PLOT = True
s = Sphere()
p = 3
diff_stencil_arm = 1
dim = 3
# As a byproduct of finding the banded grid, we already have its
# closest points, so we don't really have to call s.closest_point()
cp, distance, grid, dx = s.grid(num_blocks_per_dim=41,
levels=1,
p=p,
diff_stencil_arm=diff_stencil_arm)
cp2, distance2, _, _ = s.closest_point(grid)
assert np.allclose(cp, cp2)
assert np.allclose(distance, distance2)
# Corners of the virtual grid, superset of `grid`
ll = np.array(dim * [grid.min()]) - 3 * dx
ur = np.array(dim * [grid.max()]) + 3 * dx
virtual_grid_shape = np.abs(ur-ll) / dx + 1
# The (i,j,...) indices of the grid points, taking `ll` as origin.
int_grid = np.round((grid - ll) / dx).astype(np.int)
# Initial conditions
th, phi, r = cart2sph(grid[:, 0], grid[:, 1], grid[:, 2])
u = np.cos(phi + np.pi / 2)
# Let's keep a copy of the initial conditions
PLOT = False
s = Sphere()
p = 3
diff_stencil_arm = 1
dim = 3
# As a byproduct of finding the banded grid, we already have its
# closest points, so we don't really have to call s.closest_point()
cp, distance, grid, dx = s.grid(num_blocks_per_dim=41,
levels=1,
p=p,
diff_stencil_arm=diff_stencil_arm)
cp2, distance2, _, _ = s.closest_point(grid)
assert np.allclose(cp, cp2)
assert np.allclose(distance, distance2)
# Corners of the virtual grid, superset of `grid`
ll = np.array(dim * [grid.min()]) - 3 * dx
ur = np.array(dim * [grid.max()]) + 3 * dx
virtual_grid_shape = np.abs(ur - ll) / dx + 1
# The (i,j,...) indices of the grid points, taking `ll` as origin.
int_grid = np.round((grid - ll) / dx).astype(np.int)
# Initial conditions
th, phi, r = cart2sph(grid[:, 0], grid[:, 1], grid[:, 2])
u = np.cos(phi + np.pi / 2)
# Let's keep a copy of the initial conditions