Videos are coming out, and will all eventually be published at https://www.youtube.com/user/PyConAU/videos.
The first day consisted of "miniconfs", which are independently organised focused streams on specific topics. I attended the "Science and Data" miniconf. It is clear that this is a huge and growing component of the Python community. However, science and data still suffer from a lack of general Python community integration. The tools put in place do appear to be having a transformative effect on scientists who are adopting them (notable technologies include Ipython Notebook, scipy, numpy, and efforts such as Software Carpentry). However, general best practises around software design, team workflow, testing, version control and code review have not been so enthusiastically adopted. Going the other way, data-oriented techniques and self-measurement have not been widely adopted within open source.
On of the major "new" tools is "pandas", which provides extremely strong data management for row/column based data. This tool is a few years old, but is really coming into its own. It supports very strong indexing and data relation methods, and some basic statistical techniques for handling missing data and basic plots. More advanced techniques and plots can be achieved by using existing Python libraries for those purposes by fetching the pandas data structures as numpy arrays.
Saturday: Main Conference Day One
The main conference was opened by a keynote from Dr James Curran, who gave an inspiring presentation which discussed the new Australian national curriculum. This is to include coding from early years through to year ten as a standard part of the standard education given to all Australians. This is an amazing development for software and computing, and it looks likely the Python may have a strong tole to play in this.
I presented next on the topic of "Verification: Truth in Statistics". I can't give an unbiased review, but as a presenter, I felt comfortable with the quality of the presentation and I hope I gave the audience value.
I attended "Graphs, Networks and Python: The Power of Interconnection" by Lachlan Blackhall, which included an interesting presentation of applying the NetworkX library to a variety of network-based computing problems.
For those looking for a relevant introduction, "IPython parallel for distributed computing" by Nathan Faggian was a good overview.
"Record linkage: Join for real life" by Rhydwyn Mcguire gave an interesting discussion of techniques for identity matching, in this case for the purpose of cross-matching partially identified patients in the medical system to reduce errors and improve medical histories.
"The Quest for the Pocket-Sized Python" by Christopher Neugebauer was an informative refresh of my understanding on Python for developing mobile applications. Short version: still use Kivy.
Sunday: Main Conference Day Two
Monday and Tuesday: Developer SprintsOne of the other conference attendees (Nick Farrell) was aware of my experience in natural language generation, and suggested I help him to put together a system for providing automatic text descriptions of graphs. These text descriptions can be used by screen reader applications used by (among others) the visually impaired in order to access information not otherwise available to them.
Together with around eight other developers over the course of the next two days, I provided coordination and an initial design of a system which could do this. The approach taken is a combination of standard NLG design patterns (data transformation --> feature identification --> language realisation) and a selection of appropriate modern Python tools. We utilised "Jinja2", a web page templating language usually used for rendering dynamic web page components, for providing the language realisation. This had the distinct advantage of being a familiar technology to the developers present at the sprint, and provided a ready-to-go system for text generation. I believe this system has significant limitations around complexity which may become a problem later, however it was an excellent choice for getting the initial prototype built quickly.
You can find the code at https://github.com/tleeuwenburg/wordgraph and the documentation at https://wordgraph.readthedocs.org/en/latest/. Wordgraph is the initial working name chosen quickly during the sprints -- it may be that a more specific name should be chosen at some point. The documentation provides the acknowledgments for all the developers who volunteered their time over this period.
It was very exciting working so fast with an amazing group of co-contributors. We were able to complete a functional proof-of-concept in just two days, which was capable of providing English-language paragraph-length description of data sets produced by "graphite". This is a standard systems metric web application which produces time-series data. The wordgraph design is easily extensible to other formats and other kinds of description. It the system proved to be of wider use, there is a lot of room to grow. However, there is also a long way to go before the system could be said to be truly generally useful.