def taylor_test(J, c, dc, dJdm=None):
"""
Dummy `taylor_test` function for consistency of notation between discrete and continuous
adjoint problems.
"""
assert dJdm is not None
if isinstance(c, Function):
c = c.dat.data
elif isinstance(c, list) and isinstance(c[0], Function):
c = np.array([ci.dat.data[0] for ci in c])
if isinstance(dc, Function):
dc = c.dat.data
elif isinstance(dc, list) and isinstance(dc[0], Function):
dc = np.array([dci.dat.data[0] for dci in dc])
opt.taylor_test(J, dJdm, c, delta_m=dc, verbose=True)
if bool(args.taylor_test_okada or False):
"""
Consider the reduced functional
J(m) = e . S(m)
Then dJdm is the same as propagating e through the reverse mode of AD on S.
"""
print("Taylor test Okada...")
# np.random.seed(0)
_rf_okada = lambda m: np.sum(okada_source(m))
_gradient_okada = lambda _: gradient_okada(_, np.ones(len(op.indices)))
m_init = 0.7 * np.concatenate(
[op.control_parameters[ctrl] for ctrl in op.active_controls])
minconv = opt.taylor_test(_rf_okada, _gradient_okada, m_init, verbose=True)
assert minconv > 1.90
# --- Setup coupling
def tsunami_ic(dislocation):
"""
Set the initial velocity-elevation tuple for the tsunami
propagation model, given some dislocation field.
"""
surf = Function(P1)
surf.dat.data[op.indices] = dislocation
return surf
savefig("original_source", "plots", extensions=["jpg"])
fig, axes = plt.subplots(figsize=(7, 7))
cbar = fig.colorbar(axes.contourf(X, Y, eta_pert, **plotting_kwargs), ax=axes)
cbar.ax.tick_params(labelsize=tick_fontsize)
cbar.set_label(r"Elevation [$\mathrm m$]", fontsize=fontsize)
axes.set_xlabel("Longitude", fontsize=fontsize)
axes.set_ylabel("Latitude", fontsize=fontsize)
use_degrees(axes)
for tick in axes.xaxis.get_major_ticks():
tick.label.set_fontsize(tick_fontsize)
for tick in axes.yaxis.get_major_ticks():
tick.label.set_fontsize(tick_fontsize)
savefig("perturbed_source", "plots", extensions=["jpg"])
# Taylor test
taylor_test(reduced_functional, gradient, op.input_vector, verbose=True)
def opt_cb(m):
"""
Print progress after every successful line search.
"""
msg = "{:4d}: J = {:.4e} ||dJdm|| = {:.4e}"
counter = len(op.J_progress)
if counter % 100 == 0:
print(msg.format(counter, op.J_progress[-1], op.dJdm_progress[-1]))
# Inversion
op.J_progress = []
op.dJdm_progress = []