diff --git a/wetb/prepost/h2_vs_hs2.py b/wetb/prepost/h2_vs_hs2.py
index 5f69fcaced878660ce6177333171c071446d5338..72996d2f885b6819a9dbf3181ea0b6e550609c23 100644
--- a/wetb/prepost/h2_vs_hs2.py
+++ b/wetb/prepost/h2_vs_hs2.py
@@ -727,7 +727,7 @@ class MappingsH2HS2(object):
             msg = 'HAWC2 sensor type "%s" is missing, are they defined?'
             raise ValueError(msg % sensortype)
         sel.sort_values(['radius'], inplace=True)
-        tors_e_channels = sel.ch_name.tolist()
+        tors_e_channels = sel.unique_ch_name.tolist()
 
         # find the current case in the statistics DataFrame
         case = fname.replace('.htc', '')
@@ -737,7 +737,7 @@ class MappingsH2HS2(object):
         # join the stats with the channel descriptions DataFrames, have the
         # same name on the joining column
         df_tors_e.set_index('channel', inplace=True)
-        sel.set_index('ch_name', inplace=True)
+        sel.set_index('unique_ch_name', inplace=True)
 
         # joining happens on the index, and for which the same channel has been
         # used: the unique HAWC2 channel naming scheme
diff --git a/wetb/prepost/misc.py b/wetb/prepost/misc.py
index b0bd74982c05a716bea95f61a6c49a122b8ef956..1d3b3aff9e6500720d06b3cc075db32f82c9b3bc 100644
--- a/wetb/prepost/misc.py
+++ b/wetb/prepost/misc.py
@@ -769,6 +769,13 @@ def check_df_dict(df_dict):
     """
     Verify if the dictionary that needs to be transferred to a Pandas DataFrame
     makes sense
+
+    Returns
+    -------
+
+    collens : dict
+        Dictionary with df_dict keys as keys, len(df_dict[key]) as column.
+        In other words: the length of each column (=rows) of the soon to be df.
     """
     collens = {}
     for col, values in df_dict.items():
@@ -978,6 +985,20 @@ def histfit(hist, bin_edges, xnew):
     return shape_out, scale_out, pdf_fit
 
 
+def hist_centers2edges(centers):
+    """Given the centers of bins, return its edges and bin widths.
+    """
+
+    binw_c = centers[1:] - centers[:-1]
+    edges = np.ndarray((len(centers)+1,))
+    edges[0] = centers[0] - binw_c[0]/2.0
+    edges[-1] = centers[-1] + binw_c[-1]/2.0
+    edges[1:-1] = centers[1:] - binw_c/2.0
+    binw_e = edges[1:] - edges[:-1]
+
+    return edges, binw_e
+
+
 def df_dict_check_datatypes(df_dict):
     """
     there might be a mix of strings and numbers now, see if we can have