Plot geodata with cartopy#

underlay e.g. terrain data from a Digital Elevation Model (DEM)
overlay features such as administrative borders, rivers, catchments, rain gauges, cities, …

Here, we create a map without radar data to concentrate on the other layers.

[1]:

import wradlib as wrl
import matplotlib.pyplot as pl
import warnings

warnings.filterwarnings("ignore")
try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    pl.ion()
import numpy as np

# Some more matplotlib tools we will need...
import matplotlib.ticker as ticker
from matplotlib.colors import LogNorm
from mpl_toolkits.axes_grid1 import make_axes_locatable
import cartopy.crs as ccrs
import cartopy.feature as cfeature

/home/runner/micromamba-root/envs/wradlib-tests/lib/python3.11/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Plotting a Digital Elevation Model (DEM)#

We use a preprocessed geotiff which was created from SRTM data via gdal

gdalwarp -te 88. 20. 93. 27. srtm_54_07.tif srtm_55_07.tif srtm_54_08.tif srtm_55_08.tif bangladesh.tif

Here we - read the DEM via wradlib.io.open_raster and extracted via wradlib.georef.extract_raster_dataset. - resample the data to a (lon/lat) grid with spacing=0.005.

Note: we organise the code in functions which we can re-use in this notebook.

[2]:

def plot_dem(ax):
    filename = wrl.util.get_wradlib_data_file("geo/bangladesh.tif")
    ds = wrl.io.open_raster(filename)
    # pixel_spacing is in output units (lonlat)
    ds = wrl.georef.reproject_raster_dataset(ds, spacing=0.005)
    rastervalues, rastercoords, proj = wrl.georef.extract_raster_dataset(ds)
    # specify kwargs for plotting, using terrain colormap and LogNorm
    dem = ax.pcolormesh(
        rastercoords[..., 0],
        rastercoords[..., 1],
        rastervalues,
        cmap=pl.cm.terrain,
        norm=LogNorm(vmin=1, vmax=3000),
        transform=ccrs.PlateCarree(),
    )
    # add colorbar and title
    # we use LogLocator for colorbar
    cb = pl.gcf().colorbar(dem, ticks=ticker.LogLocator(subs=range(10)), pad=0.075)
    cb.set_label("terrain height [m]")

[3]:

map_proj = ccrs.Mercator(central_longitude=90.5)

[4]:

fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=map_proj)
plot_dem(ax)
ax.gridlines(draw_labels=True)

[4]:

<cartopy.mpl.gridliner.Gridliner at 0x7f0ad4037c90>

../../_images/notebooks_visualisation_wradlib_overlay_cartopy_11_1.png

Plotting borders#

For country borders, we use ESRI Shapfiles from Natural Earth Data.

We extract features using - the OGR.Layer AttributeFilter and - the wradlib.georef.get_vector_coordinates function.

The plot overlay is done via wradlib.vis.add_lines.

[5]:

def plot_borders(ax):
    borders = cfeature.NaturalEarthFeature(
        category="cultural",
        name="admin_0_boundary_lines_land",
        scale="10m",
        facecolor="none",
    )
    ax.add_feature(borders, edgecolor="black", lw=2, zorder=4)

[6]:

fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=map_proj)
plot_dem(ax)
plot_borders(ax)
ax.set_extent([88, 93, 20, 27], crs=ccrs.PlateCarree())
ax.gridlines(draw_labels=True)

[6]:

<cartopy.mpl.gridliner.Gridliner at 0x7f0ad3e8fdd0>

../../_images/notebooks_visualisation_wradlib_overlay_cartopy_16_1.png

Plotting Rivers#

For rivers, we use ESRI Shapfiles from FAO and Natural Earth Data.

We extract features using - the OGR.Layer SpatialFilter and - the wradlib.georef.get_vector_coordinates function.

Then we use wradlib.vis.add_lines again for the overlay.

