{"action":"created","issue":{"url":"https://api.github.com/repos/mne-tools/mne-python/issues/1495","labels_url":"https://api.github.com/repos/mne-tools/mne-python/issues/1495/labels{/name}","comments_url":"https://api.github.com/repos/mne-tools/mne-python/issues/1495/comments","events_url":"https://api.github.com/repos/mne-tools/mne-python/issues/1495/events","html_url":"https://github.com/mne-tools/mne-python/issues/1495","id":39610139,"number":1495,"title":"FIX/DOC: Documentation","user":{"login":"dengemann","id":1908618,"avatar_url":"https://avatars.githubusercontent.com/u/1908618?v=3","gravatar_id":"","url":"https://api.github.com/users/dengemann","html_url":"https://github.com/dengemann","followers_url":"https://api.github.com/users/dengemann/followers","following_url":"https://api.github.com/users/dengemann/following{/other_user}","gists_url":"https://api.github.com/users/dengemann/gists{/gist_id}","starred_url":"https://api.github.com/users/dengemann/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dengemann/subscriptions","organizations_url":"https://api.github.com/users/dengemann/orgs","repos_url":"https://api.github.com/users/dengemann/repos","events_url":"https://api.github.com/users/dengemann/events{/privacy}","received_events_url":"https://api.github.com/users/dengemann/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/mne-tools/mne-python/labels/DOC","name":"DOC","color":"009800"}],"state":"open","locked":false,"assignee":null,"milestone":{"url":"https://api.github.com/repos/mne-tools/mne-python/milestones/8","html_url":"https://github.com/mne-tools/mne-python/milestones/0.12","labels_url":"https://api.github.com/repos/mne-tools/mne-python/milestones/8/labels","id":1429321,"number":8,"title":"0.12","description":"","creator":{"login":"Eric89GXL","id":2365790,"avatar_url":"https://avatars.githubusercontent.com/u/2365790?v=3","gravatar_id":"","url":"https://api.github.com/users/Eric89GXL","html_url":"https://github.com/Eric89GXL","followers_url":"https://api.github.com/users/Eric89GXL/followers","following_url":"https://api.github.com/users/Eric89GXL/following{/other_user}","gists_url":"https://api.github.com/users/Eric89GXL/gists{/gist_id}","starred_url":"https://api.github.com/users/Eric89GXL/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Eric89GXL/subscriptions","organizations_url":"https://api.github.com/users/Eric89GXL/orgs","repos_url":"https://api.github.com/users/Eric89GXL/repos","events_url":"https://api.github.com/users/Eric89GXL/events{/privacy}","received_events_url":"https://api.github.com/users/Eric89GXL/received_events","type":"User","site_admin":false},"open_issues":21,"closed_issues":2,"state":"open","created_at":"2015-11-25T13:36:18Z","updated_at":"2015-12-14T21:23:46Z","due_on":"2016-04-29T04:00:00Z","closed_at":null},"comments":18,"created_at":"2014-08-06T10:55:30Z","updated_at":"2015-12-16T08:22:32Z","closed_at":null,"body":"Dear all. I feel over the next release cycle we should chose improving our documentation as a main goal. I know most of us including myself find it more rewarding to add new code. But we must bite that bullet, I feel. If we distribute efforts a bit and get well organized it might be less of a pain. \r\nAnd importantly, as a project, we'll be making more progress if newcomers find it easy to get started. \r\n\r\nThis includes at least the following points:\r\n\r\n- ~~reorganize examples (e.g. order by intent and processing-step)~~ #2101\r\n- add real tutorials with stronger commitment to the wording / the recommendations\r\n- make more explicit that small examples are actually API examples and shall not be taken too literally (e.g. prevent users from adding random noise-arrays to threir data when doing using spatio-temporal clustering, etc.)\r\n- ~~merge MNE manual with MNE-Python docs~~\r\n- disseminate slide shows, screen casts\r\n- send a user poll to the mailing list\r\n\r\nHelp users with pitfalls:\r\n\r\n- basic Python issues (e.g., paths #2052)\r\n- +/- in Evoked\r\n- the use of Montage\r\n- working with a template brain\r\n- integrate IPython notebooks?\r\n- `n_jobs=1` to save memory\r\n- freesurfer + source space API, vertex indices, vertex numbers\r\n\r\n\"Migrating to / working with\" section:\r\n- EEGLAB (@jona-sassenhagen?): explaining the distinction in channel types etc.\r\n\r\nBug reports (from @dengemann):\r\n\r\nSet up list of things to check + rule out before filing a bug report, inspired by https://github.com/mxcl/homebrew/wiki/Troubleshooting\r\n\r\nExtended tutorials:\r\n\r\n- epochs selection and event handling (see #1963)\r\n- artefact removal\r\n- group statistics (make explicit 2 (spatial / no spatial prior) X 2 (F/T-test) API, clarify TFCE (it's not another test, but an enhancement method)), clarify that despite t-test inputs, spatio-temporal permutation clustering is not parametric and alos it does not give a multiple comparison corrected t-map, which means it's not just a thresholding to a parametric stat. Also add recent insights on decoding stats. Discuss repeated measure designs and options (hierarchial subtraction with t-test, rANOVA, etc.)\r\n\r\nManual:\r\n\r\n- decoding section explaining how the API follows the sklearn convention. Subsections for spatial pattern methods & time gen + time decoding\r\n- time-frequency module: tfr functions vs. psd functions\r\n\r\nFor devs:\r\n- cookbook like [this] (http://pandas.pydata.org/pandas-docs/dev/cookbook.html) with common idioms, one liners\r\n\r\ncc @t3on @christianbrodbeck @mainakjas @rgoj @dgwakeman @adykstra @legitta @deep-introspection @olaf @mshamalainen @mluessi  @Eric89GXL  @agramfort ... and and and ..."},"comment":{"url":"https://api.github.com/repos/mne-tools/mne-python/issues/comments/165029599","html_url":"https://github.com/mne-tools/mne-python/issues/1495#issuecomment-165029599","issue_url":"https://api.github.com/repos/mne-tools/mne-python/issues/1495","id":165029599,"user":{"login":"jona-sassenhagen","id":4321826,"avatar_url":"https://avatars.githubusercontent.com/u/4321826?v=3","gravatar_id":"","url":"https://api.github.com/users/jona-sassenhagen","html_url":"https://github.com/jona-sassenhagen","followers_url":"https://api.github.com/users/jona-sassenhagen/followers","following_url":"https://api.github.com/users/jona-sassenhagen/following{/other_user}","gists_url":"https://api.github.com/users/jona-sassenhagen/gists{/gist_id}","starred_url":"https://api.github.com/users/jona-sassenhagen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jona-sassenhagen/subscriptions","organizations_url":"https://api.github.com/users/jona-sassenhagen/orgs","repos_url":"https://api.github.com/users/jona-sassenhagen/repos","events_url":"https://api.github.com/users/jona-sassenhagen/events{/privacy}","received_events_url":"https://api.github.com/users/jona-sassenhagen/received_events","type":"User","site_admin":false},"created_at":"2015-12-16T08:22:32Z","updated_at":"2015-12-16T08:22:32Z","body":"@dengemann what format do you mean wrt. EEGLAB conversion? A wiki or something like that? iPython notebook, wiki style, ...?\r\n\r\nOne thing (different thing) I would like to see, and would be willing to write, is a more extended example of how to get from raw files to a simple full analysis, including group statistics, for the most simple thing EEG/ERP guys will think of: just two conditions, import, data cleaning, ERP plotting, stats. Preferably just an iPython notebook."}}