Python使用matplotlib绘制动画的方法

本文实例讲述了Python使用matplotlib绘制动画的方法。分享给大家供大家参考。具体分析如下：

matplotlib从1.1.0版本以后就开始支持绘制动画

下面是几个的示例：

第一个例子使用generator，每隔两秒，就运行函数data_gen：

# -*- 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()

再看下面的例子:

# -*- 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()

希望本文所述对大家的python程序设计有所帮助。