import geopandas as gpd
from lets_plot import *
LetsPlot.setup_html()

from lets_plot import *
from lets_plot.geo_data import *

# %%

offices = [
    ["Prague", "CZ", "Headquarters", 100],
    ["Petersburg", "Russia", "R&D Center", 1000],
    ["Moscow", "Russia", "R&D Center", 100],
    ["Novosibirsk", "Russia", "R&D Center", 50],
    ["München", "Germany", "R&D Center", 200],
    ["Amsterdam", "Netherlands", "R&D Center", 100],
    ["Boston", "US", "R&D Center", 10],
    ["Marlton", "US", "Sales", 10],
    ["Foster City", "US", "Sales", 10],
]

dat = dict(
    city=[o[0] for o in offices],
    country=[o[1] for o in offices],
    kind=[o[2] for o in offices],
    size=[o[3] for o in offices],
)

# %%

# Geocoding
city_geocoder = geocode_cities(dat['city']).countries(dat['country'])

# %%

# The map of JetBrains major offices worldwide.
p = (ggplot(dat) +
     # geom_livemap() +
     geom_point(
         # aes(color='kind', shape='kind', size='size'),
         aes(color='kind'),
         size=10,
         map_join = [['city'], ['city']],
         #map=city_geocoder))
         map=city_geocoder.get_centroids()))

p.show()

gpd.datasets.available

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
print(world.size)
world[100:120]

ggplot() + geom_polygon(aes(fill='continent'), data=world) + ggsize(800, 400)

ggplot() + geom_path(data=world)

ggplot() + geom_map(data=world)

ggplot() + geom_polygon(map=world)

ggplot() + geom_map(map=world)

ggplot() + geom_point(map=world)

ggplot() + geom_rect(map=world, alpha=.3)

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

ggplot() + geom_point(data=cities)

ggplot() + geom_point(map=cities)

import random
cityNames = cities['name'].tolist()
cityValue = [random.randint(10, 100) for _ in cityNames]
cityData = dict(city=cityNames, val=cityValue)
ggplot() + geom_point(aes(color='val'), data=cityData, map=cities, map_join=('city', 'name'), size=7)

citiesWithKey = cities.rename(columns = {'name':'key'}, inplace = False)
citiesWithKey

ggplot() + geom_point(aes(color='val'), data=cityData, map=citiesWithKey, map_join=('city', 'key'), size=7)

ggplot() + ggsize(800, 400)\
+ geom_polygon(aes(fill='continent'), data=world)\
+ geom_point(data=cities, color='cyan')

ggplot() + ggsize(800, 400)\
+ geom_rect(aes(fill='continent'), data=world)\
+ geom_point(data=cities, color='cyan', size=7, alpha=.5)

nybb = gpd.read_file(gpd.datasets.get_path('nybb'))
print(nybb.size)
nybb.head()

ggplot() + geom_polygon(aes(fill='BoroName'), data=nybb)

ggplot() + geom_polygon(aes(fill='BoroName'), data=nybb) + coord_cartesian(xlim=[-74.05,-73.95],ylim=[40.675,40.725])