[SciPy-User] optimize.leastsq and improper input parameters

[ms]

Hi,

I am stuck with optimize.leastsq.
I am writing a quite complicated code that fits data to two variations 
of the same function, each of those can be with or without some 
parameters fixed. The concept is:
- write the two variations
- have a list of what parameters will be fixed and which not
- use reasonable starting points of the non-fixed params to fit, and 
keep the fixed params as fixed within the function
- then have a generalized routine that has as input the function to 
actually fit, among other things
- apply leastsq on that function

What comes out is similar, in structure, to this simplified example:
-----
import scipy as sp
import scipy.optimize as opt
import numpy as np


#Initial data
x = np.arange(0,10,1)
y = [i**2 for i in x]

#Three nice functions to fit
def xexponent1(param,i):
     exp = param[0]
     return i**exp

def xexponent2(param, i):
     exp_a=param[0]
     exp_b=param[1]
     return i**(exp_a) + i**(exp_b)

def line(param,i):
     A = param[0]
     b = param[1]
     return i*b + A


def f_to_minimize(pars,args):
     #Generalized function we use to minimize the fit
     #calculates function and squared residuals
     function,x,y = args[0],args[1],args[2]
     y_estimate = [function(pars,xi) for xi in x]

     #calculate squared residuals
     resid = [(i-j)**2 for i,j in zip(y,y_estimate)]
     return sum(resid)

def minimize(x,y,func,p0):
     #calls minimization
     args = [func,x,y]
     i = opt.leastsq(f_to_minimize, p0, args)
     print i

minimize(x,y,line,[1,2])
-----

Here you can play with p0 and the function to give to f_to_minimize as 
an argument.

What comes out is that if I give a p0 = [1] and I use a single-variable 
function, it works. As soon as I try a two-variable function (and thus I 
need two input parameters), I get:

massimo at boltzmann:~/work$ python test_norm_leastsq.py
Traceback (most recent call last):
   File "test_norm_leastsq.py", line 44, in <module>
     minimize(x,y,[1,2])
   File "test_norm_leastsq.py", line 41, in minimize
     i = opt.leastsq(f_to_minimize, p0, args)
   File "/usr/lib/python2.6/dist-packages/scipy/optimize/minpack.py", 
line 300, in leastsq
     raise errors[info][1], errors[info][0]
TypeError: Improper input parameters.

The funny thing is that it worked *before* I messed with the thing to 
simplify the function-choosing mechanism (before I had N different 
functions for each combination of fixed/nonfixed params, now I just have 
two and I fix stuff *inside* the function), and I can't see however how 
can this be different. Also, the example above leaves me perplexed -it 
seems leastsq simply doesn't want two-variable functions to be minimized 
in this case. Any hint?

Thanks a lot,
Massimo