motile_tracker.example_data

Attributes

logger

Functions

`Mouse_Embryo_Membrane`(→ list[napari.types.LayerData])	Loads the Mouse Embryo Membrane raw data and segmentation data from
`Fluo_N2DL_HeLa`(→ list[napari.types.LayerData])	Loads the Fluo-N2DL-HeLa 01 training raw data and silver truth from
`Fluo_N2DL_HeLa_crop`(→ list[napari.types.LayerData])	Loads the Fluo-N2DL-HeLa 01 training raw data and silver truth from
`read_zenodo_dataset`(→ list[napari.types.LayerData])	Read a zenodo dataset (assumes pre-downloaded)
`read_ctc_dataset`(→ list[napari.types.LayerData])	Read a CTC dataset from a zarr (assumes pre-downloaded and converted)
`download_zenodo_dataset`(→ None)	Download a sample dataset from zenodo doi and unzip it, then delete the zip. Then convert the tiffs to
`download_ctc_dataset`(→ None)	Download a dataset from the Cell Tracking Challenge
`convert_4d_arr_to_zarr`(→ None)	Convert 4D tiff file to zarr array. Deletes the tiff after conversion.
`convert_to_zarr`(→ None)	Convert a directory of tiff files to a zarr array. Deletes tiffs after conversion.

Module Contents

motile_tracker.example_data.logger

motile_tracker.example_data.Mouse_Embryo_Membrane() → list[napari.types.LayerData]

Loads the Mouse Embryo Membrane raw data and segmentation data from the appdir “user data dir”. Will download it from the Zenodo DOI if it is not present already. Returns:

list[LayerData]: An image layer of raw data and a segmentation labels
layer

motile_tracker.example_data.Fluo_N2DL_HeLa() → list[napari.types.LayerData]

Loads the Fluo-N2DL-HeLa 01 training raw data and silver truth from the appdir “user data dir”. Will download it from the CTC and convert it to zarr if it is not present already. Returns:

list[LayerData]: An image layer of 01 training raw data and a labels
layer of 01 training silver truth labels

motile_tracker.example_data.Fluo_N2DL_HeLa_crop() → list[napari.types.LayerData]

Loads the Fluo-N2DL-HeLa 01 training raw data and silver truth from the appdir “user data dir”. Will download it from the CTC and convert it to zarr if it is not present already. Returns:

list[LayerData]: An image layer of 01 training raw data and a labels
layer of 01 training silver truth labels

motile_tracker.example_data.read_zenodo_dataset(ds_name: str, raw_name: str, label_name: str, data_dir: pathlib.Path) → list[napari.types.LayerData]

Read a zenodo dataset (assumes pre-downloaded) and returns a list of layer data for making napari layers

Args:

ds_name (str): name to give to the dataset raw_name (str): name of the file that points to the intensity data label_name (str): name of the file that points to the segmentation data data_dir (Path): Path to the directory containing the images

Returns:

list[LayerData]: An image layer of raw data and a segmentation labels: layer

motile_tracker.example_data.read_ctc_dataset(ds_name: str, data_dir: pathlib.Path, crop_region=False) → list[napari.types.LayerData]

Read a CTC dataset from a zarr (assumes pre-downloaded and converted) and returns a list of layer data for making napari layers

Args:

ds_name (str): Dataset name data_dir (Path): Path to the directory containing the zarr

Returns:

list[LayerData]: An image layer of 01 training raw data and a labels: layer of 01 training silver truth labels

motile_tracker.example_data.download_zenodo_dataset(ds_name: str, raw_name: str, label_name: str, data_dir: pathlib.Path) → None

Download a sample dataset from zenodo doi and unzip it, then delete the zip. Then convert the tiffs to zarrs for the first training set consisting of 3D membrane intensity images and segmentation.

Args:: ds_name (str): Name to give to the dataset raw_name (str): Name of the file that contains the intensity data label_name (str): Name of the file that contains the label data data_dir (Path): The directory in which to store the data.

motile_tracker.example_data.download_ctc_dataset(ds_name: str, data_dir: pathlib.Path) → None

Download a dataset from the Cell Tracking Challenge and unzip it, then delete the zip. Then convert the tiffs to zarrs for the first training set images and silver truth.

Args:: ds_name (str): Dataset name, according to the CTC data_dir (Path): The directory in which to store the data.

motile_tracker.example_data.convert_4d_arr_to_zarr(tiff_file: pathlib.Path, zarr_path: pathlib.Path, zarr_group: str, relabel: bool = False) → None

Convert 4D tiff file to zarr array. Deletes the tiff after conversion.

Args:: tiff_file: Path to the 4D tiff file zarr_path: Path to the zarr store to write to zarr_group: Name of the array within the zarr store relabel: If True, relabel segmentations to be unique across time

motile_tracker.example_data.convert_to_zarr(tiff_path: pathlib.Path, zarr_path: pathlib.Path, zarr_group: str, relabel: bool = False) → None

Convert a directory of tiff files to a zarr array. Deletes tiffs after conversion.

Args:: tiff_path: Path to directory containing tiff files (one per time point) zarr_path: Path to the zarr store to write to zarr_group: Name of the array within the zarr store relabel: If True, relabel segmentations to be unique across time