Minuit不适合曲线

Question

我正在尝试使用Minuit拟合曲线，但似乎数据未正确拟合到曲线。我怀疑这与该函数的登录有关，因为一旦我从chisquare函数中删除了chiaf，就可以正确拟合这些值。问题也是am18的值变为负值，它对应于质量，并且由于所有其他值均为正值，因此，这个值不是真是很奇怪。

import numpy as np
from iminuit import Minuit

#some constants
hc = 197.3269788
mssph = 685.78 
#some data and errors to fit
muds = np.array([ 
    81.96100485,  64.96427609,  38.15520137,  45.75992993,
    27.38344029,  23.41742996,  18.73586921,  18.07486749,
     9.20589292,   4.83878931,  72.17070899,  71.08083681,
    39.57647386,  31.63373626,  30.65363538,  26.50584842,
    19.33150992,  13.45482499,   3.86943831,  68.76598171,
    66.7933219 ,  38.86139831,  38.61246743,  38.6500419 ,
    16.62670414,  16.24156579,   9.16347451,  52.48804692,
    51.95712296,  23.86333781,  23.81815279,  12.44282442
    ]) #xdata
Fr = np.array([ 
    127.31527434,  122.72790265,  110.26449558,  112.75717699,
    104.81830088,  104.35746903,  101.32016814,  100.54513274,
     96.87942478,   92.98330088,  124.9736053 ,  122.52414305,
    112.47114172,  108.74591788,  107.34258013,  108.00597616,
    102.18850331,  100.04522384,   91.47210596,  128.18641113,
    122.15516847,  108.23229985,  109.85263369,  107.69218856,
     99.14042658,   98.0902102 ,   99.1104204 ,  112.47678261,
    110.39126087,   98.373     ,   98.97391304,   95.01495652
    ])#ydata
Zsrgi = np.array([1.47, 1.5, 1.54, 1.58])

dzs = np.array([ 3.60555128,  3.60555128,  4.24264069,  1.41421356])
Za = np.array([ 0.9468,  0.9632,  0.9707,  0.9756])

Zaey = np.array([56.0, 53.0, 35.0, 15.0])

