Performant streaming to Zarr storage, on filesystem or cloud

Project description

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 single-array, 4-dimensional acquisition might look like this:

ZarrArraySettings array{
    .output_key =
      "my-array", // Optional: path within Zarr where data should be stored
    .data_type = ZarrDataType_uint16,
};

ZarrArraySettings_create_dimension_array(&array, 4);
array.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 ...

ZarrStreamSettings settings = (ZarrStreamSettings){
    .store_path = "my_stream.zarr",
    .version = ZarrVersion_3,
    .overwrite = true, // Optional: remove existing data at store_path if true
    .arrays = &array,
    .array_count = 1, // Number of arrays in the stream
};

ZarrStream* stream = ZarrStream_create(&settings);

// You can now safely free the dimensions array
ZarrArraySettings_destroy_dimension_array(&array);

size_t bytes_written;
ZarrStream_append(stream,
                  my_frame_data,
                  my_frame_size,
                  &bytes_written,
                  "my-array"); // if you have just one array configured, this can be NULL
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",
    version=aqz.ZarrVersion.V3,
    overwrite=True  # Optional: remove existing data at store_path if true
)

settings.arrays = [
    aqz.ArraySettings(
        output_key="array1",
        data_type=np.uint16,
        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)

# ... append more data as needed ...

# When done, close the stream to flush any remaining data
stream.close()

Organizing data within a Zarr container

The library allows you to stream multiple arrays to a single Zarr dataset by configuring multiple arrays. For example, a multichannel acquisition with both brightfield and fluorescence channels might look like this:

import acquire_zarr as aqz
import numpy as np

# configure the stream with two arrays
settings = aqz.StreamSettings(
    store_path="experiment.zarr",
    version=aqz.ZarrVersion.V3,
    overwrite=True,  # Remove existing data at store_path if true
    arrays=[
        aqz.ArraySettings(
            output_key="sample1/brightfield",
            data_type=np.uint16,
            dimensions=[
                aqz.Dimension(
                    name="t",
                    type=aqz.DimensionType.TIME,
                    array_size_px=0,
                    chunk_size_px=100,
                    shard_size_chunks=1
                ),
                aqz.Dimension(
                    name="c",
                    type=aqz.DimensionType.CHANNEL,
                    array_size_px=1,
                    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
                )
            ]
        ),
        aqz.ArraySettings(
            output_key="sample1/fluorescence",
            data_type=np.uint16,
            dimensions=[
                aqz.Dimension(
                    name="t",
                    type=aqz.DimensionType.TIME,
                    array_size_px=0,
                    chunk_size_px=100,
                    shard_size_chunks=1
                ),
                aqz.Dimension(
                    name="c",
                    type=aqz.DimensionType.CHANNEL,
                    array_size_px=2,  # two fluorescence channels
                    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
                )
            ]
        )
    ]
)

stream = aqz.ZarrStream(settings)

# ... append data ...
stream.append(brightfield_frame_data, key="sample1/brightfield")
stream.append(fluorescence_frame_data, key="sample1/fluorescence")

# ... append more data as needed ...

# When done, close the stream to flush any remaining data
stream.close()

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.

High-content screening workflows

The library supports high-content screening (HCS) datasets following the OME-NGFF 0.5 specification. HCS data is organized into plates, wells, and fields of view, with automatic generation of appropriate metadata.

Note: HCS is not supported for Zarr V2.

Here's an example of creating an HCS dataset in Python:

import acquire_zarr as aqz
import numpy as np

# Configure field of view arrays
fov1_array = aqz.ArraySettings(
    output_key="fov1",  # Relative to the well: plate/A/1/fov1
    data_type=np.uint16,
    dimensions=[
        aqz.Dimension(
            name="t",
            type=aqz.DimensionType.TIME,
            array_size_px=0,
            chunk_size_px=10,
            shard_size_chunks=1
        ),
        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=512,
            chunk_size_px=256,
            shard_size_chunks=2
        ),
        aqz.Dimension(
            name="x",
            type=aqz.DimensionType.SPACE,
            array_size_px=512,
            chunk_size_px=256,
            shard_size_chunks=2
        )
    ]
)

