aicsimageio.readers package

Submodules

aicsimageio.readers.arraylike_reader module

class aicsimageio.readers.arraylike_reader.ArrayLikeReader(data: Union[numpy.ndarray, dask.array.core.Array], **kwargs)[source]

Bases: aicsimageio.readers.reader.Reader

A catch all for numpy ndarray and dask array reading.

Parameters

data (Union[numpy.ndarray, da.core.Array]) – An in memory numpy ndarray or preconfigured dask array.

Notes

Because this is simply a wrapper around numpy ndarray, no metadata is returned. However, dimension order is returned with dimensions assumed in order but with extra dimensions removed depending on image shape.

property dims
property metadata

aicsimageio.readers.czi_reader module

class aicsimageio.readers.czi_reader.CziReader[source]

Bases: aicsimageio.readers.reader.Reader

CziReader wraps aicspylibczi to provide the same reading capabilities but abstracts the specifics of using the backend library to create a unified interface. This enables higher level functions to duck type the File Readers.

Parameters

  • data (types.FileLike) – A string or path to the CZI file to be read.

  • chunk_by_dims (List[str]) – The dimensions to use as the for mapping the chunks / blocks. Default: [Dimensions.SpatialZ, Dimensions.SpatialY, Dimensions.SpatialX] Note: SpatialY and SpatialX will always be added to the list if not present.

  • S (int) – If the image has different dimensions on any scene from another, the dask array construction will fail. In that case, use this parameter to specify a specific scene to construct a dask array for. Default: 0 (select the first scene)

ZEISS_10BYTE = b'ZISRAWFILE'
ZEISS_2BYTE = b'ZI'
property dims
dtype() → numpy.dtype[source]
get_channel_names(scene: int = 0)[source]

Attempts to use the image’s metadata to get the image’s channel names.

Parameters

scene (int) – The index of the scene for which to return channel names.

Returns

channels_names – List of strings representing the channel names. If channel dimension not present in file, return None.

Return type

Optional[List[str]]

get_physical_pixel_size()[source]

Attempts to retrieve physical pixel size for the specified scene. If none available, returns 1.0 for each spatial dimension.

Parameters

scene (int) – The index of the scene for which to return physical pixel sizes.

Returns

sizes – Tuple of floats representing the pixel sizes for X, Y, Z, in that order.

Return type

Tuple[float]

property metadata

Load and return the metadata from the CZI file

Returns

Return type

The lxml Element Tree of the metadata

size_c() → int[source]
size_s() → int[source]
size_t() → int[source]
size_x() → int[source]
size_y() → int[source]
size_z() → int[source]

aicsimageio.readers.default_reader module

class aicsimageio.readers.default_reader.DefaultReader[source]

Bases: aicsimageio.readers.reader.Reader

A catch all for image file reading that uses imageio for reading.

Parameters

file (types.ImageLike) – String with path to file.

property dims
get_channel_names()[source]

Attempts to use the image’s metadata to get the image’s channel names.

Parameters

scene (int) – The index of the scene for which to return channel names.

Returns

channels_names – List of strings representing the channel names. If channel dimension not present in file, return None.

Return type

Optional[List[str]]

property metadata

aicsimageio.readers.lif_reader module

class aicsimageio.readers.lif_reader.LifReader[source]

Bases: aicsimageio.readers.reader.Reader

LifReader wraps readlif.reader to provide the same reading capabilities but abstracts the specifics of using the backend library to create a unified interface. This enables higher level functions to duck type the File Readers.

Parameters

  • data (types.FileLike) – A string or path to the LIF file to be read.

  • chunk_by_dims (List[str]) – The dimensions to use as the for mapping the chunks / blocks. Default: [Dimensions.SpatialZ, Dimensions.SpatialY, Dimensions.SpatialX] Note: SpatialY and SpatialX will always be added to the list if not present.

  • S (int) – If the image has different dimensions on any scene from another, the dask array construction will fail. In that case, use this parameter to specify a specific scene to construct a dask array for. Default: 0 (select the first scene)

LIF_MAGIC_BYTE = 112
LIF_MEMORY_BYTE = 42
property dims

The dimensions for a lif file.

Returns

“STCZYX”

Return type

str

dtype() → numpy.dtype[source]

The data type of the underlying numpy ndarray, ie uint8, uint16, uint32 etc.

