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documents/Rmd/analyse-syndrome-grippal-rstudio.Rmd 0 → 100644
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---
title: "Analyse de l'incidence du syndrôme grippal"
author: "Konrad Hinsen"
output:
pdf_document:
toc: true
html_document:
toc: true
theme: journal
documentclass: article
classoption: a4paper
header-includes:
- \usepackage[french]{babel}
- \usepackage[upright]{fourier}
- \hypersetup{colorlinks=true,pagebackref=true}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Préparation des données
Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente. L'URL est:
```{r}
data_url = "http://www.sentiweb.fr/datasets/incidence-PAY-3.csv"
```
Voici l'explication des colonnes donnée sur le [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):
| Nom de colonne | Libellé de colonne |
|----------------+-----------------------------------------------------------------------------------------------------------------------------------|
| `week` | Semaine calendaire (ISO 8601) |
| `indicator` | Code de l'indicateur de surveillance |
| `inc` | Estimation de l'incidence de consultations en nombre de cas |
| `inc_low` | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |
| `inc_up` | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |
| `inc100` | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |
| `inc100_low` | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |
| `inc100_up` | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |
| `geo_insee` | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |
| `geo_name` | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |
La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skip=1`.
### Téléchargement
```{r}
data = read.csv(data_url, skip=1)
```
Regardons ce que nous avons obtenu:
```{r}
head(data)
tail(data)
```
Y a-t-il des points manquants dans nos données ?
```{r}
na_records = apply(data, 1, function (x) any(is.na(x)))
data[na_records,]
```
Les deux colonnes qui nous intéressent sont `week` et `inc`. Vérifions leurs classes:
```{r}
class(data$week)
class(data$inc)
```
Ce sont des entiers, tout va bien !
### Conversion des numéros de semaine
La gestion des dates est toujours un sujet délicat. Il y a un grand nombre de conventions différentes qu'il ne faut pas confondre. Notre jeux de données utilise un format que peu de logiciels savent traiter: les semaines en format [ISO-8601](https://en.wikipedia.org/wiki/ISO_8601). En `R`, il est géré par la bibliothèque [parsedate](https://cran.r-project.org/package=parsedate):
```{r}
library(parsedate)
```
Pour faciliter le traitement suivant, nous remplaçons ces semaines par les dates qui correspondent aux lundis. Voici une petite fonction qui fait la conversion pour une seule valeur:
```{r}
convert_week = function(w) {
ws = paste(w)
iso = paste0(substring(ws, 1, 4), "-W", substring(ws, 5, 6))
as.character(parse_iso_8601(iso))
}
```
Nous appliquons cette fonction à tous les points, créant une nouvelle colonne `date` dans notre jeu de données:
```{r}
data$date = as.Date(convert_week(data$week))
```
Vérifions qu'elle est de classe `Date`:
```{r}
class(data$date)
```
Les points sont dans l'ordre chronologique inverse, il est donc utile de les trier:
```{r}
data = data[order(data$date),]
```
C'est l'occasion pour faire une vérification: nos dates doivent être séparées d'exactement sept jours:
```{r}
all(diff(data$date) == 7)
```
### Inspection
Regardons enfin à quoi ressemblent nos données !
```{r}
plot(data$date, data$inc, type="l", xlab="Date", ylab="Incidence hebdomadaire")
```
Un zoom sur les dernières années montre mieux la localisation des pics en hiver. Le creux des incidences se trouve en été.
```{r}
with(tail(data, 200), plot(date, inc, type="l", xlab="Date", ylab="Incidence hebdomadaire"))
```
## L'incidence annuelle
### Calcul
Étant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er août de l'année $N$ au 1er août de l'année $N+1$. Nous mettons l'année $N+1$ comme étiquette sur cette année décalée, car le pic de l'épidémie est toujours au début de l'année $N+1$. Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.
L'argument `na.rm=True` dans la sommation précise qu'il faut supprimer les points manquants. Ce choix est raisonnable car il n'y a qu'un seul point manquant, dont l'impact ne peut pas être très fort.
```{r}
pic_annuel = function(annee) {
debut = paste0(annee-1,"-08-01")
fin = paste0(annee,"-08-01")
semaines = data$date > debut & data$date <= fin
sum(data$inc[semaines], na.rm=TRUE)
}
```
Nous devons aussi faire attention aux premières et dernières années de notre jeux de données. Les données commencent en octobre 1984, ce qui ne permet pas de quantifier complètement le pic attribué à 1985. Nous l'enlevons donc de notre analyse. Par contre, pour une exécution en octobre 2018, les données se terminent après le 1er août 2018, ce qui nous permet d'inclure cette année.
```{r}
annees = 1986:2018
```
Nous créons un nouveau jeu de données pour l'incidence annuelle, en applicant la fonction `pic_annuel` à chaque année:
```{r}
inc_annuelle = data.frame(annee = annees,
incidence = sapply(annees, pic_annuel))
head(inc_annuelle)
```
### Inspection
Voici les incidences annuelles en graphique:
```{r}
plot(inc_annuelle, type="p", xlab="Année", ylab="Incidence annuelle")
```
### Identification des épidémies les plus fortes
Une liste triée par ordre décroissant d'incidence annuelle permet de plus facilement repérer les valeurs les plus élevées:
```{r}
head(inc_annuelle[order(-inc_annuelle$incidence),])
```
Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population française, sont assez rares: il y en eu trois au cours des 35 dernières années.
```{r}
hist(inc_annuelle$incidence, breaks=10, xlab="Incidence annuelle", ylab="Nb d'observations", main="")
```
documents/emacs/analyse-syndrome-grippal-orgmode.org 0 → 100644
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#+TITLE: Incidence du syndrôme grippal
#+LANGUAGE: fr
#+OPTIONS: *:nil num:1 toc:t
# #+HTML_HEAD: <link rel="stylesheet" title="Standard" href="http://orgmode.org/worg/style/worg.css" type="text/css" />
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
#+HTML_HEAD: <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
#+HTML_HEAD: <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
#+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>
#+PROPERTY: header-args :session
* Préface
Pour exécuter le code de cette analyse, il faut disposer des logiciels suivants:
** Emacs 25 ou plus
Une version plus ancienne d'Emacs devrait suffire. Pour une version antérieure à 26, il faut installer une version récente (9.x) d'org-mode.
