Problem combining Scientific (leastSquaresFit) and scipy (odeint)

[Colin W.]

Harold Fellermann wrote:
> Hi,
> 
> I need to perform leastSquaresFit of a model that is given by a
> differential equation for which there seems to be no analytic
> solution. So, I am trying to solve the ODE numerically (using
> scipy.integrate.odeint) within the function I provide to
> leastSquaresFit as a model:
> 
> def func(L, t, a, k) :
> 	return -k * L * (1 - ( 1 - a*L**(-1./3) )**3.)
> 
> def model((k, L0, a), t) :
> 	solution = odeint( func, array(L0[0]), array([0,t]), args=(a,k) )
> 	return L0 - solution[1][0]
> 
> params, chisq = leastSquaresFit(model, params, data)
> 
> Unfortunately, this approach runs into an error (ValueError: shape
> mismatch: objects cannot be broadcast to a single shape) that seems to
> stem from the fact that leastSquaresFit is based on automatic
> derivation (DerivVar), and according to the manual "the function [that
> defines the model] may only use the mathematical functions known to
> the module FirstDerivatives".
> 
> What is a good solution or workaround to this problem which appears to
> be quite a standard situation to me?
> 
> Thanks for any help, harold.
You might consider using numpy.

Colin W.