I have data where my x column corresponds to the residue position and the y axis corresponds to the entropy value. I want to do a curve-fitting on the data obtained. I was trying to fit a Gaussian curve but the curve is not fitting according to what it should look like. Can anyone suggest to me what should be an optimal way to do so?

```
from scipy.optimize import curve_fit
x = [18,23,95,142,154,156,157,158,258,318,367,382,484,501,522,574,681,832,943,1071,1078,1101,1133,1153,1174,1264]
y = [0.179,0.179,0.692,0.574,0.669,0.295,0.295,0.295,0.387,0.179,0.179,0.462,0.179,0.179,0.179,0.179,0.179,0.179,0.179,0.179,0.179,0.462,0.179,0.387,0.179,0.295]
x = np.asarray(x)
y = np.asarray(y)
amp1 = 100
sigma1 = 10
cen1 = 50
def Gauss(x, A, B):
y = A*np.exp(-1*B*x**2)
return y
parameters, covariance = curve_fit(Gauss, x, y)
fit_A = parameters[0]
fit_B = parameters[1]
fit_y = Gauss(x, fit_A, fit_B)
plt.figure(figsize=(18,10))
plt.plot(x, y, label='data')
plt.plot(x, fit_y, label="fit")
plt.legend()
plt.show()
```