Beispiel #1
0
def excite_system(mesh, Hy=0):

    sim = Sim(mesh, name="dyn")

    sim.set_options(rtol=1e-10, atol=1e-12)
    sim.alpha = 0.04
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(np.load("m0.npy"))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.18
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, Hy, 2e-2], name="H")
    sim.add(zeeman)

    hx = TimeZeeman([0, 0, 1e-5], sinc_fun, name="h")
    sim.add(hx, save_field=True)

    dt = 5
    steps = 2001
    for i in range(steps):

        sim.run_until(i * dt)
Beispiel #2
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def relax_system(mesh):

    # Only relaxation
    sim = Sim(mesh, name='relax')

    # Simulation parameters
    sim.driver.set_tols(rtol=1e-8, atol=1e-10)
    sim.alpha = 0.5
    sim.driver.gamma = 2.211e5 / mu0
    sim.mu_s = 1e-27 / mu0
    sim.driver.do_precession = False

    # The initial state passed as a function
    sim.set_m(init_m)
    # sim.set_m(np.load('m0.npy'))

    # Energies
    exch = UniformExchange(J=2e-20)
    sim.add(exch)

    anis = Anisotropy(0.01 * 2e-20, axis=(0, 0, 1))
    sim.add(anis)

    # dmi = DMI(D=8e-4)
    # sim.add(dmi)

    # Start relaxation and save the state in m0.npy
    sim.relax(dt=1e-14,
              stopping_dmdt=1e4,
              max_steps=5000,
              save_m_steps=None,
              save_vtk_steps=None)

    np.save('m0.npy', sim.spin)
def relax_system(mesh):

    # Only relaxation
    sim = Sim(mesh, name='relax')

    # Simulation parameters
    sim.driver.set_tols(rtol=1e-8, atol=1e-10)
    sim.alpha = 0.5
    sim.driver.gamma = 2.211e5 / mu0
    sim.mu_s = 1e-27 / mu0
    sim.driver.do_precession = False

    # The initial state passed as a function
    sim.set_m(init_m)
    # sim.set_m(np.load('m0.npy'))

    # Energies
    exch = UniformExchange(J=2e-20)
    sim.add(exch)

    anis = Anisotropy(0.01*2e-20, axis=(0, 0, 1))
    sim.add(anis)

    # dmi = DMI(D=8e-4)
    # sim.add(dmi)

    # Start relaxation and save the state in m0.npy
    sim.relax(dt=1e-14, stopping_dmdt=1e4, max_steps=5000,
              save_m_steps=None, save_vtk_steps=None)

    np.save('m0.npy', sim.spin)
Beispiel #4
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def excite_system(mesh):

    sim = Sim(mesh, name='dyn', driver='sllg')
    sim.set_options(dt=1e-14, gamma=const.gamma, k_B=const.k_B)
    sim.alpha = 0.1
    sim.mu_s = const.mu_s_1
    sim.T = temperature_gradient

    sim.set_m(np.load("m0.npy"))

    J = 50.0 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.5 * J
    dmi = DMI(D)
    sim.add(dmi)

    Hz = 0.2 * J / const.mu_s_1
    zeeman = Zeeman([0, 0, Hz])
    sim.add(zeeman)

    dt = 2e-14 * 50  # 1e-12
    ts = np.linspace(0, 1000 * dt, 501)
    for t in ts:
        sim.run_until(t)
        sim.save_vtk()
        sim.save_m()
        print 'sim t=%g' % t
Beispiel #5
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def relax_system(mesh):

    sim=Sim(mesh,name='relax')
    sim.set_options(rtol=1e-12,atol=1e-14)
    sim.do_precession = False
    sim.alpha = 0.5
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.18
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0,0e-3,2e-2],name='H')
    sim.add(zeeman)

    sim.relax(dt=2.0, stopping_dmdt=1e-8, max_steps=10000, save_m_steps=None, save_vtk_steps=100)

    np.save('m0.npy',sim.spin)
Beispiel #6
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def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    sim.set_options(rtol=1e-12, atol=1e-14)
    sim.do_procession = False
    sim.alpha = 0.5
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.18
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0e-3, 2e-2], name='H')
    sim.add(zeeman)

    sim.relax(dt=2.0,
              stopping_dmdt=1e-8,
              max_steps=10000,
              save_m_steps=None,
              save_vtk_steps=100)

    np.save('m0.npy', sim.spin)
Beispiel #7
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def relax_system_stage1():

    mesh = CuboidMesh(nx=140 , ny=140, nz=1)

    sim = Sim(mesh, name='relax', driver='llg')
    #sim.set_options(dt=1e-14, gamma=const.gamma, k_B=const.k_B)
    sim.alpha = 0.5
    sim.do_precession = False
    sim.gamma = const.gamma
    sim.mu_s = spatial_mu

    sim.set_m(init_m)

    J = 50 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.27 * J
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman(spatial_H)
    sim.add(zeeman)

    sim.relax(dt=1e-14, stopping_dmdt=1e10, max_steps=1000,
              save_m_steps=100, save_vtk_steps=10)

    np.save('skx.npy', sim.spin)
    plot_m(mesh, 'skx.npy', comp='z')
Beispiel #8
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def excite_system(mesh, Hy=0):

    sim = Sim(mesh, name='dyn')

    sim.driver.set_tols(rtol=1e-10, atol=1e-12)
    sim.driver.alpha = 0.04
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(np.load('m0.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.18
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, Hy, 2e-2], name='H')
    sim.add(zeeman)

    hx = TimeZeeman([0, 0, 1e-5], sinc_fun, name='h')
    sim.add(hx, save_field=True)

    dt = 5
    steps = 2001
    for i in range(steps):
        sim.run_until(i * dt)
        sim.save_m()
        print("step {}/{}".format(i, steps))
Beispiel #9
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def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.driver.alpha = 1.0
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)
    # sim.set_m(random_m)
    # sim.set_m(np.load('m_10000.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.09
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0, 3.75e-3])
    sim.add(zeeman)

    sim.relax(dt=2.0,
              stopping_dmdt=1e-6,
              max_steps=1000,
              save_m_steps=100,
              save_vtk_steps=50)

    np.save('m0.npy', sim.spin)
def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    sim.set_default_options(gamma=const.gamma)
    sim.driver.alpha = 0.5
    sim.mu_s = const.mu_s_1

    sim.set_m(init_m)

