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

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

.. _sphx_glr_advanced_mathematical_optimization_auto_examples_plot_curve_fitting.py:


Curve fitting
=============

A curve fitting example




.. image:: /advanced/mathematical_optimization/auto_examples/images/sphx_glr_plot_curve_fitting_001.png
    :class: sphx-glr-single-img





.. code-block:: python


    import numpy as np
    from scipy import optimize
    import matplotlib.pyplot as plt

    np.random.seed(0)

    # Our test function
    def f(t, omega, phi):
        return np.cos(omega * t + phi)

    # Our x and y data
    x = np.linspace(0, 3, 50)
    y = f(x, 1.5, 1) + .1*np.random.normal(size=50)

    # Fit the model: the parameters omega and phi can be found in the
    # `params` vector
    params, params_cov = optimize.curve_fit(f, x, y)

    # plot the data and the fitted curve
    t = np.linspace(0, 3, 1000)

    plt.figure(1)
    plt.clf()
    plt.plot(x, y, 'bx')
    plt.plot(t, f(t, *params), 'r-')
    plt.show()


**Total running time of the script:** ( 0 minutes  0.014 seconds)


.. _sphx_glr_download_advanced_mathematical_optimization_auto_examples_plot_curve_fitting.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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