diff --git a/module3/exo2/exercice_python_en.org b/module3/exo2/exercice_python_en.org
index 5782f493934678ba782fb65634a4d86e5f3adefc..88c2aa5fbf3be2f6a73c4453c3f701479e137d09 100644
--- a/module3/exo2/exercice_python_en.org
+++ b/module3/exo2/exercice_python_en.org
@@ -1,8 +1,7 @@
-#+TITLE: Your title
-#+AUTHOR: Your name
-#+DATE: Today's date
+#+TITLE: Analysis of the incidence of chickenpox
+#+AUTHOR: Thomas Rushton
+#+DATE: 2024-04-24
#+LANGUAGE: en
-# #+PROPERTY: header-args :eval never-export
#+HTML_HEAD:
#+HTML_HEAD:
@@ -11,84 +10,185 @@
#+HTML_HEAD:
#+HTML_HEAD:
-* Some explanations
+#+PROPERTY: header-args :session
-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/.
+#+OPTIONS: ^:{}
-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.
+* Data acquisition
-Like we showed in the video, Python code is included as follows (and
-is exxecuted by typing ~C-c C-c~):
+Let's start by getting the data on chickenpox incidence from [[https://www.sentiweb.fr/][Rèseau Sentinelles]].
+#+NAME: data-url
+https://www.sentiweb.fr/datasets/incidence-PAY-7.csv
+
+#+begin_src python :results output :var data_url=data-url
+data_file = "chickenpox.csv"
+
+import os
+import urllib.request
+if not os.path.exists(data_file):
+ urllib.request.urlretrieve(data_url, data_file)
+#+end_src
+
+#+RESULTS:
+
+** Format the data
+
+Discard the first line (which is a comment) and split the remaining
+lines into columns.
+
+#+begin_src python :results silent :exports both
+data = open(data_file, 'rb').read()
+lines = data.decode('latin-1').strip().split('\n')
+data_lines = lines[1:]
+table = [line.split(',') for line in data_lines]
+# Could use the csv library instead...
+#+end_src
+
+Let's take a gander at the data:
+
+#+begin_src python :results value :exports both
+table[:5]
+#+end_src
+
+#+RESULTS:
+| week | indicator | inc | inc_low | inc_up | inc100 | inc100_low | inc100_up | geo_insee | geo_name |
+| 202416 | 7 | 19330 | 13879 | 24781 | 29 | 21 | 37 | FR | France |
+| 202415 | 7 | 24807 | 17183 | 32431 | 37 | 26 | 48 | FR | France |
+| 202414 | 7 | 16181 | 12544 | 19818 | 24 | 19 | 29 | FR | France |
+| 202413 | 7 | 18322 | 14206 | 22438 | 27 | 21 | 33 | FR | France |
+
+Alright, looks good 👍
+
+** Sanitise the data
+
+But there may be problems with the data. Let's check for the obvious
+case of missing/empty data:
#+begin_src python :results output :exports both
-print("Hello world!")
+valid_table = []
+for row in table:
+ missing = any([column == '' for column in row])
+ if missing:
+ print(row)
+ else:
+ valid_table.append(row)
+#+end_src
+
+#+RESULTS:
+
+This is kind of grim, but it does the job.
+
+** Extract required columns
+
+For a temporal analysis, we just need the =week= and =inc= columns:
+
+#+begin_src python :results silent :exports both
+week = [row[0] for row in valid_table]
+assert week[0] == 'week'
+del week[0]
+inc = [row[2] for row in valid_table]
+assert inc[0] == 'inc'
+del inc[0]
+data = list(zip(week, inc))
+#+end_src
+
+Let's check what we have, using ~None~ to tell Org where to put
+separators in the resulting table (which contains the first five and
+last five weeks' data):
+
+#+begin_src python :results value :exports both
+[('week', 'inc'), None] + data[:5] + [None] + data[-5:]
#+end_src
#+RESULTS:
-: Hello world!
+| week | inc |
+|--------+-------|
+| 202416 | 19330 |
+| 202415 | 24807 |
+| 202414 | 16181 |
+| 202413 | 18322 |
+| 202412 | 12818 |
+|--------+-------|
+| 199101 | 15565 |
+| 199052 | 19375 |
+| 199051 | 19080 |
+| 199050 | 11079 |
+| 199049 | 1143 |
+
+** Convert dates
+
+Dates are represented in ISO 8601 format (YYYYWW) so let's parse
+those. It should already be sorted chronologically, but let's make
+sure of that too.
+
+#+begin_src python :results silent :exports both
+import datetime
+converted_data = [(datetime.datetime.strptime(year_and_week + ":1", '%G%V:%u').date(),
+ int(inc))
+ for year_and_week, inc in data]
+converted_data.sort(key = lambda record: record[0])
+#+end_src
-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~.
+Let's check again:
-#+begin_src python :results output :session :exports both
-import numpy
-x=numpy.linspace(-15,15)
-print(x)
+#+begin_src python :results value :exports both
+data_as_str = [(str(date), str(inc)) for date, inc in converted_data]
+[('date', 'inc'), None] + data_as_str[:5] + [None] + data_as_str[-5:]
#+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
+| date | inc |
+|------------+-------|
+| 1990-12-03 | 1143 |
+| 1990-12-10 | 11079 |
+| 1990-12-17 | 19080 |
+| 1990-12-24 | 19375 |
+| 1990-12-31 | 15565 |
+|------------+-------|
+| 2024-03-18 | 12818 |
+| 2024-03-25 | 18322 |
+| 2024-04-01 | 16181 |
+| 2024-04-08 | 24807 |
+| 2024-04-15 | 19330 |
+
+** Visual inspection
+
+So, now we can take a look at incidence over time. (The 'flu notebook
+switches to R here, but we're going to stick with python.)
+
+#+begin_src python :results output file :var filename="./incidence.png" :exports both
import matplotlib.pyplot as plt
-plt.figure(figsize=(10,5))
-plt.plot(x,numpy.cos(x)/x)
+plt.clf()
+
+date,incidence = zip(*converted_data)
+
+plt.plot(date,incidence)
plt.tight_layout()
+plt.savefig(filename)
+print(filename)
+#+end_src
+
+#+RESULTS:
+[[file:./incidence.png]]
+
+And we can zoom in on a period of, say, five years:
-plt.savefig(matplot_lib_filename)
-print(matplot_lib_filename)
+#+begin_src python :results output file :var filename="./incidence-zoom.png" :exports both
+plt.clf()
+
+start = 10
+years = 5
+date,incidence = zip(*converted_data[52*start:52*(start+years)])
+
+plt.plot(date,incidence)
+plt.tight_layout()
+plt.savefig(filename)
+print(filename)
#+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 ~