Python と Mayavi2 を使用して粒子群最適化のアニメーションを実行しようとしています。
アニメーションは正常に動作しています。私の問題は、動きをアニメーション化している間はプロットを操作できないことです。具体的には、グラフを回転させてズームしたいと思います。多分誰かがアニメーションをした経験がありますか？
私が行う方法は、最初に粒子の位置を計算してから保存することです。計算が終了したら、point3d() を使用して最初の時点で粒子の位置をプロットし、set() メソッドを使用してデータを更新する時間を繰り返します。
グラフを回転できるようにする方法はありますか? レンダリングを無効にするスレッドに関する何かについて聞いたことがありますが、コードでそれを行う方法がわかりませんでした。他の多くのものに加えて、私は読んだ：
http://code.enthought.com/projects/mayavi//docs/development/html/mayavi/mlab_animation.html
しかし、それはそれを使用する方法を見ることができません。
助言がありますか？
これが私のコードです：
#!/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 '------------------------------------------------------'