def test_cvt_lloyd(mesh, num_steps, ref):
print(num_steps)
m = copy.deepcopy(mesh)
optimesh.optimize(m, "Lloyd", 1.0e-2, num_steps, verbose=False)
assert_norm_equality(m.points, ref, 1.0e-12)
# try the other way of calling optimesh
X, c = mesh.points.copy(), mesh.cells("points").copy()
X, _ = optimesh.optimize_points_cells(X, c, "lloyd", 1.0e-2, num_steps)
assert_norm_equality(X, ref, 1.0e-12)
def test_cvt_qnb_boundary(n=10):
X, cells = create_random_circle(n=n, radius=1.0)
def boundary_step(x):
x0 = [0.0, 0.0]
r = 1.0
# simply project onto the circle
y = (x.T - x0).T
r = np.sqrt(np.einsum("ij,ij->j", y, y))
return ((y / r * r).T + x0).T
mesh = meshplex.MeshTri(X, cells)
optimesh.optimize(mesh, "Lloyd", 1.0e-2, 100, boundary_step=boundary_step)
# X, cells = optimesh.cvt.quasi_newton_uniform_lloyd(
# X, cells, 1.0e-2, 100, boundary_step=boundary_step
# )
# X, cells = optimesh.cvt.quasi_newton_uniform_blocks(
# X, cells, 1.0e-2, 100, boundary=Circle()
# )
mesh.show()
def test_cvt_qnb(mesh, ref):
m = copy.deepcopy(mesh)
optimesh.optimize(m, "CVT (block-diagonal)", 1.0e-2, 100)
assert_norm_equality(m.points, ref, 1.0e-10)
def test_cvt_lloyd_overrelaxed(mesh, ref):
m = copy.deepcopy(mesh)
optimesh.optimize(m, "Lloyd", 1.0e-2, 100, omega=2.0)
assert_norm_equality(m.points, ref, 1.0e-12)
def test_cvt_qnf(mesh, ref):
m = copy.deepcopy(mesh)
optimesh.optimize(m, "cvt (full)", 1.0e-2, 100, omega=0.9)
m.show()
# Assert that we're dealing with the mesh we expect.
assert_norm_equality(m.points, ref, 1.0e-12)
def test_nonlinear_optimization(mesh, ref):
m = copy.deepcopy(mesh)
optimesh.optimize(m, "ODT (BFGS)", 1.0e-5, 100)
assert_norm_equality(m.points, ref, 1.0e-12)
def test_fixed_point(mesh, ref):
m = copy.deepcopy(mesh)
optimesh.optimize(m, "ODT (fixed-point)", 1.0e-3, 100)
assert_norm_equality(m.points, ref, 1.0e-12)
def test_fixed_point(mesh, ref):
optimesh.optimize(mesh, "laplace", 0.0, 10)
assert_norm_equality(mesh.points, ref, 1.0e-12)