So, even if numpy-to-flex conversion catches wrongly [1] typed arrays, users still need to know which numpy dtypes to use for which flex constructors. Here is what I have determined:

I realized that it is possible that unqualified specifiers like "numpy.float", etc, may have different meanings on different systems. Therefore, I think a better (i.e. more explicit) mapping is

flex.bool : numpy.bool8
flex.int : numpy.int32
flex.long : numpy.int64
flex.float : numpy.float32
flex.double : numpy.float64