    J = 50.0 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.5 * J
    dmi = DMI(D)
    sim.add(dmi)

    Hz = 0.2 * J / const.mu_s_1
    zeeman = Zeeman([0, 0, Hz])
    sim.add(zeeman)

    ONE_DEGREE_PER_NS = 17453292.52

    sim.relax(dt=1e-13, stopping_dmdt=0.01 * ONE_DEGREE_PER_NS,
              max_steps=1000, save_m_steps=100, save_vtk_steps=50)

    np.save('m0.npy', sim.spin)
Beispiel #11
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def relax_system(mesh, Hy=0):

    sim = Sim(mesh, name='relax')
    sim.driver.set_tols(rtol=1e-10, atol=1e-12)
    sim.driver.alpha = 0.5
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.do_precession = False

    sim.set_m(init_m)
    #sim.set_m(random_m)
    #sim.set_m(np.load('m_10000.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.18
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, Hy, 2e-2], name='H')
    sim.add(zeeman)

    sim.relax(dt=2.0,
              stopping_dmdt=1e-7,
              max_steps=10000,
              save_m_steps=100,
              save_vtk_steps=50)

    np.save('m0.npy', sim.spin)
Beispiel #12
0
def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.driver.alpha = 1.0
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)
    # sim.set_m(random_m)
    # sim.set_m(np.load('m_10000.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    Kx = Anisotropy(Ku=0.005, axis=(1, 0, 0), name='Kx')
    sim.add(Kx)

    sim.relax(dt=2.0,
              stopping_dmdt=1e-6,
              max_steps=1000,
              save_m_steps=100,
              save_vtk_steps=50)

    np.save('m0.npy', sim.spin)
Beispiel #13
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def dynamic(mesh):

    sim = Sim(mesh, name='dyn', driver='slonczewski')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(np.load('m0.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    Kx = Anisotropy(Ku=0.005, axis=(1, 0, 0), name='Kx')
    sim.add(Kx)

    sim.p = (0, 0, 1)

    sim.u0 = 0.03
    sim.driver.alpha = 0.1

    ts = np.linspace(0, 1e3, 101)
    for t in ts:
        sim.run_until(t)
        sim.save_vtk()
        print t
Beispiel #14
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def test_skx_num():

    mesh = CuboidMesh(nx=120, ny=120, nz=1, periodicity=(True, True, False))

    sim = Sim(mesh, name='skx_num')
    sim.set_tols(rtol=1e-6, atol=1e-6)
    sim.alpha = 1.0
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)

    sim.do_procession = False

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.09
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0, 5e-3])
    sim.add(zeeman)

    sim.relax(dt=2.0,
              stopping_dmdt=1e-2,
              max_steps=1000,
              save_m_steps=None,
              save_vtk_steps=None)

    skn = sim.skyrmion_number()
    print 'skx_number', skn
    assert skn > -1 and skn < -0.99
def excite_system(mesh):

    sim = Sim(mesh, name='dyn', driver='sllg')
    sim.set_options(dt=1e-14, gamma=const.gamma, k_B=const.k_B)
    sim.driver.alpha = 0.1
    sim.mu_s = const.mu_s_1
    sim.T = temperature_gradient

    sim.set_m(np.load("m0.npy"))

    J = 50.0 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.5 * J
    dmi = DMI(D)
    sim.add(dmi)

    Hz = 0.2 * J / const.mu_s_1
    zeeman = Zeeman([0, 0, Hz])
    sim.add(zeeman)

    dt = 2e-14 * 50  # 1e-12
    ts = np.linspace(0, 1000 * dt, 501)
    for t in ts:
        sim.run_until(t)
        sim.save_vtk()
        sim.save_m()
        print 'sim t=%g' % t
Beispiel #16
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def dynamic(mesh):

    sim = Sim(mesh, name='dyn', driver='slonczewski')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(np.load('m0.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    Kx = Anisotropy(Ku=0.005, axis=(1, 0, 0), name='Kx')
    sim.add(Kx)

    sim.p = (0,0,1)

    sim.u0 = 0.03
    sim.alpha = 0.1

    ts = np.linspace(0, 1e3, 101)
    for t in ts:
        sim.run_until(t)
        sim.save_vtk()
        print t
Beispiel #17
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def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.alpha = 1.0
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)
    # sim.set_m(random_m)
    # sim.set_m(np.load('m_10000.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.09
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0, 3.75e-3])
    sim.add(zeeman)

    sim.relax(dt=2.0, stopping_dmdt=1e-6, max_steps=1000,
              save_m_steps=100, save_vtk_steps=50)

    np.save('m0.npy', sim.spin)
Beispiel #18
0
def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    sim.set_default_options(gamma=const.gamma)
    sim.alpha = 0.5
    sim.mu_s = const.mu_s_1

    sim.set_m(init_m)

    J = 50.0 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.5 * J
    dmi = DMI(D)
    sim.add(dmi)

    Hz = 0.2 * J / const.mu_s_1
    zeeman = Zeeman([0, 0, Hz])
    sim.add(zeeman)

    ONE_DEGREE_PER_NS = 17453292.52

    sim.relax(dt=1e-13, stopping_dmdt=0.01 * ONE_DEGREE_PER_NS,
              max_steps=1000, save_m_steps=100, save_vtk_steps=50)

    np.save('m0.npy', sim.spin)
Beispiel #19
0
def relax_system(mesh, Hy=0):

    sim = Sim(mesh, name="relax")
    sim.set_options(rtol=1e-10, atol=1e-12)
    sim.alpha = 0.5
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.do_precession = False

    sim.set_m(init_m)
    # sim.set_m(random_m)
    # sim.set_m(np.load('m_10000.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.18
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, Hy, 2e-2], name="H")
    sim.add(zeeman)

    sim.relax(dt=2.0, stopping_dmdt=1e-8, max_steps=10000, save_m_steps=100, save_vtk_steps=50)

    np.save("m0.npy", sim.spin)
Beispiel #20
0
def relax_system_stage2():

    mesh = CuboidMesh(nx=140 , ny=140, nz=1)

    sim = Sim(mesh, name='dyn', driver='llg')
    sim.alpha = 0.1
    sim.do_precession = True
    sim.gamma = const.gamma
    sim.mu_s = spatial_mu

    sim.set_m(np.load('skx.npy'))

