def run(enable_output = True):
passed = True
field_center = []
E = []
for size in system_sizes:
with state.State("") as p_state:
parameters.llg.set_output_general(p_state, any=False)
#turn ddi on
hamiltonian.set_ddi(p_state, 1)
#turn everything else off
hamiltonian.set_exchange(p_state, 0, [])
hamiltonian.set_dmi(p_state, 0, [])
hamiltonian.set_anisotropy(p_state, 0, [0,0,1])
hamiltonian.set_boundary_conditions(p_state, [0,0,0])
hamiltonian.set_field(p_state, 0, [0,0,1])
geometry.set_n_cells(p_state, [size, size, 1])
configuration.plus_z(p_state)
nos = system.get_nos(p_state)
simulation.start(p_state, simulation.METHOD_LLG, simulation.SOLVER_VP, n_iterations = 1)
Gradient_Spirit = np.array(system.get_effective_field(p_state)).reshape(nos, 3)
E_Spirit = system.get_energy(p_state)
field_center.append(Gradient_Spirit[int(size/2 + size/2 * size)])
E.append(E_Spirit)
if enable_output:
with open(outputfile, 'w') as out:
out.write("#system_size, field_center_z, energy\n")
for i in range(len(E)):
out.write("{0}, {1}, {2}\n".format(system_sizes[i], field_center[i][2], E[i]))
def run(self):
passed = True
with state.State(self.inputfile1, quiet=True) as p_state:
configuration.plus_z(p_state)
system.update_data(p_state)
E_1 = system.get_energy(p_state)
print('>>> Result 1: mu_s = 1')
print('E_DDI = ', E_1)
with state.State(self.inputfile2, quiet=True) as p_state:
configuration.plus_z(p_state)
system.update_data(p_state)
E_2 = system.get_energy(p_state)
print('\n>>> Result 2: mu_s = 2')
print('E_DDI = ', E_2)
if np.abs(E_1 * 2**2 - E_2) > 1e-1:
passed = False
return passed
def test_energy(self, p_state, mu=None):
nos = system.get_nos(p_state)
pos = np.array(geometry.get_positions(p_state)).reshape(nos, 3)
spins = np.array(system.get_spin_directions(p_state)).reshape(nos, 3)
if type(mu) == type(None):
mu_s = np.ones(len(pos))
else:
mu_s = mu
E_BF = self.E_DDI_BF(pos, spins, mu_s)
E_Spirit = system.get_energy(p_state)
return E_BF, E_Spirit
def run(self, enable_output=True):
passed = True
self.inputfile = "test_cases/input/input_saturated_film.cfg"
theory = 0.5
N_ITERATIONS = 1
system_sizes = [10 * (i + 1) for i in range(5)]
field_center = []
E = []
for size in system_sizes:
with state.State(self.inputfile) as p_state:
parameters.llg.set_output_general(p_state, any=False)
geometry.set_n_cells(p_state, [size, size, 1])
configuration.plus_z(p_state)
nos = system.get_nos(p_state)
simulation.start(p_state,
simulation.METHOD_LLG,
simulation.SOLVER_VP,
n_iterations=1)
Gradient_Spirit = np.array(
system.get_effective_field(p_state)).reshape(nos, 3)
E_Spirit = system.get_energy(p_state)
field_center.append(Gradient_Spirit[int(size / 2 +
size / 2 * size)])
E.append(E_Spirit)
if enable_output:
with open('output_' + self.name + '.txt', 'w') as out:
out.write("#system_size, field_center_z, energy\n")
for i in range(len(E)):
out.write("{0}, {1}, {2}\n".format(system_sizes[i],
field_center[i][2],
E[i]))
def test_get_energy(self):
# NOTE: that test is trivial
E = system.get_energy(self.p_state)
p_state, n_thermalisation, n_thermalisation
) # We want n_thermalisation iterations and only a single log message
simulation.start(p_state, MC) # Start a MC simulation
# Sampling at given temperature
parameters.mc.set_iterations(
p_state, n_decorrelation * n_samples, n_decorrelation * n_samples
) # We want n_decorrelation iterations and only a single log message
simulation.start(p_state, MC,
single_shot=True) # Start a single-shot MC simulation
for n in range(n_samples):
# Run decorrelation
for i_decorr in range(n_decorrelation):
simulation.single_shot(p_state) # one MC iteration
# Get energy
E_local = system.get_energy(p_state) / NOS
# Get magnetization
M_local = np.array(quantities.get_magnetization(p_state))
M_local_tot = np.linalg.norm(M_local)
# Add to cumulative averages
E += E_local
E2 += E_local**2
M += M_local_tot
M2 += M_local_tot**2
M4 += M_local_tot**4
# Make sure the MC simulation is not running anymore
simulation.stop(p_state)
# Average over samples
E /= n_samples
E2 /= n_samples