Also:
* Assured that arguments are consistent (we always seem to use workflow_spec_id, so I made sure we use that consistently.
* Don't require named parameters - so it's cool to call it like: reset_workflow('my_workflow_id')
* Task Actions (ie create, assign, etc...) are now an enumeration in the models, and not static variables on Workflow Service, so we can reference them consistently from anywhere.
* Removed some repetitive code
* Always try to validate as much as possible in the scripts to save folks time debugging.
*
1. Avoid ever re-generating the list of scripts that can be used in a script task. Terribly expensive as we call eval constantly, and it never ever changes once the app starts. (see script.py changes, and comments)
2. Cache the DocumentStatus list in the flask session, so we calculate it at most once per API Call. It's at least .25 seconds per call. (see study_sevice)
3. We called UserFileService.get_files_for_study (which runs a db query EVERY time) for every possible document type. Now we run the query once (study service line 321)
4. When returning a workflow, we looped through every single task in that workflow's navigation, and called the expensive spiff_task_to_api_task just to figure out it's proper display name. We run a much faster and more efficient method to calculate the display name naow (see workflow_service on lie 680, and 799)
5. A hellton of @timeit and sincetime() calls, that I want to leave in, to help debug any slowness on production.
2. Added two api endpoints that will allow us to get all the logs for a specific workflow or study.
3. Assured that requests for logs are paginated, sortable, and can be limited to a specific code if needed.
4. Assure that we only use logging levels that match the log levels of Python.
Assure we raise more thoughtful error messages when running getting exceptions in engine tasks.
Field Options should always be available now due to a fix in Spiffworkflow.
Removing the spreadsheet.value.column and data.value.column so we just have value.column for both.
Improving the __str__ function in the ApiError class, to make debugging a little easier.
Adding a "validate_all" flask command, to help us track down any issues with current workflows in production (use this in concert with sync_with_testing)
Fixed logs of tests.
removed fact_runner.py, a very early and crufty bit of code.
There was a problem with the python script engine as well that wasn't handling the de-serialize properly and didn't correctly pick back up on the script engine, and the renaming of methods in PythonScriptEngine created some conflicts with the way we override functions.
We were not handling ldap looks up efficiently, and this was also breaking in Study Info.
Finally we had a bug in SpiffWorkflow that did not allow us to reset back to the previous task in some cases where nested call activities happen far later in the process and are currently active when the reset is created.
