我正在尝试使用 Python 和 Mayavi2 制作粒子群优化的动画。
动画效果很好，我的问题是在动画动画时无法与情节进行交互。具体来说，我想转动图表并缩放。也许有人有做动画的经验？
我这样做的方式是首先计算粒子的位置，然后存储它们。计算完成后，我使用 point3d() 在第一个时间点绘制粒子的位置，然后使用 set() 方法迭代时间更新数据。
有没有办法可以转动图表？我听说过一些带有线程的东西，禁用了渲染，但我不知道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
但它看不到如何使用它。
有什么建议么？
这是我的代码：
#!/usr/bin/env python
'''
@author rt
'''
import pylab as plt
from numpy import *
from mayavi import mlab
from threading import Thread # making plotting faster?
import ackley as ac
class 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],v[1],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),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,y,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,y,z]
self.trail.append([x,y,z])
def plot_trail(self,index):
[x,y,z] = self.trail[index]
mlab.points3d(x,y,z,scale_factor=self.size)
# Make the animation
mlab.figure(1, bgcolor=(0, 0, 0), size=(1300, 700)) # create a new figure with black background and size 1300x700
objective = ac.Ackley() # make an objective function
swarm = pso.Swarm(objective) # create a swarm
nr_of_particles = 25 # nr of particles in swarm
swarm.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 = ', 1000
print '# of particles in swarm = ', nr_of_particles
print '------------------------------------------------------'