Interpolation now also works for 2D problems

Author: Thomas Baumann

pySDC/implementations/convergence_controller_classes/adaptive_collocation.py

@@ -105,7 +105,7 @@ def switch_sweeper(self, S):
             P = L.prob
 
             # store solution of current level which will be interpolated to new level
-            u_old = L.u.copy()
+            u_old = [me.flatten() for me in L.u]
             nodes_old = L.sweep.coll.nodes.copy()
 
             # change sweeper
@@ -119,12 +119,13 @@ def switch_sweeper(self, S):
             # interpolate solution of old collocation problem to new one
             nodes_new = L.sweep.coll.nodes.copy()
             interpolator = LagrangeApproximation(points=np.append(0, nodes_old))
+
             u_inter = interpolator.getInterpolationMatrix(np.append(0, nodes_new)) @ u_old
 
             # assign the interpolated values to the nodes in the level
             for i in range(0, len(u_inter)):
                 me = P.dtype_u(P.init)
-                me[:] = u_inter[i]
+                me[:] = np.reshape(u_inter[i], P.init[0])
                 L.u[i] = me
 
             # reevaluate rhs

pySDC/implementations/convergence_controller_classes/adaptivity.py

@@ -448,6 +448,13 @@ def get_local_error_estimate(self, controller, S, **kwargs):
 
 
 class AdaptivityCollocation(AdaptivityBase):
+    """
+    Control the step size via a collocation based estimate of the local error.
+    The error estimate works by subtracting two solutions to collocation problems with different order. You can
+    interpolate between collocation methods as much as you want but the adaptive step size selection will always be
+    based on the last switch of quadrature.
+    """
+
     def setup(self, controller, params, description, **kwargs):
         """
         Add a default value for control order to the parameters.
@@ -537,8 +544,11 @@ def get_new_step_size(self, controller, S, **kwargs):
             lvl = S.levels[0]
 
             # compute next step size
-            order = self.status.order[-2] + 1  # local order of second to most accurate solution
+            order = (
+                min(self.status.order[-2::]) + 1
+            )  # local order of less accurate of the last two collocation problems
             e_est = self.get_local_error_estimate(controller, S)
+
             lvl.status.dt_new = self.compute_optimal_step_size(
                 self.params.beta, lvl.params.dt, self.params.e_tol, e_est, order
             )