def setup_in_volume(volume, count, h, spc=1., orig=(0.,0.,0.), offset=0., offset_inner=None, steps=100, fact=0.0001, maxneighs=1000, radius=0.):
max_x_cl = CLScalar(volume.shape[0]*spc)
max_y_cl = CLScalar(volume.shape[1]*spc)
max_z_cl = CLScalar(volume.shape[2]*spc)
particles_cl = CLReal4()
oi = None
if not offset_inner == None:
oi = offset_inner - radius
particles_cl.value = rand_particles_in_volume(volume, count, spc=spc, orig=(0.,0.,0.), offset=offset + radius, offset_inner=oi)
maxneighs_cl = CLScalar(maxneighs)
volume.shape = (volume.shape[0],volume.shape[1],volume.shape[2],1)
vol_cl = CLReal()
vol_cl.value = volume
neighsearch = Neighsearch(particles_cl, h=h, maxneighs=int(maxneighs_cl.value))
neighsearch.search()
h_cl = CLScalar(h)
fact_cl = CLScalar(fact)
vol_shp0_cl = CLScalar(volume.shape[0])
vol_shp1_cl = CLScalar(volume.shape[1])
vol_shp2_cl = CLScalar(volume.shape[2])
offset_cl = CLScalar(offset)
if offset_inner == None:
offset_inner = -pow(10., 38)
offset_inner_cl = CLScalar(offset_inner)
print "offsets: ", offset_cl, offset_inner_cl
gradient_cl = CLReal4()
gradient_cl.value = gradient_field(volume)
kern = CLTemplateKernel(src=_RELAXPART_SRC, pre=_GET_DIST_GRAD_SRC)
kern.pos = particles_cl
kern.h = h_cl
kern.maxneighs = maxneighs_cl
kern.neighbors = neighsearch.neighbors
kern.fact = fact_cl
kern.volume = vol_cl
kern.spacing = CLScalar(spc)
kern.vol_shp0 = vol_shp0_cl
kern.vol_shp1 = vol_shp1_cl
kern.vol_shp2 = vol_shp2_cl
kern.offset = offset_cl
kern.offset_inner = offset_inner_cl
kern.gradient = gradient_cl
kern.max_x = max_x_cl
kern.max_y = max_y_cl
kern.max_z = max_z_cl
kern.compile()
print 'Particle initialization'
for i in range(steps):
neighsearch.search()
kern()
kern.finish()
print 'Step', i+1, 'of', steps
ret = particles_cl.value
ret[:,0] += orig[0]
ret[:,1] += orig[1]
ret[:,2] += orig[2]
return ret
def setup_with_particles(particles_in, volume, count, h, spc=1., orig=(0.,0.,0.), offset=0., offset_inner=None, steps=100, fact=0.0001, maxneighs=300):
max_x_cl = CLScalar(volume.shape[0]*spc + orig[0])
max_y_cl = CLScalar(volume.shape[1]*spc + orig[1])
max_z_cl = CLScalar(volume.shape[2]*spc + orig[2])
min_x_cl = CLScalar(orig[0])
min_y_cl = CLScalar(orig[1])
min_z_cl = CLScalar(orig[2])
maxneighs_cl = CLScalar(maxneighs)
parts_new = rand_particles_in_volume(volume, count, spc=spc, orig=orig, offset=offset, offset_inner=offset_inner)
particles_cl = CLReal4()
particles_cl.value = np.vstack((parts_new, particles_in))
flag_cl = CLChar()
flag_cl.value = np.vstack((np.zeros((len(parts_new),1)), np.ones((len(particles_in),1))))# 1->boundary particle, 0->movable particle
grp = Syncgroup(particles_cl, flag_cl)
neighsearch = Neighsearch(particles_cl, h=h, maxneighs=int(maxneighs_cl.value), group=grp)
neighsearch.search()
h_cl = CLScalar(h)
fact_cl = CLScalar(fact)
kern = CLTemplateKernel(src=_RELAXPART_WITH_PART_SRC)
kern.pos = particles_cl
kern.flag = flag_cl
kern.h = h_cl
kern.maxneighs = maxneighs_cl
kern.neighbors = neighsearch.neighbors
kern.fact = fact_cl
kern.max_x = max_x_cl
kern.max_y = max_y_cl
kern.max_z = max_z_cl
kern.min_x = min_x_cl
kern.min_y = min_y_cl
kern.min_z = min_z_cl
kern.compile()
print 'Particle initialization'
for i in range(steps):
neighsearch.search()
kern()
print 'Step', i+1, 'of', steps
parts = particles_cl.value
ret = []
flags = flag_cl.value
for i in range(len(parts)):
if flags[i,0] == 0:
ret.append(parts[i])
return np.array(ret)