# Create acquisition metadata
acquisition = aqz.Acquisition(
    id=0,
    name="Measurement_01",
    start_time=1343731272000,  # Unix timestamp in milliseconds
    end_time=1343737645000
)

# Configure wells with fields of view
well_a1 = aqz.Well(
    row_name="A",
    column_name="1",
    images=[
        aqz.FieldOfView(
            path="fov1",
            acquisition_id=0,
            array_settings=fov1_array
        )
    ]
)

# Configure the plate
plate = aqz.Plate(
    path="experiment_plate",
    name="My HCS Experiment",
    row_names=["A", "B", "C", "D", "E", "F", "G", "H"],
    column_names=["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"],
    wells=[well_a1],  # Add more wells as needed
    acquisitions=[acquisition]
)

# Create stream with HCS configuration
settings = aqz.StreamSettings(
    store_path="hcs_experiment.zarr",
    version=aqz.ZarrVersion.V3,
    overwrite=True,
    hcs_plates=[plate]
)

stream = aqz.ZarrStream(settings)

# Write data to specific field of view
frame_data = np.random.randint(0, 2**16, (3, 512, 512), dtype=np.uint16)
stream.append(frame_data, key="experiment_plate/A/1/fov1")

# Close when done
stream.close()

You can also combine HCS plates with flat arrays in the same dataset:

# Add a labels array alongside HCS data
labels_array = aqz.ArraySettings(
    output_key="experiment_plate/A/1/labels",
    data_type=np.uint8,
    dimensions=[
        aqz.Dimension(
            name="y",
            type=aqz.DimensionType.SPACE,
            array_size_px=512,
            chunk_size_px=256,
            shard_size_chunks=2
        ),
        aqz.Dimension(
            name="x",
            type=aqz.DimensionType.SPACE,
            array_size_px=512,
            chunk_size_px=256,
            shard_size_chunks=2
        )
    ]
)

settings = aqz.StreamSettings(
    store_path="mixed_experiment.zarr",
    version=aqz.ZarrVersion.V3,
    overwrite=True,
    arrays=[labels_array],  # Flat arrays
    hcs_plates=[plate]      # HCS structure
)

stream = aqz.ZarrStream(settings)

# Write to both HCS and flat arrays
stream.append(frame_data, key="experiment_plate/A/1/fov1")
labels_data = np.zeros((512, 512), dtype=np.uint8)
stream.append(labels_data, key="experiment_plate/A/1/labels")

stream.close()

In C, the equivalent HCS workflow would look like this:

#include "acquire.zarr.h"

// Create array settings for field of view
ZarrArraySettings fov_array = {
    .output_key = "fov1",  // Relative to well: plate/A/1/fov1
    .data_type = ZarrDataType_uint16,
};

ZarrArraySettings_create_dimension_array(&fov_array, 4);
fov_array.dimensions[0] = (ZarrDimensionProperties){
    .name = "t",
    .type = ZarrDimensionType_Time,
    .array_size_px = 0,
    .chunk_size_px = 10,
    .shard_size_chunks = 1,
};
fov_array.dimensions[1] = (ZarrDimensionProperties){
    .name = "c", 
    .type = ZarrDimensionType_Channel,
    .array_size_px = 3,
    .chunk_size_px = 1,
    .shard_size_chunks = 1,
};
fov_array.dimensions[2] = (ZarrDimensionProperties){
    .name = "y",
    .type = ZarrDimensionType_Space,
    .array_size_px = 512,
    .chunk_size_px = 256,
    .shard_size_chunks = 2,
};
fov_array.dimensions[3] = (ZarrDimensionProperties){
    .name = "x",
    .type = ZarrDimensionType_Space,
    .array_size_px = 512,
    .chunk_size_px = 256,
    .shard_size_chunks = 2,
};

// Create well with field of view
ZarrHCSWell well = {
    .row_name = "A",
    .column_name = "1",
};

