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journal/Readme.md
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# Journal de bord du Mooc / Mooc's logbook # Journal de bord du Mooc
FR - 11/11/2020 : module 1 terminé, prise en main des outils.
- Modules 1 achevé.
- Premières rédactions. Les résumés seront mis dans les fichiers Readme.md inclus dans les répertoires.
- Démarrage du module 2
Espace réservé au journal de bord du Mooc
EN - 13/11/2020 : présentation de JupyterHub par un collègue et autres outils
- [Ploty](https://plotly.com/)
- [https://cantera.org/](https://cantera.org/)
- 14/11/2020 : réplication de la base en local et suivi par git.
- 19.11.2020 : exercices du module2 sur la solution emacs-org + Python
Quand on exécute un code Python, Emacs crée un buffer *Python* en arrière plan.
On peut y entrer et en particulier vérifier sur quel répertoire on est, par exemple pour la
génération de graphique. Les commandes pour vérifier le répertoire en cours et en changer sont
respectivement `os.getcwd()` et `os.chdir(..)`.
Reserved for the Mooc's logbook
\ No newline at end of file
module1/README.md
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Module 1 # Module 1 : Cahier de Notes, cahier de laboratoire
Ce module commence par un aperçu historique de la prise de note dans le contexte plus général de l'écriture et des livres. Puis il présente des entretiens avec des chercheurs de plusieurs domaines (mathématique, histoire, sience politique, recherche en laboratoire ...). Ces entretiens montrent le glissement progressif des chercheurs vers des solutions informatiques, qui présentent des intérêts techniques évidents (comme la renumérotation automatique de notes très nombreuses, comme en parle très bien l'historienne). Le module termine par la présentation des solutions informatiques.
L'exercice consiste à écrire un premier fichier dans le format markdown.
Le support du cours est téléchargeable [ici](https://www.fun-mooc.fr/asset-v1:inria+41016+self-paced+type@asset+block/C028AL_slides_module1-fr-gz.pdf)
## Aperçu historique
L'évolution du livre est passée par deux étapes en Occident:
- le *volumen*, qui est rouleau écrit sur papyrus qui se déroule au moment de la lecture (entre deux rouleaux). On en trouve ecore aujourd'hui dans les synagogues pour les rouleaux de la Tora.
- le *codex*, qui descend des tablettes de cire de la période Romaine, assemblées ensemble, puis plus tard remplacées par des feuilles de vélin, puis de papier.
La tradition Juive est née à l'époque des *volumen* et s'attache donc à ce format. Il pousse à lire à haute voix et ne permet pas de prise de note sur un autre support annexe car les deux mains sont prises par les deux rouleaux (celui à enrouler et celui à dérouler). La lecture se fait au rythme de la diction, ce qui est renforcé par la difficulté de déchiffrer un texte dans lequel les mots ne sont pas séparés (et pour l'hébreux, dans lequel les accents indiquant les sons voyelles peuvent ne pas être notés).
Les traditions Chrétiennes, puis musulmannes nées plus tard, ont utilisé le format codes dès leur origine pour leur livres traditionnels.
La séparation des mots, puis des paragraphes (d'abord s'implement indiqués par les lettrines de couleur) n'apparaitra que progressivement au cours des siècles qui suivent. La lecture silencieuse n'apparaitra qu'à la fin de cette lente évolution. Elle remplacera le lien entre la lecture et l'élocution par un lien entre la lecture et la pensée, qui nous semble si naturel aujourd'hui.
## Revu des exemples pratiques de la prise de notes aujourd'hui
À travers les entrertiens et les présentations, les impératifs de la prise de notes sont:
- être techniquement simple et légère, pour que le processus soit naturel et puisse être répétés régulièrement et sans découragements ;
- permettre une relecture simple et rapide ;
- faciliter ensuite les recherches, quand les notes se sont accumulées.
L'utilisation de l'informatique est actuellement un accélérateur de recherche. Dans son entretien, l'historienne compare ses notes écrites au début sur papier avec celles qu'elle prend aujourd'hui sur un ordinateur et avoue qu'honnêtrement, elle revient maintenant très rarement sur celles en papier, plus compliquées à utiliser. On voit donc qu'à terme, le fait de pouvoir retrouver efficacement une note sur un sujet donné est un point crucial.
Par contre, la difficulté est que nos centres d'intérêt ou de recherche peuvent changer. Le classement que l'on pense efficace aujourd'hui, le sera peut être moins demain...
<!-- exemple à donner ou développer -->
## Les solutions informatiques
Plusieurs solutions informatiques sont présentées:
- le langage de balisage [markdown](https://enacit.epfl.ch/cours/markdown-pandoc)
- les logiciels permettant de *marquer* des images.
- Les logiciels de recherche de mots à travers plusieurs fichiers.
- Le logiciel se suivi de réivison *git* et ses applications directes *gitlab* et *github*
### le format markdown
Il insiste en particulier sur le format texte, qui correspond aux fichiers encodés selon un standard de représenation simple, actuellement [utf-8](https://www.utf8-chartable.de/) étant le format plus naturel. Ces formats simples vont faciliter ensuite la recherche par d'autres outils et en particulier les outils cloud, comme dans l'exemple de ces notes.
Pour conserver une présentation agréable à la lecture, le format , est format simple avec balise qui permet d'obtenir les mises en forme dont on peut avoir besoin dans une prise de notes. Ces formats de balise permettent aussi d'inclure des notes masquées, qui faciliteront les recherches par thèmes. D'autres initiatives comme la [TEI](https://tei-c.org/release/doc/tei-p5-doc/fr/html/) visent à établir des standards, qui permettraient d'utiliser plus efficacement les textes sources, s'ils étaient universellement utilisés.
### Les recherches de mots clés à travers plusieurs fichiers
Les logiciels de traitement de texte (comme Word) permettent la recherche de mots, mais uniquement sur un seul fichier. Sous Linux, il existe une application bien pratique, *grep*, qui permet de faire ce genre de recherche à travers plusieurs fichiers textes (et en généralisant ces recherches grâce aux expressions rationnelles). Cette [page d'Ubuntu](https://doc.ubuntu-fr.org/recherche_de_fichiers) présente les différentes solutions qui existent.
#### cgvg
La [page de l'auteur donne le source](http://www.uzix.org/cgvg.html). On trouve aussi d'autres explications sur [une page de Debian](https://packages.debian.org/unstable/cgvg). Cette suite n'est a priori plus maintenue, mais fonctionne bien. Elle permet de faire des recherches avec le commande `cg` puis de visualiser les résultats dans les fichiers avec `vg`.
