How to use Cartopy - 10 common examples

The extent of the image ``(x0, y0, x1, y1)`` referenced in
        the ``img_proj`` coordinate system.

    """
    url = 'https://www.nasa.gov/sites/default/files/pia17037.jpg'
    img_handle = BytesIO(urlopen(url).read())
    raw_image = Image.open(img_handle)
    # The image is extremely high-resolution, which takes a long time to
    # plot. Sub-sampling reduces the time taken to plot while not
    # significantly altering the integrity of the result.
    smaller_image = raw_image.resize([raw_image.size[0] // 10,
                                      raw_image.size[1] // 10])
    img = np.asarray(smaller_image)
    # We define the semimajor and semiminor axes, but must also tell the
    # globe not to use the WGS84 ellipse, which is its default behaviour.
    img_globe = ccrs.Globe(semimajor_axis=285000., semiminor_axis=229000.,
                           ellipse=None)
    img_proj = ccrs.PlateCarree(globe=img_globe)
    img_extent = (-895353.906273091, 895353.906273091,
                  447676.9531365455, -447676.9531365455)
    return img, img_globe, img_proj, img_extent

# Crater catalogue.
        self.craters = igen.ReadLROCHeadCombinedCraterCSV(
            filelroc="../catalogues/LROCCraters.csv",
            filehead="../catalogues/HeadCraters.csv",
            sortlat=True)

        # Long/lat limits
        self.cdim = [-180., 180., -60., 60.]

        # Coordinate systems.
        self.iglobe = ccrs.Globe(semimajor_axis=1737400,
                                 semiminor_axis=1737400,
                                 ellipse=None)
        self.geoproj = ccrs.Geodetic(globe=self.iglobe)
        self.iproj = ccrs.PlateCarree(globe=self.iglobe)

height = 0.8
'''
left = 0.25
bottom = 0.01
width = 0.65
height = 0.98
rect = [left, bottom, width, height]

ax = plt.axes(rect, projection=ccrs.PlateCarree(), )
ax.set_extent((150, 155, -30, -23))

ax.coastlines(resolution='10m', zorder=2)

LAND_10m = cartopy.feature.NaturalEarthFeature('physical', 'land', '10m',
                                               edgecolor='face',
                                               facecolor=cartopy.feature.COLORS['land'])
RIVERS_10m = cartopy.feature.NaturalEarthFeature('physical', 'rivers_lake_centerlines', '10m',
                                                 edgecolor=cartopy.feature.COLORS['water'],
                                                 facecolor='none')
BORDERS2_10m = cartopy.feature.NaturalEarthFeature('cultural', 'admin_1_states_provinces',
                                                   '10m', edgecolor='black', facecolor='none')
ax.add_feature(LAND_10m)
ax.add_feature(RIVERS_10m)
ax.add_feature(BORDERS2_10m, edgecolor='grey')
ax.stock_img()
# stock image is good enough for example, but OCEAN_10m could be used, but very slow
#       ax.add_feature(OCEAN_10m)

ax.gridlines(draw_labels=True, xlocs=[150, 152, 154, 155])

lon0, lon1, lat0, lat1 = ax.get_extent()

bottom = 0.1
width = 0.7
height = 0.8
'''
left = 0.25
bottom = 0.01
width = 0.65
height = 0.98
rect = [left, bottom, width, height]

ax = plt.axes(rect, projection=ccrs.PlateCarree(), )
ax.set_extent((150, 155, -30, -23))

ax.coastlines(resolution='10m', zorder=2)

LAND_10m = cartopy.feature.NaturalEarthFeature('physical', 'land', '10m',
                                               edgecolor='face',
                                               facecolor=cartopy.feature.COLORS['land'])
RIVERS_10m = cartopy.feature.NaturalEarthFeature('physical', 'rivers_lake_centerlines', '10m',
                                                 edgecolor=cartopy.feature.COLORS['water'],
                                                 facecolor='none')
BORDERS2_10m = cartopy.feature.NaturalEarthFeature('cultural', 'admin_1_states_provinces',
                                                   '10m', edgecolor='black', facecolor='none')
ax.add_feature(LAND_10m)
ax.add_feature(RIVERS_10m)
ax.add_feature(BORDERS2_10m, edgecolor='grey')
ax.stock_img()
# stock image is good enough for example, but OCEAN_10m could be used, but very slow
#       ax.add_feature(OCEAN_10m)

ax.gridlines(draw_labels=True, xlocs=[150, 152, 154, 155])

self.imgsize = self.img.size

        # Crater catalogue.
        self.craters = igen.ReadLROCHeadCombinedCraterCSV(
            filelroc="../catalogues/LROCCraters.csv",
            filehead="../catalogues/HeadCraters.csv",
            sortlat=True)

