def example3(Ms):
x0 = y0 = z0 = 0
x1 = 500
y1 = 10
z1 = 500
nx = 50
ny = 1
nz = 1
mesh = df.Box(x0, y0, z0, x1, y1, z1, nx, ny, nz)
sim = Sim(mesh, Ms, unit_length=1e-9)
sim.alpha = 0.01
sim.set_m((1, 0, 0.1))
H_app = Zeeman((0, 0, 5e5))
sim.add(H_app)
exch = fe.Exchange(13.0e-12)
sim.add(exch)
demag = Demag(solver="FK")
sim.add(demag)
llg = sim.llg
max_time = 1 * np.pi / (llg.gamma * 1e5)
ts = np.linspace(0, max_time, num=100)
for t in ts:
print t
sim.run_until(t)
df.plot(llg._m)
df.interactive()
def run_finmag():
# Setup
setupstart = time.time()
mesh = df.Box(0, 0, 0, 30, 30, 100, 15, 15, 50)
sim = Simulation(mesh, Ms=0.86e6, unit_length=1e-9)
sim.set_m((1, 0, 1))
demag = Demag("FK")
demag.parameters["poisson_solver"]["method"] = "cg"
demag.parameters["poisson_solver"]["preconditioner"] = "jacobi"
demag.parameters["laplace_solver"]["method"] = "cg"
demag.parameters["laplace_solver"]["preconditioner"] = "bjacobi"
sim.add(demag)
sim.add(Exchange(13.0e-12))
# Dynamics
dynamicsstart = time.time()
sim.run_until(3.0e-10)
endtime = time.time()
# Write output to results.txt
output = open(os.path.join(MODULE_DIR, "results.txt"), "a")
output.write("\nBackend %s:\n" % df.parameters["linear_algebra_backend"])
output.write("\nSetup: %.3f sec.\n" % (dynamicsstart - setupstart))
output.write("Dynamics: %.3f sec.\n\n" % (endtime - dynamicsstart))
output.write(str(default_timer))
output.close()
def example2(Ms):
x0 = y0 = z0 = 0
x1 = 500
y1 = 10
z1 = 100
nx = 50
ny = 1
nz = 1
mesh = df.Box(x0, y0, z0, x1, y1, z1, nx, ny, nz)
S1 = df.FunctionSpace(mesh, "Lagrange", 1)
S3 = df.VectorFunctionSpace(mesh, "Lagrange", 1, dim=3)
llb = LLB(S1, S3)
llb.alpha = 0.01
llb.beta = 0.0
llb.M0 = Ms
llb.set_M((Ms, 0, 0))
llb.set_up_solver(jacobian=False)
llb.chi = 1e-4
H_app = Zeeman((0, 0, 5e5))
H_app.setup(S3, llb._M, Ms=1)
llb.interactions.append(H_app)
exchange = Exchange(13.0e-12, 1e-2)
exchange.chi = 1e-4
exchange.setup(S3, llb._M, Ms, unit_length=1e-9)
llb.interactions.append(exchange)
demag = Demag("FK")
demag.setup(S3, llb._M, Ms=1)
llb.interactions.append(demag)
max_time = 1 * np.pi / (llb.gamma * 1e5)
ts = np.linspace(0, max_time, num=100)
for t in ts:
print t
llb.run_until(t)
df.plot(llb._M)
df.interactive()
def example1(Ms=8.6e5):
x0 = y0 = z0 = 0
x1 = y1 = z1 = 10
nx = ny = nz = 1
mesh = df.Box(x0, x1, y0, y1, z0, z1, nx, ny, nz)
S1 = df.FunctionSpace(mesh, "Lagrange", 1)
S3 = df.VectorFunctionSpace(mesh, "Lagrange", 1, dim=3)
vis = df.Function(S3)
llb = LLB(S1, S3)
llb.alpha = 0.01
llb.beta = 0.0
llb.M0 = Ms
llb.set_M((Ms, 0, 0))
llb.set_up_solver(jacobian=False)
llb.chi = 1e-4
H_app = Zeeman((0, 0, 1e5))
H_app.setup(S3, llb._M, Ms=1)
llb.interactions.append(H_app)
exchange = Exchange(13.0e-12, 1e-2)
exchange.chi = 1e-4
exchange.setup(S3, llb._M, Ms, unit_length=1e-9)
llb.interactions.append(exchange)
max_time = 2 * np.pi / (llb.gamma * 1e5)
ts = np.linspace(0, max_time, num=100)
mlist = []
Ms_average = []
for t in ts:
llb.run_until(t)
mlist.append(llb.M)
vis.vector()[:] = mlist[-1]
Ms_average.append(llb.M_average)
df.plot(vis)
time.sleep(0.00)
print 'llb times', llb.call_field_times
save_plot(ts, Ms_average, 'Ms_%g-time.png' % Ms)
df.interactive()
def example1_sundials(Ms):
x0 = y0 = z0 = 0
x1 = y1 = z1 = 10
nx = ny = nz = 1
mesh = df.Box(x0, x1, y0, y1, z0, z1, nx, ny, nz)
S1 = df.FunctionSpace(mesh, "Lagrange", 1)
S3 = df.VectorFunctionSpace(mesh, "Lagrange", 1, dim=3)
vis = df.Function(S3)
llb = LLB(S1, S3)
llb.alpha = 0.00
llb.set_m((1, 1, 1))
