QE, QI and Q which are unrelated to the problem are returned by get_sweeper_mats; the matrices LHS and RHS which do include lambda as parameter are provided by get_scalar_problems_sweeper_mats

Author: Daniel Ruprecht

pySDC/sweeper_classes/imex_1st_order.py

@@ -160,3 +160,15 @@ def get_sweeper_mats(self):
       QI = self.QI[1:,1:]
       Q  = self.coll.Qmat[1:,1:]
       return QE, QI, Q
+
+    def get_scalar_problems_sweeper_mats(self, lambdas=[None, None]):
+      QE, QI, Q = self.get_sweeper_mats()
+      if lambdas==[None,None]:
+        pass
+        # should use lambdas from attached problem and make sure it is a scalar IMEX 
+        raise NotImplementedError("At the moment, the values for lambda have to be provided")
+      else:
+        lambda_fast = lambdas[0]
+        lambda_slow = lambdas[1]
+      assert abs(lambda_slow)<=abs(lambda_fast), "First entry in parameter lambdas (lambda_fast) has to be greater than second entry (lambda_slow)"
+      return QE, QI, Q

tests/test_imexsweeper.py

@@ -32,14 +32,10 @@ def setupLevelStepProblem(self):
     problem = level.prob
     return step, level, problem, nnodes
 
-  def setupQMatrices(self, level):
-    QE, QI, Q = level.sweep.get_sweeper_mats()
-    return QE, QI, Q
-
   def setupSweeperMatrices(self, step, level, problem):
     nnodes  = step.levels[0].sweep.coll.num_nodes
     # Build SDC sweep matrix
-    QE, QI, Q = self.setupQMatrices(level)
+    QE, QI, Q = level.sweep.get_sweeper_mats()
     dt = step.status.dt
     LHS = np.eye(nnodes) - step.status.dt*( problem.lambda_f[0]*QI + problem.lambda_s[0]*QE )
     RHS = step.status.dt*( (problem.lambda_f[0]+problem.lambda_s[0])*Q - (problem.lambda_f[0]*QI + problem.lambda_s[0]*QE) )
@@ -149,11 +145,12 @@ def test_updateformula(self):
   def test_collocationinvariant(self):
     for type in classes:
       self.swparams['collocation_class'] = getattr(pySDC.CollocationClasses, type)
+
       step, level, problem, nnodes = self.setupLevelStepProblem()
       level.sweep.predict()
       u0full = np.array([ level.u[l].values.flatten() for l in range(1,nnodes+1) ])
 
-      QE, QI, Q = self.setupQMatrices(level)
+      QE, QI, Q = level.sweep.get_sweeper_mats()
 
       # Build collocation matrix
       Mcoll = np.eye(nnodes) - step.status.dt*Q*(problem.lambda_s[0] + problem.lambda_f[0])