ESIP Summer Meeting – HDF Workshop and Town Hall

Lindsay Powers, The HDF Group

Please join us to learn about new HDF tools, projects and perspectives.

The HDF Group will be hosting a one-day workshop at the upcoming Federation for Earth Science Information Partners (ESIP) Summer Meeting in Asilomar, CA on Tuesday, July 14th.

There will also be an HDF Town Hall meeting on Wednesday afternoon, July 15th.

Please join us for any and all of the events.  If you are unable to join us in person, you may participate through remote access. Remote access details will be made available through the ESIP meeting website. Questions? Contact Lindsay at lpowers@hdfgroup.org.

The agenda for the July 14 HDF Group workshop:  Continue reading

HDF5 as a zero-configuration, ad-hoc scientific database for Python

Andrew Collette, Research Scientist with IMPACT, HDF Guest Blogger

“…HDF5 is that rare product which excels in two fields: archiving and sharing data according to strict standardized conventions, and also ad-hoc, highly flexible and iterative use for local data analysis. For more information on using Python together with HDF5…”

Accelerator Lab at IMPACT image used with permission
IMPACT breaks the 100 km/s speed barrier in February, 2015.  The Dust Accelerator Lab detected their fastest dust grain to date. An iron grain with a charge of 0.2 fC and diameter of 30 nm was clocked at a speed of 107.6 km/s (or 240,694 mph).  Image used with permission from IMPACT.

An enormous amount of effort has gone into the HDF ecosystem over the past decade. Because of a concerted effort between The HDF Group, standards bodies, and analysis software vendors, HDF5 is one of the best technologies on the planet for sharing numerical data. Not only is the format itself platform-independent, but nearly every analysis platform in common use can read HDF5. This investment continues with tools like HDF Product Designer and the REST-based H5Serv project, for sharing data using the HDF5 object model over the Internet.

What I’d like to talk about today is something very different: the way that I and many others in the Python world use HDF5, not for widely-shared data but for data that may never even leave the local disk…   Continue reading