{"id":24074279,"url":"https://github.com/tailhq/plasmaml","last_synced_at":"2025-10-15T09:01:52.638Z","repository":{"id":47568140,"uuid":"43759524","full_name":"tailhq/PlasmaML","owner":"tailhq","description":"Machine Learning tools for Space Weather and Plasma Physics ","archived":false,"fork":false,"pushed_at":"2022-09-01T23:02:35.000Z","size":61977,"stargazers_count":17,"open_issues_count":7,"forks_count":1,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-03-30T11:41:34.158Z","etag":null,"topics":["machine-learning","plasma-physics","scala","space-physics","space-weather"],"latest_commit_sha":null,"homepage":"https://tailhq.github.io/PlasmaML","language":"Scala","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-2.1","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tailhq.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}},"created_at":"2015-10-06T15:27:16.000Z","updated_at":"2023-09-20T10:40:18.000Z","dependencies_parsed_at":"2022-08-30T18:22:08.489Z","dependency_job_id":null,"html_url":"https://github.com/tailhq/PlasmaML","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tailhq%2FPlasmaML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tailhq%2FPlasmaML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tailhq%2FPlasmaML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tailhq%2FPlasmaML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tailhq","download_url":"https://codeload.github.com/tailhq/PlasmaML/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251357283,"owners_count":21576687,"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":["machine-learning","plasma-physics","scala","space-physics","space-weather"],"created_at":"2025-01-09T18:09:37.823Z","updated_at":"2025-10-15T09:01:52.535Z","avatar_url":"https://github.com/tailhq.png","language":"Scala","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PlasmaML\n\n[![Build Status](https://travis-ci.org/transcendent-ai-labs/PlasmaML.svg?branch=master)](https://travis-ci.org/transcendent-ai-labs/PlasmaML)\n\nMachine Learning tools for Space Weather and Plasma Physics\n---------------------------\n\u003e ![Image courtesy NASA](http://www.nasa.gov/images/content/607990main1_FAQ13-670.jpg)\n\u003e\n\u003e courtesy [NASA](www.nasa.gov)\n\n*PlasmaML* is a collection of data analysis and machine learning tools in the domain of space physics, more specifically in modelling of space plasmas \u0026 space weather prediction.\n\nThis is a multi-language project where the primary modelling is done in *Scala* while *R* is heavily leveraged for generating visualizations. The project depends on the [DynaML](https://github.com/mandar2812/DynaML) scala machine learning library and uses model and optimization implementations in it as a starting point for extensive experiments in space physics simulations and space weather prediction.\n\n## Getting Started\n\n*PlasmaML* is managed using the Simple Build Tool (sbt).\n\n### Installation\n\n#### Requirements\n\n1. Java Development Kit 8. \n2. [Scala](scala-lang.org)\n3. [sbt](http://www.scala-sbt.org/)\n4. [R](https://www.r-project.org/) with the following packages:\n\n    * `ggplot2`\n    * `reshape2`\n    * `latex2exp`\n    * `plyr`\n    * `gridExtra`\n    * `reshape2`\n    * `directlabels`\n\n\n#### Steps\n\nAfter cloning the project, PlasmaML can be installed directly from the shell or \nby first entering the sbt shell and building the source.\n\n**From the shell**\n\nFrom the root directory `PlasmaML` run the build script (with configurable parameters).\n\n```bash\n./build.sh \u003cheap size\u003e \u003ccompile with gpu support\u003e \u003cuse packaged tensorflow\u003e \u003cupdate bash env\u003e\n```\n\nFor example the following builds the project with 4 GB java heap and GPU support.\n\n```bash\n./build.sh 4096m true\n```\n\nNote that for Nvidia GPU support to work, compatible versions of CUDA and cuDNN must be installed and \nfound in the `$LD_LIBRARY_PATH` environment variable see the [DynaML docs](https://transcendent-ai-labs.github.io/DynaML/installation/installation/) for more info.\n\nUse the last parameter `\u003cupdate bash env\u003e` to add the PlasmaML executable in the bash `$PATH`.\n\nThe following build will use 4 GB of heap, with GPU support, precompiled tensorflow binaries and \nadds `plasmaml` binary to the `$PATH` variable.\n\n```\n./build.sh 4096m true false true\n```\n\n**From the sbt shell**\n\nStart the sbt shell with the script `sbt-shell.sh` having the same parameters as `build.sh`\n\n```bash\n./build.sh \u003cheap size\u003e \u003ccompile with gpu support\u003e \u003cuse packaged tensorflow\u003e\n```\n\nFrom the sbt shell, run\n\n```\nstage\n```\n\nAfter building, access the PlasmaML shell like \n\n```\n./target/universal/stage/bin/plasmaml\n```\n\nFor more information on PlasmaML and its modules, refer to the scala docs below.\n\n1. [omni](https://transcendent-ai-labs.github.io/api_docs/PlasmaML/recent/omni/io/github/mandar2812/PlasmaML/omni/index.html): Forecasting models for geomagnetic indices.\n\n2. [mag-core](https://transcendent-ai-labs.github.io/api_docs/PlasmaML/recent/mag-core/io/github/mandar2812/PlasmaML/index.html): API for Bayesian inference of radiation belt parameters.\n\n3. [helios](https://transcendent-ai-labs.github.io/api_docs/PlasmaML/recent/helios/io/github/mandar2812/PlasmaML/index.html): Machine learning models for solar wind and heliosperic data.\n\n4. [vanAllen](https://transcendent-ai-labs.github.io/api_docs/PlasmaML/recent/vanAllen/io/github/mandar2812/PlasmaML/vanAllen/index.html): Processing of van Allen probe data.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftailhq%2Fplasmaml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftailhq%2Fplasmaml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftailhq%2Fplasmaml/lists"}