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  • keewis · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1340809916 https://github.com/pydata/xarray/issues/7363#issuecomment-1340809916 https://api.github.com/repos/pydata/xarray/issues/7363 IC_kwDOAMm_X85P6yK8 keewis 14808389 2022-12-07T11:09:03Z 2022-12-07T11:09:03Z MEMBER

implementing a "grow_coordinate" function to grow / reallocate larger arrays copying the previous chunk along a coordinate

this sounds a lot like pad with mode="constant"?

is it possible that xarray makes no assumptions of this kind

xarray uses pandas indexes for alignment and indexing (if you have a recent version of xarray you should see the "Indexes" section in the HTML repr), so yes, it will always make sure to use a search that is more efficient than the linear search, as long as the data is sorted. This was also the reason why you had to use swap_dims / set_index to create an index along the coordinate you wanted to reindex.

 
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   expand dimension by re-allocating larger arrays with more space "at the end of the corresponding dimension", block copying previously existing data, and autofill newly created entry by a default value (note: alternative to reindex, but much faster for extending large arrays along, for example, the time dimension) 1479121713
1339675640 https://github.com/pydata/xarray/issues/7363#issuecomment-1339675640 https://api.github.com/repos/pydata/xarray/issues/7363 IC_kwDOAMm_X85P2dP4 keewis 14808389 2022-12-06T16:55:40Z 2022-12-06T16:55:40Z MEMBER

I'm a bit surprised. Could you post a repr of timestamps_extended_basis? That might help figuring out what exactly happened.

If everything fails, you might also create a new xarray object with just the new values, and then use xr.concat to combine both?

 
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   expand dimension by re-allocating larger arrays with more space "at the end of the corresponding dimension", block copying previously existing data, and autofill newly created entry by a default value (note: alternative to reindex, but much faster for extending large arrays along, for example, the time dimension) 1479121713
1339568566 https://github.com/pydata/xarray/issues/7363#issuecomment-1339568566 https://api.github.com/repos/pydata/xarray/issues/7363 IC_kwDOAMm_X85P2DG2 keewis 14808389 2022-12-06T15:39:20Z 2022-12-06T15:39:20Z MEMBER

I think this is because you don't have an index along the dimension. Try any of python previous_observations.set_coords(["timestamps"]).swap_dims({"time": "timestamps"}).reindex(...) previous_observations.set_index({"time": "timestamps"}).reindex(...) (the only difference is the name of the dimension / coordinate you end up with)

 
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   expand dimension by re-allocating larger arrays with more space "at the end of the corresponding dimension", block copying previously existing data, and autofill newly created entry by a default value (note: alternative to reindex, but much faster for extending large arrays along, for example, the time dimension) 1479121713

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