def update_state( self, incoming ):
# Build a callback that handles setting an input to be required at
# runtime. We still process all other parameters the user might have
# set. We also need to make sure all datasets have a dummy value
# for dependencies to see
self.post_job_actions = ActionBox.handle_incoming(incoming)
make_runtime_key = incoming.get( 'make_runtime', None )
make_buildtime_key = incoming.get( 'make_buildtime', None )
def item_callback( trans, key, input, value, error, old_value, context ):
# Dummy value for Data parameters
if isinstance( input, DataToolParameter ) or isinstance( input, DataCollectionToolParameter ):
return DummyDataset(), None
# Deal with build/runtime (does not apply to Data parameters)
if key == make_buildtime_key:
return input.get_initial_value( trans, context ), None
elif isinstance( old_value, RuntimeValue ):
return old_value, None
elif key == make_runtime_key:
return RuntimeValue(), None
elif isinstance(value, basestring) and re.search("\$\{.+?\}", str(value)):
# Workflow Parameter Replacement, so suppress error from going to the workflow level.
return value, None
else:
return value, error
# Update state using incoming values
errors = self.tool.update_state( self.trans, self.tool.inputs, self.state.inputs, incoming, item_callback=item_callback )
self.errors = errors or None
def get_post_job_actions(self, incoming):
return ActionBox.handle_incoming(incoming)
def get_post_job_actions( self, incoming=None):
if incoming is None:
return self.post_job_actions
else:
return ActionBox.handle_incoming(incoming)
def get_post_job_actions( self, incoming ):
return ActionBox.handle_incoming( incoming )
