DAS-2276: Use OPAQUE and TRANSPARENT. Reword comments.

hybig/browse.py

@@ -224,8 +224,8 @@ def create_browse_imagery(
 def convert_mulitband_to_raster(data_array: DataArray) -> ndarray[uint8]:
     """Convert multiband to a raster image.
 
-    Return a raster of a 4-band data set, the existing alpha layer is presumed to be the
-    missing data mask.
+    Return a 4-band raster, where the alpha layer is presumed to be the missing
+    data mask.
 
     Convert 3-band data into a 4-band raster by generating an alpha layer from
     any missing data in the RGB bands.
@@ -242,7 +242,7 @@ def convert_mulitband_to_raster(data_array: DataArray) -> ndarray[uint8]:
     if data_array.rio.count == 4:
         return convert_to_uint8(bands, original_dtype(data_array))
 
-    # Create a nan-based alpha layer where input NaN values are transparent.
+    # Input NaNs in any of the RGB bands are made transparent.
     nan_mask = np.isnan(bands).any(axis=0)
     nan_alpha = np.where(nan_mask, TRANSPARENT, OPAQUE)
 
@@ -277,8 +277,8 @@ def convert_to_uint8(bands: ndarray, dtype: str | None) -> ndarray[uint8]:
 def original_dtype(data_array: DataArray) -> str | None:
     """Return the original input data's type.
 
-    The input dtype is retained in the encoding dictionary and can be used to
-    understand what kind of casts are safe.
+    rastero_optn retains the input dtype in the encoding dictionary and is used
+    to understand what kind of casts are safe.
 
     """
     return data_array.encoding.get('dtype') or data_array.encoding.get('rasterio_dtype')

tests/unit/test_browse.py

@@ -553,14 +553,15 @@ def test_convert_4_multiband_uint16_to_raster(self):
         g_data = r_data.copy()
         b_data = r_data.copy()
 
-        a_data = np.ones_like(self.data) * 255
-        a_data[0, 0] = 0
+        a_data = np.ones_like(self.data) * OPAQUE
+        a_data[0, 0] = TRANSPARENT
 
         to_numpy_result = np.stack([r_data, g_data, b_data, a_data])
 
         ds.to_numpy.return_value = to_numpy_result
 
-        # expect the input data to have 0 to 400 to be scaled into 0 to 255
+        # expect the input data to have the data values from 0 to 400 to be
+        # scaled into the range 0 to 255.
         expected_raster = np.around(
             np.interp(to_numpy_result, (0, 400), (0.0, 1.0)) * 255.0
         ).astype('uint8')