UPDATE

location_data/test_data_driven_subdivision.py

@@ -1,9 +1,14 @@
 from datetime import date
+import os
+from tempfile import TemporaryDirectory
+from unittest.mock import patch
 
+from django.core.files.base import ContentFile
 from django.test import TestCase
 
 from location_data.data_driven_subdivision import (
     EmptyObservationDatasetError,
+    _persist_remote_sensing_diagnostic_artifacts,
     build_clustering_dataset,
     sync_block_subdivision_with_result,
 )
@@ -137,6 +142,78 @@ class DataDrivenSubdivisionSyncTests(TestCase):
             self.subdivision.metadata["data_driven_subdivision"]["cluster_count"],
             2,
         )
+        self.assertIn("diagnostic_artifacts", self.subdivision.metadata["data_driven_subdivision"])
+
+    def test_persist_remote_sensing_diagnostic_artifacts_saves_expected_images(self):
+        cell = AnalysisGridCell.objects.create(
+            soil_location=self.location,
+            block_subdivision=self.subdivision,
+            block_code="block-1",
+            cell_code="cell-1",
+            chunk_size_sqm=900,
+            geometry=self.boundary,
+            centroid_lat="35.689200",
+            centroid_lon="51.389200",
+        )
+        observation = AnalysisGridObservation.objects.create(
+            cell=cell,
+            run=self.run,
+            temporal_start=date(2025, 1, 1),
+            temporal_end=date(2025, 1, 31),
+            ndvi=0.5,
+            ndwi=0.2,
+            soil_vv_db=-8.0,
+        )
+        result = RemoteSensingSubdivisionResult.objects.create(
+            soil_location=self.location,
+            run=self.run,
+            block_subdivision=self.subdivision,
+            block_code="block-1",
+            chunk_size_sqm=900,
+            temporal_start=date(2025, 1, 1),
+            temporal_end=date(2025, 1, 31),
+            cluster_count=1,
+            selected_features=["ndvi", "ndwi", "soil_vv_db"],
+            metadata={"inertia_curve": [{"k": 1, "sse": 0.0}]},
+        )
+
+        with TemporaryDirectory() as temp_dir:
+            with patch.dict(os.environ, {"REMOTE_SENSING_DIAGNOSTIC_DIR": temp_dir}, clear=False), patch(
+                "location_data.data_driven_subdivision.render_elbow_plot",
+                return_value=ContentFile(b"elbow"),
+            ), patch(
+                "location_data.data_driven_subdivision._render_cluster_map_plot",
+                return_value=ContentFile(b"map"),
+            ), patch(
+                "location_data.data_driven_subdivision._render_cluster_size_plot",
+                return_value=ContentFile(b"sizes"),
+            ), patch(
+                "location_data.data_driven_subdivision._render_feature_pair_plot",
+                return_value=ContentFile(b"pairs"),
+            ):
+                artifacts = _persist_remote_sensing_diagnostic_artifacts(
+                    result=result,
+                    observations=[observation],
+                    labels=[0],
+                    cluster_summaries=[
+                        {
+                            "cluster_label": 0,
+                            "cell_count": 1,
+                            "centroid_lat": 35.6892,
+                            "centroid_lon": 51.3892,
+                            "cell_codes": ["cell-1"],
+                        }
+                    ],
+                    selected_features=["ndvi", "ndwi", "soil_vv_db"],
+                    scaled_matrix=[[0.0, 0.0, 0.0]],
+                    inertia_curve=[{"k": 1, "sse": 0.0}],
+                )
+                self.assertEqual(
+                    sorted(artifacts["files"].keys()),
+                    ["cluster_map", "cluster_sizes", "elbow_plot", "feature_pairs"],
+                )
+                for path in artifacts["files"].values():
+                    self.assertTrue(os.path.exists(path))
 
     def test_build_clustering_dataset_raises_clear_error_when_all_selected_features_are_null(self):
         cell = AnalysisGridCell.objects.create(
@@ -164,7 +241,7 @@ class DataDrivenSubdivisionSyncTests(TestCase):
             ):
                 build_clustering_dataset(
                     observations=[observation],
-                    selected_features=["ndvi", "ndwi", "lst_c", "soil_vv_db"],
+                    selected_features=["ndvi", "ndwi", "soil_vv_db"],
                     run=self.run,
                     location=self.location,
                 )