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Visualisation is an important part of scientific computation, both for
the analysis of results and their presentation. To visualise computed
results, we use MayaVi (Ramachandran, 2001), which makes use of
Kitware's VTK (Schroeder et al., 1996, Schroeder et al., 1997) as the middleware
for preparing the data prior to being rendered in either POV-Ray or a
Pixar Renderman (Pixar, 1989, Pixar, 2000) compliant raytracer.
We can exploit the features of these tools, particularly by adding features commonly found in computational fluid dynamics to further our understanding of the magnetisation patterns resulting from our simulations.
Figure 2.19 shows a typical
visualisation. Point A in the upper image shows the surface map of a
scalar, in this instance the xy angle of magnetisation. For clarity a
wireframe map showing the outline of the finite element mesh is
visible. In the lower image, the scalar surface map remains, though it
is translucent (point B). The cones indicated by point C represent the
mean magnetisation of the small area immediately surrounding the
cones; the colour shows a scalar (the component of the
magnetisation) and the direction of the cone reflects the
magnetisation vector itself. Where smaller cones are present in a
visualisation, these represent an interpolation of the vector where
source data is only available around that point rather than at the
point itself. This usually takes place at boundaries, arising from a
linear interpolation between
and 0.
To highlight points of interest, an isosurface of a scalar (such as
that indicated by point D) may be shown. The isosurface in this
example is again based on the component of the magnetisation and
attaches a visual representation to the core of a vortex. Finally,
point E shows streamlines, which are the result of tracer particles
being ``dropped'' into the system. These tracers follow the path of
the magnetisation and provide a visual cue for interesting features of
the visualisation; here they gradually follow the magnetisation around
the surface of the sample, spiralling in until they reach the vortex
core.
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Where the volume of the sample is of particular interest, a random point mask can be applied to the visualisation, such as that in figure 2.20. Here streamlines have again been used to add depth to the visualisation and by operating on a derived vector (in this case, the curl of the magnetisation) the bounds of the vortex core are clear.
Python (van Rossum, 2003) and Linux shell scripts (Ramey, 2003) were employed extensively in coordinating the process to take raw simulation results and produce camera-ready images and animations suitable for the analysis of magnetic microstructures.
Schematic drawings are occasionally used to assist understanding
physical geometry or aspects of magnetisation.
Figure 2.21 shows two schematics of a generic
sample with arbitrary shape and a symmetry in the plane (point
A), here represented by a rhombus. The axes on the left indicate the
three-dimensionality of the sample. If the applied field (point B)
were initially applied along the
direction, then two possible
vortex types emerge. The vortex shown in the left sample has an
out-of-plane vortex, where the magnetisation circulates in the
plane (point C, solid blue arrow) and the core of the vortex
(point D, dotted red arrow) points perpendicular to this symmetric
plane, i.e. in
. The vortex shown in the right sample has an
in-plane vortex -- the circulation of the magnetisation (point
C, solid blue arrow) is in the
plane and the core of the vortex
(point D, solid red arrow) is aligned with the direction of the
initial applied field, i.e. in
.
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