fixing parameters about iminuit HOT 7 CLOSED

>>> from iminuit import Minuit
>>> def f(x, y, z):
...         return (x - 2) ** 2 + (y - 3) ** 2 + (z - 4) ** 2
... 
>>> m = Minuit(f, pedantic=False)
>>> m.fitarg
{'fix_z': False, 'y': 0.0, 'z': 0.0, 'fix_y': False, 'error_x': 1.0, 'error_z': 1.0, 'limit_x': None, 'error_y': 1.0, 'limit_y': None, 'fix_x': False, 'limit_z': None, 'x': 0.0}
>>> m.fitarg['fix_x'] = True
>>> m.fitarg['fix_x']
True

from __future__ import print_function
import iminuit
import numpy as np

print("Iminuit version",iminuit.__version__)

def generateData(N=100,order=2,err=0.1):
  x = np.arange(N)
  coefs = np.random.random(order+1)
  y = np.polyval(coefs,x)+np.random.normal(scale=err,size=N)
  return coefs,x,y,err

def func(x,pars):
  return np.polyval(pars,x)
f1 = lambda x,a0,a1: a1*x+a0
f2 = lambda x,a0,a1,a2: a2*x**2+a1*x+a0
f3 = lambda x,a0,a1,a2,a3: a3*x**3+a2*x**2+a1*x+a0

class GenericChi2:
  def __init__(self, f, x, y,e=1.):
    self.f = f
    self.x = x; self.y=y; self.e=e
    args = iminuit.describe(f)#extract function signature
    self.func_code = iminuit.Struct(
      co_varnames = args[1:],#dock off independent param
      co_argcount = len(args)-1
    )
  def __call__(self, *arg):
    f = ( self.f(self.x,*arg)-self.y )/self.e
    return np.sum(f**2)

def doFit(N=100,order=2):
  # generate data
  right_values,x,y,e = generateData(N,order)
  right_values = right_values[::-1]
  # get fitting function
  f = globals()["f%d"%order]
  pars_dict = {}
  for i in range(order+1):
    pars_dict["error_a%d" % i] = 0.01
    pars_dict["a%d" % i] = 0.5
  im = iminuit.Minuit( GenericChi2(f,x,y,e),errordef=1,**pars_dict )
  im.migrad()
  p = im.values.keys(); p.sort()
  for i,pi in enumerate(p):
    print("Par %s, best fit %.4f +/- %.4f, exact value %.4f" % 
      (pi,im.values[pi],im.errors[pi],right_values[i]))


  im.fitarg["fix_a0"]=True
  im.fitarg["a0"]=0

  print("Is a0 fixed: %s"%im.is_fixed("a0"))
  im.migrad()
  p = im.values.keys(); p.sort()
  for i,pi in enumerate(p):
    print("Par %s, best fit %.4f +/- %.4f, exact value %.4f" % 
      (pi,im.values[pi],im.errors[pi],right_values[i]))

  return im

doFit()