    J = 50 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.27 * J
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman(spatial_H)
    sim.add(zeeman)

    ts = np.linspace(0, 2e-9, 201)
    for t in ts:
        sim.run_until(t)
        sim.save_vtk()
        sim.save_m()
        print(t)
Beispiel #21
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def test_dynamic():

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, name='dyn_spin', driver='llg_stt_cpp')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m((0.8, 0, -1))

    Kx = Anisotropy(Ku=-0.05, axis=(0, 0, 1), name='Kz')
    sim.add(Kx)

    sim.p = (0, 0, 1)

    sim.a_J = 0.0052
    sim.alpha = 0.1

    ts = np.linspace(0, 1200, 401)
    for t in ts:
        sim.driver.run_until(t)

    mz = sim.spin[2]
    alpha, K, u = 0.1, 0.05, 0.0052
    print(mz, u / (2 * alpha * K))

    #########################################################
    # The system used in this test can be solved analytically, which gives that mz = u/(2*alpha*K),
    # where K represents the easy-plane anisotropy.
    ###
    assert abs(mz - u / (2 * alpha * K)) / mz < 5e-4
Beispiel #22
0
def excite_system(T=0.1, H=0.15):

    mesh = CuboidMesh(nx=28 * 3, ny=16 * 5, nz=1, pbc='2d')

    sim = Sim(mesh, name='dyn', driver='sllg')
    sim.set_options(dt=1e-14, gamma=const.gamma, k_B=const.k_B)
    sim.alpha = 0.1
    sim.mu_s = const.mu_s_1

    sim.set_m(random_m)

    J = 50 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.5 * J
    dmi = DMI(D)
    sim.add(dmi)

    Hz = H * J / const.mu_s_1
    zeeman = Zeeman([0, 0, Hz])
    sim.add(zeeman)

    sim.T = J / const.k_B * T

    ts = np.linspace(0, 5e-11, 51)
    for t in ts:
        sim.run_until(t)
        # sim.save_vtk()

    np.save('m.npy', sim.spin)
    plot_m(mesh, 'm.npy', comp='z')
Beispiel #23
0
def test_skx_num():

    mesh = CuboidMesh(nx=120, ny=120, nz=1, periodicity=(True, True, False))

    sim = Sim(mesh, name='skx_num')
    sim.set_tols(rtol=1e-6, atol=1e-6)
    sim.alpha = 1.0
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)

    sim.do_procession = False

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.09
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0, 5e-3])
    sim.add(zeeman)

    sim.relax(dt=2.0, stopping_dmdt=1e-2, max_steps=1000,
              save_m_steps=None, save_vtk_steps=None)

    skn = sim.skyrmion_number()
    print 'skx_number', skn
    assert skn > -1 and skn < -0.99
def test_dynamic():

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, name='dyn_spin', driver='llg_stt_cpp')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m((0.8,0,-1))

    Kx = Anisotropy(Ku=-0.05, axis=(0, 0, 1), name='Kz')
    sim.add(Kx)

    sim.p = (0,0,1)

    sim.a_J = 0.0052
    sim.alpha = 0.1

    ts = np.linspace(0, 1200, 401)
    for t in ts:
        sim.driver.run_until(t)


    mz = sim.spin[2]
    alpha, K, u = 0.1, 0.05, 0.0052
    print(mz, u/(2*alpha*K))

    #########################################################
    # The system used in this test can be solved analytically, which gives that mz = u/(2*alpha*K),
    # where K represents the easy-plane anisotropy.
    ###
    assert abs(mz - u/(2*alpha*K))/mz< 5e-4
def excite_system(mesh):

    sim = Sim(mesh, name='dyn')

    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.driver.alpha = 0.04
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(np.load('m0.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.09
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0, 3.75e-3], name='H')
    sim.add(zeeman)

    w0 = 0.02

    def time_fun(t):
        return np.exp(-w0 * t)

    hx = TimeZeeman([0, 0, 1e-5], sinc_fun, name='h')
    sim.add(hx, save_field=True)

    ts = np.linspace(0, 20000, 5001)
    for t in ts:
        sim.run_until(t)
        print 'sim t=%g' % t
Beispiel #26
0
def excite_system(mesh):

    sim = Sim(mesh, name='dyn')

    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.alpha = 0.04
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(np.load('m0.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.09
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0, 3.75e-3], name='H')
    sim.add(zeeman)

    w0 = 0.02

    def time_fun(t):
        return np.exp(-w0 * t)

    hx = TimeZeeman([0, 0, 1e-5], sinc_fun, name='h')
    sim.add(hx, save_field=True)

    ts = np.linspace(0, 20000, 5001)
    for t in ts:
        sim.run_until(t)
        print 'sim t=%g' % t
def test_hexagonal_demags_2D():
    """
    Comparison of the FFT approach for hexagonal meshes, named
    DemagHexagonal, where it is used a system with the double number
    of nodes along the x direction (i.e. a mesh with twice the number
    of nodes of the original mesh), against the full calculation
    of the Demag field
    """
    # Number of atoms
    N = 15
    a = 0.4
    mesh = HexagonalMesh(a * 0.5, N, N,
                         unit_length=1e-9,
                         alignment='square')

    # Centre
    xc = (mesh.Lx * 0.5)
    yc = (mesh.Ly * 0.5)

    mu_s = 2 * const.mu_B

    sim = Sim(mesh)
    sim.mu_s = mu_s

    sim.set_m(lambda pos: m_init_2Dvortex(pos, (xc, yc)))
    # Brute force demag calculation
    sim.add(DemagFull())

    sim.get_interaction('demag_full').compute_field()
    sim.get_interaction('demag_full').field
    demag_full_energy = sim.compute_energy() / const.meV

    # Demag using the FFT approach and a larger mesh
    sim2 = Sim(mesh)
    sim2.mu_s = mu_s

    sim2.set_m(lambda pos: m_init_2Dvortex(pos, (xc, yc)))

    sim2.add(DemagHexagonal())
    sim2.get_interaction('demag_hex').compute_field()
    sim2.compute_energy()

    demag_2fft_energy = sim2.compute_energy() / const.meV

    # We compare both energies scaled in meV
    assert (demag_full_energy - demag_2fft_energy) < 1e-10
Beispiel #28
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def test_hexagonal_demags_2D():
    """
    Comparison of the FFT approach for hexagonal meshes, named
    DemagHexagonal, where it is used a system with the double number
    of nodes along the x direction (i.e. a mesh with twice the number
    of nodes of the original mesh), against the full calculation
    of the Demag field
    """
    # Number of atoms
    N = 15
    a = 0.4
    mesh = HexagonalMesh(a * 0.5, N, N,
                         unit_length=1e-9,
                         alignment='square')

