A minimal working example of fitting your model to data using SciPy.

Fit_line.py

# A minimal working example of fitting your model to data using SciPy. 
# I rewrote the Cookbook example (http://wiki.scipy.org/Cookbook/FittingData) for a simpler case
# and simpler syntax. 

from __future__ import division
from pylab import *
from scipy import *
import numpy as np
from scipy import optimize

def fitfunc(x, p):
	 print x.shape, 'x'
	 ret = p[0]*x+p[1] # Model
	 return ret


def errfunc(p, x, y, yerr):
	  print p, 'params'
	  ret = (y - fitfunc(x, p)) / yerr# Distance b/ween model and data -- Chi (not squared, squaring and summing up are done by SciPy)
	  return ret


# Generate data points 
num_points = 150
x = linspace(5., 8., num_points)
y = 0.8*x + 12
yerr = 0.2*np.ones((x.shape))

p0 = [0.8, 0.] # Initial guess for the parameters
p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y, yerr))


scatter(x, y, c="r", label='data') # Plot of the data and the fit
plot(x, fitfunc(x, p1), c='g', label="fit", zorder=100)
errorbar(x, y, yerr=yerr, c='k')
legend()
show()