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我试图通过使用 lmfit.Model 拟合实验数据来确定系数。

当我运行我的代码时，我收到以下错误：

Traceback (most recent call last):

  File "C:\***.py", line 82, in <module>
    Dc = fit_Dc(t_days, prod, PV, Swi, l, r)

  File "C:\***.py", line 63, in fit_Dc
    result = fmodel.fit(Son, params, t)

  File "C:\***\Anaconda3\lib\site-packages\lmfit\model.py", line 991, in fit
    var_data = kwargs[var]

KeyError: 't_days'

功能是：

我想确定直流

我试图修复参数 l&r 因为它们是已知的。t（时间）对应于我的 x 数据，儿子是我想要拟合模型的 Y 数据。

我的代码是：

t_days = np.array([0, 2.3, 4.33, 6.83])
prod = np.array([0, 3, 3.1, 3.3])
PV = 11.2
Swi = 0.1303

def calc_So(t_days, PV, prod, Swi):
    t = t_days*24
    Sw = Swi + prod/PV
    So = 1 - Sw
    Soi = 1 - Swi
    recovery = prod/Soi/PV
    Son = (So - So[-1])/(Soi - So[-1])
    return recovery, Soi, Sw, So, Son, t
def calc_So_n(t, l, r, Dc):
     c_cyl = []
     #calc C_ps
     for n in range(0, 1200):
         c_psi = ((8/(2*n+1)**2)/np.pi**2)*np.exp(-Dc*((2*n+1)**2*np.pi**2)*t/4/l**2)
         c_ps.append(c_psi)
     c_ps = np.asarray(c_ps, dtype = np.float64)
     return np.sum(c_ps, axis = None)

     #calc c_cyl
     for n in range(0, 1200):
            c_cyli = (4/((jn_zeros(0,n)[n-1])**2))*np.exp(-Dc*t*(jn_zeros(0,n)[n-1]/r)**2)
            c_cyl.append(c_cyli)
     c_cyl = np.asarray(c_cyl, dtype = np.float64)       
     return np.sum(c_cyl, axis = None)

     return c_cyl*c_ps


# fit DC ti experimental data using lmfit model
def fit_Dc(t_days, prod, PV, Swi, l, r):
    recovery, Soi, Sw, So, Son, t = calc_So(t_days, PV, prod, Swi)
    # create model
    fmodel = Model(calc_So_n)
    # giving initial values for parameters
    params = fmodel.make_params(r=r, l=l, Dc = 0)


    # fix l & r:
    params['r'].vary = False
    params['l'].vary = False
    # fit parameters to data with constant values of r, l:
    params['r'].value = r
    params['l'].value = l
    result = fmodel.fit(Son, params, t= t)
    print("Dc, Dc_error, chi-square=%f" % (result.params['Dc'].value, 
                                             result.params['Dc'].stderr,
                                             result.chisqr))
    return result.params['Dc'].value

recovery, Soi, Sw, So, Son, t = calc_So(t_days, PV, prod, Swi)
Dc = fit_Dc(t, prod, PV, Swi, l, r)

t是一个 np 数组以及Son

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