def _fitstart_poisson(self, x, fixed=None):
 a = x.min()
 eps = 0
 if (fixed is None):
  loc = (a - eps)
 elif np.isnan(fixed[(-1)]):
  loc = (a - eps)
 else:
  loc = fixed[(-1)]
 xtrans = (x - loc)
 lambd = xtrans.mean()
 return (lambd, loc)

def _fitstart_poisson ( self , x , fixed = None ) : a = x . min ( ) eps = 0 if ( fixed is None ) : loc = ( a - eps ) elif np . isnan ( fixed [ ( - 1 ) ] ) : loc = ( a - eps ) else : loc = fixed [ ( - 1 ) ] xtrans = ( x - loc ) lambd = xtrans . mean ( ) return ( lambd , loc )
maximum likelihood estimator as starting values for poisson distribution parameters x : array data for which the parameters are estimated fixed : none or array_like sequence of numbers and np .