Specify spatial and temporal dimensions

Assuming you have an Xarray object that has some atypical or unusual names for its dimensions, we can tell geoxarray what these dimensions represent. For more information on what dimensions geoxarray understands see the Dimensions documentation.

print(data_arr)

We can see from this output that there are three dimensions in this DataArray: (dim_0, dim_1, dim_2).

<xarray.DataArray (dim_0: 5, dim_1: 20, dim_2: 10)> Size: 8kB
...
Coordinates:
  * dim_1    (dim_1) float64 160B 0.0 1.0 2.0 3.0 4.0 ... 16.0 17.0 18.0 19.0
  * dim_2    (dim_2) float64 80B 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
Dimensions without coordinates: dim_0

These don’t fit geoxarray’s usual preferred names for Dimensions, but we can see if geoxarray can guess their purpose by using .geo.dims:

import geoxarray

print(data_arr.geo.dims)

And note that the names of the dimensions haven’t changed:

('dim_0', 'dim_1', 'dim_2')

We can tell geoxarray what these mean by using .geo.set_dims():

data_arr.geo.set_dims(y="dim_1", x="dim_2")
print(data_arr.geo.dims)

And notice that geoxarray now knows that the second and third dimension correspond to the y and x spatial dimensions.

('dim_0', 'y', 'x')

From now on geoxarray should be able to make decisions based on these known dimensions. Note that geoxarray hasn’t changed anything about the DataArray itself. We can rename the dimensions with geoxarray’s preferred names by doing:

new_data_arr = data_arr.geo.write_dims()
print(new_data_arr)

<xarray.DataArray (dim_0: 5, y: 20, x: 10)> Size: 8kB
...
Coordinates:
  * y        (y) float64 160B 0.0 1.0 2.0 3.0 4.0 ... 16.0 17.0 18.0 19.0
  * x        (x) float64 80B 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
Dimensions without coordinates: dim_0