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- Slicing DataArray can take longer than not slicing · 4 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 460881018 | https://github.com/pydata/xarray/issues/2004#issuecomment-460881018 | https://api.github.com/repos/pydata/xarray/issues/2004 | MDEyOklzc3VlQ29tbWVudDQ2MDg4MTAxOA== | shoyer 1217238 | 2019-02-06T02:32:46Z | 2019-02-06T02:32:46Z | MEMBER | The performance difference here does indeed to have been fixed with netCDF-C 4.6.2 (but see also https://github.com/pydata/xarray/issues/2747) |
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Slicing DataArray can take longer than not slicing 307318224 | |
| 375067743 | https://github.com/pydata/xarray/issues/2004#issuecomment-375067743 | https://api.github.com/repos/pydata/xarray/issues/2004 | MDEyOklzc3VlQ29tbWVudDM3NTA2Nzc0Mw== | shoyer 1217238 | 2018-03-21T19:29:51Z | 2018-03-21T19:29:51Z | MEMBER | H5py is doing all the hard work for this in h5netcdf. On Wed, Mar 21, 2018 at 11:51 AM Benjamin Root notifications@github.com wrote:
|
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Slicing DataArray can take longer than not slicing 307318224 | |
| 375020977 | https://github.com/pydata/xarray/issues/2004#issuecomment-375020977 | https://api.github.com/repos/pydata/xarray/issues/2004 | MDEyOklzc3VlQ29tbWVudDM3NTAyMDk3Nw== | shoyer 1217238 | 2018-03-21T17:08:15Z | 2018-03-21T17:08:15Z | MEMBER | The culprit appears to be netCDF4-python and/or netCDF-C: ``` f = netCDF4.Dataset('test.nc') %time f['xarray_dataarray_variable'][:, ::10] CPU times: user 313 ms, sys: 1.23 s, total: 1.54 s``` When I try doing the same operation with h5netcdf, it runs very quickly: ```python reopened = xr.open_dataarray('test.nc', engine='h5netcdf') %time reopened[::1, ::10].compute() CPU times: user 6.11 ms, sys: 3.63 ms, total: 9.74 ms``` |
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Slicing DataArray can take longer than not slicing 307318224 | |
| 375010010 | https://github.com/pydata/xarray/issues/2004#issuecomment-375010010 | https://api.github.com/repos/pydata/xarray/issues/2004 | MDEyOklzc3VlQ29tbWVudDM3NTAxMDAxMA== | shoyer 1217238 | 2018-03-21T16:38:59Z | 2018-03-21T16:38:59Z | MEMBER | Here's a simpler case that gets at the essence of the problem: ```python import xarray as xr import numpy as np source = xr.DataArray(np.zeros((100, 12000)), dims=['time', 'x']) source.to_netcdf('test.nc', format='NETCDF4') reopened = xr.open_dataarray('test.nc') %time reopened[::1, ::1].compute() CPU times: user 1.35 ms, sys: 6.77 ms, total: 8.12 ms%time reopened[::1, ::10].compute() CPU times: user 371 ms, sys: 1.33 s, total: 1.7 s``` |
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Slicing DataArray can take longer than not slicing 307318224 |
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