ParaView Catalyst User’s Guide
Figure 2.2: The Catalyst panel can be toggled in
View Catatalyst Export Inspector
.
2.2.1 Creating a Representative Dataset
A question that often arises is how to create a representative dataset. There are two ways to do this. The first way is to
run the simulation with Catalyst with a script that outputs the full grid with all attribute information. Appendix 6.1.2 has a
script that can be used for this purpose. The second way is by using the sources and filters in ParaView. The easiest grids
to create within the GUI are image data grids (i.e. uniform rectilinear grids), polydata and unstructured grids. For those
knowledgeable enough about VTK, the programmable source can also be used to create all grid types. If a multi-block
grid is needed, the Group Datasets filter can be used to group together multiple datasets into a single output. The next step
is to create the attribute information (i.e. point and/or cell data). This can be easily done with the Calculator filter as it can
create data with one or three components, name the array to match the name of the array provided by the adaptor, and set
an appropriate range of values for the data. Once this is done, the user should save this out and then read the file back in to
have the reader act as the source for the pipeline.
2.2.2 Manipulating Python Scripts
For users that are comfortable programming in Python, we encourage them to modify the given scripts as desired. The
following information can be helpful for doing this:
• Sphinx generated ParaView Python API documentation at
www.paraview.org/ParaView/Doc/Nightly/www/py-doc/index.html.
• Using the ParaView GUI trace functionality to determine how to create desired filters and set their parameters. This
is done with
Start Trace
and
Stop Trace
under the
Tools
menu.
• Using the ParaView GUI Python shell with tab completion. This is done with
Python Shell
under the
View
menu.
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