diff --git a/.gitignore b/.gitignore
index d7c4706..2337d2a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
+*.DS_Store
+
 # Data files
 data/
 # Checkpoints
diff --git a/stepback/plotting.py b/stepback/plotting.py
index 04b646a..77d3999 100644
--- a/stepback/plotting.py
+++ b/stepback/plotting.py
@@ -44,34 +44,15 @@ def plot_stability(R,
     if isinstance(score, str):
         score = [score]
     
-    base_df = R.base_df.copy()
-    id_df = R.id_df.copy()
-
-    grouped = base_df.groupby(['name', xaxis])
-    max_epoch = grouped['epoch'].max()
-    assert len(max_epoch.unique()) == 1, "It seems that different setups ran for different number of epochs."
-
-    if cutoff is None:
-        cutoff_epoch = (max_epoch[0], max_epoch[0])
-    else:
-        cutoff_epoch = (cutoff, max_epoch[0])
 
     fig, axs = plt.subplots(len(score),1,figsize=figsize)
 
     for j, s in enumerate(score):
-        # filter epochs
-        sub_df = base_df[(base_df.epoch >= cutoff_epoch[0]) & (base_df.epoch <= cutoff_epoch[1])] 
-        # select the columns to group by
-        grouping_cols = [c for c in id_df.columns if c not in ignore_columns]
-        # group by all id_cols 
-        df = sub_df.groupby(grouping_cols)[[s, s+'_std']].mean() # use dropna=False if we would have nan values
-        # move xaxis out of grouping
-        df = df.reset_index(level=xaxis)
-        # make xaxis float
-        df[xaxis] = df[xaxis].astype('float')
-        
-        # get method and learning rate with best score
-        # best_ind, best_x = df.index[df[s].argmax()], df[xaxis][df[s].argmax()]
+        df = R.build_sweep_df(score=s,
+                              xaxis=xaxis,
+                              ignore_columns=ignore_columns,
+                              cutoff=cutoff
+        )
 
         ax = axs.ravel()[j] if len(score) > 1 else axs 
         # .unique(level=) might be useful at some point
diff --git a/stepback/record.py b/stepback/record.py
index c943a1d..3d05828 100644
--- a/stepback/record.py
+++ b/stepback/record.py
@@ -189,6 +189,38 @@ def _build_base_df(self, agg='mean'):
         
         return df
     
+    def build_sweep_df(self, score='val_score', xaxis='lr', ignore_columns=list(), cutoff=None):
+
+        base_df = self.base_df.copy()
+        id_df = self.id_df.copy()
+
+        grouped = base_df.groupby(['name', xaxis])
+        max_epoch = grouped['epoch'].max()
+        assert len(max_epoch.unique()) == 1, "It seems that different setups ran for different number of epochs."
+
+        if cutoff is None:
+            cutoff_epoch = (max_epoch[0], max_epoch[0])
+        else:
+            cutoff_epoch = (cutoff, max_epoch[0])
+
+        # filter epochs
+        sub_df = base_df[(base_df.epoch >= cutoff_epoch[0])
+                         &
+                         (base_df.epoch <= cutoff_epoch[1])] 
+        # select the columns to group by
+        grouping_cols = [c for c in id_df.columns if c not in ignore_columns]
+        # group by all id_cols
+        df = sub_df.groupby(grouping_cols)[[score, score+'_std']].mean()
+        # move xaxis out of grouping
+        df = df.reset_index(level=xaxis)
+        # make xaxis float
+        df[xaxis] = df[xaxis].astype('float')
+        
+        # get method and learning rate with best score
+        # best_ind, best_x = df.index[df[s].argmax()], df[xaxis][df[s].argmax()]
+
+        return df
+    
     #============ DATABASE =================================
     #=======================================================