WIP/BUG: fixed ProbPlot class and tests to work with general distributions

Unsurprisingly, most of what I did to get this point was to simplify things.

~~[Here's a notebook](http://nbviewer.ipython.org/gist/phobson/9233257)~~ [New Notebook](http://nbviewer.ipython.org/gist/phobson/b3bf593a3b6bb631b921) showing how things are working so far. It shows off creating `ProbPlot` objects with three different distributions:
 1. Lognormal
 1. beta
 1. t

It also creates the object three different ways:
 1. Simple: provide data, unfrozen distribution, minimal distribution parameters (`distargs`)
 1. More specific: provide data, frozen distribution, minimal distribution parameters (`distargs`)
 1. "Automatic": provide data, unfrozen distribution, tell the class to fit the distribution

Not all the plots are perfect. But we're getting close.

 - [ ] Add option to use `ax.step` instead of `ax.plot`
 - [x] confirm that `distargs` are not needed with a frozen distribution
 - [x] look into the "binary" nature of `t` probplots when `fit=False`
 - [ ] beef up the testing and examples for the `qqlines`

[Updated notebook](http://nbviewer.ipython.org/gist/phobson/b3bf593a3b6bb631b921)
The docstring needs to be updated to reflect the changes either way. Yeah, this was in 0.5.0, so I think we need to stick with the old signature. You can add a deprecation warning if you want and do some magic to check if loc and scale are provided in distargs or just leave loc and scale as separate keywords that attach to private variables and ditch the properties. I haven't followed all the logic closely yet.