fix plist

Author: dweindl

python/sdist/amici/petab/petab_importer.py

@@ -747,16 +747,18 @@ def apply_parameters(
         experiment_id = edata.id
         experiment = self._petab_problem[experiment_id]
 
-        # TODO set plist -- estimated parameters + those mapped via placeholders
+        # plist -- estimated parameters + those mapped via placeholders
+        # TODO sufficient to set them during creation of edata or allow dynamic fixing of parameters?
+        #  store list of sensitivity parameter in class instead of using x_free_ids or estimate=True
         plist = []
         placeholder_mappings = self._get_placeholder_mapping(experiment)
         estimated_par_ids = self._petab_problem.x_free_ids
         for model_par_idx, model_par_id in enumerate(
             self._model.get_parameter_ids()
         ):
-            if (
-                model_par_id in estimated_par_ids
-                or model_par_id in placeholder_mappings
+            if model_par_id in estimated_par_ids or (
+                (maps_to := placeholder_mappings.get(model_par_id)) is not None
+                and maps_to in estimated_par_ids
             ):
                 plist.append(model_par_idx)
         edata.plist = plist

tests/benchmark_models/test_petab_benchmark.py

@@ -738,22 +738,23 @@ def test_nominal_parameters_llh_v2(problem_id):
     # check llh
     compare_to_reference(problem_id, llh)
 
+    # check gradient
+    if problem_id not in problems_for_gradient_check:
+        return None
+        # pytest.skip("Excluded from gradient check.")
+
     # sensitivities computed w.r.t. the expected parameters? (`plist` correct?)
-    # TODO: allow fixing parameters?
     ps._solver.set_sensitivity_order(SensitivityOrder.first)
     ps._solver.set_sensitivity_method(SensitivityMethod.forward)
+    ps._model.set_always_check_finite(True)
     result = ps.simulate(
         problem_parameters=problem_parameters,
     )
+    assert result[SLLH] is not None
     actual_sens_pars = set(result[SLLH].keys())
     expected_sens_pars = set(problem.x_free_ids)
     assert actual_sens_pars == expected_sens_pars
 
-    # check gradient
-    if problem_id not in problems_for_gradient_check:
-        return None
-        # pytest.skip("Excluded from gradient check.")
-
     # TODO
     scale = False