How to use the cartopy.feature.LAND function in Cartopy

transform=ccrs.PlateCarree(),
)
plt.colorbar().set_label("Air temperature (C)")
ax.quiver(
    data.longitude.values,
    data.latitude.values,
    data.wind_speed_east_knots.values,
    data.wind_speed_north_knots.values,
    width=0.003,
    transform=ccrs.PlateCarree(),
)

# Add tick labels and land and ocean features to the map.
region = (-107, -93, 25.5, 37)
crs = ccrs.PlateCarree()
ax.add_feature(cfeature.LAND, facecolor="#dddddd")
ax.add_feature(cfeature.BORDERS, linewidth=0.5)
ax.add_feature(cfeature.STATES, linewidth=0.1)
ax.set_extent(region, crs=crs)
ax.set_xticks(np.arange(-106, -92, 3), crs=crs)
ax.set_yticks(np.arange(27, 38, 3), crs=crs)
ax.xaxis.set_major_formatter(LongitudeFormatter())
ax.yaxis.set_major_formatter(LatitudeFormatter())

plt.tight_layout()
plt.show()

###########################################
# The payoff
# ----------

# Change the DPI of the resulting figure. Higher DPI drastically improves the
# look of the text rendering
plt.rcParams['savefig.dpi'] = 255

# Create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)

# Add some various map elements to the plot to make it recognizable
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS, linewidth=2)

# Set plot bounds
ax.set_extent((-118, -73, 23, 50))

#
# Here's the actual station plot
#

# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, data['longitude'], data['latitude'],

custom_layout = StationPlotLayout()
custom_layout.add_barb('eastward_wind', 'northward_wind', units='knots')
custom_layout.add_value('NW', 'air_temperature', fmt='.1f', units='degF', color='darkred')
custom_layout.add_value('SW', 'dew_point_temperature', fmt='.1f', units='degF',
                        color='darkgreen')

# Also, we'll add a field that we don't have in our dataset. This will be ignored
custom_layout.add_value('E', 'precipitation', fmt='0.2f', units='inch', color='blue')

# Create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)

# Add some various map elements to the plot to make it recognizable
ax.add_feature(feat.LAND, zorder=-1)
ax.add_feature(feat.OCEAN, zorder=-1)
ax.add_feature(feat.LAKES, zorder=-1)
ax.coastlines(resolution='110m', zorder=2, color='black')
ax.add_feature(state_boundaries, edgecolor='black')
ax.add_feature(feat.BORDERS, linewidth=2, edgecolor='black')

# Set plot bounds
ax.set_extent((-118, -73, 23, 50))

#
# Here's the actual station plot
#

# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, data['longitude'], data['latitude'],

# The background patch and axes frame are not needed in this example.
    ax.background_patch.set_facecolor('none')

    # We want the map to go down to -60 degrees latitude.
    ax.set_extent([-180, 180, -60, 90], ccrs.PlateCarree())

    # Importantly, we want the axes to be circular at the -60 latitude
    # rather than cartopy's default behaviour of zooming in and becoming
    # square.
    _, patch_radius = az_eq.transform_point(0, -60, pc)
    circular_path = matplotlib.path.Path.circle(0, patch_radius)
    ax.set_boundary(circular_path)

    if filled_land:
        ax.add_feature(
            cfeature.LAND, facecolor='white', edgecolor='none')
    else:
        ax.stock_img()

    gl = ax.gridlines(crs=pc, linewidth=3, color='white', linestyle='-')
    # Meridians every 45 degrees, and 5 parallels.
    gl.xlocator = matplotlib.ticker.FixedLocator(np.arange(0, 361, 45))
    parallels = np.linspace(-60, 70, 5, endpoint=True)
    gl.ylocator = matplotlib.ticker.FixedLocator(parallels)

    # Now add the olive branches around the axes. We do this in normalised
    # figure coordinates
    olive_leaf = olive_path()

    olives_bbox = Bbox.null()
    olives_bbox.update_from_path(olive_leaf)

custom_layout = StationPlotLayout()
custom_layout.add_barb('eastward_wind', 'northward_wind', units='knots')
custom_layout.add_value('NW', 'air_temperature', fmt='.1f', units='degF', color='darkred')
custom_layout.add_value('SW', 'dew_point_temperature', fmt='.1f', units='degF',
                        color='darkgreen')

# Also, we'll add a field that we don't have in our dataset. This will be ignored
custom_layout.add_value('E', 'precipitation', fmt='0.2f', units='inch', color='blue')

# Create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)

# Add some various map elements to the plot to make it recognizable
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS, linewidth=2)

