fix(spp_simulation): fix metric rendering, CEL profile context, and fallback logic

- Load CEL profile in _get_ideal_population_ids so the executor runs with
  the correct base domain and model context (matches _execute_targeting)
- Fix _compute_metric_results_html to read "rate" and "ratio" keys for
  coverage and ratio metrics instead of always reading "value"
- Fix distribution stats to use explicit None check so zero values render
  as numbers instead of dashes
- Add "scenario_id.target_type" to @api.depends for _compute_summary_html
- Set amount_mode = "fixed" in the multiplier-to-fixed fallback branch so
  the wizard item is created with an explicit mode
- Fix aggregate metric to use metric.aggregation as the result key so that
  sum/avg/min/max aggregations read the correct value from the CEL result

Author: jeremi

spp_simulation/models/simulation_run.py

@@ -225,6 +225,7 @@ def _compute_display_name(self):
         "equity_score",
         "scenario_id.name",
         "scenario_id.budget_amount",
+        "scenario_id.target_type",
     )
     def _compute_summary_html(self):
         for record in self:
@@ -312,7 +313,7 @@ def _compute_distribution_details_html(self):
                 ("Std Dev", dist.get("standard_deviation", 0), "{:,.2f}"),
             ]
             for label, value, fmt in stats:
-                formatted = fmt.format(value) if value else "-"
+                formatted = fmt.format(value) if value is not None else "-"
                 html_parts.append(f"<tr><td><strong>{label}</strong></td><td>{formatted}</td></tr>")
             html_parts.append("</tbody></table></div>")
 
@@ -392,7 +393,13 @@ def _compute_metric_results_html(self):
                 Markup("</tr></thead><tbody>"),
             ]
             for metric_name, metric_data in metrics.items():
-                value = metric_data.get("value", 0)
+                # coverage stores result under "rate", ratio under "ratio";
+                # fall back through all three keys using explicit None checks
+                value = metric_data.get("value")
+                if value is None:
+                    value = metric_data.get("rate")
+                if value is None:
+                    value = metric_data.get("ratio", 0)
                 metric_type = metric_data.get("type", "unknown")
                 # Format value based on type
                 if metric_type == "coverage":

spp_simulation/services/simulation_service.py

@@ -360,11 +360,15 @@ def _compute_custom_metrics(self, scenario, beneficiary_ids):
                         base_domain=[("id", "in", beneficiary_ids)],
                         limit=0,
                     )
-                    count = result.get("count", 0)
+                    # Use the aggregation type as the result key so that
+                    # sum/avg/min/max metrics read the correct key rather
+                    # than always reading "count".
+                    agg_key = metric.aggregation or "count"
+                    value = result.get(agg_key, 0)
                     results[metric.name] = {
                         "type": "aggregate",
                         "aggregation": metric.aggregation,
-                        "value": count,
+                        "value": value,
                     }
 
                 elif metric.metric_type == "coverage":
@@ -587,6 +591,7 @@ def _create_wizard_entitlement_items(self, wizard, scenario, warnings):
                 # Multiplier mode cannot be directly mapped because
                 # simulation rules use a Char field name while wizard items
                 # use a Many2one to ir.model.fields. Fall back to fixed.
+                item_vals["amount_mode"] = "fixed"
                 warnings.append(
                     _(
                         "Entitlement rule '%s' uses multiplier mode with field '%s'. "

spp_simulation/services/targeting_efficiency_service.py

@@ -56,8 +56,19 @@ def compute_targeting_efficiency(self, scenario, simulated_ids):
 
     @api.model
     def _get_ideal_population_ids(self, scenario):
-        """Get the ideal population IDs from the ideal population expression."""
-        executor = self.env["spp.cel.executor"]
+        """Get the ideal population IDs from the ideal population expression.
+
+        Loads the CEL profile matching the scenario target type so that the
+        executor evaluates the expression in the correct model context (same
+        pattern used by _execute_targeting in simulation_service.py).
+        """
+        # Load the CEL profile so the executor has the correct base domain
+        # and model context, matching _execute_targeting in simulation_service.py
+        profile = "registry_groups" if scenario.target_type == "group" else "registry_individuals"
+        registry = self.env["spp.cel.registry"]
+        cfg = registry.load_profile(profile)
+
+        executor = self.env["spp.cel.executor"].with_context(cel_cfg=cfg)
         all_ids = []
         try:
             for batch_ids in executor.compile_for_batch(