def test_compressed_octree_works_for_large_domain(self):
# Given
pa = self._make_particles(20)
# We turn on cache so it computes all the neighbors quickly for us.
nps = nnps.CompressedOctreeNNPS(dim=3, particles=[pa], cache=True)
nbrs = UIntArray()
direct = UIntArray()
nps.set_context(0, 0)
for i in range(pa.get_number_of_particles()):
nps.get_nearest_particles(0, 0, i, nbrs)
nps.brute_force_neighbors(0, 0, i, direct)
x = nbrs.get_npy_array()
y = direct.get_npy_array()
x.sort(); y.sort()
assert numpy.all(x == y)
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 test_cache_updates_with_changed_particles(self):
# Given
pa1 = self._make_random_parray('pa1', 5)
particles = [pa1]
nnps = LinkedListNNPS(dim=3, particles=particles)
cache = NeighborCache(nnps, dst_index=0, src_index=0)
cache.update()
# When
pa2 = self._make_random_parray('pa2', 2)
pa1.add_particles(x=pa2.x, y=pa2.y, z=pa2.z)
nnps.update()
cache.update()
nb_cached = UIntArray()
nb_direct = UIntArray()
for i in range(len(particles[0].x)):
nnps.get_nearest_particles_no_cache(0, 0, i, nb_direct, False)
cache.get_neighbors(0, i, nb_cached)
nb_e = nb_direct.get_npy_array()
nb_c = nb_cached.get_npy_array()
self.assertTrue(np.all(nb_e == nb_c))
def test_neighbors_cached_properly(self):
# Given
pa1 = self._make_random_parray('pa1', 5)
pa2 = self._make_random_parray('pa2', 4)
particles = [pa1, pa2]
nnps = LinkedListNNPS(dim=3, particles=particles)
for dst_index in (0, 1):
for src_idx in (0, 1):
# When
cache = NeighborCache(nnps, dst_index, src_idx)
cache.update()
nb_cached = UIntArray()
nb_direct = UIntArray()
# Then.
for i in range(len(particles[dst_index].x)):
nnps.get_nearest_particles_no_cache(
src_idx, dst_index, i, nb_direct, False)
cache.get_neighbors(src_idx, i, nb_cached)
nb_e = nb_direct.get_npy_array()
nb_c = nb_cached.get_npy_array()
self.assertTrue(np.all(nb_e == nb_c))
def test_empty_neigbors_works_correctly(self):
# Given
pa1 = self._make_random_parray('pa1', 5)
pa2 = self._make_random_parray('pa2', 2)
pa2.x += 10.0
particles = [pa1, pa2]
# When
nnps = LinkedListNNPS(dim=3, particles=particles)
# Cache for neighbors of destination 0.
cache = NeighborCache(nnps, dst_index=0, src_index=1)
cache.update()
# Then
nb_cached = UIntArray()
nb_direct = UIntArray()
for i in range(len(particles[0].x)):
nnps.get_nearest_particles_no_cache(1, 0, i, nb_direct, False)
# Get neighbors from source 1 on destination 0.
cache.get_neighbors(src_index=1, d_idx=i, nbrs=nb_cached)
nb_e = nb_direct.get_npy_array()
nb_c = nb_cached.get_npy_array()
self.assertEqual(len(nb_e), 0)
self.assertTrue(np.all(nb_e == nb_c))
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_nnps_sorts_without_gids(self):
# Given
pa, nps = self._make_particles(10)
# When
nps.set_context(0, 0)
# Test the that gids are actually huge and invalid.
self.assertEqual(numpy.max(pa.gid), numpy.min(pa.gid))
self.assertTrue(numpy.max(pa.gid) > pa.gid.size)
# Then
nbrs = UIntArray()
for i in range(pa.get_number_of_particles()):
nps.get_nearest_particles(0, 0, i, nbrs)
nb = nbrs.get_npy_array()
sorted_nbrs = nb.copy()
sorted_nbrs.sort()
self.assertTrue(numpy.all(nb == sorted_nbrs))
# 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)
# contribution from this neibhor
print "Neighbor search",nl_updates
pa = get_particle_array(name='prot', x=rx, y=ry, z=rz, h=1.0) #h=1.0 must not be changed as it affects radius_scale in nnps.LinkedListNNPS
nps = nnps.LinkedListNNPS(dim=3, particles=[pa], radius_scale=rcut2)
#nps = nnps.SpatialHashNNPS(dim=3, particles=[pa], radius_scale=rcut2)
src_index = 0
dst_index = 0
neighbors = []
tot = 0
for i in range(nca):
nbrs = UIntArray()
nps.get_nearest_particles(src_index, dst_index, i, nbrs)
tmp = nbrs.get_npy_array().tolist()
# patch
if tmp.count(i) > 1:
print "Problems: nbrs has multiple occurrences of residue %d, which might indicate something wrong in pysph (for certain radius_scale values)" %(i)
print "Patching this problem by assuming that residue %d has no neighbors other than itself" % (i)
tmp = [i]
else:
pass
nneigh = len(tmp)
tot += nneigh
#I do not want neighbors that are only the i-th particle (i.e. self)
#I want the "central" atom as the first element: THIS IS FUNDAMENTAL FOR HOW all_pairs_in_nl WORKS
if (nneigh > 1):
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)
