This spring the NumFOCUS Board of Directors awarded targeted small development grants to applicants from or approved by our sponsored and affiliated projects. In the wake of a successful 2016 end-of-year fundraising drive, NumFOCUS wanted to direct the donated funds to our projects in a way that would have impact and visibility to donors and the wider community. Each grant will help the recipient project to produce a clear outcome, achievable within 2017.

Just over $13,000 was awarded in grants to support the following projects:

MD Analysis

Widening platform availability for MDAnalysis: Full Python 3 Support

This project aims to include full Python 3 support for MDAnalysis; at the moment, only Python 2.7 is fully supported. Although about 80% of code passes unit tests in Python 3, we urgently need to close the remaining 20% gap in order to support our user base and to safeguard the long term viability of the project. MDAnalysis started almost 10 years ago when Python was around version 2.4 and interfacing with existing C code was mostly done with writing C-wrappers that directly used CPython. This legacy code has hampered a speedy full transition to Python 3 and consequently MDAnalysis lags behind the rest of the scientific Python community in fully supporting Python 3.

The grant will support a final focused drive to complete support of Python 3 while also remaining compatible with Python 2.7 for as long as it is officially supported (2020). To do this, two core developers (Richard Gowers (RG) & Tyler Reddy (TR)) will visit Arizona State University to work on the issue full-time for 2 weeks. The output of this project will be merged into the development branch and will be included in the existing Travis CI build matrix. This will then be one of the key features to be included in the upcoming 0.17 release which is targeted for September 2017 (to coincide with the inclusion of the anticipated outputs from Google Summer of Code 2017 projects). For MDAnalysis it is vital to fully support Python 3 in order to maintain and grow its user and developer base. The work supported by the grant will put MDAnalysis on track with the rest of the scientific Python community, increase package interoperability, and promote the overall move towards Python 3.

 

PyTables

h5py backend for PyTables

The goal is to define a new way to access I/O that would allow a new version of PyTables to use different backends. The main priority is for interfacing h5py so as to allow HDF5 access through it. This way PyTables can leverage h5py to access the most advanced features of HDF5 while still delivering features like advanced table management, fast table queries and easy access to advanced Blosc meta-compressors.

The goal is to define a new way to access I/O that would allow a new version of PyTables (probably v4.x) to use different backends. As h5py is a great interface for HDF5, the main priority is for interfacing h5py so as to allow HDF5 access through it. This way PyTables can leverage h5py to access the most advanced features of HDF5 while still delivering features like advanced table management, fast table queries and easy access to advanced Blosc meta-compressors (and with it, to a wide array of codecs, like LZ4, Snappy and Zstandard). You can see a more detailed blog about our vision here. In fact, work has already started on that front: in August 2016 a handful of PyTables core developers gathered with the goal to start this precise task, and although they certainly made a lot of progress on the Table object (the fundamental one in PyTables), there is still quite a bit of work to do. This grant will allow PyTables to continue the job done till now and release an alpha release with the basic Table, CArray, EArray and VLarray objects working, plus hopefully get some traction for promptly releasing a stable version unifying the best of PyTables and h5py packages. The grant work is meant to address project 1) here.

With this approach, PyTables and h5py will be close to complementary instead of having overlapping functionalities. This overlapping leads to redundant effort for both core developers and community users of PyTables and h5py; moreover, there are two places where bugs could b