Reputation: 55
I am working with a lot of temperature data which has been measured at different longitudes and latitudes and I can open it from a NetCDF file like this.
<xarray.Dataset>
Dimensions: (altitude: 801, measurement_number: 3180)
Coordinates:
* altitude (altitude) float64 0.0 100.0 200.0 300.0 400.0 500.0 ...
Dimensions without coordinates: measurement_number
Data variables:
temperature (measurement_number, altitude) float32 ...
longitude (measurement_number) float64 ...
latitude (measurement_number) float64 ...
To evaluate the results more conveniently I want to change the variables "longitude" and "latitude" into dimensions or coordinates like this.
<xarray.Dataset>
Dimensions: (altitude: 801, measurement_number: 3180, longitude: 36, latitude: 18)
Coordinates:
* altitude (altitude) float64 0.0 100.0 200.0 300.0 400.0 500.0 ...
* longitude (longitude) float64 -180, -170, -160 ...
* latitude (latitude) float64 -90, -80, -70, ...
Dimensions without coordinates: measurement_number
Data variables:
temperature (measurement_number, altitude, longitude, latitude) float32 ...
I tried to get to here by using ds.set_coords(['longitude','latitude'])
, and lots of different functions, but I cannot make the temperature depending on longitude and latitude. Can you give me a tip how to approach this problem?
Upvotes: 3
Views: 5147
Reputation: 174
Have you seen the Multi-index feature in xarray? It may not be exactly what you are looking for and there are some limitations (e.g. serialisation to netCDF is not yet supported as far as i know), but maybe that is okay for you. The idea is to turn measurement_number into a pandas multiindex with latitude and longitude as virtual coordinates. You can then use .sel on multiindex coordinates aswell -> Multi-level indexing
Upvotes: 3