Migrate xarray examples

[dcherian]

From the visualization gallery

The data is here

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imshow() and rasterio map projections

Using rasterio's projection information for more accurate plots.

This example extends recipes.rasterio and plots the image in the
original map projection instead of relying on pcolormesh and a map
transformation.

# opening raster files requires the rioxarray package
da = xr.open_rasterio("RGB.byte")

# The data is in UTM projection. We have to set it manually until
# https://github.com/SciTools/cartopy/issues/813 is implemented
crs = ccrs.UTM("18")

# Plot on a map
ax = plt.subplot(projection=crs)
da.plot.imshow(ax=ax, rgb="band", transform=crs)
ax.coastlines("10m", color="r")

Parsing rasterio geocoordinates

Converting a projection's cartesian coordinates into 2D longitudes and
latitudes.

These new coordinates might be handy for plotting and indexing, but it should
be kept in mind that a grid which is regular in projection coordinates will
likely be irregular in lon/lat. It is often recommended to work in the data's
original map projection (see recipes.rasterio_rgb).

from pyproj import Transformer
import numpy as np

# opening raster files requires the rioxarray package
da = xr.tutorial.open_rasterio("RGB.byte")

x, y = np.meshgrid(da["x"], da["y"])
transformer = Transformer.from_crs(da.crs, "EPSG:4326", always_xy=True)
lon, lat = transformer.transform(x, y)
da.coords["lon"] = (("y", "x"), lon)
da.coords["lat"] = (("y", "x"), lat)

# Compute a greyscale out of the rgb image
greyscale = da.mean(dim="band")

# Plot on a map
ax = plt.subplot(projection=ccrs.PlateCarree())
greyscale.plot(
    ax=ax,
    x="lon",
    y="lat",
    transform=ccrs.PlateCarree(),
    cmap="Greys_r",
    shading="auto",
    add_colorbar=False,
)
ax.coastlines("10m", color="r")