Python使用matplotlib繪製動畫的方法

Python使用matplotlib繪製動畫的方法

 　　本文執行個體講述了Python使用matplotlib繪製動畫的方法。分享給大家供大家參考。具體分析如下：

　　matplotlib從1.1.0版本以後就開始支援繪製動畫

　　下面是幾個的樣本：

　　第一個例子使用generator，每隔兩秒，就運行函數data_gen：

　　?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

# -*- 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)) #因為update的參數是調用函數data_gen, #所以第一個預設參數不能是framenum def update(data): line.set_ydata(data) return line, # 每次產生10個隨機資料 def data_gen(): while True: yield np.random.rand(10) ani = animation.FuncAnimation(fig, update, data_gen, interval=2*1000) plt.show()

　　第二個例子使用list(metric)，每次從metric中取一行資料作為參數送入update中：

　　?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

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) #如果是參數是list,則預設每次取list中的一個元素, #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()

　　第三個例子：

　　?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

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()

　　第四個例子:

　　?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation # 每次產生一個新的座標點 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 # 繪圖 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 = [], [] # 因為run的參數是調用函數data_gen, # 所以第一個參數可以不是framenum:設定line的資料,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秒調用函數run,run的參數為函數data_gen, # 表示圖形只更新需要繪製的元素 ani = animation.FuncAnimation(fig, run, data_gen, blit=True, interval=10, repeat=False) plt.show()

　　再看下面的例子:

　　?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation #第一個參數必須為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從1增加大25後,返回再次從1增加到25,再返回... line_ani = animation.FuncAnimation(fig1, update_line, 25,fargs=(data, l),interval=50, blit=True) #等同於 #line_ani = animation.FuncAnimation(fig1, update_line, frames=25,fargs=(data, l), # interval=50, blit=True) #忽略frames參數,framenum會從1一直增加下去知道無窮 #由於frame達到25以後,資料不再改變,所以你會發現到達25以後圖形不再變化了 #line_ani = animation.FuncAnimation(fig1, update_line, fargs=(data, l), # interval=50, blit=True) plt.show()

　　希望本文所述對大家的python程式設計有所協助。