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# -*- coding: utf-8 -*-
"""
.. _recipes.rasterio:
=================================
Parsing rasterio's 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 :ref:`recipes.rasterio_rgb`).
"""
import os
import urllib.request
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
from rasterio.warp import transform
import xarray as xr
# Download the file from rasterio's repository
url = 'https://github.com/mapbox/rasterio/raw/master/tests/data/RGB.byte.tif'
urllib.request.urlretrieve(url, 'RGB.byte.tif')
# Read the data
da = xr.open_rasterio('RGB.byte.tif')
# Compute the lon/lat coordinates with rasterio.warp.transform
ny, nx = len(da['y']), len(da['x'])
x, y = np.meshgrid(da['x'], da['y'])
# Rasterio works with 1D arrays
lon, lat = transform(da.crs, {'init': 'EPSG:4326'},
x.flatten(), y.flatten())
lon = np.asarray(lon).reshape((ny, nx))
lat = np.asarray(lat).reshape((ny, nx))
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', add_colorbar=False)
ax.coastlines('10m', color='r')
plt.show()
# Delete the file
os.remove('RGB.byte.tif')
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