Merge pull request #327 from OpenGeoscience/emphasize-ai

Modify labels to emphasize AI use

Author: annehaley

uvdat/core/rest/analytics.py

@@ -1,8 +1,11 @@
 from __future__ import annotations
 
+import inspect
+
 from django.db.models import QuerySet
 from rest_framework.decorators import action
 from rest_framework.response import Response
+from rest_framework.serializers import ModelSerializer
 from rest_framework.viewsets import ReadOnlyModelViewSet
 
 from uvdat.core.models import Project, TaskResult
@@ -34,7 +37,21 @@ def list_types(self, request, project_id: int, **kwargs):
             for k, v in instance.get_input_options().items():
                 if isinstance(v, QuerySet):
                     filtered_queryset = v.filter_by_projects(Project.objects.filter(id=project_id))
-                    options = [{"id": o.id, "name": o.name} for o in filtered_queryset]
+                    input_serializer = next(
+                        iter(
+                            [
+                                s
+                                for _, s in inspect.getmembers(uvdat_serializers, inspect.isclass)
+                                if issubclass(s, ModelSerializer)
+                                and s.Meta.model == filtered_queryset.model
+                            ]
+                        ),
+                        None,
+                    )
+                    if input_serializer is not None:
+                        options = [input_serializer(o).data for o in filtered_queryset]
+                    else:
+                        options = [{"id": o.id, "name": o.name} for o in filtered_queryset]
                 elif any(not isinstance(o, dict) for o in v):
                     options = [{"id": o, "name": o} for o in v]
                 else:
@@ -45,6 +62,7 @@ def list_types(self, request, project_id: int, **kwargs):
                     "name": instance.name,
                     "db_value": instance.db_value,
                     "description": instance.description,
+                    "details": instance.details,
                     "attribution": instance.attribution,
                     "input_options": filtered_input_options,
                     "input_types": instance.input_types,

uvdat/core/rest/serializers.py

@@ -264,6 +264,7 @@ class AnalysisTypeSerializer(serializers.Serializer):
     name = serializers.CharField(max_length=255)
     db_value = serializers.CharField(max_length=25)
     description = serializers.CharField(max_length=255)
+    details = serializers.CharField(max_length=2048, allow_null=True)
     attribution = serializers.CharField(max_length=255)
     input_options = serializers.JSONField()
     input_types = serializers.JSONField()

uvdat/core/tasks/analytics/analysis_type.py

@@ -11,6 +11,7 @@ class AnalysisType(ABC):
     def __init__(self, *args):
         self.name = ""
         self.description = ""
+        self.details = None
         self.db_value = ""  # cannot be longer than 25 characters
         self.input_types = {}
         self.output_types = {}
@@ -34,6 +35,10 @@ def validate_inputs(self, inputs):
             if input_name not in inputs:
                 raise AnalysisInputError(f"{input_name} not provided.")
 
+    @abstractmethod
+    def finalize(self, result):
+        raise NotImplementedError
+
 
 class AnalysisInputError(Exception):
     pass
@@ -55,5 +60,14 @@ def on_failure(self, exc, task_id, args, kwargs, einfo):
         task_result.write_error(err_msg)
 
     def after_return(self, status, retval, task_id, args, kwargs, einfo):  # noqa: PLR0913
+        # Avoid circular import
+        from . import analysis_types  # noqa: PLC0415
+
         task_result = self.get_task_result(args)
         task_result.complete()
+
+        analysis_type = next(
+            iter(t for t in analysis_types if t().db_value == task_result.task_type), None
+        )
+        if analysis_type is not None:
+            analysis_type().finalize(task_result)

uvdat/core/tasks/analytics/create_road_network.py

@@ -51,6 +51,9 @@ def run_task(self, *, project, **inputs):
         create_road_network.delay(result.id)
         return result
 
+    def finalize(self, result):
+        pass
+
 
 def metadata_for_row(row):
     return {

uvdat/core/tasks/analytics/flood_network_failure.py

@@ -68,6 +68,9 @@ def validate_inputs(self, inputs):
             # data is at 10 meter resolution
             raise AnalysisInputError("Station radius must be greater than 10")
 
+    def finalize(self, result):
+        pass
+
 
 def _get_station_region(point: Point, radius_meters: float) -> dict[str, Any]:
     """Get a rectangular region around a point, sized by radius_meters."""

uvdat/core/tasks/analytics/flood_simulation.py

@@ -16,8 +16,15 @@
 class FloodSimulation(AnalysisType):
     def __init__(self):
         super().__init__()
-        self.name = "Flood Simulation"
+        self.name = "AI Flood Simulation"
         self.description = "Select parameters to simulate a 24-hour flood of the Charles River"
+        self.details = (
+            "The AI extreme precipitation downscaler achieves a Mean Absolute Error (MAE) "
+            "of 3.82 mm and a MAE of Annual Precipitation Maxima of 20.46 mm when tested "
+            "on an unseen validation period of n=7305 days. The AI-optimized SIMHYD "
+            "hydrological model achieves a Nash-Sutcliffe Efficiency (NSE) of 0.70 when "
+            "tested on an unseen validation period of n=6209 days."
+        )
         self.db_value = "flood_simulation"
         self.input_types = {
             "initial_conditions_id": "string",
@@ -61,6 +68,14 @@ def run_task(self, *, project, **inputs):
         flood_simulation.delay(result.id)
         return result
 
+    def finalize(self, result):
+        seconds = (result.completed - result.created).total_seconds()
+        result.status = (
+            f"AI Simulation completed in {seconds:.2f} seconds "
+            "(compare to baseline 5-hour numerical simulation)."
+        )
+        result.save()
+
 
 @shared_task(base=AnalysisTask)
 def flood_simulation(result_id):

uvdat/core/tasks/analytics/geoai_segmentation.py

@@ -59,6 +59,9 @@ def run_task(self, *, project, **inputs):
         geoai_segmentation.delay(result.id)
         return result
 
+    def finalize(self, result):
+        pass
+
 
 @shared_task(base=AnalysisTask)
 def geoai_segmentation(result_id):  # noqa: PLR0915

uvdat/core/tasks/analytics/network_recovery.py

@@ -79,6 +79,9 @@ def validate_inputs(self, inputs):
         if mode not in RECOVERY_MODES:
             raise AnalysisInputError("Recovery mode not a valid option")
 
+    def finalize(self, result):
+        pass
+
 
 def get_network_graph(network):
     network = {

web/src/components/sidebars/AnalyticsPanel.vue

@@ -233,6 +233,14 @@ watch(
             </template>
           </v-tooltip>
         </v-card-title>
+        <v-expansion-panels v-if="analysisStore.currentAnalysisType.details">
+          <v-expansion-panel bg-color="transparent">
+            <v-expansion-panel-title class="py-3" style="min-height: 0">Details</v-expansion-panel-title>
+            <v-expansion-panel-text class="px-3">
+              {{ analysisStore.currentAnalysisType.details }}
+            </v-expansion-panel-text>
+          </v-expansion-panel>
+        </v-expansion-panels>
 
         <v-tabs v-model="analysisStore.currentAnalysisTab" align-tabs="center" fixed-tabs>
           <v-tab value="new">Run New</v-tab>

web/src/store/panel.ts

@@ -64,7 +64,7 @@ function defaultPanelArrangement(): FloatingPanelConfig[] {
         },
         {
             id: "analytics",
-            label: "Analytics",
+            label: "AI & Analytics",
             visible: false,
             closeable: true,
             collapsed: true,