issue_comments

Basic support for dask.array is merged on master.

Continued in https://github.com/xray/xray/issues/394

@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).

Yes, take_nd is very similar to fancy indexing but only non-negative indices are valid (-1 means insert NaN).

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.)