ZarrHCSWell_create_image_array(&well, 1);
well.images[0] = (ZarrHCSFieldOfView){
    .path = "fov1",
    .acquisition_id = 0,
    .has_acquisition_id = true,
    .array_settings = &fov_array,
};

// Create plate
ZarrHCSPlate plate = {
    .path = "experiment_plate",
    .name = "My HCS Experiment",
};

// Set up row and column names
ZarrHCSPlate_create_row_name_array(&plate, 8);
const char* row_names[] = {"A", "B", "C", "D", "E", "F", "G", "H"};
for (int i = 0; i < 8; i++) {
    plate.row_names[i] = row_names[i];
}

ZarrHCSPlate_create_column_name_array(&plate, 12);
const char* col_names[] = {"1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"};
for (int i = 0; i < 12; i++) {
    plate.column_names[i] = col_names[i];
}

// Add wells and acquisitions
ZarrHCSPlate_create_well_array(&plate, 1);
plate.wells[0] = well;

ZarrHCSPlate_create_acquisition_array(&plate, 1);
plate.acquisitions[0] = (ZarrHCSAcquisition){
    .id = 0,
    .name = "Measurement_01",
    .start_time = 1343731272000,
    .has_start_time = true,
    .end_time = 1343737645000,
    .has_end_time = true,
};

// Create HCS settings
ZarrHCSSettings hcs_settings = {
    .plates = &plate,
    .plate_count = 1,
};

// Configure stream
ZarrStreamSettings settings = {
    .store_path = "hcs_experiment.zarr",
    .version = ZarrVersion_3,
    .overwrite = true,
    .arrays = NULL,
    .array_count = 0,
    .hcs_settings = &hcs_settings,
};

ZarrStream* stream = ZarrStream_create(&settings);

// Write data
uint16_t* frame_data = /* your image data */;
size_t frame_size = 3 * 512 * 512 * sizeof(uint16_t);
size_t bytes_written;

ZarrStream_append(stream, frame_data, frame_size, &bytes_written, "experiment_plate/A/1/fov1");

// Cleanup
ZarrStream_destroy(stream);
ZarrHCSPlate_destroy_well_array(&plate);
ZarrArraySettings_destroy_dimension_array(&fov_array);

The resulting dataset will include proper OME-NGFF metadata for plates and wells.

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

Project details

Release history Release notifications | RSS feed

This version

0.6.0

Sep 24, 2025

0.5.2

Aug 7, 2025

0.5.1

Jul 24, 2025

0.5.0

Jul 11, 2025

0.4.0

Apr 24, 2025

0.3.1

Apr 22, 2025

0.3.0

Apr 18, 2025

0.2.4

Mar 25, 2025

0.2.3

Mar 12, 2025

0.2.2

Feb 25, 2025

0.2.1

Feb 25, 2025

0.2.0

Feb 11, 2025

0.1.0

Jan 21, 2025

0.0.5

Jan 9, 2025

0.0.2

Nov 26, 2024

0.0.1

Nov 8, 2024

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

acquire_zarr-0.6.0.tar.gz (193.5 kB view details)

Uploaded Sep 24, 2025 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

acquire_zarr-0.6.0-cp313-cp313-win_amd64.whl (2.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.13Windows x86-64

acquire_zarr-0.6.0-cp313-cp313-manylinux_2_28_x86_64.whl (4.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.13manylinux: glibc 2.28+ x86-64

acquire_zarr-0.6.0-cp313-cp313-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded Sep 24, 2025 CPython 3.13manylinux: glibc 2.28+ ARM64

acquire_zarr-0.6.0-cp313-cp313-macosx_15_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.13macOS 15.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp313-cp313-macosx_13_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.13macOS 13.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp312-cp312-win_amd64.whl (2.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.12Windows x86-64

acquire_zarr-0.6.0-cp312-cp312-manylinux_2_28_x86_64.whl (4.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.12manylinux: glibc 2.28+ x86-64

acquire_zarr-0.6.0-cp312-cp312-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded Sep 24, 2025 CPython 3.12manylinux: glibc 2.28+ ARM64

