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# <examples/doc_confidence_basic.py>
import numpy as np
import lmfit
x = np.linspace(0.3, 10, 100)
np.random.seed(0)
y = 1/(0.1*x) + 2 + 0.1*np.random.randn(x.size)
pars = lmfit.Parameters()
pars.add_many(('a', 0.1), ('b', 1))
def residual(p):
return 1/(p['a']*x) + p['b'] - y
mini = lmfit.Minimizer(residual, pars)
result = mini.minimize()
print(lmfit.fit_report(result.params))
ci = lmfit.conf_interval(mini, result)
lmfit.printfuncs.report_ci(ci)
# <end examples/doc_confidence_basic.py>
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