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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