acquire_zarr-0.6.0-cp312-cp312-macosx_15_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.12macOS 15.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp312-cp312-macosx_13_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.12macOS 13.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp311-cp311-win_amd64.whl (2.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.11Windows x86-64

acquire_zarr-0.6.0-cp311-cp311-manylinux_2_28_x86_64.whl (4.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.11manylinux: glibc 2.28+ x86-64

acquire_zarr-0.6.0-cp311-cp311-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded Sep 24, 2025 CPython 3.11manylinux: glibc 2.28+ ARM64

acquire_zarr-0.6.0-cp311-cp311-macosx_15_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.11macOS 15.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp311-cp311-macosx_13_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.11macOS 13.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp310-cp310-win_amd64.whl (2.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.10Windows x86-64

acquire_zarr-0.6.0-cp310-cp310-manylinux_2_28_x86_64.whl (4.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.10manylinux: glibc 2.28+ x86-64

acquire_zarr-0.6.0-cp310-cp310-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded Sep 24, 2025 CPython 3.10manylinux: glibc 2.28+ ARM64

acquire_zarr-0.6.0-cp310-cp310-macosx_15_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.10macOS 15.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp310-cp310-macosx_13_0_x86_64.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.10macOS 13.0+ x86-64

acquire_zarr-0.6.0-cp39-cp39-win_amd64.whl (2.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.9Windows x86-64

acquire_zarr-0.6.0-cp39-cp39-manylinux_2_28_x86_64.whl (4.8 MB view details)

Uploaded Sep 24, 2025 CPython 3.9manylinux: glibc 2.28+ x86-64

acquire_zarr-0.6.0-cp39-cp39-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded Sep 24, 2025 CPython 3.9manylinux: glibc 2.28+ ARM64

acquire_zarr-0.6.0-cp39-cp39-macosx_15_0_universal2.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.9macOS 15.0+ universal2 (ARM64, x86-64)

acquire_zarr-0.6.0-cp39-cp39-macosx_13_0_x86_64.whl (3.6 MB view details)

Uploaded Sep 24, 2025 CPython 3.9macOS 13.0+ x86-64

File details

Details for the file acquire_zarr-0.6.0.tar.gz.

File metadata

Download URL: acquire_zarr-0.6.0.tar.gz
Upload date: Sep 24, 2025
Size: 193.5 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for acquire_zarr-0.6.0.tar.gz
Algorithm	Hash digest
SHA256	`e531524cc72cbbdf44aa68cc54677654f4c4ca47620e4d02b87a2805aceb81b6`
MD5	`0b1e92f980c6e0f3d4f333dba640e6ec`
BLAKE2b-256	`e351c0a10b76016c68e641bfa1f5a63102cecce1bc42c11d7694602061924dec`

Algorithm	Hash digest
SHA256	`2286cb27de72979b95bdb1ba9e4a0342dce572c9846bf2a276e36a286574bf52`
MD5	`f33a6207fc6ab8d9d7712a4ae52ef3b9`
BLAKE2b-256	`62da5d30ac1e06e8c6ef5d85cf38a62fe545b65e638185f5609b12866a26b283`

Algorithm	Hash digest
SHA256	`acfbbd1f96baf02076fb81b94f4cd9ad7514d2bb40b12387a7666754aa31e907`
MD5	`43326e8881b33abac0aac5d9c2176244`
BLAKE2b-256	`3a9f71348226c890fca9fbbfab0b99f9b932d4b25c6a31ecb69dbdda72985823`

Algorithm	Hash digest
SHA256	`6b04def27496469487a039d9a7fdf477b96f5b759611b41cbf7b175daf64a35a`
MD5	`c494bff83a74ea59b8cc8cf80616e9f5`
BLAKE2b-256	`e0c9c73c3ffb1d889654320d749ffed5eb65a8bead1bd3353c35c7d30728bab7`

Algorithm	Hash digest
SHA256	`5548c55e22f91197e47b4c3f9bdab0fe586e10d53b5791ea76c7a4d2ff478b05`
MD5	`9378fec0d877b1d9d93de6682aa692a7`
BLAKE2b-256	`7cff97ef21d613204e39029a1a757d6bbb18b60bbd97a3264e23735b7e261b83`

