def create(self, validated_data, **kwargs): """Create the new workflow.""" wflow_name = self.context.get('name') if not wflow_name: wflow_name = self.validated_data.get('name') if not wflow_name: raise Exception(_('Unexpected empty workflow name.')) if Workflow.objects.filter(name=wflow_name, user=self.context['user']).exists(): raise Exception( _('There is a workflow with this name. ' + 'Please provide a workflow name in the import page.')) # Initial values workflow_obj = None try: workflow_obj = Workflow( user=self.context['user'], name=wflow_name, description_text=validated_data['description_text'], nrows=0, ncols=0, attributes=validated_data['attributes'], query_builder_ops=validated_data.get('query_builder_ops', {}), ) workflow_obj.save() # Create the columns column_data = ColumnSerializer(data=validated_data.get( 'columns', []), many=True, context={'workflow': workflow_obj}) # And save its content if column_data.is_valid(): columns = column_data.save() else: raise Exception(_('Unable to save column information')) # If there is any column with position = 0, recompute (this is to # guarantee backward compatibility. if any(col.position == 0 for col in columns): for idx, col in enumerate(columns): col.position = idx + 1 col.save() # Load the data frame data_frame = validated_data.get('data_frame') if data_frame is not None: # Store the table in the DB store_table( data_frame, workflow_obj.get_data_frame_table_name(), dtype={ col.name: col.data_type for col in workflow_obj.columns.all() }, ) # Reconcile now the information in workflow and columns with # the one loaded workflow_obj.ncols = validated_data['ncols'] workflow_obj.nrows = validated_data['nrows'] workflow_obj.save() # Create the actions pointing to the workflow action_data = ActionSerializer(data=validated_data.get( 'actions', []), many=True, context={ 'workflow': workflow_obj, 'columns': columns }) if action_data.is_valid(): action_data.save() else: raise Exception(_('Unable to save column information')) # Create the views pointing to the workflow view_data = ViewSerializer(data=validated_data.get('views', []), many=True, context={ 'workflow': workflow_obj, 'columns': columns }) if view_data.is_valid(): view_data.save() else: raise Exception(_('Unable to save column information')) except Exception: # Get rid of the objects created if workflow_obj: if workflow_obj.id: workflow_obj.delete() raise return workflow_obj
def workflow_delete_column( workflow: Workflow, column: Column, cond_to_delete: Optional[List[Condition]] = None, ): """Remove column from workflow. Given a workflow and a column, removes it from the workflow (and the corresponding data frame :param workflow: Workflow object :param column: Column object to delete :param cond_to_delete: List of conditions to delete after removing the column :return: Nothing. Effect reflected in the database """ # Drop the column from the DB table storing the data frame df_drop_column(workflow.get_data_frame_table_name(), column.name) # Reposition the columns above the one being deleted workflow.reposition_columns(column.position, workflow.ncols + 1) # Delete the column column.delete() # Update the information in the workflow workflow.ncols = workflow.ncols - 1 workflow.save() if not cond_to_delete: # The conditions to delete are not given, so calculate them # Get the conditions/actions attached to this workflow cond_to_delete = [ cond for cond in Condition.objects.filter(action__workflow=workflow, ) if column in cond.columns.all() ] # If a column disappears, the conditions that contain that variable # are removed actions_without_filters = [] for condition in cond_to_delete: if condition.is_filter: actions_without_filters.append(condition.action) # Formula has the name of the deleted column. Delete it condition.delete() # Traverse the actions for which the filter has been deleted and reassess # all their conditions # TODO: Explore how to do this asynchronously (or lazy) map(lambda act: act.update_n_rows_selected(), actions_without_filters) # If a column disappears, the views that contain only that column need to # disappear as well as they are no longer relevant. for view in workflow.views.all(): if view.columns.count() == 0: view.delete()