{"action":"opened","issue":{"url":"https://api.github.com/repos/soft-matter/trackpy/issues/297","labels_url":"https://api.github.com/repos/soft-matter/trackpy/issues/297/labels{/name}","comments_url":"https://api.github.com/repos/soft-matter/trackpy/issues/297/comments","events_url":"https://api.github.com/repos/soft-matter/trackpy/issues/297/events","html_url":"https://github.com/soft-matter/trackpy/issues/297","id":112046317,"number":297,"title":"ENH: Add least-squares feature identification","user":{"login":"nkeim","id":2985325,"avatar_url":"https://avatars.githubusercontent.com/u/2985325?v=3","gravatar_id":"","url":"https://api.github.com/users/nkeim","html_url":"https://github.com/nkeim","followers_url":"https://api.github.com/users/nkeim/followers","following_url":"https://api.github.com/users/nkeim/following{/other_user}","gists_url":"https://api.github.com/users/nkeim/gists{/gist_id}","starred_url":"https://api.github.com/users/nkeim/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nkeim/subscriptions","organizations_url":"https://api.github.com/users/nkeim/orgs","repos_url":"https://api.github.com/users/nkeim/repos","events_url":"https://api.github.com/users/nkeim/events{/privacy}","received_events_url":"https://api.github.com/users/nkeim/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/soft-matter/trackpy/labels/enhancement","name":"enhancement","color":"84b6eb"}],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-10-18T22:28:47Z","updated_at":"2015-10-18T22:28:47Z","closed_at":null,"body":"This is more of a long-term goal. It is possible to refine feature locations not by weighted averaging, but by least-squares fitting with a model of how all *n* particles should appear. This was developed by Mark Shattuck's group for close granular packings; there is a tutorial (and Matlab code) [here](http://gibbs.engr.ccny.cuny.edu/technical/Tracking/ChiTrack.php). And you can also check out a [paper](http://link.aps.org/doi/10.1103/PhysRevE.75.051311) that uses the technique successfully.\r\n\r\nI wanted to create an issue because I hope that someday a user will contribute this to trackpy. Maybe you!"}}