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by arpineh
4800 days ago
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The stuff you can do with current IPython notebook is already amazing and immensely useful, as the examples mentioned in the posts demonstrate. Browser is not a good place to manipulate gigabytes of data (like Python), but it is the easieast to use, most versatile rendering engine for visualizations. I keep envisioning a marriage of Light Table with IPython. If this post is any indication of Light Table's prowess, it could very well work with IPython backend: www.chris-granger.com/2012/05/21/the-future-is-specific/ I mourn the lack of language to build these editors. Schema languages only validate document's structure and content. Browser based editors either abstract editing away to wiki like syntax or hide it inside Word processor emulating widgets. Both come with a set of assumptions that makes them hard to customize to suit the needs of the data. It should be possible to create a declarative editing and visualization language (a Domain Specific Language) that would drive this browser/data structure server combination. You could describe the editing environment for one particular domain (like exploring and visualizing a statistical dataset). This should be data driven, ie. it builds the environment according to chosen dataset. Language should allow macros or modules in suitable languages for additional functionality, like complex validation, animations and transitions. You can export your view of the data, complete with your code and command history. Tools for data visualization and editing need to look like they are build for this data, for these particular visualizations. And they should be customizable, right there in the same environment so the link between the data and your knowledge of its domain stays in view. I think these tools hold the promise of becoming toolmakers tools, too. |
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I currently do most of my exploratory work (which is in turn most of my total work) in the IPython Notebook and it will be interesting to see what Light Table can do beyond what the notebook does (for starters, I imagine it will have a more refined approach to Javascript hooks for visualization and the like).
I will point out though that the only thing that gets posted to the browser in the IPython Notebook is an objects __repr__. So, unless you are looking at raw dumps of tens of gigabytes of data, you shouldn't have to worry about browser memory limitations. The IPython backend of the Notebook can handle just about anything that your standard python interpreter can handle. It could even be an EC2 instance or cluster resource (here is one example: http://www.nature.com/ismej/journal/v7/n3/full/ismej2012123a...).