add parameter values to model class

python/sdist/amici/jax.py

@@ -38,6 +38,7 @@ class JAXModel(eqx.Module):
     icoeff: float
     dcoeff: float
     maxsteps: int
+    parameters: jnp.ndarray
     term: diffrax.ODETerm
     petab_problem: petab.Problem | None
 
@@ -58,17 +59,40 @@ def __init__(self):
         )
         self.term = diffrax.ODETerm(self.xdot)
         self.petab_problem = None
+        self.parameters = jnp.array([])
 
     def set_petab_problem(self, petab_problem: petab.Problem) -> "JAXModel":
+        """
+        Set the PEtab problem for the model and updates parameters to the nominal values.
+        :param petab_problem:
+            Petab problem to set.
+        :return: JAXModel instance
+        """
         if self.petab_problem is None:
-            return eqx.tree_at(
+            model = eqx.tree_at(
                 lambda x: x.petab_problem,
                 self,
                 petab_problem,
                 is_leaf=lambda x: x is None,
             )
         else:
-            return eqx.tree_at(lambda x: x.petab_problem, self, petab_problem)
+            model = eqx.tree_at(lambda x: x.petab_problem, self, petab_problem)
+
+        nominal_values = jnp.array(
+            [
+                petab.scale(
+                    model.petab_problem.parameter_df.loc[
+                        pval, petab.NOMINAL_VALUE
+                    ],
+                    model.petab_problem.parameter_df.loc[
+                        pval, petab.PARAMETER_SCALE
+                    ],
+                )
+                for pval in model.petab_parameter_ids()
+            ]
+        )
+
+        return eqx.tree_at(lambda x: x.parameters, model, nominal_values)
 
     @staticmethod
     @abstractmethod
@@ -138,7 +162,15 @@ def getFixedParameterIds(self) -> list[str]:  # noqa: N802
         """
         return self.fixed_parameter_ids
 
-    def unscale_p(self, p, pscale):
+    def petab_parameter_ids(self) -> list[str]:
+        return self.petab_problem.parameter_df[
+            self.petab_problem.parameter_df[petab.ESTIMATE] == 1
+        ].index.tolist()
+
+    def get_petab_parameter_by_name(self, name: str) -> jnp.float_:
+        return self.parameters[self.petab_parameter_ids().index(name)]
+
+    def _unscale_p(self, p, pscale):
         return jax.vmap(
             lambda p_i, pscale_i: jnp.stack(
                 (p_i, jnp.exp(p_i), jnp.power(10, p_i))
@@ -217,7 +249,7 @@ def _run(
         checkpointed=True,
         dynamic="true",
     ):
-        ps = self.unscale_p(p, pscale)
+        ps = self._unscale_p(p, pscale)
 
         # Pre-equilibration
         if k_preeq.shape[0] > 0:
@@ -340,14 +372,7 @@ def run_simulation(
                 if isinstance(
                     pval := parameter_mapping.map_sim_var[par], Number
                 )
-                else petab.scale(
-                    self.petab_problem.parameter_df.loc[
-                        pval, petab.NOMINAL_VALUE
-                    ],
-                    self.petab_problem.parameter_df.loc[
-                        pval, petab.PARAMETER_SCALE
-                    ],
-                )
+                else self.get_petab_parameter_by_name(pval)
                 for par in self.parameter_ids
             ]
         )
@@ -471,13 +496,11 @@ def run_simulations(
 @dataclass
 class ReturnDataJAX(dict):
     x: np.array = None
-    sx: np.array = None
     y: np.array = None
-    sy: np.array = None
     sigmay: np.array = None
-    ssigmay: np.array = None
     llh: np.array = None
     sllh: np.array = None
+    s2llh: np.array = None
     stats: dict = None
 
     def __init__(self, *args, **kwargs):