    # Centre
    xc = (mesh.Lx * 0.5)
    yc = (mesh.Ly * 0.5)

    mu_s = 2 * const.mu_B

    sim = Sim(mesh)
    sim.mu_s = mu_s

    sim.set_m(lambda pos: m_init_2Dvortex(pos, (xc, yc)))
    # Brute force demag calculation
    sim.add(DemagFull())

    sim.get_interaction('DemagFull').compute_field()
    sim.get_interaction('DemagFull').field
    DemagFull_energy = sim.compute_energy() / const.meV

    # Demag using the FFT approach and a larger mesh
    sim2 = Sim(mesh)
    sim2.mu_s = mu_s

    sim2.set_m(lambda pos: m_init_2Dvortex(pos, (xc, yc)))

    sim2.add(DemagHexagonal())
    sim2.get_interaction('DemagHexagonal').compute_field()
    sim2.compute_energy()

    demag_2fft_energy = sim2.compute_energy() / const.meV

    # We compare both energies scaled in meV
    assert (DemagFull_energy - demag_2fft_energy) < 1e-10
Beispiel #29
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def relax_neb(k, maxst, simname, init_im, interp, save_every=10000):
    """
    Execute a simulation with the NEB function of the FIDIMAG code

    The simulations are made for a specific spring constant 'k' (a float),
    number of images 'init_im', interpolations between images 'interp'
    (an array) and a maximum of 'maxst' steps.
    'simname' is the name of the simulation, to distinguish the
    output files.

    --> vtks and npys are saved in files starting with the 'simname' string

    """

    # Prepare simulation
    sim = Sim(mesh, name=simname)
    sim.gamma = const.gamma

    # magnetisation in units of Bohr's magneton
    sim.mu_s = 2. * const.mu_B

    # Exchange constant in Joules: E = Sum J_{ij} S_i S_j
    J = 12. * const.meV
    exch = UniformExchange(J)
    sim.add(exch)

    # DMI constant in Joules: E = Sum D_{ij} S_i x S_j
    D = 2. * const.meV
    dmi = DMI(D, dmi_type='interfacial')
    sim.add(dmi)

    # Anisotropy along +z axis
    ku = Anisotropy(Ku=0.5 * const.meV,
                    axis=[0, 0, 1],
                    name='ku')
    sim.add(ku)

    # Initial images
    init_images = init_im

    # Number of images between each state specified before (here we need only
    # two, one for the states between the initial and intermediate state
    # and another one for the images between the intermediate and final
    # states). Thus, the number of interpolations must always be
    # equal to 'the number of initial states specified', minus one.
    interpolations = interp

    neb = NEB_Sundials(sim,
                       init_images,
                       interpolations=interpolations,
                       spring=k,
                       name=simname)

    neb.relax(max_steps=maxst,
              save_vtk_steps=save_every,
              save_npy_steps=save_every,
              stopping_dmdt=1e-2)
Beispiel #30
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def test_sim_spins(do_plot=False):

    mesh = CuboidMesh(nx=10, ny=5, nz=1)

    sim = Sim(mesh, name='10spin')

    alpha = 0.1
    gamma = 2.21e5
    sim.alpha = alpha
    sim.gamma = gamma
    sim.mu_s = 1.0

    sim.set_m((1, 0, 0))
    print(sim.spin)

    H0 = 1e5
    sim.add(Zeeman((0, 0, H0)))

    ts = np.linspace(0, 1e-9, 101)

    mx = []
    my = []
    mz = []
    real_ts = []

    for t in ts:
        sim.run_until(t)
        real_ts.append(sim.t)
        #print sim.t, abs(sim.spin_length()[0] - 1)
        av = sim.compute_average()
        mx.append(av[0])
        my.append(av[1])
        mz.append(av[2])

        #sim.save_vtk()

    mz = np.array(mz)
    # print mz
    a_mx, a_my, a_mz = single_spin(alpha, gamma, H0, ts)

    print(sim.stat())

    if do_plot:
        plot(real_ts,
             mx,
             my,
             mz,
             a_mx,
             a_my,
             a_mz,
             name='spins.pdf',
             title='integrating spins')

    print(("Max Deviation = {0}".format(np.max(np.abs(mz - a_mz)))))

    assert np.max(np.abs(mz - a_mz)) < 5e-7
Beispiel #31
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def relax_neb(k, maxst, simname, init_im, interp, save_every=10000):
    """
    Execute a simulation with the NEB function of the FIDIMAG code

    The simulations are made for a specific spring constant 'k' (a float),
    number of images 'init_im', interpolations between images 'interp'
    (an array) and a maximum of 'maxst' steps.
    'simname' is the name of the simulation, to distinguish the
    output files.

    --> vtks and npys are saved in files starting with the 'simname' string

    """

    # Prepare simulation
    sim = Sim(mesh, name=simname)
    sim.driver.gamma = const.gamma

    # magnetisation in units of Bohr's magneton
    sim.mu_s = 2. * const.mu_B

    # Exchange constant in Joules: E = Sum J_{ij} S_i S_j
    J = 12. * const.meV
    exch = UniformExchange(J)
    sim.add(exch)

    # DMI constant in Joules: E = Sum D_{ij} S_i x S_j
    D = 2. * const.meV
    dmi = DMI(D, dmi_type='interfacial')
    sim.add(dmi)

    # Anisotropy along +z axis
    ku = Anisotropy(Ku=0.5 * const.meV, axis=[0, 0, 1], name='ku')
    sim.add(ku)

    # Initial images
    init_images = init_im

    # Number of images between each state specified before (here we need only
    # two, one for the states between the initial and intermediate state
    # and another one for the images between the intermediate and final
    # states). Thus, the number of interpolations must always be
    # equal to 'the number of initial states specified', minus one.
    interpolations = interp

    neb = NEB_Sundials(sim,
                       init_images,
                       interpolations=interpolations,
                       spring=k,
                       name=simname)

    neb.relax(max_steps=maxst,
              save_vtk_steps=save_every,
              save_npy_steps=save_every,
              stopping_dmdt=1e-2)
Beispiel #32
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def test_skx_num_atomistic():
    """
    Test the *finite spin chirality* or skyrmion number for
    a discrete spins simulation in a two dimensional lattice

    The expression is (PRL 108, 017601 (2012)) :

    Q =     S_i \dot ( S_{i+1}  X  S_{j+1} )
         +  S_i \dot ( S_{i-1}  X  S_{j-1} )

    which measures the chirality taking two triangles of spins
    per lattice site i:
        S_{i} , S_{i + x} , S_{i + y}    and
        S_{i} , S_{i - x} , S_{i - y}

    The area of the two triangles cover a unit cell, thus the sum
    cover the whole area of the atomic lattice

    This test generate a skyrmion pointing down with unrealistic
    paremeters.

