fractal-lif-converters 3.0.1

Package description.

Lif to OME-Zarr Converters

This repository contains the code to convert Lif files to OME-Zarr format.

Installation

To install the package, run the following command:

pip install git+https://github.com/fractal-analytics-platform/fractal-lif-converters

Example usage

• Plate Converter:

from pathlib import Path

from lif_converters.wrappers import convert_lif_plate_to_omezarr

zarr_dir = "./exports" # Directory path where the OME-Zarr file will be saved
lif_file_path = "./testData_Leica/" # File or directory path containing the lif files
n_levels = 5 # Number of levels to be created in the OME-Zarr file
coarsening_xy = 2 # Coarsening factor for the xy dimensions

convert_lif_plate_to_omezarr(
    zarr_dir=zarr_dir,
    lif_files_path=lif_file_path,
    num_levels=n_levels,
    coarsening_xy=coarsening_xy,
    overwrite=True,
    verbose=True,
)

Where the .lif file is formatted as follows:

/Project.lif
----/Tilescan 1/A/1
----/Tilescan 1/A/2
    ...
----/Tilescan 1/B/1
    ...
----/Tilescan 2/A/1

and the OME-Zarr file will be formatted as follows:

/Project.zarr
----/A
--------/1
------------/0 # This will correspond to "Tilescan 1/A/1"
------------/1 # This will correspond to "Tilescan 2
            ...

• Single Acquisition Converter:

from pathlib import Path

from lif_converters.wrappers import convert_lif_scene_to_omezarr

zarr_dir = "./exports" # Directory path where the OME-Zarr file will be saved
lif_file_path = "./testData_Leica/" # File or directory path containing the lif files
scene_name = "Scene-1" # Name of the scene to be converted
n_levels = 5 # Number of levels to be created in the OME-Zarr file
coarsening_xy = 2 # Coarsening factor for the xy dimensions

convert_lif_plate_to_omezarr(
    zarr_dir=zarr_dir,
    lif_files_path=lif_file_path,
    num_levels=n_levels,
    scene_name=scene_name,
    coarsening_xy=coarsening_xy,
    overwrite=True,
    verbose=True,
)

Note that the if the scene_name is not provided, all the scenes in the lif file will be converted in a single Ngff Image.