def test_definition2dtype_errors():
r"""Check that error raised if type not specified."""
assert_raises(KeyError, ScalarMetaschemaProperties.definition2dtype, {})
assert_raises(RuntimeError, ScalarMetaschemaProperties.definition2dtype,
{'type': 'float'})
assert_equal(ScalarMetaschemaProperties.definition2dtype({'type': 'bytes'}),
np.dtype((ScalarMetaschemaProperties._valid_types['bytes'])))
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.
"""
dtype = ScalarMetaschemaProperties.definition2dtype(typedef)
if typedef['type'] == '1darray':
out = np.zeros(typedef.get('length', 2), dtype)
elif typedef['type'] == 'ndarray':
out = np.zeros(typedef['shape'], dtype)
else:
out = np.zeros(1, dtype)[0]
out = units.add_units(out, typedef.get('units', ''))
return out
def transform_type(cls, obj, typedef=None):
r"""Transform an object based on type info.
Args:
obj (object): Object to transform.
typedef (dict, optional): Type definition that should be used to
transform the object. Defaults to None.
Returns:
object: Transformed object.
"""
if typedef is None:
return obj
typedef0 = cls.encode_type(obj)
typedef1 = copy.deepcopy(typedef0)
typedef1.update(**typedef)
dtype = ScalarMetaschemaProperties.definition2dtype(typedef1)
arr = cls.to_array(obj).astype(dtype, casting='same_kind')
out = cls.from_array(arr, unit_str=typedef0.get('units', None), dtype=dtype)
out = cls.as_python_type(out, typedef)
return units.convert_to(out, typedef1.get('units', None))
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.
"""
bytes = backwards.base64_decode(obj.encode('ascii'))
dtype = ScalarMetaschemaProperties.definition2dtype(typedef)
arr = np.frombuffer(bytes, dtype=dtype)
# arr = np.fromstring(bytes, dtype=dtype)
if 'shape' in typedef:
arr = arr.reshape(typedef['shape'])
out = cls.from_array(arr, unit_str=typedef.get('units', None), dtype=dtype)
# Cast numpy type to native python type if they are equivalent
out = cls.as_python_type(out, typedef)
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.
"""
bytes = base64.decodebytes(obj.encode('ascii'))
dtype = ScalarMetaschemaProperties.definition2dtype(typedef)
arr = np.frombuffer(bytes, dtype=dtype)
# arr = np.fromstring(bytes, dtype=dtype)
if 'shape' in typedef:
arr = arr.reshape(typedef['shape'])
out = cls.from_array(arr,
unit_str=typedef.get('units', None),
dtype=dtype,
typedef=typedef)
return out