    """

    mesh = CuboidMesh(nx=120, ny=120, nz=1,
                      periodicity=(True, True, False))

    sim = Sim(mesh, name='skx_num')
    sim.set_tols(rtol=1e-6, atol=1e-6)
    sim.alpha = 1.0
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(lambda pos: init_m(pos, 60, 60, 20))

    sim.do_precession = False

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.09
    dmi = DMI(D)
    sim.add(dmi)

    zeeman = Zeeman([0, 0, 5e-3])
    sim.add(zeeman)

    sim.relax(dt=2.0, stopping_dmdt=1e-2, max_steps=1000,
              save_m_steps=None, save_vtk_steps=None)

    skn = sim.skyrmion_number()
    print('skx_number', skn)
    assert skn > -1 and skn < -0.99
Beispiel #33
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def test_cuboid_demags_1Dchain():
    """
    Test a brute force calculation of the demagnetising field, called
    DemagFull, based on the sum of the dipolar contributions of the whole
    system for every lattice site, against the default FFT approach for the
    demag field. We compute the energies scaled in meV.
    This test is performed in a cuboid mesh to assure that the DemagFull
    library is calculating the same than the default demag function
    """
    N = 12
    a = 0.4
    mesh = CuboidMesh(a, a, a, N, 1, 1, unit_length=1e-9)
    mu_s = 2 * const.mu_B

    sim = Sim(mesh)
    sim.mu_s = mu_s

    sim.set_m(lambda pos: m_init_dw(pos, N, a))
    # Brute force demag calculation
    sim.add(DemagFull())

    sim.get_interaction('DemagFull').compute_field()
    # print sim.get_interaction('DemagFull').field
    DemagFull_energy = sim.compute_energy() / const.meV

    # Demag using the FFT approach
    sim2 = Sim(mesh)
    sim2.mu_s = mu_s

    sim2.set_m(lambda pos: m_init_dw(pos, N, a))

    sim2.add(Demag())
    sim2.get_interaction('Demag').compute_field()
    sim2.compute_energy()

    demag_fft_energy = sim2.compute_energy() / const.meV

    # We compare both energies scaled in meV
    assert (DemagFull_energy - demag_fft_energy) < 1e-10
def test_cuboid_demags_1Dchain():
    """
    Test a brute force calculation of the demagnetising field, called
    DemagFull, based on the sum of the dipolar contributions of the whole
    system for every lattice site, against the default FFT approach for the
    demag field. We compute the energies scaled in meV.
    This test is performed in a cuboid mesh to assure that the DemagFull
    library is calculating the same than the default demag function
    """
    N = 12
    a = 0.4
    mesh = CuboidMesh(a, a, a, N, 1, 1, unit_length=1e-9)
    mu_s = 2 * const.mu_B

    sim = Sim(mesh)
    sim.mu_s = mu_s

    sim.set_m(lambda pos: m_init_dw(pos, N, a))
    # Brute force demag calculation
    sim.add(DemagFull())

    sim.get_interaction('demag_full').compute_field()
    # print sim.get_interaction('demag_full').field
    demag_full_energy = sim.compute_energy() / const.meV

    # Demag using the FFT approach
    sim2 = Sim(mesh)
    sim2.mu_s = mu_s

    sim2.set_m(lambda pos: m_init_dw(pos, N, a))

    sim2.add(Demag())
    sim2.get_interaction('demag').compute_field()
    sim2.compute_energy()

    demag_fft_energy = sim2.compute_energy() / const.meV

    # We compare both energies scaled in meV
    assert (demag_full_energy - demag_fft_energy) < 1e-10
Beispiel #35
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def test_sim_single_spin_sllg(do_plot=False):

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, name='spin', driver='sllg')

    alpha = 0.1
    gamma = 2.21e5

    sim.set_options(dt=5e-15, gamma=gamma)

    sim.alpha = alpha
    sim.mu_s = 1.0

    sim.set_m((1, 0, 0))

    H0 = 1e5
    sim.add(Zeeman((0, 0, H0)))

    ts = np.linspace(0, 1e-10, 101)

    mx = []
    my = []
    mz = []
    real_ts = []
    for t in ts:
        sim.run_until(t)
        real_ts.append(sim.t)
        print(sim.t, abs(sim.spin_length()[0] - 1))
        mx.append(sim.spin[0])
        my.append(sim.spin[1])
        mz.append(sim.spin[2])

    mz = np.array(mz)

    a_mx, a_my, a_mz = single_spin(alpha, gamma, H0, ts)

    if do_plot:
        plot(real_ts,
             mx,
             my,
             mz,
             a_mx,
             a_my,
             a_mz,
             name='spin_sllg.pdf',
             title='integrating a spin')

    print(("Max Deviation = {0}".format(np.max(np.abs(mz - a_mz)))))

    assert np.max(np.abs(mz - a_mz)) < 1e-8
Beispiel #36
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def test_sim_spins(do_plot=False):

    mesh = CuboidMesh(nx=10, ny=5, nz=1)

    sim = Sim(mesh, name='10spin')

    alpha = 0.1
    gamma = 2.21e5
    sim.alpha = alpha
    sim.gamma = gamma
    sim.mu_s = 1.0

    sim.set_m((1, 0, 0))
    print(sim.spin)

    H0 = 1e5
    sim.add(Zeeman((0, 0, H0)))

    ts = np.linspace(0, 1e-9, 101)

    mx = []
    my = []
    mz = []
    real_ts = []

    for t in ts:
        sim.run_until(t)
        real_ts.append(sim.t)
        #print sim.t, abs(sim.spin_length()[0] - 1)
        av = sim.compute_average()
        mx.append(av[0])
        my.append(av[1])
        mz.append(av[2])

