fix(map): honor raster coverage and temporal dates

backend/app/services/temporal_analysis_service.py

@@ -32,6 +32,37 @@ class TemporalAnalysisService:
         "provision_regional_grb_context.py",
     }
 
+    @staticmethod
+    def _canonical_observation_snapshots(datasets: list[Dataset]) -> list[Dataset]:
+        by_observation: dict[datetime, Dataset] = {}
+        for dataset in datasets:
+            if dataset.observed_at is None:
+                continue
+            current = by_observation.get(dataset.observed_at)
+            dataset_recency = max(
+                (
+                    value.timestamp()
+                    for value in (dataset.imported_at, dataset.updated_at, dataset.created_at)
+                    if value is not None
+                ),
+                default=0.0,
+            )
+            current_recency = max(
+                (
+                    value.timestamp()
+                    for value in (
+                        getattr(current, "imported_at", None),
+                        getattr(current, "updated_at", None),
+                        getattr(current, "created_at", None),
+                    )
+                    if value is not None
+                ),
+                default=0.0,
+            )
+            if current is None or (dataset_recency, str(dataset.id)) > (current_recency, str(current.id)):
+                by_observation[dataset.observed_at] = dataset
+        return sorted(by_observation.values(), key=lambda item: item.observed_at)
+
     @staticmethod
     def list_series(db: Session, project_id: UUID) -> list[TemporalSeriesRead]:
         rows = (
@@ -49,6 +80,7 @@ class TemporalAnalysisService:
 
         result: list[TemporalSeriesRead] = []
         for key, datasets in grouped.items():
+            datasets = TemporalAnalysisService._canonical_observation_snapshots(datasets)
             observed = [item.observed_at for item in datasets if item.observed_at is not None]
             if not observed:
                 continue
@@ -118,6 +150,13 @@ class TemporalAnalysisService:
                 bbox,
                 selection_area.geometry,
             )
+
+        def is_preclipped_to_selection_area(dataset: Dataset) -> bool:
+            return bool(
+                selection_area
+                and VectorFeatureService.can_use_full_area_fast_path(dataset, selection_area.id)
+            )
+
         summaries: dict[UUID, dict[str, Any]] = {}
 
         def summarize(dataset: Dataset) -> dict[str, Any]:
@@ -126,14 +165,9 @@ class TemporalAnalysisService:
                 return cached
             kwargs: dict[str, Any] = {"dataset": dataset, "bbox": bbox}
             if selection_area is not None:
-                kwargs["selection_geometry"] = selection_geometry
-                kwargs["full_dataset_area"] = (
-                    selection_covers_full_area
-                    and VectorFeatureService.can_use_full_area_fast_path(
-                        dataset,
-                        selection_area.id,
-                    )
-                )
+                dataset_is_preclipped = is_preclipped_to_selection_area(dataset)
+                kwargs["selection_geometry"] = None if dataset_is_preclipped else selection_geometry
+                kwargs["full_dataset_area"] = selection_covers_full_area and dataset_is_preclipped
             summary = VectorFeatureService.summarize_features_by_bbox(db, **kwargs)
             summaries[dataset.id] = summary
             return summary
@@ -164,16 +198,20 @@ class TemporalAnalysisService:
             later=later,
             bbox=bbox,
             preview_limit=payload.preview_limit,
-            selection_geometry=selection_geometry,
+            selection_geometry=(
+                None
+                if is_preclipped_to_selection_area(earlier) and is_preclipped_to_selection_area(later)
+                else selection_geometry
+            ),
             earlier_full_dataset_area=(
                 selection_covers_full_area
-                and VectorFeatureService.can_use_full_area_fast_path(earlier, selection_area.id)
+                and is_preclipped_to_selection_area(earlier)
                 if selection_area is not None
                 else False
             ),
             later_full_dataset_area=(
                 selection_covers_full_area
-                and VectorFeatureService.can_use_full_area_fast_path(later, selection_area.id)
+                and is_preclipped_to_selection_area(later)
                 if selection_area is not None
                 else False
             ),
@@ -298,8 +336,7 @@ class TemporalAnalysisService:
             )
         else:
             datasets = fallback_datasets
-        unique = {dataset.id: dataset for dataset in datasets}
-        ordered = sorted(unique.values(), key=lambda item: item.observed_at or datetime.min.replace(tzinfo=timezone.utc))
+        ordered = TemporalAnalysisService._canonical_observation_snapshots(datasets)
         observations: list[TemporalObservation] = []
         for dataset in ordered:
             if dataset.observed_at is None: