We now cache the LDAP records - so we look in our own database for the record before calling out to ldap for the details when given a straight up computing id like dhf8r.
Added "date_approved" to the approval model.
And moved the approver and primary investigator into real associated models to make it easier to dump.
Fixed a problem with the validation that was causing it to throw incorrect errors on valid workflows. Getting it to behave a little more like the front end behaves, and respecting the read-only fields. But it was mainly to do with always returning all the data with each form submission.
Also, when returning error messages, attempt to include the task data for the task that caused the error.
Also, when attempting to delete any file, respond with an API error explaining the issue, and log the details.
Added a File class, that we wrap around the FileModel so the api endpoints don't change, but File no longer holds refences to versions or dates of the file_data model, we
figure this out based on a clean database structure.
The ApprovalFile is directly related to the file_data_model - so no chance that a reviewer would review the incorrect version of a file.py
Noticed that our FileType enum called "bpmn" "bpmm", hope this doesn't screw someone up.
Workflows are directly related to the data_models that create the workflow spec it needs. So the files should always be there. There are no more hashes, and thus no more hash errors where it can't find the files to rebuild the workflow.py
Not much to report here, other than I broke every single test in the system at one point. So I'm super concerned about this, and will be testing it a lot before creating the pull request.
From an API point of view you can do the following (and only the following)
/files?workflow_spec_id=x
* You can find all files associated with a workflow_spec_id, and add a file with a workflow_spec_id
/files?workflow_id=x
* You can find all files associated with a workflow_id, and add a file that is directly associated with the workflow
/files?workflow_id=x&form_field_key=y
* You can find all files associated with a form element on a running workflow, and add a new file.
Note: you can add multiple files to the same form_field_key, IF they have different file names. If the same name, the original file is archived,
and the new file takes its place.
The study endpoints always return a list of the file metadata associated with the study. Removed /studies-files, but there is an
endpoint called
/studies/all - that returns all the studies in the system, and does include their files.
On a deeper level:
The File model no longer contains:
- study_id,
- task_id,
- form_field_key
Instead, if the file is associated with workflow - then that is the one way it is connected to the study, and we use this relationship to find files for a study.
A file is never associated with a task_id, as these change when the workflow is reloaded.
The form_field_key must match the irb_doc_code, so when requesting files for a form field, we just look up the irb_doc_code.
Using the LDAP service for checking user details in development mode - even if you are using the back door.
Added a new Flask fucntion load-example-rrt-data that loads the rrt workflow, and not the CRC wrokflows.
Modified the "load-example-data" in the tests to use some test data, rather than loading up all the workflows[
in CRC each time, with a parameter to load crc data if that is required - which is enabled for just a handful of tests.
(Tests run in 1/4 the time now)
PB_ENABLED can be set to false in the configuration (either in a file called instance/config.py, or as an environment variable)
Added a check in the base_test, to assure that we are always running tests with the test configuration, and bail out otherwise. Setting TESTING=true as an environment variable will get this, but so well the correct ordering of imports. Just be dead certain the first file every test file imports is base_test.py.
Aaron was right, and we call the Protocol Builder in all kinds of awful places. But we don't do this now. So Carlos, you should have the ability to reuse a lot of the logic in the study_service now.
I dropped the poorly named "study-update" endpoint completely. We weren't using it. POST and PUT to Study still work just fine for doing exactly that.
All the tests now run and pass with the Protocol builder disabled. Tests that specifically check PB behavior turn it back on for the test, or mock it out.
Improving the study_info script documentation to provide detailed examples of values returned based on arguments.
Making the tests a little more targetted and less subject to breaking through better mocks.
Allow all tests to pass even when ther protocol builder mock isn't running locally.
Removing the duplication of reference files in tests and static, as this seems silly to me at the moment.
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.
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'
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.
Carlos Lopez
Dan Funk
Aaron Louie