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

# # Visualization of the Titanic's Voyage
# 
# 

# 1. [Data Preparation](#1.-Data-Preparation)
# 
#    1.1 [Travellers Survival Rates by the Port of Embarkation](#1.1-Travellers-Survival-Rates-by-the-Port-of-Embarkation)
#  
# 2. [Ports of Embarkation on Map](#2.-Ports-of-Embarkation-on-Map)  
# 
#    2.1 [Spatial DataFrame (Pandas GeoDataFrame)](#2.1-Spatial-DataFrame-(Pandas-GeoDataFrame))
#    
#    2.2 [Markers on Map](#2.2-Markers-on-Map)
#  
# 3. [The "Titanic's site" Marker](#3.-The-"Titanic's-site"-Marker)  
# 
# 4. [The New York City Marker](#4.-The-New-York-City-Marker)  
# 
# 5. [Connecting Markers on Map](#5.-Connecting-Markers-on-Map)  
# 
# 6. [Pie-chart Markers on Map](#6.-Pie-chart-Markers-on-Map)  
# 
#    6.1 [Travellers Survival Rates, Now with Pie](#6.1-Travellers-Survival-Rates,-Now-with-Pie)
#    
#    6.2 [Spatial Pies](#6.2-Spatial-Pies)
#    
#    6.3 [Adjusting the Map Zoom Level and Position](#6.3-Adjusting-the-Map-Zoom-Level-and-Position)
# 

# In[1]:


import pandas as pd

from lets_plot import *
from lets_plot.geo_data import *


# In[2]:


LetsPlot.setup_html()


# #### 1. Data Preparation
# 
# The Titanic dataset is available at [kaggle](https://www.kaggle.com) : ["Titanic: cleaned data" dataset](https://www.kaggle.com/jamesleslie/titanic-cleaned-data?select=train_clean.csv) (train_clean.csv).

# In[3]:


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


# In this Titanic dataset the column `Embarked`contains a single-letter codes of the Titanic's ports of embarkation:
# - S: Southampton (UK)
# - C: Cherbourg (France)
# - Q: Cobh (Ireland)
# 
# Let's add new colum "Port" to the data:

# In[4]:


def to_port_name (row):
    if row['Embarked'] == 'S' :
        return 'Southampton'
    if row['Embarked'] == 'C' :
        return 'Cherbourg'
    if row['Embarked'] == 'Q' :
        return 'Cobh'
    return 'Other'

df['Port']=df.apply (lambda row: to_port_name(row), axis=1)
df.head(3)


# ##### 1.1 Travellers Survival Rates by the Port of Embarkation

# In[5]:


from lets_plot.mapping import as_discrete

c_surv="#E1A439"
c_lost="#6B9993"

bars = (ggplot(df) +
        geom_bar(
            aes('Port', fill=as_discrete('Survived')), 
            tooltips=layer_tooltips()
                .line('@{..count..} (@{..prop..})')
                .format('@{..prop..}', '.0%'),
            position='dodge') +
        scale_fill_manual(values=[c_lost, c_surv], labels=['no', 'yes']) +
        scale_x_discrete(limits=['Cobh', 'Cherbourg', 'Southampton']) +
        labs(x="", y="Travellers count") + 
        ggtitle("Survival by the Port of Embarkation")
)        

bars + ggsize(800, 300)


# #### 2. Ports of Embarkation on Map
# 
# Titanic's ports of of embarkation were:
# - Southampton (UK)
# - Cherbourg (France)
# - Cobh (Ireland)
# 
# Let's find geographical coordinates of these cities using `Lets-Plot` geocoding module.

# In[6]:


from lets_plot.geo_data import *

ports = ['Southampton', 'Cherbourg', 'Cobh']


# ##### 2.1 Spatial DataFrame (Pandas GeoDataFrame)

# In[7]:


ports_gcoder = (geocode_cities(ports)
                .where(ports[0], scope='England')
                .where(ports[1], scope='France'))
ports_gdf = ports_gcoder.get_centroids()
ports_gdf


# ##### 2.2 Markers on Map
# 
# `Lets-Plot` API makes it easy to create an interactive basemap layer using either its own vector tiles service or 
# by configuring a 3-rd party Z-X-Y raster tile providers.
# 
# In this notebook we will use beautifull *CARTO Antique* raster tiles by [CARTO](https://carto.com/attribution/) as our basemap.
# 

