{"id":18014741,"url":"https://github.com/alisiahkoohi/srcsep","last_synced_at":"2025-04-04T15:13:59.173Z","repository":{"id":171398242,"uuid":"647502611","full_name":"alisiahkoohi/srcsep","owner":"alisiahkoohi","description":"Code to partially reproduce results in \"Unearthing InSights into Mars: Unsupervised source separation with limited data\", ICML 2023","archived":false,"fork":false,"pushed_at":"2024-03-11T22:41:46.000Z","size":111,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-10T00:43:12.786Z","etag":null,"topics":["limited-data","scattering-covariances","scattering-networks","source-separation","unsupervised-learning","wavelet"],"latest_commit_sha":null,"homepage":"https://proceedings.mlr.press/v202/siahkoohi23a.html","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/alisiahkoohi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-05-30T23:33:40.000Z","updated_at":"2024-03-19T14:29:39.000Z","dependencies_parsed_at":"2024-03-11T23:51:19.284Z","dependency_job_id":null,"html_url":"https://github.com/alisiahkoohi/srcsep","commit_stats":null,"previous_names":["alisiahkoohi/insight_src_sep","alisiahkoohi/srcsep"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alisiahkoohi%2Fsrcsep","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alisiahkoohi%2Fsrcsep/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alisiahkoohi%2Fsrcsep/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alisiahkoohi%2Fsrcsep/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alisiahkoohi","download_url":"https://codeload.github.com/alisiahkoohi/srcsep/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247198466,"owners_count":20900081,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["limited-data","scattering-covariances","scattering-networks","source-separation","unsupervised-learning","wavelet"],"created_at":"2024-10-30T04:10:48.665Z","updated_at":"2025-04-04T15:13:59.156Z","avatar_url":"https://github.com/alisiahkoohi.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003eUnearthing InSights into Mars: unsupervised source separation with limited data\u003c/h1\u003e\n\nCode to partially reproduce results in [Unearthing InSights into Mars: unsupervised source separation with limited data](https://proceedings.mlr.press/v202/siahkoohi23a.html), published in the proceedings of ICML 2023.\n\n\n## Installation\n\nRun the commands below to install the required packages.\n\n```bash\ngit clone https://github.com/alisiahkoohi/srcsep\ncd srcsep/\nconda env create -f environment.yml\nconda activate srcsep\npip install -e .\n```\n\nAfter the above steps, you can run the example scripts by just\nactivating the environment, i.e., `conda activate srcsep`, the\nfollowing times.\n\n## Scripts\n\nDeglitching can be done for a toy example by running the following:\n\n```bash\npython scripts/toy_example.py\n```\n\nThe default command line arguments are stored at `configs/toy_example.json`. Non-default arguments can be passed to the script by for example:\n\n```bash\npython scripts/toy_example.py\n    --max_itr 1000 \\\n    --j 8,8 \\\n    --q 1,1 \\\n    --type exp_glitch\n```\n\nThe generated data is stored in `data/checkpoints/` directory. To visualize the results, run:\n\n```bash\npython scripts/visualize_results.py\n    --max_itr 1000 \\\n    --j 8,8 \\\n    --q 1,1 \\\n    --type exp_glitch\n```\n\nThe figures will be stored in the `plots/` directory.\n\n**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.\n\n## Questions\n\nPlease contact alisk@rice.edu for questions.\n\n## Authors\n\nRudy Morel and Ali Siahkoohi\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falisiahkoohi%2Fsrcsep","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falisiahkoohi%2Fsrcsep","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falisiahkoohi%2Fsrcsep/lists"}