python – 如何在动画粒子的同时与mayavi窗口进行交互？ (在粒子群优化期间)

概述我正在尝试使用 Python和Mayavi2制作粒子群优化的动画. 动画工作正常,我的问题是,当动画为动画制作动画时,无法与绘图进行交互.具体来说我想转动图形和缩放.也许有人有动画制作经验？ 我这样做的方法是首先计算粒子的位置,然后存储它们.计算完成后,我用point3d()绘制粒子在第一个时间点的位置,然后迭代使用set()方法更新数据的时间. 有没有办法可以转动图表？我听说过线程的东西,解除了 我正在尝试使用 Python和mayavi2制作粒子群优化的动画.

动画工作正常,我的问题是,当动画为动画制作动画时,无法与绘图进行交互.具体来说我想转动图形和缩放.也许有人有动画制作经验？

我这样做的方法是首先计算粒子的位置,然后存储它们.计算完成后,我用point3d()绘制粒子在第一个时间点的位置,然后迭代使用set()方法更新数据的时间.

有没有办法可以转动图表？我听说过线程的东西,解除了渲染,但我无法弄清楚如何在我的代码中做到这一点.除了很多其他的东西,我读过：

http://code.enthought.com/projects/mayavi//docs/development/html/mayavi/mlab_animating.html

http://code.enthought.com/projects/mayavi//docs/development/html/mayavi/tips.html#acceleration-mayavi-scripts

但它无法看到如何使用它.

有什么建议？

这是我的代码：

@H_@R_419_6941@_27@#!/usr/bin/env python''' @author rt'''import pylab as pltfrom numpy import *from mayavi import mlabfrom threading import Thread # making plotting faster?import ackley as acclass Swarm(Thread,object): ''' constructor for the swarm initializes all instance variables ''' def __init__(self,objective_function): Thread.__init__(self) # optimization options self.omega = 0.9 # inertial constant self.c1 = 0.06 # cognitive/private constant self.c2 = 0.06 # social constant self.objective = objective_function # function object self.max_iteration = 100 # maximal number of iterations # Swarm stuff self.number = 0 self.best = [] # gbest; the global best position self.particles = [] # empty List for particles # temporary self.min = self.objective.min self.max = self.objective.max self.best_evolution = [] # self.dimensions = 2 # dimensions NB! ''' add particles to the swarm find the best position of particle in swarm to set global best ''' def add_particles(self,n): for i in range(n): particle = Particle(self) if i == 0: # initialize self.best self.best = particle.position if particle.eval() < self._eval(): # check if there is a better and if,set it self.best = copy(particle.position) self.particles.append(particle) # append the particle to the swarm def _eval(self): return self.objective.evaluate(self.best) def plot(self): for i in range(self.max_iteration): pos_x = [] pos_y = [] pos_z = [] #print pos_x for particle in self.particles: [x,y,z] = particle.trail[i] pos_x.append(x) pos_y.append(y) pos_z.append(z) #print pos_x if i ==0: g = mlab.points3d(pos_x,pos_y,pos_z,scale_factor=0.5) ms =g.mlab_source ms.anti_aliasing_frames = 0 ms.set(x=pos_x,y = pos_y,z = pos_z,scale_factor=0.5) #updating y value #print pos_y #ms.set(x=pos_x) # update x values #ms.set(y=pos_y) #updating y value #ms.set(z=pos_z) #updating y value #for p in self.particles: #p.plot() def plot_objective(self): delta = 0.1 v = mgrID[self.min:self.max:delta,self.min:self.max:delta] z = self.objective.evaluate(v) #mlab.mesh(v[0],v[1],z) mlab.surf(v[0],z) # surf creates a more efficIEnt data structure than mesh mlab.xlabel('x-axis',object=None) mlab.ylabel('y-axis',object=None) mlab.zlabel('z-axis',object=None) def _info(self): self.plot() print '----------------------------' print 'The best result is:' print 'Coordinates:',self.best print 'Value: ',self._eval() #print 'with ',nreval,'evaluations' print 'nr of particles: ',len(self.particles) print '----------------------------' def run(self): self.plot_objective() self.best = self.particles[0].get_position() iteration = 0 while iteration < self.max_iteration: #if iteration!= 0: obj.scene.disable_render = True #disable_render = True for particle in self.particles: rnd_c1 = array([random.uniform(0,1),random.uniform(0,1)]) rnd_c2 = array([random.uniform(0,1)]) particle.veLocity = self.omega * array(particle.veLocity) + \ self.c1 * rnd_c1 * (array(particle.best) - array(particle.position)) + \ self.c2 * rnd_c2 * (array(self.best) - array(particle.position)) # Todo: change so independent rnd for components particle.position = array(particle.position) + particle.veLocity if particle.eval() < particle.best_eval(): particle.best = copy(particle.position) if particle.eval() < self._eval(): self.best = copy(particle.position) particle.update() # add the point to the trail iteration +=1 self.best_evolution.append(self._eval()) #obj.scene.disable_render = False print 'finished: ',iteration self._info()''' Class modeling particle'''class Particle(): def __init__(self,swarm): self.swarm = swarm x_rand = random.uniform(self.swarm.min,self.swarm.max) y_rand = random.uniform(self.swarm.min,self.swarm.max) self.position = array([x_rand,y_rand]) v_x_rand = random.uniform(self.swarm.min,self.swarm.max) v_y_rand = random.uniform(self.swarm.min,self.swarm.max) self.veLocity = array([v_x_rand,v_y_rand]) self.size = 0.5 self.best = self.position # visualization self.trail = [] def plot(self): [x,y] = self.position z = self.eval() mlab.points3d(x,z,scale_factor=self.size) def eval(self): return self.swarm.objective.evaluate(self.position) def best_eval(self): return self.swarm.objective.evaluate(self.best) def get_position(self): return self.position def update(self): [x,y] = self.position z = self.eval() #print [x,z] self.trail.append([x,z]) def plot_trail(self,index): [x,z] = self.trail[index] mlab.points3d(x,scale_factor=self.size)# Make the animationmlab.figure(1,bgcolor=(0,0),size=(1300,700)) # create a new figure with black background and size 1300x700objective = ac.Ackley() # make an objective functionswarm = pso.Swarm(objective) # create a swarmnr_of_particles = 25 # nr of particles in swarmswarm.add_particles(nr_of_particles) swarm.run()#swarm.start()mlab.show()print '------------------------------------------------------'print 'Particle Swarm Optimization'#objective.info()print 'Objective function to minimize has dimension = ',objective.get_dimension()print '# of iterations = ',1000print '# of particles in swarm = ',nr_of_particlesprint '------------------------------------------------------'@R_301_6120@ 在我的情况下,即使我有点能够做Brandon Rhodes建议的模拟程序( https://stackoverflow.com/questions/16617814/interacting-with-mlab-scene-while-it-is-being-drawn),我也无法转换我已经存在的大型程序.

然后我找到了这个链接：http://wiki.wxpython.org/LongRunningTasks

所以,我只是在我的循环中撒了很多wx.YIEld().这样我就不需要改变我的程序结构了,而且我能够与窗口进行交互.我想在链接中解释了更好的方法.

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