issue_comments: 75475215

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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.)

 
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