{"action":"created","comment":{"url":"https://api.github.com/repos/numpy/numpy/pulls/comments/193434409","pull_request_review_id":126405602,"id":193434409,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDE5MzQzNDQwOQ==","diff_hunk":"@@ -423,6 +424,19 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):\n     cov : bool, optional\n         Return the estimate and the covariance matrix of the estimate\n         If full is True, then cov is not returned.\n+    absolute_weights: bool, optional","path":"numpy/lib/polynomial.py","position":14,"original_position":14,"commit_id":"6f21fd82fc6cc30ea535a49b152e6ee67af34e65","original_commit_id":"c95fdb521a2bdc104469cdc65df1cb405a96c60b","user":{"login":"wummo","id":39764283,"node_id":"MDQ6VXNlcjM5NzY0Mjgz","avatar_url":"https://avatars0.githubusercontent.com/u/39764283?v=4","gravatar_id":"","url":"https://api.github.com/users/wummo","html_url":"https://github.com/wummo","followers_url":"https://api.github.com/users/wummo/followers","following_url":"https://api.github.com/users/wummo/following{/other_user}","gists_url":"https://api.github.com/users/wummo/gists{/gist_id}","starred_url":"https://api.github.com/users/wummo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wummo/subscriptions","organizations_url":"https://api.github.com/users/wummo/orgs","repos_url":"https://api.github.com/users/wummo/repos","events_url":"https://api.github.com/users/wummo/events{/privacy}","received_events_url":"https://api.github.com/users/wummo/received_events","type":"User","site_admin":false},"body":"To be honest, I like the simple `relative_weight` better. Like @jotasi already mentioned, it behaves like `absolute_sigma` in `curve_fit`. Still, if you insist, I can implement your proposal. In this case it would be nice, if you could point me to another function with a similar parameter, so I can have a look what type of parameter check is performed.","created_at":"2018-06-06T14:38:29Z","updated_at":"2018-06-06T14:38:29Z","html_url":"https://github.com/numpy/numpy/pull/11197#discussion_r193434409","pull_request_url":"https://api.github.com/repos/numpy/numpy/pulls/11197","author_association":"NONE","_links":{"self":{"href":"https://api.github.com/repos/numpy/numpy/pulls/comments/193434409"},"html":{"href":"https://github.com/numpy/numpy/pull/11197#discussion_r193434409"},"pull_request":{"href":"https://api.github.com/repos/numpy/numpy/pulls/11197"}},"in_reply_to_id":192388759},"pull_request":{"url":"https://api.github.com/repos/numpy/numpy/pulls/11197","id":191427452,"node_id":"MDExOlB1bGxSZXF1ZXN0MTkxNDI3NDUy","html_url":"https://github.com/numpy/numpy/pull/11197","diff_url":"https://github.com/numpy/numpy/pull/11197.diff","patch_url":"https://github.com/numpy/numpy/pull/11197.patch","issue_url":"https://api.github.com/repos/numpy/numpy/issues/11197","number":11197,"state":"open","locked":false,"title":"BUG/ENH: Removed non-standard scaling of the covariance matrix and added option to disable scaling completely.","user":{"login":"wummo","id":39764283,"node_id":"MDQ6VXNlcjM5NzY0Mjgz","avatar_url":"https://avatars0.githubusercontent.com/u/39764283?v=4","gravatar_id":"","url":"https://api.github.com/users/wummo","html_url":"https://github.com/wummo","followers_url":"https://api.github.com/users/wummo/followers","following_url":"https://api.github.com/users/wummo/following{/other_user}","gists_url":"https://api.github.com/users/wummo/gists{/gist_id}","starred_url":"https://api.github.com/users/wummo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wummo/subscriptions","organizations_url":"https://api.github.com/users/wummo/orgs","repos_url":"https://api.github.com/users/wummo/repos","events_url":"https://api.github.com/users/wummo/events{/privacy}","received_events_url":"https://api.github.com/users/wummo/received_events","type":"User","site_admin":false},"body":"Fixes #11196 \r\n\r\nAs discussed in the bug report polyfit uses a non-standard scaling factor for the covariance matrix, this is corrected. \r\n\r\nFurthermore, an option is added to disable the scaling of the covariance matrix completely. It would be useful in occasions, where the weights are given by 1/sigma with sigma being the (known) standard errors of (Gaussian distributed) data points, in which case the unscaled matrix is already a correct estimate for the covariance matrix.\r\n","created_at":"2018-05-30T11:57:06Z","updated_at":"2018-06-06T14:38:29Z","closed_at":null,"merged_at":null,"merge_commit_sha":"858ba1b93f4b914f8084bcf5c486b569d49cd5d7","assignee":null,"assignees":[],"requested_reviewers":[],"requested_teams":[],"labels":[{"id":6359214,"node_id":"MDU6TGFiZWw2MzU5MjE0","url":"https://api.github.com/repos/numpy/numpy/labels/00%20-%20Bug","name":"00 - Bug","color":"fef2c0","default":false},{"id":6359239,"node_id":"MDU6TGFiZWw2MzU5MjM5","url":"https://api.github.com/repos/numpy/numpy/labels/01%20-%20Enhancement","name":"01 - 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