** Python 3.6 ou plus
Nous utilisons le traitement de dates en format ISO 8601, qui a été implémenté en Python seulement avec la version 3.6.
#+BEGIN_SRC python :results output :exports both
import sys
if sys.version_info.major < 3 or sys.version_info.minor < 6:
print("Veuillez utiliser Python 3.6 (ou plus) !")
#+END_SRC
#+RESULTS:
#+BEGIN_SRC emacs-lisp :results output :exports both
(unless (featurep 'ob-python)
(print "Veuillez activer python dans org-babel (org-babel-do-languages) !"))
#+END_SRC
#+RESULTS:
** R 3.4
Nous n'utilisons que des fonctionnalités de base du langage R, une version antérieure devrait suffire.
#+BEGIN_SRC emacs-lisp :results output :exports both
(unless (featurep 'ob-R)
(print "Veuillez activer R dans org-babel (org-babel-do-languages) !"))
#+END_SRC
#+RESULTS:
* Préparation des données
Les données de l'incidence du syndrome grippal sont disponibles du site Web du [[http://www.sentiweb.fr/][Réseau Sentinelles]]. Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période d'observation. Nous téléchargeons toujours le jeu de données complet (rien d'autre n'est proposé), qui commence en 1984 et se termine avec une semaine récente. L'URL est:
#+NAME: data-url
http://www.sentiweb.fr/datasets/incidence-PAY-3.csv
Voici l'explication des colonnes donnée sur [[https://ns.sentiweb.fr/incidence/csv-schema-v1.json][le site d'origine:]]
| Nom de colonne | Libellé de colonne |
|----------------+-----------------------------------------------------------------------------------------------------------------------------------|
| ~week~ | Semaine calendaire (ISO 8601) |
| ~indicator~ | Code de l'indicateur de surveillance |
| ~inc~ | Estimation de l'incidence de consultations en nombre de cas |
| ~inc_low~ | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |
| ~inc_up~ | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |
| ~inc100~ | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |
| ~inc100_low~ | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |
| ~inc100_up~ | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |
| ~geo_insee~ | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |
| ~geo_name~ | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |
L'indication d'une semaine calendaire en format [[https://en.wikipedia.org/wiki/ISO_8601][ISO-8601]] est populaire en Europe, mais peu utilisée aux Etats-Unis. Ceci explique peut-être que peu de logiciels savent gérer ce format. Le langage Python le fait depuis la version 3.6. Nous utilisons donc ce langage pour la préparation de nos données, ce qui a l'avantage de ne nécessiter aucune bibliothèque supplémentaire. (Note: nous expliquerons dans le module 4 pourquoi il est avantageux pour la réproductibilité de se limiter à un minimum de bibliothèques.)
** Téléchargement
Après avoir téléchargé les données, nous commençons par l'extraction des données qui nous intéressent. D'abord nous découpons le contenu du fichier en lignes, dont nous jetons la première qui ne contient qu'un commentaire. Les autres lignes sont découpées en colonnes.
#+BEGIN_SRC python :results silent :var data_url=data-url :exports both
from urllib.request import urlopen
data = urlopen(data_url).read()
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 :exports both
table[:5]
#+END_SRC
#+RESULTS:
| week | indicator | inc | inc_low | inc_up | inc100 | inc100_low | inc100_up | geo_insee | geo_name |
| 201911 | 3 | 31787 | 26747 | 36827 | 48 | 40 | 56 | FR | France |
| 201910 | 3 | 49871 | 43378 | 56364 | 76 | 66 | 86 | FR | France |
| 201909 | 3 | 88354 | 79564 | 97144 | 134 | 121 | 147 | FR | France |
| 201908 | 3 | 172604 | 160024 | 185184 | 262 | 243 | 281 | FR | France |
** 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.
Nous construisons un nouveau jeu de données sans les lignes qui contiennent des champs vides. Nous affichons ces lignes pour en garder une trace.
#+BEGIN_SRC python :results output :exports both
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:
: ['198919', '3', '0', '', '', '0', '', '', 'FR', 'France']
** 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 :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
Regardons les premières et les dernières lignes. Nous insérons ~None~ pour indiquer à org-mode la séparation entre les trois sections du tableau: en-tête, début des données, fin des données.
#+BEGIN_SRC python :results value :exports both
[('week', 'inc'), None] + data[:5] + [None] + data[-5:]
#+END_SRC
#+RESULTS:
| week | inc |
|--------+--------|
| 201911 | 31787 |
| 201910 | 49871 |
| 201909 | 88354 |
| 201908 | 172604 |
| 201907 | 307338 |
|--------+--------|
| 198448 | 78620 |
| 198447 | 72029 |
| 198446 | 87330 |
| 198445 | 135223 |
| 198444 | 68422 |
** Vérification
Il est toujours prudent de vérifier si les données semblent crédibles. Nous savons que les semaines sont données par six chiffres (quatre pour l'année et deux pour la semaine), et que les incidences sont des nombres entiers positifs.
#+BEGIN_SRC python :results output :exports both
for week, inc in data:
if len(week) != 6 or not week.isdigit():
print("Valeur suspecte dans la colonne 'week': ", (week, inc))
if not inc.isdigit():
print("Valeur suspecte dans la colonne 'inc': ", (week, inc))
#+END_SRC
#+RESULTS:
Pas de problème !
** Conversions
Pour faciliter les traitements suivants, nous remplaçons les numéros de semaine ISO par les dates qui correspondent aux lundis. A cette occasion, nous trions aussi les données par la date, et nous transformons les incidences en nombres entiers.
