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added the pyplot tutorial to the users guide
svn path=/trunk/matplotlib/; revision=5234
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‎doc/users_guide/artist_api_tut.txt‎

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**********************************
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The matplotlib Artist API tutorial
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**********************************
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***************
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Artist tutorial
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***************
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There are three layers to the matplotlib API. The FigureCanvas is the
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area onto which the figure is drawn, the Renderer is the object which
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at http://matplotlib.sourceforge.net/api.pdf for a listing of
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properties for a give object.
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Getting at the objects to customize them
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========================================
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Object containers
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=================
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Now that we know how to inspect set the properties of a given
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object we want to configure, we need to now how to get at that
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section we'll review where the various container objects store the
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Artists that you want to get at.
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The Figure container
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--------------------
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Figure container
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----------------
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The top level container Artist is the matplotlib.figure.Figure, and it
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contains everything in the figure. The background of the figure is a
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In [191]: fig = plt.figure()
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In [192]: l1 = matplotlib.lines.Line2D([0, 1], [0, 1], transform=fig.transFigure, figure=fig)
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In [192]: l1 = matplotlib.lines.Line2D([0, 1], [0, 1],
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transform=fig.transFigure, figure=fig)
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In [193]: l2 = matplotlib.lines.Line2D([0, 1], [1, 0], transform=fig.transFigure, figure=fig)
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In [193]: l2 = matplotlib.lines.Line2D([0, 1], [1, 0],
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transform=fig.transFigure, figure=fig)
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In [194]: fig.lines.extend([l1, l2])
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================ ===============================================================
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The Axes container
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------------------
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Axes container
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--------------
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The matplotlib.axes.Axes is the center of the matplotlib universe --
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it contains the vast majority of all the Artists used in a figure with
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grid Turn the grid on or off for the major or minor ticks
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====================== =========================================================
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Try creating the figure below
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Here is an example, not recommended for its beauty, which customizes
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the axes and tick properties
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.. literalinclude:: figures/fig_axes_customize_simple.py
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.. image:: figures/fig_axes_customize_simple.png
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:scale: 75
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Exercise solution::
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import numpy as np
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import matplotlib.pyplot as plt
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# plt.figure creates a matplotlib.figure.Figure instance
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fig = plt.figure()
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rect = fig.figurePatch # a rectangle instance
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rect.set_facecolor('lightgoldenrodyellow')
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ax1 = fig.add_axes([0.1, 0.3, 0.4, 0.4])
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rect = ax1.axesPatch
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rect.set_facecolor('lightslategray')
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for label in ax1.xaxis.get_ticklabels():
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# label is a Text instance
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label.set_color('red')
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label.set_rotation(45)
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label.set_fontsize(16)
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for line in ax1.yaxis.get_ticklines():
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# line is a Line2D instance
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line.set_color('green')
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line.set_markersize(25)
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line.set_markeredgewidth(3)
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fig.savefig('figures/fig_axes_customize_simple.png', dpi=150)
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fig.savefig('figures/fig_axes_customize_simple.eps')
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plt.show()
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The Tick containers
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-------------------
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Tick containers
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---------------
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The matplotlib.axis.Tick is the final container object in our descent
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from the Figure to the Axes to the Axis to the Tick. The Tick
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============== ==========================================================
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Here is an example which sets the formatter for the upper ticks with
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dollar signs and colors them green on the right side of the yaxis::
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(100*np.random.rand(20))
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formatter = ticker.FormatStrFormatter('$%1.2f')
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ax.yaxis.set_major_formatter(formatter)
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for tick in ax.yaxis.get_major_ticks():
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tick.label1On = False
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tick.label2On = True
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tick.label2.set_color('green')
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plt.show()
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dollar signs and colors them green on the right side of the yaxis
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.. literalinclude:: figures/dollar_ticks.py
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.. image:: figures/dollar_ticks.png
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:scale: 75

‎doc/users_guide/pyplot_tutorial.txt‎

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****************************
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A matplotlib.pyplot tutorial
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****************************
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***************
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pyplot tutorial
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***************
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:mod:`matplotlib.pyplot` is a collection of functions that make
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matplotlib work like matlab. Each ``pyplot`` function makes some

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