ENH: Efficient interpolation on regular grids in arbitrary dimensions

Based on Johannes Buchner's *regulargrid* package (see https://github.com/JohannesBuchner/regulargrid), I implemented a new class `RegularGridInterpolator` for efficient linear and nearest-neighbour interpolation on regular (possibly unevenly spaced) grids in arbitrary dimensions.

I believe this is a significant addition to the `scipy.interpolate` package because the existing ND-interpolators perform a triangulation of the input points with becomes unfeasible with medium (~8) dimensions.  There, by taking advantage of the regular grid structure, the interpolation can be sped up quite a bit.

Another advantage of this class is that it accepts anthing that can be appropriately indexed as *values* parameter, meaning that it is possible to use this interpolation object for interpolation of disk-based data sets (netCDF, pytables, ...).

I asked for permission about inclusion in Scipy; the (positive) answer is here: https://github.com/JohannesBuchner/regulargrid/issues/2.