#### docFetcher
Ce logiciel a l'intérêt d'exister sous les trois plateformes.
#### ExifTool
Ce [logiciel](https://exiftool.org/) permet de visualiser et d'écrire directement dans les métadata d'un fichier image. On peut ainsi insérer une nouvelle propriété, qui pourra à son tour être trouvées par une recherche sur un mot clé donné.
La commande [mogrify d'ImageMagick](https://imagemagick.org/script/mogrify.php) permet aussi de faire cela, mais je n'ai pas encore approfondi la desssus.
### La gestion de versions avec Git
Le logiciel [Git](https://git-scm.com/) a été dévelopé par Linus Torwald est son équipe en 2005 pour travailler en équipe sur le noyaux linux et remplacer le logiciel *bitKeeper* qui changeait alors de licence. Ce logiciel conserve les différentes versions d'une serie de fichiers (que l'on peut choisir un à un), ce qui permet les retours en arrière et aussi d'identifier et de commenter les changements. Il fonctionne de manière non centralisée: chacun conservant une copie en local, peut ensuite développer des branches alternatives. Ceci correspond pleinement à l'esprit des logiciels libres.
Mais l'application à des prises de notes est aussi très intéressante : on prend des notes, on peut revenir en arrière tout en conservant des notes alternatives qui pourraient redevenir intéressantes plus tard...
Deux applications cloud existent sur la base de ce logiciel:
- [github](https://github.com/)
- [gitlab](https://about.gitlab.com/)
Ces deux applications appartiennent à des entrerprises privées et ont chacune un fonctionnement commercial différent.
- github est gratuit pour les étudiants, mais devient payante pour les entreprises
- gitlab permet à chacun de se faire une version personnalisée et c'est ensuite le service qui devient payant
Ce cours utilise une version Inria de Gitlab
## Des liens utiles que j'ai retenus après ce module:
- [Lien vers La BnF sur l'histoire des écritures](http://classes.bnf.fr/ecritures/)
- [table des caractères Unicode](https://www.utf8-chartable.de/)
- [Élaboration et conversion de documents avec Markdown et Pandoc, de Jean-Daniel Bonjour](https://enacit.epfl.ch/cours/markdown-pandoc/)
- [La TEI: Text Encoding Initiative](https://tei-c.org/release/doc/tei-p5-doc/fr/html/)
- [Odette, logiciel TEI, présentation de Frédéric Glorieux](http://obvil.sorbonne-universite.site/developpements/odette)
*Autres ressources intéressantes:*
- [Qu'est-ce qu'un lieu de savoir](https://books.openedition.org/oep/650)
module1/exo2/fichier-markdown.md
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...@@ -13,22 +13,22 @@ Une ligne de `code` ...@@ -13,22 +13,22 @@ Une ligne de `code`
## Sous-partie 2 : listes ## Sous-partie 2 : listes
** Liste à puces ** **Liste à puces**
- item - item
- sous-item - sous-item
- sous-item - sous-item
- item - item
- item - item
** Liste numérotée ** **Liste numérotée**
1. item 1. item
2. item 2. item
3. item 3. item
** Sous-partie 3 : code ** ## Sous-partie 3 : code **
``` ```
# Extrait de code` # Extrait de code
``` ```
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module2/README.md
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Module 2 # Module 2 : La vitrine et l'envers du décors
## Introduction
Trois outils sont présentés dans ce module
1. Jupyter : un notebook orienté pour le langage Python (mais acceptant R)
2. RStdudio: un environnement de développement pour le langage R
3. org mode: un environnement généraliste développé en particulier pour Emacs
## Jupyter
Jupyter permet de mélanger le code Python avec des descriptions et les résultats.
Il existe des méta instructions comme `matplotlib line` qui est une instruction spécifiant que les images seront intégrées au notebook. On peut en avoir la liste complète avec la macro-instruction `%lsmagic`
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"","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
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---
title: "Your title"
author: "Your name"
date: "Today's date"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some explanations
This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
```{r cars}
summary(cars)
```
It is also straightforward to include figures. For example:
```{r pressure, echo=FALSE}
plot(pressure)
```
Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
Now it's your turn! You can delete all this information and replace it by your computational document.
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#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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, R code is included as follows (and is
exxecuted by typing ~C-c C-c~):
#+begin_src R :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: [1] "Hello world!"
And now the same but in an R session. This is the most frequent
situation, because R is really an interactive language. With a
session, R'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~.
#+begin_src R :results output :session *R* :exports both
summary(cars)
#+end_src
#+RESULTS:
: speed dist
: Min. : 4.0 Min. : 2.00
: 1st Qu.:12.0 1st Qu.: 26.00
: Median :15.0 Median : 36.00
: Mean :15.4 Mean : 42.98
: 3rd Qu.:19.0 3rd Qu.: 56.00
: Max. :25.0 Max. :120.00
Finally, an example for graphical output:
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
plot(cars)
#+end_src
#+RESULTS:
[[file:./cars.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 ~cars.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 R by typing ~<r~ or ~<R~ followed by
~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module2/exo1/toy_document_orgmode_R_fr.org
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#+TITLE: Votre titre Content-Type: text/enriched
#+AUTHOR: Votre nom Text-Width: 100
#+DATE: La date du jour
#+LANGUAGE: fr <x-color><param>#73d216</param># -*- coding: utf-8-unix; mode: org; mode: auto-fill; fill-column: 100; -*-</x-color>
# #+PROPERTY: header-args :eval never-export
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/> #+TITLE: <x-color><param>pale turquoise</param>Premier exercice
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/> </x-color>#+AUTHOR: <x-color><param>pale turquoise</param>Mathieu Pouit
#+HTML_HEAD: <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script> </x-color>#+DATE: <x-color><param>pale turquoise</param>2020-11-19
#+HTML_HEAD: <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script> </x-color>#+LANGUAGE: fr
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script> <x-color><param>#73d216</param># #+PROPERTY: header-args :eval never-export</x-color>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
#+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>
* Quelques explications * Quelques explications
Ceci est un document org-mode avec quelques exemples de code Ceci est un document org-mode avec quelques exemples de code
R. Une fois ouvert dans emacs, ce document peut aisément être R. Une fois ouvert dans emacs, ce document peut aisément être
exporté au format HTML, PDF, et Office. Pour plus de détails sur exporté au format HTML, PDF, et Office. Pour plus de détails sur
org-mode vous pouvez consulter https://orgmode.org/guide/. org-mode vous pouvez consulter https://orgmode.org/guide/.
Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera
compilé en html. Tout le code contenu sera ré-exécuté, les résultats compilé en html. Tout le code contenu sera ré-exécuté, les résultats
récupérés et inclus dans un document final. Si vous ne souhaitez pas récupérés et inclus dans un document final. Si vous ne souhaitez pas
...@@ -25,25 +35,31 @@ ré-exécuter tout le code à chaque fois, il vous suffit de supprimer ...@@ -25,25 +35,31 @@ 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 le # et l'espace qui sont devant le ~#+PROPERTY:~ au début de ce
document. document.
Comme nous vous l'avons montré dans la vidéo, on inclut du code Comme nous vous l'avons montré dans la vidéo, on inclut du code
R de la façon suivante (et on l'exécute en faisant ~C-c C-c~): R de la façon suivante (et on l'exécute en faisant ~C-c C-c~):
#+begin_src R :results output :exports both #+begin_src R :results output :exports both
print("Hello world!") print("Hello world!")
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: [1] "Hello world!" : [1] "Hello world!"
Voici la même chose, mais avec une session R (c'est le cas le Voici la même chose, mais avec une session R (c'est le cas le
plus courant, R étant vraiment un langage interactif), donc une plus courant, R étant vraiment un langage interactif), donc une
persistance d'un bloc à l'autre (et on l'exécute toujours en faisant persistance d'un bloc à l'autre (et on l'exécute toujours en faisant
~C-c C-c~). ~C-c C-c~).
#+begin_src R :results output :session *R* :exports both #+begin_src R :results output :session *R* :exports both
summary(cars) summary(cars)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: speed dist : speed dist
: Min. : 4.0 Min. : 2.00 : Min. : 4.0 Min. : 2.00
...@@ -53,32 +69,39 @@ summary(cars) ...@@ -53,32 +69,39 @@ summary(cars)
: 3rd Qu.:19.0 3rd Qu.: 56.00 : 3rd Qu.:19.0 3rd Qu.: 56.00
: Max. :25.0 Max. :120.00 : Max. :25.0 Max. :120.00
Et enfin, voici un exemple de sortie graphique: Et enfin, voici un exemple de sortie graphique:
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R* #+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
plot(cars) plot(cars)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
[[file:./cars.png]] [[file:./cars.png]]
Vous remarquerez le paramètre ~:exports results~ qui indique que le code Vous remarquerez le paramètre ~:exports results~ qui indique que le code
ne doit pas apparaître dans la version finale du document. Nous vous 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 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 (indiquer ~both~) car l'objectif est que vos analyses de données soient
parfaitement transparentes pour être reproductibles. parfaitement transparentes pour être reproductibles.
Attention, la figure ainsi générée n'est pas stockée dans le document Attention, la figure ainsi générée n'est pas stockée dans le document
org. C'est un fichier ordinaire, ici nommé ~cars.png~. N'oubliez pas org. C'est un fichier ordinaire, ici nommé ~cars.png~. N'oubliez pas
de le committer si vous voulez que votre analyse soit lisible et de le committer si vous voulez que votre analyse soit lisible et
compréhensible sur GitLab. compréhensible sur GitLab.
Enfin, pour les prochains exercices, nous ne vous fournirons pas Enfin, pour les prochains exercices, nous ne vous fournirons pas
forcément de fichier de départ, ça sera à vous de le créer, par forcément de fichier de départ, ça sera à vous de le créer, par
exemple en repartant de ce document et de le commiter vers exemple en repartant de ce document et de le commiter vers
gitlab. N'oubliez pas que nous vous fournissons dans les ressources de gitlab. N'oubliez pas que nous vous fournissons dans les ressources de
ce MOOC une configuration avec un certain nombre de raccourcis ce MOOC une configuration avec un certain nombre de raccourcis
claviers permettant de créer rapidement les blocs de code R (en claviers permettant de créer rapidement les blocs de code R (en
faisant ~<r~ ou ~<R~ suivi de ~Tab~). faisant ~<<r~ ou ~<<R~ suivi de ~Tab~).
Maintenant, à vous de jouer! Vous pouvez effacer toutes ces Maintenant, à vous de jouer! Vous pouvez effacer toutes ces
informations et les remplacer par votre document computationnel. informations et les remplacer par votre document computationnel.
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#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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!")
#+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~.
#+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
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]]
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~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
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# -*- coding: utf-8-unix; mode:org; mode: auto-fill; fill-column: 80; -*-
#+TITLE: Votre titre #+TITLE: Votre titre
#+AUTHOR: Votre nom #+AUTHOR: Votre nom
#+DATE: La date du jour #+DATE: La date du jour
#+STARTUP: showall
#+STARTUP: hidestars
#+LANGUAGE: fr #+LANGUAGE: fr
# #+PROPERTY: header-args :eval never-export #+PROPERTY: header-args :eval never-export
#+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/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/> #+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
...@@ -13,22 +19,26 @@ ...@@ -13,22 +19,26 @@
* Quelques explications * Quelques explications
Ceci est un document org-mode avec quelques exemples de code Ceci est un document org-mode avec quelques exemples de code python. Une fois
python. Une fois ouvert dans emacs, ce document peut aisément être ouvert dans emacs, ce document peut aisément être exporté au format HTML, PDF,
exporté au format HTML, PDF, et Office. Pour plus de détails sur et Office. Pour plus de détails sur org-mode vous pouvez consulter
org-mode vous pouvez consulter https://orgmode.org/guide/. https://orgmode.org/guide/.
Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera compilé en
compilé en html. Tout le code contenu sera ré-exécuté, les résultats html. Tout le code contenu sera ré-exécuté, les résultats récupérés et inclus
récupérés et inclus dans un document final. Si vous ne souhaitez pas dans un document final. Si vous ne souhaitez pas ré-exécuter tout le code à
ré-exécuter tout le code à chaque fois, il vous suffit de supprimer chaque fois, il vous suffit de supprimer le # et l'espace qui sont devant le
le # et l'espace qui sont devant le ~#+PROPERTY:~ au début de ce ~#+PROPERTY:~ au début de ce document.
document.
