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Problems using fsolve to find two unknowns in Python

I've been having problems trying to estimate two variables while using fsolve. Below is the code
def f(variables):
#Ideal
n=0 #this is passed as an argument but given in this example for simplicity
P1 = 101325; T1 = 300
q = 'H2:2,O2:1,N2:{}'.format(molno[n])
mech = 'H2O2sandan.cti'
cj_speed,R2,plot_data = CJspeed(P1,T1,q,mech,fullOutput=True)
gas = PostShock_fr(cj_speed, P1, T1, q, mech)
Ta = gas.T; Ps = gas.P;
CVout1 = cvsolve(gas)
taua = CVout1['ind_time']
Tb = Ta*1.01
gas.TPX = Tb,Ps,q
CVout2 = cvsolve(gas)
taub = CVout2['ind_time']
#Constant Volume
k,Ea = variables
T_0_1= Tvn_mg_d[n]
T_vn_1 = Tvn_mg_d[n]
den = rhovn_mg_d[n]
t = np.linspace(0,0.0001,100000000)
qr = Q_mg[n]
perfect_var = T_vn_1,den,Ea,k,qr
sol_t= odeint(ode,T_0_1,t=t,args=(perfect_var,))
index = np.argmax(np.gradient(sol_t[:,0]))
tau_cv_1 = t[index]
T_0_2= Tvn_mg_d[n]*1.01
T_vn_2 = Tvn_mg_d[n]*1.01
den = rhovn_mg_d[n]
t = np.linspace(0,0.0001,100000000)
qr = Q_mg[n]
perfect_var = T_vn_2,den,Ea,k,qr
sol_t= odeint(ode,T_0_2,t=t,args=(perfect_var,))
index = np.argmax(np.gradient(sol_t[:,0]))
tau_cv_2 = t[index]
root1 = taua - t_cv_1
root2 = taub - t_cv_2
return[root1,root2]
import scipy.optimize as opt
k_guess = 95000
Ea_guess = 28*300
solution = opt.fsolve(f,(k_guess,Ea_guess))
print(solution)
I want to find values of k_guess and Ea_guess such that roo1 and roo2 are 0 (i.e. taua = t_cv_1 and taub = t_cv_2). However I don't know if I've used fsolve the right way as the values returned seem to be way off. Am I returning the right thing? I also get the below error:
lsoda-- warning..internal t (=r1) and h (=r2) are
such that in the machine, t + h = t on the next step
(h = step size). solver will continue anyway
What am I doing wrong here?

Is there a way to fix value-error problem

Hello guys i am trying to implement an algortihm to remove water from underwater images and make image more noticable , but i got an errror ValueError: max() arg is an empty sequence , at the function homomorpic on this line r = max(np.ravel(result[:,:i])) , the error is caused because the result array is empty but i filled it above .Here the code below.
import numpy as np
import cv2
def homomorpic(img):
img = np.float32(img)
#img = img/255
rows , cols , dim = img.shape
(rh,rl,cutoff) = 1.3, 0.8, 32
b,g,r = cv2.split(img)
y_log_b = np.log(b + 0.01)
y_log_g = np.log(g + 0.01)
y_log_r = np.log(r + 0.01)
y_fft_b= np.fft.fft2(y_log_b)
y_fft_g= np.fft.fft2(y_log_g)
y_fft_r= np.fft.fft2(y_log_r)
y_fft_b_shift = np.fft.fftshift(y_log_b)
y_fft_g_shift = np.fft.fftshift(y_log_g)
y_fft_r_shift = np.fft.fftshift(y_log_r)
D0=cols/cutoff
H= np.ones((rows,cols))
B= np.ones((rows,cols))
for i in range(rows):
for j in range(cols):
H[i][j] = ((rh-rl)* (1-np.exp(-((i-rows/2)**2+(j-cols/2)**2)/(2*D0**2))))+rl
result_filter_b = H* y_fft_b_shift
result_filter_g = H* y_fft_g_shift
result_filter_r = H* y_fft_r_shift
result_b_intern = np.real(np.fft.ifft2(np.fft.ifftshift(result_filter_b)))
result_g_intern = np.real(np.fft.ifft2(np.fft.ifftshift(result_filter_g)))
result_r_intern = np.real(np.fft.ifft2(np.fft.ifftshift(result_filter_r)))
result_b = np.exp(result_b_intern)
result_g = np.exp(result_g_intern)
result_r = np.exp(result_r_intern)
result = np.zeros((rows,cols,dim))
result[:,:,0] = result_b
result[:,:,1] = result_g
result[:,:,2] = result_r
ma = -1
mi = 500
for i in range(3):
r = max(np.ravel(result[:,:i]))
x = min(np.ravel(result[:,:i]))
if r > ma :
ma = r
if x < mi :
mi = x
return(result)
image = cv2.imread("eg.png")
image2 = homomorpic(image)
Thanks for any help or suggestion.

