239469432,https://api.github.com/repos/pydata/xarray/issues/964,239469432,MDEyOklzc3VlQ29tbWVudDIzOTQ2OTQzMg==,5635139,2016-08-12T14:58:15Z,2016-08-12T14:58:15Z,MEMBER,"When this is done & we can do `where`, I wonder whether
``` python
da[bool_array] = 5
```
...could be sugar for...
``` python
da.where(bool_array, 5)
```
i.e. do we get multidimensional indexing for free?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798
https://github.com/pydata/xarray/pull/964#issuecomment-239347755,https://api.github.com/repos/pydata/xarray/issues/964,239347755,MDEyOklzc3VlQ29tbWVudDIzOTM0Nzc1NQ==,5635139,2016-08-12T02:34:12Z,2016-08-12T02:34:12Z,MEMBER,"This looks awesome! Would simplify a lot of the existing op stuff!
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,170779798