Comme nous vous l'avons montré dans la vidéo, on inclue du code 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~): python de la façon suivante (et on l'exécute en faisant ~C-c C-c~):
#+begin_src python :results output :exports both Il existe une [[https://orgmode.org/worg/org-contrib/babel/languages/ob-doc-python.html][aide spécifique sur l'exécution de Python dans Org-mode]]. En
particulier, il faut indiquer que le programme python s'appelle ~python3~ sous
linux, pour ne pas confondre avec ~python2~ , qui avait pris jusqu'ici l'alias
~python~ par défaut.
#+begin_src python :results output :exports both :python python3
print("Hello world!") print("Hello world!")
#+end_src #+end_src
...@@ -38,7 +48,7 @@ print("Hello world!") ...@@ -38,7 +48,7 @@ print("Hello world!")
Voici la même chose, mais avec une session python, donc une Voici la même chose, mais avec une session python, donc une
persistance d'un bloc à l'autre (et on l'exécute toujours en faisant persistance d'un bloc à l'autre (et on l'exécute toujours en faisant
~C-c C-c~). ~C-c C-c~).
#+begin_src python :results output :session :exports both #+begin_src python :results output :session :exports both :python python3
import numpy import numpy
x=numpy.linspace(-15,15) x=numpy.linspace(-15,15)
print(x) print(x)
...@@ -46,6 +56,9 @@ print(x) ...@@ -46,6 +56,9 @@ print(x)
#+RESULTS: #+RESULTS:
#+begin_example #+begin_example
Python 3.8.5 (default, Jul 28 2020, 12:59:40)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041 [-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592 -11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143 -8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143
...@@ -59,7 +72,7 @@ print(x) ...@@ -59,7 +72,7 @@ print(x)
#+end_example #+end_example
Et enfin, voici un exemple de sortie graphique: Et enfin, voici un exemple de sortie graphique:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results #+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results :python python3
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
plt.figure(figsize=(10,5)) plt.figure(figsize=(10,5))
...@@ -73,6 +86,7 @@ print(matplot_lib_filename) ...@@ -73,6 +86,7 @@ print(matplot_lib_filename)
#+RESULTS: #+RESULTS:
[[file:./cosxsx.png]] [[file:./cosxsx.png]]
Vous remarquerez le paramètre ~:exports results~ qui indique que le code Vous remarquerez le paramètre ~:exports results~ qui indique que le code
ne doit pas apparaître dans la version finale du document. Nous vous 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 recommandons dans le cadre de ce MOOC de ne pas changer ce paramètre
......
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{
"cells": [],
"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
}
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#!/usr/bin/env python
# coding: utf-8
# # toy_notbook_fr
# March 28, 2019
# ## À propos du calcul de $\pi$
# ### En démandant à la lib maths
# Mon ordinateur m'indique de $\pi$ vaut *approximativement*
# In[1]:
from math import *
print(pi)
# ### En utilisant la méthode des aiguilles de Buffon
# Mais calculé avec la méthode des aiguilles de Buffon, on obtiendrait
# In[2]:
import numpy as np
np.random.seed(seed=42)
N = 1000
x = np.random.uniform(size=N, low=0, high=1)
theta = np.random.uniform(size=N, low=0, high=pi/2)
2/(sum((x+np.sin(theta))>1)/N)
# ### Avec un argument "fréquentiel" de surface
# 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 $P[X^2 + Y^2] = \pi/4$ (voir méthode Monte Carlo sur Wikipédia). Le code suivant illustre ce fait.
# In[5]:
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
np.random.seed(seed=42)
N = 1000
x = np.random.uniform(size=N, low=0, high=1)
y = np.random.uniform(size=N, low=0, high=1)
accept = (x*x+y*y) <= 1
reject = np.logical_not(accept)
fig, ax = plt.subplots(1)
ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)
ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)
ax.set_aspect('equal')
# 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 :
# In[6]:
4*np.mean(accept)
# In[ ]:
module2/exo2/exercice_R_en.org deleted 100644 → 0
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#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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, R code is included as follows (and is
exxecuted by typing ~C-c C-c~):
#+begin_src R :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: [1] "Hello world!"
And now the same but in an R session. This is the most frequent
situation, because R is really an interactive language. With a
session, R'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~.
#+begin_src R :results output :session *R* :exports both
summary(cars)
#+end_src
#+RESULTS:
: speed dist
: Min. : 4.0 Min. : 2.00
: 1st Qu.:12.0 1st Qu.: 26.00
: Median :15.0 Median : 36.00
: Mean :15.4 Mean : 42.98
: 3rd Qu.:19.0 3rd Qu.: 56.00
: Max. :25.0 Max. :120.00
Finally, an example for graphical output:
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
plot(cars)
#+end_src
#+RESULTS:
[[file:./cars.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 ~cars.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 R by typing ~<r~ or ~<R~ followed by
~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module2/exo2/exercice_en.Rmd deleted 100644 → 0
View file @ 66e89474
---
title: "Your title"
author: "Your name"
date: "Today's date"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some explanations
This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
```{r cars}
summary(cars)
```
It is also straightforward to include figures. For example:
```{r pressure, echo=FALSE}
plot(pressure)
```
Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
Now it's your turn! You can delete all this information and replace it by your computational document.
module2/exo2/exercice_en.ipynb deleted 100644 → 0
View file @ 66e89474
{
"cells": [],
"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
}
module2/exo2/exercice_python_en.org deleted 100644 → 0
View file @ 66e89474
#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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!")
#+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~.
#+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
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]]
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~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module2/exo2/exercice_python_fr.org
View file @ e9afcc9b
#+TITLE: Votre titre # -*- coding: utf-8-unix; mode: org; mode: auto-fill; fill-column: 100; -*-
#+AUTHOR: Votre nom
#+DATE: La date du jour #+TITLE: Module 2 Exercice 2 : première lignes de Python sous Emacs
#+AUTHOR: Mathieu Pouit
#+DATE: 19.11.2020
#+LANGUAGE: fr #+LANGUAGE: fr
# #+PROPERTY: header-args :eval never-export # #+PROPERTY: header-args :eval never-export
...@@ -28,7 +30,7 @@ document. ...@@ -28,7 +30,7 @@ document.
Comme nous vous l'avons montré dans la vidéo, on inclue du code 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~): python de la façon suivante (et on l'exécute en faisant ~C-c C-c~):
#+begin_src python :results output :exports both #+begin_src python :results output :exports both :python python3
print("Hello world!") print("Hello world!")