# In[8]:


from lets_plot import tilesets

LetsPlot.set(tilesets.CARTO_ANTIQUE_HIRES)


# In[9]:


basemap = ggplot() + geom_livemap() + ggsize(800, 350)


port_markers = geom_point(
                   map=ports_gdf, 
                   size=7, 
                   shape=21, 
                   color="black", 
                   fill="yellow")

basemap + port_markers


# #### 3. The "Titanic's site" Marker

# In[10]:


from shapely.geometry import Point, LineString
titanic_site = Point(-38.056641, 46.920255)

titanic_site_marker = geom_point(x=titanic_site.x, y = titanic_site.y, size=10, shape=9, color='red')

basemap + port_markers + titanic_site_marker


# #### 4. The New York City Marker
# 
# New York City was the Titanic's destination.

# In[11]:


NYC = geocode_cities(['New York']).get_centroids().geometry[0]
NYC_marker = geom_point(x=NYC.x, y=NYC.y, size=7, shape=21, color='black', fill='white')

(basemap + 
 port_markers +
 titanic_site_marker +
 NYC_marker
)


# #### 5. Connecting Markers on Map
# 
# To connect markers on the map we will create a `LineString` object (from `Shaply` package).
# 

# In[12]:


from geopandas import GeoSeries
from geopandas import GeoDataFrame

# Points of embarkation (GeoSeries).
port_points = ports_gdf.geometry
path_points = pd.concat([port_points, GeoSeries([titanic_site, NYC], crs=ports_gdf.crs)], ignore_index=True)

# Create a new GeoDataFrame containing a `LineString` geometry.
path_gdf = GeoDataFrame(
    dict(geometry=[ LineString(path_points) ])
)


# In[13]:


# Add "path" to the map.
titanic_path = geom_path(
    map=path_gdf, 
    color='dark-blue', 
    linetype='dotted', size=1.2)

(basemap +
 titanic_path +
 port_markers +
 titanic_site_marker +
 NYC_marker
)


# #### 6. Pie-chart Markers on Map

# ##### 6.1 Travellers Survival Rates, Now with Pie

# In[14]:


pies = (ggplot(df) +
        geom_pie(
            aes(x='Port', y="..sum..", fill=as_discrete('Survived'), size="..sum.."), 
            labels=layer_labels()
                             .line('@{..count..}')
                             .line('(@{..prop..})').format('@{..prop..}', '.0%'),
            tooltips=layer_tooltips().title("@Port (@{..sum..})"),
            stroke=1.5, 
            hole=0.5) +
        scale_fill_manual(values=[c_lost, c_surv], labels=['no', 'yes']) +
        scale_x_discrete(limits=['Cobh', 'Cherbourg', 'Southampton'], expand=[0, 0.3]) +
        scale_size(range=[3, 10], guide="none") +
        ylim(0, 800) +
        labs(x="", y="Travellers count") + 
        ggtitle("Survival by the Port of Embarkation")
)

pies + ggsize(800, 300)


# ##### 6.2 Spatial Pies
# 

# In[15]:


spatial_pies = (
        geom_pie(
            aes(x='Port', fill=as_discrete('Survived'), size="..sum.."), 
            data=df,
            map=ports_gdf, 
            map_join=['Port','city'],
            tooltips=layer_tooltips()
                     .title("@Port (@{..sum..})")
                     .line('@{..count..} (@{..prop..})')
                     .format('@{..prop..}', '.0%'),
            stroke=1.5, 
            hole=0.5,
            color='white') +
        scale_fill_manual(values=[c_lost, c_surv], labels=['lost', 'survived']) +
        scale_size(range=[3, 10], guide="none") +
        theme(legend_position=[0.5, 1], 
              legend_justification=[0.5, 1], 
              legend_direction='horizontal',
              legend_title=element_blank())
)                

(basemap + 
 titanic_path +
 spatial_pies +
 titanic_site_marker +
 NYC_marker
)


# ##### 6.3 Adjusting the Map Zoom Level and Position

# In[16]:


(ggplot() + ggsize(900, 600) + ggtitle("Titanic Survival Rate by Port of Embarkation") +
     geom_livemap(zoom=6, location=[-8.29, 51.85, -1.61, 49.63]) + 
     titanic_path +
     port_markers +
     spatial_pies +
     titanic_site_marker +
     NYC_marker +
     theme(text=element_text(family="Garamond"),
           plot_title=element_text(size=30))
)