lazystack 0.5.0

lazystack

This package provides a familiar interface for lazily reading scientific image stacks, particularly stacks of x-ray images. It's cross-platform, lightweight, and easy to use. Stacks opened via lazystack are indexable (including NumPy-style fancy indexing), iterable, sliceable, and only load the underlying data into memory when absolutely necessary.

Supported formats

• HDF (via h5py) — datasets must be (num_images, height, width) grayscale images.
• TIFF and other TIFF-family files (via tifffile) — grayscale stacks only; colour/multichannel images and hyperstacks are not supported.
• Hamamatsu DCIMG (.dcimg).
• Hamamatsu HIS (.his).

TIFF-family files that store the whole stack as a single 3D image (rather than a sequence of 2D images) require the optional zarr package (pip install zarr).

Installation

$ pip install git+https://github.com/domjurkschat/lazystack

For developers, you can clone the repository and run

$ pip install -e .

or

$ uv sync

Usage

Lazy stacks can be created by passing a path or a list/array of paths to the lazystack function. The underlying data is only materialised via integer indexing or upon being cast to a NumPy array, e.g., via np.asarray() or .asarray(). For example:

from lazystack import lazystack

with lazystack("path/to/somehis.his") as images:
    # Access familiar NDArray attributes, e.g., shape, dtype, size.
    shape = images.shape
    # Slicing the lazystack produces a view (no images in memory).
    substack = images[0:50]
    # Slicing a view or lazystack also produces a view.
    subsubstack = substack[0:10]
    # Spatial slicing also produces a view.
    subsubsubstack = subsubstack[:, 100:, 50:-50]
    # Integer indexing materialises an image from the view.
    image = subsubsubstack[5]
    # `np.asarray()` materialises the whole view.
    subsubsubstack = np.asarray(subsubsubstack)

A lazystack exposes NumPy-like attributes: shape, dtype, ndim, size (total elements), and itemsize (bytes per element). Disk usage is available via image_nbytes (bytes per frame) and nbytes (bytes for the whole stack). A one-line summary is available via str(stack) or stack.info.

It's best to open lazystacks within a context manager, but you can also open and close them manually, e.g.:

images = lazystack("path/to/somehis.his")
# Do some stuff.
# ...
# Don't forget to close!
images.close()

If you're opening HDF files, the desired dataset path (within the HDF file) must also be specified, e.g.:

images = lazystack("path/to/some/hdf5.h5", "path/to/some/dset")

Directories of files can be opened by supplying a list or array of filenames, e.g.:

filenames = sorted(input_path.glob("*.tif"))
with lazystack(filenames) as images:
    # Do some stuff.

Currently, this only supports TIFF files.

lazystack also provides iter_chunks for prefetching and yielding successive chunks along any axis of a lazystack (or regular 3D NumPy array), e.g.:

from lazystack import lazystack, iter_chunks

# How many chunks will be prefetched.
num_prefetch = 1
# Memory allowance for each chunk (in GB).
chunk_size_gb = 1.0
# Axis to chunk over (must be 0, 1, or 2).
axis = 0
# Step along the chunk axis (must be at least 1).
step = 1

with lazystack("path/to/somedcimg.dcimg") as images:
    # `start_idx` and `stop_idx` specify the index bounds of each chunk, useful 
    #   for output.
    for chunk, start_idx, stop_idx in iter_chunks(
        images, 
        chunk_size_gb=chunk_size_gb,
        axis=axis,
        step=step,
        num_prefetch=num_prefetch
    ):
        # Do some stuff.

Contributing

Contributions are very welcome! Don't hesitate to reach out if you have any questions and feel free to open an issue if you have any feedback or encounter any bugs.

Tests can be run via:

$ uv run pytest

To-do list:

• Improve test suite.
• Nested spatial indexing.
• Stacks of stacks.
• Other file formats.
• Colour/multichannel and hyperstack support.