navigate.model.data_sources.zarr_data_source.OMEZarrDataSource

class navigate.model.data_sources.zarr_data_source.OMEZarrDataSource(file_name: Optional[str] = None, mode: str = 'w')

Bases: PyramidalDataSource

OME-Zarr data source.

This class implements an OME-Zarr image data source using the Zarr v2 format.

__init__(file_name: Optional[str] = None, mode: str = 'w') → None

Initialize the OME-Zarr data source.

Parameters:

• file_name (str) – Name of the file to open.

• mode (str) – Mode to open the file in.

Methods

__init__([file_name, mode])	Initialize the OME-Zarr data source.
close()	Close the image file.
get_data([timepoint, position, channel, z, ...])	Get data according to timepoint, position, channel and z-axis id
get_shape_from_metadata()	Get the shape of the data source from the metadata.
get_slice(x, y, c[, z, t, p, subdiv])	Get a 3D slice of the dataset for a single c, t, p, subdiv.
new_position(pos, view)	Create new arrays on the fly for each position in self.positions.
read()	Reads data from the image file.
set_metadata(metadata_config)	Sets the metadata
set_metadata_from_configuration_experiment(...)	Sets the metadata from according to the microscope configuration.
setup()	Set up the Zarr writer.
write(data, **kw)	Writes 2D image to the data source.

Attributes

data	Return array representation of data stored in data source.
mode	Getter for the mode of the data source.
nbytes	Getter for image size.
resolutions	Getter for resolutions.
shape	Getter for the shape as XYCZT.
shapes	Getter for image shape.
subdivisions	Getter for subdivisions.
voxel_size	Getter for the voxel size
metadata	Metadata object for the OME-Zarr data source.

close() → None

Close the image file.

get_data(timepoint: int = 0, position: int = 0, channel: int = 0, z: int = -1, resolution: int = 1) → Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

Get data according to timepoint, position, channel and z-axis id

Parameters:

• timepoint (int) – The timepoint value

• position (int) – The position id in multi-position table

• channel (int) – The channel id

• z (int) – The index of Z in a Z-stack. Return all z if -1.

• resolution (int) – values from 1, 2, 4, 8

Returns:

data – Image data

Return type:

npt.ArrayLike

Raises:

NotImplementedError – If not implemented in a derived class.

get_shape_from_metadata()

Get the shape of the data source from the metadata.

get_slice(x, y, c, z=0, t=0, p=0, subdiv=0) → Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

Get a 3D slice of the dataset for a single c, t, p, subdiv.

Parameters:

• x (int or slice) – x indices to grab

• y (int or slice) – y indices to grab

• c (int) – Single channel

• z (int or slice) – z indices to grab

• t (int) – Single timepoint

• p (int) – Single position

• subdiv (int) – Subdivision of the dataset to index along

Returns:

3D (z, y, x) slice of data set

Return type:

npt.ArrayLike

new_position(pos, view)

Create new arrays on the fly for each position in self.positions.

Parameters:

• pos (int) – Position index

• view (str) – View name

read() → None

Reads data from the image file.

set_metadata(metadata_config: dict) → None

Sets the metadata

Parameters:

metadata_config (dict) – shape configuration: “c”, “z”, “t”, “p”, “is_dynamic”, “per_stack”

set_metadata_from_configuration_experiment(configuration: Dict[str, Any], microscope_name: Optional[str] = None) → None

Sets the metadata from according to the microscope configuration.

Parameters:

• configuration (Dict[str, Any]) – The configuration experiment.

• microscope_name (str) – The microscope name

setup()

Set up the Zarr writer.

write(data: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], **kw) → None

Writes 2D image to the data source.

Parameters:

• data (npt.ArrayLike) – 2D image data

• kw (dict) – Keyword arguments

bits

Number of bits per pixel.

Type:

int

property data: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

Return array representation of data stored in data source.

Returns:

Array representation of data stored in data source.

Return type:

npt.ArrayLike

Raises:

NotImplementedError – If not implemented in a derived class.

dc

Step size between channels (always 1)

Type:

float

dt

The time interval between frames (seconds).

Type:

float

dx

Pixel size in x dimension (microns). float: Pixel size in y dimension (microns). float: Pixel size in z dimension (microns).

Type:

float

dy

Pixel size in x dimension (microns). float: Pixel size in y dimension (microns). float: Pixel size in z dimension (microns).

Type:

float

dz

Pixel size in x dimension (microns). float: Pixel size in y dimension (microns). float: Pixel size in z dimension (microns).

Type:

float

file_name

Name of the file to read/write from.

Type:

str

logger

Logger for this class.

Type:

logging.Logger

metadata

Metadata object for the OME-Zarr data source.

Type:

OMEZarrMetadata

property mode: str

Getter for the mode of the data source.

Returns:

Mode of the data source.

Return type:

str

property nbytes: int

Getter for image size.

Size in bytes. Overrides base class. Accounts for subdivisions.

Returns:

size – The size of the image in bytes.

Return type:

int

positions

Number of positions in the data source.

Type:

int

property resolutions: ndarray[Any, dtype[ScalarType]]

Getter for resolutions.

Store as XYZ per BDV spec.

Returns:

resolutions – The resolutions.

Return type:

npt.NDArray

property shape: tuple

Getter for the shape as XYCZT.

Returns:

Shape of the data source in XYCZT format.

Return type:

tuple

shape_c

Size of the data source in c dimension.

Type:

int

shape_t

Size of the data source in t dimension.

Type:

int

shape_x

Size of the data source in x dimension.

Type:

int

shape_y

Size of the data source in y dimension.

Type:

int

shape_z

Size of the data source in z dimension.

Type:

int

property shapes: ndarray[Any, dtype[ScalarType]]

Getter for image shape.

Store as ZYX rather than XYZ, per BDV spec.

Returns:

shapes – The shapes.

Return type:

npt.NDArray

property subdivisions: ndarray[Any, dtype[ScalarType]]

Getter for subdivisions.

Store as XYZ per BDV spec.

Returns:

subdivisions – The subdivisions.

Return type:

npt.NDArray

property voxel_size: tuple

Getter for the voxel size

Returns:

Voxel size in x, y, z dimensions.

Return type:

tuple