llb.Ms = Ms
H_app = Zeeman((0, 0, 1e5))
H_app.setup(S3, llb._m, Ms=Ms)
llb.interactions.append(H_app)
exchange = BaryakhtarExchange(13.0e-12, 1e-2)
exchange.setup(S3, llb._m, llb._Ms)
llb.interactions.append(exchange)
integrator = llg_integrator(llb, llb.M, abstol=1e-10, reltol=1e-6)
max_time = 2 * np.pi / (llb.gamma * 1e5)
ts = np.linspace(0, max_time, num=50)
mlist = []
Ms_average = []
for t in ts:
integrator.advance_time(t)
mlist.append(integrator.m.copy())
llb.M = mlist[-1]
vis.vector()[:] = mlist[-1]
Ms_average.append(llb.M_average)
df.plot(vis)
time.sleep(0.0)
print llb.count
save_plot(ts, Ms_average, 'Ms_%g-time-sundials.png' % Ms)
df.interactive()
def example1(Ms=8.6e5):
x0 = y0 = z0 = 0
x1 = y1 = z1 = 10
nx = ny = nz = 1
mesh = df.Box(x0, x1, y0, y1, z0, z1, nx, ny, nz)
S1 = df.FunctionSpace(mesh, "Lagrange", 1)
S3 = df.VectorFunctionSpace(mesh, "Lagrange", 1, dim=3)
vis = df.Function(S3)
llb = LLB(S1, S3, rtol=1e-6, atol=1e-10)
llb.Ms = Ms
llb.alpha = 0.0
llb.set_m((1, 1, 1))
H_app = Zeeman((0, 0, 1e5))
H_app.setup(S3, llb._m, Ms=Ms)
llb.interactions.append(H_app)
exchange = BaryakhtarExchange(13.0e-12, 1e-5)
exchange.setup(S3, llb._m, llb._Ms)
llb.interactions.append(exchange)
max_time = 2 * np.pi / (llb.gamma * 1e5)
ts = np.linspace(0, max_time, num=100)
mlist = []
Ms_average = []
for t in ts:
llb.run_until(t)
mlist.append(llb.M)
vis.vector()[:] = mlist[-1]
Ms_average.append(llb.M_average)
df.plot(vis)
time.sleep(0.00)
print llb.count
save_plot(ts, Ms_average, 'Ms_%g-time.png' % Ms)
df.interactive()
import dolfin as df
import numpy
from scipy.integrate import odeint
from finmag.sim.llg import LLG
"""
Compute the behaviour of a one-dimensional strip of magnetic material,
with exchange interaction.
"""
nm=1e-9
xdim = 30
ydim = 30
zdim = 3
mesh = dolfin.Box(0,0,0,xdim*nm,ydim*nm, zdim*nm, xdim/3, ydim/3, zdim/3)
llg = LLG(mesh)
llg.alpha=1.0
llg.H_app=(0,0,0)
llg.A =1.3e-11
llg.D = 4e-3
#llg.set_m((
# 'MS * (2*x[0]/L - 1)',
# 'sqrt(MS*MS - MS*MS*(2*x[0]/L - 1)*(2*x[0]/L - 1))',
# '0'), L=length, MS=llg.Ms)
llg.set_m((
'MS',
'0',
import numpy as np
import dolfin as df
from finmag import Simulation
from finmag.energies import Exchange, DMI_Old, DMI
mesh = df.Box(0, 0, 0, 30e-9, 30e-9, 3e-9, 10, 10, 1)
Ms = 8.6e5
sim = Simulation(mesh, Ms)
sim.set_m((Ms, 0, 0))
A = 1.3e-11
D = 4e-3
sim.add(Exchange(A))
sim.add(DMI(D))
series = df.TimeSeries("solution/m")
t = np.linspace(0, 1e-9, 1000)
for i in t:
sim.run_until(i)
p = df.plot(sim.llg._m)
p.write_png("m_{}".format(i))
series.store(sim.llg._m.vector(), i)
df.interactive()
self.H_gradm = df.PETScVector()
self.integrator = native_llb.StochasticLLGIntegratorSTT(
self.m, self.m_pred, self._Ms, self._T, self.real_volumes,
self._alpha, self.P, self.beta, self.stochastic_update_field,
self.method)
# seems that in the presence of current, the time step have to very
# small
self.dt = 1e-14
# TODO: fix the using_u0 here.
if __name__ == "__main__":
mesh = df.Box(0, 0, 0, 5, 5, 5, 1, 1, 1)
sim = SLLG(mesh, 8.6e5, unit_length=1e-9)
sim.alpha = 0.1
sim.set_m((1, 0, 0))
ts = np.linspace(0, 1e-9, 11)
print sim.Ms
sim.T = 2000
sim.dt = 1e-14
H0 = 1e6
sim.add(Zeeman((0, 0, H0)))
A = helpers.scalar_valued_dg_function(13.0e-12, mesh)
exchange = Exchange(A)
sim.add(exchange)
In dolfin 1.1.0, the ordering of degrees of freedom does not correspond
to geometrical structures such as vertices anymore. This has to be
investigated further. For now, the re-ordering can be turned off using
the commented line below.
