{"id":20520295,"url":"https://github.com/plasmacontrol/bes-ml","last_synced_at":"2026-04-20T07:33:22.645Z","repository":{"id":49436287,"uuid":"517639392","full_name":"PlasmaControl/bes-ml","owner":"PlasmaControl","description":"BES ML data tools and models","archived":false,"fork":false,"pushed_at":"2026-01-21T03:44:45.000Z","size":60782,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2026-02-28T02:10:03.679Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PlasmaControl.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-07-25T11:35:39.000Z","updated_at":"2022-07-25T11:38:45.000Z","dependencies_parsed_at":"2025-06-16T20:41:54.062Z","dependency_job_id":null,"html_url":"https://github.com/PlasmaControl/bes-ml","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/PlasmaControl/bes-ml","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fbes-ml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fbes-ml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fbes-ml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fbes-ml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PlasmaControl","download_url":"https://codeload.github.com/PlasmaControl/bes-ml/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fbes-ml/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32037860,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-20T00:18:06.643Z","status":"online","status_checked_at":"2026-04-20T02:00:06.527Z","response_time":94,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2024-11-15T22:19:03.255Z","updated_at":"2026-04-20T07:33:22.630Z","avatar_url":"https://github.com/PlasmaControl.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# BES ML\n\n**ML models for DIII-D BES data**\n\nTo use this repo, clone it and add the repo directory to `$PYTHONPATH`.  To contribute to this repo, branch off of `main`, push the feature branch to Github, and submit PRs.  Prior to submitting PRs, pull and merge any updates from `main` and run pytest.\n\n`bes_ml/` contains modules and classes to create, train, and analyze BES ML models.  `bes_ml.base` contains the base classes, and other modules under `bes_ml` contain specific applications that import `bes_ml.base`.  Each application directory should contain `train.py` and `analyze.py` modules.  Example usage:\n\n```python\nfrom elm_regression import Trainer, Analyzer\n\nmodel = Trainer()\nmodel.train()\n\nmodel_analyzer = Analyzer()\nmodel_analyzer.plot_training()\nmodel_analyzer.run_inference()\nmodel_analyzer.plot_inference()\nmodel_analyzer.show()\n```\n\nThe primary code objects are:\n\n- Base class `_Trainer` in `bes_ml.base.train_base` and application-specific subclasses like `ELM_Classification_Trainer` in `bes_ml.elm_classification.train`.\n- Similarly, base class `_Analyzer` in `bes_ml.base.analyze_base` and applicaiton-specific subclasses like `ELM_Regression_Analyzer` in `bes_ml.elm_classification.analyze`\n- Model class `Multi_Features_Model` in `bes_ml.base.models`.  `Multi_Features_Model` is composed of different types of features such as CNN features, dense features, FFT features, etc.  All feature are a subclass of `_Base_Features` in `bes_ml.base.models`.\n\n`bes_data/` contains small sample datasets (~10 MB HDF5 files) to assist with code development and tools to package BES data on the GA cluster.  `test/` contains pytest tests.\n\nAdditional examples can be inferred from the test scripts and from `if __name__ == ...` blocks in modules.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fplasmacontrol%2Fbes-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fplasmacontrol%2Fbes-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fplasmacontrol%2Fbes-ml/lists"}