"""
Colouring Anomaly Data With Logarithmic Scaling
===============================================

In this example, we need to plot anomaly data where the values have a
"logarithmic" significance  -- i.e. we want to give approximately equal ranges
of colour between data values of, say, 1 and 10 as between 10 and 100.

As the data range also contains zero, that obviously does not suit a simple
logarithmic interpretation.  However, values of less than a certain absolute
magnitude may be considered "not significant", so we put these into a separate
"zero band" which is plotted in white.

To do this, we create a custom value mapping function (normalization) using
the matplotlib Norm class :obj:`matplotlib.colors.SymLogNorm`.
We use this to make a cell-filled pseudocolor plot with a colorbar.

.. note::

    By "pseudocolour", we mean that each data point is drawn as a "cell"
    region on the plot, coloured according to its data value.
    This is provided in Iris by the functions :meth:`iris.plot.pcolor` and
    :meth:`iris.plot.pcolormesh`, which call the underlying matplotlib
    functions of the same names (i.e., :obj:`matplotlib.pyplot.pcolor`
    and :obj:`matplotlib.pyplot.pcolormesh`).
    See also: https://en.wikipedia.org/wiki/False_color#Pseudocolor.

"""  # noqa: D205, D212, D400

import cartopy.crs as ccrs
import matplotlib.colors as mcols
import matplotlib.pyplot as plt

import iris
import iris.coord_categorisation
import iris.plot as iplt


def main():
    # Load a sample air temperatures sequence.
    file_path = iris.sample_data_path("E1_north_america.nc")
    temperatures = iris.load_cube(file_path)

    # Create a year-number coordinate from the time information.
    iris.coord_categorisation.add_year(temperatures, "time")

    # Create a sample anomaly field for one chosen year, by extracting that
    # year and subtracting the time mean.
    sample_year = 1982
    year_temperature = temperatures.extract(iris.Constraint(year=sample_year))
    time_mean = temperatures.collapsed("time", iris.analysis.MEAN)
    anomaly = year_temperature - time_mean

    # Construct a plot title string explaining which years are involved.
    years = temperatures.coord("year").points
    plot_title = "Temperature anomaly"
    plot_title += "\n{} differences from {}-{} average.".format(
        sample_year, years[0], years[-1]
    )

    # Define scaling levels for the logarithmic colouring.
    minimum_log_level = 0.1
    maximum_scale_level = 3.0

    # Use a standard colour map which varies blue-white-red.
    # For suitable options, see the 'Diverging colormaps' section in:
    # https://matplotlib.org/stable/gallery/color/colormap_reference.html
    anom_cmap = "bwr"

    # Create a 'logarithmic' data normalization.
    anom_norm = mcols.SymLogNorm(
        linthresh=minimum_log_level,
        linscale=0.01,
        vmin=-maximum_scale_level,
        vmax=maximum_scale_level,
    )
    # Setting "linthresh=minimum_log_level" makes its non-logarithmic
    # data range equal to our 'zero band'.
    # Setting "linscale=0.01" maps the whole zero band to the middle colour value
    # (i.e., 0.5), which is the neutral point of a "diverging" style colormap.

    # Create an Axes, specifying the map projection.
    plt.axes(projection=ccrs.LambertConformal())

    # Make a pseudocolour plot using this colour scheme.
    mesh = iplt.pcolormesh(anomaly, cmap=anom_cmap, norm=anom_norm)

    # Add a colourbar, with extensions to show handling of out-of-range values.
    bar = plt.colorbar(mesh, orientation="horizontal", extend="both")

    # Set some suitable fixed "logarithmic" colourbar tick positions.
    tick_levels = [-3, -1, -0.3, 0.0, 0.3, 1, 3]
    bar.set_ticks(tick_levels)

    # Modify the tick labels so that the centre one shows "+/-<minumum-level>".
    tick_levels[3] = r"$\pm${:g}".format(minimum_log_level)
    bar.set_ticklabels(tick_levels)

    # Label the colourbar to show the units.
    bar.set_label("[{}, log scale]".format(anomaly.units))

    # Add coastlines and a title.
    plt.gca().coastlines()
    plt.title(plot_title)

    # Display the result.
    iplt.show()


if __name__ == "__main__":
    main()