python编程matplotlib交互绘制Julia集示例解析
目录
- Julia集
- matplotlib绑定事件
- 缩放
所谓Julia集就是类似下面的美妙的图案
Julia集
特别地,当 c = z的初始值时,符合收敛条件的 z 的便构成大名鼎鼎的Mandelbrot集
在上图中,颜色表示该点的发散速度,可以理解为开始发散时迭代的次数。其生成代码也非常简单:
#mbrot.py import numpy as np import time import pyplotlib.pyplot as plt #生成z坐标,axis为起始位置,nx,ny为x向和y向的格点个数 def genZ(axis,nx,ny): x0,x1,y0,y1 = axis x = np.linspace(x0,x1,nx) y = np.linspace(y0,y1,ny) real, img = np.meshgrid(x,y) z = real + img*1j return z #获取Julia集,n为迭代次数,m为判定发散点,大于1即可 def getJulia(z,c,n,m=2): t = time.time() c = np.zeros_like(z)+c out = abs(z) for i in range(n): absz = abs(z) z[absz>m]=0 #对开始发散的点置零 c[absz>m]=0 out[absz>m]=i #记录发散点的发散速度 z = z*z + c print("time:",time.time()-t) return out if __name__ == "__main__": axis = np.array([-2,1,-1.5,1.5]) z0 = genZ(axis,500,500) mBrot = getJulia(z0,z0,50) plt.imshow(mBrot, cmap=cm.jet, extent=axis) plt.gca().set_axis_off() plt.show()
matplotlib绑定事件
下面希望实现点击Mandelbrot集中的一点,生成相应的Julia集。
在mpl中,事件绑定函数mpl_connect被封装在cavnas类中,调用格式为
canvas.mpl_connect('str', func)
其中func事件函数,字符串为被传入事件函数的事件标识,如下所列,望文生义即可
'button_press_event' 'button_release_event' 'draw_event' 'key_press_event' 'key_release_event' 'motion_notify_event' 'pick_event' 'resize_event' 'scroll_event' 'figure_enter_event' 'figure_leave_event' 'axes_enter_event' 'axes_leave_event' 'close_event'
简单起见,可以先检测一下鼠标点击事件'button_press_event',对此我们需要定义一个事件函数,并将上面的入口函数稍加修改:
def test(evt): print(evt.xdata) #xdata即x方向的坐标 if __name__ == "__main__": axis = np.array([-2,1,-1.5,1.5]) z0 = genZ(axis,500,500) mBrot = getJulia(z0,z0,50) fig, ax = plt.subplots() fig.canvas.mpl_connect('button_press_event', test)#调用事件函数 plt.imshow(mBrot, cmap=cm.jet, extent=axis) plt.gca().set_axis_off() plt.show()
于是点击imshow()出来的图片,即可返回相应的x坐标。
python mbrot.py time: 0.47572827339172363 -0.8652597402597402 -0.7840909090909087 -0.18344155844155807 0.23051948051948123 0.8149350649350655
缩放
那么生成Julia集只需要重新调用一次getJulia
这个函数即可。
Mandelbrot集的分形特征意味着我们所生成的图片可以无限放大,但是mpl自带的放大工具并不会重新生成数据,所以是虚假的放大。因此需要重新绑定放大操作,其思路是,当右键点击(‘button_press_event')时,记录此时的坐标,当右键释(‘button_release_event')放时重新绘制图片,为了防止与左键冲突,所以在点击所对应的事件函数中加入左右键判断。
其结果如图
此外,还可以绑定鼠标滚轮,实现Mandelbrot集在该点的真实缩放,代码如下
import matplotlib.pyplot as plt import numpy as np from matplotlib import cm import matplotlib.backend_bases as mbb import time class MandelBrot(): def __init__(self,x0,x1,y0,y1,n): self.oriAxis = np.array([x0,x1,y0,y1]) #初始坐标 self.axis = self.oriAxis self.nx,self.ny,self.nMax = n,n,n #x,y方向的网格划分个数 self.nIter = 100 #迭代次数 self.n0 = 0 #预迭代次数 self.z = genZ(self.oriAxis,self.nx,self.ny) self.DrawMandelbrot() def DrawMandelbrot(self): mBrot = getJulia(self.z,self.z,self.nIter) self.fig, ax = plt.subplots() plt.imshow(mBrot, cmap=cm.jet, extent=self.axis) plt.gca().set_axis_off() self.fig.canvas.mpl_disconnect(self.fig.canvas.manager.key_press_handler_id) self.fig.canvas.mpl_connect('button_press_event', self.OnMouse) self.fig.canvas.mpl_connect('button_release_event', self.OnRelease) self.fig.canvas.mpl_connect('scroll_event', self.OnScroll) plt.show() def DrawJulia(self,c0): z = genZ([-2,2,-2,2],800,800) julia = getJulia(z,c0,self.nIter) jFig,jAx = plt.subplots() plt.cla() plt.imshow(julia, cmap=cm.jet, extent=self.axis) plt.gca().set_axis_off() plt.show() jFig.canvas.draw_idle() #滚轮缩放 def OnScroll(self,evt): x0,y0 = evt.xdata,evt.ydata if evt.button == "up": self.axis = (self.axis+[x0,x0,y0,y0])/2 elif evt.button == 'down': self.axis = 2*self.axis-[x0,x0,y0,y0] z = genZ(self.axis,self.nx,self.ny) mBrot = getJulia(z,z,self.nIter) plt.cla() plt.imshow(mBrot, cmap=cm.jet, extent=self.axis) plt.gca().set_axis_off() mBrot[mBrot<1]==self.n0+self.nIter self.n0 = int(np.min(mBrot)) self.fig.canvas.draw_idle() pass def OnMouse(self, evt): self.xStart = evt.xdata self.yStart = evt.ydata self.fig.canvas.draw_idle() def OnRelease(self,evt): x0,y0,x1,y1 = self.xStart,self.yStart,evt.xdata,evt.ydata if evt.button == mbb.MouseButton.LEFT: self.DrawJulia(x1+y1*1j) #如果释放的是左键,那么就绘制Julia集并返回 return #右键拖动,可以对Mandelbrot集进行真实的放大 self.axis = np.array([min(x0,x1),max(x0,x1), min(y0,y1),max(y0,y1)]) nxny = self.axis[[1,3]]-self.axis[[0,2]] self.nx,self.ny = (nxny/max(nxny)*self.nMax).astype(int) z = genZ(self.axis,self.nx,self.ny) n = 100 #n为迭代次数 mBrot = getJulia(z,z,n) plt.cla() plt.imshow(mBrot, cmap=cm.jet, extent=self.axis) plt.gca().set_axis_off() mBrot[mBrot<1]==self.n0+n self.n0 = int(np.min(mBrot)) self.fig.canvas.draw_idle() def genZ(axis,nx,ny): x0,x1,y0,y1 = axis x = np.linspace(x0,x1,nx) y = np.linspace(y0,y1,ny) real, img = np.meshgrid(x,y) z = real + img*1j return z def getJulia(z,c,n,n0=0,m=2): t = time.time() c = np.zeros_like(z)+c out = abs(z) for _ in range(n0): z = z*z + c for i in range(n0,n0+n): absz = abs(z) z[absz>m]=0 c[absz>m]=0 out[absz>m]=i z = z*z + c print("time:",time.time()-t) return out if __name__ == "__main__": x,y = 0,0 brot = MandelBrot(-2,1,-1.5,1.5,1000)
以上就是python编程matplotlib交互绘制Julia集示例解析的详细内容,更多关于matplotlib交互绘制Julia集的资料请关注我们其它相关文章!
赞 (0)