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  • Examples combining multiple files · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
454533643 https://github.com/pydata/xarray/issues/436#issuecomment-454533643 https://api.github.com/repos/pydata/xarray/issues/436 MDEyOklzc3VlQ29tbWVudDQ1NDUzMzY0Mw== max-sixty 5635139 2019-01-15T20:10:37Z 2019-01-15T20:10:37Z MEMBER

Closing as stale, please reopen if still relevant

 
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   Examples combining multiple files 88897697
113340207 https://github.com/pydata/xarray/issues/436#issuecomment-113340207 https://api.github.com/repos/pydata/xarray/issues/436 MDEyOklzc3VlQ29tbWVudDExMzM0MDIwNw== shoyer 1217238 2015-06-19T02:00:27Z 2015-06-19T02:00:27Z MEMBER

@j08lue good idea! Right now that page is mostly targeted at non-climate scientists, but it would be nice to include an example for climate scientists, too.

 
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   Examples combining multiple files 88897697
113127892 https://github.com/pydata/xarray/issues/436#issuecomment-113127892 https://api.github.com/repos/pydata/xarray/issues/436 MDEyOklzc3VlQ29tbWVudDExMzEyNzg5Mg== j08lue 3404817 2015-06-18T11:47:42Z 2015-06-18T11:47:42Z CONTRIBUTOR

That is actually an excellent demonstration of the power of xray for climate data analysis, @shoyer.

Something like this (including the pandas bridge) should be included in the documentation somewhere, for example under Why xray?.

Just a thought...

 
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   Examples combining multiple files 88897697
112651491 https://github.com/pydata/xarray/issues/436#issuecomment-112651491 https://api.github.com/repos/pydata/xarray/issues/436 MDEyOklzc3VlQ29tbWVudDExMjY1MTQ5MQ== shoyer 1217238 2015-06-17T04:46:52Z 2015-06-17T04:46:52Z MEMBER

Have you tried xray.open_mfdataset? All of the examples should work the same with a dataset loaded with that function as from a single file.

In your case, something like the following should work:

``` python

load data

ds = xray.open_mfdataset('path/to/my/files/*.nc')

calculate anomalies

clim = ds.groupby('time.month').mean('time') anom = ds.groupby('time.month') - clim

plot anomalies over time

(in practice, would probably want to use .sel here to do

labeled lookups)

anom.temperature.isel(x=0, y=0).to_pandas().plot()

plot anomalies over space

plt.imshow(anom.temperature.isel(time=0).values) ```

Plotting is currently not so easy as it should be with xray (hence why you see me exporting everything to pandas), but that's something we plan to start work on very soon.

 
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   Examples combining multiple files 88897697

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