z5

C++ and Python wrapper for zarr and n5 file formats. Implements the file system specification of these formats. Implementations for cloud based storage are work in progress. Any help is highly appreciated. See issues #136 and #137 for details.

Support for the following compression codecs:

• Blosc
• Zlib / Gzip
• Bzip2
• XZ
• LZ4

Installation

Conda

Conda packages for the relevant systems and python versions are hosted on conda-forge:

$ conda install -c conda-forge z5py

From Source

The easiest way to build the library from source is using a conda-environment with all necessary dependencies. You can find the conda environment files for build environments in .environments/unix

To set up the conda environment and install the package on unix:

$ conda env create -f environments/unix/z5-dev.yaml
$ conda activate z5-dev
$ mkdir bld
$ cd bld
$ cmake -DWITH_ZLIB=ON -DWITH_BZIP2=ON -DCMAKE_INSTALL_PREFIX=/path/to/install ..
$ make install

Note that in the CMakeLists.txt, we try to infer the active conda-environment automatically. If this fails, you can set it manually via -DCMAKE_PREFIX_PATH=/path/to/conda-env. To specify where to install the package, set:

• CMAKE_INSTALL_PREFIX: where to install the C++ headers
• PYTHON_MODULE_INSTALL_DIR: where to install the python package (set to site-packages of active conda env by default)

If you want to include z5 in another C++ project, note that the library itself is header-only. However, you need to link against the compression codecs that you use.

If you don't want to use conda for dependency management, the following dependencies are necessary:

• xtensor
• nlohman_json
• pybind11 (only for python bindings)
• xtensor_python (only for python bindings)

Examples / Usage

Python

The Python API is very similar to h5py. Some differences are:

• The constructor of File takes the boolean argument use_zarr_format, which determines whether the zarr or N5 format is used (if set to None, an attempt is made to automatically infer the format).
• There is no need to close File, hence the with block isn't necessary (but supported).
• Linked datasets (my_file['new_ds'] = my_file['old_ds']) are not supported
• Broadcasting is only supported for scalars in Dataset.__setitem__
• Arbitrary leading and trailing singleton dimensions can be added/removed/rolled through in Dataset.__setitem__
• Compatibility of exception handling is a goal, but not necessarily guaranteed.
• Because zarr/N5 are usually used with large data, z5py compresses blocks by default where h5py does not. The default compressors are
  • zarr: "blosc"
  • n5: "gzip"

Some examples:

import z5py
import numpy as np

# create a file and a dataset
f = z5py.File('array.zr', use_zarr_format=True)
ds = f.create_dataset('data', shape=(1000, 1000), chunks=(100, 100), dtype='float32')

# write array to a roi
x = np.random.random_sample(size=(500, 500)).astype('float32')
ds[:500, :500] = x

# broadcast a scalar to a roi
ds[500:, 500:] = 42.

# read array from a roi
y = ds[250:750, 250:750]

# create a group and create a dataset in the group
g = f.create_group('local_group')
g.create_dataset('local_data', shape=(100, 100), chunks=(10, 10), dtype='uint32')

# open dataset from group or file
ds_local1 = f['local_group/local_data']
ds_local2 = g['local_data']

# read and write attributes
attributes = ds.attrs
attributes['foo'] = 'bar'
baz = attributes['foo']

There are convenience functions to convert n5 and zarr files to and from hdf5 or tif. Additional data formats will follow.

# convert existing h5 file to n5
# this only works if h5py is available
from z5py.converter import convert_from_h5

h5_file = '/path/to/file.h5'
n5_file = '/path/to/file.n5'
h5_key = n5_key = 'data'
target_chunks = (64, 64, 64)
n_threads = 8

convert_from_h5(h5_file, n5_file,
                in_path_in_file=h5_key,
                out_path_in_file=n5_key,
                chunks=target_chunks,
                n_threads=n_threads,
                compression='gzip')

C++

Z5 aims to supports different storage implementations. The default is to use the filesystem, implementations to also supports AWS-S3 and Google Cloud Storage are work in progress. The API implements factory functions like createFile or createDataset in the factory header. These functions need to be called with the corresponding handle, like z5::filesystem::handle::File or z5::s3::handle::File in order to specify which backend to use.

