def coerce_type(cls, obj, typedef=None, key_order=None, **kwargs):
r"""Coerce objects of specific types to match the data type.
Args:
obj (object): Object to be coerced.
typedef (dict, optional): Type defintion that object should be
coerced to. Defaults to None.
key_order (list, optional): Order that keys from a dictionary should
be used to compose an array. Defaults to None.
**kwargs: Additional keyword arguments are metadata entries that may
aid in coercing the type.
Returns:
object: Coerced object.
Raises:
RuntimeError: If obj is a dictionary, but key_order is not provided.
"""
from yggdrasil.serialize import pandas2list, numpy2list, dict2list
if isinstance(obj, pd.DataFrame):
obj = pandas2list(obj)
elif isinstance(obj, np.ndarray) and (len(obj.dtype) > 0):
obj = numpy2list(obj)
elif isinstance(obj, dict):
if (key_order is not None) or (len(obj) == 1):
obj = dict2list(obj, order=key_order)
else:
obj = [obj]
elif isinstance(typedef, dict) and (len(typedef.get('items', [])) == 1):
typedef_validated = kwargs.get('typedef_validated', False)
if cls.check_decoded([obj], typedef,
typedef_validated=typedef_validated):
obj = [obj]
return obj
def coerce_type(cls,
obj,
typedef=None,
key_order=None,
dont_wrap_single=False,
**kwargs):
r"""Coerce objects of specific types to match the data type.
Args:
obj (object): Object to be coerced.
typedef (dict, optional): Type defintion that object should be
coerced to. Defaults to None.
key_order (list, optional): Order that keys from a dictionary should
be used to compose an array. Defaults to None.
dont_wrap_single (bool, optional): If True, single element
data types will not attempt to wrap input object in
additional list. Defaults to False.
**kwargs: Additional keyword arguments are metadata entries that may
aid in coercing the type.
Returns:
object: Coerced object.
Raises:
RuntimeError: If obj is a dictionary, but key_order is not provided.
"""
from yggdrasil.serialize import pandas2list, numpy2list, dict2list
if isinstance(obj, pd.DataFrame):
obj = pandas2list(obj)
elif isinstance(obj, np.ndarray) and (len(obj.dtype) == 0):
obj = [obj]
elif isinstance(obj, np.ndarray) and (len(obj.dtype) > 0):
obj = numpy2list(obj)
elif isinstance(obj, dict):
if (key_order is not None) or (len(obj) == 1):
obj = dict2list(obj, order=key_order)
elif (isinstance(typedef, dict)
and isinstance(typedef.get('items', None), list)
and all([('title' in x) for x in typedef['items']])):
key_order = [x['title'] for x in typedef['items']]
obj = dict2list(obj, order=key_order)
else:
obj = [obj]
elif ((isinstance(typedef, dict) and (not dont_wrap_single)
and (len(typedef.get('items', [])) == 1))):
typedef_validated = kwargs.get('typedef_validated', False)
try_obj = cls.coerce_type([obj],
typedef=typedef,
key_order=key_order,
dont_wrap_single=True,
**kwargs)
if cls.check_decoded(try_obj,
typedef,
typedef_validated=typedef_validated):
obj = try_obj
return super(JSONArrayMetaschemaType, cls).coerce_type(obj,
typedef=typedef,
**kwargs)
def test_numpy2list():
r"""Test conversion of a numpy array to a list and back."""
with pytest.raises(TypeError):
serialize.numpy2list(None)
with pytest.raises(TypeError):
serialize.list2numpy(None)
nele = 5
names = ["complex", "name", "number", "value"]
dtypes = ['c16', 'S5', 'i8', 'f8']
dtype = np.dtype([(n, f) for n, f in zip(names, dtypes)])
arr_mix = np.zeros(nele, dtype)
arr_mix['name'][0] = 'hello'
test_arrs = [arr_mix]
for ans in test_arrs:
d = serialize.numpy2list(ans)
# Name provided
res = serialize.list2numpy(d, names=names)
np.testing.assert_array_equal(ans, res)