{"id":16492685,"url":"https://github.com/jeremyrajan/invite-list-jr","last_synced_at":"2025-07-08T08:07:05.474Z","repository":{"id":72058954,"uuid":"115456767","full_name":"jeremyrajan/invite-list-jr","owner":"jeremyrajan","description":"Find records within a radius(proximity) using haversine or spherical law of cosines","archived":false,"fork":false,"pushed_at":"2020-03-09T06:55:08.000Z","size":98,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-11T20:42:10.003Z","etag":null,"topics":["great-circle","haversine-formula","spherical-coordinates"],"latest_commit_sha":null,"homepage":"","language":"TypeScript","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/jeremyrajan.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-12-26T21:40:56.000Z","updated_at":"2019-07-15T21:48:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"869c734f-ef10-4213-bad7-917c57f7fccb","html_url":"https://github.com/jeremyrajan/invite-list-jr","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/jeremyrajan%2Finvite-list-jr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jeremyrajan%2Finvite-list-jr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jeremyrajan%2Finvite-list-jr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jeremyrajan%2Finvite-list-jr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jeremyrajan","download_url":"https://codeload.github.com/jeremyrajan/invite-list-jr/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241329448,"owners_count":19944982,"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":["great-circle","haversine-formula","spherical-coordinates"],"created_at":"2024-10-11T14:06:54.052Z","updated_at":"2025-03-01T06:29:59.603Z","avatar_url":"https://github.com/jeremyrajan.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Build Status](https://travis-ci.org/jeremyrajan/invite-list-jr.svg?branch=master)](https://travis-ci.org/jeremyrajan/invite-list-jr)\n[![tested with jest](https://img.shields.io/badge/tested_with-jest-99424f.svg)](https://github.com/facebook/jest)\n[![npm version](https://badge.fury.io/js/invite-list-jr.svg)](https://badge.fury.io/js/invite-list-jr)\n[![david.dm](https://david-dm.org/jeremyrajan/invite-list-jr.svg)](https://david-dm.org/jeremyrajan/invite-list-jr)\n[![codecov](https://codecov.io/gh/jeremyrajan/invite-list-jr/branch/master/graph/badge.svg)](https://codecov.io/gh/jeremyrajan/invite-list-jr)\n\n[![NPM](https://nodei.co/npm/invite-list-jr.png?downloads=true\u0026downloadRank=true\u0026stars=true)](https://nodei.co/npm/invite-list-jr/)\n\n# invite-list-jr\nFind a list of customers who are close to the proximity (in KM). Mainly used to create a invitation list of customers within a certain radius of the event.\n\n# Setup\n```\n[sudo] npm i -g invite-list-jr\n```\n\n# Usage\nThe program expects 2 inputs while looking for the right information:\n\n* Customer records in a text file (customers.txt) -- one customer per line, JSON-encoded. Example [here](https://github.com/jeremyrajan/invite-list-jr/blob/master/customers.txt).\n* `config.json` file, with the structure and information [here](https://github.com/jeremyrajan/invite-list-jr/blob/master/src/Config.d.ts).\n\nOnce you have the above information, run the following command:\n\n```\ninvite-list-jr --f=[location to customers.txt] --c=[location to config.json]\n```\n\nThis will give you a list of customers within in the proximity defined and number of results.\n\n![image](https://user-images.githubusercontent.com/2890683/34391311-c76000de-eb6a-11e7-8737-8af465721b2e.png)\n\n\n\u003e For easiness, we have provided a [customers.txt](https://github.com/jeremyrajan/invite-list-jr/blob/master/customers.txt) and [config.json](https://github.com/jeremyrajan/invite-list-jr/blob/master/config.json) example to get started with.\n\n# Calculation\nCurrently the program uses haversine function to calculate the distance and convert it to KM's. That said, we have an option to change the program to use spherical law of cosines [here](https://github.com/jeremyrajan/invite-list-jr/blob/master/src/index.ts#L21).\n\nFor more information regarding the algorithms, please refer to https://en.wikipedia.org/wiki/Great-circle_distance.\n\n# Roadmap\n* Allow the algorithm to be configurable.\n* Ability to write the results to a file and convert it to .csv for easy export.\n\n# Development\n\n```\ngit clone git@github.com:jeremyrajan/invite-list-jr.git\nnpm install\ngulp # starts a watcher\nnpm test # to run the tests using jest\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeremyrajan%2Finvite-list-jr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjeremyrajan%2Finvite-list-jr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeremyrajan%2Finvite-list-jr/lists"}