/usr/lib/python2.7/dist-packages/PySPH-1.0a4.dev0-py2.7-linux-x86_64.egg/pysph/base/tests/test_nnps.py is in python-pysph 0~20160514.git91867dc-4build1.
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You can run the tests like so:
$ nosetests -v test_nnps.py
"""
import numpy
from numpy import random
# PySPH imports
from pysph.base.point import IntPoint, Point
from pysph.base.utils import get_particle_array
from pysph.base import nnps
# Carrays from PyZoltan
from pyzoltan.core.carray import UIntArray, IntArray, DoubleArray
# Python testing framework
import unittest
class SimpleNNPSTestCase(unittest.TestCase):
"""Simplified NNPS test case
We distribute particles manually and perform sanity checks on NNPS
"""
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
)
# 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]
}
def test_cell_size(self):
"SimpleNNPS :: test cell_size"
nnps = self.dict_box_sort_nnps
self.assertAlmostEqual( nnps.cell_size, 1.0, 14 )
nnps = self.box_sort_nnps
self.assertAlmostEqual( nnps.cell_size, 1.0, 14 )
nnps = self.ll_nnps
self.assertAlmostEqual( nnps.cell_size, 1.0, 14 )
def test_cells(self):
"SimpleNNPS :: test cells"
nnps = self.dict_box_sort_nnps
cells = self.expected_cells
# check each cell for it's contents
for key in cells:
self.assertTrue( key in nnps.cells )
cell = nnps.cells.get(key)
cell_indices = list( cell.lindices[0].get_npy_array() )
expected_indices = cells.get(key)
self.assertTrue( cell_indices == expected_indices )
class NNPSTestCase(unittest.TestCase):
"""Standard nearest neighbor queries and comparison with the brute
force approach.
We randomly distribute particles in 3-space and compare the list
of neighbors using the NNPS algorithms and the brute force
approach.
"""
def setUp(self):
"""Default set-up used by all the tests
Two sets of particle arrays (a & b) are created and neighbors
are checked from a -> b, b -> a , a -> a and b -> b
"""
self.numPoints1 = numPoints1 = 1 << 10
self.numPoints2 = numPoints2 = 1 << 11
self.pa1 = pa1 = self._create_random(numPoints1)
self.pa2 = pa2 = self._create_random(numPoints2)
# the list of particles
self.particles = [pa1, pa2]
def _create_random(self, numPoints):
# average particle spacing and volume in the unit cube
dx = pow( 1.0/numPoints, 1./3. )
# create random points in the interval [-1, 1]^3
x1, y1, z1 = random.random( (3, numPoints) ) * 2.0 - 1.0
h1 = numpy.ones_like(x1) * 1.2 * dx
gid1 = numpy.arange(numPoints).astype(numpy.uint32)
# first particle array
pa = get_particle_array(
x=x1, y=y1, z=z1, h=h1, gid=gid1)
return pa
def _assert_neighbors(self, nbrs_nnps, nbrs_brute_force):
# ensure that the lengths of the arrays are the same
self.assertEqual( nbrs_nnps.length, nbrs_brute_force.length )
nnbrs = nbrs_nnps.length
_nbrs1 = nbrs_nnps.get_npy_array()
_nbrs2 = nbrs_brute_force.get_npy_array()
# sort the neighbors
nbrs1 = _nbrs1[:nnbrs]; nbrs1.sort()
nbrs2 = _nbrs2; nbrs2.sort()
# check each neighbor
for i in range(nnbrs):
self.assertEqual( nbrs1[i], nbrs2[i] )
def _test_neighbors_by_particle(
self, src_index, dst_index, dst_numPoints):
# nnps and the two neighbor lists
nps = self.nps
