{"id":39382695,"url":"https://github.com/rcv911/emd","last_synced_at":"2026-01-18T03:01:07.000Z","repository":{"id":209921900,"uuid":"116487641","full_name":"rcv911/EMD","owner":"rcv911","description":"Empirical Mode Decomposition","archived":false,"fork":false,"pushed_at":"2018-01-06T15:32:32.000Z","size":1166,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2023-11-29T20:43:06.811Z","etag":null,"topics":["data-analysis","emd","empirical-mode-decomposition"],"latest_commit_sha":null,"homepage":null,"language":"Python","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/rcv911.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":"2018-01-06T14:05:18.000Z","updated_at":"2023-11-29T20:43:08.718Z","dependencies_parsed_at":"2023-11-29T20:43:08.656Z","dependency_job_id":"f37d3c95-0675-4f40-b093-24edaa405caf","html_url":"https://github.com/rcv911/EMD","commit_stats":null,"previous_names":["rcv911/emd"],"tags_count":0,"template":null,"template_full_name":null,"purl":"pkg:github/rcv911/EMD","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcv911%2FEMD","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcv911%2FEMD/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcv911%2FEMD/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcv911%2FEMD/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rcv911","download_url":"https://codeload.github.com/rcv911/EMD/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcv911%2FEMD/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28528026,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-18T00:39:45.795Z","status":"online","status_checked_at":"2026-01-18T02:00:07.578Z","response_time":98,"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":["data-analysis","emd","empirical-mode-decomposition"],"created_at":"2026-01-18T03:00:37.647Z","updated_at":"2026-01-18T03:01:06.948Z","avatar_url":"https://github.com/rcv911.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# EMD = Empirical Mode Decomposition \n\n## Description\n\n[EMD](http://rspa.royalsocietypublishing.org/content/royprsa/454/1971/903.full.pdf) is a method for analysing non-stationary and nonlinear data. \nI'm going to tell you main things about EMD:\n- Method is locally adaptive, data-driven, multiscale, high efficient. \n- The user specifies the number of mod. \n- Fast oscillations superimposed to slow oscillations \n(First mode = fast oscillations = high frequency. Last mode = slow oscillations = low frequency).  \n- Many applications to speech analysis (biological data, astronomical data, nonlinear physics data, earthquake, climate, etc.).\n\n## Motivation\n\n## Test Data\n\nWe are going to use noise sinus:\n```python\n\tnoise = random.uniform(-0.05,0.05,10000)\n\tsignal = sin(2*pi*f*t) + noise\n```\n\n![](images/test_signal.png)\n![](images/test_signal+.png)\n\n## Results\n\nIf number of mod = 2\n![](images/modes_k2.png)\n![](images/result_k2.png)\n![](images/result+_k2.png)\n\nIf number of mod = 4\n![](images/modes_k4.png)\n![](images/modes+_k4.png)\n![](images/result_k4.png)\n![](images/result+_k4.png)\n\nResult EMD for Van der Pol oscillator. The number of mod = 4.\n![](images/emd_van_der_pol_oscillator.png)\n\n## Learn more\n\n- [Wiki EMD](https://en.wikipedia.org/wiki/Hilbert%E2%80%93Huang_transform#Empirical_mode_decomposition_(EMD))\n- [scipy.interpolate.splrep](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.splrep.html)\n- [scipy.interpolate.splev](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.splev.html)\n\n\u003c I'm going to add some useful links lately...\n\n\n## Installation\n\nYou can use [Python](https://www.python.org/) with data package: [Anaconda](https://www.anaconda.com/) or [Miniconda](https://conda.io/miniconda).\nThere's another way - use [Portable Python](http://portablepython.com/). Also you can use whatever IDE for Python.\n\n## License\n\nFree\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frcv911%2Femd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frcv911%2Femd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frcv911%2Femd/lists"}