[7]:

flist = [
    "geo/rivers_asia_37331.shx",
    "geo/rivers_asia_37331.prj",
    "geo/rivers_asia_37331.dbf",
]
[wrl.util.get_wradlib_data_file(f) for f in flist]


def plot_rivers(ax):
    # plot rivers from esri vector shape, filter spatially
    # http://www.fao.org/geonetwork/srv/en/metadata.show?id=37331

    # open the input data source and get the layer
    filename = wrl.util.get_wradlib_data_file("geo/rivers_asia_37331.shp")
    dataset, inLayer = wrl.io.open_vector(filename)

    # do spatial filtering to get only geometries inside bounding box
    inLayer.SetSpatialFilterRect(88, 20, 93, 27)
    rivers, keys = wrl.georef.get_vector_coordinates(inLayer, key="MAJ_NAME")

    # plot on ax1, and ax4
    wrl.vis.add_lines(
        ax,
        rivers,
        color=pl.cm.terrain(0.0),
        lw=0.5,
        zorder=3,
        transform=ccrs.PlateCarree(),
    )

[8]:

fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=map_proj)
plot_dem(ax)
plot_borders(ax)
plot_rivers(ax)
ax.set_extent([88, 93, 20, 27], crs=ccrs.PlateCarree())
ax.gridlines(draw_labels=True)

[8]:

<cartopy.mpl.gridliner.Gridliner at 0x7f0ac52a7090>

../../_images/notebooks_visualisation_wradlib_overlay_cartopy_21_1.png

[9]:

flist = [
    "geo/ne_10m_rivers_lake_centerlines.shx",
    "geo/ne_10m_rivers_lake_centerlines.prj",
    "geo/ne_10m_rivers_lake_centerlines.dbf",
]
[wrl.util.get_wradlib_data_file(f) for f in flist]


def plot_water(ax):
    # plot rivers from esri vector shape, filter spatially
    # plot rivers from NED
    # open the input data source and get the layer
    filename = wrl.util.get_wradlib_data_file("geo/ne_10m_rivers_lake_centerlines.shp")
    dataset, inLayer = wrl.io.open_vector(filename)
    inLayer.SetSpatialFilterRect(88, 20, 93, 27)
    rivers, keys = wrl.georef.get_vector_coordinates(inLayer)
    wrl.vis.add_lines(
        ax,
        rivers,
        color=pl.cm.terrain(0.0),
        lw=0.5,
        zorder=3,
        transform=ccrs.PlateCarree(),
    )

[10]:

fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=map_proj)
plot_dem(ax)
plot_borders(ax)
plot_rivers(ax)
plot_water(ax)
ax.set_extent([88, 93, 20, 27], crs=ccrs.PlateCarree())
ax.gridlines(draw_labels=True)

[10]:

<cartopy.mpl.gridliner.Gridliner at 0x7f0ac5296f10>

../../_images/notebooks_visualisation_wradlib_overlay_cartopy_23_1.png

Plotting Cities#

The 5 biggest cities of bangladesh are added using simple matplotlib functions.

[11]:

def plot_cities(ax):
    # plot city dots with annotation, finalize plot
    # lat/lon coordinates of five cities in Bangladesh
    lats = [23.73, 22.32, 22.83, 24.37, 24.90]
    lons = [90.40, 91.82, 89.55, 88.60, 91.87]
    cities = ["Dhaka", "Chittagong", "Khulna", "Rajshahi", "Sylhet"]
    for lon, lat, city in zip(lons, lats, cities):
        ax.plot(lon, lat, "ro", zorder=5, transform=ccrs.PlateCarree())
        ax.text(
            lon + 0.01, lat + 0.01, city, fontsize="large", transform=ccrs.PlateCarree()
        )

[12]:

fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=map_proj)
plot_dem(ax)
plot_borders(ax)
plot_rivers(ax)
plot_water(ax)
plot_cities(ax)

ax.set_extent([88, 93, 20, 27], crs=ccrs.PlateCarree())
ax.gridlines(draw_labels=True)
ax.text(
    -0.1,
    0.55,
    "latitude",
    va="bottom",
    ha="center",
    rotation="vertical",
    rotation_mode="anchor",
    transform=ax.transAxes,
)
ax.text(
    0.5,
    -0.05,
    "longitude",
    va="bottom",
    ha="center",
    rotation="horizontal",
    rotation_mode="anchor",
    transform=ax.transAxes,
)
t = ax.set_title("Bangladesh")
t.set_y(1.05)

../../_images/notebooks_visualisation_wradlib_overlay_cartopy_27_0.png

Plotting country patches#

Plotting in “geographic projection” (WGS84)#

Here, we plot countries as patches on a lat/lon (WGS84) map (data from Natural Earth Data again).

We again extract the features using - the OGR.Layer SpatialFilter and - wradlib.georef.get_vector_coordinates.

Then the patches are added one by one via wradlib.vis.add_patches.

