def __init__(
self,
keys: KeysCollection,
prefix="Data",
data_shape=True,
intensity_range=True,
data_value=False,
additional_info=None,
logger_handler: Optional[Handler] = None,
):
"""
Args:
keys: keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
prefix (string or list of string): will be printed in format: "{prefix} statistics".
data_shape (bool or list of bool): whether to show the shape of input data.
intensity_range (bool or list of bool): whether to show the intensity value range of input data.
data_value (bool or list of bool): whether to show the raw value of input data.
a typical example is to print some properties of Nifti image: affine, pixdim, etc.
additional_info (Callable or list of Callable): user can define callable function to extract
additional info from input data.
logger_handler (logging.handler): add additional handler to output data: save to file, etc.
add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html
"""
super().__init__(keys)
self.prefix = ensure_tuple_rep(prefix, len(self.keys))
self.data_shape = ensure_tuple_rep(data_shape, len(self.keys))
self.intensity_range = ensure_tuple_rep(intensity_range,
len(self.keys))
self.data_value = ensure_tuple_rep(data_value, len(self.keys))
self.additional_info = ensure_tuple_rep(additional_info,
len(self.keys))
self.logger_handler = logger_handler
self.printer = DataStats(logger_handler=logger_handler)
def __init__(
self,
keys: KeysCollection,
prefix: Union[Sequence[str], str] = "Data",
data_type: Union[Sequence[bool], bool] = True,
data_shape: Union[Sequence[bool], bool] = True,
value_range: Union[Sequence[bool], bool] = True,
data_value: Union[Sequence[bool], bool] = False,
additional_info: Optional[Union[Sequence[Callable], Callable]] = None,
logger_handler: Optional[logging.Handler] = None,
allow_missing_keys: bool = False,
) -> None:
"""
Args:
keys: keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
prefix: will be printed in format: "{prefix} statistics".
it also can be a sequence of string, each element corresponds to a key in ``keys``.
data_type: whether to show the type of input data.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
data_shape: whether to show the shape of input data.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
value_range: whether to show the value range of input data.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
data_value: whether to show the raw value of input data.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
a typical example is to print some properties of Nifti image: affine, pixdim, etc.
additional_info: user can define callable function to extract
additional info from input data. it also can be a sequence of string, each element
corresponds to a key in ``keys``.
logger_handler: add additional handler to output data: save to file, etc.
add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html
the handler should have a logging level of at least `INFO`.
allow_missing_keys: don't raise exception if key is missing.
"""
super().__init__(keys, allow_missing_keys)
self.prefix = ensure_tuple_rep(prefix, len(self.keys))
self.data_type = ensure_tuple_rep(data_type, len(self.keys))
self.data_shape = ensure_tuple_rep(data_shape, len(self.keys))
self.value_range = ensure_tuple_rep(value_range, len(self.keys))
self.data_value = ensure_tuple_rep(data_value, len(self.keys))
self.additional_info = ensure_tuple_rep(additional_info,
len(self.keys))
self.logger_handler = logger_handler
self.printer = DataStats(logger_handler=logger_handler)
