Updated the text-based examples.
Removed the largely outdated Python examples.  These are up-to-date for the most recent dev branch in another repository.
Slight change to shell matrix for newest PETSc.
Updated README.
Merge branch 'dev'
Cleanup from dev merge
Added MIT license.
Major overhaul to CMake builds system.  A lots of repeated code to generate each library was replaced by an equivalent function.  There is probably even more room to consolidate.
Initial placement of export macros for port to Windows.
Initial changes for DLL support.

The CMake build system has been modified to produce library-specific
headers with various macros supporting DLL creation.  The major
difference between a DLL on Windows and a .so on Linux (at least when
developing) is ensuring that appropriate code is "exported" for client
use.  This is done implicitly in .so creation, and the code actually
needs to be modified for doing this exporting.  This is implemented
using architecture-dependent macros facilitated quite a bit by built-in
CMake macros.
Modified CMake build for DLL support.  Now there are several macros that help with the exporting.  To be tested.
Updates in support of Windows installer.

The CMake build has been further adjusted.  Additionally, CPack is used
to create a NSIS-based Windows installer.
CMake does not need to be 2.8.9, as we include the modified macros.
Changes for compiling in Xcode .6 along with eliminating several warnings related to unused variables, style issues, etc.
Updates for OS X and general cleanup.  Tarball distribution looks like it's working, but I'll need to try on a clean system to test.
Modified library cmake macro to include the export headers in the install.
Added finder for Git to get SHA1, and added that to header for printing out.
Modified scatter source to handle adjoint group bounds
Added periodic boundary condition for SN
Added SWIG helper function to give contiguous memory views in C++ vectors as Numpy arrays.  This should facilitate things like plotting and so forth for speed.  Whenever np.asarray is used, use vec_asarray for large data.