def test_coerce(nested_approx):
r"""Test serialization of coerced types."""
from yggdrasil.metaschema.datatypes.JSONObjectMetaschemaType import (
JSONObjectMetaschemaType)
from yggdrasil import serialize
typedef = {
'type': 'object',
'properties': {
'a': {
'type': '1darray',
'subtype': 'float',
'title': 'a',
'precision': 64
}
}
}
x = JSONObjectMetaschemaType(**typedef)
key_order = ['a']
msg_recv = {'a': np.zeros(3, 'float64')}
msg_send_list = [{
'a': np.zeros(3, 'float32')
},
serialize.dict2numpy(msg_recv, order=key_order),
serialize.dict2pandas(msg_recv, order=key_order),
serialize.dict2list(msg_recv, order=key_order)]
def do_send_recv(msg_send):
msg_seri = x.serialize(msg_send, tyepdef=typedef, key_order=key_order)
assert (x.deserialize(msg_seri)[0] == nested_approx(msg_recv))
for y in msg_send_list:
do_send_recv(y)
def test_coerce():
r"""Test serialization of coerced types."""
typedef = {
'type': 'object',
'properties': {
'a': {
'type': '1darray',
'subtype': 'float',
'title': 'a',
'precision': 64
}
}
}
x = JSONObjectMetaschemaType(**typedef)
key_order = ['a']
msg_recv = {'a': np.zeros(3, 'float64')}
msg_send_list = [
serialize.dict2numpy(msg_recv, order=key_order),
serialize.dict2pandas(msg_recv, order=key_order),
serialize.dict2list(msg_recv, order=key_order)
]
def do_send_recv(msg_send):
msg_seri = x.serialize(msg_send, tyepdef=typedef, key_order=key_order)
assert_equal(x.deserialize(msg_seri)[0], msg_recv)
for y in msg_send_list:
do_send_recv(y)
def encode_data(cls, obj, typedef):
r"""Encode an object's data.
Args:
obj (object): Object to encode.
typedef (dict): Type definition that should be used to encode the
object.
Returns:
string: Encoded object.
"""
args = ArgsMetaschemaProperty.instance2args(obj)
kwargs = KwargsMetaschemaProperty.instance2kwargs(obj)
typedef_args = None
typedef_kwargs = None
if isinstance(typedef, dict):
if 'args' in typedef:
typedef_args = {'items': typedef['args']}
if 'kwargs' in typedef:
typedef_kwargs = {'properties': typedef['kwargs']}
out = [
JSONArrayMetaschemaType.encode_data(args, typedef_args),
JSONObjectMetaschemaType.encode_data(kwargs, typedef_kwargs)
]
return out
def decode_data(cls, obj, typedef):
r"""Decode an object.
Args:
obj (string): Encoded object to decode.
typedef (dict): Type definition that should be used to decode the
object.
Returns:
object: Decoded object.
"""
# TODO: Normalization can be removed if metadata is normalized
typedef = cls.normalize_definition(typedef)
args = JSONObjectMetaschemaType.decode_data(
obj, {'properties': typedef.get('args', {})})
return typedef['class'](**args)
def encode_data(cls, obj, typedef):
r"""Encode an object's data.
Args:
obj (object): Object to encode.
typedef (dict): Type definition that should be used to encode the
object.
Returns:
string: Encoded object.
"""
args = ArgsMetaschemaProperty.instance2args(obj)
if isinstance(typedef, dict) and ('args' in typedef):
typedef_args = {'properties': typedef['args']}
else:
typedef_args = None
return JSONObjectMetaschemaType.encode_data(args, typedef_args)
def decode_data(cls, obj, typedef):
r"""Decode an object.
Args:
obj (string): Encoded object to decode.
typedef (dict): Type definition that should be used to decode the
object.
Returns:
object: Decoded object.
"""
# TODO: Normalization can be removed if metadata is normalized
typedef = cls.normalize_definition(typedef)
assert (isinstance(obj, list))
assert (len(obj) == 2)
args = JSONArrayMetaschemaType.decode_data(
obj[0], {'items': typedef.get('args', [])})
kwargs = JSONObjectMetaschemaType.decode_data(
obj[1], {'properties': typedef.get('kwargs', {})})
return typedef['class'](*args, **kwargs)
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.
"""
args = JSONArrayMetaschemaType.generate_data({
'type':
'array',
'items':
typedef.get('args', [])
})
kwargs = JSONObjectMetaschemaType.generate_data({
'type':
'object',
'properties':
typedef.get('kwargs', {})
})
return typedef['class'](*args, **kwargs)