ParaView
Availability
Personal
Copy data to personal machine for analysis.
Windows and OS X
Download latest version from ParaView website
older version may work if newer fails on older OS or graphics card
Linux
Install with your package manager (can also download as above)
Debian and Ubuntu:
sudo apt-get install paraviewFedora:
sudo yum install paraview
Clusters
Remote desktop to avoid having to transfer data
Open OnDemand (OOD) (nibi, trillium)
JupyterHub (JH) (rorqual, fir)
Some general hints for picking job options
reservation: None unless taking part in a course (not this one) with a reservations,
account: ending in gpu if using a GPU (including hardware OpenGL) and cpu otherwise,
time: 8hrs or less (runs in the interactive queue) for faster start,
cores: enough cores so memory/core is 12G or less (regular memory queue) for faster start,
memory: at least 8GB for a desktop session (see cores note about keeping memory/core 12G or less),
oversubscription: select if possible (shares CPUs with other users) for faster start,
gpu configuration: if using a GPU pick type cluster has most of for faster start, and
user interface: JupyterLab (Jupter Notebook is the older version before the renaming).
OOD
Login and pick Compute Node -> Desktop (nibi) or Apps -> Trillium Desktop (trillium)
JupyterHub
Login, start job, pick Mate Desktop or XFCE4 Desktop
If the browser fails to connect to the desktop, trying waiting around thirty seconds and then refresh the failed
connection page (press the F5 key).
If you see … relocation error: …/rebind.so … in your terminal, you have run into an issue caused by
the JupyterHub desktop websockify LD_PRELOAD. Disable this by running
$ unset LD_PRELOAD
If you see warning messages about ipykernel when loading modules, you have run into issues caused by JupyterHub
pre-loading the ipython-kernel. Unload this by running
$ module --force unload ipython-kernel python
Tutorial
Working through the ParaView tutorial is one of the quickest and easiest ways to get up-to-speed on ParaView.
basic usage (what we will be going over)
batch python scripting
visualizing large models
$ cp -r /home/tyson/ParaView .
Basis of visualization
map raw data to visual data
spacial and temporal data
topology and types of grids
User interface
menu bar
tool bars
pipeline browser
properties panel
view
Basic interface
creating a source
interacting with a 3d view
modifying visualization paramaters (filter, display, view)
undo and redo (regular vs camera)
Loading data
opening file (selecting which variables to load)
representation and field coloring
scaling
Filters
selecting filters (toolbar, menu, and search)
applying a filter (contours, slices)
Multiview
creating a multiple views
linking cameras
re-arranging the views
Vector visualization
streamlines
tubes and glyphs
surface LIC
Volume rendering
Enabling
Transfer function
Plotting
histogram plot
plot over a line in space
plot over a curve
plot over time
Selections
Query and view based selections
Data vs spatial selections
Selection labels
Extract selection and spreadsheet