On Tue, Sep 22, 2009 at 12:57 AM, Neal Becker <ndbeck...@gmail.com> wrote: > David Cournapeau wrote: > >> On Mon, Sep 21, 2009 at 9:00 PM, Neal Becker <ndbeck...@gmail.com> wrote: >>> >>> numpy arrays of fpi should support all numeric operations. Also mixed >>> fpi/integer operations. >>> >>> I'm not sure how to go about implementing this. At first, I was thinking >>> to just subclass numpy array. But, I don't think this provides fpi >>> scalars, and their associated operations. >> >> Using dtype seems more straightforward. I would first try to see how >> far you could go using a pure python object as a dtype. For example >> (on python 2.6): >> >> from decimal import Decimal >> import numpy as np >> a = np.array([1, 2, 3], Decimal) >> b = np.array([2, 3, 4], Decimal) >> a + b >> >> works as expected. A lot of things won't work (e.g. most transcendent >> functions, which would require a specific implementation anyway), but >> arithmetic, etc... would work. >> >> Then, you could think about implementing the class in cython. If speed >> is an issue, then implementing your own dtype seems the way to go - I >> don't know exactly what kind of speed increase you could hope from >> going the object -> dtype, though. >> > > We don't want to create arrays of fixed-pt objects. That would be very > wasteful.
Maybe, but that would be a good way to prototype the thing. > What I have in mind is that integer_bits, frac_bits are > attributes of the entire arrays, not the individual elements. The array > elements are just plain integers. That's not really how numpy arrays are designed: type-specific info should be in the dtype, not the array class. As Robert mentioned, the recently added datetime dtype shows an example on how to do it. David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
