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  • mrocklin · 3 ✖

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  • Support out-of-core computation using dask · 3 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
75475798 https://github.com/pydata/xarray/issues/328#issuecomment-75475798 https://api.github.com/repos/pydata/xarray/issues/328 MDEyOklzc3VlQ29tbWVudDc1NDc1Nzk4 mrocklin 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?

 
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   Support out-of-core computation using dask 58310637
75417769 https://github.com/pydata/xarray/issues/328#issuecomment-75417769 https://api.github.com/repos/pydata/xarray/issues/328 MDEyOklzc3VlQ29tbWVudDc1NDE3NzY5 mrocklin 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]]) ```

 
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   Support out-of-core computation using dask 58310637
75276367 https://github.com/pydata/xarray/issues/328#issuecomment-75276367 https://api.github.com/repos/pydata/xarray/issues/328 MDEyOklzc3VlQ29tbWVudDc1Mjc2MzY3 mrocklin 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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   Support out-of-core computation using dask 58310637

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