Forcing a single type reduces ambiguity, though we also miss out on
potential performance and space gains in the future for certain types of
data. But, I really like minimizing ambiguity.


On Fri, Feb 7, 2014 at 9:05 AM, Greg Caporaso <notifications@github.com>wrote:

> In bipy/core/distance.py:
>
> >          num_sids = len(sample_ids)
> >
> >          if 0 in data.shape:
> >              raise DistanceMatrixError("Data must be at least 1x1 in size.")
> > +        elif len(data.shape) != 2:
> > +            raise DistanceMatrixError("Data must have exactly two dimensions.")
> > +        elif data.shape[0] != data.shape[1]:
> > +            raise DistanceMatrixError("Data must be square (i.e., have the "
> > +                                      "same number of rows and columns).")
> > +        elif data.dtype != np.double:
> > +            raise DistanceMatrixError("Data must contain only floating point "
>
> ok, makes sense. i don't think we should focus on this now as this will be
> fine for all current use cases.
>
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>