diff --git a/CEP/Pipeline/recipes/sip/nodes/imager_prepare.py b/CEP/Pipeline/recipes/sip/nodes/imager_prepare.py
index 6d75d26b47cdc028be6513f2cab2c05d773db552..95d0f73da5583672c2f499edd5d0bb0557ff4f55 100644
--- a/CEP/Pipeline/recipes/sip/nodes/imager_prepare.py
+++ b/CEP/Pipeline/recipes/sip/nodes/imager_prepare.py
@@ -227,6 +227,22 @@ class imager_prepare(LOFARnodeTCP):
             catch_segfaults(cmd, working_dir, environment,
                    logger, cleanup = None, usageStats=self.resourceMonitor)
 
+    def _get_nchan_from_ms(self, file):
+        """
+        Wrapper for pt call to retrieve the number of channels in a ms
+
+        Uses Pyrap functionality throws 'random' exceptions.
+        """
+
+        # open the datasetassume same nchan for all sb
+        table = pt.table(file)  # 
+           
+        # get the data column, get description, get the 
+        # shape, first index returns the number of channels
+        nchan = str(pt.tablecolumn(table, 'DATA').getdesc()["shape"][0])
+
+        return nchan
+
     def _run_dppp(self, working_dir, time_slice_dir_path, slices_per_image,
                   processed_ms_map, subbands_per_image, collected_ms_dir_name, 
                   parset, ndppp):
@@ -251,6 +267,7 @@ class imager_prepare(LOFARnodeTCP):
             # filling with zeros           
             ndppp_input_ms = []
             nchan_known = False
+
             for item in processed_ms_map[start_slice_range:end_slice_range]:
                 if item.skip:
                     ndppp_input_ms.append("SKIPPEDSUBBAND")
@@ -258,16 +275,10 @@ class imager_prepare(LOFARnodeTCP):
                     # From the first non skipped filed get the nchan
                     if not nchan_known:
                         try:
-                            # Automatically average the number of channels in 
-                            # the output to 1
-                            # open the datasetassume same nchan for all sb
-                            table = pt.table(item.file)  # 
-            
-                            # get the data column, get description, get the 
-                            # shape, first index returns the number of channels
-                            nchan_input = str(
-                                pt.tablecolumn(table, 
-                                               'DATA').getdesc()["shape"][0])
+                            # We want toAutomatically average the number 
+                            # of channels in the output to 1, get the current
+                            # nr of channels
+                            nchan_input = self._get_nchan_from_ms(item.file)
                             nchan_known = True
 
                         # corrupt input measurement set
diff --git a/CEP/Pipeline/test/recipes/nodes/imager_prepare_test.py b/CEP/Pipeline/test/recipes/nodes/imager_prepare_test.py
index 35e61a823acbc5d50635faae89d9228840efb14b..d0716889161a307c33d068ff5616a5df091992c7 100644
--- a/CEP/Pipeline/test/recipes/nodes/imager_prepare_test.py
+++ b/CEP/Pipeline/test/recipes/nodes/imager_prepare_test.py
@@ -12,6 +12,7 @@ from lofarpipe.support.utilities import create_directory
 from lofarpipe.recipes.nodes.imager_prepare import imager_prepare \
      as imager_prepare_node
 from logger import logger
+from lofarpipe.support.data_map import DataMap
 
 class ImagerPrepareTestWrapper(imager_prepare_node):
     """
@@ -29,6 +30,9 @@ class ImagerPrepareTestWrapper(imager_prepare_node):
     def _dppp_call(self, working_dir, ndppp, cmd, environment):
         self.dppp_call_vars = (working_dir, ndppp, cmd, environment)
 
+    def _get_nchan_from_ms(self, file):
+        return 4
+
 
 class ImagerPrepareTest(unittest.TestCase):
     """
@@ -104,6 +108,11 @@ class ImagerPrepareTest(unittest.TestCase):
                          ("lce072", "test_file_path2"),
                          ("lce072", "test_file_path3"),
                          ("lce072", "test_file_path4")]
+
+        input_datamap = DataMap()
+        for entry in input_map:
+            input_datamap.append(entry)
+
         subbands_per_image = 2
         collected_ms_dir_name = ""
         fp = open(os.path.join(self.test_path, "parset"), 'w')
@@ -115,7 +124,7 @@ class ImagerPrepareTest(unittest.TestCase):
 
         sut = ImagerPrepareTestWrapper()
         output = sut._run_dppp(working_dir, time_slice_dir_path, slices_per_image,
-                  input_map, subbands_per_image, collected_ms_dir_name, parset,
+                  input_datamap, subbands_per_image, collected_ms_dir_name, parset,
                   ndppp)
 
         # The output should contain two timeslices ms prepended with the time_slice_dir_path
@@ -127,6 +136,7 @@ class ImagerPrepareTest(unittest.TestCase):
 
         # Two parset should be written in the time_slice_dir_path
         parset_1_content_expected = [('replace', 'uselogger', 'True'),
+                                     ('replace', 'avg1.freqstep', '4'),
                  ('replace', 'msin', "['test_file_path1', 'test_file_path2']"),
                  ('replace', 'msout', '{0}'.format(
                     os.path.join(time_slice_dir_path, "time_slice_0.dppp.ms")))]
@@ -138,6 +148,7 @@ class ImagerPrepareTest(unittest.TestCase):
 
         # Two parset should be written in the time_slice_dir_path
         parset_2_content_expected = [('replace', 'uselogger', 'True'),
+                                     ('replace', 'avg1.freqstep', '4'),
                  ('replace', 'msin', "['test_file_path3', 'test_file_path4']"),
                  ('replace', 'msout', '{0}'.format(
                     os.path.join(time_slice_dir_path, "time_slice_1.dppp.ms")))]