Pyplot tutorial
Demo地址爲：點擊打開鏈接 一個簡單的例子：1.#
-*- coding: utf-8 -*-2.import matplotlib.pyplot
as plt3.plt.plot([1, 4, 9, 16])4.plt.ylabel('some
numbers')5.plt.show()運行結果爲：
我只指定了一組list參數，從圖中可以看書，這組參數自動分配爲了縱座標，爲什麼會這樣呢？
你可能想知道爲什麼X軸的範圍是0-3。如果你提供一個單一的列表或數組的plot()命令，matplotlib假定這是一個序列的y值（這個例子是[1,4,9,16]所以因此在零處y值是1，x=1時y=4，x=2時y=9），並自動生成X值。因爲Python範圍從0開始，默認x向量從0開始並以1爲步長自動得到X座標。因此X的數據爲[ 0, 1, 2, 3 ]。
plot()是一種通用的命令，並將採取任意數量的參數。默認X和Y的參數爲list（實際上內部都是轉化爲數組numpy），並且長度相同，否則報錯。
For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is ‘b-‘, which is a solid blue line. For example, to plot the above with red circles, you would issue
對於每一個X，Y參數對，有一個可選的第三個參數是表示顏色的和線型的格式字符串。格式字符串的字母和符號來源於MATLAB，你可以制定顏色和線型。默認的格式字符串爲“b-”，這是一個藍線實線。如上圖所示。
plot() 文檔有完整的格式化字符串參數說明。axis() 命令指定座標範圍[xmin, xmax, ymin, ymax]。
例子：
01.#
-*- coding: utf-8 -*-02.import numpy
as np03.import matplotlib.pyplot
as plt04. 05.#
evenly sampled time at 200ms intervals06.t
= np.arange(0., 5., 0.2)07.#
red dashes, blue squares and green triangles08.plt.plot(t,
t, 'r--',
t, t**2, 'bs',
t, t**3, 'g^')09.plt.show()
Controlling line properties
Lines have many attributes that you can set: linewidth線寬, dash style, antialiased抗鋸齒, etc; see matplotlib.lines.Line2D. There are several ways to set line properties 1、利用關鍵字：1.plt.plot(x,
y, linewidth=2.0)1.line1,
line2 = plot(x1,y1,x2,y2)2.line.set_antialiased(False)
# turn off antialising1.lines
= plt.plot(x1, y1, x2, y2)2.#
use key<a href="http://www.it165.net/edu/ebg/" target="_blank" class="keylink">word</a>
args3.plt.setp(lines,
color='r',
linewidth=2.0)4.#
or MATLAB style string value pairs5.plt.setp(lines, 'color', 'r', 'linewidth', 2.0)
4、To get a list of settable line properties, call the setp() function with a line or lines as argument 例如：
1.lines
= plt.plot([1,2,3])2. 3.plt.setp(lines)4.alpha: float5.animated:
[True | False]6.antialiased
or aa: [True | False]7....snipWorking 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 (amatplotlib.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.MATLAB和pyplot，有當前圖和當前軸的概念。所有的繪圖命令適用於當前軸。gca()方法返回當前軸（一個matplotlib.axes.axes實例），和gcf()方法返回當前圖形（matplotlib.figure.figure實例）。通常，你不用擔心這個，因爲它是幕後自動管理的。下面是一個腳本來創建兩個圖。
01.#
-*- coding: utf-8 -*-02.import numpy
as np03.import matplotlib.pyplot
as plt04. 05.def
f(t):06.return np.exp(-t)
* np.cos(2*np.pi*t)07. 08.t1
= np.arange(0.0, 5.0, 0.1)09.t2
= np.arange(0.0, 5.0, 0.02)10. 11.plt.figure(1)12.plt.subplot(211)13.plt.plot(t1,
f(t1), 'bo',
t2, f(t2), 'k')14. 15.plt.subplot(212)16.plt.plot(t2,
np.cos(2*np.pi*t2), 'r--')17.plt.show()
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 an axes. Thesubplot() command specifies numrows, numcols, fignum where fignum ranges from 1 to numrows*numcols. The commas in the subplot command are optional if numrows*numcols<10. Sosubplot(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, ie, not on a rectangular grid, use theaxes() 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 pylab_examples example code: axes_demo.py for an example of placing axes manually and pylab_examples example code: line_styles.py for an example with lots-o-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:
這裏的figure()指令是可選的因爲figure(1)默認會被創建，就像subplot(111)將默認創建當你不手動指定axes的情況下。該subplot()命令指定numrows，numcols，fignum範圍從1到numrows * numcols【即211爲2行1列第1幅圖，和MATLAB相同】。如果numrows * numcols＜10，subplot()命令中的逗號是可選的。您可以創建任意數量的subplots和axes。如果你想手動設置一個axes，可以使用axes()命令，它允許你指定的位置爲axes([left, bottom, width, height])，所有的值都是分數（0～1）座標。01.#
