In [1]:

import cartopy
import matplotlib.pyplot as plt
%matplotlib inline

import matplotlib
matplotlib.rcParams['savefig.dpi'] = 300

import cartopy.crs as ccrs
import numpy as np
from matplotlib import mlab
from goodies import as_table

In [2]:

import netCDF4
dataset = netCDF4.Dataset('../data/maps/avhrr-only-v2.20140101.nc')

In [3]:

lon = dataset.variables['lon'][:]
lat = dataset.variables['lat'][:]
temp = dataset.variables['sst'][:].squeeze()

In [4]:

ax = plt.axes()
ax.contourf(lon, lat, temp, 100)

Out[4]:

<matplotlib.contour.QuadContourSet instance at 0x10d619ea8>

In [5]:

ax = plt.axes(projection=ccrs.Mercator())
ax.coastlines()
ax.add_feature(cartopy.feature.BORDERS)
ax.set_global()
ax.contourf(lon, lat, temp, 20, transform=ccrs.PlateCarree())

Out[5]:

<matplotlib.contour.QuadContourSet instance at 0x112cdb998>

In [6]:

shapes = cartopy.io.shapereader.Reader(
            '../data/maps/rgp_population/rgp_population')
pop = np.array([r.attributes['POPMUN2011']/r.attributes['AREA']
                for r in shapes.records()])

In [7]:

gl = cartopy.crs.Globe(ellipse='GRS80')
prj = cartopy.crs.LambertConformal(central_latitude=46.5, 
                                   central_longitude=3.,
                                   secant_latitudes=(49, 44),
                                   false_easting=700000,
                                   false_northing=6600000)

In [10]:

from matplotlib import colors, cm
from cartopy.io.img_tiles import MapQuestOSM

imaging = MapQuestOSM()
ax = plt.axes(projection=imaging.crs)

ax.set_extent((2.0277,2.6662,48.7200,48.9987))
ax.add_image(imaging, 12)

pop_colors = cm.YlOrRd(colors.Normalize()(pop))

for c,g in zip(pop_colors, shapes.geometries()):
    ax.add_geometries(g, prj, facecolor=c, edgecolor='none', alpha=0.5)