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  • jbusecke · 4 ✖

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  • We need a fast path for open_mfdataset · 4 ✖

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  • CONTRIBUTOR · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
531945252 https://github.com/pydata/xarray/issues/1823#issuecomment-531945252 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDUzMTk0NTI1Mg== jbusecke 14314623 2019-09-16T20:29:35Z 2019-09-16T20:29:35Z CONTRIBUTOR

Wooooow. Thanks. Ill have to give this a whirl soon.

 
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   We need a fast path for open_mfdataset 288184220
373123959 https://github.com/pydata/xarray/issues/1823#issuecomment-373123959 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDM3MzEyMzk1OQ== jbusecke 14314623 2018-03-14T18:16:38Z 2018-03-14T18:16:38Z CONTRIBUTOR

Awesome, thanks for the clarification. I just looked at #1981 and it seems indeed very elegant (in fact I just now used this approach to parallelize printing of movie frames!) Thanks for that!

 
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   We need a fast path for open_mfdataset 288184220
372856076 https://github.com/pydata/xarray/issues/1823#issuecomment-372856076 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDM3Mjg1NjA3Ng== jbusecke 14314623 2018-03-13T23:40:54Z 2018-03-13T23:40:54Z CONTRIBUTOR

Would these two options be necessarily mutually exclusive?

I think parallelizing the read in sounds amazing.

But isnt there some merit in skipping some of the checks all together, if the user is sure about the structure of the data contained in the many files?

I am often working with the aforementioned type of data (many files either contain a new timestep or a different variable, but most of the dimensions/coordinates are the same).

In some cases I am finding that reading the data "lazily" consumes a significant amount of the time in my workflow. I am unsure how hard this would be to achieve, and perhaps it is not worth it after all.

Just putting out a few ideas, while I wait for my xr.open_mfdataset to finish :-)

 
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   We need a fast path for open_mfdataset 288184220
359069753 https://github.com/pydata/xarray/issues/1823#issuecomment-359069753 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDM1OTA2OTc1Mw== jbusecke 14314623 2018-01-19T19:45:00Z 2018-01-19T19:45:00Z CONTRIBUTOR

I did not really find an elegant solution. What I did was just specify all dims and coords as drop_variables and then update those from a master file with ds.update(ds_master) Perhaps this could be generalized in a sense, by reading all coords and dims just from the first file. 

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   We need a fast path for open_mfdataset 288184220

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