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  • Zeitsperre · 7 ✖

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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
1243917954 https://github.com/pydata/xarray/issues/7012#issuecomment-1243917954 https://api.github.com/repos/pydata/xarray/issues/7012 IC_kwDOAMm_X85KJK6C Zeitsperre 10819524 2022-09-12T15:33:10Z 2022-09-12T15:33:10Z CONTRIBUTOR

@mathause You're right! I noticed this first in my builds using "upstream" dependencies (xarray@main, flox@main, cftime@master, bottleneck@master). It might indeed be flox-related!

 
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   Time-based resampling drops lat/lon coordinate metadata 1367029446
678423562 https://github.com/pydata/xarray/issues/4054#issuecomment-678423562 https://api.github.com/repos/pydata/xarray/issues/4054 MDEyOklzc3VlQ29tbWVudDY3ODQyMzU2Mg== Zeitsperre 10819524 2020-08-21T18:16:16Z 2020-08-21T18:16:16Z CONTRIBUTOR

Just dicsovered that the same things is true for ~. Another thing to add to the list: ... : error: Unsupported operand type for ~ ("DataArray") [operator]

 
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   Type checking fails for multiplication 617140674
677920840 https://github.com/pydata/xarray/issues/4054#issuecomment-677920840 https://api.github.com/repos/pydata/xarray/issues/4054 MDEyOklzc3VlQ29tbWVudDY3NzkyMDg0MA== Zeitsperre 10819524 2020-08-20T21:41:51Z 2020-08-20T22:48:49Z CONTRIBUTOR

We're currently working on a library largely based on xarray and have seen the same types of errors from mypy (PR in our project that is currently trying to integrate mypy: https://github.com/Ouranosinc/xclim/pull/532). Currently working off of xarray v0.16.

I also want to note this error is raised for other operations as well (+, -, /, and *) between xarray.DataArray and xarray.Datasets. 

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   Type checking fails for multiplication 617140674
491853311 https://github.com/pydata/xarray/pull/2957#issuecomment-491853311 https://api.github.com/repos/pydata/xarray/issues/2957 MDEyOklzc3VlQ29tbWVudDQ5MTg1MzMxMQ== Zeitsperre 10819524 2019-05-13T14:46:50Z 2019-05-13T14:46:50Z CONTRIBUTOR 
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   DOC: Added xclim to related projects 443440217
462422387 https://github.com/pydata/xarray/issues/2417#issuecomment-462422387 https://api.github.com/repos/pydata/xarray/issues/2417 MDEyOklzc3VlQ29tbWVudDQ2MjQyMjM4Nw== Zeitsperre 10819524 2019-02-11T17:41:47Z 2019-02-11T17:41:47Z CONTRIBUTOR

Hi @jhamman, please excuse the lateness of this reply. It turned out that in the end all I needed to do was set OMP_NUM_THREADS to the number based on my cores I want to use (2 threads/core) before launching my processes. Thanks for the help and for keeping this open. Feel free to close this thread. 

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   Limiting threads/cores used by xarray(/dask?) 361016974
453203293 https://github.com/pydata/xarray/issues/2664#issuecomment-453203293 https://api.github.com/repos/pydata/xarray/issues/2664 MDEyOklzc3VlQ29tbWVudDQ1MzIwMzI5Mw== Zeitsperre 10819524 2019-01-10T18:30:48Z 2019-01-10T18:30:48Z CONTRIBUTOR

That certainly is the error. The workaround identified for it is good enough for now. Thanks!

 
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   Xarray fails to build with bottleneck on Travis CI 397950349
422445732 https://github.com/pydata/xarray/issues/2417#issuecomment-422445732 https://api.github.com/repos/pydata/xarray/issues/2417 MDEyOklzc3VlQ29tbWVudDQyMjQ0NTczMg== Zeitsperre 10819524 2018-09-18T15:44:03Z 2018-09-18T15:44:03Z CONTRIBUTOR

As per your suggestion, I retried with chunking and found a new error (due to the nature of my data having rotated poles, dask demanded that I save my data with astype(); this isn't my major concern so I'll deal with that somewhere else).

What I did notice was that when chunking was specified (ds = xr.open_dataset(ncfile).chunking({'time': 10})), I lost all parallelism and although I had specified different thread counts, the performance never crossed 110% (I imagine the extra 10% was due to I/O).

This is really a mystery and unfortunately, I haven't a clue how this beahviour is possible if parallel processing is disabled by default. The speed of my results when dask multprocessing isn't specified suggests that it must be using more processing power:

  • using Multiprocessing calls to CDO with 5 ForkPoolWorkers = ~2h/5 files (100% x 5 CPUs)
  • xarray without dask multiprocessing specifications = ~3min/5 files (spikes of 3500% on one CPU)

Could these spikes in CPU usage be due to other processes (e.g. memory usage, I/O)? 

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   Limiting threads/cores used by xarray(/dask?) 361016974

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