log outputs and only iterpolate active shells taus

tardis/workflows/v_inner_solver.py

@@ -34,6 +34,9 @@ def __init__(self, configuration, mean_optical_depth="rosseland", tau=None):
         if tau is not None:
             self.TAU_TARGET = np.log(tau)
 
+        self.iterations_mean_optical_depth = np.full((self.total_iterations, self.simulation_state.no_of_shells), np.nan) 
+        self.iterations_values = {'t_radiative':[], 'dilution_factor':[], 't_inner':[], 'v_inner_boundary':[], 'mask':[]}
+
         initial_v_inner = self.estimate_v_inner()
 
         self.simulation_state.geometry.v_inner_boundary = initial_v_inner
@@ -56,9 +59,11 @@ def estimate_v_inner(self):
             )[self.mean_optical_depth]
         )
 
+        self.iterations_mean_optical_depth[self.completed_iterations,self.simulation_state.geometry.v_inner_boundary_index:] = tau_integ[self.simulation_state.geometry.v_inner_boundary_index:] 
+
         interpolator = interp1d(
-            tau_integ,
-            self.simulation_state.geometry.v_inner,  # Only use the active values as we only need a numerical estimate, not an index
+            tau_integ[self.simulation_state.geometry.v_inner_boundary_index:],
+            self.simulation_state.geometry.v_inner[self.simulation_state.geometry.v_inner_boundary_index:],  # Only use the active values as we only need a numerical estimate, not an index
             fill_value="extrapolate",
         )
 
@@ -331,6 +336,8 @@ def run(self):
             converged = self.check_convergence(estimated_values)
 
             self.completed_iterations += 1
+            for key, value in estimated_values.items():
+                self.iterations_values[key].append(value)
 
             if converged and self.convergence_strategy.stop_if_converged:
                 break