add tolerance

Author: Emma Ai

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@eee2ed1
+odc-stats[ows] @ git+https://github.com/opendatacube/odc-stats@4480ab1
 
 # For ML
 tflite-runtime

odc/stats/plugins/lc_tf_urban.py

@@ -178,15 +178,15 @@ def aggregate_results_from_group(self, urban_masks):
             urban_masks = urban_masks[0]
 
         urban_masks = expr_eval(
-            "where((a/nodata)>=_l, nodata, a%nodata)",
+            "where((a/nodata)+0.5>=_l, nodata, a%nodata)",
             {"a": urban_masks},
             name="mark_nodata",
             dtype="float32",
             **{"_l": m_size, "nodata": NODATA},
         )
 
         urban_masks = expr_eval(
-            "where((a>0)&(a<nodata), _u, a)",
+            "where((a>0.5)&(a<nodata), _u, a)",
             {"a": urban_masks},
             name="output_classes_artificial",
             dtype="float32",
@@ -197,7 +197,7 @@ def aggregate_results_from_group(self, urban_masks):
         )
 
         urban_masks = expr_eval(
-            "where(a<=0, _nu, a)",
+            "where(a<0.5, _nu, a)",
             {"a": urban_masks},
             name="output_classes_natrual",
             dtype="uint8",

odc/stats/plugins/lc_treelite_cultivated.py

@@ -164,21 +164,6 @@ def generate_features(input_block, bands_indices):
         norm, 1e-8
     )  # Avoid division by zero, fine if it's nan
 
-    # reassemble the array
-    output_block = np.concatenate(
-        [output_block, input_block[..., bands_indices["sdev"]][..., np.newaxis]],
-        axis=-1,
-    ).astype("float32")
-    # scale edev \in [0, 1]
-    edev = input_block[..., bands_indices["edev"]] / 1e4
-    output_block = np.concatenate(
-        [output_block, edev[..., np.newaxis]], axis=-1
-    ).astype("float32")
-    output_block = np.concatenate(
-        [output_block, input_block[..., bands_indices["bcdev"]][..., np.newaxis]],
-        axis=-1,
-    ).astype("float32")
-
     feature_block = None
     for f, p in zip(
         [
@@ -193,15 +178,29 @@ def generate_features(input_block, bands_indices):
         ],
         feature_input_indices,
     ):
-        ib = f(output_block[..., : bands_indices["nbart_swir_2"] + 1], *p)
+        ib = f(output_block, *p)
         if feature_block is None:
             feature_block = ib[..., np.newaxis]
         else:
             feature_block = np.concatenate(
                 [feature_block, ib[..., np.newaxis]], axis=-1
             )
-
+    # reassemble the array
+    output_block = np.concatenate(
+        [output_block, input_block[..., bands_indices["sdev"]][..., np.newaxis]],
+        axis=-1,
+    ).astype("float32")
+    # scale edev \in [0, 1]
+    edev = input_block[..., bands_indices["edev"]] / 1e4
+    output_block = np.concatenate(
+        [output_block, edev[..., np.newaxis]], axis=-1
+    ).astype("float32")
+    output_block = np.concatenate(
+        [output_block, input_block[..., bands_indices["bcdev"]][..., np.newaxis]],
+        axis=-1,
+    ).astype("float32")
     output_block = np.concatenate([output_block, feature_block], axis=-1)
+
     selected_indices = np.r_[
         [
             bands_indices[k]
@@ -288,23 +287,23 @@ def aggregate_results_from_group(self, predict_output):
             predict_output = predict_output.sum(axis=0)
 
         predict_output = expr_eval(
-            "where((m/nodata)>=_l, nodata, m%nodata)",
+            "where((m/nodata)+0.5>=_l, nodata, m%nodata)",
             {"m": predict_output},
             name="mark_nodata",
             dtype="float32",
             **{"_l": m_size, "nodata": NODATA},
         )
 
         predict_output = expr_eval(
-            "where((m>0)&(m<nodata), _u, m)",
+            "where((m>0.5)&(m<nodata), _u, m)",
             {"m": predict_output},
             name="output_classes_cultivated",
             dtype="float32",
             **{"_u": self.output_classes["cultivated"], "nodata": NODATA},
         )
 
         predict_output = expr_eval(
-            "where(m<=0, _nu, m)",
+            "where(m<0.5, _nu, m)",
             {"m": predict_output},
             name="output_classes_natural",
             dtype="uint8",

odc/stats/plugins/lc_treelite_woody.py

@@ -66,7 +66,7 @@ def aggregate_results_from_group(self, predict_output):
             predict_output = predict_output.sum(axis=0)
 
         predict_output = expr_eval(
-            "where((a/nodata)>=_l, nodata, a%nodata)",
+            "where((a/nodata)+0.5>=_l, nodata, a%nodata)",
             {"a": predict_output},
             name="summary_over_classes",
             dtype="float32",
@@ -77,15 +77,15 @@ def aggregate_results_from_group(self, predict_output):
         )
 
         predict_output = expr_eval(
-            "where((a>0)&(a<nodata), _nw, a)",
+            "where((a>0.5)&(a<nodata), _nw, a)",
             {"a": predict_output},
             name="output_classes_herbaceous",
             dtype="float32",
             **{"nodata": NODATA, "_nw": self.output_classes["herbaceous"]},
         )
 
         predict_output = expr_eval(
-            "where(a<=0, _nw, a)",
+            "where(a<0.5, _nw, a)",
             {"a": predict_output},
             name="output_classes_woody",
             dtype="uint8",