def excite_system(mesh, beta=0.0):
# Specify the stt dynamics in the simulation
sim = Sim(mesh, name='dyn_%g'%beta, driver='llg_stt_cpp')
sim.driver.set_tols(rtol=1e-12, atol=1e-12)
sim.driver.alpha = 0.1
sim.driver.gamma = 2.211e5
sim.Ms = 8.6e5
# sim.set_m(init_m)
sim.set_m(np.load('m0.npy'))
# Energies
A = 1.3e-11
exch = UniformExchange(A=A)
sim.add(exch)
anis = UniaxialAnisotropy(5e4)
sim.add(anis)
# beta is the parameter in the STT torque
sim.a_J = global_const*1e11
sim.p = (1,0,0)
sim.beta = beta
# The simulation will run for 5 ns and save
# 500 snapshots of the system in the process
ts = np.linspace(0, 0.5e-9, 21)
xs=[]
thetas=[]
for t in ts:
print('time', t)
sim.run_until(t)
spin = sim.spin.copy()
x, theta = extract_dw(spin)
xs.append(x)
thetas.append(theta)
sim.save_vtk()
np.savetxt('dw_%g.txt'%beta,np.transpose(np.array([ts, xs,thetas])))
def excite_system(mesh, beta=0.0):
# Specify the stt dynamics in the simulation
sim = Sim(mesh, name='dyn_%g' % beta, driver='llg_stt_cpp')
sim.driver.set_tols(rtol=1e-12, atol=1e-12)
sim.driver.alpha = 0.1
sim.driver.gamma = 2.211e5
sim.Ms = 8.6e5
# sim.set_m(init_m)
sim.set_m(np.load('m0.npy'))
# Energies
A = 1.3e-11
exch = UniformExchange(A=A)
sim.add(exch)
anis = UniaxialAnisotropy(5e4)
sim.add(anis)
# beta is the parameter in the STT torque
sim.a_J = global_const * 1e11
sim.p = (1, 0, 0)
sim.beta = beta
# The simulation will run for 5 ns and save
# 500 snapshots of the system in the process
ts = np.linspace(0, 0.5e-9, 21)
xs = []
thetas = []
for t in ts:
print('time', t)
sim.run_until(t)
spin = sim.spin.copy()
x, theta = extract_dw(spin)
xs.append(x)
thetas.append(theta)
sim.save_vtk()
np.savetxt('dw_%g.txt' % beta, np.transpose(np.array([ts, xs, thetas])))
def excite_system(mesh):
# Specify the stt dynamics in the simulation
sim = Sim(mesh, name='dyn2', driver='llg_stt')
sim.driver.set_tols(rtol=1e-12, atol=1e-14)
sim.driver.alpha = 0.2
sim.driver.gamma = 2.211e5
sim.Ms = 8.6e5
# sim.set_m(init_m)
sim.set_m(np.load('m0.npy'))
# Energies
A = 1.3e-11
exch = UniformExchange(A=A)
sim.add(exch)
anis = UniaxialAnisotropy(5e4)
sim.add(anis)
# dmi = DMI(D=8e-4)
# sim.add(dmi)
# Set the current in the x direction, in A / m
# beta is the parameter in the STT torque
def jx_func(pos, t):
T = 1e-9
return (-1e12 + -0.1e12 * np.sin(t / T))
sim.driver.jx_function = jx_func
sim.driver.beta = 0.01
# The simulation will run for 5 ns and save
# 500 snapshots of the system in the process
ts = np.linspace(0, 10e-9, 1001)
for t in ts:
print('time', t)
sim.driver.run_until(t)
print('j = {}'.format(sim.driver._jx[0]))
sim.save_vtk()
sim.save_m()
def excite_system(mesh, time=5, snaps=501):
# Specify the stt dynamics in the simulation
sim = Sim(mesh, name='dyn', driver='llg_stt')
# Set the simulation parameters
sim.set_tols(rtol=1e-12, atol=1e-14)
sim.alpha = 0.05
sim.gamma = 2.211e5
sim.Ms = 8.6e5
# Load the initial state from the npy file saved
# in the realxation
sim.set_m(np.load('m0.npy'))
# Add the energies
A = 1.3e-11
exch = UniformExchange(A=A)
sim.add(exch)
anis = UniaxialAnisotropy(5e4)
sim.add(anis)
# dmi = DMI(D=8e-4)
# sim.add(dmi)
# Set the current in the x direction, in A / m
# beta is the parameter in the STT torque
sim.jx = -1e12
sim.beta = 1
# The simulation will run for x ns and save
# 'snaps' snapshots of the system in the process
ts = np.linspace(0, time * 1e-9, snaps)
for t in ts:
print 'time', t
sim.run_until(t)
sim.save_vtk()
sim.save_m()
def excite_system(mesh, time=5, snaps=501):
# Specify the stt dynamics in the simulation
sim = Sim(mesh, name='dyn', driver='llg_stt')
