{"id":19216583,"url":"https://github.com/jpcano/radarplot","last_synced_at":"2025-10-09T09:35:11.427Z","repository":{"id":144047624,"uuid":"87765991","full_name":"jpcano/radarplot","owner":"jpcano","description":"A library to read the CIKM AnalaytiCup 2017 dataset :rainbow:","archived":false,"fork":false,"pushed_at":"2017-05-24T12:12:31.000Z","size":1392,"stargazers_count":3,"open_issues_count":1,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-01T09:10:14.733Z","etag":null,"topics":["data-visualization","machine-learning","matplotlib","meteorology","python"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jpcano.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,"governance":null}},"created_at":"2017-04-10T04:20:08.000Z","updated_at":"2024-09-20T18:54:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"d66d1b23-1d07-4211-b8d7-b145862d8654","html_url":"https://github.com/jpcano/radarplot","commit_stats":{"total_commits":6,"total_committers":1,"mean_commits":6.0,"dds":0.0,"last_synced_commit":"d00573ad27f4f162a57d13d3d11aeb465d637b86"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jpcano/radarplot","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpcano%2Fradarplot","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpcano%2Fradarplot/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpcano%2Fradarplot/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpcano%2Fradarplot/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jpcano","download_url":"https://codeload.github.com/jpcano/radarplot/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpcano%2Fradarplot/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279001121,"owners_count":26083022,"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":"2025-10-09T02:00:07.460Z","response_time":59,"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-visualization","machine-learning","matplotlib","meteorology","python"],"created_at":"2024-11-09T14:17:43.630Z","updated_at":"2025-10-09T09:35:11.412Z","avatar_url":"https://github.com/jpcano.png","language":"Python","readme":"# radarplot \n\nA library to read the CIKM AnalaytiCup 2017 dataset.\n\nradarplot is a library intended to read efficiently the dataset released for the CIKM AnalytiCup 2017 competiton (`train.txt`) and map it to Python objects. The dataset `train.txt` is 17.1 GB long but this library takes `O(1)` to read any of the 600,000 radar maps in the dataset. The information of the different Stacks and Layers of each sequence are easily accesible in the Radar object (all the structure within Radar object can be visualized in a plot).\n\nInstallation\n------------\n\nThis library is not published in the pip platform, so if you want to install you can do it manually following these steps:\n\n```shell\ngit clone https://github.com/jpcano/radarplot.git\ncd radarplot\npip3 install -e .\n```\n\nQuick Guide\n-----------\n\nPlot the 2nd layer of the 7th stack of the location number 134 in the dataset:\n\n```python\nfrom radarplot.CIKM import *\ncikm = CIKM('train.txt', 'train.index')\ncikm.getRadars(134).getStack(6).getLayer(1).plot()\n```\n\nYou can save it as a png file in the current directory:\n\n```python\nfrom radarplot.CIKM import *\ncikm = CIKM('train.txt', 'train.index')\ncikm.getRadars(134).getStack(6).getLayer(1).plot('great_image')\n```\n\nShows an animated radar map from the radar location 3 in the dataset:\n\n```python\nfrom radarplot.CIKM import *\ncikm = CIKM('train.txt', 'train.index')\ncikm.getRadar(3).plot()\n```\n\nThis saves all the radar maps (as mp4 sequences) in the directory 'vid':\n\n```python\nfrom radarplot.CIKM import *\ncikm = CIKM('train.txt', 'train.index')\nfor radar in cikm.getAllRadars():\n\tradar.plot('vid/' + radar.getID() + '.mp4')`\n```\n\nIt is possible to save the images in reversed sorted order of the labels:\n\n```python\nfrom radarplot.CIKM import *\ncikm = CIKM('train.txt', 'train.index')\nfor radar in cikm.getAllRadars(sorted=True, reversed=True):\n\tradar.plot('vid/' + radar.getID() + '.mp4')\n```\n\n### Notes\n\nThe first time that the CIKM constructor is called it will take some time to generate the file `train.index`.\nThe next calls to that constructor will detect the index file and they will use it to speed up the random access to the images.\n\n### More examples\n\nYou can have a look at the project [radarweb](https://github.com/jpcano/radarweb). It implements a client that uses this library to generate this [web](http://jesus.engineer/radarweb).\n\nTests\n-----\n\nThere is a unitary test suite that can be run with the following command:\n\n```shell\nmake test\n```\n\nTODO\n----\n\n- Autogenerate the API docummentation.\n- Continuous Integration with Travis.\n- Integrate codecov.io with Travis for automatic test coverage reports.\n\nLicense\n-------\n\nGNU GPLv3, see [LICENSE](LICENSE)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjpcano%2Fradarplot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjpcano%2Fradarplot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjpcano%2Fradarplot/lists"}