make output band name an option for landcover final

odc/stats/plugins/lc_level34.py

@@ -2,7 +2,7 @@
 Plugin of Module A3 in LandCover PipeLine
 """
 
-from typing import Tuple, Optional, List
+from typing import Optional, List
 
 import numpy as np
 import xarray as xr
@@ -55,11 +55,6 @@ def __init__(
             water_seasonality_threshold if water_seasonality_threshold else 3
         )
 
-    @property
-    def measurements(self) -> Tuple[str, ...]:
-        _measurements = ["level3", "level4"]
-        return _measurements
-
     def native_transform(self, xx):
         return xx
 
@@ -114,10 +109,9 @@ def reduce(self, xx: xr.Dataset) -> xr.Dataset:
         attrs = xx.attrs.copy()
         attrs["nodata"] = NODATA
         dims = xx.classes_l3_l4.dims[1:]
-
         data_vars = {
-            "level3": xr.DataArray(level3.squeeze(), dims=dims, attrs=attrs),
-            "level4": xr.DataArray(level4.squeeze(), dims=dims, attrs=attrs),
+            k: xr.DataArray(v, dims=dims, attrs=attrs)
+            for k, v in zip(self.measurements, [level3.squeeze(), level4.squeeze()])
         }
 
         coords = dict((dim, xx.coords[dim]) for dim in dims)

tests/test_lc_l34.py

@@ -152,7 +152,7 @@ def test_l4_classes(image_groups):
     expected_l3 = [[216, 216, 215], [216, 216, 216], [215, 215, 215], [215, 215, 215]]
 
     expected_l4 = [[95, 97, 93], [97, 96, 96], [93, 93, 93], [93, 93, 93]]
-    stats_l4 = StatsLccsLevel4()
+    stats_l4 = StatsLccsLevel4(measurements=["level3", "level4"])
     ds = stats_l4.reduce(image_groups)
 
     assert (ds.level3.compute() == expected_l3).all()