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 = ScalarMetaschemaProperties.data2dtype(obj)
if cls.is_fixed and ('subtype' in cls.fixed_properties):
type_list = [
ScalarMetaschemaProperties._valid_types[
cls.fixed_properties['subtype']]
]
else:
type_list = ScalarMetaschemaProperties._valid_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 = ScalarMetaschemaProperties.dtype2definition(dtype)
# elif (dtype is None) and (typedef is not None):
# dtype = ScalarMetaschemaProperties.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 = ScalarMetaschemaProperties.data2dtype(out)
out = units.add_units(out, unit_str, dtype=dtype)
return out
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 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 = ScalarMetaschemaProperties.data2dtype(obj_nounits)
arr = np.array([obj_nounits], dtype=dtype)
return arr
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
def from_array(cls, arr, unit_str=None, dtype=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.
Returns:
object: Object representation of the data in the input array.
"""
if (cls.name not in ['1darray', 'ndarray']) and (arr.ndim > 0):
out = arr[0]
else:
out = arr
if unit_str is not None:
if dtype is None:
dtype = ScalarMetaschemaProperties.data2dtype(out)
out = units.add_units(out, unit_str, dtype=dtype)
return out
