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211 lines
9 KiB
ReStructuredText
.. _usersguide_plots:
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======================
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Geometry Visualization
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======================
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.. currentmodule:: openmc
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OpenMC is capable of producing two-dimensional slice plots of a geometry as well
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as three-dimensional voxel plots using the geometry plotting :ref:`run mode
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<usersguide_run_modes>`. The geometry plotting mode relies on the presence of a
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:ref:`plots.xml <io_plots>` file that indicates what plots should be created. To
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create this file, one needs to create one or more :class:`openmc.Plot`
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instances, add them to a :class:`openmc.Plots` collection, and then use the
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:class:`Plots.export_to_xml` method to write the ``plots.xml`` file.
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-----------
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Slice Plots
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-----------
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.. image:: ../_images/atr.png
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:width: 300px
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By default, when an instance of :class:`openmc.Plot` is created, it indicates
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that a 2D slice plot should be made. You can specify the origin of the plot
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(:attr:`Plot.origin`), the width of the plot in each direction
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(:attr:`Plot.width`), the number of pixels to use in each direction
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(:attr:`Plot.pixels`), and the basis directions for the plot. For example, to
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create a :math:`x` - :math:`z` plot centered at (5.0, 2.0, 3.0) with a width of
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(50., 50.) and 400x400 pixels::
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plot = openmc.Plot()
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plot.basis = 'xz'
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plot.origin = (5.0, 2.0, 3.0)
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plot.width = (50., 50.)
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plot.pixels = (400, 400)
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The color of each pixel is determined by placing a particle at the center of
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that pixel and using OpenMC's internal ``find_cell`` routine (the same one used
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for particle tracking during simulation) to determine the cell and material at
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that location.
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.. note:: In this example, pixels are 50/400=0.125 cm wide. Thus, this plot may
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miss any features smaller than 0.125 cm, since they could exist
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between pixel centers. More pixels can be used to resolve finer
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features but will result in larger files.
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By default, a unique color will be assigned to each cell in the geometry. If you
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want your plot to be colored by material instead, change the
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:attr:`Plot.color_by` attribute::
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plot.color_by = 'material'
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If you don't like the random colors assigned, you can also indicate that
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particular cells/materials should be given colors of your choosing::
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plot.colors = {
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water: 'blue',
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clad: 'black'
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}
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# This is equivalent
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plot.colors = {
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water: (0, 0, 255),
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clad: (0, 0, 0)
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}
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Note that colors can be given as RGB tuples or by a string indicating a valid
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`SVG color <https://www.w3.org/TR/SVG11/types.html#ColorKeywords>`_.
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When you're done creating your :class:`openmc.Plot` instances, you need to then
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assign them to a :class:`openmc.Plots` collection and export it to XML::
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plots = openmc.Plots([plot1, plot2, plot3])
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plots.export_to_xml()
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# This is equivalent
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plots = openmc.Plots()
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plots.append(plot1)
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plots += [plot2, plot3]
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plots.export_to_xml()
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To actually generate the plots, run the :func:`openmc.plot_geometry` function.
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Alternatively, run the :ref:`scripts_openmc` executable with the ``--plot``
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command-line flag. When that has finished, you will have one or more ``.png``
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files. Alternatively, if you're working within a `Jupyter
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<https://jupyter.org/>`_ Notebook or QtConsole, you can use the
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:func:`openmc.plot_inline` to run OpenMC in plotting mode and display the
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resulting plot within the notebook.
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.. _usersguide_voxel:
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-----------
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Voxel Plots
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-----------
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.. image:: ../_images/3dba.png
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:width: 200px
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The :class:`openmc.Plot` class can also be told to generate a 3D voxel plot
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instead of a 2D slice plot. Simply change the :attr:`Plot.type` attribute to
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'voxel'. In this case, the :attr:`Plot.width` and :attr:`Plot.pixels` attributes
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should be three items long, e.g.::
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vox_plot = openmc.Plot()
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vox_plot.type = 'voxel'
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vox_plot.width = (100., 100., 50.)
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vox_plot.pixels = (400, 400, 200)
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The voxel plot data is written to an :ref:`HDF5 file <io_voxel>`. The voxel file
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can subsequently be converted into a standard mesh format that can be viewed in
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`ParaView <https://www.paraview.org/>`_, `VisIt
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<https://wci.llnl.gov/simulation/computer-codes/visit>`_, etc. This typically
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will compress the size of the file significantly. The provided
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:ref:`scripts_voxel` script can convert the HDF5 voxel file to VTK formats. Once
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processed into a standard 3D file format, colors and masks can be defined using
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the stored ID numbers to better explore the geometry. The process for doing this
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will depend on the 3D viewer, but should be straightforward.
