Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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from typing import List
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from SpiffWorkflow import WorkflowException
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from crc import db, session
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from crc.api.common import ApiError
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2020-04-29 20:07:39 +00:00
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from crc.models.file import FileModel, FileDataModel
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Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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from crc.models.protocol_builder import ProtocolBuilderStudy, ProtocolBuilderStatus
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2020-04-06 17:08:17 +00:00
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from crc.models.stats import WorkflowStatsModel, TaskEventModel
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Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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from crc.models.study import StudyModel, Study, Category, WorkflowMetadata
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from crc.models.workflow import WorkflowSpecCategoryModel, WorkflowModel, WorkflowSpecModel, WorkflowState, \
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WorkflowStatus
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2020-04-23 23:25:01 +00:00
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from crc.scripts.documents import Documents
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from crc.services.file_service import FileService
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Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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from crc.services.protocol_builder import ProtocolBuilderService
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from crc.services.workflow_processor import WorkflowProcessor
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class StudyService(object):
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"""Provides common tools for working with a Study"""
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@staticmethod
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def get_studies_for_user(user):
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"""Returns a list of all studies for the given user."""
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db_studies = session.query(StudyModel).filter_by(user_uid=user.uid).all()
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studies = []
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for study_model in db_studies:
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studies.append(StudyService.get_study(study_model.id, study_model))
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return studies
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@staticmethod
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def get_study(study_id, study_model: StudyModel = None):
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"""Returns a study model that contains all the workflows organized by category.
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IMPORTANT: This is intended to be a lightweight call, it should never involve
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loading up and executing all the workflows in a study to calculate information."""
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if not study_model:
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study_model = session.query(StudyModel).filter_by(id=study_id).first()
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study = Study.from_model(study_model)
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study.categories = StudyService.get_categories()
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workflow_metas = StudyService.__get_workflow_metas(study_id)
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status = StudyService.__get_study_status(study_model)
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study.warnings = StudyService.__update_status_of_workflow_meta(workflow_metas, status)
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# Group the workflows into their categories.
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for category in study.categories:
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category.workflows = {w for w in workflow_metas if w.category_id == category.id}
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return study
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@staticmethod
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def delete_study(study_id):
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2020-04-06 17:08:17 +00:00
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session.query(TaskEventModel).filter_by(study_id=study_id).delete()
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2020-04-29 20:07:39 +00:00
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session.query(WorkflowStatsModel).filter_by(study_id=study_id).delete()
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for workflow in session.query(WorkflowModel).filter_by(study_id=study_id):
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StudyService.delete_workflow(workflow.id)
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Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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session.query(StudyModel).filter_by(id=study_id).delete()
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2020-04-08 17:28:43 +00:00
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session.commit()
|
Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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2020-04-29 20:07:39 +00:00
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@staticmethod
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def delete_workflow(workflow_id):
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for file in session.query(FileModel).filter_by(workflow_id=workflow_id).all():
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FileService.delete_file(file.id)
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session.query(TaskEventModel).filter_by(workflow_id=workflow_id).delete()
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session.query(WorkflowStatsModel).filter_by(workflow_id=workflow_id).delete()
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session.query(WorkflowModel).filter_by(id=workflow_id).delete()
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Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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@staticmethod
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def get_categories():
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"""Returns a list of category objects, in the correct order."""
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cat_models = db.session.query(WorkflowSpecCategoryModel) \
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.order_by(WorkflowSpecCategoryModel.display_order).all()
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categories = []
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for cat_model in cat_models:
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categories.append(Category(cat_model))
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return categories
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2020-04-23 18:40:05 +00:00
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@staticmethod
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def get_approvals(study_id):
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2020-04-24 13:45:55 +00:00
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"""Returns a list of non-hidden approval workflows."""
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study = StudyService.get_study(study_id)
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cat = next(c for c in study.categories if c.name == 'approvals')
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2020-04-23 18:40:05 +00:00
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approvals = []
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2020-04-24 13:45:55 +00:00
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for wf in cat.workflows:
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if wf.state is WorkflowState.hidden:
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continue
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workflow = db.session.query(WorkflowModel).filter_by(id=wf.id).first()
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2020-04-23 18:40:05 +00:00
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approvals.append({
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2020-04-24 03:32:20 +00:00
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'study_id': study_id,
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2020-04-24 13:45:55 +00:00
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'workflow_id': wf.id,
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'display_name': wf.display_name,
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'display_order': wf.display_order or 0,
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'name': wf.name,
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'state': wf.state.value,
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'status': wf.status.value,
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2020-04-23 18:40:05 +00:00
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'workflow_spec_id': workflow.workflow_spec_id,
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})
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2020-04-24 12:54:14 +00:00
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approvals.sort(key=lambda k: k['display_order'])
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2020-04-23 18:40:05 +00:00
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return approvals
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2020-04-23 23:25:01 +00:00
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@staticmethod
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def get_documents_status(study_id):
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"""Returns a list of required documents and related workflow status."""
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doc_service = Documents()
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# Get PB required docs
|
2020-04-24 01:02:08 +00:00
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pb_docs = ProtocolBuilderService.get_required_docs(study_id=study_id, as_objects=True)
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2020-04-23 23:25:01 +00:00
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# Get required docs for study
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study_docs = doc_service.get_documents(study_id=study_id, pb_docs=pb_docs)
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# Container for results
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documents = []
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# For each required doc, get file(s)
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for code, doc in study_docs.items():
|
2020-04-24 03:32:20 +00:00
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|
if not doc['required']:
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continue
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|
2020-04-23 23:25:01 +00:00
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doc['study_id'] = study_id
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doc['code'] = code
|
2020-04-24 03:32:20 +00:00
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# Make a display name out of categories if none exists
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if 'Name' in doc and len(doc['Name']) > 0:
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doc['display_name'] = doc['Name']
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else:
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|
name_list = []
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|
for cat_key in ['category1', 'category2', 'category3']:
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|
if doc[cat_key] not in ['', 'NULL']:
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name_list.append(doc[cat_key])
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|
doc['display_name'] = ' '.join(name_list)
|
2020-04-23 23:25:01 +00:00
|
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|
|
# For each file, get associated workflow status
|
2020-04-24 03:32:20 +00:00
|
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doc_files = FileService.get_files(study_id=study_id, irb_doc_code=code)
|
2020-04-23 23:25:01 +00:00
|
|
|
for file in doc_files:
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|
doc['file_id'] = file.id
|
2020-04-24 03:32:20 +00:00
|
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|
doc['task_id'] = file.task_id
|
2020-04-23 23:25:01 +00:00
|
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|
doc['workflow_id'] = file.workflow_id
|
2020-04-24 03:32:20 +00:00
|
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|
doc['workflow_spec_id'] = file.workflow_spec_id
|
2020-04-23 23:25:01 +00:00
|
|
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|
|
|
|
if doc['status'] is None:
|
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|
|
workflow: WorkflowModel = session.query(WorkflowModel).filter_by(id=file.workflow_id).first()
|
|
|
|
doc['status'] = workflow.status.value
|
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documents.append(doc)
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return documents
|
|
|
|
|
Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
|
|
|
@staticmethod
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|
|
|
def synch_all_studies_with_protocol_builder(user):
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|
|
"""Assures that the studies we have locally for the given user are
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|
in sync with the studies available in protocol builder. """
|
|
|
|
# Get studies matching this user from Protocol Builder
|
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|
pb_studies: List[ProtocolBuilderStudy] = ProtocolBuilderService.get_studies(user.uid)
|
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|
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# Get studies from the database
|
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|
|
db_studies = session.query(StudyModel).filter_by(user_uid=user.uid).all()
