def make_condensational_growth_solver(nr, r_min, r_max, GC_max, grid_layout, psi_coord, pdf_of_r, drdt_of_r, opts: Options):
# psi = psi(p)
dp_dr = psi_coord.dx_dr
dx_dr = grid_layout.dx_dr
xh, dx = np.linspace(
grid_layout.x(r_min),
grid_layout.x(r_max),
nr + 1,
retstep=True
)
rh = grid_layout.r(xh)
x = np.linspace(
xh[0] + dx / 2,
xh[-1] - dx / 2,
nr
)
r = grid_layout.r(x)
def pdf_of_r_over_psi(r):
return pdf_of_r(r) / psi_coord.dx_dr(r)
psi = discretised_analytical_solution(rh, pdf_of_r_over_psi, midpoint_value=True, r=r)
dp_dt = drdt_of_r(rh) * dp_dr(rh)
G = dp_dr(r) / dx_dr(r)
# C = dr_dt * dt / dr
# GC = dp_dr / dx_dr * dr_dt * dt / dr =
# \ \_____ / _..____/
# \_____.._____/ \_ dt/dx
# |
# dp_dt
dt = GC_max * dx / np.amax(dp_dt)
GCh = dp_dt * dt / dx
# CFL condition
np.testing.assert_array_less(np.abs(GCh), 1)
g_factor = ScalarField(G.astype(dtype=opts.dtype), halo=opts.n_halo, boundary_conditions=(ExtrapolatedBoundaryCondition(),))
state = ScalarField(psi.astype(dtype=opts.dtype), halo=opts.n_halo, boundary_conditions=(ConstantBoundaryCondition(0),))
GC_field = VectorField([GCh.astype(dtype=opts.dtype)], halo=opts.n_halo, boundary_conditions=(ConstantBoundaryCondition(0),))
stepper = Stepper(
options=opts,
n_dims=1,
non_unit_g_factor=True
)
return (
Solver(stepper=stepper, g_factor=g_factor, advectee=state, advector=GC_field),
r,
rh,
dx,
dt,
g_factor
)
def test_timing_2d(benchmark,
options,
dtype,
grid_static_str,
concurrency_str,
plot=False):
if grid_static_str == "static":
grid_static = True
elif grid_static_str == "dynamic":
grid_static = False
else:
raise ValueError()
if concurrency_str == "serial":
numba.set_num_threads(1)
else:
numba.set_num_threads(numba.config.NUMBA_NUM_THREADS)
setup = Setup(n_rotations=6)
_, __, z = from_pdf_2d(setup.pdf,
xrange=setup.xrange,
yrange=setup.yrange,
gridsize=setup.grid)
C = (-.5, .25)
grid = z.shape
advector_data = [
np.full((grid[0] + 1, grid[1]), C[0], dtype=options.dtype),
np.full((grid[0], grid[1] + 1), C[1], dtype=options.dtype)
]
advector = VectorField(advector_data,
halo=options.n_halo,
boundary_conditions=(PeriodicBoundaryCondition(),
PeriodicBoundaryCondition()))
advectee = ScalarField(data=z.astype(dtype=options.dtype),
halo=options.n_halo,
boundary_conditions=(PeriodicBoundaryCondition(),
PeriodicBoundaryCondition()))
if grid_static:
stepper = Stepper(options=options, grid=grid)
else:
stepper = Stepper(options=options, n_dims=2)
solver = Solver(stepper=stepper, advectee=advectee, advector=advector)
def set_z():
solver.advectee.get()[:] = z
benchmark.pedantic(solver.advance, (setup.nt, ),
setup=set_z,
warmup_rounds=1,
rounds=3)
if options.n_iters == 1 or options.flux_corrected_transport:
np.testing.assert_almost_equal(np.amin(solver.advectee.get()), h0)
assert np.amax(solver.advectee.get()) < 10 * h
if plot:
pyplot.imshow(solver.advectee.get())
pyplot.colorbar()
pyplot.show()
def test_make_upwind(self):
# Arrange
psi_data = np.array((0, 1, 0))
flux_data = np.array((0, 0, 1, 0))
options = Options()
halo = options.n_halo
traversals = Traversals(grid=psi_data.shape,
halo=halo,
jit_flags={},
n_threads=1)
upwind = make_upwind(options=options,
non_unit_g_factor=False,
traversals=traversals)
bc = [PeriodicBoundaryCondition()]
psi = ScalarField(psi_data, halo, bc)
psi_impl = psi.impl
flux_impl = VectorField((flux_data, ), halo, bc).impl
null_impl = ScalarField.make_null(len(psi_data.shape)).impl
# Act
upwind(psi_impl[0], *flux_impl, *null_impl)
# Assert
np.testing.assert_array_equal(psi.get(), np.roll(psi_data, 1))
