def __init__(self, particles):
super(VoronoiMesh2D, self).__init__(particles)
face_vars = {
"area": "double",
"velocity-x": "double",
"velocity-y": "double",
"normal-x": "double",
"normal-y": "double",
"com-x": "double",
"com-y": "double",
"pair-i": "longlong",
"pair-j": "longlong",
}
self.faces = ParticleContainer(var_dict=face_vars)
def __init__(self, *arg, **kw):
super(VoronoiMesh2D, self).__init__(*arg, **kw)
self.dim = 2
self["neighbors"] = None
self["number of neighbors"] = None
self["faces"] = None
self["voronoi vertices"] = None
face_vars = {
"area": "double",
"velocity-x": "double",
"velocity-y": "double",
"normal-x": "double",
"normal-y": "double",
"com-x": "double",
"com-y": "double",
"pair-i": "longlong",
"pair-j": "longlong",
}
self.faces = ParticleContainer(var_dict=face_vars)
class VoronoiMesh2D(VoronoiMeshBase):
"""
2d voronoi mesh class
"""
def __init__(self, *arg, **kw):
super(VoronoiMesh2D, self).__init__(*arg, **kw)
self.dim = 2
self["neighbors"] = None
self["number of neighbors"] = None
self["faces"] = None
self["voronoi vertices"] = None
face_vars = {
"area": "double",
"velocity-x": "double",
"velocity-y": "double",
"normal-x": "double",
"normal-y": "double",
"com-x": "double",
"com-y": "double",
"pair-i": "longlong",
"pair-j": "longlong",
}
self.faces = ParticleContainer(var_dict=face_vars)
def compute_cell_info(self, particles):
"""
compute volume and center of mass of all real particles and compute areas, center of mass, normal
face pairs, and number of faces for faces
"""
num_faces = mesh.number_of_faces(particles, self["neighbors"],
self["number of neighbors"])
self.faces.resize(num_faces)
vol = particles["volume"]
xcom = particles["com-x"]
ycom = particles["com-y"]
vol[:] = 0.0
xcom[:] = 0.0
ycom[:] = 0.0
mesh.cell_face_info_2d(particles, self.faces, self["neighbors"],
self["number of neighbors"], self["faces"],
self["voronoi vertices"])
def tessellate(self, particles):
"""
create 2d voronoi tesselation from particle positions
"""
# create the tesselation
vor = Voronoi(particles.T)
# total number of particles
num_particles = particles.shape[1]
# create neighbor and face graph
neighbor_graph = [[] for i in range(num_particles)]
face_graph = [[] for i in range(num_particles)]
# loop through each face collecting the two particles
# that made that face as well as the face itself
for i, face in enumerate(vor.ridge_points):
p1, p2 = face
neighbor_graph[p1].append(p2)
neighbor_graph[p2].append(p1)
face_graph[p1] += vor.ridge_vertices[i]
face_graph[p2] += vor.ridge_vertices[i]
# sizes for 1d graphs
neighbor_graph_sizes = np.array([len(n) for n in neighbor_graph],
dtype=np.int32)
# graphs in 1d
neighbor_graph = np.array(list(
itertools.chain.from_iterable(neighbor_graph)),
dtype=np.int32)
face_graph = np.array(list(itertools.chain.from_iterable(face_graph)),
dtype=np.int32)
self["neighbors"] = neighbor_graph
self["number of neighbors"] = neighbor_graph_sizes
self["faces"] = face_graph
self["voronoi vertices"] = vor.vertices
if rank == 2:
num_particles = 3
x = np.array([4.0, 3.0, 0.0], dtype=np.float64)
y = np.array([3.0, 3.0, 4.0], dtype=np.float64)
gid = np.array([18, 19, 20], dtype=np.int32)
if rank == 3:
num_particles = 4
x = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64)
y = np.array([4.0, 4.0, 4.0, 4.0], dtype=np.float64)
gid = np.array([21, 22, 23, 24], dtype=np.int32)
# create particle data structure
pc = ParticleContainer(num_particles)
pc['position-x'][:] = x
pc['position-y'][:] = y
pc.register_property(x.size, 'gid', 'long')
pc['gid'][:] = gid
# Gather the global data on root
X = np.zeros(shape=25, dtype=np.float64)
Y = np.zeros(shape=25, dtype=np.float64)
GID = np.zeros(shape=25, dtype=np.int32)
displacements = np.array([12, 6, 3, 4], dtype=np.int32)
comm.Gatherv(sendbuf=x, recvbuf=[X, (displacements, None)], root=0)
comm.Gatherv(sendbuf=y, recvbuf=[Y, (displacements, None)], root=0)
def update_ghost_particles(self, particles, mesh, domain, load_balance, comm):
rank = comm.Get_rank()
size = comm.Get_size()
# allocate arrays for boundary indices
indices = LongArray()
corner_ghost = ParticleContainer()
send_particles = np.zeros(size, dtype=np.int32)
recv_particles = np.zeros(size, dtype=np.int32)
# we are puting new ghost at the end of the array
current_size = particles.get_number_of_particles()