nbrs1 = UIntArray()
nbrs2 = UIntArray()
# get the neighbors and sort the result
for i in range(dst_numPoints):
nps.get_nearest_particles(src_index, dst_index, i, nbrs1)
nps.brute_force_neighbors(src_index, dst_index, i, nbrs2)
# ensure that the neighbor lists are the same
self._assert_neighbors(nbrs1, nbrs2)
class DictBoxSortNNPSTestCase(NNPSTestCase):
"""Test for the original box-sort algorithm"""
def setUp(self):
NNPSTestCase.setUp(self)
self.nps = nnps.DictBoxSortNNPS(
dim=3, particles=self.particles, radius_scale=2.0
)
def test_neighbors_aa(self):
self._test_neighbors_by_particle(src_index=0, dst_index=0, dst_numPoints=self.numPoints1)
def test_neighbors_ab(self):
self._test_neighbors_by_particle(src_index=0, dst_index=1, dst_numPoints=self.numPoints2)
def test_neighbors_ba(self):
self._test_neighbors_by_particle(src_index=1, dst_index=0, dst_numPoints=self.numPoints1)
def test_neighbors_bb(self):
self._test_neighbors_by_particle(src_index=1, dst_index=1, dst_numPoints=self.numPoints2)
class BoxSortNNPSTestCase(DictBoxSortNNPSTestCase):
"""Test for the original box-sort algorithm"""
def setUp(self):
NNPSTestCase.setUp(self)
self.nps = nnps.BoxSortNNPS(
dim=3, particles=self.particles, radius_scale=2.0
)
class LinkedListNNPSTestCase(DictBoxSortNNPSTestCase):
"""Test for the original box-sort algorithm"""
def setUp(self):
NNPSTestCase.setUp(self)
self.nps = nnps.LinkedListNNPS(
dim=3, particles=self.particles, radius_scale=2.0
)
def test_cell_index_positivity(self):
nps = self.nps
ncells_tot = nps.ncells_tot
ncells_per_dim = nps.ncells_per_dim
dim = nps.dim
# cell indices should be positive. We iterate over the
# flattened indices, get the unflattened version and check
# that each component remains positive
for cell_index in range(ncells_tot):
cid = nnps.py_unflatten( cell_index, ncells_per_dim, dim )
self.assertTrue( cid.x > -1 )
self.assertTrue( cid.y > -1 )
self.assertTrue( cid.z > -1 )
class TestBoxSortNNPSOnLargeDomain(unittest.TestCase):
def _make_particles(self, nx=20):
x, y, z = numpy.random.random((3, nx, nx, nx))
x = numpy.ravel(x)
y = numpy.ravel(y)
z = numpy.ravel(z)
h = numpy.ones_like(x)*1.3/nx
pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h)
# Place one particle far far away
# On Linux and OSX this works even if sz is 100000.
# However, on Windows this fails but works with 1000,
# hence we set it to 1000.
sz = 1000.0
pa.add_particles(x=[sz], y=[sz], z=[sz])
return pa
def test_linked_list_nnps_raises_exception_for_large_domain(self):
# Given/When
pa = self._make_particles(20)
# Then
self.assertRaises(
RuntimeError, nnps.LinkedListNNPS, dim=3, particles=[pa], cache=True
)
def test_box_sort_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.BoxSortNNPS(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)
class TestNNPSWithSorting(unittest.TestCase):
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_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))
def test_nnps_sorts_with_valid_gids(self):
# Given
pa, nps = self._make_particles(10)
pa.gid[:] = numpy.arange(pa.x.size)
nps.update()
# When
nps.set_context(0, 0)
