boiling-flow 0.1.1

Phase screen generation for aero-optics using boiling flow

This project includes a data-driven algorithm that generates synthetic time-series of images (of arbitrary duration) by estimating statistical parameters from an input time-series of images. Full documentation is available at https://boiling-flow.readthedocs.io .

Installing

1. Clone or download the repository:

   git clone [e-mail]:jeffreyutley/boiling_flow

2. Install the conda environment and package

   1. Option 1: Clean install from dev_scripts

      *****You can skip all other steps if you do a clean install.****

      To do a clean install, use the command:

      cd dev_scripts
      source clean_install_all.sh

   2. Option 2: Manual install

      Create a conda environment boiling_flow using the environment.yml file.

      conda env create -f environment.yml

      Anytime you want to use this package, this boiling_flow environment should be activated with the following:

      conda activate boiling_flow

Running Demo(s)

There are three demo scripts: parameter_estimates_from_measured_data.py, generate_phase_screen_data.py, and results_from_simulated_data.py. The former two scripts show an example of the boiling flow algorithm on measured data sets, while the latter first generates simulated data and then runs the boiling flow algorithm. The results_from_simulated_data.py script can be run without downloading any external data sets.

Before running the former two demo scripts, download the measured data sets:

Option 1. Install using shell script

Use the script get_demo_data_server.sh inside of the demo folder to automatically install the data and place it in the proper folder for the scripts parameter_estimates_from_measured_data.py and generate_phase_screen_data.py.

Inside of the parent directory (the boiling_flow folder containing this file), run the following:

source demo/get_demo_data_server.sh

Option 2. Manual install

To manually install the data sets, visit the Bouman data repository <https://www.datadepot.rcac.purdue.edu/bouman/> and download the .zip file TBL_data.zip.

Unzip the file and place the folder TBL_data inside of the data/demo directory.

Run any of the demo scripts from the parent directory (the boiling_flow folder containing this file) with the following command:

python demo/demo_file.py

The script generate_phase_screen_data.py loads an .npy file that is saved by parameter_estimates_from_measured_data.py, so the latter script must be run before the former.

Disclaimer

Approved for public release; distribution is unlimited. Public Affairs release approval # AFRL-2025-5580.