doc_confidence_basic.py

[[Variables]]
    a:  0.09943896 +/- 1.9322e-04 (0.19%) (init = 0.1)
    b:  1.98476942 +/- 0.01222678 (0.62%) (init = 1)
[[Correlations]] (unreported correlations are < 0.100)
    C(a, b) = +0.6008
      99.73%    95.45%    68.27%    _BEST_    68.27%    95.45%    99.73%
 a:  -0.00059  -0.00039  -0.00019   0.09944  +0.00019  +0.00039  +0.00060
 b:  -0.03764  -0.02477  -0.01229   1.98477  +0.01229  +0.02477  +0.03764

# <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.create_params(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>