#+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
Regardons de nouveau les premières et les dernières lignes:
#+BEGIN_SRC python :results value :exports both
str_data = [(str(date), str(inc)) for date, inc in converted_data]
[('date', 'inc'), None] + str_data[:5] + [None] + str_data[-5:]
#+END_SRC
#+RESULTS:
| date | inc |
|------------+--------|
| 1984-10-29 | 68422 |
| 1984-11-05 | 135223 |
| 1984-11-12 | 87330 |
| 1984-11-19 | 72029 |
| 1984-11-26 | 78620 |
|------------+--------|
| 2019-02-11 | 307338 |
| 2019-02-18 | 172604 |
| 2019-02-25 | 88354 |
| 2019-03-04 | 49871 |
| 2019-03-11 | 31787 |
** Vérification des dates
Nous faisons encore une vérification: nos dates doivent être séparées d'exactement une semaine, sauf autour du point manquant.
#+BEGIN_SRC python :results output :exports both
dates = [date for date, _ in converted_data]
for date1, date2 in zip(dates[:-1], dates[1:]):
if date2-date1 != datetime.timedelta(weeks=1):
print(f"Il y a {date2-date1} entre {date1} et {date2}")
#+END_SRC
#+RESULTS:
: Il y a 14 days, 0:00:00 entre 1989-05-01 et 1989-05-15
** Passage Python -> R
Nous passons au langage R pour inspecter nos données, parce que l'analyse et la préparation de graphiques sont plus concises en R, sans nécessiter aucune bibliothèque supplémentaire.
Nous utilisons le mécanisme d'échange de données proposé par org-mode, ce qui nécessite un peu de code Python pour transformer les données dans le bon format.
#+NAME: data-for-R
#+BEGIN_SRC python :results silent :exports both
[('date', 'inc'), None] + [(str(date), inc) for date, inc in converted_data]
#+END_SRC
En R, les données arrivent sous forme d'un data frame, mais il faut encore convertir les dates, qui arrivent comme chaînes de caractères.
#+BEGIN_SRC R :results output :var data=data-for-R :exports both
data$date <- as.Date(data$date)
summary(data)
#+END_SRC
#+RESULTS:
: date inc
: Min. :1984-10-29 Min. : 0
: 1st Qu.:1993-06-07 1st Qu.: 5137
: Median :2002-01-07 Median : 16182
: Mean :2002-01-06 Mean : 62939
: 3rd Qu.:2010-08-09 3rd Qu.: 51746
: Max. :2019-03-11 Max. :1001824
** Inspection
Regardons enfin à quoi ressemblent nos données !
#+BEGIN_SRC R :results output graphics :file inc-plot.png :exports both
plot(data, type="l", xlab="Date", ylab="Incidence hebdomadaire")
#+END_SRC
#+RESULTS:
[[file:inc-plot.png]]
Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été.
#+BEGIN_SRC R :results output graphics :file inc-plot-zoom.png :exports both
plot(tail(data, 200), type="l", xlab="Date", ylab="Incidence hebdomadaire")
#+END_SRC
#+RESULTS:
[[file:inc-plot-zoom.png]]
* Étude de l'incidence annuelle
** Calcul de l'incidence annuelle
Étant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er août de l'année /N/ au 1er août de l'année /N+1/. Nous mettons l'année /N+1/ comme étiquette sur cette année décalée, car le pic de l'épidémie est toujours au début de l'année /N+1/. Comme l'incidence du syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.
Voici une fonction qui calcule l'incidence annuelle en appliquant ces conventions.
#+BEGIN_SRC R :results silent :exports both
pic_annuel = function(annee) {
debut = paste0(annee-1,"-08-01")
fin = paste0(annee,"-08-01")
semaines = data$date > debut & data$date <= fin
sum(data$inc[semaines], na.rm=TRUE)
}
#+END_SRC
Nous devons aussi faire attention aux premières et dernières années de notre jeux de données. Les données commencent en octobre 1984, ce qui ne permet pas de quantifier complètement le pic attribué à l'année 1985. Nous le supprimons donc de notre analyse. Pour la même raison, nous arrêtons en 2018. Nous devons attendre les données pour juillet 2019 avant d'augmenter la dernière année à 2019.
#+BEGIN_SRC R :results silent :exports both
annees <- 1986:2018
#+END_SRC
#+BEGIN_SRC R :results value :exports both
inc_annuelle = data.frame(annee = annees,
incidence = sapply(annees, pic_annuel))
head(inc_annuelle)
#+END_SRC
#+RESULTS:
| 1986 | 5100540 |
| 1987 | 2861556 |
| 1988 | 2766142 |
| 1989 | 5460155 |
| 1990 | 5233987 |
| 1991 | 1660832 |
** Inspection
Voici les incidences annuelles en graphique.
#+BEGIN_SRC R :results output graphics :file annual-inc-plot.png :exports both
plot(inc_annuelle, type="p", xlab="Année", ylab="Incidence annuelle")
#+END_SRC
#+RESULTS:
[[file:annual-inc-plot.png]]
** Identification des épidémies les plus fortes
Une liste triée par ordre décroissant d'incidence annuelle permet de plus facilement repérer les valeurs les plus élevées:
#+BEGIN_SRC R :results output :exports both
head(inc_annuelle[order(-inc_annuelle$incidence),])
#+END_SRC
#+RESULTS:
: annee incidence
: 4 1989 5460155
: 5 1990 5233987
: 1 1986 5100540
: 28 2013 4182265
: 25 2010 4085126
: 14 1999 3897443
Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population française, sont assez rares: il y en eu trois au cours des 35 dernières années.
