Gaussian Curve fitting
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Entering edit mode
10 weeks ago
satyam85cool ▴ 10

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()
Curve Fitting Gaussian Python • 89 views
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