Introduction au Python pour des scientifiques Module1: Initiation

Transcription

1 Introduction au Python pour des scientifiques Module1: Initiation Vincent Noel, Dmitry Khvorostyanov, Jean-Yves Peterschmitt avec des contributions précieuses de Ludmila Klenov, Yohann Morille LMD, IPSL/CNRS-X-ENS-UPMC Journée de formation IPSL 14 octobre 2013 Ecole Polytechnique, Palaiseau, France

2 Programme de la journée Matin (maintenant) 9:30 accueil café 10h00-12h30: Introduction générale à Python (amphi Becquerel) Midi 12:30 14:00 repas: salle Club Magnan Après-midi 14H 17h30: Travaux Pratiques (salles informatiques 31 et 33 de l'x)

3 Presentation outline Introduction to Python (Dmitry) Language syntax introduction (Jean-Yves) Coffee break/questions Scientific Python libraries (Vincent) Practical sessions overview (Dmitry)

4 Python: Brief History February 1991, the 1 st published version (0.9.0): classes, exception handling, functions, core datatypes (list, dict, str...), module system January 1994, v1.0: functional programming paradigm tools lambda, map, filter and reduce. October 1996, v1.4: keyword arguments [ex.: c = jump(high = True)], complex numbers October, 2000, v2.0: list comprehensions (we'll see one today!), a garbage collection system (makes life easier with memory management) December, 2001, v2.2: new-style classes (unification of Python's types (written in C) and classes (written in Python) into one hierarchy; generators December, 2008, v3.0: "Reduce feature duplication by removing old ways of doing things" => no more backward compatibility! June, 2009, v3.1, July, 2010, v2.7. Python 2.7 was released to coincide with Python 3.1, included some features 3.1, as well as warnings for old features. The 2.7 was the last 2.x release. Van Rossum at OSCON 2006 Photo taken from WikiPedia Guido Van Rossum: "Benevolent Dictator For Life" Python creator, he oversees the development process, making decisions

5 Why Python? High-level flexible programming language Development speed Code readability Collaborative development, program component (re-)usability combined with Rich variety of libraries for wide range of applications: Scientific computing Data analysis and statistics, input/output Data visualization and plotting Image/signal processing, data mining, machine learning... Shell programming (a readable bash alternative!) Web / Databases Human-computer interaction (graphical user interfaces, virtual reality...) Video-games, networking, sound/text processing, linguistics, neurosciences... Embedded systems Free, open-source, permissive license (PSFL) Large, friendly, dynamically growing community

6 Large, friendly, dynamically growing community - AFPY: rencontres, forums, formations, documents éducatifs - "Planets" et "User Groups" de Python: forums, blogs, groupes thématiques... Stack Overflow: réponses à presque toutes vos questions! - EuroSciPy, PyCon, EuroPython, PyCon UK, OSCON,... - échanges de compétences, tutoriaux, événements sociaux - Google, BitTorrent, SpamBayes, Plone CMS, MailMan, Zope, Django - Python-powered! Des initiatives humanitaires dans des pays en développement: - Argentine Py Group: CDPedia - One Laptop Per Child: Sugar - Bungeni: système d'information pour les parlements africains + ouverts et accessibles - Python African Tour: Formations Maroc, Sénégal, Zambie, Nigéria...

7 High-level and flexible programming language Multiple ways to do the same thing, often with very compact syntax: dates = array([datetime(*map(int,row[:5])) for row in data]) => see TP1 for three other ways to do this! Imperative def filter_div(v,n) : y = [] for x in v: if (x % N == 0): y.append(x) return y filter_div([1,2,6,5,7],3) >> [6] Three programming paradigms Object-oriented class ncvar: def init (self, file): self.var = [] self.nc = netcdf4.dataset(file) def read(self, varname): self.var = self.nc.variables [varname][:] Functional V = ncvar('o3.nc') V.read('tem2') print(v.var.shape) map(lambda x: round(x**2, 1), [0.1, 0.4, 5]) >> [0.0, 0.2, 25.0] filter(lambda x: x % 3 == 0, [1,2,6,5,7]) >> [6] reduce(lambda x, y: x * y, [1,2,3,4]) >> 24

8 High-level and flexible language + libraries Can get very quickly what you need with a simple straightforward code: import cartopy.crs as ccrs import netcdf4 nc = netcdf4.dataset('sinwave.nc') lons = nc.variables['lon'][:] lats = nc.variables['lat'][:] data = nc.variables['data'][:] ax = plt.axes(projection=ccrs.mollweide()) ax.contourf(lons, lats, data, transform=ccrs.platecarree(),cmap='spectral') ax.coastlines() ax.set_global() show() import cartopy.crs as ccrs import matplotlib.pyplot as plt ax = plt.axes(projection=ccrs.mollweide()) ax.stock_img() plt.show()

9 High-level and flexible language + libraries...and create complex software architectures : Twisted : ~ 208,000 lines of code (network framework) Zope : ~ 404,000 (web application server & framework) Plone : ~ 573,000 (content managment system)

10 High-level and flexible language: résumé Python est utilisé dans de nombreux domaines: programmation système, réseaux, automatisation, ingénierie, serveurs web, bases de données, images de synthèse, manipulation de texte, jeux vidéos, data mining, applications commerciales et en sciences ( Ouah! ) Python s adapte à plusieurs types de programmation : Séquentielle / parallèle Procédurale/ fonctionnele / orientée objet Test-driven Bête/intuitive (populaire dans les labos)

11 High-level and flexible language + framework Available with Linux (standard distribution included), Mac OS X (standard included), Windows, Android, etc. Interactive mode (python, ipython, notebook) or automatic (scripts, libraries, ressources) Interpreters and compilers (CPython, PyPy, Cython, py2exe) Interfaces with other languages : Fortran (f2py), C/C++, Java (Jython, JPype, Jepp)...

12 Est-ce que Python va dominer le monde? [ du travail ] Percentage of jobs in web job listings since 2005 (from indeed.com) % job postings % growth

13 Distributions Python python.org = python + librairies standard o numpy, etc à installer soi-meme : complexe distributions "intégrées" python scientifique o python + librairie standard + numpy + lecture/écriture + etc. o plus facile, à privilégier si possible o vérifier la license... (parfois payant) CANOPY (Enthought Python Distribution, conseillé) Anaconda ( UV-CDAT ( SAGE (calcul formel) python(x,y)

14 II. La syntaxe : brève intro Presentation of Jean-Yves goes here!

15

16 III. Python pour la science : outils et librairies Presentation of Vincent goes here!

17

18 Pour aller plus loin Restez pour l'après-midi Google : Python, science, numpy, matplotlib, tutorials... Bouquins (souvent en anglais, mais pas toujours)

19 TPs de cet après-midi login machine python2... python35 Mot de pass: sera communiqué discrètement Actualiser les PATHs: $ export $HOME/../python1/set_paths.sh Données, énoncés, solutions (seront communiqué au fur et à mésure de votre progréssion): cp $HOME/../python1/TPx. ou x=['intro, 1, 2, 3, 'Bonus']

20 Merci pour votre attention Questions?