Welcome to aicsimageio’s documentation!

AICSImageIO

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Delayed Parallel Image Reading for Microscopy Images in Python


Features

  • Supports reading metadata and imaging data for:

    • CZI

    • OME-TIFF

    • TIFF

    • LIF

    • Any additional format supported by imageio

  • Supports writing metadata and imaging data for:

    • OME-TIFF

Installation

Stable Release: pip install aicsimageio
Development Head: pip install git+https://github.com/AllenCellModeling/aicsimageio.git

Documentation

For full package documentation please visit allencellmodeling.github.io/aicsimageio.

Quick Start

Full Image Reading

from aicsimageio import AICSImage, imread

# Get an AICSImage object
img = AICSImage("my_file.tiff")
img.data  # returns 6D STCZYX numpy array
img.dims  # returns string "STCZYX"
img.shape  # returns tuple of dimension sizes in STCZYX order
img.get_image_data("CZYX", S=0, T=0)  # returns 4D CZYX numpy array

# Get 6D STCZYX numpy array
data = imread("my_file.tiff")

Delayed Image Reading

from aicsimageio import AICSImage, imread_dask

# Get an AICSImage object
img = AICSImage("my_file.tiff")
img.dask_data  # returns 6D STCZYX dask array
img.dims  # returns string "STCZYX"
img.shape  # returns tuple of dimension sizes in STCZYX order
img.size("STC")  # returns tuple of dimensions sizes for just STC
img.get_image_dask_data("CZYX", S=0, T=0)  # returns 4D CZYX dask array

# Read specified portion of dask array
lazy_s0t0 = img.get_image_dask_data("CZYX", S=0, T=0)  # returns 4D CZYX dask array
s0t0 = lazy_s0t0.compute()  # returns 4D CZYX numpy array

# Or use normal numpy array slicing
lazy_data = imread_dask("my_file.tiff")
lazy_s0t0 = lazy_data[0, 0, :]
s0t0 = lazy_s0t0.compute()

Quick Start Notes

In short, if the word “dask” appears in the function or property name, the function utilizes delayed reading. If not, the requested image will be loaded immediately and the internal implementation may result in loading the entire image even if only a small chunk was requested. Currently, AICSImage.data and AICSImage.get_image_data load and cache the entire image in memory before performing their operation. AICSImage.dask_data and AICSImage.get_image_dask_data do not load any image data until the user calls compute on the dask.Array object and only the requested chunk will be loaded into memory instead of the entire image.

Metadata Reading

from aicsimageio import AICSImage

# Get an AICSImage object
img = AICSImage("my_file.tiff")
img.metadata  # returns the metadata object for this image type
img.get_channel_names()  # returns a list of string channel names found in the metadata

Performance Considerations

  • If your image fits in memory: use AICSImage.data, AICSImage.get_image_data, or Reader equivalents.

  • If your image is too large to fit in memory: use AICSImage.dask_data, AICSImage.get_image_dask_data, or Reader equivalents.

Napari Interactive Viewer

napari is a fast, interactive, multi-dimensional image viewer for python and it is pretty useful for imaging data that this package tends to interact with.

We have also released napari-aicsimageio, a plugin that allows use of all the functionality described in this library, but in the napari default viewer itself.

Notes

  • Image data and dask_data are always returned as six dimensional in dimension order STCZYX or Scene, Time, Channel, Z, Y, and X.

  • Each file format may use a different metadata parser it is dependent on the reader’s implementation.

  • The AICSImage object will only pull the Scene, Time, Channel, Z, Y, X dimensions from the reader. If your file has dimensions outside of those, use the base reader classes CziReader, OmeTiffReader, TiffReader, or DefaultReader.

  • We make some choices for the user based off the image data during img.view_napari. If you don’t want this behavior, simply pass the img.dask_data into napari.view_image instead.

Development

See CONTRIBUTING.md for information related to developing the code.

Free software: BSD-3-Clause

Indices and tables