[SciPy-user] Again on Double Precision
Sat Sep 1 14:16:57 CDT 2007
Lorenzo Isella wrote:
> Dear All,
> I know this is related to a thread which had been going on for a while,
> but I am about to publish some results of a simulation making use of
> integrate.odeint and I would like to be sure I have not misunderstood
> anything fundamental.
> I was using all my arrays and functions to be dealt with by
> integrate.odeint without ever bothering too much about the details, i.e.
> I never specified explicitly the "type" of arrays I was using..
> I assumed that integrate.odeint was a thin layer to some Fortran
> routine and it would automatically convert to Fortran double-precision
> all the due quantities.
> Is this what is happening really? I actually have no reason to think
> that my results are somehow inaccurate, but a you never know.
> I was getting worried after looking at:
> Apologies if this is too basic for the forum, but in Fortran I always
> used double precision as a standard and in R all the numbers/arrays
> are stored as double precision objects and you do not have to worry
> (practically the only languages I use apart from Python). In the end of
> the day, double precision is a specific case of floating point numbers
> and I wonder if, when working with the default floating arrays in SciPy,
> I attain the same accuracy I would get with double-precision Fortran
The default floating point type in Python, numpy, and scipy is double-precision.
Unless if you have explicitly constructed arrays using float32, your
calculations will be done in double-precision.
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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