        # Long/lat limits
        self.cdim = [-180., 180., -60., 60.]

        # Coordinate systems.
        self.iglobe = ccrs.Globe(semimajor_axis=1737400,
                                 semiminor_axis=1737400,
                                 ellipse=None)
        self.geoproj = ccrs.Geodetic(globe=self.iglobe)
        self.iproj = ccrs.PlateCarree(globe=self.iglobe)

# ---------------------------------Overview Location Map ------------------------
#
# set up index map 20% height, left 16% of figure
left = 0.03
bottom = 0
width = 0.16
height = 0.2
rect = [left, bottom, width, height]

ax2 = plt.axes(rect, projection=ccrs.PlateCarree(), )
ax2.set_extent((110, 160, -45, -10))
#  ax2.set_global()  will show the whole world as context

ax2.coastlines(resolution='110m', zorder=2)
ax2.add_feature(cfeature.LAND)
ax2.add_feature(cfeature.OCEAN)

ax2.gridlines()

lon0, lon1, lat0, lat1 = ax.get_extent()
box_x = [lon0, lon1, lon1, lon0, lon0]
box_y = [lat0, lat0, lat1, lat1, lat0]

plt.plot(box_x, box_y, color='red', transform=ccrs.Geodetic())

# -------------------------------- Title -----------------------------
# set up map title top 4% of figure, right 80% of figure

left = 0.2
bottom = 0.95
width = 0.8
height = 0.04

def test_colorfill_no_colorbar():
    """Test that we can use ContourFillPlot."""
    data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))

    contour = FilledContourPlot()
    contour.data = data
    contour.level = 700 * units.hPa
    contour.field = 'Temperature'
    contour.colormap = 'coolwarm'
    contour.colorbar = None

    panel = MapPanel()
    panel.area = (-110, -60, 25, 55)
    panel.layers = [cfeature.STATES]
    panel.plots = [contour]

    pc = PanelContainer()
    pc.panel = panel
    pc.size = (8, 8)
    pc.draw()

    return pc.figure

# Convert the fraction value into a code of 0-8, which can be used to pull out
# the appropriate symbol
data['cloud_coverage'] = (8 * data_arr['cloud_fraction']).fillna(10).values.astype(int)

# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx_text = data_arr['weather'].fillna('')
data['present_weather'] = [wx_code_map[s.split()[0] if ' ' in s else s] for s in wx_text]

###########################################
# All the data wrangling is finished, just need to set up plotting and go:
# Set up the map projection and set up a cartopy feature for state borders
proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
                             standard_parallels=[35])
state_boundaries = feat.NaturalEarthFeature(category='cultural',
                                            name='admin_1_states_provinces_lines',
                                            scale='110m', facecolor='none')

###########################################
# 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)

plt.show()

########################################################################################
# We can estimate the polynomial coefficients for this trend:

trend = vd.Trend(degree=1).fit(coordinates, data.air_temperature_c)
print(trend.coef_)

########################################################################################
# More importantly, we can predict the trend values and remove them from our data:

trend_values = trend.predict(coordinates)
residuals = data.air_temperature_c - trend_values

fig, axes = plt.subplots(
    1, 2, figsize=(10, 6), subplot_kw=dict(projection=ccrs.Mercator())
)

ax = axes[0]
ax.set_title("Trend")
tmp = ax.scatter(
    data.longitude,
    data.latitude,
    c=trend_values,
    s=60,
    cmap="plasma",
    transform=ccrs.PlateCarree(),
)
plt.colorbar(tmp, ax=ax, orientation="horizontal", pad=0.06)
vd.datasets.setup_texas_wind_map(ax)

ax = axes[1]

data_arr['wind_dir'].values * units.degree)
data['eastward_wind'], data['northward_wind'] = u, v

# Convert the fraction value into a code of 0-8, which can be used to pull out
# the appropriate symbol
data['cloud_coverage'] = (8 * data_arr['cloud_fraction']).fillna(10).values.astype(int)

# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx_text = data_arr['weather'].fillna('')
data['present_weather'] = [wx_code_map[s.split()[0] if ' ' in s else s] for s in wx_text]

###########################################
# All the data wrangling is finished, just need to set up plotting and go:
# Set up the map projection and set up a cartopy feature for state borders
proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
                             standard_parallels=[35])
state_boundaries = feat.NaturalEarthFeature(category='cultural',
                                            name='admin_1_states_provinces_lines',
                                            scale='110m', facecolor='none')

###########################################
# 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')