.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/gallery/general/plot_polynomial_fit.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_generated_gallery_general_plot_polynomial_fit.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_generated_gallery_general_plot_polynomial_fit.py:


Fitting a Polynomial
====================

This example demonstrates computing a polynomial fit to 1D data from an Iris
cube, adding the fit to the cube's metadata, and plotting both the 1D data and
the fit.

.. GENERATED FROM PYTHON SOURCE LINES 10-55



.. image-sg:: /generated/gallery/general/images/sphx_glr_plot_polynomial_fit_001.png
   :alt: Trend of US air temperature over time
   :srcset: /generated/gallery/general/images/sphx_glr_plot_polynomial_fit_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    import iris
    import iris.quickplot as qplt


    def main():
        # Load some test data.
        fname = iris.sample_data_path("A1B_north_america.nc")
        cube = iris.load_cube(fname)

        # Extract a single time series at a latitude and longitude point.
        location = next(cube.slices(["time"]))

        # Calculate a polynomial fit to the data at this time series.
        x_points = location.coord("time").points
        y_points = location.data
        degree = 2

        p = np.polyfit(x_points, y_points, degree)
        y_fitted = np.polyval(p, x_points)

        # Add the polynomial fit values to the time series to take
        # full advantage of Iris plotting functionality.
        long_name = "degree_{}_polynomial_fit_of_{}".format(degree, cube.name())
        fit = iris.coords.AuxCoord(y_fitted, long_name=long_name, units=location.units)
        location.add_aux_coord(fit, 0)

        qplt.plot(location.coord("time"), location, label="data")
        qplt.plot(
            location.coord("time"),
            location.coord(long_name),
            "g-",
            label="polynomial fit",
        )
        plt.legend(loc="best")
        plt.title("Trend of US air temperature over time")

        qplt.show()


    if __name__ == "__main__":
        main()


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

   **Total running time of the script:** (0 minutes 0.250 seconds)


.. _sphx_glr_download_generated_gallery_general_plot_polynomial_fit.py:

.. only:: html

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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

    .. container:: sphx-glr-download sphx-glr-download-python

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


.. only:: html

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

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