.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_documentation_confidence_basic.py: doc_confidence_basic.py ======================= .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [[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) = 0.601 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: -0.03766 -0.02478 -0.01230 1.98477 +0.01230 +0.02478 +0.03761 | .. code-block:: default ## import warnings warnings.filterwarnings("ignore") ## # 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) # .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.286 seconds) .. _sphx_glr_download_examples_documentation_confidence_basic.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: confidence_basic.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: confidence_basic.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_