cr-connect-workflow/example_data.py

import datetime
import glob
import os

from crc import app, db, session
from crc.models.file import FileType, FileModel, FileDataModel
from crc.models.study import StudyModel
from crc.models.workflow import WorkflowSpecModel
import xml.etree.ElementTree as ElementTree
from crc.services.workflow_processor import WorkflowProcessor

class ExampleDataLoader:
    def make_data(self):
        studies = [
            StudyModel(
                id=1,
                title='The impact of fried pickles on beer consumption in bipedal software developers.',
                last_updated=datetime.datetime.now(),
                protocol_builder_status='in_process',
                primary_investigator_id='dhf8r',
                sponsor='Sartography Pharmaceuticals',
                ind_number='1234'
            ),
            StudyModel(
                id=2,
                title='Requirement of hippocampal neurogenesis for the behavioral effects of soft pretzels',
                last_updated=datetime.datetime.now(),
                protocol_builder_status='in_process',
                primary_investigator_id='dhf8r',
                sponsor='Makerspace & Co.',
                ind_number='5678'
            ),
        ]

        workflow_specifications = \
            self.create_spec(id="m2_demo",
                             name="m2_demo",
                             display_name="Milestone 2 Demo",
                             description='A simplified CR Connect workflow for demonstration purposes.')
        # workflow_specifications += \
        #     self.create_spec(id="crc_study_workflow",
        #                      name="crc_study_workflow",
        #                      display_name="CR Connect Study Workflow",
        #                      description='Draft workflow for CR Connect studies.')
        workflow_specifications += \
            self.create_spec(id="random_fact",
                             name="random_fact",
                             display_name="Random Fact Generator",
                             description='Displays a random fact about a topic of your choosing.')
        workflow_specifications += \
            self.create_spec(id="two_forms",
                             name="two_forms",
                             display_name="Two dump questions on two separate tasks",
                             description='the name says it all')
        workflow_specifications += \
            self.create_spec(id="decision_table",
                             name="decision_table",
                             display_name="Form with Decision Table",
                             description='the name says it all')
        workflow_specifications += \
            self.create_spec(id="parallel_tasks",
                             name="parallel_tasks",
                             display_name="Parallel tasks",
                             description='Four tasks that can happen simultaneously')
        workflow_specifications += \
            self.create_spec(id="exclusive_gateway",
                             name="exclusive_gateway",
                             display_name="Exclusive Gateway Example",
                             description='How to take different paths based on input.')
        workflow_specifications += \
            self.create_spec(id="docx",
                            name="docx",
                            display_name="Form with document generation",
                            description='the name says it all')

        all_data = studies + workflow_specifications
        return all_data

    def create_spec(self, id, name, display_name="", description="", filepath=None):
        """Assumes that a directory exists in static/bpmn with the same name as the given id.
           further assumes that the [id].bpmn is the primary file for the workflow.
           returns an array of data models to be added to the database."""
        models = []
        spec = WorkflowSpecModel(id=id,
                                 name=name,
                                 display_name=display_name,
                                 description=description)
        models.append(spec)
        if not filepath:
            filepath = os.path.join(app.root_path, 'static', 'bpmn', id, "*")
        files = glob.glob(filepath)
        for file_path in files:
            noise, file_extension = os.path.splitext(file_path)
            filename = os.path.basename(file_path)
            if file_extension.lower() == '.bpmn':
                type = FileType.bpmn
            elif file_extension.lower() == '.dmn':
                type = FileType.dmn
            elif file_extension.lower() == '.svg':
                type = FileType.svg
            elif file_extension.lower() == '.docx':
                type = FileType.docx
            else:
                raise Exception("Unsupported file type:" + file_path)
                continue

            is_primary = filename.lower() == id + ".bpmn"
            file_model = FileModel(name=filename, type=type, content_type='text/xml', version="1",
                                   last_updated=datetime.datetime.now(), primary=is_primary,
                                   workflow_spec_id=id)
            models.append(file_model)
            try:
                file = open(file_path, "rb")
                data = file.read()
                if(is_primary):
                    bpmn: ElementTree.Element = ElementTree.fromstring(data)
                    spec.primary_process_id = WorkflowProcessor.get_process_id(bpmn)
                    print("Locating Process Id for " + filename + "  " + spec.primary_process_id)
                models.append(FileDataModel(data=data, file_model=file_model))
            finally:
                file.close()
        return models


    @staticmethod
    def clean_db():
        session.flush()  # Clear out any transactions before deleting it all to avoid spurious errors.
        for table in reversed(db.metadata.sorted_tables):
            session.execute(table.delete())
        session.flush()

    def load_all(self):
        for data in self.make_data():
            session.add(data)
            session.commit()
        session.flush()