|
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|
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|
|
# Update all studies from the protocol builder, create new studies as needed.
|
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|
|
# Futher assures that every active study (that does exist in the protocol builder)
|
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|
|
# has a reference to every available workflow (though some may not have started yet)
|
|
|
|
for pb_study in pb_studies:
|
|
|
|
db_study = next((s for s in db_studies if s.id == pb_study.STUDYID), None)
|
|
|
|
if not db_study:
|
|
|
|
db_study = StudyModel(id=pb_study.STUDYID)
|
|
|
|
session.add(db_study)
|
|
|
|
db_studies.append(db_study)
|
|
|
|
db_study.update_from_protocol_builder(pb_study)
|
|
|
|
StudyService._add_all_workflow_specs_to_study(db_study)
|
|
|
|
|
|
|
|
# Mark studies as inactive that are no longer in Protocol Builder
|
|
|
|
for study in db_studies:
|
|
|
|
pb_study = next((pbs for pbs in pb_studies if pbs.STUDYID == study.id), None)
|
|
|
|
if not pb_study:
|
2020-04-21 21:13:30 +00:00
|
|
|
study.protocol_builder_status = ProtocolBuilderStatus.ABANDONED
|
Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
|
|
|
|
|
|
|
db.session.commit()
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
def __update_status_of_workflow_meta(workflow_metas, status):
|
|
|
|
# Update the status on each workflow
|
|
|
|
warnings = []
|
|
|
|
for wfm in workflow_metas:
|
|
|
|
if wfm.name in status.keys():
|
|
|
|
if not WorkflowState.has_value(status[wfm.name]):
|
|
|
|
warnings.append(ApiError("invalid_status",
|
|
|
|
"Workflow '%s' can not be set to '%s', should be one of %s" % (
|
|
|
|
wfm.name, status[wfm.name], ",".join(WorkflowState.list())
|
|
|
|
)))
|
|
|
|
else:
|
|
|
|
wfm.state = WorkflowState[status[wfm.name]]
|
|
|
|
else:
|
|
|
|
warnings.append(ApiError("missing_status", "No status specified for workflow %s" % wfm.name))
|
|
|
|
return warnings
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
def __get_workflow_metas(study_id):
|
|
|
|
# Add in the Workflows for each category
|
2020-04-23 23:25:01 +00:00
|
|
|
workflow_models = db.session.query(WorkflowModel). \
|
|
|
|
join(WorkflowSpecModel). \
|
|
|
|
filter(WorkflowSpecModel.is_master_spec == False). \
|
|
|
|
filter(WorkflowModel.study_id == study_id). \
|
2020-03-30 18:01:57 +00:00
|
|
|
all()
|
Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
|
|
|
workflow_metas = []
|
|
|
|
for workflow in workflow_models:
|
|
|
|
workflow_metas.append(WorkflowMetadata.from_workflow(workflow))
|
|
|
|
return workflow_metas
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@staticmethod
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def __get_study_status(study_model):
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"""Uses the Top Level Workflow to calculate the status of the study, and it's
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workflow models."""
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master_specs = db.session.query(WorkflowSpecModel). \
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filter_by(is_master_spec=True).all()
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if len(master_specs) < 1:
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raise ApiError("missing_master_spec", "No specifications are currently marked as the master spec.")
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if len(master_specs) > 1:
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raise ApiError("multiple_master_specs",
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"There is more than one master specification, and I don't know what to do.")
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2020-03-30 18:01:57 +00:00
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return WorkflowProcessor.run_master_spec(master_specs[0], study_model)
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Created a "StudyService" and moved all complex logic around study manipulation out of the study api, and this service, as things were getting complicated. The Workflow Processor no longer creates the WorkflowModel, the study object handles that, and only passes the model into the workflow processor when it is ready to start the workflow.
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.
2020-03-30 12:00:16 +00:00
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@staticmethod
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def _add_all_workflow_specs_to_study(study):
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existing_models = session.query(WorkflowModel).filter(WorkflowModel.study_id == study.id).all()
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existing_specs = list(m.workflow_spec_id for m in existing_models)
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new_specs = session.query(WorkflowSpecModel). \
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filter(WorkflowSpecModel.is_master_spec == False). \
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filter(WorkflowSpecModel.id.notin_(existing_specs)). \
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all()
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errors = []
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for workflow_spec in new_specs:
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try:
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StudyService._create_workflow_model(study, workflow_spec)
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except WorkflowException as we:
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errors.append(ApiError.from_task_spec("workflow_execution_exception", str(we), we.sender))
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return errors
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@staticmethod
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def _create_workflow_model(study, spec):
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workflow_model = WorkflowModel(status=WorkflowStatus.not_started,
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study_id=study.id,
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workflow_spec_id=spec.id)
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session.add(workflow_model)
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session.commit()
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return workflow_model
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