#+BEGIN_SRC R :results output graphics :file annual-inc-hist.png :exports both
hist(inc_annuelle$incidence, breaks=10, xlab="Incidence annuelle", ylab="Nb d'observations", main="")
#+END_SRC
#+RESULTS:
[[file:annual-inc-hist.png]]
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documents/notebooks/calcul_statistiques.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Moyenne : 14.113000000000001\n",
"Écart-type : 4.312369534258399\n",
"Valeur minimale : 2.8\n",
"Médiane : 14.5\n",
"Valeur maximale : 23.4\n"
]
}
],
"source": [
"# Importer les bibliothèques nécessaires\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Données fournies\n",
"data = [\n",
" 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, \n",
" 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, \n",
" 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, \n",
" 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, \n",
" 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, \n",
" 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, \n",
" 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, \n",
" 15.7, 10.2, 8.9, 21.0\n",
"]\n",
"\n",
"# Calcul des statistiques\n",
"mean_value = np.mean(data)\n",
"std_dev = np.std(data)\n",
"min_value = np.min(data)\n",
"median_value = np.median(data)\n",
"max_value = np.max(data)\n",
"\n",
"# Afficher les résultats\n",
"print(\"Moyenne :\", mean_value)\n",
"print(\"Écart-type :\", std_dev)\n",
"print(\"Valeur minimale :\", min_value)\n",
"print(\"Médiane :\", median_value)\n",
"print(\"Valeur maximale :\", max_value)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# a. Affichage graphique de la séquence des données\n",
"plt.figure(figsize=(10, 5))\n",
"plt.plot(data, marker='o')\n",
"plt.title('Sequence Plot')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Value')\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# b. Affichage graphique de l'histogramme des données\n",
"plt.figure(figsize=(10, 5))\n",
"plt.hist(data, bins=20, edgecolor='black')\n",
"plt.title('Histogramme')\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Frequency')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
documents/notebooks/explanation.ipynb 0 → 100644
View file @ d040a708
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse du risque de défaillance des joints toriques de la navette Challenger"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le 27 Janvier 1986, veille du décollage de la navette *Challenger*, eu\n",
"lieu une télé-conférence de trois heures entre les ingénieurs de la\n",
"Morton Thiokol (constructeur d'un des moteurs) et de la NASA. La\n",
"discussion portait principalement sur les conséquences de la\n",
"température prévue au moment du décollage de 31°F (juste en dessous de\n",
"0°C) sur le succès du vol et en particulier sur la performance des\n",
"joints toriques utilisés dans les moteurs. En effet, aucun test\n",
"n'avait été effectué à cette température.\n",
"\n",
"L'étude qui suit reprend donc une partie des analyses effectuées cette\n",
"nuit là et dont l'objectif était d'évaluer l'influence potentielle de\n",
"la température et de la pression à laquelle sont soumis les joints\n",
"toriques sur leur probabilité de dysfonctionnement. Pour cela, nous\n",
"disposons des résultats des expériences réalisées par les ingénieurs\n",
"de la NASA durant les 6 années précédant le lancement de la navette\n",
"Challenger.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chargement des données\n",
"Nous commençons donc par charger ces données:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4/12/81</td>\n",
" <td>6</td>\n",
" <td>66</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3/22/82</td>\n",
" <td>6</td>\n",
" <td>69</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11/11/82</td>\n",
" <td>6</td>\n",
" <td>68</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/04/83</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6/18/82</td>\n",
" <td>6</td>\n",
" <td>72</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8/30/83</td>\n",
" <td>6</td>\n",
" <td>73</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11/28/83</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/29/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"data = pd.read_csv(\"shuttle.csv\")\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le jeu de données nous indique la date de l'essai, le nombre de joints\n",
"toriques mesurés (il y en a 6 sur le lançeur principal), la\n",
"température (en Farenheit) et la pression (en psi), et enfin le\n",
"nombre de dysfonctionnements relevés. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspection graphique des données\n",
"Les vols où aucun incident n'est relevé n'apportant aucun information\n",
"sur l'influence de la température ou de la pression sur les\n",
"dysfonctionnements, nous nous concentrons sur les expériences où au\n",
"moins un joint a été défectueux.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"1 11/12/81 6 70 50 1\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"13 1/24/85 6 53 200 2\n",
