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

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

.. _sphx_glr_examples_documentation_model_with_nan_policy.py:


doc_model_with_nan_policy.py
============================



.. image:: /examples/documentation/images/sphx_glr_model_with_nan_policy_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    [[Model]]
        Model(gaussian)
    [[Fit Statistics]]
        # fitting method   = leastsq
        # function evals   = 22
        # data points      = 99
        # variables        = 3
        chi-square         = 3.27990355
        reduced chi-square = 0.03416566
        Akaike info crit   = -331.323278
        Bayesian info crit = -323.537918
    [[Variables]]
        amplitude:  8.82064765 +/- 0.11686065 (1.32%) (init = 5)
        center:     5.65906365 +/- 0.01055590 (0.19%) (init = 6)
        sigma:      0.69165290 +/- 0.01060625 (1.53%) (init = 1)
        fwhm:       1.62871808 +/- 0.02497581 (1.53%) == '2.3548200*sigma'
        height:     5.08771012 +/- 0.06488211 (1.28%) == '0.3989423*amplitude/max(2.220446049250313e-16, sigma)'
    [[Correlations]] (unreported correlations are < 0.100)
        C(amplitude, sigma) =  0.610






|


.. code-block:: default

    ##
    import warnings
    warnings.filterwarnings("ignore")
    ##
    # <examples/doc_model_with_nan_policy.py>
    import matplotlib.pyplot as plt
    import numpy as np

    from lmfit.models import GaussianModel

    data = np.loadtxt('model1d_gauss.dat')
    x = data[:, 0]
    y = data[:, 1]

    y[44] = np.nan
    y[65] = np.nan

    # nan_policy = 'raise'
    # nan_policy = 'propagate'
    nan_policy = 'omit'

    gmodel = GaussianModel()
    result = gmodel.fit(y, x=x, amplitude=5, center=6, sigma=1,
                        nan_policy=nan_policy)

    print(result.fit_report())

    # make sure nans are removed for plotting:
    x_ = x[np.where(np.isfinite(y))]
    y_ = y[np.where(np.isfinite(y))]

    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_with_nan_policy.py>


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

   **Total running time of the script:** ( 0 minutes  0.117 seconds)


.. _sphx_glr_download_examples_documentation_model_with_nan_policy.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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