        #sim.save_vtk()

    mz = np.array(mz)
    # print mz
    a_mx, a_my, a_mz = single_spin(alpha, gamma, H0, ts)

    print(sim.stat())

    if do_plot:
        plot(real_ts, mx, my, mz, a_mx, a_my, a_mz, name='spins.pdf', title='integrating spins')

    print(("Max Deviation = {0}".format(
        np.max(np.abs(mz - a_mz)))))

    assert np.max(np.abs(mz - a_mz)) < 5e-7
Beispiel #37
0
def relax_system():

    # 1D chain of 50 spins with a lattice constant of 0.27 A
    mesh = CuboidMesh(
        nx=nx,
        dx=dx,
        unit_length=1e-9,
        # pbc='1d'
    )

    # Initiate the simulation
    sim = Sim(mesh, name=sim_name)
    sim.gamma = const.gamma

    # magnetisation in units of Bohr's magneton
    sim.mu_s = 2 * const.mu_B

    # sim.set_options(gamma=const.gamma, k_B=const.k_B)

    # Initial magnetisation profile
    sim.set_m(init_m)

    # Exchange constant in Joules: E = Sum J_{ij} S_i S_j
    J = 12. * const.meV
    exch = UniformExchange(J)
    sim.add(exch)

    # DMI constant in Joules: E = Sum D_{ij} S_i x S_j
    D = 2. * const.meV
    dmi = DMI(D, dmi_type='interfacial')
    sim.add(dmi)

    # Anisotropy along +z axis
    ku = Anisotropy(Ku=0.5 * const.meV, axis=[0, 0, 1], name='ku')
    sim.add(ku)

    # Faster convergence
    sim.alpha = 0.5
    sim.do_precession = False

    sim.relax(dt=1e-13,
              stopping_dmdt=0.05,
              max_steps=700,
              save_m_steps=1000,
              save_vtk_steps=1000)

    # Save the last relaxed state
    np.save(sim_name + '.npy', sim.spin)
Beispiel #38
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def relax_system():

    # 1D chain of 50 spins with a lattice constant of 0.27 A
    mesh = CuboidMesh(nx=nx,
                  dx=dx,
                  unit_length=1e-9,
                  # pbc='1d'
                  )

    # Initiate the simulation. PBCs are specified in the mesh
    sim = Sim(mesh, name=sim_name)
    sim.gamma = const.gamma

    # magnetisation in units of Bohr's magneton
    sim.mu_s = 2. * const.mu_B

    # sim.set_options(gamma=const.gamma, k_B=const.k_B)

    # Initial magnetisation profile
    sim.set_m((0, 0, 1))

    # Exchange constant in Joules: E = Sum J_{ij} S_i S_j
    J = 12. * const.meV
    exch = UniformExchange(J)
    sim.add(exch)

    # DMI constant in Joules: E = Sum D_{ij} S_i x S_j
    D = 2. * const.meV
    dmi = DMI(D, dmi_type='interfacial')
    sim.add(dmi)

    # Anisotropy along +z axis
    ku = Anisotropy(Ku=0.5 * const.meV,
                    axis=[0, 0, 1],
                    name='ku')
    sim.add(ku)

    # Faster convergence
    sim.alpha = 0.5
    sim.do_precession = False

    sim.relax(dt=1e-13, stopping_dmdt=0.05,
              max_steps=700,
              save_m_steps=1000, save_vtk_steps=1000)

    # Save the last relaxed state
    np.save(sim_name + '.npy', sim.spin)
Beispiel #39
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def test_dw_dmi_atomistic(do_plot=False):

    mesh = CuboidMesh(nx=300, ny=1, nz=1)

    sim = Sim(mesh, name='relax')
    sim.set_default_options(gamma=const.gamma)
    sim.alpha = 0.5
    sim.mu_s = const.mu_s_1
    sim.do_precession = False

    sim.set_m(m_init_dw)

    J = 50.0 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.01 * J
    dmi = DMI(D)
    sim.add(dmi)

    K = 0.005 * J
    anis = Anisotropy(K, axis=[1, 0, 0])
    sim.add(anis)

    ONE_DEGREE_PER_NS = 17453292.52

    sim.relax(dt=1e-13,
              stopping_dmdt=0.01 * ONE_DEGREE_PER_NS,
              max_steps=1000,
              save_m_steps=100,
              save_vtk_steps=50)

    np.save('m0.npy', sim.spin)

    xs = np.array([p[0] for p in mesh.coordinates]) - 150

    mx, my, mz = analytical(xs, A=J / 2.0, D=-D, K=K)
    mxyz = sim.spin.copy()
    mxyz = mxyz.reshape(-1, 3).T

    assert max(abs(mxyz[0, :] - mx)) < 0.001
    assert max(abs(mxyz[1, :] - my)) < 0.001
    assert max(abs(mxyz[2, :] - mz)) < 0.0006

    if do_plot:

        save_plot(xs, mxyz, mx, my, mz)
Beispiel #40
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def relax_system(rtol=1e-10, atol=1e-12):
    """numerical solution"""
    mesh = CuboidMesh(nx=1, ny=1, nz=1)
    sim = Sim(mesh, name='relax')
    sim.driver.set_tols(rtol=rtol, atol=atol)
    sim.driver.alpha = 0.5
    sim.driver.gamma = 2.21e5
    sim.mu_s = 1.0

    sim.set_m((1.0, 0, 0))

    sim.add(Zeeman((0, 0, 1e5)))

    ts = np.linspace(0, 1e-9, 1001)

    for t in ts:
        sim.driver.run_until(t)
def relax_system(rtol=1e-10, atol=1e-12):
    """numerical solution"""
    mesh = CuboidMesh(nx=1, ny=1, nz=1)
    sim = Sim(mesh, name='relax')
    sim.driver.set_tols(rtol=rtol, atol=atol)
    sim.driver.alpha = 0.5
    sim.driver.gamma = 2.21e5
    sim.mu_s = 1.0

    sim.set_m((1.0, 0, 0))

    sim.add(Zeeman((0, 0, 1e5)))

    ts = np.linspace(0, 1e-9, 1001)

    for t in ts:
        sim.driver.run_until(t)
Beispiel #42
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def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    sim.mu_s = 1e-23
    sim.driver.gamma = 1.76e11
    sim.driver.alpha = 1.0
    J = 1e-22
    exch = UniformExchange(J)
    sim.add(exch)
    demag = Demag()
    sim.add(demag)
    sim.set_m(init_m)

    ts = np.linspace(0, 5e-10, 101)
    for t in ts:
        sim.driver.run_until(t)
        sim.save_vtk()
    np.save('m0.npy', sim.spin)
Beispiel #43
0
def test_sim_single_spin_sllg(do_plot=False):

