def test_flatten(self):
sequence = [[1, 2, 3], [], [[2, [3], 4], 6]]
expected_value = [1, 2, 3, 2, 3, 4, 6]
self.assertEqual(flatten(sequence), expected_value)
max_recursion = 2
expected_value = [1, 2, 3, 2, [3], 4, 6]
self.assertEqual(flatten(sequence, max_recursion=max_recursion),
expected_value)
def is_numeric_sequence(obj: Sequence[Number]) -> bool:
try:
from cereja.array import flatten
sum(flatten(obj))
except (TypeError, ValueError):
return False
return True
def test_get_shape(self):
for expected_shape, seq in zip(self.shapes, self.sequences):
shape_received = get_shape(seq)
sequence_length = len(flatten(seq))
self.assertEqual(
sequence_length,
prod(shape_received),
msg=f"array shape {shape_received} is inconsistent")
self.assertEqual(shape_received, expected_shape)
def normalize_data(cls, data: Any, *args, **kwargs) -> dict:
if data is None:
return {}
if isinstance(data, (dict, set)):
return cls.parse(data)
if is_sequence(data):
if len(data) == 2:
if len(flatten(data)) == 2:
return cls.parse(cls._parse_key_value(data))
normalized_data = {}
for item in data:
k, v = cls._parse_key_value(item, take_tuple=True)
assert k not in normalized_data, ValueError(
f'Data contains duplicate keys <{k}>')
normalized_data[k] = v
return cls.parse(normalized_data)
return cls.parse({data: None})
def flatten(self):
return flatten(self.lines)
def flatten(self):
return flatten(self._data)