def remove_overlap_particles(fluid_parray, solid_parray, dx_solid, dim=3):
"""
This function will take 2 particle arrays as input and will remove all
the particles of the first particle array which are in the vicinity of
the particles from second particle array. The function will remove all
the particles within the dx_solid vicinity so some particles are removed
at the outer surface of the particles from the second particle array.
This uses a pysph nearest neighbour particles search which will output
the particles within some range for every given particle.
Parameters
----------
fluid_parray : a pysph particle array object
solid_parray : a pysph particle array object
dx_solid : a number which is the dx of the second particle array
dim : dimensionality of the problem
The particle arrays should atleast contain x, y and h values for a 2d case
and atleast x, y, z and h values for a 3d case
Returns
-------
particle_array : pysph wcsph_particle_array with x, y, z and h values
"""
x = fluid_parray.x
x1 = solid_parray.x
y = fluid_parray.y
y1 = solid_parray.y
z = fluid_parray.z
z1 = solid_parray.z
h = fluid_parray.h
if dim == 2:
z = np.zeros_like(x)
z1 = np.zeros_like(x1)
modified_points = []
h_new = []
ll_nnps = LinkedListNNPS(dim, [fluid_parray, solid_parray])
for i in range(len(x)):
nbrs = UIntArray()
ll_nnps.get_nearest_particles(1, 0, i, nbrs)
point_i = np.array([x[i], y[i], z[i]])
near_points = nbrs.get_npy_array()
distances = []
for ind in near_points:
dest = [x1[ind], y1[ind], z1[ind]]
distances.append(distance(point_i, dest))
if len(distances) == 0:
modified_points.append(point_i)
h_new.append(h[i])
elif min(distances) >= (dx_solid * (1.0 - 1.0e-07)):
modified_points.append(point_i)
h_new.append(h[i])
modified_points = np.array(modified_points)
x_new = modified_points[:, 0]
y_new = modified_points[:, 1]
z_new = modified_points[:, 2]
p_array = get_particle_array_wcsph(x=x_new, y=y_new, z=z_new, h=h_new)
return p_array
def time_pysph_nnps(pa, nbrs, num_particles):
start_time = time.time()
r = 1/1.101
nn = LinkedListNNPS(dim=3, particles=[pa], radius_scale=r, cache=True)
nn.set_context(0,0)
for i in range(num_particles):
nn.get_nearest_particles(0,0,i,nbrs)
return time.time() - start_time
def find_overlap_particles(fluid_parray, solid_parray, dx_solid, dim=3):
"""This function will take 2 particle arrays as input and will find all the
particles of the first particle array which are in the vicinity of the
particles from second particle array. The function will find all the
particles within the dx_solid vicinity so some particles may be identified
at the outer surface of the particles from the second particle array.
The particle arrays should atleast contain x, y and h values for a 2d case
and atleast x, y, z and h values for a 3d case.
Parameters
----------
fluid_parray : a pysph particle array object
solid_parray : a pysph particle array object
dx_solid : a number which is the dx of the second particle array
dim : dimensionality of the problem
Returns
-------
list of particle indices to remove from the first array.
"""
x = fluid_parray.x
x1 = solid_parray.x
y = fluid_parray.y
y1 = solid_parray.y
z = fluid_parray.z
z1 = solid_parray.z
if dim == 2:
z = np.zeros_like(x)
z1 = np.zeros_like(x1)
to_remove = []
ll_nnps = LinkedListNNPS(dim, [fluid_parray, solid_parray])
for i in range(len(x)):
nbrs = UIntArray()
ll_nnps.get_nearest_particles(1, 0, i, nbrs)
point_i = np.array([x[i], y[i], z[i]])
near_points = nbrs.get_npy_array()
distances = []
for ind in near_points:
dest = [x1[ind], y1[ind], z1[ind]]
distances.append(distance(point_i, dest))
if len(distances) == 0:
continue
elif min(distances) < (dx_solid * (1.0 - 1.0e-07)):
to_remove.append(i)
return to_remove
def test_linked_list_nnps_large_num_cells(self):
'''
Tests LinkedListNNPS for large number of cells.
Fails with a malloc error
'''
print ""
for key in self.dataset_large_domain:
x, y, z, h = self.dataset_large_domain[key]
pa = get_particle_array(x=x, y=y, z=z, h=h)
qid = randint(0,pa.get_number_of_particles()-1)
nn_bf = brute_force_neighbours(pa, pa.x[qid], pa.y[qid],
pa.z[qid], pa.h[qid])
r = 1/1.101
sp = LinkedListNNPS(dim=3, particles=[pa], radius_scale=r, cache=True)
nn_sp = UIntArray()
sp.set_context(0,0)
sp.get_nearest_particles(0,0,qid,nn_sp)
print " - " + key
assert equal(nn_bf, nn_sp)
def test_setting_use_cache_does_cache(self):
# Given
pa = self._make_random_parray('pa1', 3)
pa.h[:] = 1.0
nnps = LinkedListNNPS(dim=3, particles=[pa], cache=False)
n = pa.get_number_of_particles()
# When
nnps.set_use_cache(True)
nbrs = UIntArray()
nnps.set_context(0, 0)
for i in range(n):
nnps.get_nearest_particles(0, 0, i, nbrs)
# Then
self.assertEqual(nbrs.length, n)
# Find the length of all cached neighbors,
# in this case, each particle has n neighbors,
# so we should have n*n neighbors in all.
total_length = sum(x.length for x in nnps.cache[0]._neighbor_arrays)
self.assertEqual(total_length, n * n)
nps = LinkedListNNPS(dim=2, particles=[pa,], radius_scale=k.radius_scale, domain=domain)
# container for neighbors
nbrs = UIntArray()
# arrays including ghosts
x, y, h, m = pa.get('x', 'y', 'h', 'm', only_real_particles=False)
# iterate over destination particles
t1 = time()
max_ngb = -1
for i in range( pa.num_real_particles ):
xi = x[i]; yi = y[i]; hi = h[i]
# get list of neighbors
nps.get_nearest_particles(0, 0, i, nbrs)
neighbors = nbrs.get_npy_array()
max_ngb = max( neighbors.size, max_ngb )
# iterate over the neighbor set
rho_sum = 0.0; wij_sum = 0.0
for j in neighbors:
xij = xi - x[j]
yij = yi - y[j]
rij = numpy.sqrt( xij**2 + yij**2 )
hij = 0.5 * (h[i] + h[j])
_wij = k.kernel( [xij, yij, 0.0], rij, hij)
# container for neighbors
nbrs = UIntArray()
# arrays including ghosts
x, y, h, m = pa.get('x', 'y', 'h', 'm', only_real_particles=False)
# iterate over destination particles
t1 = time()
max_ngb = -1
for i in range(pa.num_real_particles):
xi = x[i]
yi = y[i]
hi = h[i]
# get list of neighbors
nps.get_nearest_particles(0, 0, i, nbrs)
neighbors = nbrs.get_npy_array()
max_ngb = max(neighbors.size, max_ngb)
# iterate over the neighbor set
rho_sum = 0.0
wij_sum = 0.0
for j in neighbors:
xij = xi - x[j]
yij = yi - y[j]
rij = numpy.sqrt(xij**2 + yij**2)
hij = 0.5 * (h[i] + h[j])
_wij = k.kernel([xij, yij, 0.0], rij, hij)