refactor: methods for SimulateEIS, updt. fitting_example

examples/scripts/eis_fitting.py

@@ -5,7 +5,7 @@
 
 # Define model
 parameter_set = pybop.ParameterSet.pybamm("Chen2020")
-model = pybop.lithium_ion.DFN(
+model = pybop.lithium_ion.SPM(
     parameter_set=parameter_set, options={"surface form": "differential"}
 )
 
@@ -39,33 +39,10 @@
 signal = ["Impedance"]
 # Generate problem, cost function, and optimisation class
 problem = pybop.EISProblem(model, parameters, dataset, signal=signal)
-prediction_1 = problem.evaluate(np.array([1.0, 60e-6]))
-prediction_2 = problem.evaluate(np.array([10.0, 40e-6]))
+prediction_1 = problem.evaluate(np.array([0.1, 50e-6]))
+prediction_2 = problem.evaluate(np.array([10, 70e-6]))
+
+# Plot
 fig = px.scatter(x=prediction_1["Impedance"].real, y=-prediction_1["Impedance"].imag)
 fig.add_scatter(x=prediction_2["Impedance"].real, y=-prediction_2["Impedance"].imag)
 fig.show()
-# cost = pybop.SumSquaredError(problem)
-# optim = pybop.CMAES(cost, max_iterations=100)
-
-# # Run the optimisation
-# x, final_cost = optim.run()
-# print("True parameters:", parameters.true_value())
-# print("Estimated parameters:", x)
-
-# # Plot the time series
-# pybop.plot_dataset(dataset)
-
-# # Plot the timeseries output
-# pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison")
-
-# # Plot convergence
-# pybop.plot_convergence(optim)
-
-# # Plot the parameter traces
-# pybop.plot_parameters(optim)
-
-# # Plot the cost landscape
-# pybop.plot2d(cost, steps=15)
-
-# # Plot the cost landscape with optimisation path
-# pybop.plot2d(optim, steps=15)

pybop/models/base_model.py

@@ -126,8 +126,8 @@ def build(
         else:
             if not self.pybamm_model._built:
                 self.pybamm_model.build_model()
-            self.set_params(eis=self.eis)
 
+            self.set_params(eis=self.eis)
             self._mesh = pybamm.Mesh(self.geometry, self.submesh_types, self.var_pts)
             self._disc = pybamm.Discretisation(
                 mesh=self.mesh,
@@ -460,8 +460,8 @@ def simulateEIS(self, inputs: Inputs, f_eval: list) -> dict[str, np.ndarray]:
         inputs : dict or array-like
             The input parameters for the simulation. If array-like, it will be
             converted to a dictionary using the model's fit keys.
-        t_eval : array-like
-            An array of time points at which to evaluate the solution.
+        f_eval : array-like
+            An array of frequency points at which to evaluate the solution.
 
         Returns
         -------
@@ -492,31 +492,25 @@ def simulateEIS(self, inputs: Inputs, f_eval: list) -> dict[str, np.ndarray]:
             zs = [self.calculate_impedance(frequency) for frequency in f_eval]
             return {"Impedance": np.asarray(zs) * self.z_scale}
 
-    def initialise_eis_simulation(self, inputs: Inputs = None):
-        # Get the mass matrix
+    def initialise_eis_simulation(self, inputs: Optional[Inputs] = None):
+        # Set mass matrix, and solver
         self.M = self._built_model.mass_matrix.entries
+        self._solver.set_up(self._built_model, inputs=inputs)
 
-        if inputs is not None:
-            casadi_inputs = (
-                casadi.vertcat(*[x for x in inputs.values()])
-                if self._built_model.convert_to_format == "casadi"
-                else inputs
-            )
-
-            # Set up the solver for new inputs
-            self._solver.set_up(self._built_model, inputs=inputs)
+        # Convert inputs to casadi format if needed
+        casadi_inputs = (
+            casadi.vertcat(*inputs.values())
+            if inputs is not None and self._built_model.convert_to_format == "casadi"
+            else inputs or []
+        )
 
-            # Extract necessary attributes from the model
-            self.y0 = self._built_model.concatenated_initial_conditions.evaluate(
-                0, inputs=inputs
-            )
-            self.J = self._built_model.jac_rhs_algebraic_eval(
-                0, self.y0, casadi_inputs
-            ).sparse()
-        else:
-            # Extract necessary attributes from the model
-            self.y0 = self._built_model.concatenated_initial_conditions.entries
-            self.J = self._built_model.jac_rhs_algebraic_eval(0, self.y0, []).sparse()
+        # Extract necessary attributes from the model
+        self.y0 = self._built_model.concatenated_initial_conditions.evaluate(
+            0, inputs=inputs
+        )
+        self.J = self._built_model.jac_rhs_algebraic_eval(
+            0, self.y0, casadi_inputs
+        ).sparse()
 
         # Convert to Compressed Sparse Column format
         self.M = csc_matrix(self.M)
@@ -679,9 +673,7 @@ def predict(
                         parameter_values=parameter_set,
                     ).solve(initial_soc=init_soc)
             else:
-                raise ValueError(
-                    "This sim method currently only supports PyBaMM models"
-                )
+                raise ValueError("This method currently only supports PyBaMM models")
 
         else:
             return [np.inf]