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, name='spin', driver='sllg')

    alpha = 0.1
    gamma = 2.21e5

    sim.set_options(dt=5e-15, gamma=gamma)

    sim.alpha = alpha
    sim.mu_s = 1.0

    sim.set_m((1, 0, 0))

    H0 = 1e5
    sim.add(Zeeman((0, 0, H0)))

    ts = np.linspace(0, 1e-10, 101)

    mx = []
    my = []
    mz = []
    real_ts = []
    for t in ts:
        sim.run_until(t)
        real_ts.append(sim.t)
        print(sim.t, abs(sim.spin_length()[0] - 1))
        mx.append(sim.spin[0])
        my.append(sim.spin[1])
        mz.append(sim.spin[2])

    mz = np.array(mz)

    a_mx, a_my, a_mz = single_spin(alpha, gamma, H0, ts)

    if do_plot:
        plot(real_ts, mx, my, mz, a_mx, a_my, a_mz, name='spin_sllg.pdf', title='integrating a spin')

    print(("Max Deviation = {0}".format(
        np.max(np.abs(mz - a_mz)))))

    assert np.max(np.abs(mz - a_mz)) < 1e-8
Beispiel #44
0
def test_dw_dmi_atomistic(do_plot=False):

    mesh = CuboidMesh(nx=300, ny=1, nz=1)

    sim = Sim(mesh, name='relax')
    sim.set_default_options(gamma=const.gamma)
    sim.alpha = 0.5
    sim.mu_s = const.mu_s_1
    sim.do_procession = False

    sim.set_m(m_init_dw)

    J = 50.0 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.01 * J
    dmi = DMI(D)
    sim.add(dmi)

    K = 0.005 * J
    anis = Anisotropy(K, axis=[1,0,0])
    sim.add(anis)

    ONE_DEGREE_PER_NS = 17453292.52

    sim.relax(dt=1e-13, stopping_dmdt=0.01 * ONE_DEGREE_PER_NS,
              max_steps=1000, save_m_steps=100, save_vtk_steps=50)

    np.save('m0.npy', sim.spin)

    xs = np.array([p[0] for p in mesh.coordinates]) - 150

    mx, my, mz = analytical(xs, A=J/2.0, D=-D, K=K)
    mxyz = sim.spin.copy()
    mxyz = mxyz.reshape(-1, 3).T

    assert max(abs(mxyz[0, :] - mx)) < 0.001
    assert max(abs(mxyz[1, :] - my)) < 0.001
    assert max(abs(mxyz[2, :] - mz)) < 0.0006

    if do_plot:

        save_plot(xs, mxyz, mx, my, mz)
Beispiel #45
0
def test_sim_single_spin_vode(do_plot=False):

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, name='spin')

    alpha = 0.1
    gamma = 2.21e5
    sim.driver.alpha = alpha
    sim.driver.gamma = gamma
    sim.mu_s = 1.0

    sim.set_m((1, 0, 0))

    H0 = 1e5
    sim.add(Zeeman((0, 0, H0)))

    ts = np.linspace(0, 1e-9, 101)

    mx = []
    my = []
    mz = []
    real_ts = []
    for t in ts:
        sim.driver.run_until(t)
        real_ts.append(sim.driver.t)
        #print sim.driver.t, abs(sim.spin_length()[0] - 1)
        mx.append(sim.spin[0])
        my.append(sim.spin[1])
        mz.append(sim.spin[2])

    mz = np.array(mz)
    a_mx, a_my, a_mz = single_spin(alpha, gamma, H0, ts)

    print(sim.driver.stat())

    if do_plot:
        plot(real_ts, mx, my, mz, a_mx, a_my, a_mz)

    print(("Max Deviation = {0}".format(
        np.max(np.abs(mz - a_mz)))))

    assert np.max(np.abs(mz - a_mz)) < 5e-7
Beispiel #46
0
def test_sim_single_spin_vode(do_plot=False):

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, name='spin')

    alpha = 0.1
    gamma = 2.21e5
    sim.alpha = alpha
    sim.gamma = gamma
    sim.mu_s = 1.0

    sim.set_m((1, 0, 0))

    H0 = 1e5
    sim.add(Zeeman((0, 0, H0)))

    ts = np.linspace(0, 1e-9, 101)

    mx = []
    my = []
    mz = []
    real_ts = []
    for t in ts:
        sim.run_until(t)
        real_ts.append(sim.t)
        #print sim.t, abs(sim.spin_length()[0] - 1)
        mx.append(sim.spin[0])
        my.append(sim.spin[1])
        mz.append(sim.spin[2])

    mz = np.array(mz)
    a_mx, a_my, a_mz = single_spin(alpha, gamma, H0, ts)

    print sim.stat()

    if do_plot:
        plot(real_ts, mx, my, mz, a_mx, a_my, a_mz)

    print("Max Deviation = {0}".format(
        np.max(np.abs(mz - a_mz))))

    assert np.max(np.abs(mz - a_mz)) < 5e-7
Beispiel #47
0
def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    sim.set_default_options(gamma=const.gamma)
    sim.alpha = 0.5
    sim.mu_s = const.mu_s_1
    sim.do_procession = False

    sim.set_m(m_init_dw)

    J = 50.0 * const.k_B
    exch = UniformExchange(J)
    sim.add(exch)

    D = 0.1 * J
    dmi = DMI(D, dmi_type='interfacial')
    sim.add(dmi)

    K = 0.02 * J
    anis = Anisotropy(K, axis=[0, 0, 1])
    sim.add(anis)

    ONE_DEGREE_PER_NS = 17453292.52

    sim.relax(dt=1e-13,
              stopping_dmdt=0.01 * ONE_DEGREE_PER_NS,
              max_steps=1000,
              save_m_steps=100,
              save_vtk_steps=50)

    np.save('m0.npy', sim.spin)

    xs = np.array([p[0] for p in mesh.pos]) - 150

    mx, my, mz = analytical(xs, A=J / 2.0, D=-D, K=K)
    mxyz = sim.spin.copy()
    mxyz.shape = (3, -1)

    save_plot(xs, mxyz, mx, my, mz)
Beispiel #48
0
def dynamic(mesh):

    sim = Sim(mesh, name='dyn_spin', driver='slonczewski')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m((0.8, 0, -1))

