diff --git a/setup.py b/setup.py
index 6ec23cb..cea3b86 100644
--- a/setup.py
+++ b/setup.py
@@ -14,7 +14,7 @@
     name=__pkg_name__,
     license="MIT",
     description='xpore is a python package for Nanopore data analysis.',
-    version='v0.5.5',
+    version='v0.5.6',
     long_description=README,
     long_description_content_type='text/markdown',
     url='https://github.com/GoekeLab/xpore',
diff --git a/xpore/scripts/dataprep.py b/xpore/scripts/dataprep.py
index 5547564..e54312e 100644
--- a/xpore/scripts/dataprep.py
+++ b/xpore/scripts/dataprep.py
@@ -41,49 +41,54 @@ def combine(read_name,eventalign_per_read,out_paths,locks):
     eventalign_result = pd.DataFrame.from_records(eventalign_per_read)
 
     cond_successfully_eventaligned = eventalign_result['reference_kmer'] == eventalign_result['model_kmer']
-    eventalign_result = eventalign_result[cond_successfully_eventaligned]
+    
+    if cond_successfully_eventaligned.sum() != 0:
 
-    keys = ['read_index','contig','position','reference_kmer'] # for groupby
-    eventalign_result['length'] = pd.to_numeric(eventalign_result['end_idx'])-pd.to_numeric(eventalign_result['start_idx'])
-    eventalign_result['sum_norm_mean'] = pd.to_numeric(eventalign_result['event_level_mean']) * eventalign_result['length']
+        eventalign_result = eventalign_result[cond_successfully_eventaligned]
 
-    eventalign_result = eventalign_result.groupby(keys)  
-    sum_norm_mean = eventalign_result['sum_norm_mean'].sum() 
-    start_idx = eventalign_result['start_idx'].min()
-    end_idx = eventalign_result['end_idx'].max()
-    total_length = eventalign_result['length'].sum()
+        keys = ['read_index','contig','position','reference_kmer'] # for groupby
+        eventalign_result['length'] = pd.to_numeric(eventalign_result['end_idx'])-pd.to_numeric(eventalign_result['start_idx'])
+        eventalign_result['sum_norm_mean'] = pd.to_numeric(eventalign_result['event_level_mean']) * eventalign_result['length']
 
-    eventalign_result = pd.concat([start_idx,end_idx],axis=1)
-    eventalign_result['norm_mean'] = sum_norm_mean/total_length
+        eventalign_result = eventalign_result.groupby(keys)  
+        sum_norm_mean = eventalign_result['sum_norm_mean'].sum() 
+        start_idx = eventalign_result['start_idx'].min()
+        end_idx = eventalign_result['end_idx'].max()
+        total_length = eventalign_result['length'].sum()
 
-    eventalign_result.reset_index(inplace=True)
+        eventalign_result = pd.concat([start_idx,end_idx],axis=1)
+        eventalign_result['norm_mean'] = sum_norm_mean/total_length
 
-    # eventalign_result['transcript_id'] = [contig.split('.')[0] for contig in eventalign_result['contig']]
-    eventalign_result['transcript_id'] = eventalign_result['contig']
-    eventalign_result['transcriptomic_position'] = pd.to_numeric(eventalign_result['position']) + 2 # the middle position of 5-mers.
-    # eventalign_result = misc.str_encode(eventalign_result)
-    eventalign_result['read_id'] = [read_name]*len(eventalign_result)
+        eventalign_result.reset_index(inplace=True)
 
-    # features = ['read_id','transcript_id','transcriptomic_position','reference_kmer','norm_mean','start_idx','end_idx']
-    # features_dtype = np.dtype([('read_id', 'S36'), ('transcript_id', 'S15'), ('transcriptomic_position', '<i8'), ('reference_kmer', 'S5'), ('norm_mean', '<f8'), ('start_idx', '<i8'), ('end_idx', '<i8')])
-    features = ['read_id','transcript_id','transcriptomic_position','reference_kmer','norm_mean']
+        # eventalign_result['transcript_id'] = [contig.split('.')[0] for contig in eventalign_result['contig']]    
+        eventalign_result['transcript_id'] = eventalign_result['contig']
 
-    df_events_per_read = eventalign_result[features]
-    # print(df_events_per_read.head())
-    
-    # write to file.
-    df_events_per_read = df_events_per_read.set_index(['transcript_id','read_id'])
 
-    with locks['hdf5'], h5py.File(out_paths['hdf5'],'a') as hf:
-        for tx_id,read_id in df_events_per_read.index.unique():
-            df2write = df_events_per_read.loc[[(tx_id,read_id)],:].reset_index() 
-            events = np.rec.fromrecords(misc.str_encode(df2write[features]),names=features) #,dtype=features_dtype
+        eventalign_result['transcriptomic_position'] = pd.to_numeric(eventalign_result['position']) + 2 # the middle position of 5-mers.
+        # eventalign_result = misc.str_encode(eventalign_result)
+        eventalign_result['read_id'] = [read_name]*len(eventalign_result)
 
-            hf_tx = hf.require_group('%s/%s' %(tx_id,read_id))
-            if 'events' in hf_tx:
-                continue
-            else:
-                hf_tx['events'] = events
+        # features = ['read_id','transcript_id','transcriptomic_position','reference_kmer','norm_mean','start_idx','end_idx']
+        # features_dtype = np.dtype([('read_id', 'S36'), ('transcript_id', 'S15'), ('transcriptomic_position', '<i8'), ('reference_kmer', 'S5'), ('norm_mean', '<f8'), ('start_idx', '<i8'), ('end_idx', '<i8')])
+        features = ['read_id','transcript_id','transcriptomic_position','reference_kmer','norm_mean']
+
+        df_events_per_read = eventalign_result[features]
+        # print(df_events_per_read.head())
+
+        # write to file.
+        df_events_per_read = df_events_per_read.set_index(['transcript_id','read_id'])
+
+        with locks['hdf5'], h5py.File(out_paths['hdf5'],'a') as hf:
+            for tx_id,read_id in df_events_per_read.index.unique():
+                df2write = df_events_per_read.loc[[(tx_id,read_id)],:].reset_index() 
+                events = np.rec.fromrecords(misc.str_encode(df2write[features]),names=features) #,dtype=features_dtype
+
+                hf_tx = hf.require_group('%s/%s' %(tx_id,read_id))
+                if 'events' in hf_tx:
+                    continue
+                else:
+                    hf_tx['events'] = events
     
     with locks['log'], open(out_paths['log'],'a') as f:
         f.write('%s\n' %(read_name))