diff --git a/nengo_gui/examples/hbb_tutorials/chapter3/1-addition.py b/nengo_gui/examples/hbb_tutorials/chapter3/1-addition.py
index 87a6c454..5e5b7d6a 100644
--- a/nengo_gui/examples/hbb_tutorials/chapter3/1-addition.py
+++ b/nengo_gui/examples/hbb_tutorials/chapter3/1-addition.py
@@ -1,32 +1,32 @@
 # Addition
 
-# In this model, you will see a transformation which is the basic property of 
-# single neurons (i.e., addition). Addition transforms two inputs into a single 
-# output which is their sum. You will construct a network that adds two inputs. 
-# The network utilizes two communication channels going into the same neural 
-# population. Addition is somewhat ‘free’, since the incoming currents from 
+# In this model, you will see a transformation which is the basic property of
+# single neurons (i.e., addition). Addition transforms two inputs into a single
+# output which is their sum. You will construct a network that adds two inputs.
+# The network utilizes two communication channels going into the same neural
+# population. Addition is somewhat 'free', since the incoming currents from
 # different synaptic connections interact linearly.
 
-# This model has ensembles A and B which represent the two inputs to be added. 
-# The 'Sum' ensemble represents the added value. All the parameters used in the 
-# model are as described in the book, with the sum ensemble having a radius of 
+# This model has ensembles A and B which represent the two inputs to be added.
+# The 'Sum' ensemble represents the added value. All the parameters used in the
+# model are as described in the book, with the sum ensemble having a radius of
 # 2 to account for the maximum range of summing the input values.
 
-# While connecting the inputs to the ensembles A and B, the transform is 
-# set to 1 (which is the default value) since this should be a communication 
-# channel. However as described in the book, you can scale a represented 
-# variable by a constant value by changing the transform. Example: if you  
-# set the transform of ensemble B to 0 and ensemble A to 2 
-# (i.e., nengo.Connection(input_A, A, transform=[2]) ), the sum will be twice 
-# of the input_A. You will also need to set an appropriate radius for the 
-# Sum ensemble to avoid saturation when you change the transform values. 
+# While connecting the inputs to the ensembles A and B, the transform is
+# set to 1 (which is the default value) since this should be a communication
+# channel. However as described in the book, you can scale a represented
+# variable by a constant value by changing the transform. Example: if you
+# set the transform of ensemble B to 0 and ensemble A to 2
+# (i.e., nengo.Connection(input_A, A, transform=[2]) ), the sum will be twice
+# of the input_A. You will also need to set an appropriate radius for the
+# Sum ensemble to avoid saturation when you change the transform values.
 
 # Press the play button to run the simulation.
-# The input_A and input_B graphs show the inputs to ensembles A and B 
-# respectively. The graphs A and B show the decoded value of the activity of 
-# ensembles A and B respectively. The sum graph shows that the decoded value of 
+# The input_A and input_B graphs show the inputs to ensembles A and B
+# respectively. The graphs A and B show the decoded value of the activity of
+# ensembles A and B respectively. The sum graph shows that the decoded value of
 # the activity in the Sum ensemble provides a good estimate of the sum of inputs
-# A and B. You can use the sliders to change the input values provided by the 
+# A and B. You can use the sliders to change the input values provided by the
 # input_A and input_B nodes.
 
 
@@ -39,23 +39,23 @@
 model = nengo.Network(label='Scalar Addition')
 
 with model:
-    #Inputs to drive the activity in ensembles A and B 
+    #Inputs to drive the activity in ensembles A and B
     input_A = nengo.Node(Piecewise({0: -0.75, 1.25: 0.5, 2.5: 0.70, 3.75: 0}))
     input_B = nengo.Node(Piecewise({0: 0.25, 1.25: -0.5, 2.5: 0.85, 3.75: 0}))
 
     #Ensembles with 100 LIF neurons each
     # Represents the first input
-    A = nengo.Ensemble(100, dimensions=1, max_rates=Uniform(100, 200))             
+    A = nengo.Ensemble(100, dimensions=1, max_rates=Uniform(100, 200))
     # Represents the second input
-    B = nengo.Ensemble(100, dimensions=1, max_rates=Uniform(100, 200))   
+    B = nengo.Ensemble(100, dimensions=1, max_rates=Uniform(100, 200))
     # Reprsents the sum of two inputs
-    Sum = nengo.Ensemble(100, dimensions=1, max_rates=Uniform(100, 200), 
-                                    radius=2) 
-        
+    Sum = nengo.Ensemble(100, dimensions=1, max_rates=Uniform(100, 200),
+                                    radius=2)
+
     #Connecting the input nodes to ensembles
     nengo.Connection(input_A, A)
     nengo.Connection(input_B, B)
-    
+
     #Connecting ensembles A and B to the Sum ensemble
     nengo.Connection(A, Sum)
     nengo.Connection(B, Sum)