{"action":"closed","issue":{"url":"https://api.github.com/repos/agnwinds/python/issues/256","repository_url":"https://api.github.com/repos/agnwinds/python","labels_url":"https://api.github.com/repos/agnwinds/python/issues/256/labels{/name}","comments_url":"https://api.github.com/repos/agnwinds/python/issues/256/comments","events_url":"https://api.github.com/repos/agnwinds/python/issues/256/events","html_url":"https://github.com/agnwinds/python/issues/256","id":233470274,"number":256,"title":"Creating tables of useful data for each ionization cycle","user":{"login":"kslong","id":2530901,"avatar_url":"https://avatars3.githubusercontent.com/u/2530901?v=3","gravatar_id":"","url":"https://api.github.com/users/kslong","html_url":"https://github.com/kslong","followers_url":"https://api.github.com/users/kslong/followers","following_url":"https://api.github.com/users/kslong/following{/other_user}","gists_url":"https://api.github.com/users/kslong/gists{/gist_id}","starred_url":"https://api.github.com/users/kslong/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kslong/subscriptions","organizations_url":"https://api.github.com/users/kslong/orgs","repos_url":"https://api.github.com/users/kslong/repos","events_url":"https://api.github.com/users/kslong/events{/privacy}","received_events_url":"https://api.github.com/users/kslong/received_events","type":"User","site_admin":false},"labels":[{"id":25197018,"url":"https://api.github.com/repos/agnwinds/python/labels/enhancement","name":"enhancement","color":"84b6eb","default":true},{"id":114390304,"url":"https://api.github.com/repos/agnwinds/python/labels/high","name":"high","color":"eb6420","default":false}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2017-06-05T00:23:04Z","updated_at":"2017-06-05T02:41:49Z","closed_at":"2017-06-05T02:41:49Z","body":"At present, we have very disparate ways of getting data on a cycle by cycle basis.  One possibility, which is sometimes used, is to write the data to the log file, and then to write something that parses the log file to extract the data.  James did this recently for the single cell data.  Another is to put special write statements into the code so special files are written out.  A third is to write all all the windsave files and then use windsave2table or py_wind to read the data.  This option is useful if we want to root around in the structure, but is generally very disk inefficient.\r\n\r\nInstead, what we need is something more generic that can extract a variable or variables  from the structure on the fly and write this to a standardized table.  A simple way to do this would be to use a system call to run windsave2table on the dumped windsave file and then delete it, but this is frowned upon for security reasons.  Instead, what seems more reasonable is to split windsave2table into a main routine (so it can be run from the command line, and to a subroutines which can be called from within python.  Then we can use a switch or advanced commands to have these files produced for each ionization cycle.  \r\n\r\nThis should make diagnosing what is happening in the cool optically thick cells much easier."}}
{"actor":{"display_login":"kslong"}}