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Graphes - Matplotlib

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Color example code: — Matplotlib 2.0.2 documentation. (Source code) """==================Colormap reference================== Reference for colormaps included with Matplotlib.

color example code: — Matplotlib 2.0.2 documentation

This reference example shows all colormaps included with Matplotlib. Note thatany colormap listed here can be reversed by appending "_r" (e.g., "pink_r").These colormaps are divided into the following categories: Sequential: These colormaps are approximately monochromatic colormaps varying smoothly between two color tones---usually from low saturation (e.g. white) to high saturation (e.g. a bright blue). Sequential colormaps are ideal for representing most scientific data since they show a clear progression from low-to-high values. Diverging: These colormaps have a median value (usually light in color) and vary smoothly to two different color tones at high and low values.

Text introduction — Matplotlib 1.5.0 documentation. Matplotlib has excellent text support, including mathematical expressions, truetype support for raster and vector outputs, newline separated text with arbitrary rotations, and unicode support.

Text introduction — Matplotlib 1.5.0 documentation

Because we embed the fonts directly in the output documents, e.g., for postscript or PDF, what you see on the screen is what you get in the hardcopy. freetype2 support produces very nice, antialiased fonts, that look good even at small raster sizes. matplotlib includes its own matplotlib.font_manager, thanks to Paul Barrett, which implements a cross platform, W3C compliant font finding algorithm. You have total control over every text property (font size, font weight, text location and color, etc) with sensible defaults set in the rc file. And significantly for those interested in mathematical or scientific figures, matplotlib implements a large number of TeX math symbols and commands, to support mathematical expressions anywhere in your figure.

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 Deux histogrammes sur une même figure avec matplotlib. 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. legend handle The original object which is used to generate an appropriate entry in the legend. Controlling the legend entries Calling legend() with no arguments automatically fetches the legend handles and their associated labels. Python - matplotlib: combine different figures and put them in a single subplot sharing a common legend. Python - surface plots in matplotlib.

The figure above is functional, but it does not (yet) satisfy the criteria for a figure used in a publication.

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 -

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.

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:

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

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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