[PYTHON MATRIX-SIG] Type Coercion

[Jim Fulton]

I agree.

On Feb 7,  1:44pm, Paul. Dubois wrote:
> Subject: Re: [PYTHON MATRIX-SIG] Type Coercion
> I hold these truths to be self-evident, your argument notwithstanding:
> a. A PyFloat is a C double
>    The fact that some of the Python API lets you convert one to a C
> float easily doesn't change this.
>
> b. It is a GOOD THING that you can count on the precision of a PyFloat.
>     Writing portable code between platforms is easy with Python, since
> even on 64 bit machines C doubles are implemented as 64 bit floats. (I
> know that isn't for certain but as a practical truth, it is true).
>
> c. It is a GOOD THING not to lose precision.
> d. It is a GOOD THING not to get an exception.
>
> The original design functioned perfectly, as far as I was concerned.
>
> The change has been made because of a desire not to GAIN precision. This
> is only a concern if you have a concern about space, really. (The time
> penalty on most workstations for double vs. float is nowhere near 2).
> So this means you must have a humongous amount of data. In which case,
> Python is a lousy choice anyway, since it will make lots of temporaries
> in the act of expression evaluation, while a nice Fortran routine won't.
>
> In short, I'd argue that it is not a good idea to pervert the matrix
> class in Python to satisfy a marginal need.
>
> But the alternative is unclean. The minute you find yourself proposing
> that elements of an array are zero-d arrays you should realize the slope
> has gotten too slippery.
>
> I return to my previous horror example:
>
> (1./3.) * a_float[i]
> vs.
> ((1./3.) * a_float)[i]
>
> Can you really stand for those to be different? Vectorize a loop and
> change the answer?
> --
> Paul F. Dubois, L-472				(510)-422-5426
> Lawrence Livermore National Laboratory		FAX (510)-423-9969
> Livermore, CA 94550				dubois1@llnl.gov
> Consulting: PaulDubois@aol.com
>
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>-- End of excerpt from Paul. Dubois



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