boundary_indices = {
"left" : LongArray(),
"right" : LongArray(),
"bottom" : LongArray(),
"top" : LongArray(),
"left-top" : LongArray(),
"left-bottom" : LongArray(),
"right-top" : LongArray(),
"right-bottom" : LongArray()
}
# relabel all particles
particles["tag"][:] = ParticleTAGS.Undefined
particles["type"][:] = ParticleTAGS.Undefined
# flag particles that have left the domain and particles
# that remained
load_balance.flag_migrate_particles(particles, rank)
# find particles that have left the domain
export_indices = np.where(particles["type"] == ParticleTAGS.ExportInterior)[0]
if export_indices.size > 0:
# extract export particles
export_particles = particles.extract_items(export_indices)
# put particles in process order
ind = np.argsort(export_particles["process"])
for field in export_particles.properties.keys():
array = export_particles[field]
array[:] = array[ind]
export_particles["tag"][:] = ParticleTAGS.Real
export_particles["type"][:] = ParticleTAGS.Undefined
else:
export_particles = ParticleContainer()
# bin particle process
send_particles[:] = np.bincount(export_particles["process"], minlength=size)
# how many particles are being sent from each process
comm.Alltoall(sendbuf=send_particles, recvbuf=recv_particles)
# create container for incoming particles
import_particles = ParticleContainer(np.sum(recv_particles))
exchange_particles(import_particles, export_particles, send_particles, recv_particles,
0, comm)
# copy import particle data to ghost place holders and turn to real particles
migrate.transfer_migrate_particles(particles, import_particles)
# flag export particles back to interior ghost particles
particles["type"][export_indices] = ParticleTAGS.Interior
ghost_indices = np.where(particles["tag"] == ParticleTAGS.OldGhost)[0]
# find indices of interior/exterior ghost particles
cumsum_neighbors = mesh["number of neighbors"].cumsum()
exterior_ghost_indices = np.where(particles["type"] == ParticleTAGS.Exterior)[0]
interior_ghost_indices = np.where(particles["type"] == ParticleTAGS.Interior)[0]
#---------- create exterior ghost particles ----------#
num_exterior_ghost = 0
if exterior_ghost_indices.size > 0:
num_exterior_ghost = create_reflect_ghost(particles, boundary_indices,
domain, exterior_ghost_indices, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, -1)
#---------- create interior ghost particles ----------#
send_particles[:] = 0
recv_particles[:] = 0
interior_ghost_proc = particles["process"][interior_ghost_indices]
# arrange particles in process order
ind = interior_ghost_proc.argsort()
interior_ghost_proc = interior_ghost_proc[ind]
interior_ghost_indices = interior_ghost_indices[ind]
# bin processors
interior_ghost_proc_bin = np.bincount(interior_ghost_proc, minlength=size)
cumsum_neighbors = mesh["number of neighbors"].cumsum()
# collect the indices of particles to be export to each process
cumsum_proc = interior_ghost_proc_bin.cumsum()
for proc in range(size):
if interior_ghost_proc_bin[proc] != 0:
start = cumsum_proc[proc] - interior_ghost_proc_bin[proc]
end = cumsum_proc[proc]
send_particles[proc] = find_boundary_particles(indices, interior_ghost_indices[start:end], ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, False)
# extract data to send and remove the particles
send_data = {}
for prop in particles.properties.keys():
send_data[prop] = np.ascontiguousarray(particles[prop][indices.get_npy_array()])
send_data["tag"][:] = ParticleTAGS.Ghost
# how many particles are being sent from each process
comm.Alltoall(sendbuf=send_particles, recvbuf=recv_particles)
num_interior_ghost = np.sum(recv_particles)
# resize arrays to give room for incoming particles
sp = particles.get_number_of_particles()
particles.extend(num_interior_ghost)
exchange_particles(particles, send_data, send_particles, recv_particles,
sp, comm)
#---------- create exterior corner ghost particles ----------#
indices.reset()
send_particles[:] = 0
recv_particles[:] = 0
if boundary_indices['left'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["left"], indices, send_particles, 'x', domain.xmin, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
if boundary_indices['right'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["right"], indices, send_particles, 'x', domain.xmax, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
