Code to partially reproduce results in Unearthing InSights into Mars: unsupervised source separation with limited data, published in the proceedings of ICML 2023.
Run the commands below to install the required packages.
git clone https://github.com/alisiahkoohi/srcsep
cd srcsep/
conda env create -f environment.yml
conda activate srcsep
pip install -e .
After the above steps, you can run the example scripts by just
activating the environment, i.e., conda activate srcsep
, the
following times.
Deglitching can be done for a toy example by running the following:
python scripts/toy_example.py
The default command line arguments are stored at configs/toy_example.json
. Non-default arguments can be passed to the script by for example:
python scripts/toy_example.py
--max_itr 1000 \
--j 8,8 \
--q 1,1 \
--type exp_glitch
The generated data is stored in data/checkpoints/
directory. To visualize the results, run:
python scripts/visualize_results.py
--max_itr 1000 \
--j 8,8 \
--q 1,1 \
--type exp_glitch
The figures will be stored in the plots/
directory.
Note regarding caching: The scattering covariance computation caches the results in srcsep/_cached_dir
and following runs with the same exact setup will simply load the results. Feel free to delete the cache when needed.
Please contact alisk@rice.edu for questions.
Rudy Morel and Ali Siahkoohi