Introduction

XRADIO is an open-source Python package that leverages xarray to provide an interface for radio astronomy data. It provides for each dataset type:

  • Documentation and checkers (for documentation and reference for other software packages)

  • Converters from existing storage formats (such as measurement sets v2)

  • Convenience functions for loading, storing and navigating data

  • Guides and tutorials

Following Xarray terminology, data in XRADIO is organized into:

This means that XRADIO enables:

  • Usage of any storage back-end that is compatible with xarray (such as zarr and all of its backends)

  • Flexible indexing and selection as explained in the xarray indexing and selection guide.

  • Native support for lazy loading and distribution using dask.

Dataset types

XRADIO is actively developing support for various types of radio astronomy data:

Dataset type

Version

Zarr Extension

Description

Status

Measurement Set (v4)

4.0.0

.ps.zarr

Interferometer data (Visibilities) and Single Dish data (Spectrum)

Released

Sky and Aperture Images

0.0.0

?

Representation of celestial objects and antenna patterns

Schema design in progress

Calibration Data

0.0.0

?

Information for instrument calibration

Schema design in progress

Aperture Models

–

–

Antenna dish models using Zernike polynomials

Work scheduled

Simulation Component Lists

–

–

Data for simulating radio astronomy observations

Work scheduled

Additional data types will be added based on community needs and contributions.

Schema Versioning

Each schema in XRADIO follows semantic versioning (MAJOR.MINOR.PATCH):

  • MAJOR: Changes to existing dimensions, coordinates, data variables, attributes, or measures. (This will not occur without wider community consultation.)

  • MINOR: Addition of new datasets. (Backward compatible)

  • PATCH: Addition of new coordinates, data variables, attributes, or measures to existing datasets. (Backward compatible)

For example:

  • v4.0.0 to v5.0.0: Major change in the Measurement Set structure

  • v4.0.0 to v4.1.0: Addition of a new dataset type

  • v4.0.0 to v4.0.1: Addition of new attributes to an existing dataset

The Measurement Set schema starts at v4.0.0, building upon the work of Measurement Set V2 and Measurement Set v3 (which was never fully implemented).

An XRADIO release will be tied to specific versions of each available schema. All generated data will include both the XRADIO version and the schema version in the attribute section.

Installation

XRADIO can be installed in virtual environments via pip. It is recommended to use the conda environment manager from miniforge to create a clean, self-contained runtime where XRADIO and all its dependencies can be installed, for example:

conda create --name xradio python=3.13 --no-default-packages
conda activate xradio

On macOS, if one wants to use the functions to convert MSv2=>MSv4, it is required to pre-install python-casacore. That can be done using conda install -c conda-forge python-casacore. XRADIO can now be installed using:

pip install xradio

This installs only the minimal dependencies for XRADIO, which allow you to use the schema checker and export schemas to JSON. Note that if only the minimal dependencies are installed, the functionality to open data stored using zarr and to convert MSv2 to MSv4 will not be available.

To install the zarr backend use:

pip install "xradio[zarr]"

This allows for opening data stored using zarr.

To install the zarr backend and the interactive components (JupyterLab) use:

pip install "xradio[interactive]"

To install the casacore backend along with the zarr backend which enables conversion from MSv2 to MSv4 use (this only works for Linux):

pip install "xradio[casacore]"

To installs all the needed packages to run the unit tests:

pip install "xradio[test]"

This also installs the zarr backend and the casacore backend on Linux. Note the tests will fail on MacOS if python-casacore is not installed separately using conda.

Multiple-dependencies can be installed using:

pip install "xradio[interactive,casacore,test]"

To install a more complete set of dependencies:

pip install "xradio[all]"

This will include the dependencies required to run the interactive Jupyter notebooks, run tests, build documentation, and python-casacore to enable MSv2=>MSv4 functionality on Linux.

Instruction of how to setup a developer environment can be found at Development.

Instruction of how to setup a developer environment using casatools instead of python-casacore can be found at casatools I/O backend guide.