113 lines
5.6 KiB
Python
113 lines
5.6 KiB
Python
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from SpiffWorkflow.bpmn.workflow import BpmnWorkflow
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from pandas import ExcelFile
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from crc.api.common import ApiError
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from crc.models.api_models import Task
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import jinja2
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from jinja2 import Template
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from crc.services.file_service import FileService
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from crc.services.workflow_processor import WorkflowProcessor, CustomBpmnScriptEngine
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from SpiffWorkflow import Task as SpiffTask, WorkflowException
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class WorkflowService(object):
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"""Provides tools for processing workflows and tasks. This
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should at some point, be the only way to work with Workflows, and
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the workflow Processor should be hidden behind this service.
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This will help maintain a structure that avoids circular dependencies.
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But for now, this contains tools for converting spiff-workflow models into our
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own API models with additional information and capabilities."""
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@classmethod
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def test_spec(cls, spec_id):
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"""Runs a spec through it's paces to see if it results in any errors. Not full proof, but a good
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sanity check."""
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spec = WorkflowProcessor.get_spec(spec_id)
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bpmn_workflow = BpmnWorkflow(spec, script_engine=CustomBpmnScriptEngine())
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bpmn_workflow.data[WorkflowProcessor.STUDY_ID_KEY] = 1
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bpmn_workflow.data[WorkflowProcessor.WORKFLOW_ID_KEY] = spec_id
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bpmn_workflow.data[WorkflowProcessor.VALIDATION_PROCESS_KEY] = True
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while not bpmn_workflow.is_completed():
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try:
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bpmn_workflow.do_engine_steps()
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tasks = bpmn_workflow.get_tasks(SpiffTask.READY)
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for task in tasks:
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task_api = WorkflowService.spiff_task_to_api_task(
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task) # Assure we try to process the documenation, and raise those errors.
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WorkflowProcessor.populate_form_with_random_data(task)
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task.complete()
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except WorkflowException as we:
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raise ApiError.from_task_spec("workflow_execution_exception", str(we),
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we.sender)
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@staticmethod
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def spiff_task_to_api_task(spiff_task):
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documentation = spiff_task.task_spec.documentation if hasattr(spiff_task.task_spec, "documentation") else ""
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task = Task(spiff_task.id,
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spiff_task.task_spec.name,
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spiff_task.task_spec.description,
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spiff_task.task_spec.__class__.__name__,
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spiff_task.get_state_name(),
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None,
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documentation,
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spiff_task.data)
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# Only process the form and documentation if this is something that is ready or completed.
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if not (spiff_task._is_predicted()):
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if hasattr(spiff_task.task_spec, "form"):
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task.form = spiff_task.task_spec.form
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for field in task.form.fields:
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WorkflowService._process_options(spiff_task, field)
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if documentation != "" and documentation is not None:
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WorkflowService._process_documentation(task, documentation)
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return task
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@staticmethod
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def _process_documentation(task, documentation):
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"""Runs the given documentation string through the Jinja2 processor to inject data
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create loops, etc..."""
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try:
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template = Template(documentation)
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task.documentation = template.render(**task.data)
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except jinja2.exceptions.TemplateError as ue:
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raise ApiError(code="template_error", message="Error processing template for task %s: %s" %
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(task.name, str(ue)), status_code=500)
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# TODO: Catch additional errors and report back.
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@staticmethod
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def _process_options(spiff_task, field):
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""" Checks to see if the options are provided in a separate lookup table associated with the
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workflow, and populates these if possible. """
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if field.has_property(Task.ENUM_OPTIONS_FILE_PROP):
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if not field.has_property(Task.EMUM_OPTIONS_VALUE_COL_PROP) or \
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not field.has_property(Task.EMUM_OPTIONS_LABEL_COL_PROP):
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raise ApiError.from_task("invalid_emum",
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"For emumerations based on an xls file, you must include 3 properties: %s, "
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"%s, and %s, you supplied %s" % (Task.ENUM_OPTIONS_FILE_PROP,
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Task.EMUM_OPTIONS_VALUE_COL_PROP,
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Task.EMUM_OPTIONS_LABEL_COL_PROP),
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task=spiff_task)
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# Get the file data from the File Service
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file_name = field.get_property(Task.ENUM_OPTIONS_FILE_PROP)
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value_column = field.get_property(Task.EMUM_OPTIONS_VALUE_COL_PROP)
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label_column = field.get_property(Task.EMUM_OPTIONS_LABEL_COL_PROP)
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data_model = FileService.get_workflow_file_data(spiff_task.workflow, file_name)
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xls = ExcelFile(data_model.data)
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df = xls.parse(xls.sheet_names[0])
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if value_column not in df:
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raise ApiError("invalid_emum",
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"The file %s does not contain a column named % s" % (file_name, value_column))
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if label_column not in df:
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raise ApiError("invalid_emum",
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"The file %s does not contain a column named % s" % (file_name, label_column))
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for index, row in df.iterrows():
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field.options.append({"id": row[value_column],
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"name": row[label_column]})
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