{"action":"created","issue":{"url":"https://api.github.com/repos/igmhub/picca/issues/442","repository_url":"https://api.github.com/repos/igmhub/picca","labels_url":"https://api.github.com/repos/igmhub/picca/issues/442/labels{/name}","comments_url":"https://api.github.com/repos/igmhub/picca/issues/442/comments","events_url":"https://api.github.com/repos/igmhub/picca/issues/442/events","html_url":"https://github.com/igmhub/picca/pull/442","id":327526928,"node_id":"MDExOlB1bGxSZXF1ZXN0MTkxMjk5NzIz","number":442,"title":"Add subsample covar co","user":{"login":"londumas","id":3166436,"node_id":"MDQ6VXNlcjMxNjY0MzY=","avatar_url":"https://avatars0.githubusercontent.com/u/3166436?v=4","gravatar_id":"","url":"https://api.github.com/users/londumas","html_url":"https://github.com/londumas","followers_url":"https://api.github.com/users/londumas/followers","following_url":"https://api.github.com/users/londumas/following{/other_user}","gists_url":"https://api.github.com/users/londumas/gists{/gist_id}","starred_url":"https://api.github.com/users/londumas/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/londumas/subscriptions","organizations_url":"https://api.github.com/users/londumas/orgs","repos_url":"https://api.github.com/users/londumas/repos","events_url":"https://api.github.com/users/londumas/events{/privacy}","received_events_url":"https://api.github.com/users/londumas/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":10,"created_at":"2018-05-29T23:58:07Z","updated_at":"2018-06-05T17:41:21Z","closed_at":null,"author_association":"CONTRIBUTOR","pull_request":{"url":"https://api.github.com/repos/igmhub/picca/pulls/442","html_url":"https://github.com/igmhub/picca/pull/442","diff_url":"https://github.com/igmhub/picca/pull/442.diff","patch_url":"https://github.com/igmhub/picca/pull/442.patch"},"body":"Add computation of the co-variance matrix with sub-sampling for the auto-correlation and cross-correlation of objects.\r\nIn progress..."},"comment":{"url":"https://api.github.com/repos/igmhub/picca/issues/comments/394798041","html_url":"https://github.com/igmhub/picca/pull/442#issuecomment-394798041","issue_url":"https://api.github.com/repos/igmhub/picca/issues/442","id":394798041,"node_id":"MDEyOklzc3VlQ29tbWVudDM5NDc5ODA0MQ==","user":{"login":"londumas","id":3166436,"node_id":"MDQ6VXNlcjMxNjY0MzY=","avatar_url":"https://avatars0.githubusercontent.com/u/3166436?v=4","gravatar_id":"","url":"https://api.github.com/users/londumas","html_url":"https://github.com/londumas","followers_url":"https://api.github.com/users/londumas/followers","following_url":"https://api.github.com/users/londumas/following{/other_user}","gists_url":"https://api.github.com/users/londumas/gists{/gist_id}","starred_url":"https://api.github.com/users/londumas/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/londumas/subscriptions","organizations_url":"https://api.github.com/users/londumas/orgs","repos_url":"https://api.github.com/users/londumas/repos","events_url":"https://api.github.com/users/londumas/events{/privacy}","received_events_url":"https://api.github.com/users/londumas/received_events","type":"User","site_admin":false},"created_at":"2018-06-05T17:41:21Z","updated_at":"2018-06-05T17:41:21Z","author_association":"CONTRIBUTOR","body":"@ngbusca, Here is the evolution of the variance compared to Poisson for:\r\n- nb_random = 1 x nb_data\r\n- nb_random = 2 x nb_data\r\n- nb_random = 5 x nb_data\r\n\r\nI don't have 10 times because for some reasons my Nersc jobs did't work.\r\nYou can see that the variance gets closer and closer to the Poisson (i.e. not correlated) case.\r\nHowever, I would expect to get very close to the Poisson case with no contribution from the variance of the randoms. I want to send the calculation again but only up to `r = 20` and see how it goes.\r\n\r\n> why you want to put the same pairs on different subsamples\r\n\r\nI don't want to, I have tried to get a correct computation of the covariance matrix without, but it doesn't work. This is a pain because it produces a computation that is twice as long as before.\r\nWe can choose:\r\n- get a faster computation but no evaluation of the true variance and of the correlation matrix\r\n- computation twice as slow and all of the above.\r\n\r\n \r\n![capture d ecran de 2018-06-05 11-34-26](https://user-images.githubusercontent.com/3166436/40992619-8839311a-68b4-11e8-8d8e-b95098903059.png)\r\n"}}
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