#!/usr/bin/env python
# coding: utf-8

# ## Visualization of Airport Data on Map.
# 
# The idea and the airport's data for this demo was found in the "Towards Data Science" article [How to Visualize Data on top of a Map in Python using the Geoviews library](https://towardsdatascience.com/how-to-visualize-data-on-top-of-a-map-in-python-using-the-geoviews-library-c4f444ca2929) by [Christos Zeglis](https://medium.com/@christoszeglis).
# 
# The paper demonstrates how to make a plot to visualize the passengers volume for the busiest airports in Greece, and the neighbor country, Turkey, for comparison reasons.
# 
# The author uses Geoviews and Bokeh libraries to achieve such visualisation. We are going to use the [Lets-Plot](https://github.com/JetBrains/lets-plot/blob/master/README.md) library solely to do the same job and, on top of it, fill both countries boundaries with semi-transparent colors.
# 
# The tasks completed in this tutorial:
# 
# - Configuring map-tiles for the interactive base-map layer.
# - Obtaining boundaries of Greece and Turkey using Lets-Plot geo-coding module.
# - Creating a proportional symbols map by combining base-map, polygons and point layers.
# - Castomizing the tooltip contents.

# In[1]:


import numpy as np
import pandas as pd

from lets_plot import *


# In[2]:


LetsPlot.setup_html()


# ### Configuring the basemap.
# 
# For the purposes of this tutorial, we are going to use "CityLights 2012" map-tiles [© NASA Global Imagery Browse Services (GIBS)](https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/gibs).

# In[3]:


LetsPlot.set(maptiles_zxy(
    url='https://gibs.earthdata.nasa.gov/wmts/epsg3857/best/VIIRS_CityLights_2012/default//GoogleMapsCompatible_Level8/{z}/{y}/{x}.jpg',
    attribution='<a href="https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/gibs">© NASA Global Imagery Browse Services (GIBS)</a>',
    max_zoom=8
))


# ### Loading the "airports" dataset.
# 
# The "airports" dataset is already cleaned and only contains data on Greece and Turkey airports.

# In[4]:


airports = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/airports.csv")
airports.head(3)


# ### Obtaining boundaries (or polygons) of Greece and Turkey.
# 
# On this step we are using built-in geo-coding capabilities of the Lets-Plot library.

# In[5]:


from lets_plot.geo_data import *

countries_gcoder = geocode_countries(['GR', 'TR'])
countries_gcoder.get_geocodes()


# ### Showing the data on map.
# 
# - Add an interactive base-map layer with custom initial location and zoom level.
# - Add polygons layer to fill the country boundaries with semi-transparent colors.
# - Add points layer marking the airports location with the point size proportional to the airport's passengers volume.
# - Customize tooltip on the points layer to show all the airport data.
# - Use the 'scale_fill_manual()' function in order to fill polygons and points with blue and red colors.
# - Setup the desired marker's size using the 'scale_size()' function.

# In[6]:


(ggplot() 
 + geom_livemap(location=[26.65, 38.61],
                zoom=6)
 + geom_polygon(aes(fill='country'), 
                data=countries_gcoder.inc_res().get_boundaries(),
                size=0,
                alpha=.2)
 + geom_point(aes('longitude', 'latitude', fill='country', size='passengers'),
              data=airports,
              shape=21,
              alpha=.7,
              color='white',
              tooltips=layer_tooltips()
                        .format('passengers', '{.1f} m' )
                        .format('^x', '.2f').format('^y', '.2f')
                        .line('@|@IATA')
                        .line('Passengers|@passengers') 
                        .line('City|@city') 
                        .line('Country|@country') 
                        .line('Longitude|^x')
                        .line('Latitude|^y'))
 + scale_fill_manual(values=['#30a2da', '#fc4f30'])
 + scale_size(range=[10, 40], trans='sqrt')
 + theme(legend_position='none')
 + ggsize(900, 520)
)