'how can I calculate in curve fit the 95% confidence interval from the pcov values?

What are the values I print now with sigma_ab and how can I calculate the confidence interval at 95?

for g in all:
    c0 = 5
    c2 = 0.2
    c3 = 0.7
    start = g['y'].iloc[0]
    
    p0 = np.array([c0, c2, c3]), # Construct initial guess array

    popt, pcov = curve_fit(
         model, g['x'], g['y'],
         absolute_sigma=True, maxfev=100000
    )
    
    sigma_ab = np.sqrt(np.diagonal(pcov))
    n = g.name
    print(n+' Estimated parameters: \n', popt)
    print(n + ' Approximated errors: \n', sigma_ab)

These are the estimated parameters

[0.24803625 0.06072472 0.46449578]

This is sigma_ab but I don't know exactly what it is. I would like to calculate the upper and lower limit of the mean with 95% confidence interval.

[1.32778766 0.64261562 1.47915215]

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'how can I calculate in curve fit the 95% confidence interval from the pcov values?

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