====== Visualization ======

===== Synopsis =====

Visualization is important and makes our data human readable. It also gives us a quick status check on what is going on with the data and could help us spot issues before they become too large. 


===== Current Status =====

We currently have very little visualization - we have a python-based script that can run through CSV files and map them. We also have some GNU Plot based scripts that are still a little clunky to use. We have two approaches that are being considered:

==== Image Based Approach ====

The image based approach means rendering a .png or other image of different sample sets. This approach has the advantage of being very computationally light - rendering work is only done once a day (or however often it needs to be done). 



==== Javascript Based Approach ====

This approach is based around building an API surrounding the database, specifically for data access. This approach might be more interactive and fun, but may be a little more heavy on the database access.  

===== Resources ======


  * [[ http://d3js.org/ | D3.js ]] (general visualization lib for JS)
    * [[ https://www.codecademy.com/courses/web-beginner-en-kcP9b/0/1 | Codeacademy D3.js tutorial]]
  * [[ http://square.github.io/cubism/  | cubism.js]] (library for visualizing time series data)
  * [[weatherbox:visualization:tutorials:Installing Matplotlib]]
  * https://github.com/GraphAlchemist/Alchemy
  * https://github.com/plotly/plotly.js
===== Examples =====

  * [[ http://www.nytimes.com/interactive/2013/03/29/sports/baseball/Strikeouts-Are-Still-Soaring.html?ref=baseball&_r=0 | NY Times baseball strikeouts  ]]