NCL_panel_4.py

Note: The colormap has been changed from the original NCL colormap in order to follow

best practices for colormaps. See more examples here: https://geocat-examples.readthedocs.io/en/latest/gallery/index.html#colors

This script illustrates the following concepts:

• Paneling three plots vertically on a page

• Adding a common title to paneled plots

• Adding a common colorbar to paneled plots

• Adding additional text at the bottom of a series of paneled plots

• Subsetting a color map

See following URLs to see the reproduced NCL plot & script:

• Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/panel_4.ncl

• Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/panel_4_lg.png

Import Packages

import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LongitudeFormatter, LatitudeFormatter
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
import xarray as xr

import geocat.datafiles as gdf
import geocat.viz as gv

Read in data:

# Open a netCDF data file using xarray default engine and save as a variable
ds = xr.open_dataset(gdf.get("netcdf_files/uv300.nc"))

# save the zonal and meridional wind separately, select July data
zonal = ds.U.isel(time=1)
meridional = ds.V.isel(time=1)

Define plotting helper function

# Define a utility plotting function in order not to repeat many lines of code
# since we need to make the same figure with two different variables.


def plot_labelled_filled_contours(data, ax=None):
    """A utility function for plotting labelled, filled contours with black
    contour outlines marking each level.

    Parameters
    ----------

    data : :class:`xarray.DataArray`:
        A two-dimensional array with longitude and latitude as dimensions.

    ax : :class:`cartopy.mpl.geoaxes.GeoAxesSubplot`:
        An axes object from Matplotlib package with projection from Cartopy package.

    Returns
    -------

    handles : :class:`dict`:
        A dictionary containing three objects corresponding to the filled contours, the black
        contour outlines, and the contour labels.

    Description
    -----------

        Produce labeled and filled contour on the world map with tickmarks and
        tick labels.
    """

    handles = dict()
    handles["filled"] = data.plot.contourf(
        ax=ax,  # this is the axes we want to plot to
        cmap='viridis',  # our colormap
        levels=levels,  # contour levels specified outside this function
        transform=projection,  # data projection
        add_colorbar=False,  # don't add individual colorbars for each plot call
        add_labels=False,  # turn off xarray's automatic Lat, lon labels
    )

    # matplotlib's "contourf" doesn't let you specify "edgecolors",
    # instead we use matplotlib's "contour" to plot contour lines on top of the filled contours
    handles["contour"] = data.plot.contour(
        ax=ax,
        levels=levels,
        colors="black",  # note plurals in this and following kwargs
        linestyles="-",
        linewidths=0.5,
        add_labels=False,  # again turn off automatic labels
    )

    # Label the contours
    ax.clabel(
        handles["contour"],
        levels=np.arange(-10, 50, 10),
        fontsize=8,
        fmt="%.0f",  # Turn off decimal points
    )

    # Add coastlines and make them semitransparent for plot legibility
    ax.coastlines(linewidth=0.5, alpha=0.75)

    # Use geocat.viz.util convenience function to set axes tick values
    gv.set_axes_limits_and_ticks(ax,
                                 xticks=np.arange(-180, 181, 30),
                                 yticks=np.arange(-90, 91, 30))

    # Use geocat.viz.util convenience function to add minor and major tick lines
    gv.add_major_minor_ticks(ax, labelsize=8)

    # Use geocat.viz.util convenience function to make plots look like NCL plots by using latitude, longitude tick labels
    gv.add_lat_lon_ticklabels(ax)
    # Remove degree symbol from tick labels
    ax.yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
    ax.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))

    # Use geocat.viz.util convenience function to add main title as well as titles to left and right of the plot axes.
    gv.set_titles_and_labels(ax,
                             lefttitle=data.attrs['long_name'],
                             lefttitlefontsize=10,
                             righttitle=data.attrs['units'],
                             righttitlefontsize=10)

    return handles

Plot

# Make three panels (i.e. subplots in matplotlib) specifying white space
# between them using gridspec_kw and hspace
# Generate figure and axes using Cartopy projection
projection = ccrs.PlateCarree()
fig, ax = plt.subplots(3,
                       1,
                       figsize=(6, 10),
                       gridspec_kw=dict(hspace=0.3),
                       subplot_kw={"projection": projection})
# Define the contour levels
levels = np.linspace(-10, 50, 13)

# Contour-plot U data, save "handles" to add a colorbar later
handles = plot_labelled_filled_contours(zonal, ax=ax[0])

# Set a common title
plt.suptitle("A common title", fontsize=16, y=0.94)

# Contour-plot V data
plot_labelled_filled_contours(meridional, ax=ax[1])

# Contour-plot U data again but in the bottom axes
plot_labelled_filled_contours(zonal, ax=ax[2])

# Create inset axes for colorbar
cax = inset_axes(ax[2],
                 width='100%',
                 height='10%',
                 loc='lower left',
                 bbox_to_anchor=(0, -0.25, 1, 1),
                 bbox_transform=ax[2].transAxes,
                 borderpad=0)
# Add horizontal colorbar
cbar = plt.colorbar(handles["filled"],
                    cax=cax,
                    orientation="horizontal",
                    ticks=levels[:-1],
                    drawedges=True,
                    aspect=30,
                    extendrect=True,
                    extendfrac='auto',
                    shrink=1)
cbar.ax.tick_params(labelsize=10)

# Add figure label underneath subplots
fig.text(0.5,
         0.015,
         "Figure 1: A nifty panel plot",
         horizontalalignment='center',
         fontsize=14)

# Show the plot
plt.show()