The library is intended to be used with a multiarray, that holds data in memory. By default xtensor is used, see implementation. There also exists an interface for marray, see implementation. To interface with other multiarray implementation, reimplement readSubarray and writeSubarray. Pull requests for additional multiarray support are welcome.

Some examples:

#include "json.hpp"
#include "xtensor/xarray.hpp"

// factory functions to create files, groups and datasets
#include "z5/factory.hxx"
// handles for z5 filesystem objects
#include "z5/filesystem/handle.hxx"
// io for xtensor multi-arrays
#include "z5/multiarray/xtensor_access.hxx"
// attribute functionality
#include "z5/attributes.hxx"

int main() {

  // get handle to a File on the filesystem
  z5::filesystem::handle::File f("data.zr");
  // if you wanted to use a different backend, for example AWS, you
  // would need to use this instead:
  // z5::s3::handle::File f;

  // create the file in zarr format
  const bool createAsZarr = true;
  z5::createFile(f, createAsZarr);

  // create a new zarr dataset
  const std::string dsName = "data";
  std::vector<size_t> shape = { 1000, 1000, 1000 };
  std::vector<size_t> chunks = { 100, 100, 100 };
  auto ds = z5::createDataset(f, dsName, "float32", shape, chunks);

  // write array to roi
  z5::types::ShapeType offset1 = { 50, 100, 150 };
  xt::xarray<float>::shape_type shape1 = { 150, 200, 100 };
  xt::xarray<float> array1(shape1, 42.0);
  z5::multiarray::writeSubarray<float>(ds, array1, offset1.begin());

  // read array from roi (values that were not written before are filled with a fill-value)
  z5::types::ShapeType offset2 = { 100, 100, 100 };
  xt::xarray<float>::shape_type shape2 = { 300, 200, 75 };
  xt::xarray<float> array2(shape2);
  z5::multiarray::readSubarray<float>(ds, array2, offset2.begin());

  // get handle for the dataset
  const auto dsHandle = z5::filesystem::handle::Dataset(f, dsName);

  // read and write json attributes
  nlohmann::json attributesIn;
  attributesIn["bar"] = "foo";
  attributesIn["pi"] = 3.141593;
  z5::writeAttributes(dsHandle, attributesIn);

  nlohmann::json attributesOut;
  z5::readAttributes(dsHandle, attributesOut);
  
  return 0;
}

C

There are external efforts to implement a C-Api wrapper for z5. You can check it out here.

R

There exists a prototype by @gdkrmr to provide R bindings for z5. It is still in an early stage, but looks very promising.

Citation

If you use this library in your research, please cite it via the associated DOI:

@misc{pape_z5_2019,
  doi = {10.5281/ZENODO.3585752},
  url = {https://zenodo.org/record/3585752},
  author = {Pape,  Constantin},
  title = {constantinpape/z5},
  publisher = {Zenodo},
  year = {2019}
}

When to use this library?

This library implements the zarr and n5 data specification in C++ and Python. Use it, if you need access to these formats from these languages. Zarr / n5 have native implementations in Python / Java. If you only need access in the respective native language, it is recommended to use these implementations, which are more thoroughly tested.

Current Limitations / TODOs

• No thread / process synchronization -> writing to the same chunk in parallel will lead to undefined behavior.
• Supports only little endianness and C-order for the zarr format.

A note on axis ordering

Internally, n5 uses column-major (i.e. x, y, z) axis ordering, while z5 uses row-major (i.e. z, y, x). While this is mostly handled internally, it means that the metadata does not transfer 1 to 1, but needs to be reversed for most shapes. Concretely: