New docs for reporters and other modules
Added new docs for data and data_analysis modules
Initial move for package restructuring
Docstrings for data files
Added all todos to todo.rst
Code review, docs, and tests for nbd_analysis

- Wrapped all lines to 80 chars
- Deleted extra whitespace
- Added tests (mostly simple smoke tests) of all functions
- Changed plotting to not display unless requested
- Moved comments section into top-level docstring
Refactored MCMC files into mcmc module

And added appropriate docs. Still to do: see if the fitting routines for
the "phenomenological" models can be unified to use the same fitting
script (and reports?) as the mechanistic ones.

Also, the fitting routines need tests, smoke tests at the very least.
Docs and refactoring of submit_jobs

Also eliminated the very unnecessary script nbd_mcmc_pysb_lsf.py.
Moved some stuff to /old
Moved initial value file to mcmc
Explicit namespacing for all numpy/scipy funcs
Deleted useless file test.py
Begin rewrite of grid_analysis using pandas

Started with a reimplementation of the plot_timecourse function. Still
can't figure out why I can't take a cross section of the data using the
liposome axis, so right now it just works with a single lipo
concentration.

Added a test to make sure the plot_timecourses works, but nose isn't
working with the current version of numpy (1.7) which I just upgraded
to. Arggh.
Added testing script
Substantial rewrite of grid_analysis

Created a class that is initialized with the appropriate data set,
and uses dataframes to handle cross-sections of the data. Re-implemented
the plot_timecourses function and the get_rate_regression function.
Added smoke tests for both.
Rewrote pore calculation function

Pore calculation now uses dataframes and stores the pore result in a
field of the class. The plot_timecourses function takes an argument to
determine whether the raw data or the plot data should be plotted.
Rewrote grid_analysis to use row-based dataframe

Got indexing to work for fixed axis as well.
Made plot_timecourses a package function, not a method

This way it can take any timecourse dataframe as an argument, rather
than having to specified which type of data to use.
Calculate fit parameters and store in dataframe

Required some fiddling with the pandas API.

I also moved some code around to follow a better logical order, though
that kills the diff.
Rewrote plot_dose_response as plot_titration to take dataframe