plt.plot(x, y, linewidth=2.0)

2、利用
setter方法

line1, line2 = plot(x1,y1,x2,y2)
line.set_antialiased(False) # turn off antialising

3、使用
 
setp()
 命令

lines = plt.plot(x1, y1, x2, y2)
# use keyword args
plt.setp(lines, color='r', linewidth=2.0)
# or MATLAB style string value pairs
plt.setp(lines, 'color', 'r', 'linewidth', 2.0)

Here are the available 
Line2D
 properties.

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

plt.setp(lines)
  alpha: float
  animated: [True | False]
  antialiased or aa: [True | False]
  ...snip

以上为调用setp()第二种方法。

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.

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt

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()

结果为：

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:

这里的figure()指令是可选的由于
figure(1)默认会被创建，就像subplot(111)将
默认创建当
你不手动指定axes的情况下。该subplot()命令指定numrows，numcols，fignum范围从1到numrows * numcols【即211为2行1列第1幅图。和MATLAB同样】。

假设numrows * numcols＜10，subplot()命令中的逗号是可选的。您能够创建随意数量的subplots和axes。假设你想手动设置一个axes，能够使用axes()命令，它同意你指定的位置为axes([left, bottom, width, height])。全部的值都是分数（0～1）坐标。

# -*- coding: utf-8 -*-
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
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)

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# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)


plt.xlabel('Smarts')
plt.ylabel(u'概率', fontproperties='SimHei')
plt.title(u'IQ直方图', fontproperties='SimHei')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()

结果例如以下所看到的：

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.

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

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt

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()

结果为：

from matplotlib import pyplot as plt
from numpy import sin, exp,  absolute, pi, arange
from numpy.random import normal


def f(t):
    s1 = sin(2 * pi * t)
    e1 = exp(-t)
    return absolute((s1 * e1)) + .05


t = arange(0.0, 5.0, 0.1)
s = f(t)
nse = normal(0.0, 0.3, t.shape) * s

fig = plt.figure(figsize=(12, 6))
vax = fig.add_subplot(121)
hax = fig.add_subplot(122)

vax.plot(t, s + nse, 'b^')
vax.vlines(t, [0], s)
vax.set_xlabel('time (s)')
vax.set_title('Vertical lines demo')

hax.plot(s + nse, t, 'b^')
hax.hlines(t, [0], s, lw=2)
hax.set_xlabel('time (s)')
hax.set_title('Horizontal lines demo')

plt.show()

结果为：

import numpy as np
import matplotlib.pyplot as plt


N = 50
x = np.random.rand(N)
y = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses

plt.scatter(x, y, s=area, alpha=0.5)
plt.show()

结果为：

未完待续。

。

。随时更新。

欢迎提问。共同学习，一起进步。

本文由@The_Third_Wave（Blog地址：http://blog.csdn.net/zhanh1218）原创。不定期更新，有错误请指正。

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