def test_outside_box():
grid = StructuredGrid()
# test by setting the scalar field to the y coordinate
inside = grid.inside(grid.barycentre + 5)
assert np.all(
~inside == np.any((grid.barycentre + 5) > grid.maximum, axis=1))
inside = grid.inside(grid.barycentre - 5)
assert np.all(
~inside == np.any((grid.barycentre - 5) < grid.origin, axis=1))
cix, ciy, ciz = grid.position_to_cell_index(grid.barycentre - 5)
assert np.all(cix[inside] < grid.nsteps_cells[0])
assert np.all(ciy[inside] < grid.nsteps_cells[1])
assert np.all(ciz[inside] < grid.nsteps_cells[2])
cornersx, cornersy, cornersz = grid.cell_corner_indexes(cix, ciy, ciz)
assert np.all(cornersx[inside] < grid.nsteps[0])
assert np.all(cornersy[inside] < grid.nsteps[1])
assert np.all(cornersz[inside] < grid.nsteps[2])
globalidx = grid.global_indicies(
np.dstack([cornersx, cornersy, cornersz]).T)
# print(globalidx[inside],grid.n_nodes,inside)
assert np.all(globalidx[inside] < grid.n_nodes)
inside = grid.inside(grid.barycentre - 5)
inside, grid.position_to_cell_corners(grid.barycentre - 5)
vector = grid.evaluate_gradient(grid.barycentre - 5, grid.nodes[:, 1])
assert np.sum(np.mean(vector[inside, :], axis=0) -
np.array([0, 1, 0])) == 0
vector = grid.evaluate_gradient(grid.nodes, grid.nodes[:, 1])
def test_evaluate_gradient():
grid = StructuredGrid()
# test by setting the scalar field to the y coordinate
vector = grid.evaluate_gradient(grid.barycentre, grid.nodes[:, 1])
assert np.sum(vector - np.array([0, 1, 0])) == 0
# same test but for a bigger grid, making sure scaling for cell is ok
grid = StructuredGrid(step_vector=np.array([100, 100, 100]))
vector = grid.evaluate_gradient(grid.barycentre, grid.nodes[:, 1])
assert np.sum(vector - np.array([0, 1, 0])) == 0
def test_evaluate_gradient2():
# this test is the same as above but we will use a random vector
np.random.seed(0)
for i in range(10):
step = np.random.uniform(0, 100)
grid = StructuredGrid(step_vector=np.array([step, step, step]))
# define random vector
n = np.random.random(3)
n /= np.linalg.norm(n)
distance = (n[0] * grid.nodes[:, 0] + n[1] * grid.nodes[:, 1] +
n[2] * grid.nodes[:, 2])
vector = grid.evaluate_gradient(np.random.uniform(1, 8, size=(100, 3)),
distance)
assert np.all(np.isclose(np.sum(vector - n[None, :], axis=1),
0)) == True
assert i == 9
def test_evaluate_gradient():
grid = StructuredGrid()
grid.update_property('Y', grid.nodes[:, 1])
vector = np.mean(grid.evaluate_gradient(grid.barycentre(), 'Y'), axis=0)
# vector/=np.linalg.norm(vector)
assert np.sum(vector - np.array([0, grid.step_vector[1], 0])) == 0