dmfel = np.array([ 
     2743.82452989,  1388.6310737 ,   689.77566743,   652.7213305 ,
     348.31861265,   309.31427565,   134.12993165,   157.95193019,
      60.83965035,    19.80905202,  3482.5565895 ,  3378.1685061 ,
     949.07161448,   570.68604168,   492.42222648,   801.32561084,
     362.30696375,   152.61749342,    26.39235787,  2733.32712033,
    2329.20044751,   788.45797536,   754.06472854,  1098.9552043 ,
     279.63806125,   213.46692781,   169.87677303,  2181.08350448,
    2018.45039786,   696.73902333,   763.51325268,   416.74304481
    ])
af = np.array([ 
    0.0575465 ,  0.05547301,  0.04983955,  0.05096624,  0.04737787,
    0.04716958,  0.04579672,  0.0454464 ,  0.0437895 ,  0.04202845,
    0.04717754,  0.04625286,  0.04245786,  0.04105158,  0.04052182,
    0.04077226,  0.03857616,  0.03776707,  0.03453072,  0.0414683 ,
    0.0395172 ,  0.03501315,  0.03553733,  0.03483842,  0.03207193,
    0.03173218,  0.03206222,  0.03104359,  0.03046799,  0.02715095,
    0.0273168 ,  0.02622413
    ])
afe = np.array([ 
    0.00039571,  0.00046823,  0.00034898,  0.00071523,  0.00052648,
    0.00051809,  0.00072373,  0.00059257,  0.00054912,  0.00065308,
    0.000283  ,  0.00028516,  0.00031909,  0.0003537 ,  0.00035294,
    0.0002573 ,  0.00034244,  0.00034139,  0.00055726,  0.00038002,
    0.00044435,  0.00050214,  0.00052077,  0.00037143,  0.00036278,
    0.00045654,  0.00039068,  0.00025011,  0.00024068,  0.00031897,
    0.00037707,  0.0003581
    ])
amssr = np.array([ 
        0.35006,  0.34592,  0.33967,  0.3175 ,  0.31341,  0.34965,
        0.33393,  0.31033,  0.30807,  0.32813,  0.29895,  0.26546,
        0.29559,  0.29298,  0.26026,  0.29264,  0.29094,  0.29074,
        0.25928,  0.3888 ,  0.23213,  0.23284,  0.22768,  0.20825,
        0.22591,  0.20437,  0.22353,  0.2036 ,  0.18987,  0.20088,
        0.18743,  0.18801
        ])
amudr = np.array([
    0.01737619,  0.01377279,  0.00808912,  0.00970136,  0.00580544,
    0.00496463,  0.00397211,  0.00383197,  0.0019517 ,  0.00102585,
    0.01227267,  0.01208733,  0.00673   ,  0.00537933,  0.00521267,
    0.00450733,  0.00328733,  0.002288  ,  0.000658  ,  0.00950714,
    0.00923442,  0.00537273,  0.00533831,  0.00534351,  0.0022987 ,
    0.00224545,  0.00126688,  0.00588165,  0.00582215,  0.00267405,
    0.00266899,  0.0013943
    ])
amudre = np.array([
     8.75479519e-04,   7.34296720e-04,   4.51927448e-04,
     5.98343236e-04,   3.81187530e-04,   3.18993055e-04,
     4.05218892e-04,   3.28835732e-04,   2.14302222e-04,
     1.80198850e-04,   6.27776371e-04,   6.18193827e-04,
     3.56742512e-04,   3.04857443e-04,   3.08046099e-04,
     2.32852126e-04,   1.84120801e-04,   1.62566426e-04,
     7.02801597e-05,   5.87470469e-04,   5.70265978e-04,
     3.42904960e-04,   3.42757519e-04,   3.31231953e-04,
     1.52175013e-04,   1.61067797e-04,   8.21035930e-05,
     1.40038557e-04,   1.42199518e-04,   7.54950814e-05,
     7.13899224e-05,   3.64415759e-05
     ])
mse = np.array([ 
    0.00049366,  0.00064125,  0.00045706,  0.00094021,  0.0009005 ,
    0.00074027,  0.00093984,  0.00068264,  0.00086371,  0.00083934,
    0.0004669 ,  0.00043174,  0.00057567,  0.00059908,  0.00064938,
    0.0005022 ,  0.00082   ,  0.00090521,  0.00106902,  0.00073246,
    0.00075007,  0.00098326,  0.0011934 ,  0.0006229 ,  0.00081216,
    0.00058873,  0.00052038,  0.00055326,  0.0005316 ,  0.00088051,
    0.00105233,  0.00053235
    ])

a = np.array([ 0.0904,  0.0755,  0.0647,  0.0552])

B = 2.61
Fc = 92.8
def Ffitr(X, s, pp, k, B=2.61, Fc=92.8, mu=770, Za=0.9468): #fit function
    logBXmu = np.log( (2 * B * X) / mu**2)
    temp1 = (2 * B * X) / (4 * pi * Fc)**2
    temp2 = temp1 * (afij[0] + 
                     afij[1] * logBXmu)
    temp3 = temp1**2*( afij[2] + 
                       afij[3] * logBXmu + 
                       afij[4] * logBXmu**2)
    temp4 = temp1**3 * (afij[5] + 
                        afij[6] * logBXmu + 
                        afij[7] * logBXmu**2 +
                        afij[8] * logBXmu**3)
    return Fc * pp * (1 + k * s) * (1 + temp2 + temp3 + temp4)

