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_compressed_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 = CompressedOctree(pa.get_number_of_particles() + 1)
tree.build_tree(pa)
# Test that depth of the tree is 1
assert tree.depth == 1
def setUp(self):
N = int(1e5)
self.x = np.linspace(0, 1, num=N)
self.y = np.zeros_like(self.x)
self.z = np.zeros_like(self.x)
self.h = np.ones_like(self.x)
self.tree = CompressedOctree(100)
def setUp(self):
N = 500
x, y = np.mgrid[0:1:N * 1j, 0:1:N * 1j]
self.x = x.ravel()
self.y = y.ravel()
self.z = np.zeros_like(self.x)
self.h = np.ones_like(self.x)
self.tree = CompressedOctree(10)
def setUp(self):
N = 50
x, y, z = np.mgrid[-1:1:N * 1j, -1:1:N * 1j, -1:1:N * 1j]
self.x = x.ravel()
self.y = y.ravel()
self.z = z.ravel()
x1 = np.array([-1e-20])
y1 = np.array([1e-20])
z1 = np.array([1e-20])
self.x = np.concatenate([self.x, x1])
self.y = np.concatenate([self.y, y1])
self.z = np.concatenate([self.z, z1])
self.h = np.ones_like(self.x)
self.tree = CompressedOctree(10)