# Set the simulation parameters
sim.driver.set_tols(rtol=1e-12, atol=1e-14)
sim.driver.alpha = 0.05
sim.gamma = 2.211e5
sim.Ms = 8.6e5
# Load the initial state from the npy file saved
# in the realxation
sim.set_m(np.load('m0.npy'))
# Add the energies
A = 1.3e-11
exch = UniformExchange(A=A)
sim.add(exch)
anis = UniaxialAnisotropy(5e4)
sim.add(anis)
# dmi = DMI(D=8e-4)
# sim.add(dmi)
# Set the current in the x direction, in A / m
# beta is the parameter in the STT torque
sim.driver.jx = -1e12
sim.driver.beta = 1
# The simulation will run for x ns and save
# 'snaps' snapshots of the system in the process
ts = np.linspace(0, time * 1e-9, snaps)
for t in ts:
print('time', t)
sim.driver.run_until(t)
sim.save_vtk()
sim.save_m()
def excite_system(mesh):
sim = Sim(mesh, name='dyn')
sim.driver.set_tols(rtol=1e-10, atol=1e-14)
sim.driver.alpha = 0.01
sim.driver.gamma = 2.211e5
sim.Ms = spatial_Ms
# sim.set_m(init_m)
sim.set_m(np.load('m0.npy'))
A = 1.3e-11
exch = UniformExchange(A=A)
sim.add(exch)
demag = Demag(pbc_2d=True)
sim.add(demag)
mT = 795.7747154594767
sigma = 0.08e-9
def gaussian_fun(t):
return np.exp(-0.5 * (t / sigma)**2)
zeeman = TimeZeeman((80 * mT, 0, 0), time_fun=gaussian_fun, name='hx')
#zeeman = Zeeman((100*mT,0,0), name='hx')
sim.add(zeeman, save_field=True)
ts = np.linspace(0, 1e-9, 501)
for t in ts:
print('time', t)
print('length:', sim.spin_length()[0:200])
sim.run_until(t)
sim.save_vtk()
def excite_system(mesh):
sim = Sim(mesh, name='dyn', driver='llg_stt')
sim.set_tols(rtol=1e-8, atol=1e-10)
sim.alpha = 0.5
sim.gamma = 2.211e5
sim.Ms = 8.6e5
sim.set_m(np.load('m0.npy'))
exch = UniformExchange(A=1.3e-11)
sim.add(exch)
dmi = DMI(D=-4e-3)
sim.add(dmi)
zeeman = Zeeman((0, 0, 4e5))
sim.add(zeeman, save_field=True)
sim.jx = -5e12
sim.beta = 0
ts = np.linspace(0, 0.5e-9, 101)
for t in ts:
print 'time', t
sim.run_until(t)
sim.save_vtk()
def excite_system(mesh):
sim = Sim(mesh, name='dyn', driver='llg_stt')
sim.set_tols(rtol=1e-8, atol=1e-10)
sim.alpha = 0.5
sim.gamma = 2.211e5
sim.Ms = 8.6e5
sim.set_m(np.load('m0.npy'))
exch = UniformExchange(A=1.3e-11)
sim.add(exch)
dmi = DMI(D=-4e-3)
sim.add(dmi)
zeeman = Zeeman((0, 0, 4e5))
sim.add(zeeman, save_field=True)
sim.jx = -5e12
sim.beta = 0
ts = np.linspace(0, 0.5e-9, 101)
for t in ts:
print 'time', t
sim.run_until(t)
sim.save_vtk()
# fig.show()
fig.canvas.draw()
else:
# Fidimag automatically saves the last state
sim.do_precession = False
sim.relax(dt=1e-13,
stopping_dmdt=args.stopping_dmdt,
max_steps=args.max_steps,
save_m_steps=args.save_files,
save_vtk_steps=args.save_files)
# Save final states
sim.save_m()
sim.save_vtk()
# -------------------------------------------------------------------------
# Files -------------------------------------------------------------------
# -------------------------------------------------------------------------
npy_dir = 'npys/'
vtk_dir = 'vtks/'
txt_dir = 'txts/'
if not os.path.exists(npy_dir):
os.makedirs(npy_dir)
if not os.path.exists(vtk_dir):
os.makedirs(vtk_dir)
if not os.path.exists(txt_dir):
os.makedirs(txt_dir)
# Prepare simulation
# We define the cylinder with the Magnetisation function
sim = Sim(mesh, name='skyrmion')
sim.Ms = cylinder
# To get a faster relaxation, we tune the LLG equation parameters
sim.do_precession = False
sim.alpha = 0.5
# Initial magnetisation:
sim.set_m(init_m)
# Energies:
# Exchange
sim.add(UniformExchange(A=A))
# Bulk DMI
sim.add(DMI(D=D))
# Relax the system
sim.relax(dt=1e-12, stopping_dmdt=0.0001, max_steps=5000,
save_m_steps=None,
save_vtk_steps=None
)
# Save the final relaxed state and a vtk file
np.save('sk_up.npy', sim.spin)
sim.save_vtk()
