我不会使用Python Effect of delay in diagnosis on transmission of COVID-19和curve_fit来实现odeint的SEIR模型（几乎没有修改）。没有curve_fit，我的代码是这样的：
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
def func_ode(y,t,qS,qSq,betaV1,betaV2,beta1,beta2,e1,eta1,eta2,etaH,delta2,deltaH,theta2,f1,f2,fH,d):
S_q,S,E1,E2,H,R,D,V=y
dSq=qS*S-qSq*S_q
dS=qSq*S_q-(betaV1*V+betaV2*V+beta1*E1+beta2*E2)*S
dE1=(betaV1*V+beta1*E1)*S-(e1+eta1)*E1
dE2=(betaV2*V+beta2*E2)*S+e1*E1-(eta2+delta2+theta2)*E2
dH=theta2*E2-(etaH+deltaH)*H # theta is for recovered
dR=eta1*E1+eta2*E2+etaH*H # eta is for recovered
dD=delta2*E2+deltaH*H # delta is for death
dV=f1*E1+f2*E2+fH*H-d*V
dy=[dSq,dS,dE1,dE2,dH,dR,dD,dV]
return dy
if __name__ == "__main__":
## Parameters (dummy)
qS,d = \
0,1e-4,4e-9,1e-9,1/100,1/21,1/104,1/10,1/200,1/10400,1/3.5,1400,1000,1700,144
## Initial (dummy)
y_0=[1000,100000000,10,1,100]
## Solve
t= np.linspace(1,60,60)
result=odeint(func_ode,y_0,args=(qS,d))
## Plot
plt.plot(t,result[:,0],label='Sq')
plt.plot(t,1],label='S')
plt.plot(t,2],label='E1')
plt.plot(t,3],label='E2')
plt.plot(t,4],label='H')
plt.plot(t,5],label='R')
plt.plot(t,6],label='D')
plt.plot(t,7],label='V')
plt.legend(loc='best')
plt.xlabel('t')
plt.grid()
plt.show()
pass
要对输入数据使用优化的参数，这是我的无效代码：
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import os
import pandas as pd
def func_ode(y,dV]
return dy
def func_y(t,d,y_0):
"""
Solution to the ODE y'(t) = f(t,y,parameters) with initial condition y(0) = y_0
"""
y = odeint(func_ode,d))
return y[1:,:]
if __name__ == "__main__":
file_name='Data Dummy.xlsx'
current_path=os.getcwd()
file_path=os.path.join(current_path,file_name)
sheet_name='Sheet1'
df_raw=pd.read_excel(file_path,sheet_name=sheet_name)
numpy_data=df_raw[[
'Self-quarantine susceptible','Susceptible','E1 (OTG)','E2 (ODP)','H (Hospitalized: PDP + Positif)','R (Sembuh)','D (Meninggal)','V (Virus)'
]].to_numpy()
## Parameters (dummy)
qS,144
# Used Data
y_0=numpy_data[0,:].tolist()
numpy_data=numpy_data[1:60,:]
## Reference Time
number_of_reference_time,_=np.shape(numpy_data)
## Solve
param = qS,y_0
t= np.linspace(1,number_of_reference_time,number_of_reference_time)
popt,cov = curve_fit(func_y,numpy_data,p0=[param])
qS,d = popt
## Check Result
result=odeint(func_ode,label='V')
plt.legend(loc='best')
plt.xlabel('t')
plt.grid()
plt.show()
pass
错误结果显示：
File "...\Programs\Python\Python37\lib\site-packages\scipy\optimize\minpack.py",line 458,in func_wrapped
return func(xdata,*params) - ydata
ValueError: operands could not be broadcast together with shapes (58,8) (59,8)
似乎curve_fit不能容纳有多个图形的odeint吗？或者我在这里想念什么？
编辑：
我将固定的y[1:,:]编辑为y.flatten()，将popt,p0=[param])编辑为popt,numpy_data.flatten(),p0=[param])。另外，将输入更改为numpy.array(list)，可以在pastebin中看到该代码。现在问题变成了：
File "....py",line 164,in <module>
popt,p0=[param])
File "...\Python\Python37\lib\site-packages\scipy\optimize\minpack.py",line 752,in curve_fit
res = leastsq(func,p0,Dfun=jac,full_output=1,**kwargs)
File "...\Python\Python37\lib\site-packages\scipy\optimize\minpack.py",line 396,in leastsq
gtol,maxfev,epsfcn,factor,diag)
TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe'