def test_pde_check_kernels(ctx_factory, knl_info, order=5):
dim = knl_info.kernel.dim
tctx = t.ToyContext(ctx_factory(), knl_info.kernel,
extra_source_kwargs=knl_info.extra_kwargs)
pt_src = t.PointSources(
tctx,
np.random.rand(dim, 50) - 0.5,
np.ones(50))
from pytools.convergence import EOCRecorder
from sumpy.point_calculus import CalculusPatch
eoc_rec = EOCRecorder()
for h in [0.1, 0.05, 0.025]:
cp = CalculusPatch(np.array([1, 0, 0])[:dim], h=h, order=order)
pot = pt_src.eval(cp.points)
pde = knl_info.pde_func(cp, pot)
err = la.norm(pde)
eoc_rec.add_data_point(h, err)
print(eoc_rec)
assert eoc_rec.order_estimate() > order - knl_info.nderivs + 1 - 0.1
def main():
from sumpy.kernel import ( # noqa: F401
YukawaKernel, HelmholtzKernel, LaplaceKernel)
tctx = t.ToyContext(
cl.create_some_context(),
#LaplaceKernel(2),
YukawaKernel(2),
extra_kernel_kwargs={"lam": 5},
#HelmholtzKernel(2), extra_kernel_kwargs={"k": 0.3},
)
pt_src = t.PointSources(tctx, np.random.rand(2, 50) - 0.5, np.ones(50))
fp = FieldPlotter([3, 0], extent=8)
if 0 and plt is not None:
t.logplot(fp, pt_src, cmap="jet")
plt.colorbar()
plt.show()
mexp = t.multipole_expand(pt_src, [0, 0], 5)
mexp2 = t.multipole_expand(mexp, [0, 0.25]) # noqa: F841
lexp = t.local_expand(mexp, [3, 0])
lexp2 = t.local_expand(lexp, [3, 1], 3)
#diff = mexp - pt_src
#diff = mexp2 - pt_src
diff = lexp2 - pt_src
print(t.l_inf(diff, 1.2, center=lexp2.center))
if 1 and plt is not None:
t.logplot(fp, diff, cmap="jet", vmin=-3, vmax=0)
plt.colorbar()
plt.show()
def test_toy_p2e2e2p(ctx_factory, case):
dim = case.dim
src = case.source.reshape(dim, -1)
tgt = case.target.reshape(dim, -1)
if not 0 <= case.conv_factor <= 1:
raise ValueError("convergence factor not in valid range: %e" %
case.conv_factor)
from sumpy.expansion.local import VolumeTaylorLocalExpansion
from sumpy.expansion.multipole import VolumeTaylorMultipoleExpansion
cl_ctx = ctx_factory()
ctx = t.ToyContext(cl_ctx, LaplaceKernel(dim),
VolumeTaylorMultipoleExpansion,
VolumeTaylorLocalExpansion)
errors = []
src_pot = t.PointSources(ctx, src, weights=np.array([1.]))
pot_actual = src_pot.eval(tgt).item()
for order in ORDERS_P2E2E2P:
expn = case.expansion1(src_pot, case.center1, order=order)
expn2 = case.expansion2(expn, case.center2, order=order)
pot_p2e2e2p = expn2.eval(tgt).item()
errors.append(np.abs(pot_actual - pot_p2e2e2p))
conv_factor = approx_convergence_factor(1 + np.array(ORDERS_P2E2E2P),
errors)