{"id":19076488,"url":"https://github.com/torfsen/clusterpolate","last_synced_at":"2025-04-30T01:54:12.416Z","repository":{"id":1878022,"uuid":"45171913","full_name":"torfsen/clusterpolate","owner":"torfsen","description":"Inter- and extrapolation for scattered data","archived":false,"fork":false,"pushed_at":"2023-10-03T20:52:29.000Z","size":261,"stargazers_count":4,"open_issues_count":3,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-30T01:54:06.630Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://clusterpolate.readthedocs.org","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/torfsen.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":"2015-10-29T09:05:31.000Z","updated_at":"2023-09-20T09:39:37.000Z","dependencies_parsed_at":"2022-09-02T07:41:39.130Z","dependency_job_id":null,"html_url":"https://github.com/torfsen/clusterpolate","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torfsen%2Fclusterpolate","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torfsen%2Fclusterpolate/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torfsen%2Fclusterpolate/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torfsen%2Fclusterpolate/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/torfsen","download_url":"https://codeload.github.com/torfsen/clusterpolate/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251626888,"owners_count":21617741,"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":[],"created_at":"2024-11-09T01:59:31.200Z","updated_at":"2025-04-30T01:54:12.374Z","avatar_url":"https://github.com/torfsen.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Clusterpolate\n\n## Inter- and extrapolation for clustered data\n\nTraditional approaches for inter- and extrapolation of scattered data\nwork on a filled rectangular area surrounding the data points or in\ntheir filled convex hull. However, scattered data often consists of\ndifferent clusters of irregular shapes and usually contains areas where\nthere simply is no data. Forcing such data into a traditional inter-\nor extrapolation scheme often does not lead to the desired results.\n\nHeatmaps, on the other hand, deal well with scattered data but often do\nnot provide real interpolation: Instead they usually use raw sums of\nkernel functions which overestimate the target value in densely\npopulated areas.\n\nClusterpolation is a hybrid inter- and extrapolation scheme to fix this.\nIt uses kernel functions for a weighted inter- and extrapolation of\nlocal values, as well as for a density estimation of the data. The\nlatter is used to assign a membership degree to clusterpolated points:\nPoints with a low membership degree lie in an area where there's just\nnot enough data.\n\n\n## Example\n\nGiven scattered data like\n\n![Plot of raw data](docs/plot.png \"Raw data\")\n\nthe `clusterpolate.image` function produces\n\n![Image of clusterpolated data](docs/clusterpolated.png \"Clusterpolated data\")\n\nNote how the values are cleanly interpolated even within dense regions\nand how extrapolation only occurs close to existing data points.\n\nOf course you can also use clusterpolation on your data without\ngenerating any images: simply use the `clusterpolate.clusterpolate`\nfunction.\n\n\n## Installation\n\n    pip install clusterpolate\n\n\n## Documentation\n\nThe documentation is [hosted on ReadTheDocs](https://clusterpolate.readthedocs.org).\n\n\n## History\n\n* *0.2.0:* Support for multiprocessing\n* *0.1.0:* First release\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorfsen%2Fclusterpolate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftorfsen%2Fclusterpolate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorfsen%2Fclusterpolate/lists"}