if boundary_indices['bottom'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["bottom"], indices, send_particles, 'y', domain.ymin, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
if boundary_indices['top'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["top"], indices, send_particles, 'y', domain.ymax, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
comm.Alltoall(sendbuf=send_particles, recvbuf=recv_particles)
sp = particles.get_number_of_particles()
particles.extend(np.sum(recv_particles))
# to export corners have to be reorderd by process
if corner_ghost.get_number_of_particles() > 0:
ind = np.argsort(corner_ghost['process'])
for field in corner_ghost.properties.keys():
array = corner_ghost[field]
array[:] = array[ind]
corner_ghost["process"][:] = -1
# exchange patch corners
exchange_particles(particles, corner_ghost, send_particles, recv_particles,
sp, comm)
# finally remove old ghost particles from previous time step
# and also put real particles in front and ghost in the back
particles.remove_tagged_particles(ParticleTAGS.OldGhost)
particles.align_particles()
particles['type'][:] = ParticleTAGS.Undefined
def create_ghost_particles(self, particles, mesh, domain, load_balance, comm, iteration=6):
"""Create initial ghost particles that hug the boundary after
load balance
"""
rank = comm.Get_rank()
size = comm.Get_size()
# remove current (if any) ghost particles
particles.remove_tagged_particles(ParticleTAGS.Ghost)
current_size = particles.get_number_of_particles()
# create initial ghost particles, particles is now larger
# these particles are centered in neighboring boundary leaf
# cells of the octree
load_balance.create_boundary_particles(particles, rank)
# reorder ghost in processors order: exterior have a process id of -1 so
# their put before interior ghost particles
ghost_proc = np.array(particles["process"][current_size:])
ind = np.argsort(ghost_proc)
ghost_proc = ghost_proc[ind]
for field in particles.properties.keys():
array = particles[field][current_size:]
array[:] = array[ind]
# allocate arrays for boundary indices
indices = LongArray()
corner_ghost = ParticleContainer()
# sides
boundary_indices = {
"left" : LongArray(),
"right" : LongArray(),
"bottom" : LongArray(),
"top" : LongArray(),
"left-top" : LongArray(),
"left-bottom" : LongArray(),
"right-top" : LongArray(),
"right-bottom" : LongArray()
}
send_particles = np.zeros(size, dtype=np.int32)
recv_particles = np.zeros(size, dtype=np.int32)
# create ghost interior and exterior particles by iteration, using
# the mesh to extract the needed neighbors
for i in range(iteration):
# build the mesh
mesh.tessellate()
cumsum_neighbors = mesh["number of neighbors"].cumsum()
#---------- create exterior ghost particles ----------#
# create indices for ghost particles
ghost_indices = np.arange(current_size, particles.get_number_of_particles())
# label current ghost as old ghost
particles['tag'][ghost_indices] = ParticleTAGS.OldGhost
# select exterior ghost particles
exterior_ghost = ghost_proc == -1
exterior_ghost_indices = ghost_indices[exterior_ghost]
if np.sum(exterior_ghost_indices) > 0:
num_exterior_ghost = create_reflect_ghost(particles, boundary_indices,
domain, exterior_ghost_indices, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, -1)
#---------- create interior ghost particles ----------#
interior_ghost_indices = ghost_indices[~exterior_ghost]
interior_ghost_proc = ghost_proc[~exterior_ghost]
# bin processors - they are in order
interior_ghost_proc_bin = np.bincount(interior_ghost_proc, minlength=size)
send_particles[:] = 0
recv_particles[:] = 0
indices.reset()
# collect the indices of particles to be export to each process
cumsum_proc = interior_ghost_proc_bin.cumsum()
for proc in range(size):
if interior_ghost_proc_bin[proc] != 0:
start = cumsum_proc[proc] - interior_ghost_proc_bin[proc]
end = cumsum_proc[proc]
send_particles[proc] = find_boundary_particles(indices, interior_ghost_indices[start:end], ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, False)
# extract data to send and remove the particles
send_data = {}
for prop in particles.properties.keys():
send_data[prop] = np.ascontiguousarray(particles[prop][indices.get_npy_array()])
send_data["tag"][:] = ParticleTAGS.Ghost
# how many particles are being sent from each process
comm.Alltoall(sendbuf=send_particles, recvbuf=recv_particles)