"20 10/30/85 6 75 200 2\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = data[data.Malfunction>0]\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Très bien, nous avons une variabilité de température importante mais\n",
"la pression est quasiment toujours égale à 200, ce qui devrait\n",
"simplifier l'analyse.\n",
"\n",
"Comment la fréquence d'échecs varie-t-elle avec la température ?\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n",
"\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"À première vue, ce n'est pas flagrant mais bon, essayons quand même\n",
"d'estimer l'impact de la température $t$ sur la probabilité de\n",
"dysfonctionnements d'un joint. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimation de l'influence de la température\n",
"\n",
"Supposons que chacun des 6 joints toriques est endommagé avec la même\n",
"probabilité et indépendamment des autres et que cette probabilité ne\n",
"dépend que de la température. Si on note $p(t)$ cette probabilité, le\n",
"nombre de joints $D$ dysfonctionnant lorsque l'on effectue le vol à\n",
"température $t$ suit une loi binomiale de paramètre $n=6$ et\n",
"$p=p(t)$. Pour relier $p(t)$ à $t$, on va donc effectuer une\n",
"régression logistique."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>19:11:24</td> <th> Pearson chi2: </th> <td> 0.236</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n",
"Model: GLM Df Residuals: 5\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5250\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n",
"Time: 19:11:24 Pearson chi2: 0.236\n",
"No. Iterations: 4 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n",
"Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.api as sm\n",
"\n",
"data[\"Success\"]=data.Count-data.Malfunction\n",
"data[\"Intercept\"]=1\n",
"\n",
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"L'estimateur le plus probable du paramètre de température est 0.0014\n",
"et l'erreur standard de cet estimateur est de 0.122, autrement dit on\n",
"ne peut pas distinguer d'impact particulier et il faut prendre nos\n",
"estimations avec des pincettes.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimation de la probabilité de dysfonctionnant des joints toriques\n",
"La température prévue le jour du décollage est de 31°F. Essayons\n",
"d'estimer la probabilité de dysfonctionnement des joints toriques à\n",
"cette température à partir du modèle que nous venons de construire:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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5KgqSDz7YzSHPPLnf9RSHS1tfE2awbHy57KYS1ErUSNTUQI1ErYQENTUdt0WNoLZmb7u9j8nuqylaRo0K5teI2hoxvOPxUr4sAFFbky1b+bq7PK5omWv68HSUFfSSLgC+B9QCd0fEXxbd/3VgPtAKvAH8SUT8qg/19Nk+r/qljgAKjhpKvcruEzzFO0vnEUL2uP0dtaz/TSvvvbRjnzCIotvdBl3Rq3rbfu7b35HBPvOLQ6rLkcq+Ry7dHYE1N3/AyFVPdd7X2ZcSwVNYb9fneyD/Qnpp3UsH9PDC8FBhQOThURwEtTXZziyU7/D7Pr62RojC8MkeO6y2hkOG7Q2cjmDqWEbn8vL17vztbzn6qCP3CaWOdvvczutUPr+wPqlrqGX9UNewLFFDTan11AhREKI1RcuVgL3PRZdw7QjfGvHsM88we/YZBbUL1RQ9FwXPrfLlVotb+vCYHoNeUi1wB3Ae0ASskrQsIjYWNFsDnBoRH0j6r8BfAVfsb7nbm9s596//uXQQlArtff716Ro8Q9Lavrz2ltbxh93lFb9oR+g86ug4AqnpLmz2hkoWGnuPHrJ1lA6O2jwUJHizpoWjfv/wLjt58Tpqa9Sl9pJHPwV9KWyX9TN7LHmfawtrrum6jNqa7LkR+4ZNjYAuobdvUBTWvnrV85x+2qx9gqXrkV/R9ig6EhzK4dHY2EhDw8mDXUa/+djIGj4+ZuRglzGklHNEPxPYGhGvAkh6ELgY6Az6iHiqoP2zwBd6WujwGlH/e2M6d8CSQdBNKOzdWfNQoGvoFR/xFP6bpOLbRUc1xUddxQGyN/T2hlvx0UiNxIurVzNr1oyCYN7fkR37HAWpKJiGmiwspg12Gf2iaXQNnxg3erDLMKuYcoJ+AvB6wXQTMGs/7b8E/GOpOyQtABYAjB8/nssmvFdmmX0Q+U8FteU/5RirD2ja+EJlCxhCmpubaWxsHOwy+kXKfQP372BUTtCXOpwsGaGSvgCcCvxhqfsjYjGwGKC+vj4aGhrKq7IKZUe8DYNdRr9JuX8p9w3cv4NROUHfBBxTMD0R2FHcSNK5wP8E/jAiPqxMeWZmdqDK+cDUKmCKpMmSRgBXAssKG0iaBtwFzI2InZUv08zM+qrHoI+IVuBaYAWwCVgSERsk3Sxpbt5sEXAo8BNJayUt62ZxZmY2wMq6jj4ilgPLi+Z9q+D2uRWuy6zXlq7ZzqIVm9nxTgtHH17Hwjn1APvMu2TahAGtoT/X1xvfXLqeB557na+dsIcv3bCcebOO4ZZLThzssmwA+JOxloSla7ZzwyPradmTXRe1/Z0WFv7kJRDsaYvOeTc8sh6gX8K3VA39ub7e+ObS9fzw2dc6p9siOqcd9unzoGaWhEUrNncGbIc97dEZ8h1a9rSxaMXmAauhP9fXGw8893qv5ltaHPSWhB3vtPRL20rU0F/r6422bsab6G6+pcVBb0k4+vC6fmlbiRr6a329UdvNp6u7m29pcdBbEhbOqadueG2XecNrxPDarkFWN7y2803agaihP9fXG/NmHdOr+ZYWvxlrSeh4s3Mwr7rprobBfiMW9r7h2nFOvlbyVTcHEQe9JeOSaRNKhupABm13NQwFt1xyIrdcciKNjY3821UNg12ODSCfujEzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBJXVtBLukDSZklbJX2jxP2HSHoov/85SZMqXaiZmfVNj0EvqRa4A7gQOB6YJ+n4omZfAt6OiH8HfBf4X5Uu1MzM+qacI/qZwNaIeDUiPgIeBC4uanMx8A/57YeBz0hS5co0M7O+GlZGmwnA6wXTTcCs7tpERKukd4FxwJuFjSQtABbkkx9KerkvRVeJIynqf2JS7l/KfQP3r9rV9/YB5QR9qSPz6EMbImIxsBhA0uqIOLWM9Vcl9696pdw3cP+qnaTVvX1MOadumoBjCqYnAju6ayNpGDAW+F1vizEzs8orJ+hXAVMkTZY0ArgSWFbUZhnwx/ntS4F/ioh9jujNzGzg9XjqJj/nfi2wAqgF7o2IDZJuBlZHxDLgHuB+SVvJjuSvLGPdiw+g7mrg/lWvlPsG7l+163X/5ANvM7O0+ZOxZmaJc9CbmSVuQIJe0khJz0t6SdIGSd/O50/Oh0zYkg+hMGIg6ukPkmolrZH0s3w6pb5tk7Re0tqOS7skHSHp8bx/j0v62GDX2VeSDpf0sKT/J2mTpNNT6Z+k+ny7dfy8J+lrCfXvv+WZ8rKkB/KsSWnf+/O8bxskfS2f1+ttN1BH9B8C50TEycBU4AJJp5ENlfDdiJgCvE02lEK1+nNgU8F0Sn0DODsiphZcn/wN4Mm8f0/m09Xqe8DPI+I44GSy7ZhE/yJic77dpgKnAB8Aj5JA/yRNAK4DTo2IE8guFrmSRPY9SScAXyYbneBk4HOSptCXbRcRA/oDjAJeJPt07ZvAsHz+6cCKga6nQn2amD/h5wA/I/sAWRJ9y+vfBhxZNG8zcFR++yhg82DX2ce+HQb8kvzChNT6V9Sn84GnU+kfez+RfwTZFYQ/A+aksu8BlwF3F0z/BfA/+rLtBuwcfX5qYy2wE3gc+DfgnYhozZs0kW24anQ72QZoz6fHkU7fIPuU80pJL+TDWAD8XkT8GiD//fFBq+7AfBJ4A/j7/NTb3ZJGk07/Cl0JPJDfrvr+RcR24DbgNeDXwLvAC6Sz770MnCVpnKRRwGfJPpja6203YEEfEW2R/fs4kexfkU+XajZQ9VSKpM8BOyPihcLZJZpWXd8KzI6I6WQjmP6ZpLMGu6AKGgZMB+6MiGnA+1ThaYye5Oep5wI/GexaKiU/N30xMBk4GhhN9jdarCr3vYjYRHYa6nHg58BLQOt+H9SNAb/qJiLeARqB04DD8yEToPTQCtVgNjBX0jaykT3PITvCT6FvAETEjvz3TrLzuzOB30o6CiD/vXPwKjwgTUBTRDyXTz9MFvyp9K/DhcCLEfHbfDqF/p0L/DIi3oiIPcAjwBmkte/dExHTI+Issg+jbqEP226grroZL+nw/HYd2QbaBDxFNmQCZEMoPDYQ9VRSRNwQERMjYhLZv8b/FBFXkUDfACSNljSm4zbZed6X6TrsRdX2LyJ+A7wuqWNEwM8AG0mkfwXmsfe0DaTRv9eA0ySNyodF79h2Sex7AJI+nv8+FviPZNuw19tuQD4ZK+kksvHqa8leXJZExM2SPkl2FHwEsAb4QkR82O8F9RNJDcD1EfG5VPqW9+PRfHIY8OOIuFXSOGAJcCzZDndZRFTlQHaSpgJ3AyOAV4Evkv+dkkb/RpG9afnJiHg3n5fE9ssv1b6C7JTGGmA+2Tn5qt/3ACT9C9l7fnuAr0fEk33Zdh4Cwcwscf5krJlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4sr5cnCzAZVfPvZkPvn7QBvZMAUAMyPio0EpbD8k/QmwPL8u32xI8eWVNqRJuglojojbhkAttRHR1s19/wpcGxFre7G8YQVjspj1G5+6saoi6Y+VfbfBWkl/I6lG0jBJ70haJOlFSSskzZL0z5JelfTZ/LHzJT2a379Z0jfLXO4tkp4HZkr6tqRV+Rjhf6vMFWTDbz+UP36EpKaCT4OfJumJ/PYtku6S9DjZQGrDJP11vu51kuYP/LNqqXPQW9XIx+f+PHBGPkDeMPZ+Ef1YYGU++NpHwE1kH4m/DLi5YDEz88dMB/6zpKllLPfFiJgZEc8A34uIGcCJ+X0XRMRDwFrgisjGfu/p1NI04KKI+CNgAdmgeDOBGWSDxh3bl+fHrDs+R2/V5FyyMFydDW1CHdlH+wFaIuLx/PZ64N2IaJW0HphUsIwVEfE2gKSlwH8g2w+6W+5H7B0CAuAzkhYCI4EjyYbF/cde9uOxiNid3z4f+LSkwheWKWQfbTerCAe9VRMB90bEX3SZmY1UWHgU3U72rWYdtwv/zovflIoeltsS+RtZ+Zgx/xeYHhHbJd1CFviltLL3P+biNu8X9elPI+JJzPqJT91YNXkCuFzSkZBdndOH0xznK/uO2FFkY5k/3Yvl1pG9cLyZj+j5nwru2wWMKZjeRvbVfRS1K7YC+NOOYXWVfcdrXS/7ZLZfPqK3qhER6/PRCp+QVEM2ot9/oXfjjf8r8GPgD4D7O66SKWe5EfGWpH8gG6b5V8BzBXf/PXC3pBay9wFuAv5O0m+A5/dTz11koxCuzU8b7SR7ATKrGF9eaQeN/IqWEyLia4Ndi9lA8qkbM7PE+YjezCxxPqI3M0ucg97MLHEOejOzxDnozcwS56A3M0vc/wcowwoTqhaBUgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
"data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
"data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
"plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hideCode": false,
"hidePrompt": false,
"scrolled": true
},
"source": [
"Comme on pouvait s'attendre au vu des données initiales, la\n",
"température n'a pas d'impact notable sur la probabilité d'échec des\n",
"joints toriques. Elle sera d'environ 0.2, comme dans les essais\n",
"précédents où nous il y a eu défaillance d'au moins un joint. Revenons\n",
"à l'ensemble des données initiales pour estimer la probabilité de\n",
"défaillance d'un joint:\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06521739130434782\n"
]
}
],
"source": [
"data = pd.read_csv(\"shuttle.csv\")\n",
"print(np.sum(data.Malfunction)/np.sum(data.Count))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cette probabilité est donc d'environ $p=0.065$, sachant qu'il existe\n",
"un joint primaire un joint secondaire sur chacune des trois parties du\n",
"lançeur, la probabilité de défaillance des deux joints d'un lançeur\n",
"est de $p^2 \\approx 0.00425$. La probabilité de défaillance d'un des\n",
"lançeur est donc de $1-(1-p^2)^3 \\approx 1.2%$. Ça serait vraiment\n",
"pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n",
"lieu demain comme prévu.\n",
"\n",
"Seulement, le lendemain, la navette Challenger explosera et emportera\n",
"avec elle ses sept membres d'équipages. L'opinion publique est\n",
"fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n",
"joints toriques sera directement mise en cause. Au delà des problèmes\n",
"de communication interne à la NASA qui sont pour beaucoup dans ce\n",
"fiasco, l'analyse précédente comporte (au moins) un petit\n",
"problème... Saurez-vous le trouver ? Vous êtes libre de modifier cette\n",
"analyse et de regarder ce jeu de données sous tous les angles afin\n",
"d'expliquer ce qui ne va pas."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"EXPLICATION :\n",
"\n",
"Il y a un certain nombre d'erreurs \"grossières\" dans l'analyse que nous vous avons fournie. Nous vous invitons à la comparer avec celle-ci (et sur laquelle nous reviendrons dans le module 4).\n",
"Comme vous pouvez le constater, lorsque l'on effectue l'analyse sur l'ensemble des données, sans exclure les vols où aucune défaillance n'a été observée, il apparaît bien plus clairement que pour des températures moins froides, il y a bien moins de pannes. Après coup, l'exclusion de ces données parait une erreur énorme quand on cherche à déterminer l'origine des pannes mais elles ont pourtant été longuement discutées ce soir-là.\n",
"\n",
"On y perçoit également à quel point une extrapolation sur une zone aussi éloignée des observations dont l'on dispose semble hasardeux et ce d'autant plus que cette extrapolation fait l'hypothèse de linéarité dans la régression logistique. C'est une hypothèse classique en statistiques en l'absence d'information additionnelle mais qui ne se base sur aucune hypothèse \"physique\"... Une telle prédiction devrait être considérée avec beaucoup de suspicion. Au final, cette régression nous indique qu'on ne peut a priori pas éliminer l'hypothèse que la température ait un impact mais ne doit pas être utilisée pour faire une prédiction. À ce sujet, le dernier graphique qui indique l'incertitude associée à la régression logistique est particulièrement éloquent. On peut en gros y lire que la probabilité de panne à 30 Farenheit est comprise ... entre 0 et 1 ! Ça c'est de l'information.\n",
