def energies(path, kkind="iso"):
"""
This loops over all times of a given run
and return the magnetic and kinetic energy
as a function of time.
"""
r = Run(path)
times = get_times_from_h5(r.path+"/EM_B.h5")
Bnrj = np.zeros_like(times)
K = np.zeros_like(times)
for it,t in enumerate(times):
B = r.GetB(t)
protons = r.GetParticles(t, "protons")
Bnrj[it] = mag_energy(B)
K[it] = total_kinetic(protons,kind=kkind)
return r, Bnrj, K, times
def test_restarts(self, dim, interp, simInput):
print(f"test_restarts dim/interp:{dim}/{interp}")
simput = copy.deepcopy(simInput)
for key in ["cells", "dl", "boundary_types"]:
simput[key] = [simput[key]] * dim
if "refinement" not in simput:
b0 = [[10 for i in range(dim)], [19 for i in range(dim)]]
simput["refinement_boxes"] = {"L0": {"B0": b0}}
else: # https://github.com/LLNL/SAMRAI/issues/199
# tagging can handle more than one timestep as it does not
# appear subject to regridding issues, so we make more timesteps
# to confirm simulations are still equivalent
simput["time_step_nbr"] = 10
# if restart time exists it "loads" from restart file
# otherwise just saves restart files based on timestamps
assert "restart_time" not in simput["restart_options"]
simput["interp_order"] = interp
time_step = simput["time_step"]
time_step_nbr = simput["time_step_nbr"]
restart_idx = 4
restart_time = time_step * restart_idx
timestamps = [time_step * restart_idx, time_step * time_step_nbr]
# first simulation
local_out = f"{out}/test/{dim}/{interp}/mpi_n/{cpp.mpi_size()}/id{self.ddt_test_id()}"
simput["restart_options"]["dir"] = local_out
simput["diag_options"]["options"]["dir"] = local_out
ph.global_vars.sim = None
ph.global_vars.sim = ph.Simulation(**simput)
assert "restart_time" not in ph.global_vars.sim.restart_options
model = setup_model()
dump_all_diags(model.populations, timestamps=np.array(timestamps))
Simulator(ph.global_vars.sim).run().reset()
self.register_diag_dir_for_cleanup(local_out)
diag_dir0 = local_out
# second restarted simulation
local_out = f"{local_out}_n2"
simput["diag_options"]["options"]["dir"] = local_out
simput["restart_options"]["restart_time"] = restart_time
ph.global_vars.sim = None
ph.global_vars.sim = ph.Simulation(**simput)
assert "restart_time" in ph.global_vars.sim.restart_options
model = setup_model()
dump_all_diags(model.populations, timestamps=np.array(timestamps))
Simulator(ph.global_vars.sim).run().reset()
self.register_diag_dir_for_cleanup(local_out)
diag_dir1 = local_out
def check(qty0, qty1, checker):
checks = 0
for ilvl, lvl0 in qty0.patch_levels.items():
patch_level1 = qty1.patch_levels[ilvl]
for p_idx, patch0 in enumerate(lvl0):
patch1 = patch_level1.patches[p_idx]
for pd_key, pd0 in patch0.patch_datas.items():
pd1 = patch1.patch_datas[pd_key]
self.assertNotEqual(id(pd0), id(pd1))
checker(pd0, pd1)
checks += 1
return checks
def check_particles(qty0, qty1):
return check(
qty0, qty1,
lambda pd0, pd1: self.assertEqual(pd0.dataset, pd1.dataset))
def check_field(qty0, qty1):
return check(
qty0, qty1, lambda pd0, pd1: np.testing.assert_equal(
pd0.dataset[:], pd1.dataset[:]))
def count_levels_and_patches(qty):
n_levels = len(qty.patch_levels)
n_patches = 0
for ilvl, lvl in qty.patch_levels.items():
n_patches += len(qty.patch_levels[ilvl].patches)
return n_levels, n_patches
n_quantities_per_patch = 20
pops = model.populations
for time in timestamps:
checks = 0
run0 = Run(diag_dir0)
run1 = Run(diag_dir1)
checks += check_particles(run0.GetParticles(time, pops),
run1.GetParticles(time, pops))
checks += check_field(run0.GetB(time), run1.GetB(time))
checks += check_field(run0.GetE(time), run1.GetE(time))
checks += check_field(run0.GetNi(time), run1.GetNi(time))
checks += check_field(run0.GetVi(time), run1.GetVi(time))
for pop in pops:
checks += check_field(run0.GetFlux(time, pop),
run1.GetFlux(time, pop))
checks += check_field(run0.GetN(time, pop),
run1.GetN(time, pop))
n_levels, n_patches = count_levels_and_patches(run0.GetB(time))
self.assertEqual(n_levels, 2) # at least 2 levels
self.assertGreaterEqual(n_patches,
n_levels) # at least one patch per level
self.assertEqual(checks, n_quantities_per_patch * n_patches)