# Background image in cartopy

Plot map of the temperature distribution in the ocean with some nice picture on the background.



Solution:

background_img function in cartopy 0.15

The cartopy is a great tool for creating maps in many ways more advanced than the usual workhorse for map creation in pyhton - the Basemap module. However I really missed one nice feature that Basemap have - easy way to add background image to the map. With the Basemap instance one can just write m.etopo() and get a relativelly nice map of the ETOPO topography overplayed or m.bluemarble() for the NASA's "Blue marble" image of the Earth . In the cartopy there is similar feature stock_img() but there is only one available image. In the newest version of cartopy developers add a background_img() method, that allows to add background images in a more convenient way. I will show you how to setup cartopy to use custom background images.

Imports

In [2]:
from netCDF4 import Dataset, MFDataset, num2date
import matplotlib.pylab as plt
%matplotlib inline
import numpy as np
from matplotlib import cm
import cartopy.crs as ccrs
from cmocean import cm as cmo

import sys
import os
from cartopy.util import add_cyclic_point


We will download World Ocean Atlas temperatures file to have something to plot.

In [5]:
#!wget http://data.nodc.noaa.gov/thredds/fileServer/woa/WOA09/NetCDFdata/temperature_annual_1deg.nc


Open the file and read in coordinates

In [3]:
flf = Dataset('./temperature_annual_1deg.nc')
lat = flf.variables['lat'][:]
lon = flf.variables['lon'][:]


Read in temperature from the first layer.

In [4]:
temp = flf.variables['t_an'][0,0,:,:]


Make a basic cartopy plot:

In [5]:
plt.figure(figsize=(13,6.2))

ax = plt.subplot(111, projection=ccrs.PlateCarree())

mm = ax.pcolormesh(lon,\
lat,\
temp,\
vmin=-2,\
vmax=30,\
transform=ccrs.PlateCarree(),cmap=cmo.thermal )
ax.coastlines();


Ok, there is an ugly white line along 0 longitude, there is a simple way to fix it:

In [6]:
temp_cyc, lon_cyc = add_cyclic_point(temp, coord=lon)

In [22]:
plt.figure(figsize=(13,6.2))

ax = plt.subplot(111, projection=ccrs.PlateCarree())

mm = ax.pcolormesh(lon_cyc,\
lat,\
temp_cyc,\
vmin=-2,\
vmax=30,\
transform=ccrs.PlateCarree(),\
cmap=cmo.balance )
ax.coastlines();


Looks fine. Now we will use default cartopy backgroung:

In [20]:
plt.figure(figsize=(13,6.2))

ax = plt.subplot(111, projection=ccrs.PlateCarree())

mm = ax.pcolormesh(lon_cyc,\
lat,\
temp_cyc,\
vmin=-2,\
vmax=30, \
transform=ccrs.PlateCarree(),\
cmap=cmo.balance )
ax.coastlines();
ax.stock_img();


Not that bad, but not exactly what I want.

Let's setup cartopy for use of the custom background. You have to have a folder that contain background images and a json file that describes images (see explination below). Then you have to create environment variable that contains the path to the folder:

In [9]:
os.environ["CARTOPY_USER_BACKGROUNDS"] = "/home/magik/PYTHON/cartopy/BG/"


Now you can specify name of the image in it's resolution in the background_img() method:

In [19]:
plt.figure(figsize=(13,6.2))

ax = plt.subplot(111, projection=ccrs.PlateCarree())
ax.background_img(name='BM', resolution='low')
mm = ax.pcolormesh(lon_cyc,\
lat,\
temp_cyc,\
vmin=-2,\
vmax=30,\
transform=ccrs.PlateCarree(),\
cmap=cmo.balance )
ax.coastlines(resolution='110m');


And get a map with a nice background (Blue Marble in this case).

You can get a lot of nice map backgrounds from this NASA website https://neo.sci.gsfc.nasa.gov/.

The json file that you have to put in to the background folder should be called images.json and look like this:

{"__comment__": "JSON file specifying the image to use for a given type/name and
"BM": {
"__comment__": "Blue Marble Next Generation, July ",
"__source__": "https://neo.sci.gsfc.nasa.gov/view.php?datasetId=BlueMarbleNG
-TB",
"__projection__": "PlateCarree",
"low": "BM.jpeg",
"high": "BMNG_hirez.png"},

"pop": {
"__comment__": "Population density from Nasa earth observations website",
"__source__": "https://neo.sci.gsfc.nasa.gov/view.php?datasetId=SEDAC_POP",
"__projection__": "PlateCarree",
"high": "population.jpg"}
}

As you can see one can specify different "resolutions" for the same image name which will just point to different files.

That's it. Here is the video of ocean circulation that I have generated using this method. Below are just two more examples.

In [26]:
plt.figure(figsize=(13,6.2))

ax = plt.subplot(111, projection=ccrs.Mercator(central_longitude=180, min_latitude=-30, max_latitude=67))
ax.background_img(name='BM', resolution='high')
ax.set_extent([30,-70,-30,70])
mm = ax.pcolormesh(lon,\
lat,\
temp,vmin=-2, vmax=30, transform=ccrs.PlateCarree(),cmap=cmo.balance )
ax.coastlines(resolution='10m');