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# <examples/doc_model_uncertainty.py>
import matplotlib.pyplot as plt
from numpy import exp, loadtxt, pi, sqrt
from lmfit import Model
data = loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]
def gaussian(x, amp, cen, wid):
"""1-d gaussian: gaussian(x, amp, cen, wid)"""
return (amp / (sqrt(2*pi) * wid)) * exp(-(x-cen)**2 / (2*wid**2))
gmodel = Model(gaussian)
result = gmodel.fit(y, x=x, amp=5, cen=5, wid=1)
print(result.fit_report())
dely = result.eval_uncertainty(sigma=3)
plt.plot(x, y, 'bo')
plt.plot(x, result.init_fit, 'k--', label='initial fit')
plt.plot(x, result.best_fit, 'r-', label='best fit')
plt.fill_between(x, result.best_fit-dely, result.best_fit+dely,
color="#ABABAB", label=r'3-$\sigma$ uncertainty band')
plt.legend(loc='best')
plt.show()
# <end examples/doc_model_uncertainty.py>
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