On Fri, Apr 2, 2010 at 3:30 PM, G B <[email protected]> wrote:
> That did it.  I found the section in the tutorial explaining the
> different interpretations of functions more fully, and while I don't
> quite have my head around it, I think I understand the problem at a
> basic level to be that my f(x) is a symbolic expression, but
> get_random_element() is a Python function, and there's some odd
> interaction under the hood.
>
> Is this something that will ever be able to be worked around
> (excepting things like trying to symbolically integrate a Python
> function)?  Is Sage doomed to have these similar but not quite
> compatible entities interacting in strangely subtle ways, or is there

Yep.  Except when you understand them, they do not seem similar or strange.

William

> the hope that as the package matures everything will begin to play
> more nicely?



>
> My concern is that, as a newbie, I'm carefully double checking each
> step of my calculation because I know that I don't know what I'm
> doing-- but eventually I'm liable to gain confidence, lose track of
> these various function types, and not realize that the results I'm
> getting are incorrect.
>
>
> On Mar 30, 7:04 pm, William Stein <[email protected]> wrote:
>> On Tue, Mar 30, 2010 at 7:01 PM, G B <[email protected]> wrote:
>> > Apologies for being dense, but I'm missing something.  All three forms
>> > ( f(x), def f(x) and f=lambda x: ) are giving the same results.
>>
>> > Trying:
>> > ---------
>> > var('x')
>> > T=RealDistribution('gaussian',1)
>> > def f(x):
>> >    return sin(x)+ T.get_random_element()
>> > plot(f(x),(x,0,2*pi))
>> > ---------
>>
>> > and
>> > --------
>> > var('x')
>> > T=RealDistribution('gaussian',1)
>> > f=lambda x: sin(x)+ T.get_random_element()
>> > plot(f(x),(x,0,2*pi))
>> > ---------
>>
>> > all give me a clean sine with a random offset, rather than sine
>> > +noise...
>>
>> Use
>>
>>   plot(f, (0,2*pi))
>>
>> William
>>
>>
>>
>>
>>
>>
>>
>> > On Mar 30, 2:30 pm, William Stein <[email protected]> wrote:
>> >> On Tue, Mar 30, 2010 at 2:21 PM, G B <[email protected]> wrote:
>> >> > Hi--
>>
>> >> > I'm trying to figure out how to use RealDistributions to model noise.
>> >> > For example, I would like to model a signal+noise and tried using this
>> >> > construct:
>>
>> >> > T=RealDistribution('gaussian',1)
>> >> > f(x)=sin(x)+T.get_random_element()
>> >> > plot(f(x),(x,0,2*pi))
>>
>> >> > Unfortunately, that only calls get_random_element() once, at the
>> >> > definition of f(x) and results in a perfect sinusoid offset by a
>> >> > random value.
>>
>> >> def f(x):
>> >>      return sin(x) + T.get_random_element()
>>
>> >> or
>>
>> >>  f = lambda x : sin(x) + T.get_random_element()
>>
>> >> William
>>
>> >> > How do I write this so that get_random_element() is called each time
>> >> > f(x) is evaluated?  I would like to see a noisy sinusoid.
>>
>> >> > Thanks--
>> >> >  Greg
>>
>> >> > --
>> >> > To post to this group, send email to [email protected]
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>>
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>> >> > the words "REMOVE ME" as the subject.
>>
>> >> --
>> >> William Stein
>> >> Associate Professor of Mathematics
>> >> University of Washingtonhttp://wstein.org
>>
>> > --
>> > To post to this group, send email to [email protected]
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>>
>> --
>> William Stein
>> Associate Professor of Mathematics
>> University of Washingtonhttp://wstein.org
>
> --
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>



-- 
William Stein
Associate Professor of Mathematics
University of Washington
http://wstein.org

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