In this loop for i in range(3): the first value of i would be 0.
This will later on lead to this r = max(np.ravel(result[:,:0])) where the result from the slicing would be empty.
You would want to shift yourrange forward like this:
for i in range(1, 3+1):

How to square the individual matrix value using python?

Cost function implemented with Python:
**Thanks for help to achieve this.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
load_data = pd.read_csv('C:\python_program\ex1data1.txt',sep = ",",header = None)
feature_vale = load_data[0]
y = np.matrix(load_data[1])
m = len(feature_vale)
plt.scatter(load_data[0],load_data[1],marker='+',c = 'r')
plt.title("Cost_Function")
plt.xlabel("Population of City in 10,000s")
plt.ylabel("Profit in $10,000s")
df = pd.DataFrame(pd.Series(1,index= range(0,m)))
df[1] = load_data[0]
X = np.matrix(df)
row_theta = np.zeros(2,dtype = int)
theta = np.array([row_theta]) # Transpose the array
prediction = np.dot(X,theta.T)
error = (prediction-y.T)
error_df = pd.DataFrame(error)
#square the error
squared_error = np.square(error_df)
sum = np.sum(squared_error)
print(sum)
J = np.sum(squared_error) / (2 * m)
print(J)
Data reference link: searchcode.com/codesearch/view/5404318

repeat the following steps and let me know
load_data = pd.read_csv('data.txt',sep = ",",header = None)
feature_vale = load_data[0]
y = np.matrix(load_data[1])
m = len(feature_vale)
#print(m)
#plt.scatter(load_data[0],load_data[1])
df = pd.DataFrame(pd.Series(1,index= range(0,m)))
df[1] = load_data[0]
X = np.matrix(df)
row_theta = np.zeros(2,dtype = int)
theta = np.array([row_theta]) # Transpose the array
print(theta.T)
prediction = np.matmul(X,theta.T)
error = (prediction-y)
error_df = pd.DataFrame(error)
squared_error = np.square(error_df)
print(squared_error)

TypeError: 'int' object is not callable Modeling in python

I am getting the following error in python, and I am not sure why. I am trying to model how meth affects mice.
Here is my code, and the functions that are created in my code:
from scipy import array, linspace
from scipy import integrate
from matplotlib.pyplot import *
def Temp2(z, t, Ta, Te, wexc, yexc, winhib, yinhib, whd, yhd, wexctoinhib, winhibtomdl, whdtospn, yspn, Tt):
# Dependence of Meth Concentration
# dx
# -- = -x/Ta
# dt
#
# dy
# -- = x/Ta - y/Te
# dt
# x = interperitoneal
# y = blood
# Ta is the time constant of Meth in the absorbtion
# Te is the time constant of Meth in elimination
x = z[0] # Rabbits density
y = z[1] # Sheep density
T = z[2]
D = int(x=1)
yt = D(Ta/Te -1)**-1 * (e**-t/Ta - e**-t/Te)
Pexc = (1+tanhx)*[wexc*yt*yexc]
Pinhib = (1+tanhx)*[winhib*yt*yinhib]
Phd = (1+tanhx)*[whd*yt*yhd]
Pmdl = wexctoinghib*Pexc-winhibtomdl*Pinhib
Pspn = Pmdl + whdtospn*Phd+yspn
V = array([-x/Ta, x/Ta - y/Te, (Pspn-(T-T0))/Tt])
return V
def main():
# set up our initial conditions
IC0 = 1
BC0 = 0
T0 = 37
z0 = array([IC0, BC0, T0])
# Parameters
Ta = 8.25
Te = 57.5
wexc = 1.225
yexc = -0.357
winhib = 1.335
yinhib = 1.463
whd = 0.872
yhd = -3.69
wexctoinhib = 7.47
winhibtomdl = 6.38
whdtospn = 5.66
yspn = -3.35
Tt = 89.2
# choose the time's we'd like to know the approximate solution
t = linspace(0., 1., 60)
# and solve
xode= integrate.odeint(Temp2, z0, t, args=(Ta, Te, wexc, yexc, winhib, yinhib, whd, yhd, wexctoinhib, winhibtomdl, whdtospn, yspn, Tt))
print (xode)
main()
Ignore the #s as they do not relate to what the code is saying. Here is the error I am getting:
yt = D(Ta/Te -1)**-1 * (e**-t/Ta - e**-t/Te)
TypeError: 'int' object is not callable
I am not sure what is wrong, and how I can fix this? Can anyone help me?

