from owslib.wms import WebMapService

url = 'http://mesonet.agron.iastate.edu/cgi-bin/wms/goes/conus_ir.cgi?'
wms = WebMapService(url)
layers = list(wms.contents)
print 'Request type: {}'.format(wms.identification.type)
print 'Request title: {}'.format(wms.identification.title)
print 'Available layers: {}'.format(layers)

layer = layers[1]

print 'Layer title: {}'.format(wms[layer].title)
print 'Bounding box: {}'.format(wms[layer].boundingBoxWGS84)
print 'Projection: {}'.format(wms[layer].crsOptions)

import cartopy.crs as ccrs
import matplotlib.pyplot as plt

us_extent = [-126.0, -66.0, 24.0, 50.0]

def set_up_axes(proj, coast_colour='y', extent=us_extent):
    fig = plt.figure(figsize=(14, 9))
    ax = plt.axes(projection=proj)
    ax.coastlines(color=coast_colour)
    ax.set_extent(extent, crs=ccrs.PlateCarree())
    return ax

proj = ccrs.PlateCarree()
ax = set_up_axes(proj)

ax.add_wms(url, layer)

plt.show()

import matplotlib.patheffects as mpath

# Set up the axes and add a WMS layer, as above.
proj = ccrs.PlateCarree()
ax = set_up_axes(proj)

ax.add_wms(url, layer)

# Add the locations of our three cities.
cities = {'Washington DC': [38.928, -76.981],
          'Austin': [30.308, -97.753],
          'San Francisco': [37.758, -122.438],}
city_red = '#b80000'

for name, latlon in cities.iteritems():
    plt.scatter(latlon[1], latlon[0], c=city_red, s=50, linewidths=0, marker='*')
    plt.text(latlon[1]-1.0, latlon[0]+1.0, name, color='w', size=12,
             path_effects=[mpath.withStroke(linewidth=2, foreground='k')])

plt.show()

import iris
import iris.quickplot as qplt

year_2015 = iris.Constraint(time=lambda cell: cell.point.year == 2015)
with iris.FUTURE.context(cell_datetime_objects=True):
    cube = iris.load_cube(iris.sample_data_path('A1B_north_america.nc'), year_2015)

# Set up the axes, add a WMS layer and city locations; all as above.
proj = ccrs.PlateCarree()
ax = set_up_axes(proj, coast_colour='k')

ax.add_wms(url, layer)

for name, latlon in cities.iteritems():
    plt.scatter(latlon[1], latlon[0], c=city_red, s=50, linewidths=0, marker='*')
    plt.text(latlon[1]-1.0, latlon[0]+1.0, name, color='w', size=12,
             path_effects=[mpath.withStroke(linewidth=2, foreground='k')])
    
# Add the average air temperature data to the plot as a filled contour.
qplt.contourf(cube, alpha=0.25)

plt.show()

proj = ccrs.LambertConformal()
ax = set_up_axes(proj, coast_colour='k')

ax.add_wms(url, layer)

# Now that we've changed projection we need to specify the native projection
# of our city locations.
for name, latlon in cities.iteritems():
    plt.scatter(latlon[1], latlon[0],
                c=city_red, s=50, linewidths=0, marker='*',
                transform=ccrs.PlateCarree())
    plt.text(latlon[1]-1.0, latlon[0]+1.0, name, color='w', size=12,
             path_effects=[mpath.withStroke(linewidth=2, foreground='k')],
             transform=ccrs.PlateCarree())

qplt.contourf(cube, alpha=0.25)

plt.show()