def validate(cls, obj, raise_errors=False):
r"""Validate an object to check if it could be of this type.
Args:
obj (object): Object to validate.
raise_errors (bool, optional): If True, errors will be raised when
the object fails to be validated. Defaults to False.
Returns:
bool: True if the object could be of this type, False otherwise.
"""
if isinstance(obj, np.ndarray) and (obj.ndim == 0):
obj = obj.reshape((1, ))[0]
if super(ScalarMetaschemaType,
cls).validate(units.get_data(obj), raise_errors=raise_errors):
dtype = data2dtype(obj)
if cls.is_fixed and ('subtype' in cls.fixed_properties):
type_list = [
constants.VALID_TYPES[cls.fixed_properties['subtype']]
]
else:
type_list = constants.NUMPY_TYPES
if dtype.name.startswith(tuple(type_list)):
return True
else:
if raise_errors:
raise ValueError(
("dtype %s dosn't corresponding with any " +
"of the accepted types: %s") %
(str(dtype), str(type_list)))
return False
def from_array(cls, arr, unit_str=None, dtype=None, typedef=None):
r"""Get object representation of the data.
Args:
arr (np.ndarray): Numpy array.
unit_str (str, optional): Units that should be added to returned
object.
dtype (np.dtype, optional): Numpy data type that should be maintained
as a base class when adding units. Defaults to None and is
determined from the object or typedef (if provided).
typedef (dict, optional): Type definition that should be used to
decode the object. Defaults to None and is determined from the
object or dtype (if provided).
Returns:
object: Object representation of the data in the input array.
"""
# if (typedef is None) and (dtype is not None):
# typedef = dtype2definition(dtype)
# elif (dtype is None) and (typedef is not None):
# dtype = definition2dtype(typedef)
if (cls.name not in ['1darray', 'ndarray']) and (arr.ndim > 0):
out = arr[0]
else:
out = arr
if typedef is not None:
# Cast numpy type to native python type if they are equivalent
out = cls.as_python_type(out, typedef)
if unit_str is not None:
if dtype is None:
dtype = data2dtype(out)
out = units.add_units(out, unit_str, dtype=dtype)
return out
def func_deserialize(self, msg):
r"""Deserialize a message.
Args:
msg: Message to be deserialized.
Returns:
obj: Deserialized message.
"""
if self.format_str is None:
raise RuntimeError("Format string is not defined.")
if self.as_array:
out = serialize.table_to_array(
msg,
self.format_str,
use_astropy=self.use_astropy,
names=self.get_field_names(as_bytes=True))
out = self.datatype.coerce_type(out, typedef=self.typedef)
out = self.datatype.transform_type(out, typedef=self.typedef)
else:
out = list(serialize.process_message(msg, self.format_str))
field_units = self.get_field_units()
if field_units is not None:
out = [
units.add_units(x, u, dtype=data2dtype(x))
for x, u in zip(out, field_units)
]
return out
def encode(cls, instance, typedef=None):
r"""Encoder for the 'subtype' scalar property."""
dtype = data2dtype(instance)
out = None
for k, v in constants.VALID_TYPES.items():
if dtype.name.startswith(v):
out = k
break
if out is None:
raise MetaschemaTypeError(
'Cannot find subtype string for dtype %s' % dtype)
return out
def to_array(cls, obj):
r"""Get np.array representation of the data.
Args:
obj (object): Object to get array for.
Returns:
np.ndarray: Array representation of object.
"""
obj_nounits = units.get_data(obj)
if isinstance(obj_nounits, np.ndarray):
arr = obj_nounits
else:
dtype = data2dtype(obj_nounits)
arr = np.array([obj_nounits], dtype=dtype)
return arr
def encode(cls, instance, typedef=None):
r"""Encoder for the 'precision' scalar property."""
dtype = data2dtype(instance)
out = dtype.itemsize * 8 # in bits
return out
def test_data2dtype_errors():
r"""Check that error is raised for list, dict, & tuple objects."""
with pytest.raises(metaschema.MetaschemaTypeError):
metaschema.data2dtype([])