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()
到此这篇关于Matplotlib animation模块实现动态图 的文章就介绍到这了,更多相关Matplotlib 动态图 内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持！
