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.
I noticed the validation sometimes looks ahead for files, so looking at all the tasks now, not just the ready tasks for the lookup field.
Ran into an issue with validation where a workflow model was required, so I create one and delete it. Another refactor for another day.
Another speed improvement - data in the FileDataModel is deferred, and not queried until it is specifically used, as the new data structures need to use this model frequently.
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.
Modifed the request_approval to take a list of arguments, which works better for us... today.
UpdateStudy correctly handles validation.
WorkflowService correctly populates random values from lookup tables.
And several fixes down in Spiffworkflow, including a big bug where only the last item in a decision table made it through.
Fixing a bug where the validation of forms did not correctly process auto-complete fields.
Fixing a bug where the approvals script and the update study script could not process dot notation correctly.
Moved populate_random_data into the WorkflowService where it makes more sense.
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)
Added the following columns:
* date_created - so we know when the file was created
* renamed workflow_version to just "version", because everything has a version, this is the version of the request.
* workflow_hash - this is just a quick way to see what files and versions are associated with the request, it could be factored out.
* study - a quick relationship link to the study, so that this model is easier to use.
* workflow - ditto
* approval_files - these is a list from a new link table that links an approval to specific files and versions.
The RequestApproval is logically sound, but still needs some additional pieces in place to be callable from a BPMN workflow diagram.
Altered the file service to pick up on changes to files vs adding new files, so that versions are picked up correctly as
users modify their submission - adding new files or replacing existing ones. Deleting files worries me, and I will need to revisit this.
The damn base test keeps giving me a headache, so I made changes there to see if clearing and dropping the database each time won't allow the tests to pass more consistently.
Lots more tests around the file service to make sure it is versioning user uploaded files correctly.
The "Test Request Approval Script" tries to find to assure the correct behavior as this is likely to be called many times repeatedly and with little knowledge of the internal system. So it should just "do the right thing".
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.
updaing the user 'sso' endpoint to provide additional information for debugging.
Pulling information from ldap to stay super consistent on where we get our information.
Refactored calls into a new lookup_service to keep things tidy.
New keys for all enum/auto-complete fields:
PROP_OPTIONS_FILE = "spreadsheet.name"
PROP_OPTIONS_VALUE_COLUMN = "spreadsheet.value.column"
PROP_OPTIONS_LABEL_COL = "spreadsheet.label.column"
PROP_LDAP_LOOKUP = "ldap.lookup"
FIELD_TYPE_AUTO_COMPLETE = "autocomplete"
No Previous Task, No Last Task, No Task List. Just the current task, and the Navigation.
Use the token endpoint to set the current task, even if it is a "READY" task in the api.
Previous Task can be set by identifying the prior task in the Navigation (I'm hoping)
Prefering camel case to snake case on all new apis. Maybe clean the rest up later.
running all extension/properties through the Jinja template processor so you can have custom display names using data, very helpful for building multi-instance displays.
Properties was returned as an array of key/value pairs, which is just mean. Switched this to a dictionary.
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.
Added error checking such that attempting to submit data for a task that is not in the "READY" state throws an error message.
For some reason I'm getting lots of errors in the tests as they try to hit API endpoints they were not hitting before, so adding a number of mocks to some of the study service tests.
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'
Aaron Louie
Carlos Lopez
Dan Funk