{"action":"created","issue":{"url":"https://api.github.com/repos/nipy/dipy/issues/651","labels_url":"https://api.github.com/repos/nipy/dipy/issues/651/labels{/name}","comments_url":"https://api.github.com/repos/nipy/dipy/issues/651/comments","events_url":"https://api.github.com/repos/nipy/dipy/issues/651/events","html_url":"https://github.com/nipy/dipy/pull/651","id":77330621,"number":651,"title":"Added estimate_sigma bias correction + update example","user":{"login":"samuelstjean","id":3030760,"avatar_url":"https://avatars.githubusercontent.com/u/3030760?v=3","gravatar_id":"","url":"https://api.github.com/users/samuelstjean","html_url":"https://github.com/samuelstjean","followers_url":"https://api.github.com/users/samuelstjean/followers","following_url":"https://api.github.com/users/samuelstjean/following{/other_user}","gists_url":"https://api.github.com/users/samuelstjean/gists{/gist_id}","starred_url":"https://api.github.com/users/samuelstjean/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/samuelstjean/subscriptions","organizations_url":"https://api.github.com/users/samuelstjean/orgs","repos_url":"https://api.github.com/users/samuelstjean/repos","events_url":"https://api.github.com/users/samuelstjean/events{/privacy}","received_events_url":"https://api.github.com/users/samuelstjean/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":21,"created_at":"2015-05-17T15:20:50Z","updated_at":"2015-05-22T17:37:44Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/nipy/dipy/pulls/651","html_url":"https://github.com/nipy/dipy/pull/651","diff_url":"https://github.com/nipy/dipy/pull/651.diff","patch_url":"https://github.com/nipy/dipy/pull/651.patch"},"body":"So I added the correction factor for computing the std of magnitude images. Since they do not include any negative value, the variance is lower and the mean is shifted as compared ot a gaussian distribution. This factor corrects that fact and returns a closer estimate of the variance.\r\nI also updated the example to show this fact and changed it to show a slice of diffusion instea,d since b0 images are always relatively clean.\r\n\r\nSince estimate_sigma is the same as computing the variance over the background, the correction factor needs an estimate of the local magnitude SNR, but it is zero in that specific case. Hence, the value can be precomputed without using hyp1f1. Next step is to have it on a voxelwise basis with said hyp1f1 function in need of love over here : https://github.com/nipy/dipy/issues/650"},"comment":{"url":"https://api.github.com/repos/nipy/dipy/issues/comments/104722909","html_url":"https://github.com/nipy/dipy/pull/651#issuecomment-104722909","issue_url":"https://api.github.com/repos/nipy/dipy/issues/651","id":104722909,"user":{"login":"arokem","id":118582,"avatar_url":"https://avatars.githubusercontent.com/u/118582?v=3","gravatar_id":"","url":"https://api.github.com/users/arokem","html_url":"https://github.com/arokem","followers_url":"https://api.github.com/users/arokem/followers","following_url":"https://api.github.com/users/arokem/following{/other_user}","gists_url":"https://api.github.com/users/arokem/gists{/gist_id}","starred_url":"https://api.github.com/users/arokem/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/arokem/subscriptions","organizations_url":"https://api.github.com/users/arokem/orgs","repos_url":"https://api.github.com/users/arokem/repos","events_url":"https://api.github.com/users/arokem/events{/privacy}","received_events_url":"https://api.github.com/users/arokem/received_events","type":"User","site_admin":false},"created_at":"2015-05-22T17:37:44Z","updated_at":"2015-05-22T17:37:44Z","body":"Just checking back in here: did you have a chance to run the RESTORE example on your branch?"}}