"""
# df.parameters.reorder_dofs_serial = False # for dolfin 1.1.0
"""
The goal is to compute the values of f(r, t) = sin(x) * sin(t) on a mesh for
different points in time. The cost of updating f a couple of times is measured.
"""
L = math.pi / 2
n = 10
mesh = df.Box(0, 0, 0, L, L, L, n, n, n)
#mesh = df.BoxMesh(0, 0, 0, L, L, L, n, n, n) # dor dolfin 1.1.0
ts = np.linspace(0, math.pi / 2, 100)
# dolfin expression code
S = df.FunctionSpace(mesh, "CG", 1)
expr = df.Expression("sin(x[0]) * sin(t)", t=0.0)
start = time.time()
for t in ts:
expr.t = t
f_dolfin = df.interpolate(expr, S)
t_dolfin = time.time() - start
print "Time needed for dolfin expression: {:.2g} s.".format(t_dolfin)
import dolfin as df
import numpy as np
from finmag.native import sundials
from finmag.util.timings import default_timer
from finmag.energies import Demag
if __name__ == '__main__':
x0 = y0 = z0 = 0
x1 = 500e-9
y1 = 500e-9
z1 = 2e-9
nx = 120
ny = 120
nz = 3
mesh = df.Box(x0, y0, z0, x1, y1, z1, nx, ny, nz)
print mesh.num_vertices()
Vv = df.VectorFunctionSpace(mesh, 'Lagrange', 1)
Ms = 8.6e5
expr = df.Expression(('1+x[0]', '1+2*x[1]','1+3*x[2]'))
m = df.project(expr, Vv)
m = df.project(df.Constant((1, 0, 0)), Vv)
demag = Demag("FK")
demag.setup(Vv, m, Ms)
demag.compute_field()
print default_timer
import numpy from scipy.integrate import odeint from finmag.sim.llg import LLG """ Compute the behaviour of a one-dimensional strip of magnetic material, with exchange interaction. """ nm=1e-9 xdim = 30 ydim = 30 mesh = dolfin.Rectangle(0,0,xdim*nm,ydim*nm, xdim/3, ydim/3) plotmesh = dolfin.Box(0,0,0,xdim*nm,ydim*nm, nm, xdim/3, ydim/3,1) plotV = dolfin.VectorFunctionSpace(plotmesh,"CG",1) plotM = dolfin.Function(plotV) llg = LLG(mesh) llg.alpha=1.0 llg.H_app=(0,0,0) llg.A =1.3e-11 llg.D = 4e-3 #llg.set_m(( # 'MS * (2*x[0]/L - 1)', # 'sqrt(MS*MS - MS*MS*(2*x[0]/L - 1)*(2*x[0]/L - 1))',
import dolfin as df
import numpy
from scipy.integrate import odeint
from finmag.sim.llg import LLG
"""
Compute the behaviour of a one-dimensional strip of magnetic material,
with exchange interaction.
"""
length = 60e-9 # in meters
simplexes = 20
#mesh = dolfin.Interval(simplexes, 0, length)
#mesh = dolfin.Rectangle(0,0,length,length, simplexes, simplexes)
mesh = dolfin.Box(0,0,0,length,length, length/20, simplexes, simplexes, simplexes/20)
llg = LLG(mesh)
llg.alpha=0.1
llg.H_app=(0,0,0)
llg.A = 1.3e-11
llg.D = 4e-3
#llg.set_m((
# 'MS * (2*x[0]/L - 1)',
# 'sqrt(MS*MS - MS*MS*(2*x[0]/L - 1)*(2*x[0]/L - 1))',
# '0'), L=length, MS=llg.Ms)
llg.set_m((
'MS',
"""
import dolfin
import dolfin as df
import numpy
from scipy.integrate import odeint
from finmag.sim.llg import LLG
"""
Compute the behaviour of a one-dimensional strip of magnetic material,
with exchange interaction.
"""
nm = 1e-9
simplexes = 20
mesh = dolfin.Box(0, 0, 0, 60 * nm, 3 * nm, 3 * nm, simplexes, 1, 1)
llg = LLG(mesh)
llg.alpha = 1.0
llg.H_app = (0, 0, 0)
llg.A = 1.3e-11
llg.D = 4e-3
#llg.set_m((
# 'MS * (2*x[0]/L - 1)',
# 'sqrt(MS*MS - MS*MS*(2*x[0]/L - 1)*(2*x[0]/L - 1))',
# '0'), L=length, MS=llg.Ms)
llg.set_m(('MS', '0', '0'), MS=llg.Ms)
llg.setup(use_dmi=True, use_exchange=True)
llg.pins = []