Adding a Seaborn interface

This pull request adds support for wrapping around a number of Seaborn plot types. It is a soft dependency providing convenient access to a number of powerful new View types:

* TimeSeries - Contains a number curves indexed by the same x-dimension. Plots error bars and confidence intervals among many customizable options.
* Bivariate - Plots a two-dimensional kernel density plot stored as an array of 2D coordinates (inherits from Scatter).
* Regression - Computes a regression using sns.regplot, providing numerous options to control the fit and style.
* Distribution - Visualizes the distribution of a 1D array via sns.distplot, with options to plot kernel density estimates, histograms and rug plots.
* SNSFrame - Inherits from DFrame therefore already implementing a number conversion methods, which
can be extended to the basic (non-Pandas based) Seaborn View types. Additionally it can be visualized in the form of high dimensional plots like correlation and interact plots or as regressions and linear models along some dimensions along the dframe data, which can be specified via optional x, x2, and y parameters.

A non-exhaustive list of all the plot types and styling options can be seen here [DataViews Seaborn Interface](http://philippjfr.com/work/notebooks/dataviews-seaborn-interface/)
Try something like this:

``style_opts = [el for key in dframe_options for el in dframe_options[key]]``