# Test the that gids are actually valid.
self.assertEqual(numpy.max(pa.gid), pa.gid.size-1)
self.assertEqual(numpy.min(pa.gid), 0)
# 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))
def test_large_number_of_neighbors():
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_flatten_unflatten():
# first consider the 2D case where we assume a 4 X 5 grid of cells
dim = 2
ncells_per_dim = IntArray(3)
ncells_per_dim[0] = 4; ncells_per_dim[1] = 5; ncells_per_dim[2] = 0
# valid un-flattened cell indices
cids = [ [i, j] for i in range(4) for j in range(5) ]
for _cid in cids:
cid = IntPoint( _cid[0], _cid[1], 0)
flattened = nnps.py_flatten( cid, ncells_per_dim, dim )
unflattened = nnps.py_unflatten( flattened, ncells_per_dim, dim )
# the unflattened index should match with cid
assert( cid == unflattened )
# 3D
dim = 3
ncells_per_dim = IntArray(3)
ncells_per_dim[0] = 4; ncells_per_dim[1] = 5; ncells_per_dim[2] = 2
# valid un-flattened indices
cids = [ [i, j, k] for i in range(4) for j in range(5) for k in range(2) ]
for _cid in cids:
cid = IntPoint( _cid[0], _cid[1], _cid[2])
flattened = nnps.py_flatten( cid, ncells_per_dim, dim )
unflattened = nnps.py_unflatten( flattened, ncells_per_dim, dim )
# the unflattened index should match with cid
assert( cid == unflattened )
def test_1D_get_valid_cell_index():
dim = 1
# simulate a dummy distribution such that 10 cells are along the
# 'x' direction
n_cells = 10
ncells_per_dim = IntArray(3)
ncells_per_dim[0] = n_cells
ncells_per_dim[1] = 0
ncells_per_dim[2] = 0
# target cell
cx = 1; cy = cz = 0
# as long as cy and cz are 0, the function should return the valid
# flattened cell index for the cell
for i in [-1, 0, 1]:
index = nnps.py_get_valid_cell_index(
IntPoint(cx+i, cy, cz), ncells_per_dim, dim, n_cells)
assert index != -1
# index should be -1 whenever cy and cz are > 1. This is
# specifically the case that was failing earlier.
for j in [-1, 1]:
for k in [-1, 1]:
index = nnps.py_get_valid_cell_index(
IntPoint(cx,cy+j,cz+k), ncells_per_dim, dim, n_cells)
assert index == -1
def test_get_centroid():
cell = nnps.Cell(IntPoint(0, 0, 0), cell_size=0.1, narrays=1)
centroid = Point()
cell.get_centroid(centroid)
assert(abs(centroid.x - 0.05) < 1e-10)
assert(abs(centroid.y - 0.05) < 1e-10)
assert(abs(centroid.z - 0.05) < 1e-10)
cell = nnps.Cell(IntPoint(1, 2, 3), cell_size=0.5, narrays=1)
cell.get_centroid(centroid)
assert(abs(centroid.x - 0.75) < 1e-10)
assert(abs(centroid.y - 1.25) < 1e-10)
assert(abs(centroid.z - 1.75) < 1e-10)
def test_get_bbox():
cell_size = 0.1
cell = nnps.Cell(IntPoint(0, 0, 0), cell_size=cell_size, narrays=1)
centroid = Point()
boxmin = Point()
boxmax = Point()
cell.get_centroid(centroid)
cell.get_bounding_box(boxmin, boxmax)
assert(abs(boxmin.x - (centroid.x - 1.5*cell_size)) < 1e-10)
assert(abs(boxmin.y - (centroid.y - 1.5*cell_size)) < 1e-10)
assert(abs(boxmin.z - (centroid.z - 1.5*cell_size)) < 1e-10)
assert(abs(boxmax.x - (centroid.x + 1.5*cell_size)) < 1e-10)
assert(abs(boxmax.y - (centroid.y + 1.5*cell_size)) < 1e-10)
assert(abs(boxmax.z - (centroid.z + 1.5*cell_size)) < 1e-10)
cell_size = 0.5
cell = nnps.Cell(IntPoint(1, 2, 0), cell_size=cell_size, narrays=1)
cell.get_centroid(centroid)
cell.get_bounding_box(boxmin, boxmax)
assert(abs(boxmin.x - (centroid.x - 1.5*cell_size)) < 1e-10)
assert(abs(boxmin.y - (centroid.y - 1.5*cell_size)) < 1e-10)
assert(abs(boxmin.z - (centroid.z - 1.5*cell_size)) < 1e-10)
assert(abs(boxmax.x - (centroid.x + 1.5*cell_size)) < 1e-10)
assert(abs(boxmax.y - (centroid.y + 1.5*cell_size)) < 1e-10)
assert(abs(boxmax.z - (centroid.z + 1.5*cell_size)) < 1e-10)
if __name__ == '__main__':
unittest.main()
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