def test_apply_scalar(n_threads, halo, grid, loop):
n_dims = len(grid)
if n_dims == 1 and n_threads > 1:
return
# arrange
traversals = Traversals(grid, halo, jit_flags, n_threads)
sut = traversals.apply_scalar(loop=loop)
scl_null_arg_impl = ScalarField.make_null(n_dims).impl
vec_null_arg_impl = VectorField.make_null(n_dims).impl
out = ScalarField(np.zeros(grid), halo,
[ConstantBoundaryCondition(np.nan)] * n_dims)
# act
sut(_cell_id_scalar, _cell_id_scalar, *out.impl[0],
*vec_null_arg_impl[0], *vec_null_arg_impl[1],
*scl_null_arg_impl[0], *scl_null_arg_impl[1],
*scl_null_arg_impl[0], *scl_null_arg_impl[1],
*scl_null_arg_impl[0], *scl_null_arg_impl[1])
# assert
data = out.get()
assert data.shape == grid
focus = (-halo, -halo)
for i in range(halo, halo + grid[0]):
for j in (-1, ) if n_dims == 1 else range(halo, halo + grid[1]):
value = indexers[n_dims].at[0](focus, data, i, j)
assert value == (n_dims if loop else 1) * cell_id(i, j)
assert scl_null_arg_impl[0][0][meta_halo_valid]
assert vec_null_arg_impl[0][0][meta_halo_valid]
assert not out.impl[0][0][meta_halo_valid]
def test_diffusion_only_2d(
data0=np.array([[0, 0, 0], [0, 1., 0], [0, 0, 0]]), mu=(.1, .1), nt=1):
# Arrange
options = Options(non_zero_mu_coeff=True)
bc = [PeriodicBoundaryCondition()] * 2
advectee = ScalarField(data0, options.n_halo, bc)
advector = VectorField(data=(np.zeros(
(data0.shape[0] + 1, data0.shape[1])),
np.zeros(
(data0.shape[0], data0.shape[1] + 1))),
halo=options.n_halo,
boundary_conditions=bc)
solver = Solver(stepper=Stepper(options=options, grid=data0.shape),
advector=advector,
advectee=advectee)
# Act
solver.advance(nt=nt, mu_coeff=mu)
# Assert
data1 = solver.advectee.get()
np.testing.assert_almost_equal(actual=np.sum(data1), desired=np.sum(data0))
assert np.amax(data0) > np.amax(data1)
assert np.amin(data1) >= 0
assert np.count_nonzero(data1) == 5
def test_scalar_1d(self, data, halo, side):
# arrange
field = ScalarField(data, halo, (PeriodicBoundaryCondition(),))
meta_and_data, fill_halos = field.impl
traversals = Traversals(grid=data.shape, halo=halo, jit_flags={})
sut, _ = traversals.make_boundary_conditions()
# act
sut(*meta_and_data, *fill_halos)
# assert
if side == LEFT:
np.testing.assert_array_equal(field.data[:halo], data[-halo:])
elif side == RIGHT:
np.testing.assert_array_equal(field.data[-halo:], data[:halo])
else:
raise ValueError()
def test_scalar_2d(self, halo, n_threads):
# arrange
data = np.array([[1, 6], [2, 7], [3, 8], [4, 9]])
bc = (PeriodicBoundaryCondition(),
PolarBoundaryCondition(data.shape, 0, 1))
field = ScalarField(data, halo, bc)
meta_and_data, fill_halos = field.impl
traversals = Traversals(grid=data.shape,
halo=halo,
jit_flags={},
n_threads=n_threads)
sut = traversals._fill_halos_scalar
# act
for thread_id in numba.prange(n_threads):
sut(thread_id, *meta_and_data, *fill_halos)
# assert
np.testing.assert_array_equal(
field.data[halo:-halo, :halo],
np.roll(field.get()[:, :halo], data.shape[0] // 2, axis=0))
np.testing.assert_array_equal(
field.data[halo:-halo, -halo:],
np.roll(field.get()[:, -halo:], data.shape[0] // 2, axis=0))
def test_1d_contiguous():
grid = (44, )
data = np.empty(grid)
bc = (PeriodicBoundaryCondition(), )
sut = ScalarField(data, halo=1, boundary_conditions=bc)
assert sut.get().data.contiguous
def test_2d_second_dim_contiguous():
grid = (44, 44)
data = np.empty(grid)
bc = (PeriodicBoundaryCondition(), PeriodicBoundaryCondition())
sut = ScalarField(data, halo=1, boundary_conditions=bc)
assert sut.get()[0, :].data.contiguous