Prabhu Ramachandran
Prabhu Ramachandran is a faculty member at the Department of Aerospace
Engineering, IIT Bombay. He has run several workshops at SciPy which have been
generally well received. See here https://www.youtube.com/watch?v=r6OD07Qq2mw
and https://www.youtube.com/watch?v=2dd4BduDkG8. Prabhu has been using Python
for more than two decades and has been teaching Python and Python related
tools in various capacities for many years. Prabhu is also the main author of
automan which he wrote to save himself from dealing with the drudgery of
management of hundreds of simulation results for one of his papers. Prabhu
also gave a talk on automan at SciPy 2022 titled "The (Surprising) Road to
Reproducibility: Automation!" which you can see here
(https://www.youtube.com/watch?v=zvBotV6r9AY).
Sessions
In research involving any kind of computer simulation, we often have to
execute several simulations that might become a part of the final manuscript.
It is found that automating these simulations and their post-processing
introduces significant personal benefit in the form of improving research
output and productivity. Automation makes it much easier to run large
parameter sweeps and studies and allows you to focus on the important
questions to ask rather than managing hundreds or thousands of simulations
manually. This takes the drudgery of data/file management out of your hands,
systematizes your research, and makes it possible to incrementally improve and
refine your work. The added nice benefit is that your research also becomes
much easier to reproduce.
In scientific research, visualization of data plays a pivotal role in
comprehending complex numerical simulations. Visual representations not
only aid in understanding simulation outputs but also prove invaluable
during the development and debugging phases of numerical algorithms, long
before reaching production stages.
In this tutorial, we adopt a pragmatic approach to visualization,
introducing attendees to two robust packages: Mayavi
(https://docs.enthought.com/mayavi/mayavi/) and ParaView
(https://www.paraview.org/). Mayavi, a versatile tool for scripting and
visualizing data from within Python, offers swift and efficient visualization
capabilities. On the other hand, ParaView excels in handling large-scale
datasets and visualizations at scale, making it indispensable for
researchers dealing with extensive data volumes.
Both Mayavi and ParaView leverage the Visualization Toolkit (VTK),
providing a common foundation. Our aim is to acquaint researchers with the
importance of rapid visualization and guide them on discerning when to
transition to a more potent tool like ParaView.
By mastering these visualization tools, researchers can unlock deeper
insights from their data, accelerate development cycles, and effectively
communicate their findings to broader audiences.