.. _graphics-hovmoller:


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.



.. plot:: /net/home/h05/itpp/git/iris/iris_main/docs/iris/example_code/graphics/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.
    
    """
    import matplotlib.pyplot as plt
    import matplotlib.dates as mdates
    
    import iris
    import iris.quickplot as qplt
    import iris.unit
    
    
    def main():
        fname = iris.sample_data_path('ostia_monthly.nc')
    
        # load a single cube of surface temperature between +/- 5 latitude
        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'))
    
        plt.show()
    
    
    if __name__ == '__main__':
        main()