Returns

The data format used to store the data in the Leica lif file and the read numpy.ndarray.

Return type

numpy.dtype

get_channel_names()[source]

Get the channel names for the scene

Parameters

scene (int) – The index of the scene from which to retrieve the channel names

Returns

A list of descriptive names of the channels of the form “Gray–TL-BF–EMP_BF” and “Green–FLUO–GFP”

Return type

List[str]

get_physical_pixel_size()[source]

Get the (X, Y, Z) pixel size. If the value is not set it returns 1.0.

Parameters

scene (int) – The scene to retrieve the dimensions from

Returns

Return type

(X, Y, Z) in m.

static get_pixel_type(meta: xml.etree.ElementTree.Element, scene: int = 0) → numpy.dtype[source]

This function parses the metadata to assign the appropriate numpy.dtype

Parameters

  • meta (xml.etree.ElementTree.Element) – The root Element of the metadata etree

  • scene (int) – The index of the scene, scenes could have different storage data types.

Returns

The appropriate data type to construct the matrix with.

Return type

numpy.dtype

property metadata

Load and return the metadata from the LIF file

Returns

Return type

The xml Element Tree of the metadata

size_c() → int[source]
size_s() → int[source]
size_t() → int[source]
size_x() → int[source]
size_y() → int[source]
size_z() → int[source]

aicsimageio.readers.ome_tiff_reader module

class aicsimageio.readers.ome_tiff_reader.OmeTiffReader(data: Union[str, pathlib.Path, bytes, io.BufferedIOBase], **kwargs)[source]

Bases: aicsimageio.readers.tiff_reader.TiffReader

Opening and processing the contents of an OME Tiff file

get_channel_names(scene: int = 0)[source]

Attempts to use the image’s metadata to get the image’s channel names.

Parameters

scene (int) – The index of the scene for which to return channel names.

Returns

channels_names – List of strings representing the channel names. If channel dimension not present in file, return None.

Return type

Optional[List[str]]

get_physical_pixel_size()[source]

Attempts to retrieve physical pixel size for the specified scene. If none available, returns 1.0 for each spatial dimension.

Parameters

scene (int) – The index of the scene for which to return physical pixel sizes.

Returns

sizes – Tuple of floats representing the pixel sizes for X, Y, Z, in that order.

Return type

Tuple[float]

is_ome()[source]
property metadata
size_c() → int[source]
size_s() → int[source]
size_t() → int[source]
size_x() → int[source]
size_y() → int[source]
size_z() → int[source]

aicsimageio.readers.reader module

class aicsimageio.readers.reader.Reader[source]

Bases: abc.ABC

property client
close()[source]

Always close the Dask Client connection. If connected to strictly a LocalCluster, close it down as well.

property cluster
property dask_data
property data
abstract property dims
get_channel_names()[source]

Attempts to use the image’s metadata to get the image’s channel names.

Parameters

scene (int) – The index of the scene for which to return channel names.

Returns

channels_names – List of strings representing the channel names. If channel dimension not present in file, return None.

Return type

Optional[List[str]]

get_image_dask_data(out_orientation: Optional[str] = None, **kwargs) → dask.array.core.Array[source]

Get specific dimension image data out of an image as a dask array.

Parameters

  • out_orientation (Optional[str]) – A string containing the dimension ordering desired for the returned ndarray. Default: The current image dimensions. i.e. self.dims

  • kwargs –

    • C=1: specifies Channel 1

    • T=3: specifies the fourth index in T

    • D=n: D is Dimension letter and n is the index desired. D should not be present in the out_orientation.

    • D=[a, b, c]: D is Dimension letter and a, b, c is the list of indicies desired. D should be present in the out_orientation.

    • D=(a, b, c): D is Dimension letter and a, b, c is the tuple of indicies desired. D should be present in the out_orientation.

    • D=range(…): D is Dimension letter and range is the standard Python range function. D should be present in the out_orientation.

    • D=slice(…): D is Dimension letter and slice is the standard Python slice function. D should be present in the out_orientation.

Returns

data – The read data with the dimension ordering that was specified with out_orientation.

Return type

dask array

Examples

Specific index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... c1 = img.get_image_dask_data("ZYX", C=1)

List of index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... first_and_second = img.get_image_dask_data("CZYX", C=[0, 1])

Tuple of index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... first_and_last = img.get_image_dask_data("CZYX", C=(0, -1))

Range of index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... first_three = img.get_image_dask_data("CZYX", C=range(3))

Slice selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... every_other = img.get_image_dask_data("CZYX", C=slice(0, -1, 2))

Notes

  • If a requested dimension is not present in the data the dimension is added with a depth of 1.

See aicsimageio.transforms.reshape_data for more details.

get_image_data(out_orientation: Optional[str] = None, **kwargs) → numpy.ndarray[source]

Read the image as a numpy array then return specific dimension image data.

Parameters

  • out_orientation (Optional[str]) – A string containing the dimension ordering desired for the returned ndarray. Default: The current image dimensions. i.e. self.dims

  • kwargs –

    • C=1: specifies Channel 1

    • T=3: specifies the fourth index in T

    • D=n: D is Dimension letter and n is the index desired. D should not be present in the out_orientation.

    • D=[a, b, c]: D is Dimension letter and a, b, c is the list of indicies desired. D should be present in the out_orientation.

    • D=(a, b, c): D is Dimension letter and a, b, c is the tuple of indicies desired. D should be present in the out_orientation.

    • D=range(…): D is Dimension letter and range is the standard Python range function. D should be present in the out_orientation.

    • D=slice(…): D is Dimension letter and slice is the standard Python slice function. D should be present in the out_orientation.

Examples

Specific index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... c1 = img.get_image_data("ZYX", C=1)

List of index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... first_and_second = img.get_image_data("CZYX", C=[0, 1])

Tuple of index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... first_and_last = img.get_image_data("CZYX", C=(0, -1))

Range of index selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... first_three = img.get_image_data("CZYX", C=range(3))

Slice selection

>>> img = Reader("s_1_t_1_c_10_z_20.ome.tiff")
... every_other = img.get_image_data("CZYX", C=slice(0, -1, 2))
Returns

data – The read data with the dimension ordering that was specified with out_orientation.

Return type

np.ndarray

Notes

  • If a requested dimension is not present in the data the dimension is added with a depth of 1.

  • This will preload the entire image before returning the requested data.

See aicsimageio.transforms.reshape_data for more details.

get_physical_pixel_size()[source]

Attempts to retrieve physical pixel size for the specified scene. If none available, returns 1.0 for each spatial dimension.

Parameters

scene (int) – The index of the scene for which to return physical pixel sizes.

Returns

sizes – Tuple of floats representing the pixel sizes for X, Y, Z, in that order.

Return type

Tuple[float]

static guess_dim_order()[source]
classmethod is_this_type(data: Union[str, pathlib.Path, bytes, io.BufferedIOBase, numpy.ndarray, dask.array.core.Array]) → bool[source]
abstract property metadata
property shape
Returns

shape – A tuple with the size of all dimensions.

Return type

Tuple[int]

size()[source]
Parameters

dims (str) – A string containing a list of dimensions being requested. The default is to return the six standard dims.

Returns

size – A tuple with the requested dimensions filled in.

Return type

Tuple[int]

aicsimageio.readers.reader.use_dask(setting: bool)[source]

Enable or disable Dask for image reading. When True, image reads are first attempted to be handled by a distributed cluster. When False, image reads are never routed to a distributed cluster and are instead read immediately in the running process.

aicsimageio.readers.tiff_reader module

class aicsimageio.readers.tiff_reader.TiffReader(data: Union[str, pathlib.Path, bytes, io.BufferedIOBase], S: int = 0, **kwargs)[source]

Bases: aicsimageio.readers.reader.Reader

TiffReader wraps tifffile to provide the same reading capabilities but abstracts the specifics of using the backend library to create a unified interface. This enables higher level functions to duck type the File Readers.

Parameters

  • data (types.FileLike) – A string or path to the TIFF file to be read.

  • S (int) – If the image has different dimensions on any scene from another, the dask array construction will fail. In that case, use this parameter to specify a specific scene to construct a dask array for. Default: 0 (select the first scene)

property dims
dtype()[source]
static get_image_description(buffer: io.BufferedIOBase) → Optional[bytearray][source]

Retrieve the image description as one large string.

load_slice(slice_index: int = 0) → numpy.ndarray[source]
property metadata

Module contents