"\n",
"\n",
"En revanche, il n'y a a priori pas d'erreur de calcul dans l'analyse, les statistiques utilisées ne sont certainement pas trop élaborées, au contraire, et si vous comparez la pertinence d'un modèle avec et sans la pression, comme cela est fait dans l'article de Dalal et al. vous verrez que ce paramètre n'apporte effectivement rien.\n",
"\n",
"Cette étude de cas est très célèbre et en particulier le rôle de Richard Feynman dans [la Commission Rogers](https://fr.wikipedia.org/wiki/Commission_Rogers) chargée de l'enquête. Feynman fit une démonstration célèbre sur la manière dont les joints circulaires perdent de leur efficacité par températures glaciales en plongeant tout simplement un échantillon de joint dans un verre rempli d'eau glacée... Une telle expérience vaut bien plus que toutes les statistiques élaborées qu'on pourrait imaginer.\n",
"\n",
"Pour ceux qui souhaite en savoir plus, nous vous proposons de lire cet extrait de [\"Visual Explanations: Images and Quantities,Evidence and Narrative\" par Edward R. Tufte, ISBN-13 : 978-1930824157.](https://lms.fun-mooc.fr/asset-v1:inria+41016+self-paced+type@asset+block/Tufte_Visual_Explanations_Challenger.pdf)"
]
}
],
"metadata": {
"celltoolbar": "Hide code",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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documents/notebooks/toy_notebook_fr.ipynb 0 → 100644
View file @ d040a708
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# À propos du calcul de $\\pi$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## En demandant à la lib maths\n",
"Mon ordinateur m'indique que $\\pi$ vaut *approximativement*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.141592653589793\n"
]
}
],
"source": [
"from math import *\n",
"print(pi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## En utilisant la méthode des aiguilles de Buffon\n",
"Mais calculé avec la __méthode__ des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme __approximation__ :"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.128911138923655"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.random.seed(seed=42)\n",
"N = 10000\n",
"x = np.random.uniform(size=N, low=0, high=1)\n",
"theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
"2/(sum((x+np.sin(theta))>1)/N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Avec un argument \"fréquentiel\" de surface\n",
"Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d'appel à la fonction sinus se base sur le fait que si $X\\sim U(0,1)$ et $Y\\sim U(0,1)$ alors $P[X^2+Y^2\\leq 1] = \\pi/4$ (voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80)). Le code suivant illustre ce fait :"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline \n",
"import matplotlib.pyplot as plt\n",
"\n",
"np.random.seed(seed=42)\n",
"N = 1000\n",
"x = np.random.uniform(size=N, low=0, high=1)\n",
"y = np.random.uniform(size=N, low=0, high=1)\n",
"\n",
"accept = (x*x+y*y) <= 1\n",
"reject = np.logical_not(accept)\n",
"\n",
"fig, ax = plt.subplots(1)\n",
"ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n",
"ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n",
"ax.set_aspect('equal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il est alors aisé d'obtenir une approximation (pas terrible) de $\\pi$ en comptant combien de fois, en moyenne, $X^2 + Y^2$ est inférieur à 1 :"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.112"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"4*np.mean(accept)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
journal-de-bord/.ipynb_checkpoints/tutorial-checkpoint.ipynb 0 → 100644
View file @ d040a708
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Titre du document"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"x=10\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"x = x + 10\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Petit exemple de completion"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma = 100, 15"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"x = np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1a57acff98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.hist(x)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utilisation d'autres langages"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"plot(cars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
journal-de-bord/tutorial.Rmd 0 → 100644
View file @ d040a708
---
title: "Mon premier document"
author: "Arnaud Legrand"
date: "10 octobre 2017"
output: word_document
---
## R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
```{r cars}
summary(cars)
```
## Including Plots
You can also embed plots, for example:
```{r pressure, echo=FALSE}
plot(pressure)
```
Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
