I noticed we were saving the workflow every time we loaded it up, rather than only when we were making changes to it. Refactored this to be a little more careful.
Centralized the saving of the workflow into one location in the processor, so we can make sure we update all the details about that workflow every time we save.
The workflow service has a method that will log any task action taken in a consistent way.
The stats models were removed from the API completely. Will wait for a use case for dealing with this later.
Because this changes the endpoint for all existing document details, I've modified all the test and static bpmn files to use the new format.
Shorting up the SponsorsList.xls file makes for slightly faster tests. seems senseless to load 5000 everytime we reset the data.
Tried to test all of this carefully in the test_study_details_documents.py test.
Because the name field is now used to expose workflow/sub-process information on tasks, we can't use it to store the workflow_version, so that is now just stored on the database model. Which is much cleaner and removes a duplication.
INCOMPLETE = 'Incomplete in Protocol Builder',
ACTIVE = 'Active / Ready to roll',
HOLD = 'On Hold',
OPEN = 'Open - this study is in progress',
ABANDONED = 'Abandoned, it got deleted in Protocol Builder'
Moving the primary process id from the workflow model to the file model, and assuring it is updated properly. This was causing a bug that would "lose" the workflow.
Found a problem where the documentation for elements was being processed BEFORE data was loaded from a script. There still may be some issues here.
Ran into an issue with circular dependencies - handling it with a new workflow_service, and pulling computational logic out of the api_models - it was the right thing to do.
some ugly fixes in the file_service for improving panda output from spreadsheet processing that I need to revist.
now that the spiff-workflow handles multi-instance, we can't have random multi-instance tasks around.
Improved tests around study deletion.
Adding id and spec_version to the workflow metadata.
Refactoring the processing of the master_spec so that it doesn't polute the workflow database.
Adding tests to assure that the status and counts are updated on the workflow model as users make progress.
Created a Study object (seperate from the StudyModel) that can cronstructed on request, and contains a different data structure than we store in the DB. This allows us to return underlying Categories and Workflows in a clean way.
Added a new status to workflows called "not_started", meaning we have not yet instantiated a processor or created a BPMN, they have no version yet and no stored data, just the possiblity of being started.
The Top Level Workflow or "Master" workflow is now a part of the sample data, and loaded at all times.
Removed the ability to "add a workflow to a study" and "remove a workflow from a study", a study contains all possible workflows by definition.
Example data no longer creates users or studies, it just creates the specs.
Added a validate_workflow_specification endpoint that allows you to check if the workflow will execute from beginning to end using random data.
Minor fixes to existing bpmns to allow them to pass.
All scripts must include a "do_task_validate_only" that restricts external calls and database modifications, but performs as much logic as possible.
Required Documents is becoming complicated, so making this it's own script task, removing it from study_info.py
The file_service is now very aware of this irb_documents file, so it will always need to exist. We seed this file
during setup, but it can be overwritten by the configurator.
Dan Funk
Aaron Louie