neuralib.scan.lsm.TiffScanner

final class neuralib.scan.lsm.TiffScanner

Bases: AbstractScanner

LSM confocal image data.

TODO: multi-scene and different dimension data are lacking of testing

__init__(filepath)

Parameters:

filepath (str | Path | PathLike[str])

Methods

__init__(filepath)
close()	Explicitly close any open resources associated with the scanner.
get_channel_names([scene_idx])	Get the names of the fluorescence channels for a specific scene.
view([channel, depth, project_type, norm])	Generates a view of the image data based on the provided parameters.
z_projection(stacks[, project_type, axis])	Computes a z-projection of a stack of images along a specified axis using a chosen method of projection.

Attributes

dimcode	Get dimension code for the image array
file_type	file type of the image
filepath	The absolute path to the loaded confocal file.
image	image array
metadata	Return the metadata dictionary parsed from the file.
n_scenes	Total number of scenes (positions/series) in the file.

__init__(filepath)

Parameters:

filepath (str | Path | PathLike[str])

close()

Explicitly close any open resources associated with the scanner. Subclasses should override this if they open files or other resources.

property image: ndarray

image array

property file_type: str

file type of the image

property dimcode: str

Get dimension code for the image array

property n_scenes: int

Total number of scenes (positions/series) in the file.

get_channel_names(scene_idx=None)

Get the names of the fluorescence channels for a specific scene.

Parameters:

scene_idx – The 0-based index of the scene.

Returns:

A list of strings representing the channel names. Returns generic names (e.g., [‘Channel 1’, ‘Channel 2’]) if specific names are not available.

Raises:

IndexError – If scene_idx is out of bounds.

Return type:

list[str]

view(channel=0, depth=None, project_type='max', norm=True, **kwargs)

Generates a view of the image data based on the provided parameters. Only provide a single scene view for now.

Parameters:

• channel (int) – The channel index to select from the image. Defaults to 0.

• depth (int | slice | ndarray | None) – Depth levels to process, which can be an integer, a slice, or a NumPy array. If None, all depth levels are used.

• project_type (Literal['avg', 'max', 'min', 'std', 'median']) – Type of Z-projection to apply if multiple Z-slices are selected. Options include ‘avg’, ‘max’, ‘min’, ‘std’, ‘median’. Defaults to ‘max’.

• norm (bool) – A flag indicating whether to normalize the projected image by its maximum intensity value. Defaults to True.

• kwargs (Any)

Returns:

A NumPy array representing the resulting image after applying depth projection, channel selection, and optional normalization.

Return type:

ndarray