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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Matplotlib,动态图
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