[SciPy-User] soft limiter function

[Athanasios Anastasiou]

" I'm modeling
amplifiers, nothing to do with audio"

This is so "wrong" in so many levels!!! :D

I am just joking about the "wrong" but the clipping characteristics of
instrument amplifiers, their "sound signature", is exactly why one would
like to produce such a model. See here for example:
http://en.m.wikipedia.org/wiki/Amplifier_modeling

Anyway, all the best for your project.
Athanasios Anastasiou
 On 10 Mar 2015 17:21, "Matthieu Brucher" <matthieu.brucher at gmail.com>
wrote:

> You can still modify the functions to have such derivative at the
> origin (this is often used to model amps in audio processing). Just
> use a scaling factor.
>
> Cheers,
>
> 2015-03-10 15:30 GMT+00:00 Neal Becker <ndbecker2 at gmail.com>:
> > Matt Newville wrote:
> >
> >> Neal,
> >>
> >>
> >> On Tue, Mar 10, 2015 at 9:10 AM, Neal Becker <ndbecker2 at gmail.com>
> wrote:
> >>
> >>> I'm looking for a parameterized set of functions, similar to logistic,
> >>> where
> >>> a parameter determines the 'sharpness' of the transition from the
> linear
> >>> region to the flat region.  I need to keep all the same scaling and
> >>> derivative near the origin - so like a family of logistic functions
> that
> >>> would overlay near the origin, but would become increasingly sharp
> >>> limiters
> >>> as the parameter was varied.  In the limit, would approach the ideal
> >>> limiter
> >>>
> >>>       x |x<1|
> >>> y = { 1 x > 1
> >>>      -1 x < -1
> >>>
> >>
> >> This might be too simplistic, but have you considered the "classic"
> >> step-like functions (here, going from 0 to 1, but not necessarily at
> >> x=+/-1):
> >>
> >>     arctan:     y(a) = 0.5 + arctan(a) / pi
> >>     error fcn:  y(a) = 0.5 * (1 + erf(a))
> >>     logistic:   y(a) = 1.0 - 1.0 /(1.0 + exp(a))
> >>
> >>  where a = (x-x0)/sigma ?     That gives you a knob (sigma) to control
> the
> >> sharpness of the step.
> >>
> >> --Matt
> >
> > Thanks, but I also need the derivative near the origin to be 1 - cannot
> > change the steepness near the origin
> >
> > --
> > Those who fail to understand recursion are doomed to repeat it
> >
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>
>
> --
> Information System Engineer, Ph.D.
> Blog: http://matt.eifelle.com
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
> Music band: http://liliejay.com/
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