#+end_src #+end_src
...@@ -38,7 +40,7 @@ print("Hello world!") ...@@ -38,7 +40,7 @@ print("Hello world!")
Voici la même chose, mais avec une session python, donc une Voici la même chose, mais avec une session python, donc une
persistance d'un bloc à l'autre (et on l'exécute toujours en faisant persistance d'un bloc à l'autre (et on l'exécute toujours en faisant
~C-c C-c~). ~C-c C-c~).
#+begin_src python :results output :session :exports both #+begin_src python :results output :session :exports both :python python3
import numpy import numpy
x=numpy.linspace(-15,15) x=numpy.linspace(-15,15)
print(x) print(x)
...@@ -59,7 +61,7 @@ print(x) ...@@ -59,7 +61,7 @@ print(x)
#+end_example #+end_example
Et enfin, voici un exemple de sortie graphique: Et enfin, voici un exemple de sortie graphique:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results #+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results :python python3
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
plt.figure(figsize=(10,5)) plt.figure(figsize=(10,5))
...@@ -91,3 +93,34 @@ faisant ~<p~, ~<P~ ou ~<PP~ suivi de ~Tab~). ...@@ -91,3 +93,34 @@ faisant ~<p~, ~<P~ ou ~<PP~ suivi de ~Tab~).
Maintenant, à vous de jouer! Vous pouvez effacer toutes ces Maintenant, à vous de jouer! Vous pouvez effacer toutes ces
informations et les remplacer par votre document computationnel. informations et les remplacer par votre document computationnel.
* Mon exercice en Python
#+begin_src python :results output :exports both :python python3 :session
import numpy as np
data = np.array([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('la moyenne est : %5.3f' % np.mean(data))
print('le minimum est : %3.1f' % np.min(data))
print('le maximum est : %3.1f' % np.max(data))
print('la médiane est : %5.3f' % np.median(data))
print('l\'écart type est : %5.3f' % np.std(data, ddof=1))
#+end_src
#+RESULTS:
: la moyenne est : 14.113
: le minimum est : 2.8
: le maximum est : 23.4
: la médiane est : 14.500
: l'écart type est : 4.334
module2/exo3/exercice_R_en.org deleted 100644 → 0
View file @ 66e89474
#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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, R code is included as follows (and is
exxecuted by typing ~C-c C-c~):
#+begin_src R :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: [1] "Hello world!"
And now the same but in an R session. This is the most frequent
situation, because R is really an interactive language. With a
session, R'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~.
#+begin_src R :results output :session *R* :exports both
summary(cars)
#+end_src
#+RESULTS:
: speed dist
: Min. : 4.0 Min. : 2.00
: 1st Qu.:12.0 1st Qu.: 26.00
: Median :15.0 Median : 36.00
: Mean :15.4 Mean : 42.98
: 3rd Qu.:19.0 3rd Qu.: 56.00
: Max. :25.0 Max. :120.00
Finally, an example for graphical output:
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
plot(cars)
#+end_src
#+RESULTS:
[[file:./cars.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 ~cars.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 R by typing ~<r~ or ~<R~ followed by
~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module2/exo3/exercice_en.Rmd deleted 100644 → 0
View file @ 66e89474
---
title: "Your title"
author: "Your name"
date: "Today's date"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some explanations
This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
```{r cars}
summary(cars)
```
It is also straightforward to include figures. For example:
```{r pressure, echo=FALSE}
plot(pressure)
```
Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
Now it's your turn! You can delete all this information and replace it by your computational document.
module2/exo3/exercice_en.ipynb deleted 100644 → 0
View file @ 66e89474
{
"cells": [],
"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
}
module2/exo3/exercice_python_en.org deleted 100644 → 0
View file @ 66e89474
#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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!")
#+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~.
#+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
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]]
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~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module2/exo3/exercice_python_fr.org
View file @ e9afcc9b
#+TITLE: Votre titre # -*- coding: utf-8-unix; mode: org; mode: auto-fill; fill-column: 100; -*-
#+AUTHOR: Votre nom
#+DATE: La date du jour #+TITLE: Module 2 Exercice 3 : premier graphique avec Python
#+AUTHOR: Mathieu Pouit
#+DATE: 19.11.2020
#+LANGUAGE: fr #+LANGUAGE: fr
# #+PROPERTY: header-args :eval never-export # #+PROPERTY: header-args :eval never-export
...@@ -91,3 +93,65 @@ faisant ~<p~, ~<P~ ou ~<PP~ suivi de ~Tab~). ...@@ -91,3 +93,65 @@ faisant ~<p~, ~<P~ ou ~<PP~ suivi de ~Tab~).
Maintenant, à vous de jouer! Vous pouvez effacer toutes ces Maintenant, à vous de jouer! Vous pouvez effacer toutes ces
informations et les remplacer par votre document computationnel. informations et les remplacer par votre document computationnel.
* mon exercice de génération de courbe
#+begin_src shell :results output :exports both
pwd
#+end_src
#+RESULTS:
: /home/mathieu/Qsync/informatique/rechercheReproductible/mooc-rr/module2/exo3
#+begin_src python :results output file :session :var matplot_lib_filename="./sequencePlot.png" :exports results :python python3
import numpy as np
import matplotlib.pyplot as plt
data = np.array([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])
fig=plt.figure(figsize=(3,2))
plt.plot(data)
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
fig.tight_layout()
#+end_src
#+RESULTS:
[[file:./sequencePlot.png]]
#+begin_src python :results output file :session :var matplot_lib_filename="./histogram.png" :exports results :python python3
import numpy as np
import matplotlib.pyplot as plt
data = np.array([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])
fig=plt.figure(figsize=(3,2))
plt.hist(data)
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
fig.tight_layout()
#+end_src
#+RESULTS:
[[file:./histogram.png]]
module2/exo4/exercice_R_en.org deleted 100644 → 0
View file @ 66e89474
#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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, R code is included as follows (and is
exxecuted by typing ~C-c C-c~):
#+begin_src R :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: [1] "Hello world!"
And now the same but in an R session. This is the most frequent
situation, because R is really an interactive language. With a
session, R'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~.