    Kx = Anisotropy(Ku=-0.05, axis=(0, 0, 1), name='Kz')
    sim.add(Kx)

    sim.p = (0, 0, 1)

    sim.u0 = 0.005
    sim.alpha = 0.1

    ts = np.linspace(0, 1200, 401)
    for t in ts:
        sim.run_until(t)
        #sim.save_vtk()
        print t
def dynamic(mesh):

    sim = Sim(mesh, name='dyn_spin', driver='slonczewski')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.driver.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m((0.8,0,-1))

    Kx = Anisotropy(Ku=-0.05, axis=(0, 0, 1), name='Kz')
    sim.add(Kx)

    sim.p = (0,0,1)

    sim.u0 = 0.005
    sim.driver.alpha = 0.1

    ts = np.linspace(0, 1200, 401)
    for t in ts:
        sim.run_until(t)
        #sim.save_vtk()
        print t
Beispiel #50
0
def excite_system(mesh, time=0.1, snaps=11):

    # 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-12)
    sim.driver.gamma = 2.211e5 / mu0
    sim.mu_s = 1e-27 / mu0
    sim.alpha = 0.05

    sim.set_m(np.load('m0.npy'))

    # Energies
    exch = UniformExchange(J=2e-20)
    sim.add(exch)

    anis = Anisotropy(0.01 * 2e-20, axis=(0, 0, 1))
    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.jz = -1e12
    sim.driver.beta = 0.1

    # 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()
    np.save('m1.npy', sim.spin)

    print(np.load('m1.npy')[:100])
Beispiel #51
0
def single_spin(alpha=0.01):

    mat = Material()

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, driver='sllg')
    sim.alpha = alpha
    sim.gamma = mat.gamma
    sim.mu_s = mat.mu_s
    sim.T = 10000

    sim.set_m((1, 1, 1))

    #sim.add(Zeeman(1,(0, 0, 1)))

    anis = Anisotropy(mat.K, direction=(0, 0, 1))
    sim.add(anis)

    dt = 0.5e-12
    ts = np.linspace(0, 1000 * dt, 1001)

    sx = []
    sy = []
    for t in ts:
        sim.run_until(t)
        sx.append(sim.spin[0])
        sy.append(sim.spin[1])
        print(t)

    plt.plot(sx, sy)
    plt.xlabel("$S_x$")
    plt.ylabel("$S_y$")
    plt.grid()
    plt.axis((-0.9, 0.9, -0.9, 0.9))
    plt.axes().set_aspect('equal')

    plt.savefig("macrospin.pdf")
Beispiel #52
0
def single_spin(alpha=0.01):

    mat = Material()

    mesh = CuboidMesh(nx=1, ny=1, nz=1)

    sim = Sim(mesh, driver='sllg')
    sim.alpha = alpha
    sim.gamma = mat.gamma
    sim.mu_s = mat.mu_s
    sim.T = 10000

    sim.set_m((1, 1, 1))

    #sim.add(Zeeman(1,(0, 0, 1)))

    anis = Anisotropy(mat.K, direction=(0, 0, 1))
    sim.add(anis)

    dt = 0.5e-12
    ts = np.linspace(0, 1000 * dt, 1001)

    sx = []
    sy = []
    for t in ts:
        sim.run_until(t)
        sx.append(sim.spin[0])
        sy.append(sim.spin[1])
        print(t)

    plt.plot(sx, sy)
    plt.xlabel("$S_x$")
    plt.ylabel("$S_y$")
    plt.grid()
    plt.axis((-0.9, 0.9, -0.9, 0.9))
    plt.axes().set_aspect('equal')

    plt.savefig("macrospin.pdf")
def excite_system(mesh, time=0.1, snaps=11):

    # 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-12)
    sim.driver.gamma = 2.211e5 / mu0
    sim.mu_s = 1e-27 / mu0
    sim.alpha = 0.05

    sim.set_m(np.load('m0.npy'))

    # Energies
    exch = UniformExchange(J=2e-20)
    sim.add(exch)

    anis = Anisotropy(0.01*2e-20, axis=(0, 0, 1))
    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.jz = -1e12
    sim.driver.beta = 0.1

    # 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()
    np.save('m1.npy', sim.spin)

    print(np.load('m1.npy')[:100])
Beispiel #54
0
def relax_system(mesh):

    sim = Sim(mesh, name='relax')
    # sim.set_options(rtol=1e-10,atol=1e-14)
    sim.alpha = 1.0
    sim.gamma = 1.0
    sim.mu_s = 1.0

    sim.set_m(init_m)
    # sim.set_m(random_m)
    # sim.set_m(np.load('m_10000.npy'))

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)

    Kx = Anisotropy(Ku=0.005, axis=(1, 0, 0), name='Kx')
    sim.add(Kx)

    sim.relax(dt=2.0, stopping_dmdt=1e-6, max_steps=1000,
              save_m_steps=100, save_vtk_steps=50)

    np.save('m0.npy', sim.spin)
def relax_system(mesh):

    sim = Sim(mesh, name='dmi_2d')
    sim.driver.alpha = 0.1
    sim.driver.gamma=1.76e11
    sim.mu_s = 1e-22

    J = 1e-20
    exch = UniformExchange(J)
    sim.add(exch)

    dmi = DMI(0.1 * J)
    sim.add(dmi)

    sim.set_m(init_m)    

    ts = np.linspace(0, 5e-10, 101)
    for t in ts:
        print(t)
        sim.run_until(t)
        #sim.save_vtk()

    return sim.spin
Beispiel #56
0
def create_sim():

    mesh = CuboidMesh(nx=121,ny=121,nz=1)
    sim=Sim(mesh,name='relax')
    
    sim.alpha = 1.0
    sim.gamma = 0.5
    sim.mu_s = mu_s
    
    sim.set_m(init_m)

    J = 1.0
    exch = UniformExchange(J)
    sim.add(exch)
    
    D = 0.08
    dmi = DMI(D)
    sim.add(dmi)
    
    K = 4e-3
    anis=Anisotropy(K, direction=(0,0,1),name='Ku')
    sim.add(anis)
    
    return sim