Modifying the value of a float-like object

[Eric.Le.Bigot at spectro.jussieu.fr]

It looks like Dan found what is in effect a mutable float
(numpy.array).

Now, with respect to the initial problem of having mutable floats that
also contain an uncertainty attribute, I'd like to note that
numpy.ndarray can be subclassed: it now looks possible to create a
mutable float class that also contains an uncertainty attribute!

So, I'll see how/whether this can be implemented without pain...  I'll
leave a message here when I've got news!

Thanks again everybody for helping me out!

On Apr 15, 10:44 am, Dan Goodman <dg.gm... at thesamovar.net> wrote:
> Eric.Le.Bi... at spectro.jussieu.fr wrote:
> > Hello,
>
> > Is there a way to easily build an object that behaves exactly like a
> > float, but whose value can be changed?  The goal is to maintain a list
> > [x, y,…] of these float-like objects, and to modify their value on the
> > fly (with something like x.value = 3.14) so that any expression like "x
> > +y" uses the new value.
>
> Hi Eric,
>
> Numpy's array object can do something like what you want:
>
> In [27]: x=array(0.0)
>
> In [28]: print x, sin(x)
> 0.0 0.0
>
> In [29]: x.itemset(pi/2)
>
> In [30]: print x, sin(x)
> 1.57079632679 1.0
>
> Not sure if this a recommended way of using array or not, but it seems
> to work. The problem is that any calculation you do with such an object
> will result in a float, and not another numpy array (although inplace
> operations work):
>
> In [40]: print (x*2).__class__
> <type 'numpy.float64'>
>
> In [41]: x *= 2
>
> In [42]: print x.__class__
> <type 'numpy.ndarray'>
>
> So it's not a perfect solution, but it might be OK for what you need.
>
> Dan