Matplotlib animation模块实现动态图

matplotlib 画图功能非常强大，目前也只能根据官网 提供的例子简单地画几张图。最近学习了能画动态图的animation模块，作个简单地记录。

在matplotlib作图中，比较常用的是matplotlib.pyplot模块，这个模块有非常多的属性和方法，简要列举下这次用到的方法：
matplotlib.pyplot.subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)
返回fig和ax对象！

例子1. 动态画出sin函数曲线

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

fig, ax = plt.subplots()
xdata, ydata = [], []
ln, = ax.plot([], [], 'r-', animated=False)

def init():
  ax.set_xlim(0, 2*np.pi)
  ax.set_ylim(-1, 1)
  return ln,

def update(frame):
  xdata.append(frame)
  ydata.append(np.sin(frame))
  ln.set_data(xdata, ydata)
  return ln,

ani = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128),
          init_func=init, blit=True)
plt.show()

画这类图的关键是要给出不断更新的函数，这里就是update 函数了。注意, line, = ax.plot([], [], 'r-', animated=False) 中的, 表示创建tuple类型。迭代更新的数据frame 取值从frames 取得。

例子2. 动态显示一个动点，它的轨迹是sin函数。

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

"""
animation example 2
author: Kiterun
"""

fig, ax = plt.subplots()
x = np.linspace(0, 2*np.pi, 200)
y = np.sin(x)
l = ax.plot(x, y)
dot, = ax.plot([], [], 'ro')

def init():
  ax.set_xlim(0, 2*np.pi)
  ax.set_ylim(-1, 1)
  return l

def gen_dot():
  for i in np.linspace(0, 2*np.pi, 200):
    newdot = [i, np.sin(i)]
    yield newdot

def update_dot(newd):
  dot.set_data(newd[0], newd[1])
  return dot,

ani = animation.FuncAnimation(fig, update_dot, frames = gen_dot, interval = 100, init_func=init)
ani.save('sin_dot.gif', writer='imagemagick', fps=30)

plt.show()

这里我们把生成的动态图保存为gif图片，前提要预先安装imagemagic。

例子3. 单摆（没阻尼&有阻尼）

无阻尼的单摆力学公式：

附加阻尼项：

这里需要用到scipy.integrate的odeint模块，具体用法找时间再专门写一篇blog吧，动态图代码如下：

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

from math import sin, cos
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import matplotlib.animation as animation

g = 9.8
leng = 1.0
b_const = 0.2

# no decay case:
def pendulum_equations1(w, t, l):
  th, v = w
  dth = v
  dv = - g/l * sin(th)
  return dth, dv

# the decay exist case:
def pendulum_equations2(w, t, l, b):
  th, v = w
  dth = v
  dv = -b/l * v - g/l * sin(th)
  return dth, dv

t = np.arange(0, 20, 0.1)
track = odeint(pendulum_equations1, (1.0, 0), t, args=(leng,))
#track = odeint(pendulum_equations2, (1.0, 0), t, args=(leng, b_const))
xdata = [leng*sin(track[i, 0]) for i in range(len(track))]
ydata = [-leng*cos(track[i, 0]) for i in range(len(track))]

fig, ax = plt.subplots()
ax.grid()
line, = ax.plot([], [], 'o-', lw=2)
time_template = 'time = %.1fs'
time_text = ax.text(0.05, 0.9, '', transform=ax.transAxes)

def init():
  ax.set_xlim(-2, 2)
  ax.set_ylim(-2, 2)
  time_text.set_text('')
  return line, time_text

def update(i):
  newx = [0, xdata[i]]
  newy = [0, ydata[i]]
  line.set_data(newx, newy)
  time_text.set_text(time_template %(0.1*i))
  return line, time_text

ani = animation.FuncAnimation(fig, update, range(1, len(xdata)), init_func=init, interval=50)
#ani.save('single_pendulum_decay.gif', writer='imagemagick', fps=100)
ani.save('single_pendulum_nodecay.gif', writer='imagemagick', fps=100)
plt.show()

 

例子4. 滚动的球

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

fig = plt.figure(figsize=(6, 6))
ax = plt.gca()
ax.grid()
ln1, = ax.plot([], [], '-', lw=2)
ln2, = ax.plot([], [], '-', color='r', lw=2)
theta = np.linspace(0, 2*np.pi, 100)
r_out = 1
r_in = 0.5

def init():
  ax.set_xlim(-2, 2)
  ax.set_ylim(-2, 2)
  x_out = [r_out*np.cos(theta[i]) for i in range(len(theta))]
  y_out = [r_out*np.sin(theta[i]) for i in range(len(theta))]
  ln1.set_data(x_out, y_out)
  return ln1,

def update(i):
  x_in = [(r_out-r_in)*np.cos(theta[i])+r_in*np.cos(theta[j]) for j in range(len(theta))]
  y_in = [(r_out-r_in)*np.sin(theta[i])+r_in*np.sin(theta[j]) for j in range(len(theta))]
  ln2.set_data(x_in, y_in)
  return ln2,

ani = animation.FuncAnimation(fig, update, range(len(theta)), init_func=init, interval=30)
ani.save('roll.gif', writer='imagemagick', fps=100)

plt.show()

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