num_interior_ghost = np.sum(recv_particles)
# resize arrays to give room for incoming particles
sp = particles.get_number_of_particles()
#particles.resize(current_size + num_exterior_ghost + num_interior_ghost)
particles.extend(num_interior_ghost)
exchange_particles(particles, send_data, send_particles, recv_particles,
sp, comm)
#---------- create exterior corner ghost particles ----------#
indices.reset()
send_particles[:] = 0
recv_particles[:] = 0
# clear out corner ghost
corner_ghost.resize(0)
if boundary_indices['left'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["left"], indices, send_particles, 'x', domain.xmin, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
if boundary_indices['right'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["right"], indices, send_particles, 'x', domain.xmax, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
if boundary_indices['bottom'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["bottom"], indices, send_particles, 'y', domain.ymin, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
if boundary_indices['top'].length > 0:
export_reflect(particles, corner_ghost, boundary_indices["top"], indices, send_particles, 'y', domain.ymax, ghost_indices,
mesh['neighbors'], mesh['number of neighbors'], cumsum_neighbors, load_balance, current_size, rank, size)
#print rank, current_size, particles.get_number_of_particles(), current_size + num_exterior_ghost + num_interior_ghost
sp = particles.get_number_of_particles()
comm.Alltoall(sendbuf=send_particles, recvbuf=recv_particles)
particles.extend(np.sum(recv_particles))
# to export corners have to be reorderd by process
if corner_ghost.get_number_of_particles() > 0:
ind = np.argsort(corner_ghost['process'])
for field in corner_ghost.properties.keys():
array = corner_ghost[field]
array[:] = array[ind]
corner_ghost["process"][:] = -1
# exchange patch corners
exchange_particles(particles, corner_ghost, send_particles, recv_particles,
sp, comm)
for bd in boundary_indices:
boundary_indices[bd].reset()
particles.remove_tagged_particles(ParticleTAGS.OldGhost)
# put particles in process order for next loop
ind = np.argsort(particles["process"][current_size:])
for field in particles.properties.keys():
array = particles[field][current_size:]
array[:] = array[ind]
ghost_proc = np.array(particles["process"][current_size:])
print 'rank:', rank, 'fraction of real to ghost:', (particles.get_number_of_particles()-current_size)*1.0/particles.get_number_of_particles()
class VoronoiMesh2D(VoronoiMeshBase):
"""
2d voronoi mesh class
"""
def __init__(self, particles):
super(VoronoiMesh2D, self).__init__(particles)
face_vars = {
"area": "double",
"velocity-x": "double",
"velocity-y": "double",
"normal-x": "double",
"normal-y": "double",
"com-x": "double",
"com-y": "double",
"pair-i": "longlong",
"pair-j": "longlong",
}
self.faces = ParticleContainer(var_dict=face_vars)
def compute_cell_info(self):
"""
compute volume and center of mass of all real particles and compute areas, center of mass, normal
face pairs, and number of faces for faces
"""
num_faces = mesh.number_of_faces(self.particles,
self.graph["neighbors"],
self.graph["number of neighbors"])
self.faces.resize(num_faces)
# the algorithms are cumulative so we have to zero out the data
self.particles["volume"][:] = 0.0
self.particles["com-x"][:] = 0.0
self.particles["com-y"][:] = 0.0
mesh.cell_face_info_2d(self.particles, self.faces,
self.graph["neighbors"],
self.graph["number of neighbors"],
self.graph["faces"],
self.graph["voronoi vertices"])
def update_boundary_particles(self):
cumsum = np.cumsum(self.graph["number of neighbors"], dtype=np.int32)
mesh.flag_boundary_particles(self.particles, self.graph["neighbors"],
self.graph["number of neighbors"], cumsum)
def update_second_boundary_particles(self):
cumsum = np.cumsum(self.graph["number of neighbors"], dtype=np.int32)
mesh.flag_second_boundary_particles(self.particles,
self.graph["neighbors"],
self.graph["number of neighbors"],
cumsum)
def tessellate(self):
"""
create 2d voronoi tesselation from particle positions
"""
pos = np.array(
[self.particles["position-x"], self.particles["position-y"]],
dtype=np.float64)
# create the tesselation
vor = Voronoi(pos.T)
# total number of particles
num_particles = self.particles.get_number_of_particles()
