diff --git a/python/trans_basis.py b/python/trans_basis.py
index f00140d6..7bd2ee4e 100644
--- a/python/trans_basis.py
+++ b/python/trans_basis.py
@@ -60,6 +60,10 @@ class TransBasis:
                               - 'eal' : local hamiltonian (i.e. crystal field)
                               - 'dm' : local density matrix
 
+        calc_in_solver_blocks : bool, optional
+                                Whether the property shall be diagonalized in the
+                                full sumk structure, or just in the solver structure.
+
         Returns
         -------
         wsqr : double
@@ -78,26 +82,27 @@ class TransBasis:
 
         if calc_in_solver_blocks:
             trafo = self.SK.block_structure.transformation
-            self.SK.block_structure.transform = None
+            self.SK.block_structure.transformation = None
+
             prop_solver = self.SK.block_structure.convert_matrix(prop, space_from='sumk', space_to='solver')
-            v= []
+            v= {}
             for name in prop_solver:
                 v[name] = numpy.linalg.eigh(prop_solver[name])[1]
             self.w = self.SK.block_structure.convert_matrix(v, space_from='solver', space_to='sumk')['ud' if self.SK.SO else 'up']
             self.T = numpy.dot(self.T.transpose().conjugate(),
                                self.w).conjugate().transpose()
-            self.SK.block_structure.transform = trafo
-
-        if self.SK.SO == 0:
-            self.eig, self.w = numpy.linalg.eigh(prop['up'])
-            # calculate new Transformation matrix
-            self.T = numpy.dot(self.T.transpose().conjugate(),
-                               self.w).conjugate().transpose()
+            self.SK.block_structure.transformation = trafo
         else:
-            self.eig, self.w = numpy.linalg.eigh(prop['ud'])
-            # calculate new Transformation matrix
-            self.T = numpy.dot(self.T.transpose().conjugate(),
-                               self.w).conjugate().transpose()
+            if self.SK.SO == 0:
+                self.eig, self.w = numpy.linalg.eigh(prop['up'])
+                # calculate new Transformation matrix
+                self.T = numpy.dot(self.T.transpose().conjugate(),
+                                   self.w).conjugate().transpose()
+            else:
+                self.eig, self.w = numpy.linalg.eigh(prop['ud'])
+                # calculate new Transformation matrix
+                self.T = numpy.dot(self.T.transpose().conjugate(),
+                                   self.w).conjugate().transpose()
 
         # measure for the 'unity' of the transformation:
         wsqr = sum(abs(self.w.diagonal())**2) / self.w.diagonal().size