{"id":20507453,"url":"https://github.com/hahnec/multimodal_emg","last_synced_at":"2025-10-28T23:45:56.558Z","repository":{"id":107359170,"uuid":"517367588","full_name":"hahnec/multimodal_emg","owner":"hahnec","description":"Multimodal Exponentially Modified Gaussians with Optional Oscillation","archived":false,"fork":false,"pushed_at":"2023-03-29T11:29:46.000Z","size":2517,"stargazers_count":5,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-13T21:53:09.459Z","etag":null,"topics":["acoustic-features","acoustics","exponentially-modified-gaussian","feature-engineering","gaussian-mixture-models","mixture-model","multimodal","oscillation","oscillations","regression","regression-models","superposition"],"latest_commit_sha":null,"homepage":"","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/hahnec.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}},"created_at":"2022-07-24T15:50:20.000Z","updated_at":"2023-11-21T23:06:56.000Z","dependencies_parsed_at":"2023-03-13T14:36:48.515Z","dependency_job_id":"51886c6f-aae1-44e6-b190-a0f43c161195","html_url":"https://github.com/hahnec/multimodal_emg","commit_stats":{"total_commits":74,"total_committers":3,"mean_commits":"24.666666666666668","dds":"0.14864864864864868","last_synced_commit":"a87f2bf8811e72d8638d7c90c8944c307f4491f9"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hahnec/multimodal_emg","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hahnec%2Fmultimodal_emg","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hahnec%2Fmultimodal_emg/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hahnec%2Fmultimodal_emg/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hahnec%2Fmultimodal_emg/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hahnec","download_url":"https://codeload.github.com/hahnec/multimodal_emg/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hahnec%2Fmultimodal_emg/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279017358,"owners_count":26086052,"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","status":"online","status_checked_at":"2025-10-13T02:00:06.723Z","response_time":61,"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":["acoustic-features","acoustics","exponentially-modified-gaussian","feature-engineering","gaussian-mixture-models","mixture-model","multimodal","oscillation","oscillations","regression","regression-models","superposition"],"created_at":"2024-11-15T20:14:08.414Z","updated_at":"2025-10-14T00:39:33.452Z","avatar_url":"https://github.com/hahnec.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Multimodal Exponentially Modified Gaussians\n\n[![arXiv paper link](https://img.shields.io/badge/paper-arXiv:2209.12202-red)](https://arxiv.org/pdf/2209.12202.pdf)\n\n## Quick Facts\n- multiple asymmetric Gaussian distributions for the univariate case\n- optional oscillation term for wave approximations\n- based on [analytical derivation](./docs/exp_mod_gauss_wave_partial_derivative.pdf)\n- accepts ```numpy``` as well as ```torch``` data types\n\n## Kick Start\n\nBelow is a code excerpt for fitting multi-modal skewed Gaussian distributions:\n\n```python\n\nfrom multimodal_emg import gaussian_envelope_model, emg_envelope_model, emg_wave_model\nfrom multimodal_emg.regression.derivatives import gaussian_jac, emg_jac, oemg_jac\n\n# multimodal optimization\np_star, result = multimodal_fit(\n    data,\n    features = [[1, 24, 2, 0],[.5, 48, 3, -1]], # amplitude, location, spread, skew\n    components = 2,\n    x = x,\n    fun = emg_envelope_model,\n    jac_fun = emg_jac,\n)\n\nprint(p_star)\n\nimport matplotlib.pyplot as plt\nplt.plot(result)\nplt.show()\n```\n\n## Oscillating Regression\n\nThe oscillation regression can be found in the accompanied [Jupyter Notebook](./single_synth_memg_example.ipynb) which yields the below result:\n\n[![Oscillating Multi-Modal EMG](./docs/figs/denoising_emg.svg)](./single_synth_memg_example.ipynb)\n\n## Citation\n\n```\n@inproceedings{Hahne:2022,\n    author = {Christopher Hahne},\n    title = {Multimodal Exponentially Modified Gaussian Oscillators},\n    booktitle= {2022 IEEE International Ultrasonics Symposium (IUS)},\n    address={},\n    month={Okt},\n    year={2022},\n    pages={1-4},\n}\n```\n\n## Acknowledgment\n\nThis research is funded by the Hasler foundation under project number 22027.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhahnec%2Fmultimodal_emg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhahnec%2Fmultimodal_emg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhahnec%2Fmultimodal_emg/lists"}