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GIỚI THIỆU SƠ LƯỢT VỀ THƯ VIỆN PYLOT CỦA MATPLOTLIP - PHẦN 2

Pyplot tutorial 2

 

Working with multiple figures and axes

MATLAB, and pyplot, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function gca() returns the current axes (a matplotlib.axes.Axes instance), and gcf() returns the current figure (matplotlib.figure.Figure instance). Normally, you don't have to worry about this, because it is all taken care of behind the scenes. Below is a script to create two subplots.

def f(t):

    return np.exp(-t) * np.cos(2*np.pi*t)

 

t1 = np.arange(0.0, 5.0, 0.1)

t2 = np.arange(0.0, 5.0, 0.02)

 

plt.figure(1)

plt.subplot(211)

plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

 

plt.subplot(212)

plt.plot(t2, np.cos(2*np.pi*t2), 'r--')

plt.show()

../../_images/sphx_glr_pyplot_007.png

The figure() command here is optional because figure(1) will be created by default, just as a subplot(111) will be created by default if you don't manually specify any axes. The subplot() command specifies numrows, numcols, plot_number where plot_number ranges from 1 tonumrows*numcols. The commas in the subplot command are optional if numrows*numcols<10. So subplot(211) is identical to subplot(2, 1, 1).

You can create an arbitrary number of subplots and axes. If you want to place an axes manually, i.e., not on a rectangular grid, use the axes()command, which allows you to specify the location as axes([left, bottom, width, height]) where all values are in fractional (0 to 1) coordinates. See Axes Demo for an example of placing axes manually and Basic Subplot Demo for an example with lots of subplots.

You can create multiple figures by using multiple figure() calls with an increasing figure number. Of course, each figure can contain as many axes and subplots as your heart desires:

import matplotlib.pyplot as plt

plt.figure(1)                # the first figure

plt.subplot(211)             # the first subplot in the first figure

plt.plot([1, 2, 3])

plt.subplot(212)             # the second subplot in the first figure

plt.plot([4, 5, 6])

 

 

plt.figure(2)                # a second figure

plt.plot([4, 5, 6])          # creates a subplot(111) by default

 

plt.figure(1)                # figure 1 current; subplot(212) still current

plt.subplot(211)             # make subplot(211) in figure1 current

plt.title('Easy as 1, 2, 3') # subplot 211 title

You can clear the current figure with clf() and the current axes with cla(). If you find it annoying that states (specifically the current image, figure and axes) are being maintained for you behind the scenes, don't despair: this is just a thin stateful wrapper around an object oriented API, which you can use instead (see Artist tutorial)

If you are making lots of figures, you need to be aware of one more thing: the memory required for a figure is not completely released until the figure is explicitly closed with close(). Deleting all references to the figure, and/or using the window manager to kill the window in which the figure appears on the screen, is not enough, because pyplot maintains internal references until close() is called.

Working with text

The text() command can be used to add text in an arbitrary location, and the xlabel(), ylabel() and title() are used to add text in the indicated locations (see Text in Matplotlib Plots for a more detailed example)

mu, sigma = 100, 15

x = mu + sigma * np.random.randn(10000)

 

# the histogram of the data

n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)

 

plt.xlabel('Smarts')

plt.ylabel('Probability')

plt.title('Histogram of IQ')

plt.text(60, .025, r'$\mu=100,\ \sigma=15$')

plt.axis([40, 160, 0, 0.03])

plt.grid(True)

plt.show()

../../_images/sphx_glr_pyplot_008.png

All of the text() commands return an matplotlib.text.Text instance. Just as with with lines above, you can customize the properties by passing keyword arguments into the text functions or using setp():

t = plt.xlabel('my data', fontsize=14, color='red')

These properties are covered in more detail in Text properties and layout.

Using mathematical expressions in text

matplotlib accepts TeX equation expressions in any text expression. For example to write the expression σi=15σi=15 in the title, you can write a TeX expression surrounded by dollar signs:

plt.title(r'$\sigma_i=15$')

The r preceding the title string is important -- it signifies that the string is a raw string and not to treat backslashes as python escapes. matplotlib has a built-in TeX expression parser and layout engine, and ships its own math fonts -- for details see Writing mathematical expressions. Thus you can use mathematical text across platforms without requiring a TeX installation. For those who have LaTeX and dvipng installed, you can also use LaTeX to format your text and incorporate the output directly into your display figures or saved postscript -- see Text rendering With LaTeX.

Annotating text

The uses of the basic text() command above place text at an arbitrary position on the Axes. A common use for text is to annotate some feature of the plot, and the annotate() method provides helper functionality to make annotations easy. In an annotation, there are two points to consider: the location being annotated represented by the argument xy and the location of the text xytext. Both of these arguments are (x,y) tuples.

ax = plt.subplot(111)

 

t = np.arange(0.0, 5.0, 0.01)

s = np.cos(2*np.pi*t)

line, = plt.plot(t, s, lw=2)

 

plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),

             arrowprops=dict(facecolor='black', shrink=0.05),

             )

 

plt.ylim(-2, 2)

plt.show()

../../_images/sphx_glr_pyplot_009.png

In this basic example, both the xy (arrow tip) and xytext locations (text location) are in data coordinates. There are a variety of other coordinate systems one can choose -- see Basic annotation and Advanced Annotation for details. More examples can be found in Annotating Plots

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