diff --git a/doc/Projects/2018/Project2/pdf/Project2.pdf b/doc/Projects/2018/Project2/pdf/Project2.pdf
index 9dfb20775..668bc511c 100644
Binary files a/doc/Projects/2018/Project2/pdf/Project2.pdf and b/doc/Projects/2018/Project2/pdf/Project2.pdf differ
diff --git a/doc/Projects/2018/Project2/pdf/Project2.tex b/doc/Projects/2018/Project2/pdf/Project2.tex
index 10d6fabda..1f165fe84 100644
--- a/doc/Projects/2018/Project2/pdf/Project2.tex
+++ b/doc/Projects/2018/Project2/pdf/Project2.tex
@@ -247,7 +247,7 @@ Here is the Python code you need to generate the training data, see
 also the \href{{https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVI-linreg_ising.html}}{notebook of Mehta et
 al}.
 
-\begin{print}
+\begin{verbatim}
 import numpy as np
 import scipy.sparse as sp
 np.random.seed(12)
@@ -276,7 +276,7 @@ def ising_energies(states,L):
     return E
 # calculate Ising energies
 energies=ising_energies(states,L)
-\end{print}
+\end{verbatim}
 
 We can now recast the problem as a linear regression model using our codes from project 1.
 The way we are going to build our model mimicks the way we could think of finding say the gravitional constant for the graviational force between two planets.