#+begin_src R :results output :session *R* :exports both
summary(cars)
#+end_src
#+RESULTS:
: speed dist
: Min. : 4.0 Min. : 2.00
: 1st Qu.:12.0 1st Qu.: 26.00
: Median :15.0 Median : 36.00
: Mean :15.4 Mean : 42.98
: 3rd Qu.:19.0 3rd Qu.: 56.00
: Max. :25.0 Max. :120.00
Finally, an example for graphical output:
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
plot(cars)
#+end_src
#+RESULTS:
[[file:./cars.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 ~cars.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 R by typing ~<r~ or ~<R~ followed by
~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module2/exo4/exercice_en.Rmd deleted 100644 → 0
View file @ 66e89474
---
title: "Your title"
author: "Your name"
date: "Today's date"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some explanations
This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
```{r cars}
summary(cars)
```
It is also straightforward to include figures. For example:
```{r pressure, echo=FALSE}
plot(pressure)
```
Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
Now it's your turn! You can delete all this information and replace it by your computational document.
module2/exo4/exercice_en.ipynb deleted 100644 → 0
View file @ 66e89474
{
"cells": [],
"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
}
module2/exo4/exercice_python_en.org deleted 100644 → 0
View file @ 66e89474
#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+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>
* 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!")
#+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~.
#+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
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]]
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~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module2/exo4/exercice_python_fr.org
View file @ e9afcc9b
#+TITLE: Votre titre # -*- coding: utf-8-unix; mode: org; mode: auto-fill; fill-column: 100; -*-
#+AUTHOR: Votre nom
#+TITLE: Module 2 Exercice 4
#+AUTHOR: Mathieu Pouit
#+DATE: La date du jour #+DATE: La date du jour
#+LANGUAGE: fr #+LANGUAGE: fr
# #+PROPERTY: header-args :eval never-export # #+PROPERTY: header-args :eval never-export
......
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#+TITLE: Analysis of the risk of failure of the O-rings on the Challenger shuttle
#+AUTHOR: Arnaud Legrand
#+LANGUAGE: en
#+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>
#+LATEX_HEADER: \usepackage[utf8]{inputenc}
#+LATEX_HEADER: \usepackage[T1]{fontenc}
#+LATEX_HEADER: \usepackage[a4paper,margin=.8in]{geometry}
#+LATEX_HEADER: \usepackage[french]{babel}
# #+PROPERTY: header-args :session :exports both
On January 27, 1986, the day before the takeoff of the shuttle /Challenger/, had
a three-hour teleconference was held between
Morton Thiokol (the manufacturer of one of the engines) and NASA. The
discussion focused on the consequences of the
temperature at take-off of 31°F (just below
0°C) for the success of the flight and in particular on the performance of the
O-rings used in the engines. Indeed, no test
had been performed at this temperature.
The following study takes up some of the analyses carried out that
night with the objective of assessing the potential influence of
the temperature and pressure to which the O-rings are subjected
on their probability of malfunction. Our starting point is
the results of the experiments carried out by NASA engineers
during the six years preceding the launch of the shuttle
Challenger.
* Loading the data
We start by loading this data:
#+begin_src R :results output :session *R* :exports both
data = read.csv("shuttle.csv",header=T)
data
#+end_src
#+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
#+end_example
The data set shows us the date of each test, the number of O-rings
(there are 6 on the main launcher), the
temperature (in Fahrenheit) and pressure (in psi), and finally the
number of identified malfunctions.
* Graphical inspection
Flights without incidents do not provide any information
on the influence of temperature or pressure on malfunction.
We thus focus on the experiments in which at least one O-ring was defective.
#+begin_src R :results output :session *R* :exports both
data = data[data$Malfunction>0,]
data
#+end_src
#+RESULTS:
: Date Count Temperature Pressure Malfunction
: 2 11/12/81 6 70 50 1
: 9 2/03/84 6 57 200 1
: 10 4/06/84 6 63 200 1
: 11 8/30/84 6 70 200 1
: 14 1/24/85 6 53 200 2
: 21 10/30/85 6 75 200 2
: 23 1/12/86 6 58 200 1
We have a high temperature variability but
the pressure is almost always 200, which should
simplify the analysis.
How does the frequency of failure vary with temperature?
#+begin_src R :results output graphics :file "freq_temp.png" :exports both :width 600 :height 400 :session *R*
plot(data=data, Malfunction/Count ~ Temperature, ylim=c(0,1))
#+end_src
#+RESULTS:
[[file:freq_temp.png]]
At first glance, the dependence does not look very important, but let's try to
estimate the impact of temperature $t$ on the probability of O-ring malfunction.
* Estimation of the temperature influence
Suppose that each of the six O-rings is damaged with the same
probability and independently of the others and that this probability
depends only on the temperature. If $p(t)$ is this probability, the
number $D$ of malfunctioning O-rings during a flight at
temperature $t$ follows a binomial law with parameters $n=6$ and
$p=p(t)$. To link $p(t)$ to $t$, we will therefore perform a
logistic regression.
#+begin_src R :results output :session *R* :exports both
logistic_reg = glm(data=data, Malfunction/Count ~ Temperature, weights=Count,
family=binomial(link='logit'))
summary(logistic_reg)
#+end_src
#+RESULTS:
#+begin_example
Call:
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
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
(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
Number of Fisher Scoring iterations: 4
#+end_example
The most likely estimator of the temperature parameter is 0.001416
and the standard error of this estimator is 0.049, in other words we
cannot distinguish any particular impact and we must take our
estimates with caution.
* Estimation of the probability of O-ring malfunction
The expected temperature on the take-off day is 31°F. Let's try to
estimate the probability of O-ring malfunction at
this temperature from the model we just built:
#+begin_src R :results output graphics :file "proba_estimate.png" :exports both :width 600 :height 400 :session *R*
# shuttle=shuttle[shuttle$r!=0,]
tempv = seq(from=30, to=90, by = .5)
rmv <- predict(logistic_reg,list(Temperature=tempv),type="response")
plot(tempv,rmv,type="l",ylim=c(0,1))
points(data=data, Malfunction/Count ~ Temperature)
#+end_src
#+RESULTS:
[[file:proba_estimate.png]]
As expected from the initial data, the
temperature has no significant impact on the probability of failure of the
O-rings. It will be about 0.2, as in the tests
where we had a failure of at least one joint. Let's get back to the initial dataset to estimate the probability of failure:
#+begin_src R :results output :session *R* :exports both
data_full = read.csv("shuttle.csv",header=T)
sum(data_full$Malfunction)/sum(data_full$Count)
#+end_src
#+RESULTS:
: [1] 0.06521739
This probability is thus about $p=0.065$. Knowing that there is
a primary and a secondary O-ring on each of the three parts of the
launcher, the probability of failure of both joints of a launcher
is $p^2 \approx 0.00425$. The probability of failure of any one of the
launchers is $1-(1-p^2)^3 \approx 1.2%$. That would really be
bad luck.... Everything is under control, so the takeoff can happen
tomorrow as planned.
