import matplotlib.pyplot as plt
import cartopy.crs as ccrs
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_boundary(star_path, transform=ccrs.PlateCarree())
plt.show()

import matplotlib.pyplot as plt
import numpy as np

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


def sample_data(shape=(20, 30)):
    """
    Return ``(x, y, u, v, crs)`` of some vector data
    computed mathematically. The returned crs will be a rotated
    pole CRS, meaning that the vectors will be unevenly spaced in
    regular PlateCarree space.

    """
    crs = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5)

    x = np.linspace(311.9, 391.1, shape[1])
    y = np.linspace(-23.6, 24.8, shape[0])

    x2d, y2d = np.meshgrid(x, y)
    u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2)
    v = 20 * np.cos(6 * np.deg2rad(x2d))

    return x, y, u, v, crs



fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(-10, 45))

ax.add_feature(cfeature.OCEAN, zorder=0)
ax.add_feature(cfeature.LAND, zorder=0, edgecolor='black')

ax.set_global()
ax.gridlines()

x, y, u, v, vector_crs = sample_data()
ax.quiver(x, y, u, v, transform=vector_crs)

plt.show()

import matplotlib.pyplot as plt
import numpy as np

import cartopy.crs as ccrs


def sample_data(shape=(20, 30)):
    """
    Return ``(x, y, u, v, crs)`` of some vector data
    computed mathematically. The returned CRS will be a North Polar
    Stereographic projection, meaning that the vectors will be unevenly
    spaced in a PlateCarree projection.

    """
    crs = ccrs.NorthPolarStereo()
    scale = 1e7
    x = np.linspace(-scale, scale, shape[1])
    y = np.linspace(-scale, scale, shape[0])

    x2d, y2d = np.meshgrid(x, y)
    u = 10 * np.cos(2 * x2d / scale + 3 * y2d / scale)
    v = 20 * np.cos(6 * x2d / scale)

    return x, y, u, v, crs


fig = plt.figure(figsize=(8, 10))

x, y, u, v, vector_crs = sample_data(shape=(50, 50))
ax1 = fig.add_subplot(2, 1, 1, projection=ccrs.PlateCarree())
ax1.coastlines('50m')
ax1.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())
ax1.quiver(x, y, u, v, transform=vector_crs)

ax2 = fig.add_subplot(2, 1, 2, projection=ccrs.PlateCarree())
ax2.set_title('The same vector field regridded')
ax2.coastlines('50m')
ax2.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())
ax2.quiver(x, y, u, v, transform=vector_crs, regrid_shape=20)

plt.show()

import matplotlib.pyplot as plt
import numpy as np

import cartopy.crs as ccrs


def sample_data(shape=(73, 145)):
    """Return ``lons``, ``lats`` and ``data`` of some fake data."""
    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

    return lons, lats, data

fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Mollweide())

lons, lats, data = sample_data()

ax.contourf(lons, lats, data,
            transform=ccrs.PlateCarree(),
            cmap='nipy_spectral')
ax.coastlines()
ax.set_global()
plt.show()