def __init__(
self,
keys: KeysCollection,
sigma: Union[Sequence[float], float],
approx: str = "erf",
allow_missing_keys: bool = False,
) -> None:
super().__init__(keys, allow_missing_keys)
self.converter = GaussianSmooth(sigma, approx=approx)
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
d = dict(data)
self.randomize()
if not self._do_transform:
return d
for key in self.keys:
sigma = ensure_tuple_size(tup=(self.x, self.y, self.z), dim=d[key].ndim - 1)
d[key] = GaussianSmooth(sigma=sigma, approx=self.approx)(d[key])
return d
def __call__(self, data):
d = dict(data)
self.randomize()
if not self._do_transform:
return d
for key in self.keys:
sigma = ensure_tuple_size(tup=(self.x, self.y, self.z),
dim=d[key].ndim - 1)
d[key] = GaussianSmooth(sigma=sigma)(d[key])
return d
def __init__(self, keys: KeysCollection, sigma: Union[Sequence[float],
float]):
super().__init__(keys)
self.converter = GaussianSmooth(sigma)