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.. note:: 3D voxel plotting can be very computer intensive for the viewing
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program (Visit, ParaView, etc.) if the number of voxels is large (>10
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million or so). Thus if you want an accurate picture that renders
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smoothly, consider using only one voxel in a certain direction.
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----------------
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Projection Plots
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----------------
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.. image:: ../_images/hexlat_anim.gif
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:width: 200px
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The :class:`openmc.ProjectionPlot` class presents an alternative method
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of producing 3D visualizations of OpenMC geometries. It was developed to
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overcome the primary shortcoming of voxel plots, that an enormous number
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of voxels must be employed to capture detailed geometric features.
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Projection plots perform volume rendering on material or
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cell volumes, with colors specified in the same manner as slice plots.
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This is done using the native ray tracing capabilities within OpenMC,
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so any geometry in which particles successfully run without overlaps
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or leaks will work with projection plots.
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One drawback of projection plots is that particle tracks cannot be overlaid
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on them at present. Moreover, checking for overlap regions is not currently possible with projection plots. The image heading this section can
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be created by adding the following code to the hexagonal lattice example packaged
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with OpenMC, before exporting to plots.xml.
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::
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r = 5
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import numpy as np
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for i in range(100):
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phi = 2 * np.pi * i/100
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thisp = openmc.ProjectionPlot(plot_id = 4 + i)
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thisp.filename = 'frame%s'%(str(i).zfill(3))
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thisp.look_at = [0, 0, 0]
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thisp.camera_position = [r * np.cos(phi), r * np.sin(phi), 6 * np.sin(phi)]
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thisp.pixels = [200, 200]
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thisp.color_by = 'material'
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thisp.colorize(geometry)
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thisp.set_transparent(geometry)
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thisp.xs[fuel] = 1.0
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thisp.xs[iron] = 1.0
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thisp.wireframe_domains = [fuel]
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thisp.wireframe_thickness = 2
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plot_file.append(thisp)
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This generates a sequence of png files which can be joined to form a gif.
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Each image specifies a different camera position using some simple periodic
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functions to create a perfectly looped gif. :attr:`ProjectionPlot.look_at`
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defines where the camera's centerline should point at.
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:attr:`ProjectionPlot.camera_position` similarly defines where the camera
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is situated in the universe level we seek to plot. The other settings
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resemble those employed by :class:`openmc.Plot`, with the exception of
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the :class:`ProjectionPlot.set_transparent` method and :attr:`ProjectionPlot.xs`
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dictionary. These are used to control volume rendering of material
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volumes. "xs" here stands for cross section, and it defines material
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opacities in units of inverse centimeters. Setting this value to a
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large number would make a material or cell opaque, and setting it to
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zero makes a material transparent. Thus, the :class:`ProjectionPlot.set_transparent`
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can be used to make all materials in the geometry transparent. From there,
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individual material or cell opacities can be tuned to produce the
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desired result.
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Two camera projections are available when using these plots, perspective
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and orthographic. The default, perspective projection,
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is a cone of rays passing through each pixel which radiate from the camera
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position and span the field of view in the x and y positions. The horizontal
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field of view can be set with the :attr: `ProjectionPlot.horizontal_field_of_view` attribute,
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which is to be specified in units of degrees. The field of view only influences
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behavior in perspective projection mode.
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In the orthographic projection, rays follow the same angle but originate from
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different points. The horizontal width of this plane of ray starting points
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may be set with the :attr: `ProjectionPlot.orthographic_width` element. If this element
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is nonzero, the orthographic projection is employed. Left to its default value
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of zero, the perspective projection is employed.
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Lastly, projection plots come packaged with wireframe generation that
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can target either all surface/cell/material boundaries in the geometry,
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or only wireframing around specific regions. In the above example, we
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have set only the fuel region from the hexagonal lattice example to have
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a wireframe drawn around it. This is accomplished by setting the
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:attr: `ProjectionPlot.wireframe_domains`, which may be set to either material
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IDs or cell IDs. The :attr:`ProjectionPlot.wireframe_thickness`
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attribute sets the wireframe thickness in units of pixels.
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.. note:: When setting specific material or cell regions to have wireframes
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drawn around them, the plot must be colored by materials if wireframing
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around specific materials and similarly colored by cell instance if
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wireframing around specific cells.
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