diff --git a/freeforestml/model.py b/freeforestml/model.py
index 7d7e7ce4efc53310859b65007d8a81bcca7ab4ae..c0b62838af76e9a7533ef962d5cc363a84f6240a 100644
--- a/freeforestml/model.py
+++ b/freeforestml/model.py
@@ -183,7 +183,7 @@ class ClassicalCV(CrossValidator):
             return (slice_id / (self.k * 2.0) <= variable) \
                    & (variable < (slice_id + 1.0) / (self.k * 2))
 
-    def select_training(self, df, fold_i):
+    def select_training(self, df, fold_i, for_predicting = False):
         """
         Returns the index array to select all training events from the dataset for the
         given fold.
@@ -249,7 +249,7 @@ class NoTestCV(CrossValidator):
             return (slice_id / self.k <= variable) \
                    & (variable < (slice_id + 1.0) / self.k)
 
-    def select_training(self, df, fold_i):
+    def select_training(self, df, fold_i, for_predicting = False):
         """
         Returns the index array to select all training events from the
         dataset. The fold_i parameter has no effect.
@@ -309,7 +309,7 @@ class BinaryCV(CrossValidator):
             return (slice_id / self.k <= variable) \
                    & (variable < (slice_id + 1.0) / self.k)
 
-    def select_training(self, df, fold_i):
+    def select_training(self, df, fold_i, for_predicting = False):
         """
         Returns the index array to select all training events from the dataset for the
         given fold.
diff --git a/freeforestml/plot.py b/freeforestml/plot.py
index 3eda8b4b8ed6fbbfcbb5a053dbf329eef346f08a..c1bc65bb3b3cfb5f52707c2834e8474c9a127f3a 100644
--- a/freeforestml/plot.py
+++ b/freeforestml/plot.py
@@ -333,9 +333,9 @@ def hist(dataframe, variable, bins, stacks, selection=None,
                           for yield_name in stack_item.yield_names]
         return sum(process_totals)
 
-    if y_log:
-        for stack in uhepp_obj.stacks:
-            stack.content.sort(key=lambda x: total_stackitem(uhepp_obj, x))
+    #if y_log:
+        #for stack in uhepp_obj.stacks:
+            #stack.content.sort(key=lambda x: total_stackitem(uhepp_obj, x))
 
     ##################
     # Vertical lines
diff --git a/freeforestml/stack.py b/freeforestml/stack.py
index 0aba6319ba80725e335d7bde08c74157bf090d90..ba6511175b15866885961327ac2c2698e986d56e 100644
--- a/freeforestml/stack.py
+++ b/freeforestml/stack.py
@@ -68,7 +68,7 @@ class Stack:
         self.data_uncertainties.append(data_uncertainty)
         self.aux.append(aux)
 
-    def get_hist(self, df, i, bins, variable, weight, include_outside=False):
+    def get_hist(self, df, i, bins, variable, weight, range = None, include_outside=False):
         """
         Returns the yields per bin for the i-th process in the stack. The bins
         argument specifies the bin edges.
@@ -93,7 +93,7 @@ class Stack:
         else:
             func = np.histogram
 
-        total, _ = func(variable, bins=bins, weights=weight)
+        total, _ = func(variable, bins=bins, weights=weight, range = range)
         return total
 
     def get_total(self, df, bins, variable, weight, include_outside=False):
@@ -108,7 +108,7 @@ class Stack:
 
         return total
 
-    def get_uncertainty(self, df, i, bins, variable, weight,
+    def get_uncertainty(self, df, i, bins, variable, weight, range = None, 
                               include_outside=False):
         """
         Returns the uncertainty of the total yield per bin. The bins argument
@@ -121,11 +121,11 @@ class Stack:
         uncertainty_2 = 0
 
         if self.is_data_uncertainty(i):
-            uncertainty_2 += self.get_hist(df, i, bins, variable, weight,
+            uncertainty_2 += self.get_hist(df, i, bins, variable, weight, range = range, 
                                            include_outside=include_outside)
         else:
             uncertainty_2 += self.get_hist(df, i, bins, variable,
-                                           weight=weight_2,
+                                           weight=weight_2, range = range, 
                                            include_outside=include_outside)
 
         return np.sqrt(uncertainty_2)