PyTables Tag

Anthony Scopatz, Assistant Professor at the University of South Carolina, HDF guest blogger "Python is great and its ecosystem for scientific computing is world class. HDF5 is amazing and is rightly the gold standard for persistence for scientific data. Many people use HDF5 from Python, and this number is only growing due to pandas’ HDFStore. However, using HDF5 from Python has at least one more knot than it needs to.  Let’s change that." Almost immediately when going to use HDF5 from Python you are faced with a choice between two fantastic packages with overlapping capabilities: h5py and PyTables.  h5py wraps the HDF5 API more closely using autogenerated Cython.  PyTables, while also wrapping HDF5, focuses more on a Table data structure and adds...

David Dotson, doctoral student, Center for Biological Physics, Arizona State University; HDF Guest Blogger

Recently I had the pleasure of meeting Anthony Scopatz for the first time at SciPy 2015, and we talked shop. I was interested in his opinions on MDSynthesis, a Python package our lab has designed to help manage the complexity of raw and derived data sets from molecular dynamics simulations, about which I was

Mohamad Chaarawi, The HDF Group

Second in a series: Parallel HDF5

NERSC’s Cray Sonexion system provides data storage for its Mendel scientific computing cluster.

In my previous blog post, I discussed the need for parallel I/O and a few paradigms for doing parallel I/O from applications. HDF5 is an I/O middleware library that supports (or will support in the near future) most of the I/O paradigms we talked about.

In this blog post I will discuss how to use HDF5 to implement some of the parallel I/O methods and some of the ongoing research to support new I/O paradigms. I will not discuss pros and cons of each method since we discussed those in the previous blog post.

But before getting on with how HDF5 supports parallel I/O, let’s address a question that comes up often, which is,

“Why do I need Parallel HDF5 when the MPI standard already provides an interface for doing I/O?”

John Readey, The HDF Group

Editor’s Note: Since this post was written in 2015, The HDF Group has developed HDF Cloud, a new product that addresses the challenges of adapting large scale array-based computing to the cloud and object storage while intelligently handling the full data management life cycle. If this is something that interests you, we’d love to hear from you.

 

Interestingly enough, in addition to being known as the place to go for BBQ and live music, Austin, Texas is a major hub of Python development.  Each year, Austin is host to the annual confab of Python developers known as the SciPy Conference.  Enthought, a local Python-based company, was the major sponsor of the conference and did a great job of organizing the event.  By the way, Enthought is active in Python-based training, and I thought the tutorial sessions I attended were very well done.  If you would like to get some expert training on various aspects of Python, check out their offerings.

As a first-time conference attendee, I found attending the talks and tutorials very informative and entertaining.  The conference’s focus is the set of packages that form the core of the SciPy ecosystem (SciPy, iPython, NumPy, Pandas, Matplotlib, and SymPy) and the ever-increasing number of specialized packages around this core.     

* With Python’s Help

Gerd Heber, The HDF Group

Before the recent release of our PyHexad Excel add-in for HDF5[1], the title might have sounded like the slogan of a global coffee and baked goods chain. That was then. Today, it is an expression of hope for the spreadsheet users who run this country and who either felt neglected by the HDF5 community or who might suffer from a medical condition known as data-bulging workbook stress disorder. In this article, I would like to give you a quick overview of the novel PyHexad therapy and invite you to get involved (after consulting with your doctor).

To access the data in HDF5 files from Excel is a frontrunner among the all-time TOP 10 most frequently asked for features. A spreadsheet tool might be a convenient window into, and user interface for, certain data stored in HDF5 files. Such a tool could help overcome Excel storage and performance limitations, and allow data to be freely “shuttled” between worksheets and HDF5 data containers. PyHexad ([4],[5],[6],[7]) is an attempt to further explore this concept.