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

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# 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()

# and these cover a margin below the main map
left2 = left1
right2 = right1
bottom2 = bottom1
top2 = bottom1 + 0.2 * (top1 - bottom1)

# now we overlay a new axes object on to the plot
# These are in unitless percentages of the figure size. (0,0 is bottom left)
left, bottom, width, height = [0.05, 0.3, 0.9, 0.6]
ax1 = fig.add_axes([left, bottom, width, height], projection=tifproj)
ax1.set_adjustable('box-forced')

# add coastlines etc.
ax1.coastlines(resolution='10m', color='navy', linewidth=1)
ax1.gridlines()
ax1.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax1.add_feature(cartopy.feature.RIVERS)
BORDERS.scale = '10m'
ax1.add_feature(BORDERS, color='red')

# axes ticks
xticks = np.arange(left1, right1, 100000)
yticks = np.arange(bottom1, top1, 100000)
ax1.set_xticks(xticks, crs=tifproj)
ax1.set_yticks(yticks, crs=tifproj)
# stagger x gridline / tick labels
#labels = ax1.set_xticklabels(xticks)
#for i, label in enumerate(labels):
#    label.set_y(label.get_position()[1] - (i % 2) * 0.1)
# rotate the font orientation of the axis tick labels
plt.setp(ax1.get_xticklabels(), rotation=30, horizontalalignment='right')

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# 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()

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'],
                          transform=ccrs.PlateCarree(), fontsize=12)

# 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'],
                          transform=ccrs.PlateCarree(), fontsize=12)

from metpy.testing import get_test_data

ds = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
data_var = ds.metpy.parse_cf('Temperature')

x = data_var.x
y = data_var.y
im_data = data_var.isel(time=0).sel(isobaric=1000.)

fig = plt.figure(figsize=(14, 14))
ax = fig.add_subplot(1, 1, 1, projection=data_var.metpy.cartopy_crs)

ax.imshow(im_data, extent=(x.min(), x.max(), y.min(), y.max()),
          cmap='RdBu', origin='lower' if y[0] < y[-1] else 'upper')
ax.coastlines(color='tab:green', resolution='10m')
ax.add_feature(cfeature.LAKES.with_scale('10m'), facecolor='none', edgecolor='tab:blue')
ax.add_feature(cfeature.RIVERS.with_scale('10m'), edgecolor='tab:blue')

plt.show()

#
# This should help us visualize where the precipitation event was strongest!

proj = ccrs.LambertConformal(central_longitude=-105, central_latitude=0,
                             standard_parallels=[35])

fig = plt.figure(figsize=(20, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)

state_boundaries = feat.NaturalEarthFeature(category='cultural',
                                            name='admin_1_states_provinces_lines',
                                            scale='110m', facecolor='none')

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((-109.9, -101.8, 36.5, 41.3))

# Plot each station, labeling based on departure
for stn in range(len(pcpn)):
    if pcpn_dep[stn] >= 0 and pcpn_dep[stn] < 2:
        ax.plot(lon[stn], lat[stn], 'g+', markersize=7, transform=ccrs.Geodetic())
        ax.text(lon[stn], lat[stn], pcpn[stn], transform=ccrs.Geodetic())
    elif pcpn_dep[stn] >= 2:
        ax.plot(lon[stn], lat[stn], 'm+', markersize=7, transform=ccrs.Geodetic())
        ax.text(lon[stn], lat[stn], pcpn[stn], transform=ccrs.Geodetic())
    elif pcpn_dep[stn] < 0:

from cartopy import feature as cf

from .element import Feature

borders   = Feature(cf.BORDERS, group='Borders')
coastline = Feature(cf.COASTLINE, group='Coastline')
land      = Feature(cf.LAND, group='Land')
lakes     = Feature(cf.LAKES, group='Lakes')
ocean     = Feature(cf.OCEAN, group='Ocean')
rivers    = Feature(cf.RIVERS, group='Rivers')
states    = FEATURE(cf.STATES, group='States')
grid      = Feature(cf.NaturalEarthFeature(category='physical',
                                           name='graticules_30',
                                           scale='110m'), group='Grid')

)
ax.set_global()
ax.outline_patch.set_visible(True)
ax.gridlines()
ax.coastlines()
ax.add_feature(cartopy.feature.BORDERS)

ax = gplt.sankey(
    la_flights, scale='Passengers', hue='Passengers', cmap='Purples', ax=axarr[1][1]
)
ax.set_global()
ax.outline_patch.set_visible(True)
ax.coastlines()
ax.add_feature(cartopy.feature.LAND)
ax.add_feature(cartopy.feature.OCEAN)
ax.add_feature(cartopy.feature.LAKES)
ax.add_feature(cartopy.feature.RIVERS)

plt.savefig("los-angeles-flights.png", bbox_inches='tight', pad_inches=0.1)

shape_feature = ShapelyFeature(Reader(shapefile).geometries(),
                                   crs=shapeproj, edgecolor='yellow',
                                   facecolor='none')
    ax.add_feature(shape_feature)

    # add a title
    plt.title(plottitle)

    # set map extent
    ax.set_extent(mapextent, tifproj)

    # add coastlines
    ax.coastlines(resolution='10m', color='navy', linewidth=1)

    # add lakes and rivers
    ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
    ax.add_feature(cartopy.feature.RIVERS)

    # add borders
    BORDERS.scale = '10m'
    ax.add_feature(BORDERS, color='red')

    # format the gridline positions nicely
    xticks, yticks = get_gridlines(mapextent[0], mapextent[1],
                                   mapextent[2], mapextent[3],
                                   nticks=10)

    # add gridlines
    gl = ax.gridlines(crs=tifproj, xlocs=xticks, ylocs=yticks,
                      linestyle='--', color='grey', alpha=1, linewidth=1)

    # add ticks