Fixes and tests

odc/geo/masking.py

@@ -64,8 +64,8 @@ class LANDSAT_C2L2_PIXEL_QA(Enum):
 
 def bits_to_bool(
     xx: DataArray,
-    bits: Sequence[int] | None,
-    bitflags: int | None,
+    bits: Sequence[int] | None = None,
+    bitflags: int | None = None,
     invert: bool = False,
 ) -> DataArray:
     """
@@ -121,8 +121,8 @@ def enum_to_bool(
 
 def scale_and_offset(
     xx: DataArray | Dataset,
-    scale: float | None,
-    offset: float | None,
+    scale: float | None = None,
+    offset: float | None = None,
     clip: Annotated[Sequence[int | float], 2] | None = None,
 ) -> DataArray | Dataset:
     """

tests/test_masking.py

@@ -6,20 +6,21 @@
     mask_invalid_data,
 )
 
-from xarray import DataArray
+
+from xarray import DataArray, Dataset
 
 # Top left is cloud, top right is cloud shadow
 # Bottom left is both cloud and cloud shadow, bottom right is neither
 xx_bits = DataArray(
-    [[0b00010000, 0b00001000], [0b00011000, 0b00000000]], dims=("y", "x")
+    [[0b00010000, 0b00001000], [0b00011000, 0b00000000]], dims=("y", "x"), attrs={"nodata": 0}
 )
 
 # Set up a 2x2 8 bit integer DataArray with some
 # values set to 3 (shadow), 9 (high confidence cloud).
-xx_values = DataArray([[3, 9], [3, 0]], dims=("y", "x"))
+xx_values = DataArray([[3, 9], [3, 0]], dims=("y", "x"), attrs={"nodata": 0})
 
 # Array with some zeros
-xx_with_nodata = DataArray([[1, 2], [0, 0]], dims=("y", "x"), attrs={"nodata": 0})
+xx_with_nodata = DataArray([[0, 1], [2, 3]], dims=("y", "x"), attrs={"nodata": 0})
 
 
 # Test bits_to_bool
@@ -54,17 +55,37 @@ def test_scale_and_offset():
     mask = scale_and_offset(xx_values, scale=1.0, offset=0.0)
     assert mask.equals(DataArray([[3, 9], [3, 0]], dims=("y", "x")))
 
-    mask = scale_and_offset(xx_values, scale=None, offset=None, ignore_missing=True)
+    mask = scale_and_offset(xx_values)
     assert mask.equals(DataArray([[3, 9], [3, 0]], dims=("y", "x")))
 
     mask = scale_and_offset(xx_values, scale=2.0, offset=1.0)
-    assert mask.equals(DataArray([[7, 19], [7, 1]], dims=("y", "x")))
+    assert mask.equals(DataArray([[7, 19], [7, 0]], dims=("y", "x")))
 
 
 # Test mask_invalid
 def test_mask_invalid_data():
     mask = mask_invalid_data(xx_with_nodata)
-    assert mask.equals(DataArray([[1.0, 2.0], [np.nan, np.nan]], dims=("y", "x")))
+    assert mask.equals(DataArray([[np.nan, 1.0], [2.0, 3.0]], dims=("y", "x")))
 
     mask = mask_invalid_data(xx_with_nodata, nodata=1)
-    assert mask.equals(DataArray([[np.nan, 2], [0, 0]], dims=("y", "x")))
+    assert mask.equals(DataArray([[0, np.nan], [2, 3]], dims=("y", "x")))
+
+
+# Test landsat masking
+def test_mask_landsat():
+    xx = Dataset({"pixel_qa": xx_bits, "red": scale_and_offset(xx_with_nodata, offset=20000)})
+    print(xx)
+
+    xx = xx.odc.mask_ls()
+
+    assert xx["red"].equals(DataArray([[np.nan, np.nan], [np.nan, 0.3500825]], dims=("y", "x")))
+
+
+def test_mask_sentinel2():
+    xx = Dataset({"scl": xx_values, "red": scale_and_offset(xx_with_nodata, offset=8000)})
+
+    xx = xx.odc.mask_s2()
+
+    assert xx["red"].equals(DataArray([[np.nan, np.nan], [np.nan, 0.7003]], dims=("y", "x")))
+
+    assert xx.red.odc.nodata is not None