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

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

.. _sphx_glr_examples_documentation_model_two_components.py:


doc_model_two_components.py
===========================



.. image:: /examples/documentation/images/sphx_glr_model_two_components_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    [[Model]]
        (Model(gaussian) + Model(line))
    [[Fit Statistics]]
        # fitting method   = leastsq
        # function evals   = 44
        # data points      = 101
        # variables        = 5
        chi-square         = 2.57855517
        reduced chi-square = 0.02685995
        Akaike info crit   = -360.457020
        Bayesian info crit = -347.381417
    [[Variables]]
        amp:        8.45931062 +/- 0.12414515 (1.47%) (init = 5)
        cen:        5.65547873 +/- 0.00917678 (0.16%) (init = 5)
        wid:        0.67545524 +/- 0.00991686 (1.47%) (init = 1)
        slope:      0.26484404 +/- 0.00574892 (2.17%) (init = 0)
        intercept: -0.96860202 +/- 0.03352202 (3.46%) (init = 1)
    [[Correlations]] (unreported correlations are < 0.100)
        C(slope, intercept) = -0.795
        C(amp, wid)         =  0.666
        C(amp, intercept)   = -0.222
        C(amp, slope)       = -0.169
        C(cen, slope)       = -0.162
        C(wid, intercept)   = -0.148
        C(cen, intercept)   =  0.129
        C(wid, slope)       = -0.113






|


.. code-block:: default

    ##
    import warnings
    warnings.filterwarnings("ignore")
    ##
    # <examples/doc_model_two_components.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] + 0.25*x - 1.0


    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))


    def line(x, slope, intercept):
        """a line"""
        return slope*x + intercept


    mod = Model(gaussian) + Model(line)
    pars = mod.make_params(amp=5, cen=5, wid=1, slope=0, intercept=1)

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

    print(result.fit_report())

    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.legend(loc='best')
    plt.show()
    # <end examples/doc_model_two_components.py>


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

   **Total running time of the script:** ( 0 minutes  0.142 seconds)


.. _sphx_glr_download_examples_documentation_model_two_components.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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