The issue is here
yt = D(Ta/Te -1)**-1 * (e**-t/Ta - e**-t/Te)
There is no implicit multiplication in python, so when you attempt to do D(Ta/Te - 1) it is being interpreted as a function call rather than D multiplied by what is in the bracket.
Rewrite it like this
yt = D*(Ta/Te -1)**-1 * (e**-t/Ta - e**-t/Te)

TypeError: 'float' object has no attribute '__getitem__' in while statement

I am getting TypeError: 'float' object has no attribute 'getitem' for my while statement below. I am not sure what the problem is in this case. I am using sys in the code
s1P = 4.51775*10.0**16.0
ii= 1
while s1P[ii-1] > 0.0
sys.stdout.write('\rstep={0}'.format(ii))
sys.stdout.flush()
Rad = s1r[ii-1]+delrms1[ii-1]*delm
Mas = s1m[ii-1]*[i]
Pres = s1P[ii-1]+delPms1[ii-1]*delm
lPres = np.log10(Pres)
Temp = s1T[ii-1]+delTms1[ii-1]*delm
lTemp = np.log10(Temp)
Lum = s1L[ii-1]+deLms1[ii-1]*delm
Rho = (Pres - 1.0/3.0*a*Temp**4.0)*mu/(NA*k*Temp)
lRho = np.log10(Rho)
lR = np.log10(10.0**lRho/(10.0**Temp/10.0**6)**3.0)
lK = interpolate.bisplev(lTemp,lR,tck)
K = 10.0**lK
T_n = Temp/(10.0**9.0)
epp = 2.4*10.0**4.0*(Rho*X**2.0/T_n**(2.0/3.0))*np.exp(-3.38/T_n**(1.0/3.0))
ecno = 4.4*10.0**25.0*(Rho*X*Z/T_n**(2.0/3.0))*np.exp(-15.228/T_n**(1.0/3.0))
eta = 5.0*10.0**8.0*(Rho**2.0*Y**3.0/T_n**3.0)*np.exp(-4.4/T_n)
ec = epp+ecno+eta
Bt = NA*k*Rhoc*T_n/(Pres*mu)
Gam2 =(32.0-24.0*Bt-3.0*Bt**2.0)/(24.0-18.0*Bt-3.0*Bt**2.0)
drm = 1.0/(4.0*np.pi*Rad**2.0*Rho)
dPm = -G*Mas*1.99*10.0**33.0/(4.0*np.pi*Rad**4.0)
dLm = eg
Term1 = 16.0*np.pi*a*c*G/(3.0*K)
Term2 = (1.0-1.0/Gam2)
Term3 = Temp**4.0*(Mas*1.99*10.0**33.0)/Pres
Tal = Term1+Term2+Term3
CR = Lum/Tal
dTrm = -3.0*s1K*s1L/(64.0*np.pi**2.0*a*c*s1r**4.0*s1T**3.0)
dTcm = -(1.0-1.0/s1gam2)*(G*s1m*1.99*10.0**33.0*s1T/(4.0*np.pi*s1r**4.0*s1P))
dTm = np.where(Lum > Tal, dTcm, dTrm)
sys.stdout.write('\n')
s1m.append(Mas)
s1r.append(Rad)
s1L.append(Lum)
r_c.append(CR)
s1T.append(Temp)
logs1T.append(lTemp)
s1P.append(Pres)
logs1P.append(lPres)
s1rho.append(Rho)
logs1rho.append(lRho)
logs1K.append(lK)
slK.append(K)
s1eg.append(ec)
s1gam2.append(Gam2)
delrms1.append(drm)
delPms1.append(dPm)
delLms1.append(dLm)
delTm1.append(dTm)
ii = ii+1
sys.stdout.write('\n')
s1m=s1m[:-1]
s1r=s1r[:-1]
s1L=s1L[:-1]
r_c=r_c[:-1]
s1T=s1T[:-1]
logs1T=logs1T[:-1]
s1P=s1P[:-1]
logs1P=logs1P[:-1]
s1rho=s1rho[:-1]
logs1rho=logs1K[:-1]
logs1K=logs1K[:-1]
slK=s1K[:-1]
s1eg=s1eg[:-1]
s1gam2=s1gam2[:-1]
delrms1=delrms1[:-1]
delPms1=delPms1[:-1]
delLms1=delLms1[:-1]
delTm1=delTm1[:-1]
Could still be a problem from appending the outputs of my statement?

If s1P is supposed to be a list of numbers then you need to initialize it like
s1P = [4.51775*10.0**16.0]
instead of s1P = 4.51775*10.0**16.0, which will make it a single number.