TableFunctionsFactory_transformers.TextEncodingDictTransformer Class Reference

Inheritance diagram for TableFunctionsFactory_transformers.TextEncodingDictTransformer:

Collaboration diagram for TableFunctionsFactory_transformers.TextEncodingDictTransformer:

Public Member Functions
def	visit_udtf_node
Public Member Functions inherited from TableFunctionsFactory_transformers.AstTransformer
def	visit_udtf_node
def	visit_composed_node
def	visit_arg_node
def	visit_primitive_node
def	visit_template_node
def	visit_annotation_node
Public Member Functions inherited from TableFunctionsFactory_transformers.AstVisitor
def	visit_udtf_node
def	visit_composed_node
def	visit_arg_node
def	visit_primitive_node
def	visit_annotation_node
def	visit_template_node

Detailed Description

Definition at line 252 of file TableFunctionsFactory_transformers.py.

Member Function Documentation

def TableFunctionsFactory_transformers.TextEncodingDictTransformer.visit_udtf_node	(		self,
			udtf_node
	)

* Add default_input_id to Column(List)<TextEncodingDict> without one

Definition at line 253 of file TableFunctionsFactory_transformers.py.

References run_benchmark_import.type.

    def visit_udtf_node(self, udtf_node):
        """
        * Add default_input_id to Column(List)<TextEncodingDict> without one
        """
        udtf_node = super(type(self), self).visit_udtf_node(udtf_node)
        # add default input_id
        default_input_id = None
        for idx, t in enumerate(udtf_node.inputs):

            if not isinstance(t.type, tf_node.ComposedNode):
                continue
            if default_input_id is not None:
                pass
            elif t.type.is_column_text_encoding_dict() or t.type.is_column_array_text_encoding_dict():
                default_input_id = tf_node.AnnotationNode('input_id', 'args<%s>' % (idx,))
            elif t.type.is_column_list_text_encoding_dict():
                default_input_id = tf_node.AnnotationNode('input_id', 'args<%s, 0>' % (idx,))

        for t in udtf_node.outputs:
            if isinstance(t.type, tf_node.ComposedNode) and t.type.is_any_text_encoding_dict():
                for a in t.annotations:
                    if a.key == 'input_id':
                        break
                else:
                    if default_input_id is None:
                        raise TypeError('Cannot parse line "%s".\n'
                                        'Missing TextEncodingDict input?' %
                                        (udtf_node.line))
                    t.annotations.append(default_input_id)

        return udtf_node