On 21.02.2008, at 18:40, Alan G Isaac wrote: >>>> x = N.array([1,2],dtype='float') >>>> x0 = x[0] >>>> type(x0) > <type 'numpy.float64'> >>>> > > So a "float64 value" is whatever a numpy.float64 is, > and that is part of what is under discussion.
numpy.float64 is a very recent invention. During the first decade of numerical arrays in Python (Numeric), typ(x0) was the standard Python float type. And even today, what you put into an array (via the array constructor or by assignment) is Python scalar objects, mostly int, float, and complex. The reason for defining special types for the scalar elements of arrays was efficiency considerations. Python has only a single float type, there is no distinction between single and double precision. Extracting an array element would thus always yield a double precision float, and adding it to a single-precision array would yield a double precision result, meaning that it was extremely difficult to maintain single-precision storage across array arithmetic. For huge arrays, that was a serious problem. However, the intention was always to have numpy's scalar objects behave as similarly as possible to Python scalars. Ideally, application code should not see a difference at all. This was largely successful, with the notable exception of the coercion problem that I mentioned a few mails ago. Konrad. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
