%matplotlib inline 

import matplotlib.path as mpath
import matplotlib.pyplot as plt
import numpy as np

import cartopy.crs as ccrs
import cartopy.feature as cfeature


fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.SouthPolarStereo())
ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.SouthPolarStereo(),
                      sharex=ax1, sharey=ax1)
fig.subplots_adjust(bottom=0.05, top=0.95,
                    left=0.04, right=0.95, wspace=0.02)

# Limit the map to -60 degrees latitude and below.
ax1.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())

ax1.add_feature(cfeature.LAND)
ax1.add_feature(cfeature.OCEAN)

ax1.gridlines()
ax2.gridlines()

ax2.add_feature(cfeature.LAND)
ax2.add_feature(cfeature.OCEAN)

# Compute a circle in axes coordinates, which we can use as a boundary
# for the map. We can pan/zoom as much as we like - the boundary will be
# permanently circular.
theta = np.linspace(0, 2*np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)

ax2.set_boundary(circle, transform=ax2.transAxes)

plt.show()

import cartopy.crs as ccrs
from cartopy.mpl.geoaxes import GeoAxes
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import numpy as np


def sample_data_3d(shape):
    """Return `lons`, `lats`, `times` and fake `data`"""
    ntimes, nlats, nlons = shape
    lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)
    lons = np.linspace(0, 2 * np.pi, nlons)
    lons, lats = np.meshgrid(lons, lats)
    wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)
    mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)

    lats = np.rad2deg(lats)
    lons = np.rad2deg(lons)
    data = wave + mean

    times = np.linspace(-1, 1, ntimes)
    new_shape = data.shape + (ntimes, )
    data = np.rollaxis(data.repeat(ntimes).reshape(new_shape), -1)
    data *= times[:, np.newaxis, np.newaxis]

    return lons, lats, times, data



projection = ccrs.PlateCarree()
# axes_class = (GeoAxes, dict(map_projection=projection))
axes_class = (GeoAxes, dict(projection=projection))

lons, lats, times, data = sample_data_3d((6, 73, 145))

fig = plt.figure()
axgr = AxesGrid(fig, 111, axes_class=axes_class,
                nrows_ncols=(3, 2),
                axes_pad=0.6,
                cbar_location='right',
                cbar_mode='single',
                cbar_pad=0.2,
                cbar_size='3%',
                label_mode='L')  # note the empty label_mode

for i, ax in enumerate(axgr):
    ax.coastlines()
    ax.set_xticks(np.linspace(-180, 180, 5), crs=projection)
    ax.set_yticks(np.linspace(-90, 90, 5), crs=projection)
    lon_formatter = LongitudeFormatter(zero_direction_label=True)
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)

    p = ax.contourf(lons, lats, data[i, ...],
                    transform=projection,
                    cmap='RdBu')

axgr.cbar_axes[0].colorbar(p)

plt.show()