dacapo.experiments.datasplits.datasets.arrays.zarr_array_config

Classes

ZarrArrayConfig

This config class provides the necessary configuration for a zarr array.

Module Contents

class dacapo.experiments.datasplits.datasets.arrays.zarr_array_config.ZarrArrayConfig

This config class provides the necessary configuration for a zarr array.

A zarr array is a container for large, multi-dimensional arrays. It is similar to HDF5, but is designed to work with large arrays that do not fit into memory. Zarr arrays can be stored on disk or in the cloud and can be accessed concurrently by multiple processes. Zarr arrays can be compressed and support chunked, N-dimensional arrays.

file_name: Path The file name of the zarr container.

dataset: str The name of your dataset. May include ‘/’ characters for nested heirarchies

snap_to_grid: Optional[Coordinate] If you need to make sure your ROI’s align with a specific voxel_size

_axes: Optional[List[str]] The axes of your data!

verify() → Tuple[bool, str]: Check whether this is a valid Array

Note

This class is a subclass of ArrayConfig.

array_type

file_name: upath.UPath

dataset: str

snap_to_grid: funlib.geometry.Coordinate | None

mode: str | None

verify() → Tuple[bool, str]

Check whether this is a valid Array

Returns:: A tuple of a boolean and a string. The boolean indicates whether the Array is valid or not. The string provides a reason why the Array is not valid.
Return type:: Tuple[bool, str]
Raises:: NotImplementedError – This method is not implemented for this Array

Examples

>>> zarr_array_config = ZarrArrayConfig(
...     file_name=Path("data.zarr"),
...     dataset="data",
...     snap_to_grid=Coordinate(1, 1, 1),
...     _axes=["x", "y", "z"]
... )
>>> zarr_array_config.verify()
(True, 'No validation for this Array')

Note

This method is not implemented for this Array