Ghosting

def forward(self, data: Any) -> Any:
    """Apply the transform.

    The output type always matches the input type.

    Args:
        data: Input data to transform.
    """
    if self.copy:
        data = _copy.deepcopy(data)
    batch, unwrap = self._wrap(data)
    # When per-element gating is active, the transform handles the
    # probability itself (masked-out elements get identity params),
    # so skip the batch-wide coin flip here. Apply iff rand < p, so
    # p=0 is always a no-op and p=1 always applies.
    if not self._per_instance_p_active(batch) and torch.rand(1).item() >= self.p:
        return unwrap(batch)
    params = self.make_params(batch)
    batch = self.apply_transform(batch, params)
    # Record history on the batch, unless every element was gated out by
    # per-element probability: that is an exact no-op, and recording it
    # would let history replay (e.g. an invertible spatial transform)
    # trigger an unnecessary identity resample.
    if not _all_elements_gated_out(params):
        trace = AppliedTransform(name=type(self).__name__, params=params)
        if not hasattr(batch, "applied_transforms"):
            batch.applied_transforms = []
        batch.applied_transforms.append(trace)
    result = unwrap(batch)
    # Propagate history to outputs that can carry it
    if (
        hasattr(batch, "applied_transforms")
        and not isinstance(result, (SubjectsBatch, Tensor, np.ndarray))
        and not isinstance(result, dict)
    ):
        with contextlib.suppress(AttributeError):
            result.applied_transforms = list(batch.applied_transforms)
    return result

def inverse(self, params: dict[str, Any]) -> Transform:
    """Return a transform that undoes this one.

    Override in invertible subclasses. The returned transform,
    when applied, reverses the effect of the forward pass with
    the given parameters.

    Args:
        params: The parameters recorded in the forward pass.

    Returns:
        A new `Transform` instance that inverts this one.
    """
    msg = f"{type(self).__name__} is not invertible"
    raise NotImplementedError(msg)

def to_hydra(self) -> dict[str, Any]:
    """Export as a Hydra-compatible config dict.

    Returns a dict with `_target_` set to the fully qualified
    class name and only non-default field values included.

    Returns:
        Dict suitable for `hydra.utils.instantiate()`.
    """
    from .parameter_range import _ParameterRange

    cls = type(self)
    target = f"torchio.{cls.__qualname__}"
    cfg: dict[str, Any] = {"_target_": target}

    for name, default in _collect_init_params(cls).items():
        value = getattr(self, name, default)
        if isinstance(value, _ParameterRange):
            if value._original == default:
                continue
            value = _hydra_value(value._original)
        elif value == default:
            continue
        else:
            value = _hydra_value(value)
        cfg[name] = value
    return cfg

def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
    """Sample ghosting parameters (per element when batched)."""
    restore = self.restore if self.restore is not None else 0.0
    n = self._resolve_n(batch)
    if n is None:
        num_ghosts = max(1, round(self.num_ghosts.sample_1d()))
        axis = self.axes[int(torch.randint(len(self.axes), (1,)).item())]
        return {
            "num_ghosts": num_ghosts,
            "axis": axis,
            "intensity": self.intensity.sample_1d(),
            "restore": restore,
        }
    keep = self._keep_mask(batch, n)
    num_ghosts_list: list[int] = []
    axis_list: list[int] = []
    intensity_list: list[float] = []
    for batch_index in range(n):
        if keep is not None and not keep[batch_index]:
            num_ghosts_list.append(0)
            axis_list.append(self.axes[0])
            intensity_list.append(0.0)
            continue
        num_ghosts_list.append(max(1, round(self.num_ghosts.sample_1d())))
        axis_list.append(self.axes[int(torch.randint(len(self.axes), (1,)).item())])
        intensity_list.append(self.intensity.sample_1d())
    params = {
        "num_ghosts": num_ghosts_list,
        "axis": axis_list,
        "intensity": intensity_list,
        "restore": restore,
    }
    self._tag_batched(
        params,
        batch,
        n,
        keep,
        ["num_ghosts", "axis", "intensity"],
    )
    return params

def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Add ghosting artifacts to each selected image."""
    per_instance = self._is_per_instance_params(params)
    restore = params["restore"]
    for _name, img_batch in self._get_images(batch).items():
        if per_instance:
            img_batch.data = _add_ghosting_per_element(
                img_batch.data,
                num_ghosts=params["num_ghosts"],
                axis=params["axis"],
                intensity=params["intensity"],
                restore=restore,
            )
        else:
            img_batch.data = _add_ghosting(
                img_batch.data,
                num_ghosts=params["num_ghosts"],
                axis=params["axis"],
                intensity=params["intensity"],
                restore=restore,
            )
    return batch