如何在python中为数据集分配纬度/经度坐标？

Question

I have three xarray datasets, LATITUDE, LONGITUDE, WIND SPEED all having the same x,y dimensions. I want to assign the LATITUDE and LONGITUDE datasets as coordinates of WIND SPEED at each point in the x,y frame, so that the variable WIND SPEED has dimensions like this: WIND SPEED(LATITUDE, LONGITUDE).

How should I proceed? The input data is the output of a gridded weather model in Netcdf format. I have done some calculations from the input and I want to assign the coordinates to the outputs of the calculations(WIND SPEED). Later I want to do spatial interpolation with with nearest neighbor method, so that I can get a value at any lat,lon within the dataset. Latitude XArray Sample after importing:

array([[21.821693, 21.821693, 21.821693, ..., 21.821693, 21.821693,
        21.821693],
        ......................................................
       [30.20221 , 30.20221 , 30.20221 , ..., 30.20221 , 30.20221 ,
        30.20221 ]], dtype=float32)

Wind Speed Xarray:

array([[8.725852, 8.758366, 8.728758, ...,      nan,      nan,      nan],
       [8.502903, 8.563703, 8.574378, ...,      nan,      nan,      nan],
       ........]] dtype=float32)

Answer 1

One solution would be to construct an list of dicts:

speed_lat_long_list_dict = 
[ {id:1, speed:'1', lat:'20.8', long: '-18.5}, 
  {id:2, speed:'3', lat:'24.8', long: '-14.5},
  ....
  {id:n, speed:'n', lat:'n', long: 'n} ]

This would avoid confusion of setting co-ordinates to a duplicate speed value. eg what do we do if we have different co-ordinates for the same speed measurement.

This can be passed to a DataFrame should you want or you can process it using for loops or list comprehensions

Answer 2

You can merge your datasets and then assign the desired variables as coordinates:

data = np.random.rand(50,50)

windspeed = xr.Dataset({'windspeed':(['x','y'], data)})
lattitude = xr.Dataset({'lattitude':(['x','y'], np.cos(data))})
longitude = xr.Dataset({'longitude':(['x','y'], np.sin(data))})

ds = xr.merge([windspeed, lattitude, longitude])

ds.set_coords(['lattitude','longitude'])

 <xarray.Dataset> Dimensions: (x: 50, y: 50) Coordinates: lattitude (x, y) float64 0.7035 0.9987 0.917 0.9958 ... 0.593 0.93 0.7624 longitude (x, y) float64 0.7107 0.05069 0.3988 ... 0.8052 0.3675 0.6471 Dimensions without coordinates: x, y Data variables: windspeed (x, y) float64 0.7905 0.05071 0.4102 ... 0.936 0.3763 0.7037