#chisquare
def chiwork( a0, a1, a2, a3,
             b0, b1, b2, b3, b4, b5, b6, b7, b8, b9, 
             b10, b11, b12, b13, b14, b15, b16, b17, b18, b19, 
             b20, b21, b22, b23, b24, b25, b26, b27, b28, b29, 
             b30, b31,
             z0, z1, z2, z3, za0, za1, za2, za3,
             am0, am1, am2, am3, am4, am5, am6, am7, am8, am9, 
             am10,am11, am12, am13, am14, am15, am16, am17, am18, am19,
             am20, am21, am22, am23, am24, am25, am26, am27, am28, am29,
             am30, am31,
             k, y1):
    B = 2.61
    Fc = 92.8
    hc = 197.3269788
    mu = 770
    ana = np.array([a0, a1, a2, a3])
    zs = np.array([z0, z1, z2, z3])
    za = np.array([za0, za1, za2, za3])
    am = np.array([am0, am1, am2, am3, am4, am5, am6, am7, am8, am9, 
                   am10, am11, am12, am13, am14, am15, am16, am17, am18, am19, 
                   am20, am21, am22, am23, am24, am25, am26, am27, am28, am29, 
                   am30, am31])
    #am[numpy.isnan(am)] = 10**(-50)
    am[am <= 0] = 10**(-50)
    bss = np.array([b0, b1, b2, b3, b4, b5, b6, b7, b8, b9,
                    b10, b11, b12, b13, b14, b15, b16, b17,b18,b19, 
                    b20, b21, b22, b23, b24, b25, b26, b27, b28, b29,
                    b30, b31])
    betalat = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
               1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 
               2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 
               3, 3]
    chia = sum( ( (ana - a) / (ast  * 10**(-4) ) )**2)  +\
           sum(  ( (bss * a[betalat] / hc - amssr) / mse )**2)
    chiz = sum( ( (zs - 1 / Zsrgi) / (dzs * 10**(-2) ) )**2) +\
           sum( ( (za - Za) / (Zaey * 10**(-4) ) )**2)
    chiamd = sum( ( (amudr  - (1 + y1 * ana[betalat]**2) *
                     ana[betalat] * am * zs[betalat] / hc) / amudre)**2)
    chiaf = sum( ( ( af - Ffitr(am, bss**2 - mssph**2, ana[betalat] / 0.95, k) / hc) / afe)**2)
    return chia + chiz + chiamd + chiaf

#minuit part
mc=minuit.Minuit(
           chiwork, a0=0.09, z0=0.68, za0=0.95, am0=62,
                    b0=755, k=5.124105506705958e-07, y1=1,
                    error_a0=0.02, error_z0=0.02,
                    error_za0=0.02, error_am0=13, error_b0=4,
                    error_k=10**(-8), error_y1=0.1,errordef=1
                 )

fminc, paramc = mc.migrad()

print(mc.values)

#gtting values to plot
asp = []
bs = []
Zs = []
zas = []
mdus = []

for i in range(4):
    asp.append(mc.values[i])
for i in range(4, 36):
    bs.append(mc.values[i])
for i in range(36, 40):
    Zs.append(mc.values[i])
for i in range(40, 44):
    zas.append(mc.values[i])
for i in range(44,76):
    mdus.append(mc.values[i])
anna = np.array(asp)
bna = np.array(bs)
Zsn = np.array(Zs)
zasn = np.array(zas)
mduss = np.array(mdus)
mnew = np.linspace(min(mduss), max(mduss), 100)
bnew = np.linspace(min(bna), max(bna), 100)
annas = np.linspace(min(anna), max(anna), 100)
zasns = np.linspace(min(zasn), max(zasn), 100)
zsn = np.linspace(min(Zsn), max(Zsn), 100)

plt.figure(figsize=( 14, 8.5))
plt.ylim(90, 200)
plt.plot(muds, Fr, "b^")
plt.plot()
plt.plot(mnew, Ffitr(mnew, bnew**2 - mssph**2, annas  / zasns, mc.values["k"]) / annas)

我得到的是