CODEWITHSUNDEEP
pythonslicenetcdfpython-xarray
Vivien
Vivien

Reputation: 67

xarray slice function alternative for calculating average along a dimension

I'm using Xarray and netCDF meteorological data. I have the usual dimensions time, latitude and longitude and two main variables: the wind speed (time, lat, lon) and a latitudinal position (time, lon).

 <xarray.Dataset>
Dimensions:             (lon: 53, time: 25873, lat: 20)
Coordinates:
  * lon                 (lon) float64 -80.0 -77.5 -75.0 -72.5 ... 45.0 47.5 50.0
  * time                (time) datetime64[ns] 1950-01-31 ... 2020-12-01
  * lat                 (lat) float32 70.0 67.5 65.0 62.5 ... 27.5 25.0 22.5
Data variables:
    uwnd                (time, lat, lon) float32 -0.0625 0.375 ... -1.812 -2.75
    positions           (time, lon) float64 40.0 40.0 45.0 ... 70.0 70.0 70.0

For each time, lon, I'd like to calculate a latitudinal average around the positions.

If I do a loop, I would do this (for a +-2.5° latitude average):

for i in ds.lon.values:
        for t in ds.time.values:
              wind_averaged.loc[t,i]=ds.uwnd.sel(lon=i,time=t).sel(lat=slice(2.5+ds.positions.sel(lon=i,time=t).values,ds.positions.sel(lon=i,time=t).values-2.5)).mean('lat')

This is obviously very bad and I wanted to use slice() like this:

wind_averaged=ds.uwnd.sel(lat=slice(2.5+ds.jet_positions.values,ds.jet_positions.values-2.5)).mean('lat')

but it gives an error because I

cannot use non-scalar arrays in a slice for xarray indexing

Is there any alternative to do what I want without doing two for loops by using Xarray power?

Thanks

Upvotes: 1

Views: 570

Answers (1)

Tim Foreman
Tim Foreman

Reputation: 31

I believe you are looking for the multidimensional groupby. If I understand correctly, there is a tutorial for this problem here: https://xarray.pydata.org/en/stable/examples/multidimensional-coords.html

Upvotes: 0

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