matplotlib简易入门教程及动画

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非常重要的，否则对数据连个基本的感性认识都没有，如何进行下一步的design

点击打开链接

另一个很棒的资料  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))    #因为update的参数是调用函数data_gen,所以第一个默认参数不能是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()  

第二个例子使用list（metric），每次从metric中取一行数据作为参数送入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)  #如果是参数是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()  

第三个例子：

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()

第四个例子:

# -*- 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    # 绘图   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 = [], []    # 因为run的参数是调用函数data_gen,所以第一个参数可以不是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的参数为函数data_gen,   # 表示图形只更新需要绘制的元素   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    #第一个参数必须为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增加大25后,返回再次从1增加到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参数,framenum会从1一直增加下去知道无穷   #由于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()