def test_compare_energies():
ref = np.loadtxt(os.path.join(MODULE_DIR, "energies_ref.txt"))
if not os.path.isfile(os.path.join(MODULE_DIR, "energies.txt")) \
or (os.path.getctime(os.path.join(MODULE_DIR, "energies.txt")) <
os.path.getctime(os.path.abspath(__file__))):
run_finmag()
computed = np.loadtxt(os.path.join(MODULE_DIR, "energies.txt"))
assert np.size(ref) == np.size(computed), "Compare number of energies."
vol = mesh_volume(mesh) * unit_length**mesh.topology().dim()
#30x30x100nm^3 = 30x30x100=9000
# Compare exchange energy...
exch = computed[:, 0] / vol # <-- ... density!
exch_nmag = ref[:, 0]
diff = abs(exch - exch_nmag)
rel_diff = np.abs(diff / max(exch))
print "Exchange energy, max relative error:", max(rel_diff)
assert max(rel_diff) < 0.002, \
"Max relative error in exchange energy = {} is larger than " \
"tolerance (= {})".format(max(rel_diff), REL_TOLERANCE)
# Compare demag energy
demag = computed[:, 1] / vol
demag_nmag = ref[:, 1]
diff = abs(demag - demag_nmag)
rel_diff = np.abs(diff / max(demag))
print "Demag energy, max relative error:", max(rel_diff)
# Don't really know why this is ten times higher than everyting else.
assert max(rel_diff) < REL_TOLERANCE*10, \
"Max relative error in demag energy = {} is larger than " \
"tolerance (= {})".format(max(rel_diff), REL_TOLERANCE)
# Plot
p.plot(exch_nmag, 'o', mfc='w', label='nmag')
p.plot(exch, label='finmag')
p.xlabel("time step")
p.ylabel("$e_\mathrm{exch}\, (\mathrm{Jm^{-3}})$")
p.legend()
p.savefig(os.path.join(MODULE_DIR, "exchange_energy.pdf"))
p.savefig(os.path.join(MODULE_DIR, "exchange_energy.png"))
p.close()
p.plot(demag_nmag, 'o', mfc='w', label='nmag')
p.plot(demag, label='finmag')
p.xlabel("time step")
p.ylabel("$e_\mathrm{demag}\, (\mathrm{Jm^{-3}})$")
p.legend()
p.savefig(os.path.join(MODULE_DIR, "demag_energy.pdf"))
p.savefig(os.path.join(MODULE_DIR, "demag_energy.png"))
#p.show()
p.close()
print "Energy plots written to exchange_energy.pdf and demag_energy.pdf"
def set_default_values(self):
self.Ms = Field(df.FunctionSpace(self.mesh, 'DG', 0),
8.6e5) # A/m saturation magnetisation
self._pins = np.array([], dtype="int")
self.volumes = df.assemble(
df.dot(df.TestFunction(self.S3), df.Constant([1, 1, 1])) *
df.dx).array()
self.Volume = mesh_volume(self.mesh)
self.real_volumes = self.volumes * self.unit_length**3
self.m_pred = np.zeros(self.m.shape)
self.integrator = native_llb.StochasticSLLGIntegrator(
self.m, self.m_pred, self._Ms, self._T, self.real_volumes,
self._alpha, self.stochastic_update_field, self.method)
self.alpha = 0.1
self._gamma = consts.gamma
self._seed = np.random.random_integers(4294967295)
self.dt = 1e-13
self.T = 0
def test_cubic_anisotropy_energy():
mesh = df.BoxMesh(df.Point(0, 0, 0), df.Point(1, 1, 40), 1, 1, 40)
volume = mesh_volume(mesh) * unit_length**3
S3 = df.VectorFunctionSpace(mesh, "Lagrange", 1)
S1 = df.FunctionSpace(mesh, "Lagrange", 1)
m = Field(S3)
m.set((0, 0, 1))
Ms_dg = Field(df.FunctionSpace(mesh, 'DG', 0), Ms)
