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- std interprets continents as zero not nan · 2 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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490421774 | https://github.com/pydata/xarray/issues/2946#issuecomment-490421774 | https://api.github.com/repos/pydata/xarray/issues/2946 | MDEyOklzc3VlQ29tbWVudDQ5MDQyMTc3NA== | andytraumueller 10809480 | 2019-05-08T09:44:25Z | 2019-05-08T09:49:02Z | NONE | interesting fact i just learned. when you have to process over a huge dataset, first export it as a complete single netcdf file, then calculate its aggregation function. Its a workaround, i suppose bottleneck or dask needs to have its complete set first. For mean it just simply works because of the easy calculation method, for std i think dask or bottleneck assume a nan as a zero for calculation purposes.
It could be problematic by huuuuge datasets in the tb size. |
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std interprets continents as zero not nan 441222339 | |
490394601 | https://github.com/pydata/xarray/issues/2946#issuecomment-490394601 | https://api.github.com/repos/pydata/xarray/issues/2946 | MDEyOklzc3VlQ29tbWVudDQ5MDM5NDYwMQ== | andytraumueller 10809480 | 2019-05-08T08:18:21Z | 2019-05-08T09:01:56Z | NONE | fixed: synthetic dataset of the polar region -60 - -90, in the mean calculation everything is proper and nans are ignored. std still looks suspicious. ```python import xarray as xr import glob import numpy as np data = xr.open_dataset(r"test.nc")
data.mean(dim="time", skipna=True).to_netcdf(r"mean_test.nc")
Dropbox to files: https://www.dropbox.com/sh/yuf114u143mj2l3/AABuQfC5wu4nrWDH4GsGgFyJa?dl=0 |
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std interprets continents as zero not nan 441222339 |
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