def _generate_data(cls, typedef):
r"""Generate mock data for the specified type.
Args:
typedef (dict): Type definition.
Returns:
object: Python object of the specified type.
"""
typedef0 = cls.typedef_fixed2base(typedef)
return generate_data(typedef0)
def _generate_data(cls, typedef, **kwargs):
r"""Generate mock data for the specified type.
Args:
typedef (dict): Type definition.
Returns:
object: Python object of the specified type.
"""
if isinstance(typedef[cls._json_property], dict):
nitems = typedef.get('minItems', 1)
out = cls._container_type()
for i in range(nitems):
cls._assign(
out, i, generate_data(typedef[cls._json_property],
**kwargs))
else:
out = super(JSONArrayMetaschemaType,
cls)._generate_data(typedef, **kwargs)
return out
def transform_datatype(self, datatype):
r"""Determine the datatype that will result from applying the transform
to the supplied datatype.
Args:
datatype (dict): Datatype to transform.
Returns:
dict: Transformed datatype.
"""
try:
out = encode_type(self(generate_data(datatype)))
if (((out['type'] == 'array') and (datatype['type'] == 'array')
and isinstance(out['items'], list)
and isinstance(datatype['items'], list)
and (len(out['items']) == len(datatype['items'])))):
for x, y in zip(out['items'], datatype['items']):
if 'title' in y:
x.setdefault('title', y['title'])
return out
except NotImplementedError: # pragma: debug
return datatype