`@ -158,7 +158,7 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,` ``` ``` ` """` ` global last_fit_rslt, last_chi_sqr` ` from scipy.optimize import fmin, leastsq, anneal` ` from scipy.optimize import fmin, fmin_bfgs, leastsq, anneal` ` # We want to minimize this error:` ` if Data != None: # an alternative way to specifying x and y` ` y = Data[0]` `@ -200,8 +200,9 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,` ` full_output=1,` ` **opts` ` )` ` keys = ('xopt', 'cov_x', 'infodict', 'mesg', 'ier')` ` keys = ('xopt', 'cov_x', 'infodict', 'mesg', 'ier') # ier = error message code from MINPACK` ` elif method == 'fmin':` ` # Nelder-Mead Simplex algorithm` ` rslt = fmin(fun_err2,` ` x0=Guess, # initial coefficient guess` ` args=(x,y), # data onto which the function is fitted` `@ -209,6 +210,15 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,` ` **opts` ` )` ` keys = ('xopt', 'fopt', 'iter', 'funcalls', 'warnflag', 'allvecs')` ` elif method == 'fmin_bfgs' or method == 'bfgs':` ` # Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm` ` rslt = fmin_bfgs(fun_err2,` ` x0=Guess, # initial coefficient guess` ` args=(x,y), # data onto which the function is fitted` ` full_output=1,` ` **opts` ` )` ` keys = ('xopt', 'fopt', 'funcalls', 'gradcalls', 'warnflag', 'allvecs')` ` elif method == 'anneal':` ` rslt = anneal(fun_err2,` ` x0=Guess, # initial coefficient guess`