.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_examples_documentation_model_savemodelresult2.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_documentation_model_savemodelresult2.py:


doc_model_savemodelresult2.py
=============================




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    [[Model]]
        ((Model(gaussian, prefix='g1_') + Model(gaussian, prefix='g2_')) + Model(exponential, prefix='exp_'))
    [[Fit Statistics]]
        # fitting method   = leastsq
        # function evals   = 46
        # data points      = 250
        # variables        = 8
        chi-square         = 1247.52821
        reduced chi-square = 5.15507524
        Akaike info crit   = 417.864631
        Bayesian info crit = 446.036318
    [[Variables]]
        exp_amplitude:  99.0183282 +/- 0.53748735 (0.54%) (init = 162.2102)
        exp_decay:      90.9508861 +/- 1.10310509 (1.21%) (init = 93.24905)
        g1_amplitude:   4257.77318 +/- 42.3833640 (1.00%) (init = 2000)
        g1_center:      107.030954 +/- 0.15006784 (0.14%) (init = 105)
        g1_sigma:       16.6725753 +/- 0.16048161 (0.96%) (init = 15)
        g1_fwhm:        39.2609138 +/- 0.37790530 (0.96%) == '2.3548200*g1_sigma'
        g1_height:      101.880231 +/- 0.59217099 (0.58%) == '0.3989423*g1_amplitude/max(2.220446049250313e-16, g1_sigma)'
        g2_amplitude:   2493.41771 +/- 36.1694729 (1.45%) (init = 2000)
        g2_center:      153.270100 +/- 0.19466742 (0.13%) (init = 155)
        g2_sigma:       13.8069484 +/- 0.18679415 (1.35%) (init = 15)
        g2_fwhm:        32.5128783 +/- 0.43986659 (1.35%) == '2.3548200*g2_sigma'
        g2_height:      72.0455934 +/- 0.61722093 (0.86%) == '0.3989423*g2_amplitude/max(2.220446049250313e-16, g2_sigma)'
    [[Correlations]] (unreported correlations are < 0.100)
        C(g1_amplitude, g1_sigma)      =  0.824
        C(g2_amplitude, g2_sigma)      =  0.815
        C(exp_amplitude, exp_decay)    = -0.695
        C(g1_sigma, g2_center)         =  0.684
        C(g1_center, g2_amplitude)     = -0.669
        C(g1_center, g2_sigma)         = -0.652
        C(g1_amplitude, g2_center)     =  0.648
        C(g1_center, g2_center)        =  0.621
        C(g1_center, g1_sigma)         =  0.507
        C(exp_decay, g1_amplitude)     = -0.507
        C(g1_sigma, g2_amplitude)      = -0.491
        C(g2_center, g2_sigma)         = -0.489
        C(g1_sigma, g2_sigma)          = -0.483
        C(g2_amplitude, g2_center)     = -0.476
        C(exp_decay, g2_amplitude)     = -0.427
        C(g1_amplitude, g1_center)     =  0.418
        C(g1_amplitude, g2_sigma)      = -0.401
        C(g1_amplitude, g2_amplitude)  = -0.307
        C(exp_amplitude, g2_amplitude) =  0.282
        C(exp_decay, g1_sigma)         = -0.252
        C(exp_decay, g2_sigma)         = -0.233
        C(exp_amplitude, g2_sigma)     =  0.171
        C(exp_decay, g2_center)        = -0.151
        C(exp_amplitude, g1_amplitude) =  0.148
        C(exp_decay, g1_center)        =  0.105






|


.. code-block:: default

    ##
    import warnings
    warnings.filterwarnings("ignore")
    ##
    # <examples/doc_model_savemodelresult2.py>
    import numpy as np

    from lmfit.model import save_modelresult
    from lmfit.models import ExponentialModel, GaussianModel

    dat = np.loadtxt('NIST_Gauss2.dat')
    x = dat[:, 1]
    y = dat[:, 0]

    exp_mod = ExponentialModel(prefix='exp_')
    pars = exp_mod.guess(y, x=x)

    gauss1 = GaussianModel(prefix='g1_')
    pars.update(gauss1.make_params())
    pars['g1_center'].set(value=105, min=75, max=125)
    pars['g1_sigma'].set(value=15, min=3)
    pars['g1_amplitude'].set(value=2000, min=10)

    gauss2 = GaussianModel(prefix='g2_')
    pars.update(gauss2.make_params())
    pars['g2_center'].set(value=155, min=125, max=175)
    pars['g2_sigma'].set(value=15, min=3)
    pars['g2_amplitude'].set(value=2000, min=10)

    mod = gauss1 + gauss2 + exp_mod

    init = mod.eval(pars, x=x)

    result = mod.fit(y, pars, x=x)

    save_modelresult(result, 'nistgauss_modelresult.sav')

    print(result.fit_report())
    # <end examples/doc_model_savemodelresult2.py>


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.058 seconds)


.. _sphx_glr_download_examples_documentation_model_savemodelresult2.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: model_savemodelresult2.py <model_savemodelresult2.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: model_savemodelresult2.ipynb <model_savemodelresult2.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_