matplotlib简易新手教程及动画

做数据分析，首先是要熟悉和理解数据。所以掌握一个趁手的可视化工具是很重要的，否则对数据连个主要的感性认识都没有，怎样进行下一步的design

点击打开链接

还有一个非常棒的资料  Matplotlib Tutorial(译)

使用python绘制动态图的四个栗子：

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

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation  

fig = plt.figure()
axes1 = fig.add_subplot(111)
line, = axes1.plot(np.random.rand(10))  

#由于update的參数是调用函数data_gen,所以第一个默认參数不能是framenum
def update(data):
    line.set_ydata(data)
    return line,
# 每次生成10个随机数据
def data_gen():
    while True:
        yield np.random.rand(10)  

ani = animation.FuncAnimation(fig, update, data_gen, interval=2*1000)
plt.show()

第二个样例使用list（metric），每次从metric中取一行数据作为參数送入update中：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation  

start = [1, 0.18, 0.63, 0.29, 0.03, 0.24, 0.86, 0.07, 0.58, 0]  

metric =[[0.03, 0.86, 0.65, 0.34, 0.34, 0.02, 0.22, 0.74, 0.66, 0.65],
         [0.43, 0.18, 0.63, 0.29, 0.03, 0.24, 0.86, 0.07, 0.58, 0.55],
         [0.66, 0.75, 0.01, 0.94, 0.72, 0.77, 0.20, 0.66, 0.81, 0.52]
        ]  

fig = plt.figure()
window = fig.add_subplot(111)
line, = window.plot(start)
#假设是參数是list,则默认每次取list中的一个元素,即metric[0],metric[1],...
def update(data):
    line.set_ydata(data)
    return line,  

ani = animation.FuncAnimation(fig, update, metric, interval=2*1000)
plt.show()

第三个样例：

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation  

# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 2), ylim=(-2, 2))
line, = ax.plot([], [], lw=2)  

# initialization function: plot the background of each frame
def init():
    line.set_data([], [])
    return line,  

# animation function.  This is called sequentially
# note: i is framenumber
def animate(i):
    x = np.linspace(0, 2, 1000)
    y = np.sin(2 * np.pi * (x - 0.01 * i))
    line.set_data(x, y)
    return line,  

# call the animator.  blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
                               frames=200, interval=20, blit=True)  

#anim.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])   

plt.show()

第四个样例:

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

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation  

# 每次产生一个新的坐标点
def data_gen():
    t = data_gen.t
    cnt = 0
    while cnt < 1000:
        cnt+=1
        t += 0.05
        yield t, np.sin(2*np.pi*t) * np.exp(-t/10.)
data_gen.t = 0  

# 画图
fig, ax = plt.subplots()
line, = ax.plot([], [], lw=2)
ax.set_ylim(-1.1, 1.1)
ax.set_xlim(0, 5)
ax.grid()
xdata, ydata = [], []  

# 由于run的參数是调用函数data_gen,所以第一个參数能够不是framenum:设置line的数据,返回line
def run(data):
    # update the data
    t,y = data
    xdata.append(t)
    ydata.append(y)
    xmin, xmax = ax.get_xlim()  

    if t >= xmax:
        ax.set_xlim(xmin, 2*xmax)
        ax.figure.canvas.draw()
    line.set_data(xdata, ydata)  

    return line,  

# 每隔10秒调用函数run,run的參数为函数data_gen,
# 表示图形仅仅更新须要绘制的元素
ani = animation.FuncAnimation(fig, run, data_gen, blit=True, interval=10,
    repeat=False)
plt.show()

最后一个：

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

#第一个參数必须为framenum
def update_line(num, data, line):
    line.set_data(data[...,:num])
    return line,  

fig1 = plt.figure()  

data = np.random.rand(2, 15)
l, = plt.plot([], [], 'r-')
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel('x')
plt.title('test')  

#framenum从1添加大25后,返回再次从1添加到25,再返回...
line_ani = animation.FuncAnimation(fig1, update_line, 25,fargs=(data, l),interval=50, blit=True)  

#等同于
#line_ani = animation.FuncAnimation(fig1, update_line, frames=25,fargs=(data, l),
#    interval=50, blit=True)   

#忽略frames參数,framenum会从1一直添加下去知道无穷
#因为frame达到25以后,数据不再改变,所以你会发现到达25以后图形不再变化了
#line_ani = animation.FuncAnimation(fig1, update_line, fargs=(data, l),
#    interval=50, blit=True)   

plt.show()