ca = CubicAnisotropy(u1, u2, K1, K2, K3)
ca.setup(m, Ms_dg, unit_length)
energy = ca.compute_energy()
# energy_expected = 8.3e-20 # oommf cubicEight_100pc.mif -> ErFe2.odt
energy_expected = compute_cubic_energy() * volume
print "cubic anisotropy energy = {}, expected {}.".format(
energy, energy_expected)
rel_diff = abs(energy - energy_expected) / abs(energy_expected)
assert rel_diff < 1e-10
def test_compare_energies():
#Dictionary for finmag exchange results
exch = {}
#Dictionary for finmag demag results
demag = {}
for demagsolver in demagsolvers:
ref = np.loadtxt(
os.path.join(MODULE_DIR, demagsolver + "energies_ref.txt"))
if not os.path.isfile(os.path.join(MODULE_DIR, demagsolver+"energies.txt")) \
or (os.path.getctime(os.path.join(MODULE_DIR, demagsolver+"energies.txt")) <
os.path.getctime(os.path.abspath(__file__))):
run_finmag(demagsolver)
computed = np.loadtxt(
os.path.join(MODULE_DIR, demagsolver + "energies.txt"))
assert np.size(ref) == np.size(computed), "Compare number of energies."
vol = mesh_volume(mesh) * unit_length**mesh.topology().dim()
#30x30x100nm^3 = 30x30x100=9000
# Compare exchange energy
exch[demagsolver] = computed[:, 0] / vol
exch_nmag = ref[:, 0]
diff = abs(exch[demagsolver] - exch_nmag)
rel_diff = np.abs(diff / max(exch[demagsolver]))
print "Exchange energy Finmag %s Nmag , max relative error:" % demagsolver, max(
rel_diff)
assert max(rel_diff) < REL_TOLERANCE, \
"Max relative error in Finmag %s Nmag exchange energy is %g"%(demagsolver, max(rel_diff))
# Compare demag energy
demag[demagsolver] = computed[:, 1] / vol
demag_nmag = ref[:, 1]
diff = abs(demag[demagsolver] - demag_nmag)
rel_diff = np.abs(diff / max(demag[demagsolver]))
print "Finmag %s Nmag Demag energy, max relative error: %g" % (
demagsolver, max(rel_diff))
# Don't really know why this is ten times higher than everyting else.
assert max(rel_diff) < REL_TOLERANCE*10, \
"Max relative error in Finmag %s Nmag demag energy is %g" %(demagsolver,max(rel_diff))
# Plot both FK and GCR demags on the same plot.
p.title("Finmag vs Nmag Exchange energy")
p.plot(exch_nmag, 'o', mfc='w', label='Nmag')
p.plot(exch["FK"], label='Finmag FK')
p.plot(exch["GCR"], label='Finmag GCR')
p.xlabel("Time step")
p.ylabel("$\mathsf{E_{exch}}$")
p.legend()
p.savefig(os.path.join(MODULE_DIR, "exchange_energy.png"))
p.close()
p.plot(demag_nmag, 'o', mfc='w', label='Nmag')
p.plot(demag, label='Finmag')
p.xlabel("Time step")
p.ylabel("$\mathsf{E_{demag}}$")
p.legend()
p.savefig(os.path.join(MODULE_DIR, "demag_energy.png"))
#p.show()%
p.close()
print "Energy plots written to exchange_energy.png and demag_energy.png"
def setup(self, S3, m, Ms, unit_length=1):
mesh = S3.mesh()
n_dim = mesh.topology().dim()
self.V = mesh_volume(mesh=mesh) * unit_length**n_dim
self.Ms = Ms
self.output_shape = df.Function(S3).vector().array().shape
def normalise_phi(phi, mesh):
volume = mesh_volume(mesh)
average = df.assemble(phi * df.dx)
phi.vector()[:] = phi.vector().array() - average / volume