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)