simulate_petab: Avoid warning when simulating with default parameters

python/sdist/amici/petab/simulations.py

@@ -107,11 +107,14 @@ def simulate_petab(
         Experimental data. Parameters are inserted in-place for simulation.
     :param parameter_mapping:
         Optional precomputed PEtab parameter mapping for efficiency, as
-        generated by :py:func:`create_parameter_mapping`.
+        generated by :py:func:`create_parameter_mapping` with
+        ``scaled_parameters=True``.
     :param scaled_parameters:
         If ``True``, ``problem_parameters`` are assumed to be on the scale
         provided in the PEtab parameter table and will be unscaled.
         If ``False``, they are assumed to be in linear scale.
+        If `parameter_mapping` is provided, this must match the value of
+        `scaled_parameters` used to generate the mapping.
     :param log_level:
         Log level, see :mod:`amici.logging` module.
     :param num_threads:
@@ -140,14 +143,6 @@ def simulate_petab(
     if solver is None:
         solver = amici_model.getSolver()
 
-    # Switch to scaled parameters.
-    problem_parameters = _default_scaled_parameters(
-        petab_problem=petab_problem,
-        problem_parameters=problem_parameters,
-        scaled_parameters=scaled_parameters,
-    )
-    scaled_parameters = True
-
     # number of amici simulations will be number of unique
     # (preequilibrationConditionId, simulationConditionId) pairs.
     # Can be optimized by checking for identical condition vectors.
@@ -165,10 +160,35 @@ def simulate_petab(
         parameter_mapping = create_parameter_mapping(
             petab_problem=petab_problem,
             simulation_conditions=simulation_conditions,
-            scaled_parameters=scaled_parameters,
+            # we will always use scaled parameters internally
+            scaled_parameters=True,
             amici_model=amici_model,
         )
 
+    if problem_parameters is None:
+        # scaled PEtab nominal values
+        problem_parameters = dict(
+            zip(
+                petab_problem.x_ids,
+                petab_problem.x_nominal_scaled,
+            )
+        )
+        # depending on `fill_fixed_parameters` for parameter mapping, the
+        #  parameter mapping may contain values instead of symbols for fixed
+        #  parameters. In this case, we need to filter them here to avoid
+        #  warnings in `fill_in_parameters`.
+        free_parameters = parameter_mapping.free_symbols
+        problem_parameters = {
+            par_id: par_value
+            for par_id, par_value in problem_parameters.items()
+            if par_id in free_parameters
+        }
+
+    elif not scaled_parameters:
+        problem_parameters = petab_problem.scale_parameters(problem_parameters)
+
+    scaled_parameters = True
+
     # Get edatas
     if edatas is None:
         # Generate ExpData with all condition-specific information
@@ -461,41 +481,3 @@ def rdatas_to_simulation_df(
     )
 
     return df.rename(columns={MEASUREMENT: SIMULATION})
-
-
-def _default_scaled_parameters(
-    petab_problem: petab.Problem,
-    problem_parameters: Optional[dict[str, float]] = None,
-    scaled_parameters: bool = False,
-) -> Optional[dict[str, float]]:
-    """
-    Helper method to handle an unscaled or unspecified parameter vector.
-
-    The parameter vector defaults to the nominal values in the PEtab
-    parameter table.
-
-    Unscaled parameter values are scaled.
-
-    :param petab_problem:
-        The PEtab problem.
-    :param problem_parameters:
-        Keys are PEtab parameter IDs, values are parameter values on the scale
-        defined in the PEtab parameter table. Defaults to the nominal values in
-        the PEtab parameter table.
-    :param scaled_parameters:
-        Whether `problem_parameters` are on the scale defined in the PEtab
-        parameter table.
-
-    :return:
-        The scaled parameter vector.
-    """
-    if problem_parameters is None:
-        problem_parameters = dict(
-            zip(
-                petab_problem.x_ids,
-                petab_problem.x_nominal_scaled,
-            )
-        )
-    elif not scaled_parameters:
-        problem_parameters = petab_problem.scale_parameters(problem_parameters)
-    return problem_parameters