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()
# 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()