diff --git a/module2/exo1/toy_document_orgmode_python_en.org b/module2/exo1/toy_document_orgmode_python_en.org
index 9e167bee5f8a94cb6bd481211fe3713d592f3458..6c66c86517ecf4f823a8472aeb7dcc636e5bcd50 100644
--- a/module2/exo1/toy_document_orgmode_python_en.org
+++ b/module2/exo1/toy_document_orgmode_python_en.org
@@ -11,42 +11,36 @@
#+HTML_HEAD:
#+HTML_HEAD:
-* Table of Contents
+#+PROPERTY: header-args :session :exports both
-1. Asking the math libraryo
-2. * Buffon's needle
-3. Using a surface fraction argument
-
-* 1 Asking the math library
+* Asking the math library
My computer tells me that $\pi$ is /approximatively/
-#+begin_src python :results output :exports both
-import math
-print(math.pi)
+#+begin_src python :results value :session *python* :exports both
+from math import *
+pi
#+end_src
#+RESULTS:
: 3.141592653589793
-* 2 * Buffon's needle
-Applying the method of [[https://en.wikipedia.org/wiki/Buffon%27s_needle_problem][Buffon's needle]], we get the approximation
+* * Buffon's needle
+Applying the method of [[https://en.wikipedia.org/wiki/Buffon%27s_needle_problem][Buffon's needle]], we get the *approximation*
-#+begin_src python :results output :exports both
-import math
+#+begin_src python :results value :session *python* :exports both
import numpy as np
np.random.seed(seed=42)
N = 10000
x = np.random.uniform(size=N, low=0, high=1)
-theta = np.random.uniform(size=N, low=0, high=math.pi/2)
-print(2/(sum((x+np.sin(theta))>1)/N))
+theta = np.random.uniform(size=N, low=0, high=pi/2)
+2/(sum((x+np.sin(theta))>1)/N)
#+end_src
#+RESULTS:
: 3.128911138923655
-* 3. 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
+* 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 ocde uses this approach:
#+begin_src python :results output file :session :var matplot_lib_filename="C:/Users/Utilisateur/mooc-rr/module2/exo1/PictureRes.png" :exports results