def fit_mps(dist, data, x0=None):
 xsorted = np.sort(data)
 if (x0 is None):
  x0 = getstartparams(dist, xsorted)
 args = (xsorted, dist)
 print(x0)
 return optimize.fmin(logmps, x0, args=args)

def fit_mps ( dist , data , x0 = None ) : xsorted = np . sort ( data ) if ( x0 is None ) : x0 = getstartparams ( dist , xsorted ) args = ( xsorted , dist ) print ( x0 ) return optimize . fmin ( logmps , x0 , args = args )
estimate distribution parameters with maximum product-of-spacings parameters params : array_like .