In order to calculate the stray field outside the sample, we have to define an "airbox" which is going to contain our sample. In this example we define a box with 100 nm edgle length as a mesh which then contains a magnetic sample which is a cube with 50 nm dimensions. We achieve this by implementing a Python fuction for defining the Ms (
norm_fun). Outside our sample the value of saturation magnetisation is zero.
import discretisedfield as df import micromagneticmodel as mm import oommfc as oc %matplotlib inline region = df.Region(p1=(-100e-9, -100e-9, -100e-9), p2=(100e-9, 100e-9, 100e-9)) mesh = df.Mesh(region=region, cell=(5e-9, 5e-9, 5e-9)) def norm_fun(pos): x, y, z = pos if -50e-9 <= x <= 50e-9 and -50e-9 <= y <= 50e-9 and -50e-9 <= z <= 50e-9: return 8e5 else: return 0 system = mm.System(name='airbox_method') system.energy = mm.Exchange(A=1e-12) + mm.Demag() system.dynamics = mm.Precession(gamma0=mm.consts.gamma0) + mm.Damping(alpha=1) system.m = df.Field(mesh, dim=3, value=(0, 0, 1), norm=norm_fun)
We can now plot the norm to confirm our definition.
In the next step, we can relax the system and show its magnetisation.
md = oc.MinDriver() md.drive(system) system.m.plane('z').mpl(figsize=(10, 10))
Running OOMMF (ExeOOMMFRunner) [2020/06/12 00:55]... (7.2 s)
Stray field can now be calculated as an effective field for the demagnetisation energy.
stray_field = oc.compute(system.energy.demag.effective_field, system)
Running OOMMF (ExeOOMMFRunner) [2020/06/12 00:55]... (2.2 s)
stray_field is a
df.Field and all operations characteristic to vector fields can be performed.
stray_field.plane('z').mpl(figsize=(8, 8), vector_scale=1e6)