def tstep_hook(t, tstep, stats_intv, statsfile, field_to_subspace,
field_to_subproblem, subproblems, w_, enable_PF, **namespace):
info_blue("Timestep = {}".format(tstep))
if enable_PF and stats_intv and tstep % stats_intv == 0:
# GL: Seems like a rather awkward way of doing this,
# but any other way seems to f**k up the simulation.
# Anyhow, a better idea could be to move some of this to a post-processing stage.
# GL: Move into common/utilities at a certain point.
subproblem_name, subproblem_i = field_to_subproblem["phi"]
phi = w_[subproblem_name].split(deepcopy=True)[subproblem_i]
bubble = 0.5 * (1. - df.sign(phi))
mass = df.assemble(bubble * df.dx)
massy = df.assemble(bubble * df.Expression("x[1]", degree=1) * df.dx)
if mpi_is_root():
with file(statsfile, "a") as outfile:
outfile.write("{} {} {} \n".format(t, mass, massy))
def tstep_hook(t, tstep, stats_intv, statsfile, field_to_subspace,
field_to_subproblem, subproblems, w_, enable_PF, **namespace):
info_blue("Timestep = {}".format(tstep))
if stats_intv and tstep % stats_intv == 0 and enable_PF:
# GL: Seems like a rather awkward way of doing this, but any
# other way seems to f**k up the simulation. Anyhow, a better
# idea could be to move some of this to a post-processing
# stage.
subproblem_name, subproblem_i = field_to_subproblem["phi"]
Q = w_[subproblem_name].split(deepcopy=True)[subproblem_i]
bubble = df.interpolate(Q, field_to_subspace["phi"].collapse())
bubble = 0.5 * (1. - df.sign(bubble))
mass = df.assemble(bubble * df.dx)
massy = df.assemble(bubble * df.Expression("x[1]", degree=1) * df.dx)
if mpi_is_root():
with file(statsfile, "a") as outfile:
outfile.write("{} {} {} \n".format(t, mass, massy))