But the next day, the Challenger shuttle exploded and took away
with her the seven crew members. The public was shocked and in
the subsequent investigation, the reliability of the
O-rings was questioned. Beyond the internal communication problems
of NASA, which have a lot to do with this fiasco, the previous analysis
includes (at least) a small problem.... Can you find it?
You are free to modify this analysis and to look at this dataset
from all angles in order to to explain what's wrong.
module2/exo5/exo5_en.Rmd deleted 100644 → 0
View file @ 66e89474
---
title: "Analysis of the risk of failure of the O-rings on the Challenger shuttle"
author: "Arnaud Legrand"
date: "28 juin 2018"
output: html_document
---
On January 27, 1986, the day before the takeoff of the shuttle _Challenger_, had
a three-hour teleconference was held between
Morton Thiokol (the manufacturer of one of the engines) and NASA. The
discussion focused on the consequences of the
temperature at take-off of 31°F (just below
0°C) for the success of the flight and in particular on the performance of the
O-rings used in the engines. Indeed, no test
had been performed at this temperature.
The following study takes up some of the analyses carried out that
night with the objective of assessing the potential influence of
the temperature and pressure to which the O-rings are subjected
on their probability of malfunction. Our starting point is
the results of the experiments carried out by NASA engineers
during the six years preceding the launch of the shuttle
Challenger.
# Loading the data
We start by loading this data:
```{r}
data = read.csv("shuttle.csv",header=T)
data
```
The data set shows us the date of each test, the number of O-rings
(there are 6 on the main launcher), the
temperature (in Fahrenheit) and pressure (in psi), and finally the
number of identified malfunctions.
# Graphical inspection
Flights without incidents do not provide any information
on the influence of temperature or pressure on malfunction.
We thus focus on the experiments in which at least one O-ring was defective.
```{r}
data = data[data$Malfunction>0,]
data
```
We have a high temperature variability but
the pressure is almost always 200, which should
simplify the analysis.
How does the frequency of failure vary with temperature?
```{r}
plot(data=data, Malfunction/Count ~ Temperature, ylim=c(0,1))
```
At first glance, the dependence does not look very important, but let's try to
estimate the impact of temperature $t$ on the probability of O-ring malfunction.
# Estimation of the temperature influence
Suppose that each of the six O-rings is damaged with the same
probability and independently of the others and that this probability
depends only on the temperature. If $p(t)$ is this probability, the
number $D$ of malfunctioning O-rings during a flight at
temperature $t$ follows a binomial law with parameters $n=6$ and
$p=p(t)$. To link $p(t)$ to $t$, we will therefore perform a
logistic regression.
```{r}
logistic_reg = glm(data=data, Malfunction/Count ~ Temperature, weights=Count,
family=binomial(link='logit'))
summary(logistic_reg)
```
The most likely estimator of the temperature parameter is 0.001416
and the standard error of this estimator is 0.049, in other words we
cannot distinguish any particular impact and we must take our
estimates with caution.
# Estimation of the probability of O-ring malfunction
The expected temperature on the take-off day is 31°F. Let's try to
estimate the probability of O-ring malfunction at
this temperature from the model we just built:
```{r}
# shuttle=shuttle[shuttle$r!=0,]
tempv = seq(from=30, to=90, by = .5)
rmv <- predict(logistic_reg,list(Temperature=tempv),type="response")
plot(tempv,rmv,type="l",ylim=c(0,1))
points(data=data, Malfunction/Count ~ Temperature)
```
As expected from the initial data, the
temperature has no significant impact on the probability of failure of the
O-rings. It will be about 0.2, as in the tests
where we had a failure of at least one joint. Let's get back to the initial dataset to estimate the probability of failure:
```{r}
data_full = read.csv("shuttle.csv",header=T)
sum(data_full$Malfunction)/sum(data_full$Count)
```
This probability is thus about $p=0.065$. Knowing that there is
a primary and a secondary O-ring on each of the three parts of the
launcher, the probability of failure of both joints of a launcher
is $p^2 \approx 0.00425$. The probability of failure of any one of the
launchers is $1-(1-p^2)^3 \approx 1.2%$. That would really be
bad luck.... Everything is under control, so the takeoff can happen
tomorrow as planned.
But the next day, the Challenger shuttle exploded and took away
with her the seven crew members. The public was shocked and in
the subsequent investigation, the reliability of the
O-rings was questioned. Beyond the internal communication problems
of NASA, which have a lot to do with this fiasco, the previous analysis
includes (at least) a small problem.... Can you find it?
You are free to modify this analysis and to look at this dataset
from all angles in order to to explain what's wrong.
module2/exo5/exo5_en.ipynb deleted 100644 → 0
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module2/exo5/exo5_python_en.org deleted 100644 → 0
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#+TITLE: Analysis of the risk of failure of the O-rings on the Challenger shuttle
#+AUTHOR: Arnaud Legrand
#+LANGUAGE: en
#+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>
#+LATEX_HEADER: \usepackage{a4}
#+LATEX_HEADER: \usepackage[french]{babel}
# #+PROPERTY: header-args :session :exports both
On January 27, 1986, the day before the takeoff of the shuttle /Challenger/, had
a three-hour teleconference was held between
Morton Thiokol (the manufacturer of one of the engines) and NASA. The
discussion focused on the consequences of the
temperature at take-off of 31°F (just below
0°C) for the success of the flight and in particular on the performance of the
O-rings used in the engines. Indeed, no test
had been performed at this temperature.
The following study takes up some of the analyses carried out that
night with the objective of assessing the potential influence of
the temperature and pressure to which the O-rings are subjected
on their probability of malfunction. Our starting point is
the results of the experiments carried out by NASA engineers
during the six years preceding the launch of the shuttle
Challenger.