Algorithm	Hash digest
SHA256	`748247fee45fa9b9933922ab66b823cc7bd6ef44dd489bfdeefcd54aeebcbb48`
MD5	`2a3c677bea6a8d910d082ca60e872ebb`
BLAKE2b-256	`36d8d9101b4c12098242588cbcc6ac9bdfe65f74c26164074ab21f0f914593b0`

Algorithm	Hash digest
SHA256	`b3164ba12de3d2419440d59777622b99497d4c25dc6fdb2ae9770389c79657b9`
MD5	`e96e7af302793e512086f9961e318f0f`
BLAKE2b-256	`39cc58cadf341736ed955a9edfaf17d811f4afcab4ecad4c77321ab2105de321`

Algorithm	Hash digest
SHA256	`3fe151537f9e1ff3345cf037f34e808960be7e44c9800a9bfbdb44fae32cbd36`
MD5	`2f4ffb9fb95be6fe09810148a501cb11`
BLAKE2b-256	`48d2496362141073cb506cf69f22b6055e54e59eaaec8f4f75802f8090573e3c`

Algorithm	Hash digest
SHA256	`1f33bf8ff3d07a6e8725410c648db367614cf1fc7a962c0f41cfe8bd77be5510`
MD5	`b1a9eb3dccbdae78312af18ac74fa5b5`
BLAKE2b-256	`0b6ffd3fb0235056c291b6473d7633078a5958ac0cc39a95ab7c2b3a99f599a6`

Algorithm	Hash digest
SHA256	`d92bd1684fd0019c7d4c6ff1b9f25bb0ed480f1961bd1d2120884359c34a85ce`
MD5	`a6549ec5e3f165748176682315d9fcfa`
BLAKE2b-256	`e45354abb1abf534a7abbca640263d78e220b2666f1e8393e14353c52ed3d34e`

Algorithm	Hash digest
SHA256	`1a9007137192ae04e9f02cd34ae804539860be17a7e242d4f57e7be57cca738b`
MD5	`da9db8c6c962bae480d7fa62c9bb9bd7`
BLAKE2b-256	`6d5d6f2eb87a69074485c68c11b57497ccad9e2ca1059f131c5c5f4a0995b727`

Algorithm	Hash digest
SHA256	`9d66530150dc461f27af68a3726a6b942967c402be4df50c29b232756459725b`
MD5	`944f352a64293e4c0227c5c354091456`
BLAKE2b-256	`c86b9f1ca98c3fe55a705dd4ac5f0e86d77524eee8174ad10b341e48724e06d8`

Algorithm	Hash digest
SHA256	`ccdc0b1a01531b9762b75eb72e6862f3d66c98d27bbae9d9b0d1517fa6b8e57f`
MD5	`e96ac936ee38a928858e08de53c6dd21`
BLAKE2b-256	`c4306c95f35117c7118106ddc2d9e32d880db2705c3d943132ac244014e6faf3`

Algorithm	Hash digest
SHA256	`c8677bd705770c42faaeedabed6470bd23d982f240e882642a636937154e26ec`
MD5	`f897169cd3cff2493486157899cd7991`
BLAKE2b-256	`d115c38a28c7e65e4f9c216ab4c8dafa8f36e467ac6e8ed891f60e7c0f5e57d5`

Algorithm	Hash digest
SHA256	`a48a3ac79e58d4e9cb29861fc06882ef8d080056c7fd9cc6968b174fbfe5a567`
MD5	`35352aa0958180522c791a5dbb95f520`
BLAKE2b-256	`0a83509fe2cf8da5abb51d66076ced247510d1b0580e8e96d7fd61ffc050e47e`

Algorithm	Hash digest
SHA256	`e0de443d0ea272df3ed88dd0fbd1af0dffa84c688e62bdd43493bf47d1908ff9`
MD5	`d65d4634f61374178b218c1c305d7be0`
BLAKE2b-256	`949caf9597efb64bcde30c141a1c77ad509bf8896edcf72f9c2d18f3ea099cad`