[13]:

flist = [
    "geo/ne_10m_admin_0_countries.shx",
    "geo/ne_10m_admin_0_countries.prj",
    "geo/ne_10m_admin_0_countries.dbf",
]
[wrl.util.get_wradlib_data_file(f) for f in flist]


def plot_wgs84(ax):
    from osgeo import osr

    wgs84 = osr.SpatialReference()
    wgs84.ImportFromEPSG(4326)
    # some testing on additional axes
    # add Bangladesh to countries
    countries = ["India", "Nepal", "Bhutan", "Myanmar", "Bangladesh"]
    # create colors for country-patches
    cm = pl.cm.jet
    colors = []
    for i in range(len(countries)):
        colors.append(cm(1.0 * i / len(countries)))

    # open the input data source and get the layer
    filename = wrl.util.get_wradlib_data_file("geo/ne_10m_admin_0_countries.shp")
    dataset, layer = wrl.io.open_vector(filename)

    # filter spatially and plot as PatchCollection on ax3
    layer.SetSpatialFilterRect(88, 20, 93, 27)
    patches, keys = wrl.georef.get_vector_coordinates(layer, dest_srs=wgs84, key="name")
    i = 0
    for name, patch in zip(keys, patches):
        # why comes the US in here?
        if name in countries:
            wrl.vis.add_patches(
                ax,
                patch,
                facecolor=colors[i],
                cmap=pl.cm.viridis,
                alpha=0.4,
                transform=ccrs.PlateCarree(),
            )
            i += 1
    ax.autoscale(True)
    # ax.set_aspect('equal')
    # ax.set_xlabel('Longitude')
    # ax.set_ylabel('Latitude')
    ax.set_extent([66, 102, 5, 37], crs=ccrs.PlateCarree())
    ax.gridlines(draw_labels=True)
    ax.text(
        -0.07,
        0.55,
        "latitude",
        va="bottom",
        ha="center",
        rotation="vertical",
        rotation_mode="anchor",
        transform=ax.transAxes,
    )
    ax.text(
        0.5,
        -0.075,
        "longitude",
        va="bottom",
        ha="center",
        rotation="horizontal",
        rotation_mode="anchor",
        transform=ax.transAxes,
    )
    t = ax.set_title("South Asia - PlateCarree()")
    t.set_y(1.05)

[14]:

fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=ccrs.PlateCarree())
plot_wgs84(ax)

../../_images/notebooks_visualisation_wradlib_overlay_cartopy_34_0.png

Plotting with a map projection#

Here, we plot countries as patches on a projected map.

We extract the features using - the OGR.Layer AttributeFilter and - the wradlib.georef.get_vector_coordinates function.

The coordinates of the features are reprojected on the fly using the dest_srs keyword of wradlib.georef.get_vector_coordinates.

Then, the patches are added to the map via wradlib.vis.add_patches.

[15]:

def plot_lambert(ax):
    # add Bangladesh to countries
    countries = ["India", "Nepal", "Bhutan", "Myanmar", "Bangladesh"]
    # create colors for country-patches
    cm = pl.cm.jet
    colors = []
    for i in range(len(countries)):
        colors.append(cm(1.0 * i / len(countries)))

    # open the input data source and get the layer
    filename = wrl.util.get_wradlib_data_file("geo/ne_10m_admin_0_countries.shp")
    dataset, layer = wrl.io.open_vector(filename)
    # iterate over countries, filter by attribute,
    # plot single patches on ax2
    for i, item in enumerate(countries):
        fattr = "name = '" + item + "'"
        layer.SetAttributeFilter(fattr)
        # get country patches
        patches, keys = wrl.georef.get_vector_coordinates(layer, key="name")
        wrl.vis.add_patches(
            pl.gca(), patches, facecolor=colors[i], transform=ccrs.PlateCarree()
        )

    ax.set_extent([66, 102, 5, 37], crs=ccrs.PlateCarree())
    ax.gridlines()
    ax.text(
        -0.07,
        0.55,
        "latitude",
        va="bottom",
        ha="center",
        rotation="vertical",
        rotation_mode="anchor",
        transform=ax.transAxes,
    )
    ax.text(
        0.5,
        -0.075,
        "longitude",
        va="bottom",
        ha="center",
        rotation="horizontal",
        rotation_mode="anchor",
        transform=ax.transAxes,
    )
    t = ax.set_title("South Asia in LambertAzimuthalEqualArea")
    t.set_y(1.05)

[16]:

fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(
    111,
    projection=ccrs.LambertAzimuthalEqualArea(
        central_longitude=84, central_latitude=20
    ),
)
plot_lambert(ax)

../../_images/notebooks_visualisation_wradlib_overlay_cartopy_40_0.png

Plot geodata with cartopy

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