-*- coding: utf-8 -*-02.import matplotlib.pyplot
as plt03.plt.figure(1)
# the first figure04.plt.subplot(211)
# the first subplot in the first figure05.plt.plot([1,2,3])06.plt.subplot(212)
# the second subplot in the first figure07.plt.plot([4,5,6])08. 09. 10.plt.figure(2)
# a second figure11.plt.plot([4,5,6])
# creates a subplot(111)
by default12. 13.plt.figure(1)
# figure 1 current;
subplot(212)
still current14.plt.subplot(211)
# make subplot(211)
in figure1 current15.plt.title('Easy
as 1,2,3') # subplot 211 title16.plt.show()You can clear the current figure with clf() and the current axes with cla(). If you find this statefulness, annoying, 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 a long sequence 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 introduction for a more detailed example)添加標籤！怎麼添加中文標籤？！
01.#
-*- coding: utf-8 -*-02.import numpy
as np03.import matplotlib.pyplot
as plt04. 05.mu,
sigma = 100, 1506.x
= mu + sigma * np.random.randn(10000)07. 08.#
the histogram of the data09.n,
bins, patches = plt.hist(x, 50,
normed=1,
facecolor='g',
alpha=0.75)10. 11. 12.plt.xlabel('Smarts')13.plt.ylabel(u'概率',
fontproperties='SimHei')14.plt.title(u'IQ直方圖',
fontproperties='SimHei')15.plt.text(60,
.025,
r'$mu=100,
sigma=15$')16.plt.axis([40, 160, 0, 0.03])17.plt.grid(True)18.plt.show()
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():
1.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
在文本中使用的數學表達式。matplotlib accepts TeX equation expressions in any text expression. For example to write the expression 
in
the title, you can write a TeX expression surrounded by dollar signs:
1.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 and 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 case of 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.
01.#
-*- coding: utf-8 -*-02.import numpy
as np03.import matplotlib.pyplot
as plt04. 05.ax
= plt.subplot(111)06. 07.t
= np.arange(0.0, 5.0, 0.01)08.s
= np.cos(2*np.pi*t)09.line,
= plt.plot(t, s, lw=2)10. 11.plt.annotate('local
max', xy=(2, 1),
xytext=(3, 1.5),12.arrowprops=dict(facecolor='black',
shrink=0.05),13.)14. 15.plt.ylim(-2,2)16.plt.show()
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 – seeAnnotating text and Annotating Axes for details. More examples can be found in pylab_examples example code: annotation_demo.py.
其他
這部分內容具體請看：點擊打開鏈接橫向圖形：
01.from
matplotlib import pyplot
as plt02.from
numpy import sin,
exp, absolute, pi, arange03.from
numpy.random import normal04. 05. 06.def
f(t):07.s1
= sin(2 *
pi * t)08.e1
= exp(-t)09.return absolute((s1
* e1)) + .0510. 11. 12.t
= arange(0.0, 5.0, 0.1)13.s
= f(t)14.nse
= normal(0.0, 0.3,
t.shape) * s15. 16.fig
= plt.figure(figsize=(12, 6))17.vax
= fig.add_subplot(121)18.hax
= fig.add_subplot(122)19. 20.vax.plot(t,
s + nse, 'b^')21.vax.vlines(t,
[0],
s)22.vax.set_xlabel('time
(s)')23.vax.set_title('Vertical
lines demo')24. 25.hax.plot(s
+ nse, t, 'b^')26.hax.hlines(t,
[0],
s, lw=2)27.hax.set_xlabel('time
(s)')28.hax.set_title('Horizontal
lines demo')29. 30.plt.show()
點狀分佈圖：
01.import numpy
as np02.import matplotlib.pyplot
as plt03. 04. 05.N
= 5006.x
= np.random.rand(N)07.y
= np.random.rand(N)08.area
= np.pi * (15 *
np.random.rand(N))**2 # 0 to 15 point
radiuses09. 10.plt.scatter(x,
y, s=area, alpha=0.5)11.plt.show()
總結
1、顏色控制：
b：blue ，c：cyan，g：green，k：black，m：magenta，r：red ，w：white， y：yellow。控制顏色方法： 簡稱或者全稱：如上所列； 16進制：FF00FF； RGB或RGBA元組：(1,0,1,1）；
灰度強度如：0.7；（大量顏色處理適用，不重複的隨機數即可）
2、線型控制：
- 實線； -- 短線； -. 短點相間線； ： 虛點線
