This week is the half-way point of this year’s Google Summer of Code. Let me report the progress during the term.

Progress on the front-end library

I am developing Nyaplot as the front-end library for my work.

One topic on Nyaplot is API design. That is a difficult problem since each plotting library has very different methods for creating plots. For example, matplotlib and Bokeh are both written in Python, but their demo code is written in very different styles. After discussing with my mentor, I decided to implement the function-based API.

As an exmaple of that API, here is a minimal working example for generating bar charts with Nyaplot:

1 2 3 4 require 'nyaplot' plot = Nyaplot :: Plot . new plot . add ( :bar , [ 'nya1' , 'nya2' , 'nya3' ] , [ 10 , 20 , 30 ] ) plot . show

If you want to change options (e.g., color or title), put the return value of Nyaplot::Plot.add into a variable and call its methods.

1 2 bar = plot . add ( :bar , [ 'nya1' , 'nya2' , 'nya3' ] , [ 10 , 20 , 30 ] ) bar . title ( "the number of cats" ) # name the bar chart

IRuby integration

Interaction with IRuby is a priority for Nyaplot. IRuby is a web-based Ruby lab notebook-like environment, based on IPython, which is also useful for fast prototyping.

The image at the top of this blog post is a hyperbolic spiral which I generated with Nyaplot in IRuby. Have a look at the notebook on nbviewer, an IRuby and IPython notebook hosting services.

Progress on the back-end library

Nyaplot uses Nyaplotjs as its back-end library. I spent a lot of time working to implement interactivity among multiple panes to Nyaplotjs. See an example here.

The input data source is a TSV file of 2,044 lines. Multiple-pane interactivity is especially important when visualizing such a large dataset.

Have a look at the radio buttons beside the Venn diagram. The left three panels decide which set to put into each of three circles (VENN1, VENN2, VENN3). The right panel decides which data in each area (overlapping area, non-overlapping area, and all) to use in the other two panes.

The gray box on the histogram decides the range of PNR values. If you select the range 0.3 to 0.7 with it, the right bar chart will reflect that and show how many of the selected data are in that range.

Nyaplotjs provides this interactivity using an event handler connected to a dataframe object. That allows us to handle unidirectional update method (e.g., histogram updates bar chart, but bar chart does not update histogram).

Conclusion

I finished the first half term of Google Summer of Code 2014, but I still have a lot of things to do. I would like to continue to add interactivity to both the front-end and back-end libraries.

I am developing those two libraries in separate repositories on GitHub. If you are interested, feel free to raise issues or send pull-requests, even during the GSoC term.