def _make_particles(self, nx=20):
x = numpy.linspace(0, 1, nx)
h = numpy.ones_like(x) / (nx - 1)
pa = get_particle_array(name='fluid', x=x, h=h)
nps = nnps.LinkedListNNPS(dim=1, particles=[pa], sort_gids=True)
return pa, nps
def test_use_3d_for_1d_data_with_llnps():
y = numpy.array([1.0, 1.5])
h = numpy.ones_like(y)
pa = get_particle_array(name='fluid', y=y, h=h)
nps = nnps.LinkedListNNPS(dim=3, particles=[pa], cache=False)
nbrs = UIntArray()
nps.get_nearest_particles(0, 0, 0, nbrs)
print(nbrs.length)
assert nbrs.length == len(y)
def post_process(self, info_fname):
"""
Comparing wave height 0.05 m to the left of the tank
"""
self.read_info(info_fname)
if len(self.output_files) == 0:
return
from pysph.solver.utils import iter_output
import matplotlib.pyplot as plt
from cyarray.api import UIntArray
from pysph.base import nnps
files = self.output_files
wave_height, t = [], []
for sd, arrays1, arrays2 in iter_output(files, 'fluid', 'solid'):
t.append(sd['t'])
x_left = arrays2.x.min()
probe_x = x_left + 0.05 + self.dx * (n_layers - 1)
probe_y = np.linspace(0, arrays1.y.max(), 50)
probe = get_particle_array(x=probe_x * np.ones_like(probe_y),
y=probe_y)
pa_list = [arrays1, probe]
nps = nnps.LinkedListNNPS(dim=2, particles=pa_list, radius_scale=1)
src_index, dst_index = 0, 1
nps.set_context(src_index=0, dst_index=1)
nbrs = UIntArray()
wh = 0.0
for i in range(len(probe_y)):
nps.get_nearest_particles(src_index, dst_index, i, nbrs)
neighbours = nbrs.get_npy_array()
for j in range(len(neighbours)):
if arrays1.y[neighbours[j]] > wh:
wh = arrays1.y[neighbours[j]]
wave_height.append(wh)
wave_height = np.array(wave_height)
wave_height = wave_height - h
import pysph.examples.st_exp_data as st
exp_t, exp_H = st.get_bouscasse_data()
T = 2 * pi / omega
t = np.array(t) / T
plt.plot(t, wave_height, label="Computed")
plt.scatter(exp_t, exp_H, label="Experiment: Bouscasse et al, 2013")
plt.xlabel("t/T")
plt.ylabel("Wave Height (m)")
plt.xlim(8.5, 11)
plt.legend()
plt.savefig(os.path.join(self.output_dir, 'H_vs_t.png'))
plt.show()
def test_large_number_of_neighbors_linked_list():
x = numpy.random.random(1 << 14) * 0.1
y = x.copy()
z = x.copy()
h = numpy.ones_like(x)
pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h)
nps = nnps.LinkedListNNPS(dim=3, particles=[pa], cache=False)
nbrs = UIntArray()
nps.get_nearest_particles(0, 0, 0, nbrs)
# print(nbrs.length)
assert nbrs.length == len(x)
def test_neighbor_cache_update_doesnt_leak():
# Given
x, y, z = numpy.random.random((3, 1000))
pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=0.05)
nps = nnps.LinkedListNNPS(dim=3, particles=[pa], cache=True)
nps.set_context(0, 0)
nps.cache[0].find_all_neighbors()
old_length = sum(x.length for x in nps.cache[0]._neighbor_arrays)
# When
nps.update()
nps.set_context(0, 0)
nps.cache[0].find_all_neighbors()
# Then
new_length = sum(x.length for x in nps.cache[0]._neighbor_arrays)
assert new_length == old_length
def setUp(self):
NNPSTestCase.setUp(self)
self.nps = nnps.LinkedListNNPS(
dim=3, particles=self.particles, radius_scale=2.0
)
def setUp(self):
"""Default set-up used by all the tests
Particles with the following coordinates (x, y, z) are placed in a box
0 : -1.5 , 0.25 , 0.5
1 : 0.33 , -0.25, 0.25
2 : 1.25 , -1.25, 1.25
3 : 0.05 , 1.25 , -0.5
4 : -0.5 , 0.5 , -1.25
5 : -0.75, 0.75 , -1.25
6 : -1.25, 0.5 , 0.5
7 : 0.5 , 1.5 , -0.5
8 : 0.5 , -0.5 , 0.5
9 : 0.5 , 1.75 , -0.75
The cell size is set to 1. Valid cell indices and the
particles they contain are given below:
(-2, 0, 0) : particle 0, 6
(0, -1, 0) : particle 1, 8
(1, -2, 1) : particle 2
(0, 1, -1) : particle 3, 7, 9
(-1, 0, -2): particle 4, 5
"""
x = numpy.array([
-1.5, 0.33, 1.25, 0.05, -0.5, -0.75, -1.25, 0.5, 0.5, 0.5])
y = numpy.array([
0.25, -0.25, -1.25, 1.25, 0.5, 0.75, 0.5, 1.5, -0.5, 1.75])
z = numpy.array([
0.5, 0.25, 1.25, -0.5, -1.25, -1.25, 0.5, -0.5, 0.5, -0.75])
# using a degenrate (h=0) array will set cell size to 1 for NNPS
h = numpy.zeros_like(x)
pa = get_particle_array(x=x, y=y, z=z, h=h)
self.dict_box_sort_nnps = nnps.DictBoxSortNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.box_sort_nnps = nnps.BoxSortNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.ll_nnps = nnps.LinkedListNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.sp_hash_nnps = nnps.SpatialHashNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.ext_sp_hash_nnps = nnps.ExtendedSpatialHashNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.strat_radius_nnps = nnps.StratifiedHashNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
# these are the expected cells
self.expected_cells = {
IntPoint(-2, 0, 0): [0, 6],
IntPoint(0, -1, 0): [1, 8],
IntPoint(1, -2, 1): [2],
IntPoint(0, 1, -1): [3, 7, 9],
IntPoint(-1, 0, -2): [4, 5]
}
update_nstlist = False
# update_dyn has the priority over update_nstlist
update = False
if update_dyn:
update = True
else:
update = update_nstlist
if update:
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)
