diff --git a/CAL/CalibrationProcessing/lib/processing/averaging.py b/CAL/CalibrationProcessing/lib/processing/averaging.py
index 5e713713363ee6c92caa2780eabb69dd736d35ef..dfa8f59232f104585a7446e7054a7ea603ddb904 100644
--- a/CAL/CalibrationProcessing/lib/processing/averaging.py
+++ b/CAL/CalibrationProcessing/lib/processing/averaging.py
@@ -45,20 +45,20 @@ def average_values_by_sample(data_array, window_size, field_name=None):
 
         result['mean'][i] = numpy.nanmean(data_array_view)
         if numpy.issubdtype(field_dtype, numpy.complexfloating):
-            result['std_real'][i] = numpy.std(numpy.real(data_array_view))
-            result['std_imag'][i] = numpy.std(numpy.imag(data_array_view))
+            result['std_real'][i] = numpy.nanstd(numpy.real(data_array_view))
+            result['std_imag'][i] = numpy.nanstd(numpy.imag(data_array_view))
         else:
-            result['std'][i] = numpy.std(data_array_view)
+            result['std'][i] = numpy.nanstd(data_array_view)
         # Counts the values that are not nan therefore the one used to do the mean
         result['averaged_samples'][i] = numpy.count_nonzero(~numpy.isnan(data_array_view))
 
     data_array_view = data_array[(new_array_size - 1) * window_size:]
     result['mean'][-1] = numpy.nanmean(data_array_view)
     if numpy.issubdtype(field_dtype, numpy.complexfloating):
-        result['std_real'][-1] = numpy.std(numpy.real(data_array_view))
-        result['std_imag'][-1] = numpy.std(numpy.imag(data_array_view))
+        result['std_real'][-1] = numpy.nanstd(numpy.real(data_array_view))
+        result['std_imag'][-1] = numpy.nanstd(numpy.imag(data_array_view))
     else:
-        result['std'][-1] = numpy.std(data_array_view)
+        result['std'][-1] = numpy.nanstd(data_array_view)
     # Counts the values that are not nan therefore the one used to do the mean
     result['averaged_samples'][-1] = numpy.count_nonzero(~numpy.isnan(data_array_view))
 
@@ -464,6 +464,7 @@ def weighted_average_dataset_per_station(dataset, input_data_table):
     :return:
     """
     stations = dataset.reference_stations
+    first_station = stations[0]
 
     frequencies = dataset.frequencies
 
@@ -473,8 +474,7 @@ def weighted_average_dataset_per_station(dataset, input_data_table):
         frequency_string = str(frequency)
         result_per_frequency = dict()
         result_ALL[str(frequency)] = result_per_frequency
-
-        for beam_str in input_data_table[frequency_string]:
+        for beam_str in input_data_table[first_station][frequency_string]:
 
             result_per_beam = numpy.zeros(1, dtype=HDS_data_sample_type)