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  • niowniow · 8 ✖

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  • CONTRIBUTOR · 8 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
904597086 https://github.com/pydata/xarray/issues/5727#issuecomment-904597086 https://api.github.com/repos/pydata/xarray/issues/5727 IC_kwDOAMm_X8416w5e niowniow 5802846 2021-08-24T12:28:18Z 2021-08-24T12:31:21Z CONTRIBUTOR

Thank you! You are right. I guess based on the docs for Assigning values with indexing I assumed only .loc features item assignment . I vaguely remember there where issues with assigning via integer-based indexing. Well, propably my bad.

Should we add an example how to perform masking this way to the docs? There are many examples about masking using different ways, but not this simple, intuitive way.

Even now, it's not really clear to me why "boolean"-masking should only work with integer-indexing and not label-indexing. Given that the highlighted maybe_cast_to_coords_dtype is the only reason it does not work for label-based indexing.

edit: probably the reason is to support label-based indexing for boolean coords?!

 
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   Setting item with loc and boolean mask fails 976207971
888250482 https://github.com/pydata/xarray/issues/3608#issuecomment-888250482 https://api.github.com/repos/pydata/xarray/issues/3608 IC_kwDOAMm_X8408aBy niowniow 5802846 2021-07-28T11:58:28Z 2021-07-28T11:58:28Z CONTRIBUTOR

You are right. It's quite confusing. I've already added a stride parameter in my PR #3607 I didn't follow through with it and at the moment the checks are not successful anymore. Maybe someone else could give an opinion on the pro/cons of a stride parameter in rolling? 

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   Feature Request: Efficient rolling with strides 535703663
851469496 https://github.com/pydata/xarray/issues/3608#issuecomment-851469496 https://api.github.com/repos/pydata/xarray/issues/3608 MDEyOklzc3VlQ29tbWVudDg1MTQ2OTQ5Ng== niowniow 5802846 2021-05-31T12:50:37Z 2021-05-31T12:50:37Z CONTRIBUTOR

Quickly glancing over sliding_window_view I didn't immediately understand how to use it with stride. Would I need to 1. transform the DataArray to dask array using chunk (which may involve an overhead!?), 2. then use rolling which itself uses sliding_window_view because its a dask array!? 3. Then use isel with stride on the new dimension?

reduce can easily support stride by passing it on here:

I think that's what I did in #3607. It's been a while

 
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   Feature Request: Efficient rolling with strides 535703663
839758891 https://github.com/pydata/xarray/issues/5290#issuecomment-839758891 https://api.github.com/repos/pydata/xarray/issues/5290 MDEyOklzc3VlQ29tbWVudDgzOTc1ODg5MQ== niowniow 5802846 2021-05-12T13:08:51Z 2021-05-12T20:28:10Z CONTRIBUTOR

Thanks a lot! Very helpful comments. I'll check out your PR.

If i understand it correct, zarr does some autochunking while saving coordinates even without setting specific encodings, at least for bigger coordinate arrays. I can get what I want by creating a zarr store with compute=False then deleting everything except the metadata manually on the filesystem level. Then each call to_zarr() with region results in only one coordinate chunk being created on disk. Reading with xr.open_zarr() works as expected: the coordinate contains nan except for the region written before. The (potentially very large) coordinate still needs to fit in memory though... either when creating the dummy zarr store (which could be done differently) or when opening it. Is that correct? That wont work for my use case when the coordinate is very large.

Do you know an alternative? Would it help if I store the coordinate with a non-dimension name? i guess it all boils down to the way xarray recreates the Dataset from zarr store. The only way I can think of right know to make useful "chunked indices"  are some form of hierachical indexing. Each chunk is represented by the first index in that chunk. Which would probably only work for sequential indices. I dont know if such indexing exists for pandas. Maybe a hierachical chunking could be useful for some very large datasets!? I dont know if that would create too much overhead but it would be a structured way to access long-term high-res data. In a way I think thats what I'm trying to implement. I would be happy about any pointers to existing solutions.

Regarding the documentation: I could provide an example with a time coordinate, which would illustrate two issues I encountered. * region requires index space coordinates (I know: it's already explained in the docs... :) * the before mentioned "coordinates need to be predefined" issue.

(Sorry if this bug report is not the right place to ask all these questions)

 
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   Inconclusive error messages using to_zarr with regions 887711474
573060295 https://github.com/pydata/xarray/pull/3610#issuecomment-573060295 https://api.github.com/repos/pydata/xarray/issues/3610 MDEyOklzc3VlQ29tbWVudDU3MzA2MDI5NQ== niowniow 5802846 2020-01-10T14:38:03Z 2020-01-10T14:38:03Z CONTRIBUTOR

Okay, this looks better. Thanks for the help! I had to try it again myself, because you cherry-picked some commits I accidentally merged into this branch (non-related to this fix). Could you check if this is good now?

 
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   Fix zarr append with groups 536214141
573021208 https://github.com/pydata/xarray/pull/3610#issuecomment-573021208 https://api.github.com/repos/pydata/xarray/issues/3610 MDEyOklzc3VlQ29tbWVudDU3MzAyMTIwOA== niowniow 5802846 2020-01-10T12:44:05Z 2020-01-10T12:44:05Z CONTRIBUTOR

I don't mind, thanks :)

 
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   Fix zarr append with groups 536214141
573019892 https://github.com/pydata/xarray/pull/3610#issuecomment-573019892 https://api.github.com/repos/pydata/xarray/issues/3610 MDEyOklzc3VlQ29tbWVudDU3MzAxOTg5Mg== niowniow 5802846 2020-01-10T12:39:58Z 2020-01-10T12:39:58Z CONTRIBUTOR

I tried to merge with master yesterday and did it again today. There might be a problem with the way I tried to fix the whats-new.rst issues. It might be faster if I open a new pull request and re-insert the changes!?

 
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   Fix zarr append with groups 536214141
564023352 https://github.com/pydata/xarray/issues/3608#issuecomment-564023352 https://api.github.com/repos/pydata/xarray/issues/3608 MDEyOklzc3VlQ29tbWVudDU2NDAyMzM1Mg== niowniow 5802846 2019-12-10T13:05:10Z 2019-12-10T13:05:10Z CONTRIBUTOR 
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   Feature Request: Efficient rolling with strides 535703663

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