Work on matplotlib
This sprint will have three components. The first will be interacting with other community members and creating readers for new data formats into yt. The second will be to develop IPython widgets that integrate with yt tasks. Final, we will be extending existing yt functionality for z-buffering and solid-body volume rendering.
Parallel and asynchronous computing in python is crippled by pickle's poor object serialization. Dill, a more robust serialization package, strives to serialize all of python. Dill has been used to enable state persistence and recovery, and the coordination of distributed parallel calculations. Help us enable your favorite package leverage dill. https://github.com/uqfoundation/dill
This sprint will be a chance for new contributors to join the project, interact with core Astropy developers, and start tackling easy issues. In addition, more experienced Astropy developers will be able to use this opportunity to discuss and work in person on challenging issues and features. Development will center on both the astropy core package, as well as existing and new affiliated packages.
This sprint will bring together a number of core PySAL developers to focus on key issues for the 1.8 release cycle. It also offers a chance for interested developers to explore and get involved in the project.
Let's make all our tools work better together! Do you want to figure out a open and reproducible workflow for your research? Not sure how to integrate workflow and authoring? Do you want to contribute to OSS in the name of open science?
Ask questions, make suggestions or hack on links between tools with the authors of Dexy and the Open Science Framework. All levels of Python experience welcome.
Blaze (blaze.pydata.org) is an expressive, compact set of abstractions for composing computations over large amounts of semi-structured data. Recently, Blaze has gone through a significant refactoring. This sprint will focus on computations involving Apache Spark, as well as improvements to the general "expr" and "compute" layers for symbolical computation.
The primary goal is to help people create examples that could be included in the interactive gallery at http://bokeh.pydata.org. Especially sought are examples that show Bokeh used with other SciPy packages, that demonstrate a novel visualization, or that tackle a compelling real-world dataset. Of course, anyone interested in contributing bug fixes, features, docs, or tests will also be welcomed!
Often at the end of the conference, I learn quite a few new things from tutorials and talks, but struggle getting started once I leave the conference.
For those of us new to Scipy stack, I would like to propose a Data Hackathon on Friday as a sprint to sharpen our Scipy chops. The goal is to help each other out, get the baby steps out of the way and start running.
A lot of the IPython team, including Fernando, Brian and Min will be sprinting and helping others to sprint on various aspects of the IPython project in general and the notebook in particular including documentation, and new features. More details and ideas of things to sprint on at https://github.com/ipython/ipython/wiki/Sprints:-SciPy2014-sprint-ideas