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/issues/328#issuecomment-94074862,https://api.github.com/repos/pydata/xarray/issues/328,94074862,MDEyOklzc3VlQ29tbWVudDk0MDc0ODYy,1217238,2015-04-17T21:03:12Z,2015-04-17T21:03:12Z,MEMBER,"Basic support for dask.array is merged on master.
Continued in https://github.com/xray/xray/issues/394
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58310637
https://github.com/pydata/xarray/issues/328#issuecomment-87509188,https://api.github.com/repos/pydata/xarray/issues/328,87509188,MDEyOklzc3VlQ29tbWVudDg3NTA5MTg4,1217238,2015-03-30T01:39:02Z,2015-03-30T01:39:02Z,MEMBER,"@mrocklin It occurs to me now that a much simpler version of the functionality I'm looking for with `take_nd` would be `dask.array.insert` modeled after `np.insert`, which we could combine with array indexing. For the purposes of xray, we would only need support for insert with a scalar value, e.g., like `da.insert(x, [1, 5, 6], np.nan, axis=1)`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58310637
https://github.com/pydata/xarray/issues/328#issuecomment-75476521,https://api.github.com/repos/pydata/xarray/issues/328,75476521,MDEyOklzc3VlQ29tbWVudDc1NDc2NTIx,1217238,2015-02-23T00:56:51Z,2015-02-23T00:56:51Z,MEMBER,"Yes, take_nd is very similar to fancy indexing but only non-negative indices are valid (-1 means insert NaN).
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58310637
https://github.com/pydata/xarray/issues/328#issuecomment-75475798,https://api.github.com/repos/pydata/xarray/issues/328,75475798,MDEyOklzc3VlQ29tbWVudDc1NDc1Nzk4,306380,2015-02-23T00:42:39Z,2015-02-23T00:42:39Z,MEMBER,"Am I right in thinking that this is almost equivalent to fancy indexing with a list of indices?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58310637
https://github.com/pydata/xarray/issues/328#issuecomment-75475215,https://api.github.com/repos/pydata/xarray/issues/328,75475215,MDEyOklzc3VlQ29tbWVudDc1NDc1MjE1,1217238,2015-02-23T00:30:02Z,2015-02-23T00:31:00Z,MEMBER,"> support for interleaved concatenation (necessary for transformations by group, which are quite common)
Turns out what I was thinking of here can be written as a one liner in terms of `concatenate` and `take`:
```
def interleaved_concatenate(arrays, indices, axis=0):
return np.take(np.concatenate(arrays, axis), np.concatenate(indices))
```
So I've crossed that one off the line.
> support super-imposing array values inter-leaved on top of a constant array of NaN (necessary for many alignment operations)
What I need here is something similar to the private `take_nd` functions that pandas defines that works like `np.take`, but that uses `-1` as a sentinel value for ""missing"":
```
In [1]: import pandas
In [2]: import numpy as np
In [3]: x = np.arange(5)
In [4]: pandas.core.common.take_nd(x, [0, -1, 1, -1, 2])
Out[4]: array([ 0., nan, 1., nan, 2.])
```
(In xray, I implement this a little differently so that I can take along all multiple axes simultaneously using array indexing, but this version would suffice.)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58310637
https://github.com/pydata/xarray/issues/328#issuecomment-75417769,https://api.github.com/repos/pydata/xarray/issues/328,75417769,MDEyOklzc3VlQ29tbWVudDc1NDE3NzY5,306380,2015-02-22T03:37:22Z,2015-02-22T03:37:22Z,MEMBER,"> support super-imposing array values inter-leaved on top of a constant array of NaN (necessary for many alignment operations)
@shoyer can you clarify this one? Would the `np.choose` interface satisfy this?
``` Python
In [1]: import numpy as np
In [2]: a = np.arange(4).reshape(2, 2)
In [3]: a
Out[3]:
array([[0, 1],
[2, 3]])
In [4]: x = np.array([[True, False], [True, True]])
In [5]: np.choose(x, [-10, a])
Out[5]:
array([[ 0, -10],
[ 2, 3]])
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58310637
https://github.com/pydata/xarray/issues/328#issuecomment-75276367,https://api.github.com/repos/pydata/xarray/issues/328,75276367,MDEyOklzc3VlQ29tbWVudDc1Mjc2MzY3,306380,2015-02-20T17:06:41Z,2015-02-20T17:06:41Z,MEMBER,"- support for NaN skipping aggregations
Presumably we could drop in `numbagg` here. The reductions are generally pretty straightforward to extend. I can do this relatively soon. See https://github.com/ContinuumIO/dask/blob/master/dask/array/reductions.py#L43-L111
- support for interleaved concatenation (necessary for transformations by group, which are quite common)
Do we have this already? Or rather can you point me to how you would do this with NumPy.
- support super-imposing array values inter-leaved on top of a constant array of NaN (necessary for many alignment operations)
Would this be solved by an elementwise `ifelse` operation? `ifelse(condition, x, y)`
- support ""orthogonal"" MATLAB-like array-based indexing along multiple dimensions
You can do this now by repeated slicing `x[[1, 2, 3], :][:, [4, 5, 6]]` and get a fully efficient solution. I can roll this in to the normal syntax though. I might pause for a bit as I think about the break that this causes with NumPy but I'll probably go ahead anyway.
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