vue.js + three.js轻松构建3d场景的动画图形 container.appendChild(this.renderer.domElement) }, animate() { // 使用动画效果,浏览器全新的动画效果...this.mesh.rotation.y += 0.02 // 将场景和图形动态的渲染到渲染器上去 this.renderer.render(this.scene,...this.scene.add(pointLight) this.rend...
ax=plt.subplots()x=np.linspace(0,2*np.pi,100)line,=ax.plot(x,np.sin(x))# 初始化函数definit():line.set_ydata([np.nan]*len(x))# 初始状态下隐藏线条returnline,# 动画函数defanimate(i):line.set_ydata(np.sin(x+i/10.0))# 更新 y 数据returnline,# 创建动画对象ani=FuncAnimation...
ax1.plot(xs, ys) plt.xlabel('Date') plt.ylabel('Price') plt.title('Live graph with matplotlib') ani = animation.FuncAnimation(fig, animate, interval=1000) plt.show() 现在,打开终端并运行 python 脚本。你将得到如下图所示的图表,该图表会自动更新: 这里的间隔是 1000 毫秒或一秒。 3D 图动画...
3)) line, = ax.plot(x, y) plt.axis([0, 4*np.pi, -1, 1]) def func_animate(i): ...
fig,ax=plt.subplots()x=np.linspace(0,2*np.pi,100)line,=ax.plot(x,np.sin(x))line.set_animated(True)defanimate(frame):line.set_ydata(np.sin(x+frame/10))returnline,ani=animation.FuncAnimation(fig,animate,frames=200,interval=50,blit=True)plt.title('Optimized Animation - how2matp...
1#外部获得数据2myCurve = np.fromfile('data.bin', dtype=np.float).reshape(-1,3)3xLength,j =myCurve.shape4fig =plt.figure()5ax1=plt.axes(projection='3d')6#初始化绘制7line, = ax1.plot([],[],[],animated=True)8#定义动画函数9defanimate(i):10line.set_xdata(myCurve[:i+1,0])...
(2)camera = Camera(fig)t = np.linspace(0, 2 * np.pi, 128, endpoint=False)for i in t:axes[0].plot(t, np.sin(t + i), color='blue')axes[1].plot(t, np.sin(t - i), color='blue')camera.snap()animation = camera.animate()animation.save('celluloid_subplots.gif', writer =...
func=animate, frames=100, init_func=init, interval=20, blit=False) plt.show() 1. 2. 3. 4. 5. 6. 7. 实为动图 (五)交互图ion 神经网络训练时需要动态看到loss的变化 参考: plt.ion():开启交互模式 plt.ioff():关闭交互模式 plt.clf():清除当前figure ...
line, = ax.plot(x,np.sin(x)) def animate(i): line.set_ydata(np.sin(x + i/10.0))#updata the data return line, def init(): line.set_ydata(np.sin(x)) return line, # call the animator. blit=True means only re-draw the parts that have changed. ...
%matplotlib widgetfig, ax = plt.subplots()ax.plot(x, y1, color="blue", label="y(x)")ax.plot(x, y2, color="red", label="y'(x)")ax.plot(x, y3, color="green", label="y''(x)")ax.set_xlabel("x")ax.set_ylabel("y")ax.legend(loc='lower right') ...