Algorithm	Hash digest
SHA256	`9528c85d72b6fa68daad4fc179e59eeb13a23a3a40884e5a1f3b33aa4465b3bd`
MD5	`a3dae8f1a6c1cea0e613049801955921`
BLAKE2b-256	`70ac31558a6b82e31c40063428f2edf8b8ec846c9a1aa47cb1d1f4d3b94ebe51`

Algorithm	Hash digest
SHA256	`7261f39fda6a1d1fedffc4aa0d468b9124f7fff784030ce797bec0e3c3984ce9`
MD5	`6550981fdb2d7841f6ddc4c028358289`
BLAKE2b-256	`34eda02dda158b5c795c7f68908166b55cb2e434b639b0683db7aea439b84ab7`

Algorithm	Hash digest
SHA256	`3c2ec5f95f5a157e9feabca1a1a7483f36d230c059bdd77c61255b861955e166`
MD5	`6513798b46d0778137dabfa8eeabb8cd`
BLAKE2b-256	`bbc840720f54d08bebdd2746482fc92462c8121cde42a0ee167787294de8b5bb`

Algorithm	Hash digest
SHA256	`1133efd4f4cbbaac0146d11862aea4f67430512992d3344bd8ba9355ac7fd951`
MD5	`4cce59ce939134f1ce9a95f47c3719d8`
BLAKE2b-256	`1fdcc49a9b03f46be08e3e368f00b2edec3d2485c9ce14d53c7ee1c6f317355d`

Algorithm	Hash digest
SHA256	`7bd050e2013881dcd984daad0c29206282fadc9401ee0315f6c02ad34e4bf482`
MD5	`252eabc916f8aba300d8c4e975c35d7d`
BLAKE2b-256	`73aa38647edfbe043209cf18d3e39657f6fa754e62fb9191f3b3817354641fc5`

acquire-zarr 0.6.0

Navigation

Verified details

Owner

Maintainers

Unverified details

Meta

Project description

Acquire Zarr streaming library

Installing

Precompiled binaries

Python

Building

Installing dependencies

Configuring

Building

Installing for Python

Usage

Organizing data within a Zarr container

High-content screening workflows

S3

Anaconda GLIBCXX issue

Project details

Verified details

Owner

Maintainers

Unverified details

Meta

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

Algorithm	Hash digest
SHA256	`b7ed3adc78e6ed3343c58274dd9abfe94c882265766963c01f41a30a38084910`
MD5	`76aa504d765c6a7e6e899ff2e9cbaf2c`
BLAKE2b-256	`495b53efdb8f73bf299adc8f41d9c279e8c59010a5ccf6c049e4f98cede7a636`

Algorithm	Hash digest
SHA256	`55e93970bab142eb81f2be3ce446182a63514600532c3f8a1157419f669dd787`
MD5	`c6dcb3418f003dd783a8c27993cd0ad7`
BLAKE2b-256	`e19665d647008c239a4260dbd8816c1fbd5a6600c692b3f31af2e5c7d1f1f971`

Algorithm	Hash digest
SHA256	`d01c8cf7639bea3a35b52887083a1030419af01700728f5c5c71286811913236`
MD5	`a82659fa1816ea481bb9f86017b51d2d`
BLAKE2b-256	`0025a6812e4eae897cd7a999f1d3d8020682fdd8c7e679d8d33021a8eeb6f6ea`

Algorithm	Hash digest
SHA256	`d96c220b92423720c67834a1ad0257dea5911308622bbeaa17244b414b3d0c90`
MD5	`67290a87a16f01a94112c973a937c995`
BLAKE2b-256	`d5bf6609cb447f723e4dd705a587c1f379d604187fe65a458b913436c089fa00`

Algorithm	Hash digest
SHA256	`fe01f0fabf3b72d831271fcd6316ec0ab5572030d32d345ea03cdcdf1a5f8acb`
MD5	`9a044c2521cba7bd6f3bb8cc5ee82f72`
BLAKE2b-256	`793391b4dd2f3ec238c7723813e62285abcc9fc8a424d8bd6657f037b0e9babb`