I'd caution against using abs, as abs(x) is not differentiable around 0 and could cause a gradient descent solver to get stuck/confused. x**2 on the other hand is fully differentiable, but requires you to take the sqrt of the parameters after fitting. ------------------------------ On Thu, May 17, 2012 9:51 PM NZST federico vaggi wrote:

Afaik,

there was a big discussion about this a while ago, and the short answer is, currently there is no 'automatic' way to do it. However, in your case, it's pretty easy.

Simply define:

def func (x, a,b, r): a = abs(a) b = abs(b) r = abs(r) return r + a*np.power(x,-b)

And that will do the trick. If you need to more complex boundaries, you can simply use a combination of period functions with a given amplitude or what have you. Alternatively, there are *a lot* of optimization libraries available for Python that are not a part of scipy that offer the possibility to specify boundaries.

For example:

http://newville.github.com/lmfit-py/ http://ab-initio.mit.edu/wiki/index.php/NLopt_Python_Reference

Federico

Date: Wed, 16 May 2012 18:20:27 +0200

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From: servant mathieu <servant.mathieu@gmail.com> Subject: [SciPy-User] is it possible to constrain the scipy.optimize.curve_fit function? To: scipy-user@scipy.org Message-ID: <CALnu5bM+c9L7taG_CBHdJhw7xpe5amHSVBRUEX7pa7gnRN+-7Q@mail.gmail.com

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Dear scipy users,

I'm trying to fit to data a power law of the form :

def func (x, a,b, r):

return r + a*np.power(x,-b)

I would like to constrain the curve_fit routine to only allow positive parameter values. How is it possible to do so?

Kind regards,

Mathieu