# Set plot bounds
ax.set_extent((-118, -73, 23, 50))

#
# Here's the actual station plot
#

# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, data['longitude'], data['latitude'],

def draw_map(size, qsos_by_section):
    """
    make the choropleth with Cartopy & section shapefiles
    """
    logging.debug('draw_section map()')
    width_inches = size[0] / 100.0
    height_inches = size[1] / 100.0
    fig = plt.Figure(figsize=(width_inches, height_inches), dpi=100, facecolor='black')

    projection = ccrs.PlateCarree()
    ax = fig.add_axes([0, 0, 1, 1], projection=projection)
    ax.set_extent([-168, -52, 10, 60], ccrs.Geodetic())
    ax.add_feature(cfeature.OCEAN, color='#000080')
    ax.add_feature(cfeature.LAKES, color='#000080')
    ax.add_feature(cfeature.LAND, color='#113311')

    ax.coastlines('50m')
    ax.annotate('Sections Worked', xy=(0.5, 1), xycoords='axes fraction', ha='center', va='top',
                color='white', size=48, weight='bold')
    
    ax.text(0.83, 0,datetime.datetime.utcnow().strftime("%d %b %Y %H:%M %Zz"),
               transform = ax.transAxes,style='italic', size=14,color='white')
    ranges = [0, 1, 2, 10, 20, 50, 100]  # , 500]  # , 1000]
    num_colors = len(ranges)
    color_palette = matplotlib.cm.viridis(np.linspace(0.33, 1, num_colors + 1))

    for section_name in CONTEST_SECTIONS.keys():
        qsos = qsos_by_section.get(section_name)
        if qsos is None:
            qsos = 0

def create_plot(self):
        """! Create the plot, using Cartopy.

        """

        # Use PlateCarree projection for now
        #use central meridian for central longitude
        cm_lon = 180
        ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=cm_lon))
        # ax = plt.axes(projection=ccrs.LambertCylindrical(central_longitude=0.0))

        # Add land, coastlines, and ocean
        ax.add_feature(cfeature.LAND)
        ax.coastlines()
        ax.add_feature(cfeature.OCEAN)

        # Add grid lines for longitude and latitude
        ax.gridlines(draw_labels=False, xlocs=[180, -180])
        gl = ax.gridlines(crs=ccrs.PlateCarree(central_longitude=0.0),
                          draw_labels=True, linewidth=1, color='gray',
                          alpha=0.5, linestyle='--')
        gl.xlabels_top = False
        gl.ylabels_left = False
        gl.xlines = True
        gl.xlocator = mticker.FixedLocator(
            [ -180,-140, -100, -60, -20, 20, 60, 100, 140, 180])
        gl.xformatter = LONGITUDE_FORMATTER
        gl.yformatter = LATITUDE_FORMATTER
        gl.xlabel_style = {'size': 9, 'color': 'blue'}

ax.barbs(x[wind_slice], y[wind_slice],
         data_level['u'].metpy.unit_array[wind_slice, wind_slice].to('knots'),
         data_level['v'].metpy.unit_array[wind_slice, wind_slice].to('knots'),
         length=6)

# Plot heights and temperature as contours
h_contour = ax.contour(x, y, data_level['height'], colors='k', levels=range(5400, 6000, 60))
h_contour.clabel(fontsize=8, colors='k', inline=1, inline_spacing=8,
                 fmt='%i', rightside_up=True, use_clabeltext=True)
t_contour = ax.contour(x, y, data_level['temperature'], colors='xkcd:deep blue',
                       levels=range(-26, 4, 2), alpha=0.8, linestyles='--')
t_contour.clabel(fontsize=8, colors='xkcd:deep blue', inline=1, inline_spacing=8,
                 fmt='%i', rightside_up=True, use_clabeltext=True)

# Add geographic features
ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor=cfeature.COLORS['land'])
ax.add_feature(cfeature.OCEAN.with_scale('50m'), facecolor=cfeature.COLORS['water'])
ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='#c7c783', zorder=0)
ax.add_feature(cfeature.LAKES.with_scale('50m'), facecolor=cfeature.COLORS['water'],
               edgecolor='#c7c783', zorder=0)

# Set a title and show the plot
ax.set_title('500 hPa Heights (m), Temperature (\u00B0C), Humidity (%) at '
             + time[0].dt.strftime('%Y-%m-%d %H:%MZ').item())
plt.show()

# Change the DPI of the resulting figure. Higher DPI drastically improves the
# look of the text rendering.
plt.rcParams['savefig.dpi'] = 255

# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)

# Set up a cartopy feature for state borders.
state_boundaries = feat.NaturalEarthFeature(category='cultural',
                                            name='admin_1_states_provinces_lines',
                                            scale='110m', facecolor='none')

# Add some various map elements to the plot to make it recognizable.
ax.add_feature(feat.LAND, zorder=-1)
ax.add_feature(feat.OCEAN, zorder=-1)
ax.add_feature(feat.LAKES, zorder=-1)
ax.coastlines(resolution='110m', zorder=2, color='black')
ax.add_feature(state_boundaries, edgecolor='black')
ax.add_feature(feat.BORDERS, linewidth=2, edgecolor='black')

# Set plot bounds
ax.set_extent((-118, -73, 23, 50))

#
# Here's the actual station plot
#

# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, data['lon'].values, data['lat'].values, clip_on=True,

###########################################
# The payoff
# ----------

# Change the DPI of the resulting figure. Higher DPI drastically improves the
# look of the text rendering
plt.rcParams['savefig.dpi'] = 255

# Create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)

# Add some various map elements to the plot to make it recognizable
ax.add_feature(feat.LAND, zorder=-1)
ax.add_feature(feat.OCEAN, zorder=-1)
ax.add_feature(feat.LAKES, zorder=-1)
ax.coastlines(resolution='110m', zorder=2, color='black')
ax.add_feature(state_boundaries, edgecolor='black')
ax.add_feature(feat.BORDERS, linewidth=2, edgecolor='black')

# Set plot bounds
ax.set_extent((-118, -73, 23, 50))

#
# Here's the actual station plot
#

# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, data['longitude'], data['latitude'],