pparams['u0'] = 1
# Fill description dictionary for easy hierarchy creation
description = {}
description['problem_class'] = swfw_scalar
description['problem_params'] = pparams
description['dtype_u'] = mesh
description['dtype_f'] = rhs_imex_mesh
description['collocation_class'] = collclass.CollGaussLobatto
description['num_nodes'] = [3]
description['do_LU'] = False
description['sweeper_class'] = imex_1st_order
description['level_params'] = lparams
# quickly generate block of steps
MS = mp.generate_steps(num_procs, sparams, description)
# setup parameters "in time"
t0 = 0
dt = 1.0
Tend = 1.0
P = MS[0].levels[0].prob
uinit = P.u_exact(t0)
print('Init:', uinit.values)
# call main function to get things done...
uend, stats = mp.run_pfasst(MS, u0=uinit, t0=t0, dt=dt, Tend=Tend)
uex = P.u_exact(Tend)
description['num_nodes'] = 3
elif order == 5:
description['num_nodes'] = 3
sparams['maxiter'] = order
for ii in range(0, np.shape(nsteps)[1]):
ns = nsteps[order - 3, ii]
if ((order == 3) or (order == 4)):
pparams['nvars'] = [(2, 2 * ns)]
elif order == 5:
pparams['nvars'] = [(2, 2 * ns)]
# quickly generate block of steps
MS = mp.generate_steps(num_procs, sparams, description)
dt = Tend / float(ns)
# get initial values on finest level
P = MS[0].levels[0].prob
rkimex = rk_imex(P.A.astype('complex'), P.Dx.astype('complex'),
order)
uinit = P.u_exact(t0)
y0 = np.concatenate((uinit.values[0, :], uinit.values[1, :]))
y0 = y0.astype('complex')
if ii == 0:
print("Time step: %4.2f" % dt)
print("Fast CFL number: %4.2f" % (pparams['cs'] * dt / P.dx))
print("Slow CFL number: %4.2f" % (pparams['cadv'] * dt / P.dx))
