def test_compressed_octree_has_lesser_depth_than_octree():
N = 50
x, y, z = np.mgrid[0:1:N * 1j, 0:1:N * 1j, 0:1:N * 1j]
x = x.ravel()
y = y.ravel()
z = z.ravel()
h = np.ones_like(x)
pa = get_particle_array(x=x, y=y, z=z, h=h)
x1 = np.array([20])
y1 = np.array([20])
z1 = np.array([20])
# For a dataset where one particle is far away from a
# cluster of particles
pa.add_particles(x=x1, y=y1, z=z1)
tree = Octree(10)
comp_tree = CompressedOctree(10)
depth_tree = tree.build_tree(pa)
depth_comp_tree = comp_tree.build_tree(pa)
# Test that the depth of compressed octree for the same
# leaf_max_particles is lesser than that of octree
assert depth_comp_tree < depth_tree
def test_single_level_octree():
N = 50
x, y, z = np.mgrid[0:1:N * 1j, 0:1:N * 1j, 0:1:N * 1j]
x = x.ravel()
y = y.ravel()
z = z.ravel()
h = np.ones_like(x)
pa = get_particle_array(x=x, y=y, z=z, h=h)
# For maximum leaf particles greater that total number
# of particles
tree = Octree(pa.get_number_of_particles() + 1)
tree.build_tree(pa)
# Test that depth of the tree is 1
assert tree.depth == 1
class SimpleOctreeTestCase(unittest.TestCase):
"""Simple test case for Octree
"""
def setUp(self):
N = 50
x, y, z = np.mgrid[0:1:N * 1j, 0:1:N * 1j, 0:1:N * 1j]
self.x = x.ravel()
self.y = y.ravel()
self.z = z.ravel()
self.h = np.ones_like(self.x)
self.tree = Octree(10)
def test_levels_in_tree(self):
pa = get_particle_array(x=self.x, y=self.y, z=self.z, h=self.h)
self.tree.build_tree(pa)
root = self.tree.get_root()
def _check_levels(node, level):
# Test that levels for nodes are correctly set
# starting from 0 at root
self.assertTrue(node.level == level)
children = node.get_children()
for child in children:
if child == None:
continue
_check_levels(child, level + 1)
_check_levels(root, 0)
self.tree.delete_tree()
def test_parent_for_node(self):
pa = get_particle_array(x=self.x, y=self.y, z=self.z, h=self.h)
self.tree.build_tree(pa)
root = self.tree.get_root()
# Test that parent of root is 'None'
self.assertTrue(root.get_parent() == None)
def _check_parent(node):
children = node.get_children()
for child in children:
if child == None:
continue
# Test that the parent is set correctly for all nodes
self.assertTrue(child.get_parent() == node)
_check_parent(child)
_check_parent(root)
self.tree.delete_tree()
def test_sum_of_indices_lengths_equals_total_number_of_particles(self):
pa = get_particle_array(x=self.x, y=self.y, z=self.z, h=self.h)
self.tree.build_tree(pa)
root = self.tree.get_root()
sum_indices = [0]
def _calculate_sum(node, sum_indices):
indices = node.get_indices(self.tree)
sum_indices[0] += indices.length
children = node.get_children()
for child in children:
if child == None:
continue
_calculate_sum(child, sum_indices)
_calculate_sum(root, sum_indices)
# Test that sum of lengths of all indices is equal to total
# number of particles
self.assertTrue(pa.get_number_of_particles() == sum_indices[0])
self.tree.delete_tree()
def test_plot_root(self):
pa = get_particle_array(x=self.x, y=self.y, z=self.z, h=self.h)
self.tree.build_tree(pa)
root = self.tree.get_root()
mpl = importorskip("matplotlib")
mpl.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
root.plot(ax)
for line in ax.lines:
xs = line.get_xdata()
ys = line.get_ydata()
line_length = (xs[0] - xs[1])**2 + (ys[0] - ys[1])**2
# Test that the lengths of sides 2D projection of the node
# is equal to the length of the side of the node
self.assertTrue(line_length == root.length**2 or \
line_length == 0)
self.tree.delete_tree()