exercices

module2/exo3/exercice_python_en.org

@@ -11,84 +11,35 @@
 #+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
 #+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
 
-* Some explanations
 
-This is an org-mode document with code examples in R.  Once opened in
-Emacs, this document can easily be exported to HTML, PDF, and Office
-formats. For more information on org-mode, see
-https://orgmode.org/guide/.
 
-When you type the shortcut =C-c C-e h o=, this document will be
-exported as HTML. All the code in it will be re-executed, and the
-results will be retrieved and included into the exported document. If
-you do not want to re-execute all code each time, you can delete the #
-and the space before ~#+PROPERTY:~ in the header of this document.
-
-Like we showed in the video, Python code is included as follows (and
-is exxecuted by typing ~C-c C-c~):
-
-#+begin_src python :results output :exports both
-print("Hello world!")
+#+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
+import numpy as np
+import matplotlib.pyplot as plt
+l= [14.0, 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, 16.0, 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, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 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, 15.0, 14.3, 16.8, 14.0, 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, 21.0]
+print(len(l))
+x=numpy.linspace(0,100,100)
+plt.figure(figsize=(10,5))
+plt.plot(x,l)
+plt.savefig(matplot_lib_filename)
+matplot_lib_filename
 #+end_src
 
 #+RESULTS:
-: Hello world!
-
-And now the same but in an Python session. With a session, Python's
-state, i.e. the values of all the variables, remains persistent from
-one code block to the next. The code is still executed using ~C-c
-C-c~.
+[[file:/tmp/babel-XsHjUo/figure3nkNUU.png]]
 
-#+begin_src python :results output :session :exports both
-import numpy
-x=numpy.linspace(-15,15)
-print(x)
-#+end_src
 
-#+RESULTS:
-#+begin_example
-[-15.         -14.3877551  -13.7755102  -13.16326531 -12.55102041
- -11.93877551 -11.32653061 -10.71428571 -10.10204082  -9.48979592
-  -8.87755102  -8.26530612  -7.65306122  -7.04081633  -6.42857143
-  -5.81632653  -5.20408163  -4.59183673  -3.97959184  -3.36734694
-  -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
 
-Finally, an example for graphical output:
-#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results
+#+begin_src python :results file :session :var matplot_lib_file=(org-babel-temp-file "figure2" ".png") :exports both
+import numpy as np
 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)
+print(len(l))
+num_bins = 10  
+n, bins, patches = plt.hist(l,num_bins, facecolor = 'blue', alpha = 0.5, edgecolor= 'black')
+plt.savefig(matplot_lib_file)
+matplot_lib_file
 #+end_src
 
 #+RESULTS:
-[[file:./cosxsx.png]]
-
-Note the parameter ~:exports results~, which indicates that the code
-will not appear in the exported document. We recommend that in the
-context of this MOOC, you always leave this parameter setting as
-~:exports both~, because we want your analyses to be perfectly
-transparent and reproducible.
-
-Watch out: the figure generated by the code block is /not/ stored in
-the org document. It's a plain file, here named ~cosxsx.png~. You have
-to commit it explicitly if you want your analysis to be legible and
-understandable on GitLab.
-
-Finally, don't forget that we provide in the resource section of this
-MOOC a configuration with a few keyboard shortcuts that allow you to
-quickly create code blocks in Python by typing ~<p~, ~<P~ or ~<PP~
-followed by ~Tab~.
+[[file:/tmp/babel-XsHjUo/figure2jJAtRy.png]]
 
-Now it's your turn! You can delete all this information and replace it
-by your computational document.

module2/exo5/exo5_R_fr.org

@@ -42,30 +42,31 @@ data
 
 #+RESULTS:
 #+begin_example
-        Date Count Temperature Pressure Malfunction
-1    4/12/81     6          66       50           0
-2   11/12/81     6          70       50           1
-3    3/22/82     6          69       50           0
-4   11/11/82     6          68       50           0
-5    4/04/83     6          67       50           0
-6    6/18/82     6          72       50           0
-7    8/30/83     6          73      100           0
-8   11/28/83     6          70      100           0
-9    2/03/84     6          57      200           1
-10   4/06/84     6          63      200           1
-11   8/30/84     6          70      200           1
-12  10/05/84     6          78      200           0
-13  11/08/84     6          67      200           0
-14   1/24/85     6          53      200           2
-15   4/12/85     6          67      200           0
-16   4/29/85     6          75      200           0
-17   6/17/85     6          70      200           0
-18 7/29/85     6          81      200           0
-19   8/27/85     6          76      200           0
-20  10/03/85     6          79      200           0
-21  10/30/85     6          75      200           2
-22  11/26/85     6          76      200           0
-23   1/12/86     6          58      200           1
+
+       Date Count Temperature Pressure Malfunction
+1   4/12/81     6          66       50           0
+2  11/12/81     6          70       50           1
+3   3/22/82     6          69       50           0
+4  11/11/82     6          68       50           0
+5   4/04/83     6          67       50           0
+6   6/18/82     6          72       50           0
+7   8/30/83     6          73      100           0
+8  11/28/83     6          70      100           0
+9   2/03/84     6          57      200           1
+10  4/06/84     6          63      200           1
+11  8/30/84     6          70      200           1
+12 10/05/84     6          78      200           0
+13 11/08/84     6          67      200           0
+14  1/24/85     6          53      200           2
+15  4/12/85     6          67      200           0
+16  4/29/85     6          75      200           0
+17  6/17/85     6          70      200           0
+18  7/29/85     6          81      200           0
+19  8/27/85     6          76      200           0
+20 10/03/85     6          79      200           0
+21 10/30/85     6          75      200           2
+22 11/26/85     6          76      200           0
+23  1/12/86     6          58      200           1
 #+end_example
 
