我正在使用minuit拟合一条简单的曲线，但是由于某种原因，我得到了
ValueError：具有多个元素的数组的真值是
使用a.any()或a.all()。
我不知道这是从哪里来的。我以前尝试过做类似的事情，但一直有效，我所更改的只是函数，所以这是错误，很奇怪，我不理解。
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
])
a = np.array([ 0.0904, 0.0755, 0.0647, 0.0552])
B = 2.61
Fc = 92.8
#functions to fit
def Fpapper(X,s,pp,k,l1,B=2.61,Fc=92.8,mu=770,Za=0.9468):
temp1 = (2*B*X)/(4*pi*Fc)**2
return Fc*pp*(1+k*s)*(1+l1*temp1)
def Bpapper(X,s,fa,pp,y1,y1s,l2,B=2.61,Fc=92.8,mu=770,Za=0.9468):
temp1 = (2*B*X)/(4*pi*Fc)**2
return pp*(1-fa**2*y1+y1s*s)*B*(1-l2)
#parameters initial values and errors
paramfelp=np.array([ 1.01980390e-03, 1.14017543e-03, 1.14017543e-03,
8.06225775e-04, 4.93659802e-04, 6.41248782e-04,
4.57055795e-04, 9.40212742e-04, 9.00499861e-04,
7.40270221e-04, 9.39840412e-04, 6.82641927e-04,
8.63712915e-04, 8.39344983e-04, 4.66904701e-04,
4.31740663e-04, 5.75673519e-04, 5.99082632e-04,
6.49384324e-04, 5.02195181e-04, 8.20000000e-04,
9.05207159e-04, 1.06901824e-03, 7.32461603e-04,
7.50066664e-04, 9.83259884e-04, 1.19339851e-03,
6.22896460e-04, 8.12157620e-04, 5.88727441e-04,
5.20384473e-04, 5.53263048e-04, 5.31601354e-04,
8.80511215e-04, 1.05233075e-03, 5.32353266e-04,
1.30000000e-03, 1.30000000e-03, 1.80000000e-03,
2.00000000e-04, 5.60000000e-03, 5.30000000e-03,
3.50000000e-03, 1.50000000e-03, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e-08, 1.00000000e-01,
1.00000000e-03, 1.00000000e-05, 2.00000000e-03,
4.00000000e-01, 5.00000000e-01]) #parameters error
parametersp=np.array([ 9.04000000e-02, 7.55000000e-02, 6.47000000e-02,
5.52000000e-02, 7.64118166e+02, 7.55081289e+02,
7.41438660e+02, 6.93045528e+02, 6.84117792e+02,
7.63223209e+02, 7.28909270e+02, 6.77394705e+02,
6.72461531e+02, 7.16248911e+02, 7.81336428e+02,
6.93806885e+02, 7.72554724e+02, 7.65733222e+02,
6.80216152e+02, 7.64844597e+02, 7.60401473e+02,
7.59878753e+02, 6.77654822e+02, 1.18579180e+03,
7.07967722e+02, 7.10133134e+02, 6.94395773e+02,
6.35136682e+02, 6.88997493e+02, 6.23303163e+02,
6.81738788e+02, 7.27821973e+02, 6.78740461e+02,
7.18098614e+02, 6.70018037e+02, 6.72091400e+02,
6.80272109e-01, 6.66666667e-01, 6.49350649e-01,
6.32911392e-01, 9.46800000e-01, 9.63200000e-01,
9.70700000e-01, 9.75600000e-01, 8.19610049e+01,
6.49642761e+01, 3.81552014e+01, 4.57599299e+01,
2.73834403e+01, 2.34174300e+01, 1.87358692e+01,
1.80748675e+01, 9.20589292e+00, 4.83878931e+00,
7.21707090e+01, 7.10808368e+01, 3.95764739e+01,
3.16337363e+01, 3.06536354e+01, 2.65058484e+01,
1.93315099e+01, 1.34548250e+01, 3.86943831e+00,
6.87659817e+01, 6.67933219e+01, 3.88613983e+01,
3.86124674e+01, 3.86500419e+01, 1.66267041e+01,
1.62415658e+01, 9.16347451e+00, 5.24880469e+01,
5.19571230e+01, 2.38633378e+01, 2.38181528e+01,
1.24428244e+01, 3.23442596e-07, 1.42713877e+01,
9.00000000e-02, 1.12143829e-04, 9.00000000e-02,
3.70000000e+00, 4.00000000e+00]) #parameters initial value
#chisquare
def chiworkp(paras):
B=2.61
Fc=92.8
hc=197.3269788
mu=770
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(((paras[0:4]-a)/(ast*10**(-4)))**2)+sum(((paras[4:36]*(paras[0:4][betalat])/hc-amssr)/(mse))**2)
chiz=sum(((paras[36:40]-1/Zsrgi)/(dzs*10**(-2)))**2)+sum(((paras[40:44]-Za)/(Zaey*10**(-4)))**2)
chiamd=sum(((amudr-(1+paras[77:78]*paras[0:4][betalat]**2)*paras[0:4][betalat]*paras[44:76]*paras[36:40][betalat]/hc)/amudre)**2)
chiaf=sum(((af-Fpapper(paras[44:76],paras[4:36]**2-mssph**2,paras[0:4][betalat]/paras[40:44][betalat],paras[76:77],paras[77:78])/hc)/afe)**2)
chiaB=sum((((ampir**2/(2*amudr))-Bpapper(paras[44:76],paras[4:36]**2-mssph**2,paras[0:4][betalat],paras[0:4][betalat]/paras[36:40][betalat],paras[77:78],paras[80:81])/hc)/Bfel)**2)
return chia+chiz+chiamd+chiaf+chiaB
#minuit part
mc=minuit.Minuit(chiworkp,parametersp[:],paramfelp[:],errordef=1)
fminc, paramc = mc.migrad()
print(mc.values)