# create neighbor and face graph
neighbor_graph = [[] for i in range(num_particles)]
face_graph = [[] for i in range(num_particles)]
# loop through each face collecting the two particles
# that made that face as well as the face itself
for i, face in enumerate(vor.ridge_points):
p1, p2 = face
neighbor_graph[p1].append(p2)
neighbor_graph[p2].append(p1)
face_graph[p1] += vor.ridge_vertices[i]
face_graph[p2] += vor.ridge_vertices[i]
# sizes for 1d graphs
neighbor_graph_sizes = np.array([len(n) for n in neighbor_graph],
dtype=np.int32)
# graphs in 1d
neighbor_graph = np.array(list(
itertools.chain.from_iterable(neighbor_graph)),
dtype=np.int32)
face_graph = np.array(list(itertools.chain.from_iterable(face_graph)),
dtype=np.int32)
self.graph["neighbors"] = neighbor_graph
self.graph["number of neighbors"] = neighbor_graph_sizes
self.graph["faces"] = face_graph
self.graph["voronoi vertices"] = vor.vertices
def build_geometry(self, gamma):
pc = self.particles
self.tessellate()
self.update_boundary_particles()
self.update_second_boundary_particles() #tmp delete
self.compute_cell_info()
indices = np.where((pc['tag'] == ParticleTAGS.Real)
| (pc['type'] == ParticleTAGS.Boundary)
| (pc['type'] == ParticleTAGS.BoundarySecond))[0]
# now update primitive variables
vol = pc['volume'][indices]
mass = pc['mass'][indices]
momx = pc['momentum-x'][indices]
momy = pc['momentum-y'][indices]
ener = pc['energy'][indices]
# update primitive variables
pc['density'][indices] = mass / vol
pc['velocity-x'][indices] = momx / mass
pc['velocity-y'][indices] = momy / mass
pc['pressure'][indices] = (ener / vol - 0.5 * (mass / vol) *
((momx / mass)**2 +
(momy / mass)**2)) * (gamma - 1.0)
gid = np.arange(90, 90 + num_particles, dtype=np.int32)
#gid = np.array( [18, 19, 20], dtype=np.int32 )
if rank == 3:
num_particles = 4*5
x = np.array( 5*[1.0, 2.0, 3.0, 4.0], dtype=np.float64)
y = np.array( 5*[4.0, 4.0, 4.0, 4.0], dtype=np.float64)
z = np.repeat(np.arange(5), 4).astype(np.float64)
gid = np.arange(105, 105 + num_particles, dtype=np.int32)
#gid = np.array( [21, 22, 23, 24], dtype=np.int32 )
# create particle data structure
pa = ParticleContainer(num_particles)
pa.register_property(num_particles, 'position-z', 'double')
pa['position-x'][:] = x
pa['position-y'][:] = y
pa['position-z'][:] = z
pa.register_property(x.size, 'gid', 'long')
pa['gid'][:] = gid
# Gather the global data on root
X = np.zeros(shape=125, dtype=np.float64)
Y = np.zeros(shape=125, dtype=np.float64)
Z = np.zeros(shape=125, dtype=np.float64)
GID = np.zeros(shape=125, dtype=np.int32)
displacements = 5*np.array([12, 6, 3, 4], dtype=np.int32)
ys = None
ids = None
lengths = None
disp = None
send = None
send = comm.scatter(send, root=0)
xlocal = np.empty(send,dtype=np.float64)
ylocal = np.empty(send,dtype=np.float64)
idlocal = np.empty(send,dtype=np.int32)
comm.Scatterv( [xs, (lengths, disp)], xlocal)
comm.Scatterv( [ys, (lengths, disp)], ylocal)
comm.Scatterv( [ids, (lengths, disp)], idlocal)
pc = ParticleContainer(send)
pc['position-x'][:] = xlocal
pc['position-y'][:] = ylocal
pc['process'][:] = rank
pc['type'][:] = ParticleTAGS.Undefined
#pc.register_property(send, "ids", "long")
pc["ids"][:] = idlocal
# perform the load decomposition
order = 21
dom = DomainLimits(dim=2, xmin=0., xmax=1.)
load_b = LoadBalance(pc, dom, comm=comm, order=order)
load_b.decomposition()
mesh = VoronoiMesh2D(pc)
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
# generate random particles in a unit box for each process
np.random.seed(rank)
my_num_particles = np.random.randint(64, 256)
#my_num_particles = np.random.randint(512, 1024)
my_particles = np.random.random(2 * my_num_particles).reshape(
2, my_num_particles).astype(np.float64)
#my_particles = np.random.normal(0.5, 0.1, 2*my_num_particles).reshape(2, my_num_particles).astype(np.float64)
#pa = ParticleArray(my_num_particles)
pa = ParticleContainer(my_num_particles)
pa['position-x'][:] = my_particles[0, :]
pa['position-y'][:] = my_particles[1, :]
pa['process'][:] = rank
pa['type'][:] = ParticleTAGS.Undefined
#plot initial distribuition
#plt.scatter(pc['position-x'], pc['position-y'])
#plt.savefig("plot_init_proc_%d.png" % rank)
#plt.clf()
# perform load balance
#lb = LoadBalance(pc, comm=comm)
#global_tree = lb.decomposition()
# perform the load decomposition
