Add test/fix for raising error on bad input types to dshape()
Throw some NotImplementedErrors, handle 'type X = ...' case better
Add more simple datashape tests, tighten up the accepted datashape structure
Separate the dimensions/measure in NumPy dshape interop
Switch the parser test to just call the parser, not the dshape constructor
Some more cleanup of NumPy <-> dshape interop
Reverse the order of the DataShape __mul__/__rmul__

The way it looked is kind of a C++-style operator overloading
hack to allow integers for datashapes like "3 * 4 * int32" by
writing them as "int32 * 4 * 3". I think we don't want to do this.
Adjust test so it doesn't hit the new checks in datashape construction
Switch assert_ to assertEqual so the test results show the failing values
Merge pull request #39 from mwiebe/blaze_dshape_parser

Blaze dshape parser error checking