* Loading the data
We start by loading this data:
#+begin_src python :results value :session *python* :exports both
import numpy as np
import pandas as pd
data = pd.read_csv("shuttle.csv")
data
#+end_src
#+RESULTS:
#+begin_example
Date Count Temperature Pressure Malfunction
0 4/12/81 6 66 50 0
1 11/12/81 6 70 50 1
2 3/22/82 6 69 50 0
3 11/11/82 6 68 50 0
4 4/04/83 6 67 50 0
5 6/18/82 6 72 50 0
6 8/30/83 6 73 100 0
7 11/28/83 6 70 100 0
8 2/03/84 6 57 200 1
9 4/06/84 6 63 200 1
10 8/30/84 6 70 200 1
11 10/05/84 6 78 200 0
12 11/08/84 6 67 200 0
13 1/24/85 6 53 200 2
14 4/12/85 6 67 200 0
15 4/29/85 6 75 200 0
16 6/17/85 6 70 200 0
17 7/29/85 6 81 200 0
18 8/27/85 6 76 200 0
19 10/03/85 6 79 200 0
20 10/30/85 6 75 200 2
21 11/26/85 6 76 200 0
22 1/12/86 6 58 200 1
#+end_example
The data set shows us the date of each test, the number of O-rings
(there are 6 on the main launcher), the
temperature (in Fahrenheit) and pressure (in psi), and finally the
number of identified malfunctions.
* Graphical inspection
Flights without incidents do not provide any information
on the influence of temperature or pressure on malfunction.
We thus focus on the experiments in which at least one O-ring was defective.
#+begin_src python :results value :session *python* :exports both
data = data[data.Malfunction>0]
data
#+end_src
#+RESULTS:
: Date Count Temperature Pressure Malfunction
: 1 11/12/81 6 70 50 1
: 8 2/03/84 6 57 200 1
: 9 4/06/84 6 63 200 1
: 10 8/30/84 6 70 200 1
: 13 1/24/85 6 53 200 2
: 20 10/30/85 6 75 200 2
: 22 1/12/86 6 58 200 1
We have a high temperature variability but
the pressure is almost always 200, which should
simplify the analysis.
How does the frequency of failure vary with temperature?
#+begin_src python :results output file :var matplot_lib_filename="freq_temp_python.png" :exports both :session *python*
import matplotlib.pyplot as plt
plt.clf()
data["Frequency"]=data.Malfunction/data.Count
data.plot(x="Temperature",y="Frequency",kind="scatter",ylim=[0,1])
plt.grid(True)
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
#+RESULTS:
[[file:freq_temp_python.png]]
At first glance, the dependence does not look very important, but let's try to
estimate the impact of temperature $t$ on the probability of O-ring malfunction.
* Estimation of the temperature influence
Suppose that each of the six O-rings is damaged with the same
probability and independently of the others and that this probability
depends only on the temperature. If $p(t)$ is this probability, the
number $D$ of malfunctioning O-rings during a flight at
temperature $t$ follows a binomial law with parameters $n=6$ and
$p=p(t)$. To link $p(t)$ to $t$, we will therefore perform a
logistic regression.
#+begin_src python :results value :session *python* :exports both
import statsmodels.api as sm
data["Success"]=data.Count-data.Malfunction
data["Intercept"]=1
# logit_model=sm.Logit(data["Frequency"],data[["Intercept","Temperature"]]).fit()
logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()
logmodel.summary()
#+end_src
#+RESULTS:
#+begin_example
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: Frequency No. Observations: 7
Model: GLM Df Residuals: 5
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0
Method: IRLS Log-Likelihood: -3.6370
Date: Fri, 20 Jul 2018 Deviance: 3.3763
Time: 16:56:08 Pearson chi2: 0.236
No. Iterations: 5
===============================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953
Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240
===============================================================================
#+end_example
The most likely estimator of the temperature parameter is 0.0014
and the standard error of this estimator is 0.122, in other words we
cannot distinguish any particular impact and we must take our
estimates with caution.
* Estimation of the probability of O-ring malfunction
The expected temperature on the take-off day is 31°F. Let's try to
estimate the probability of O-ring malfunction at
this temperature from the model we just built:
#+begin_src python :results output file :var matplot_lib_filename="proba_estimate_python.png" :exports both :session *python*
import matplotlib.pyplot as plt
data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})
data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])
data_pred.plot(x="Temperature",y="Frequency",kind="line",ylim=[0,1])
plt.scatter(x=data["Temperature"],y=data["Frequency"])
plt.grid(True)
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
#+RESULTS:
[[file:proba_estimate_python.png]]
As expected from the initial data, the
temperature has no significant impact on the probability of failure of the
O-rings. It will be about 0.2, as in the tests
where we had a failure of at least one joint. Let's get back to the initial dataset to estimate the probability of failure:
#+begin_src python :results output :session *python* :exports both
data = pd.read_csv("shuttle.csv")
print(np.sum(data.Malfunction)/np.sum(data.Count))
#+end_src
#+RESULTS:
: 0.06521739130434782
This probability is thus about $p=0.065$. Knowing that there is
a primary and a secondary O-ring on each of the three parts of the
launcher, the probability of failure of both joints of a launcher
is $p^2 \approx 0.00425$. The probability of failure of any one of the
launchers is $1-(1-p^2)^3 \approx 1.2%$. That would really be
bad luck.... Everything is under control, so the takeoff can happen
tomorrow as planned.
But the next day, the Challenger shuttle exploded and took away
with her the seven crew members. The public was shocked and in
the subsequent investigation, the reliability of the
O-rings was questioned. Beyond the internal communication problems
of NASA, which have a lot to do with this fiasco, the previous analysis
includes (at least) a small problem.... Can you find it?
You are free to modify this analysis and to look at this dataset
from all angles in order to to explain what's wrong.
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# -*- mode: org -*-
#+TITLE: Ressources externes pour découvrir git
#+DATE: June, 2018
#+STARTUP: overview indent
#+OPTIONS: num:nil toc:t
#+PROPERTY: header-args :eval never-export
Pour les débutants Git, voici quelques liens qui vous permettront de vous familiariser avec cet outil très puissant :
- Pour apprendre à utiliser Git en ligne de commande , voici [[http://swcarpentry.github.io/git-novice/][le cours proposé par Software Carpentry]], sous forme de TP avec des explications très progressives (cours en anglais).
- Le livre [[https://git-scm.com/book/fr/v2][Pro Git]] est disponible, gratuitement, légalement. La lecture des deux premiers chapitres vous suffira pour démarrer (livre en français)
- [[https://learngitbranching.js.org/][Learn Git Branching]] vous permettra d'apprendre Git de façon interactive et de bien comprendre les notions de branches (cours en français)