This repo contains Python scripts which run LOST evaluations, generating appropriate tables and graphs which can be subsequently included into the paper.
Compile LOST. Symlink the lost folder into the lost-evals repo (the lost executable should end up at lost-evals/lost/lost).
Clone OpenStarTracker. Install all the dependencies they say they need, including the dependencies necessary for "calibration and unit testing". Also get the Astrometry index files, which are provided in the Debian/Ubuntu package astrometry-data-tycho2-10-19 for wide-angle cameras (like the ones we use). (these index files are smaller than a TETRA database -- maybe astrometry is more suitable for star tracking than I thought?)
Go into their beast folder and run make (you may need to override the PYTHONHEADERS variable
in the Makefile for your installed version of python).
Then, symlink calibrate.py and startracker.py from the tests directly into the root directory here.
Clone Our fork of C-Tetra. Run make from their C_Tetra directory to compile. Then, you'll need to generate a few things:
- A 20 degree FOV catalog:
./Generate_Catalog 0.494 5.0 tetra_pattern_catalog_20_5.0 stars_5.0 - A 45-degree FOV catalog:
./Generate_Catalog 1.111 4.0 tetra_pattern_catalog_45_4.0 stars_4.0
Then, symlink all the generated pattern_catalog and stars files into the lost-evals folder.
Install Python dependencies from requirements.txt. There are any number of ways to do this, but I recommend creating either a virtualenv or a conda environment, then running pip install -r requirements.txt.
Python 3.11 recommended.
Conda is probably the best way to go, because it also lets you install the correct Python version. You'd first create an environment by running conda create -n lost-evals python=3.11, then whenever you need to use it, run conda activate lost-evals. Be sure to activate the environment before pip install -r requirements.txt.
To do everything (just kidding, I only mean run all the LOST evaluations), run make -j4, where 4 is the number of cpu cores you have.
To generate only the graphs, do make graphs. To only do the comprehensive tests, do make comprehensive (also plus -jN).
As a side-effect of using Make, it's really easy to run only certain evaluations! Just run make out/centroid-speed.png, for example.
To send graphs to the out5/ folder, for example, use make OUT_PREFIX=out5. Note that to specify a certain file to generate when using a custom prefix, you must also use the custom prefix if you want to run only one evaluation. For example, make OUT_PREFIX=out5 out5/centroid-speed.png.
To send comprehensive to a certain directory, use SCENARIOS_PREFIX=scenarios5 for example.