aomodel 0.1

Synthetic time-series data generation

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://aomodel-public.readthedocs.io .

Installing

1. Clone or download the repository:

   git clone [e-mail]:jeffreyutley/aomodel_public

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 aomodel using the environment.yml file.

      conda env create -f environment.yml

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

      conda activate aomodel

Running Demo(s)

The demo scripts demo_1_data_generation.py and demo_2_parameter_estimation.py show examples of how to use the ReVAR algorithm to i) generate synthetic data that matches the statistics of measured data sets and ii) estimate the parameters of ReVAR from measured data.

Before running either demo script, download the measured data sets. Before running demo_1_data_generation.py, you must also download pre-trained models. There are two options to download the data:

Option 1. Install using shell script

Use the script get_demo_data_server.sh inside of the demo folder to automatically install both the data and the pre-trained models. This script also places the files in the proper folders for the scripts demo_1_data_generation.py and demo_2_parameter_estimation.py.

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

source demo/get_demo_data_server.sh

Option 2. Manual install

To manually install the data sets and pre-trained models, visit the Bouman data repository <https://www.datadepot.rcac.purdue.edu/bouman/> and download the .zip files TBL_data.zip and pre_trained_models.zip (respectively).

Unzip the two files, then place the folder TBL_data inside of the demo/data directory and the files F06_pre_trained_model.npz and F12_pre_trained_model.npz inside of the demo/pre_trained_models directory.

Run either of the demo scripts from the parent directory (the aomodel_public folder containing this file) with something like the following command:

python demo/demo_file.py

Disclaimer

Approved for public release; distribution is unlimited. Public Affairs release approval # AFRL-2026-1309.