import xarray as xr
import hvplot.pandas  # noqa
import hvplot.xarray  # noqa
import cartopy.crs as ccrs

from bokeh.sampledata.airport_routes import airports

airports.head(3)

airports.hvplot.points('Longitude', 'Latitude', geo=True, color='red', alpha=0.2,
                       xlim=(-180, -30), ylim=(0, 72), tiles='ESRI')

import geopandas as gpd

cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))

cities.hvplot(global_extent=True, frame_height=450, tiles=True)

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

world.hvplot(geo=True) * cities.hvplot(geo=True, color='orange')

world.hvplot(geo=True) + world.hvplot(c='continent', geo=True)

import spatialpandas as spd

spd_world = spd.GeoDataFrame(world)

spd_world.hvplot(datashade=True, project=True, aggregator='count_cat', c='continent', color_key='Category10')

air_ds = xr.tutorial.open_dataset('air_temperature').load()

air_ds.hvplot.quadmesh(
    'lon', 'lat', 'air', projection=ccrs.Orthographic(-90, 30),
    global_extent=True, frame_height=540, cmap='viridis',
    coastline=True
)

air_ds.hvplot.quadmesh(
    'lon', 'lat', 'air', projection='LambertConformal',
)

rasm = xr.tutorial.open_dataset('rasm').load()



rasm.hvplot.quadmesh(
    'xc', 'yc', crs=ccrs.PlateCarree(), projection=ccrs.PlateCarree(),
    ylim=(0, 90), cmap='viridis', project=True, geo=True,
    rasterize=True, coastline=True, frame_width=800, dynamic=False,
)

rasm.hvplot.contourf(
    'xc', 'yc', crs=ccrs.PlateCarree(), projection=ccrs.PlateCarree(),
    ylim=(0, 90), frame_width=800, cmap='viridis', levels=10,
    coastline=True
)