Acquire Zarr streaming library

This library supports chunked, compressed, multiscale streaming to Zarr, both version 2 and version 3, with OME-NGFF metadata.

This code builds targets for Python and C.

Note: Zarr Version 2 is deprecated and will be removed in a future release. We recommend using Zarr Version 3 for new projects.

Installing

Precompiled binaries

C headers and precompiled binaries are available for Windows, Mac, and Linux on our releases page.

Python

The library is available on PyPI and can be installed using pip:

pip install acquire-zarr

Building

Installing dependencies

This library has the following dependencies:

• c-blosc v1.21.5
• nlohmann-json v3.11.3
• minio-cpp v0.3.0
• crc32c v1.1.2

We use vcpkg to install them, as it integrates well with CMake. To install vcpkg, clone the repository and bootstrap it:

git clone https://github.com/microsoft/vcpkg.git
cd vcpkg && ./bootstrap-vcpkg.sh

and then add the vcpkg directory to your path. If you are using bash, you can do this by running the following snippet from the vcpkg/ directory:

cat >> ~/.bashrc <<EOF
export VCPKG_ROOT=${PWD}
export PATH=\$VCPKG_ROOT:\$PATH
EOF

If you're using Windows, learn how to set environment variables here. You will need to set both the VCPKG_ROOT and PATH variables in the system control panel.

On the Mac, you will also need to install OpenMP using Homebrew:

brew install libomp

Configuring

To build the library, you can use CMake:

cmake --preset=default -B /path/to/build /path/to/source

On Windows, you'll need to specify the target triplet to ensure that all dependencies are built as static libraries:

cmake --preset=default -B /path/to/build -DVCPKG_TARGET_TRIPLET=x64-windows-static /path/to/source

Aside from the usual CMake options, you can choose to disable tests by setting BUILD_TESTING to OFF:

cmake --preset=default -B /path/to/build -DBUILD_TESTING=OFF /path/to/source

To build the Python bindings, make sure pybind11 is installed. Then, you can set BUILD_PYTHON to ON:

cmake --preset=default -B /path/to/build -DBUILD_PYTHON=ON /path/to/source

Building

After configuring, you can build the library:

cmake --build /path/to/build

Installing for Python

To install the Python bindings, you can run:

pip install .

Note

It is highly recommended to use virtual environments for Python, e.g. using venv or conda. In this case, make sure pybind11 is installed in this environment, and that the environment is activated before installing the bindings.

Usage

The library provides two main interfaces. First, ZarrStream, representing an output stream to a Zarr dataset. Second, ZarrStreamSettings to configure a Zarr stream.

A typical use case for a 4-dimensional acquisition might look like this:

ZarrStreamSettings settings = (ZarrStreamSettings){
    .store_path = "my_stream.zarr",
    .data_type = ZarrDataType_uint16,
    .version = ZarrVersion_3,
    .output_key = "dataset1",  // Optional: path within Zarr where data should be stored
    .overwrite = true,         // Optional: remove existing data at store_path if true
};

ZarrStreamSettings_create_dimension_array(&settings, 4);
settings.dimensions[0] = (ZarrDimensionProperties){
    .name = "t",
    .type = ZarrDimensionType_Time,
    .array_size_px = 0,      // this is the append dimension
    .chunk_size_px = 100,    // 100 time points per chunk
    .shard_size_chunks = 10, // 10 chunks per shard
};

// ... rest of dimensions configuration ...

ZarrStream* stream = ZarrStream_create(&settings);

size_t bytes_written;
ZarrStream_append(stream, my_frame_data, my_frame_size, &bytes_written);
assert(bytes_written == my_frame_size);

Look at acquire.zarr.h for more details.

This acquisition in Python would look like this:

import acquire_zarr as aqz
import numpy as np

settings = aqz.StreamSettings(
    store_path="my_stream.zarr",
    data_type=aqz.DataType.UINT16,
    version=aqz.ZarrVersion.V3,
    output_key="dataset1",  # Optional: path within Zarr where data should be stored
    overwrite=True  # Optional: remove existing data at store_path if true
)

settings.dimensions = [
    aqz.Dimension(
        name="t",
        type=aqz.DimensionType.TIME,
        array_size_px=0,
        chunk_size_px=100,
        shard_size_chunks=10
    ),
    aqz.Dimension(
        name="c",
        type=aqz.DimensionType.CHANNEL,
        array_size_px=3,
        chunk_size_px=1,
        shard_size_chunks=1
    ),
    aqz.Dimension(
        name="y",
        type=aqz.DimensionType.SPACE,
        array_size_px=1080,
        chunk_size_px=270,
        shard_size_chunks=2
    ),
    aqz.Dimension(
        name="x",
        type=aqz.DimensionType.SPACE,
        array_size_px=1920,
        chunk_size_px=480,
        shard_size_chunks=2
    )
]

