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Graphes - Matplotlib

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Text introduction — Matplotlib 1.5.0 documentation. Python - setting y-axis limit in matplotlib. Les histogrammes avec Matplotlib. Introduction Rappels: différence entre diagramme et histogramme: On utilise l'histogramme pour représenter la distribution observée d'une variable continue et les diagrammes à barres, bandes ou batons pour une variable discrète.

Les histogrammes avec Matplotlib

Les effectifs ou les fréquences correspondent dans un histogramme à la surface des rectangles contigus. Pour un diagramme en barres ce sont les hauteurs des barres qui correspondent à ces effectifs ou fréquences. (source) Un simple histogramme Deux histogrammes sur une même figure. Pyplot tutorial. Matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB.

Pyplot tutorial

Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that “axes” here and in most places in the documentation refers to the axespart of a figure and not the strict mathematical term for more than one axis). Python - matplotlib share x axis but don't show x axis tick labels for both, just one. Legend guide — Matplotlib 1.4.3 documentation. This legend guide is an extension of the documentation available at legend() - please ensure you are familiar with contents of that documentation before proceeding with this guide.

Legend guide — Matplotlib 1.4.3 documentation

This guide makes use of some common terms, which are documented here for clarity: legend entry A legend is made up of one or more legend entries. An entry is made up of exactly one key and one label. legend key The colored/patterned marker to the left of each legend label. legend label The text which describes the handle represented by the key.

Python - matplotlib: combine different figures and put them in a single subplot sharing a common legend. Python - surface plots in matplotlib. Nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb. The figure above is functional, but it does not (yet) satisfy the criteria for a figure used in a publication.

nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb

First and foremost, we need to have LaTeX formatted text, and second, we need to be able to adjust the font size to appear right in a publication. Matplotlib has great support for LaTeX. All we need to do is to use dollar signs encapsulate LaTeX in any text (legend, title, label, etc.). For example, "$y=x^3$". But here we can run into a slightly subtle problem with LaTeX code and Python text strings.

Cookbook/Matplotlib/Show colormaps - Show Matplotlib colormaps Toggle line numbers 1 from pylab import * 2 from numpy import outer 3 rc('text', usetex=False) 4 a=outer(arange(0,1,0.01),ones(10)) 5 figure(figsize=(10,5)) 6 subplots_adjust(top=0.8,bottom=0.05,left=0.01,right=0.99) 7 maps=[m for m in cm.datad if not m.endswith("_r")] 8 maps.sort() 9 l=len(maps)+1 10 for i, m in enumerate(maps): 11 subplot(1,l,i+1) 12 axis("off") 13 imshow(a,aspect='auto',cmap=get_cmap(m),origin="lower") 14 title(m,rotation=90,fontsize=10) 15 savefig("colormaps.png",dpi=100,facecolor='gray') But, what if I think those colormaps are ugly?

Cookbook/Matplotlib/Show colormaps -

Well, just make your own using matplotlib.colors.LinearSegmentedColormap. First, create a script that will map the range (0,1) to values in the RGB spectrum. As you see, the colormap has a break halfway through. Here a slightly modified version of the above code which allows for displaying a selection of the pre-defined colormaps as well as self-created registered colormaps. Graphiques — FAQPython 2.0 documentation. Modifier le graphique Dans cette section, nous traiterons de l’édition simple d’un graphique : mettre en place un titre, des étiquettes sur les axes, une légende ou mettre en forme les différentes courbes Pour inclure un titre et des étiquettes sur les axes, on utilise les codes title(ur"Titre")xlim(lim1, lim2)xlabel(u"LabelX") Voici une utilisation sur un exemple.

Graphiques — FAQPython 2.0 documentation

Tracer les courbes — Bien démarrer avec Numpy/Scipy/Matplotlib valpha documentation. Le module Matplotlib est chargé de tracer les courbes: >>> import matplotlib.pyplot as plt D’une manière générale les fonctions plt.plot attendent des vecteur/matrice, bref des tableaux de points du plan.

Tracer les courbes — Bien démarrer avec Numpy/Scipy/Matplotlib valpha documentation

Selon les options, ces points du plan sont reliés entre eux de façon ordonnée par des segments : le résultat est une courbe. Commençons par la fonction sinus. Tables RGB Tables. Matplotlib : "Forcer" l'échelle des axes. Python - How do I set figure title and axes labels font size in matplotlib?

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