Code Resources

A big part of the work BLISS does involves coding. Find resources BLISS members have developed or think are helpful.

Getting Started with Python

For folks that are new to coding or new to Python, there are many resources online to help get started. People affiliated with the University of Oklahoma have access to LinkedIn Learning, which offers some specific options:


Several BLISS team members host their own code on their own Github accounts. Check out the People page and look for the small Github logo under a person’s name to find their Github. The BLISS team also hosts a group Github account where collaborative work and instrument support code can be hosted. This account originated from the group that was formerly known as CASS and was used to support the Coptersonse, but it is growing in the BLISS space. BLISS Github

Getting Started with CLAMPS data

A brief tutorial on how to access CLAMPS data and start visualizing them is available online, courtesy of Dr. Tyler Bell. You can find it on his Github account under CLAMPS-Demos.


The ropospheric Remotely Observed Profiling via Optimal Estimation is an optical estimation based retreival method that was formerly known as AERIoe (as in the AERI instrument). BLISS team scientists Dr. Joshua Gebauer and Dr. Tyler Bell invested their time to translate and reframed into an opensorce language. You can learn more about it here

Lidar Toolbox

As the number of Doppler lidars the BLISS team works with grows, Dr. Joshua Gebauer has been taking his own tools and compiling the tools of some other scientists and gathering them in a Lidar Toolbox which you can find on his Github.

Multi-instrument boundary-layer height detection

Given CLAMPS unique combination of instruments, Dr. Smith collaborated with Dr. Jacob Carlin (CIWRO/NSSL) to create an algorithm that can estimate the boundary-layer hieight from the sensors onboard. You can find the algorithm on her github. A publication is in preparation.


Dr. Jeremy Gibbs has developed a tool which operates on the 1D Stochastic Burgers Equation, allowing insight into turbulence without having to generalize to the fully-3D case and thus remaining very computationally cheap. PyBurgers offers a direct numerical simularion mode and an LES mode with 4 subgridscale options. You can read more about it and find the the code on the [PyBurgers repository] (