avoid unwanted optimization

docker/requirements.txt

@@ -11,7 +11,7 @@ odc-dscache>=0.2.3
 odc-stac @ git+https://github.com/opendatacube/odc-stac@69bdf64
 
 # odc-stac is in PyPI
-odc-stats[ows] @ git+https://github.com/opendatacube/odc-stats@8dfd442
+odc-stats[ows] @ git+https://github.com/opendatacube/odc-stats@1065599
 
 # For ML
 tflite-runtime

odc/stats/plugins/lc_treelite_cultivated.py

@@ -22,28 +22,30 @@
 
 # pylint: disable=invalid-name
 def feature_MNDWI(input_block, nbart_green, nbart_swir_1):
-    return ne.evaluate(
+    res = ne.evaluate(
         "(a-b)/(a+b)",
         local_dict={
             "a": input_block[..., nbart_green],
             "b": input_block[..., nbart_swir_1],
         },
     ).astype("float32")
+    return res
 
 
 def feature_BUI(input_block, nbart_swir_1, nbart_nir, nbart_red):
-    return ne.evaluate(
+    res = ne.evaluate(
         "((a-b)/(a+b))-((b-c)/(b+c))",
         local_dict={
             "a": input_block[..., nbart_swir_1],
             "b": input_block[..., nbart_nir],
             "c": input_block[..., nbart_red],
         },
     ).astype("float32")
+    return res
 
 
 def feature_BSI(input_block, nbart_swir_1, nbart_red, nbart_nir, nbart_blue):
-    return ne.evaluate(
+    res = ne.evaluate(
         "((a+b)-(c+d))/((a+b)+(c+d))",
         local_dict={
             "a": input_block[..., nbart_swir_1],
@@ -52,6 +54,7 @@ def feature_BSI(input_block, nbart_swir_1, nbart_red, nbart_nir, nbart_blue):
             "d": input_block[..., nbart_blue],
         },
     ).astype("float32")
+    return res
 
 
 def feature_TCW(
@@ -63,7 +66,7 @@ def feature_TCW(
     nbart_swir_1,
     nbart_swir_2,
 ):
-    return ne.evaluate(
+    res = ne.evaluate(
         "(0.0315*a+0.2021*b+0.3102*c+0.1594*d+-0.6806*e+-0.6109*f)",
         local_dict={
             "a": input_block[..., nbart_blue],
@@ -74,22 +77,24 @@ def feature_TCW(
             "f": input_block[..., nbart_swir_2],
         },
     ).astype("float32")
+    return res
 
 
 def feature_NDMI(input_block, nbart_nir, nbart_swir_1):
-    return ne.evaluate(
+    res = ne.evaluate(
         "(a-b)/(a+b)",
         local_dict={
             "a": input_block[..., nbart_nir],
             "b": input_block[..., nbart_swir_1],
         },
     ).astype("float32")
+    return res
 
 
 def feature_AWEI_sh(
     input_block, nbart_blue, nbart_green, nbart_nir, nbart_swir_1, nbart_swir_2
 ):
-    return ne.evaluate(
+    res = ne.evaluate(
         "(a+2.5*b-1.5*(c+d)-0.25*e)",
         local_dict={
             "a": input_block[..., nbart_blue],
@@ -99,27 +104,30 @@ def feature_AWEI_sh(
             "e": input_block[..., nbart_swir_2],
         },
     ).astype("float32")
+    return res
 
 
 def feature_BAEI(input_block, nbart_red, nbart_green, nbart_swir_1):
-    return ne.evaluate(
+    res = ne.evaluate(
         "(a+0.3)/(b+c)",
         local_dict={
             "a": input_block[..., nbart_red],
             "b": input_block[..., nbart_green],
             "c": input_block[..., nbart_swir_1],
         },
     ).astype("float32")
+    return res
 
 
 def feature_NDSI(input_block, nbart_green, nbart_swir_1):
-    return ne.evaluate(
+    res = ne.evaluate(
         "(a-b)/(a+b)",
         local_dict={
             "a": input_block[..., nbart_green],
             "b": input_block[..., nbart_swir_1],
         },
     ).astype("float32")
+    return res
 
 
 def generate_features(input_block, bands_indices):