# Generate some random data: one time point, all channels, full frame
my_frame_data = np.random.randint(0, 2 ** 16, (3, 1080, 1920), dtype=np.uint16)

stream = aqz.ZarrStream(settings)
stream.append(my_frame_data)

Organizing Data within a Zarr Container

The library allows organizing data within a Zarr container through the output_key setting. This feature enables you to:

1. Store multiple datasets in a single Zarr container
2. Create hierarchical data organization
3. Manage related acquisitions within a structured layout

Example of storing multiple acquisitions in a single Zarr container:

import acquire_zarr as aqz
import numpy as np

# Create a Zarr container with multiple datasets
container_path = "experiment.zarr"

# First acquisition - store in "sample1/brightfield"
settings1 = aqz.StreamSettings(
    store_path=container_path,
    data_type=aqz.DataType.UINT16,
    version=aqz.ZarrVersion.V3,
    output_key="sample1/brightfield",
    overwrite=False  # Don't overwrite existing data
)
# ... configure dimensions ...
stream1 = aqz.ZarrStream(settings1)
# ... append data ...

# Second acquisition - store in "sample1/fluorescence"
settings2 = aqz.StreamSettings(
    store_path=container_path,
    data_type=aqz.DataType.UINT16,
    version=aqz.ZarrVersion.V3,
    output_key="sample1/fluorescence",
    overwrite=False  # Don't overwrite existing data
)
# ... configure dimensions ...
stream2 = aqz.ZarrStream(settings2)
# ... append data ...

The output_key parameter accepts a path-like string using forward slashes as separators. Intermediate groups will be automatically created in the Zarr hierarchy. If output_key is not specified, data will be written at the root of the Zarr container. The overwrite parameter controls whether existing data at the store_path is removed. When set to true, the entire directory specified by store_path will be removed if it exists. When set to false, the stream will use the existing directory if it exists, or create a new one if it doesn't.

S3

The library supports writing directly to S3-compatible storage. We authenticate with S3 through environment variables or an AWS credentials file. If you are using environment variables, set the following:

• AWS_ACCESS_KEY_ID: Your AWS access key
• AWS_SECRET_ACCESS_KEY: Your AWS secret key
• AWS_SESSION_TOKEN: Optional session token for temporary credentials

These must be set in the environment where your application runs.

Important Note: You should ensure these environment variables are set before running your application or importing the library or Python module. They will not be available if set after the library is loaded. Configuration requires specifying the endpoint, bucket name, and region:

// ensure your environment is set up for S3 access before running your program
#include <acquire.zarr.h>

ZarrStreamSettings settings = { /* ... */ };

// Configure S3 storage
ZarrS3Settings s3_settings = {
    .endpoint = "https://s3.amazonaws.com",
    .bucket_name = "my-zarr-data",
    .region = "us-east-1"
};

settings.s3_settings = &s3_settings;

In Python, S3 configuration looks like:

# ensure your environment is set up for S3 access before importing acquire_zarr
import acquire_zarr as aqz

settings = aqz.StreamSettings()
# ...

# Configure S3 storage
s3_settings = aqz.S3Settings(
    endpoint="s3.amazonaws.com",
    bucket_name="my-zarr-data",
    region="us-east-1"
)

# Apply S3 settings to your stream configuration
settings.s3 = s3_settings

Anaconda GLIBCXX issue

If you encounter the error GLIBCXX_3.4.30 not found when working with the library in Python, it may be due to a mismatch between the version of libstdc++ that ships with Anaconda and the one used by acquire-zarr. This usually manifests like so:

ImportError: /home/eggbert/anaconda3/envs/myenv/lib/python3.10/site-packages/acquire_zarr/../../../lib/libstdc++.so.6: version `GLIBCXX_3.4.30` not found (required by /home/eggbert/anaconda3/envs/myenv/lib/python3.10/site-packages/acquire_zarr/../../../lib/libacquire_zarr.so)

To resolve this, you can install the libstdcxx-ng package from conda-forge:

conda install -c conda-forge libstdcxx-ng