.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/gallery/meteorology/plot_hovmoller.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_meteorology_plot_hovmoller.py>`
        to download the full example code

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

.. _sphx_glr_generated_gallery_meteorology_plot_hovmoller.py:


Hovmoller Diagram of Monthly Surface Temperature
================================================

This example demonstrates the creation of a Hovmoller diagram with fine control
over plot ticks and labels. The data comes from the Met Office OSTIA project
and has been pre-processed to calculate the monthly mean sea surface
temperature.

.. GENERATED FROM PYTHON SOURCE LINES 11-52



.. image-sg:: /generated/gallery/meteorology/images/sphx_glr_plot_hovmoller_001.png
   :alt: Surface temperature
   :srcset: /generated/gallery/meteorology/images/sphx_glr_plot_hovmoller_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.dates as mdates
    import matplotlib.pyplot as plt

    import iris
    import iris.plot as iplt
    import iris.quickplot as qplt


    def main():
        # load a single cube of surface temperature between +/- 5 latitude
        fname = iris.sample_data_path("ostia_monthly.nc")
        cube = iris.load_cube(
            fname,
            iris.Constraint("surface_temperature", latitude=lambda v: -5 < v < 5),
        )

        # Take the mean over latitude
        cube = cube.collapsed("latitude", iris.analysis.MEAN)

        # Now that we have our data in a nice way, lets create the plot
        # contour with 20 levels
        qplt.contourf(cube, 20)

        # Put a custom label on the y axis
        plt.ylabel("Time / years")

        # Stop matplotlib providing clever axes range padding
        plt.axis("tight")

        # As we are plotting annual variability, put years as the y ticks
        plt.gca().yaxis.set_major_locator(mdates.YearLocator())

        # And format the ticks to just show the year
        plt.gca().yaxis.set_major_formatter(mdates.DateFormatter("%Y"))

        iplt.show()


    if __name__ == "__main__":
        main()


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

   **Total running time of the script:** (0 minutes 0.302 seconds)


.. _sphx_glr_download_generated_gallery_meteorology_plot_hovmoller.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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