HDFITS: porting the FITS data model to HDF5

TL;DR

HDFITS demonstrates that porting the FITS data model to HDF5 enables faster data access, better compression, and easier integration with existing astronomy software, promoting wider adoption of HDF5 in astronomy.

Contribution

The paper introduces a method to port the FITS data model to HDF5 and presents a conversion tool, enabling improved performance and compatibility for astronomy data storage.

Findings

HDFITS allows up to 100x faster data reading than FITS.

HDFITS achieves higher compression ratios and throughput.

HDFITS can be integrated with existing FITS tools with minimal changes.

Abstract

The FITS (Flexible Image Transport System) data format has been the de facto data format for astronomy-related data products since its inception in the late 1970s. While the FITS file format is widely supported, it lacks many of the features of more modern data serialization, such as the Hierarchical Data Format (HDF5). The HDF5 file format offers considerable advantages over FITS, such as improved I/O speed and compression, but has yet to gain widespread adoption within astronomy. One of the major holdbacks is that HDF5 is not well supported by data reduction software packages and image viewers. Here, we present a comparison of FITS and HDF5 as a format for storage of astronomy datasets. We show that the underlying data model of FITS can be ported to HDF5 in a straightforward manner, and that by doing so the advantages of the HDF5 file format can be leveraged immediately. In addition, we present a software tool, fits2hdf, for converting between FITS and a new `HDFITS' format, where data are stored in HDF5 in a FITS-like manner. We show that HDFITS allows faster reading of data (up to 100x of FITS in some use cases), and improved compression (higher compression ratios and higher throughput). Finally, we show that by only changing the import lines in Python-based FITS utilities, HDFITS formatted data can be presented transparently as an in-memory FITS equivalent.