diff --git a/python/examples/example.py b/python/examples/example.py
index 6c5b0d799c78703a16a6cd541f7ad5c5f37bfa4b..510b75f395d345c803660573b021dec822392dfe 100644
--- a/python/examples/example.py
+++ b/python/examples/example.py
@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 import numpy as np
-from pyelpa import ProcessorLayout, DistributedMatrix, Elpa
-from mpi4py import MPI
+from pyelpa import ProcessorLayout, DistributedMatrix
 import sys
 
 # set some parameters for matrix layout
@@ -9,37 +8,37 @@ na = 1000
 nev = 200
 nblk = 16
 
-# initialize processor layout, needed for calling ELPA
-comm = MPI.COMM_WORLD
-layout_p = ProcessorLayout(comm)
+# create distributed matrix
+a = DistributedMatrix.from_comm_world(na, nev, nblk)
 
-# create arrays
-a = DistributedMatrix(layout_p, na, nev, nblk)
-eigenvectors = DistributedMatrix(layout_p, na, nev, nblk)
-eigenvalues = np.zeros(na, dtype=np.float64)
-
-# initialize elpa
-e = Elpa.from_distributed_matrix(a)
-
-# set input matrix (a.data) on this core (a is stored in a block-cyclic
-# distributed layout; local size: a.na_rows x a.na_cols)
-a.data[:, :] = np.random.rand(a.na_rows, a.na_cols).astype(np.float64)
+def set_matrix(a):
+    for global_row, global_col in a.global_indices():
+        a.set_data_for_global_index(global_row, global_col,
+                                    global_row*global_col)
 
 print("Call ELPA eigenvectors")
 sys.stdout.flush()
+
+set_matrix(a)
 # now compute nev of na eigenvectors and eigenvalues
-e.eigenvectors(a.data, eigenvalues, eigenvectors.data)
+data = a.compute_eigenvectors()
+eigenvalues = data['eigenvalues']
+eigenvectors = data['eigenvectors']
+
 print("Done")
 
 # now eigenvectors.data contains the local part of the eigenvector matrix
-# which is stored in a block-cyclic distributed layout
-
-# now eigenvalues contains all computed eigenvalues on all cores
+# which is stored in a block-cyclic distributed layout and eigenvalues contains
+# all computed eigenvalues on all cores
 
 print("Call ELPA eigenvalues")
 sys.stdout.flush()
+
+set_matrix(a)
 # now compute nev of na eigenvalues
-e.eigenvalues(a.data, eigenvalues)
+set_matrix(a)
+eigenvalues = a.compute_eigenvalues()
+
 print("Done")
 
 # now eigenvalues contains all computed eigenvalues on all cores