 Le jeu de données nous indique la date de l'essai, le nombre de joints
@@ -104,7 +105,6 @@ plot(data=data, Malfunction/Count ~ Temperature, ylim=c(0,1))
 #+end_src
 
 #+RESULTS:
-[[file:freq_temp.png]]
 
 À première vue, ce n'est pas flagrant mais bon, essayons quand même
 d'estimer l'impact de la température $t$ sur la probabilité de
@@ -134,21 +134,23 @@ glm(formula = Malfunction/Count ~ Temperature, family = binomial(link = "logit")
     data = data, weights = Count)
 
 Deviance Residuals: 
-      2        9       10       11       14       21       23  
--0.3015  -0.2836  -0.2919  -0.3015   0.6891   0.6560  -0.2850  
+     Min        1Q    Median        3Q       Max  
+-0.95227  -0.78299  -0.54117  -0.04379   2.65152  
 
 Coefficients:
-             Estimate Std. Error z value Pr(>|z|)
-(Intercept) -1.389528   3.195752  -0.435    0.664
-Temperature  0.001416   0.049773   0.028    0.977
+            Estimate Std. Error z value Pr(>|z|)  
+(Intercept)  5.08498    3.05247   1.666   0.0957 .
+Temperature -0.11560    0.04702  -2.458   0.0140 *
+---
+codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
 (Dispersion parameter for binomial family taken to be 1)
 
-    Null deviance: 1.3347  on 6  degrees of freedom
-Residual deviance: 1.3339  on 5  degrees of freedom
-AIC: 18.894
+    Null deviance: 24.230  on 22  degrees of freedom
+Residual deviance: 18.086  on 21  degrees of freedom
+AIC: 35.647
 
-Number of Fisher Scoring iterations: 4
+Number of Fisher Scoring iterations: 5
 #+end_example
 
 L'estimateur le plus probable du paramètre de température est 0.001416

module3/exo1/analyse-syndrome-grippal.org

@@ -80,6 +80,47 @@ Regardons ce que nous avons obtenu:
 table[:5]
 #+END_SRC
 
+** Utilisation de données locales
+Dans le cas ou internet est indisponible, ou si le jeu de données
+fourni en ligne est susceptible d’être modifié, mieux vaut travailler
+sur des données téléchargées, en local.
+
+On vérifie que le jeu de données n’existe pas déja
+#+begin_src shell :session *shell* :results output :exports both
+cd /home/lcs/Documents/césure/mooc-rr/module3/exo1
+ls
+#+end_src
+
+#+RESULTS:
+: 
+: analyse-syndrome-grippal.ipynb    incidence-PAY-3.csv
+: analyse-syndrome-grippal.org#'   influenza-like-illness-analysis.ipynb
+:  analyse-syndrome-grippal.org      influenza-like-illness-analysis.org
+:  analyse-syndrome-grippal.Rmd      influenza-like-illness-analysis.Rmd
+
+
+ 
+#+BEGIN_SRC python :results silent :var data_url=data-url
+#import panda as pd
+#data = pd.read_csv('incidence-PAY-3.csv',header = 1)
+
+import csv
+with open('incidence-PAY-3.csv') as csv_file:
+    csv_read=csv.reader(csv_file, delimiter=',')
+
+
+lines = data.decode('latin-1').strip().split('\n')
+data_lines = lines[1:]
+table = [line.split(',') for line in data_lines]
+#+END_SRC
+
+Regardons ce que nous avons obtenu:
+#+BEGIN_SRC python :results value
+table[:5]
+#+END_SRC
+
+#+RESULTS:
+
 ** Recherche de données manquantes
 Il y a malheureusement beaucoup de façon d'indiquer l'absence d'un point de données. Nous testons ici seulement pour la présence de champs vides. Il faudrait aussi rechercher des valeurs non-numériques dans les colonnes à priori numériques. Nous ne le faisons pas ici, mais une vérification ultérieure capterait des telles anomalies.
 
@@ -94,6 +135,13 @@ for row in table:
         valid_table.append(row)
 #+END_SRC
 
+#+RESULTS:
+: Traceback (most recent call last):
+:   File "<stdin>", line 1, in <module>
+:   File "/tmp/babel-XsHjUo/python-WeKugl", line 2, in <module>
+:     for row in table:
+: NameError: name 'table' is not defined
+
 ** Extraction des colonnes utilisées
 Il y a deux colonnes qui nous intéressent: la première (~"week"~) et la troisième (~"inc"~). Nous vérifions leurs noms dans l'en-tête, que nous effaçons par la suite. Enfin, nous créons un tableau avec les deux colonnes pour le traitement suivant.
 #+BEGIN_SRC python :results silent