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  • jhamman · 2 ✖

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  • unexpected positional indexing behavior with Dataset and DataArray "isel" · 2 ✖

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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
107307140 https://github.com/pydata/xarray/issues/411#issuecomment-107307140 https://api.github.com/repos/pydata/xarray/issues/411 MDEyOklzc3VlQ29tbWVudDEwNzMwNzE0MA== jhamman 2443309 2015-06-01T05:03:29Z 2015-06-01T05:03:38Z MEMBER

Thanks @shoyer . I think #412 suffices for this issue.

I will touch base regarding some point-wise/intersection/diagonal indexing (#214) when I return in July. 

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   unexpected positional indexing behavior with Dataset and DataArray "isel" 83000406
107210623 https://github.com/pydata/xarray/issues/411#issuecomment-107210623 https://api.github.com/repos/pydata/xarray/issues/411 MDEyOklzc3VlQ29tbWVudDEwNzIxMDYyMw== jhamman 2443309 2015-05-31T15:45:49Z 2015-05-31T15:45:49Z MEMBER

Okay. Glad to hear I was missing something. For large datasets, I was getting a MemoryError with orthogonal indexing as the array size increased like it did in my example.

The indexing details link you show does explain the behavior better but it is somewhat contradictory to the quote I listed above. I think it would be worth reconciling the docs on that point.

The station selection use case you bring up is exactly what I was going for. It would be nice if we could stay in xray / pandas after the indexing. Ideally, I don't want to do: xr_data.values[:, ys, xs] and then recreate a DataArray.

Lastly, is there an issue open for support of numpy fancy indexing? This is something that would be useful for my work and I could contribute to the effort beginning in July.

 
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   unexpected positional indexing behavior with Dataset and DataArray "isel" 83000406

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