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<body>
<div id="content" class="content">
<h1 class="title">Exercice 2</h1>
<p>
Récuperer les données et les visualiser:
</p>
<div class="org-src-container">
<pre class="src src-python"><span style="color: #b877db;">import</span> numpy <span style="color: #b877db;">as</span> np
<span style="color: #e95678;">data</span> = np.genfromtxt(<span style="color: #fab795;">'data.csv'</span>, delimiter=<span style="color: #fab795;">', '</span>)
<span style="color: #b877db;">print</span>(data) <span style="color: #6c6f93; background-color: #292b36; font-style: italic;"># </span><span style="color: #6c6f93; background-color: #292b36; font-style: italic;">visualizing the data</span>
</pre>
</div>
<pre class="example">
[14. 7.6 11.2 12.8 12.5 9.9 14.9 9.4 16.9 10.2 14.9 18.1 7.3 9.8
10.9 12.2 9.9 2.9 2.8 15.4 15.7 9.7 13.1 13.2 12.3 11.7 16. 12.4
17.9 12.2 16.2 18.7 8.9 11.9 12.1 14.6 12.1 4.7 3.9 16.9 16.8 11.3
14.4 15.7 14. 13.6 18. 13.6 19.9 13.7 17. 20.5 9.9 12.5 13.2 16.1
13.5 6.3 6.4 17.6 19.1 12.8 15.5 16.3 15.2 14.6 19.1 14.4 21.4 15.1
19.6 21.7 11.3 15. 14.3 16.8 14. 6.8 8.2 19.9 20.4 14.6 16.4 18.7
16.8 15.8 20.4 15.8 22.4 16.2 20.3 23.4 12.1 15.5 15.4 18.4 15.7 10.2
8.9 21. ]
</pre>
<p>
Statistiques:
</p>
<div class="org-src-container">
<pre class="src src-python"><span style="color: #b877db;">print</span>(f<span style="color: #fab795;">"Mean: </span>{np.mean(data)}<span style="color: #f09383;">\n</span><span style="color: #fab795;">Min: </span>{np.<span style="color: #b877db;">min</span>(data)}<span style="color: #f09383;">\n</span><span style="color: #fab795;">Max: </span>{np.<span style="color: #b877db;">max</span>(data)}<span style="color: #f09383;">\n</span><span style="color: #fab795;">Median: </span>{np.median(data)}<span style="color: #f09383;">\n</span><span style="color: #fab795;">Std: </span>{np.std(data, ddof=1)}<span style="color: #fab795;">"</span>)
</pre>
</div>
<pre class="example">
Mean: 14.113000000000001
Min: 2.8
Max: 23.4
Median: 14.5
Std: 4.334094455301447
</pre>
</div>
<div id="postamble" class="status">
<p class="date">Date: 2024-06-06 Thu 00:00</p>
<p class="author">Auteur: Antoine Geimer</p>
<p class="date">Created: 2024-06-06 Thu 13:37</p>
<p class="validation"><a href="https://validator.w3.org/check?uri=referer">Validate</a></p>
</div>
</body>
</html>
module2/exo2/exercice_python_fr.org
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#+TITLE: Votre titre #+TITLE: Exercice 2
#+AUTHOR: Votre nom #+AUTHOR: Antoine Geimer
#+DATE: La date du jour #+DATE: [2024-06-06 Thu]
#+LANGUAGE: fr #+LANGUAGE: fr
# #+PROPERTY: header-args :eval never-export # #+PROPERTY: header-args :eval never-export
...@@ -11,83 +11,33 @@ ...@@ -11,83 +11,33 @@
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#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script> #+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
* Quelques explications
Ceci est un document org-mode avec quelques exemples de code Récuperer les données et les visualiser:
python. Une fois ouvert dans emacs, ce document peut aisément être #+begin_src python :results output :exports both :session *python*
exporté au format HTML, PDF, et Office. Pour plus de détails sur import numpy as np
org-mode vous pouvez consulter https://orgmode.org/guide/.
Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera data = np.genfromtxt('data.csv', delimiter=', ')
compilé en html. Tout le code contenu sera ré-exécuté, les résultats print(data) # visualizing the data
récupérés et inclus dans un document final. Si vous ne souhaitez pas
ré-exécuter tout le code à chaque fois, il vous suffit de supprimer
le # et l'espace qui sont devant le ~#+PROPERTY:~ au début de ce
document.
Comme nous vous l'avons montré dans la vidéo, on inclue du code
python de la façon suivante (et on l'exécute en faisant ~C-c C-c~):
#+begin_src python :results output :exports both
print("Hello world!")
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: Hello world! : [14. 7.6 11.2 12.8 12.5 9.9 14.9 9.4 16.9 10.2 14.9 18.1 7.3 9.8
: 10.9 12.2 9.9 2.9 2.8 15.4 15.7 9.7 13.1 13.2 12.3 11.7 16. 12.4
Voici la même chose, mais avec une session python, donc une : 17.9 12.2 16.2 18.7 8.9 11.9 12.1 14.6 12.1 4.7 3.9 16.9 16.8 11.3
persistance d'un bloc à l'autre (et on l'exécute toujours en faisant : 14.4 15.7 14. 13.6 18. 13.6 19.9 13.7 17. 20.5 9.9 12.5 13.2 16.1
~C-c C-c~). : 13.5 6.3 6.4 17.6 19.1 12.8 15.5 16.3 15.2 14.6 19.1 14.4 21.4 15.1
#+begin_src python :results output :session :exports both : 19.6 21.7 11.3 15. 14.3 16.8 14. 6.8 8.2 19.9 20.4 14.6 16.4 18.7
import numpy : 16.8 15.8 20.4 15.8 22.4 16.2 20.3 23.4 12.1 15.5 15.4 18.4 15.7 10.2
x=numpy.linspace(-15,15) : 8.9 21. ]
print(x)
Statistiques:
#+begin_src python :results output :exports both :session *python*
print(f"Mean: {np.mean(data)}\nMin: {np.min(data)}\nMax: {np.max(data)}\nMedian: {np.median(data)}\nStd: {np.std(data, ddof=1)}")
#+end_src #+end_src
#+RESULTS: #+RESULTS:
#+begin_example : Mean: 14.113000000000001
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041 : Min: 2.8
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592 : Max: 23.4
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143 : Median: 14.5
-5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694 : Std: 4.334094455301447
-2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245
0.30612245 0.91836735 1.53061224 2.14285714 2.75510204
3.36734694 3.97959184 4.59183673 5.20408163 5.81632653
6.42857143 7.04081633 7.65306122 8.26530612 8.87755102
9.48979592 10.10204082 10.71428571 11.32653061 11.93877551
12.55102041 13.16326531 13.7755102 14.3877551 15. ]
#+end_example
Et enfin, voici un exemple de sortie graphique:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.plot(x,numpy.cos(x)/x)
plt.tight_layout()
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
#+RESULTS:
[[file:./cosxsx.png]]
Vous remarquerez le paramètre ~:exports results~ qui indique que le code
ne doit pas apparaître dans la version finale du document. Nous vous
recommandons dans le cadre de ce MOOC de ne pas changer ce paramètre
(indiquer ~both~) car l'objectif est que vos analyses de données soient
parfaitement transparentes pour être reproductibles.
Attention, la figure ainsi générée n'est pas stockée dans le document
org. C'est un fichier ordinaire, ici nommé ~cosxsx.png~. N'oubliez pas
de le committer si vous voulez que votre analyse soit lisible et
compréhensible sur GitLab.
Enfin, n'oubliez pas que nous vous fournissons dans les ressources de
ce MOOC une configuration avec un certain nombre de raccourcis
claviers permettant de créer rapidement les blocs de code python (en
faisant ~<p~, ~<P~ ou ~<PP~ suivi de ~Tab~).
Maintenant, à vous de jouer! Vous pouvez effacer toutes ces
informations et les remplacer par votre document computationnel.
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