diff --git a/module2/exo1/toy_document_orgmode_python_en.org b/module2/exo1/toy_document_orgmode_python_en.org
index 7eb86625be46cdd6bff74cede1e601293a8b3c8e..29e71857a703c69f996b78bcec892b3a95c9de04 100644
--- a/module2/exo1/toy_document_orgmode_python_en.org
+++ b/module2/exo1/toy_document_orgmode_python_en.org
@@ -31,10 +31,7 @@ theta = np.random.uniform(size=N, low=0, high=pi/2)
 #+end_src
 
 * Using a surface fraction argument
-A method that is easier to understand and does not make use of the
-$\sin$ function is based on the fact that if $X\sim U(0,1)$ and $Y\sim
-U(0,1)$, then $P[X^2+Y^2\leq 1] = \pi/4$ (see [[https://en.wikipedia.org/wiki/Monte_Carlo_method]["Monte Carlo method" on
-Wikipedia]]). The following code uses this approach:
+A method that is easier to understand and does not make use of the $\sin$ function is based on the fact that if $X\sim U(0,1)$ and $Y\sim U(0,1)$, then $P[X^2+Y^2\leq 1] = \pi/4$ (see [[https://en.wikipedia.org/wiki/Monte_Carlo_method]["Monte Carlo method" on Wikipedia]]). The following code uses this approach:
 
 #+begin_src python :results output file :var matplot_lib_filename="figure_pi_mc2.png" :exports both :session *python* 
 import matplotlib.pyplot as plt
@@ -56,9 +53,7 @@ plt.savefig(matplot_lib_filename)
 print(matplot_lib_filename)
 #+end_src
 
-It is then straightforward to obtain a (not really good) approximation
-to $\pi$ by counting how many times, on average, $X^2 + Y^2$ is smaller than 1:
-
+It is then straightforward to obtain a (not really good) approximation to $\pi$ by counting how many times, on average, $X^2 + Y^2$ is smaller than 1:
 #+begin_src python :results output :session *python* :exports both
 4*np.mean(accept)
 #+end_src