## Ma propre section
```{r}
x=1
x
```
```{r}
x = x + 10
x
```
```{r}
hist(rnorm(1000))
```
```{r}
hist(rnorm(100,mean = 2, sd = .2))
```
## Un petit exemple avec d'autres langages
```{python}
from math import *
x = 3.14
print(sin(x))
```
```{python}
print(sin(x))
```
journal-de-bord/tutorial.ipynb 0 → 100644
View file @ d040a708
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Titre du document"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"x=10\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"x = x + 10\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Petit exemple de completion"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma = 100, 15"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x = np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.hist(x)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utilisation d'autres langages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Unable to determine R library path: Command '('C:\\\\Users\\\\Utilisateur\\\\miniconda3\\\\envs\\\\mooc-rr-jupyter\\\\lib\\\\R\\\\bin\\\\Rscript', '-e', 'cat(Sys.getenv(\"LD_LIBRARY_PATH\"))')' returned non-zero exit status 1.\n"
]
}
],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%R\n",
"plot(cars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
journal-de-bord/tutorial.org 0 → 100644
View file @ d040a708
# -*- coding: utf-8 -*-
# -*- mode: org -*-
#+TITLE: Une petite démo d'Org-Mode
#+AUTHOR: Arnaud Legrand
#+STARTUP: overview indent inlineimages logdrawer
#+LANGUAGE: en
#+HTML_HEAD: <link rel="stylesheet" title="Standard" href="http://orgmode.org/worg/style/worg.css" type="text/css" />
#+PROPERTY: header-args :eval never-export
* Section 1
** Sous-section
* Section 2
** Foo
*** Hello
Avec du texte
- ici du *gras*
- et là, de /l'italique/
*** Salut
*** Etc
** Bar
** Baz
* Exécution de calculs :noexport:
#+begin_src R :results output :session *R* :exports both
cars
#+end_src
#+RESULTS:
#+begin_example
speed dist
1 4 2
2 4 10
3 7 4
4 7 22
5 8 16
6 9 10
7 10 18
8 10 26
9 10 34
10 11 17
11 11 28
12 12 14
13 12 20
14 12 24
15 12 28
16 13 26
17 13 34
18 13 34
19 13 46
20 14 26
21 14 36
22 14 60
23 14 80
24 15 20
25 15 26
26 15 54
27 16 32
28 16 40
29 17 32
30 17 40
31 17 50
32 18 42
33 18 56
34 18 76
35 18 84
36 19 36
37 19 46
38 19 68
39 20 32
40 20 48
41 20 52
42 20 56
43 20 64
44 22 66
45 23 54
46 24 70
47 24 92
48 24 93
49 24 120
50 25 85
#+end_example
#+begin_src R :results output graphics :file (org-babel-temp-file "figure" ".png") :exports results :width 600 :height 400 :session *R*
plot(cars)
#+end_src
#+RESULTS:
[[file:/tmp/babel-148945lI/figure14894n0r.png]]
#+begin_src R :results output :session *R* :exports both
(x=10)
#+end_src
#+RESULTS:
: [1] 10
#+begin_src R :results output :session *R* :exports both
(x = x+10)
#+end_src
#+RESULTS:
: [1] 20
* Autres langages
#+begin_src python :results output :exports both
print(2+2)
#+end_src
#+RESULTS:
: 4
#+begin_src shell :results output :exports both
ls /tmp
#+end_src
#+RESULTS:
#+begin_example
babel-148945lI
babel-1933r-E
babel-7506nSG
emacs1000
emacs14894axZ
firefox-esr_alegrand
mozilla_alegrand0
pulse-PKdhtXMmr18n
RtmpsK10QZ
RtmpvMPlZs
ScientificMethodologyProjectGithub.ipynb
ssh-KQXcWTA8Cx6u
systemd-private-0461cab7d3944a9e974b73d23efc09af-apache2.service-QPpUU4
systemd-private-0461cab7d3944a9e974b73d23efc09af-colord.service-wdsVAi
systemd-private-0461cab7d3944a9e974b73d23efc09af-iio-sensor-proxy.service-UYGEGU
systemd-private-0461cab7d3944a9e974b73d23efc09af-ModemManager.service-FKfsh9
systemd-private-0461cab7d3944a9e974b73d23efc09af-rtkit-daemon.service-43AVDL
systemd-private-0461cab7d3944a9e974b73d23efc09af-systemd-timesyncd.service-4pB1fo
thunderbird_alegrand
tracker-extract-files.1000
tutoriel.pdf
#+end_example
#+begin_src shell :session *shell* :results output :exports both
hostname
#+end_src
#+RESULTS:
:
: icarus
#+begin_src shell :session *shell* :results output :exports both
ssh nipmuk
#+end_src
#+RESULTS:
: The programs included with the Debian GNU/Linux system are free software;
: the exact distribution terms for each program are described in the
: individual files in /usr/share/doc/*/copyright.
:
: Debian GNU/Linux comes with ABSOLUTELY NO WARRANTY, to the extent
: permitted by applicable law.
: Last login: Tue Apr 10 12:10:47 2018 from ligone.imag.fr
#+begin_src shell :session *shell* :results output :exports both
hostname
ls /tmp/
#+end_src
#+RESULTS:
: nipmuk
: ATN452-P5785-Linux-X64.bin tina_update vgauthsvclog.txt.0
: P57 tn_pipe vmware-root
: ssh-0xgYrn2tUz upgrade_linux.batch
module1/exo2/fichier-markdown.md 0 → 100644
View file @ d040a708
# Partie 1
## Sous-partie 1 : texte
Une phrase sans rien
*Une phrase en italique*
**Une phrase en gras**
Un lien vers [fun-mooc.fr](https://www.fun-mooc.fr/fr/)
Une ligne de ```code```
## Sous-partie 2 : listes
**Liste à puce** :
* Item
*sous-item
*sous-item
* Item
* Item
**Liste numérotée** :
1. Item
2. Item
3. Item
## Sous-partie 3 : code
```python
# Extrait de code
#!/usr/bin/python3
import sys
if len(sys.argv) > 1:
print("Hello " + sys.argv[1])
else:
print("Hello")
```