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

# In[1]:


import pandas as pd
import folium
import utils


# In[2]:


# Load the data
ListA = pd.read_csv('List A.csv')
ListB = pd.read_csv('List B.csv')
ListC = pd.read_csv('List C.csv')
ListD = pd.read_csv('List D.csv')


# In[3]:


# Create two centroids, one in the North End and one in the Seaport District
centroids = [[42.365, -71.054], [42.351, -71.045]]

northeastern_coordinate = "-71.09033,42.33976"


# In[4]:


# Combine the two lists and add a column to indicate the list
ListA['list'] = 'A'
ListB['list'] = 'B'
ListC['list'] = 'C'
ListD['list'] = 'D'

TotalList = pd.concat([ListA, ListB, ListC, ListD])


# In[5]:


# Remove all columns but name and gps
TotalList = TotalList[['name', 'gps', 'list']]


# In[6]:


# Convert the gps column to a list of lists for k-means
TotalList['gps'] = TotalList['gps'].apply(lambda x: x.strip('[]').split(','))
TotalList['gps'] = TotalList['gps'].apply(lambda x: [float(i) for i in x])


# In[7]:


display(TotalList)


# # 2 Routes

# ## Cluster and Minimize

# In[8]:


# Cluster and minimize the data
_, routes = utils.cluster_and_optimize(TotalList, centroids, northeastern_coordinate,
                                       time_diff=0.25, max_time=24)

route_1_coordinates = routes[0]
route_2_coordinates = routes[1]


# ## Create JSON

# In[9]:


# Create a JSON request for the API
# This is the data we want to get from the API
route_1 = utils.list_to_string(route_1_coordinates)
route_2 = utils.list_to_string(route_2_coordinates)


# In[10]:


# Create a dataframe from the JSON
df1 = utils.create_json_df(route_1, utils.list_to_string([centroids[0]]), northeastern_coordinate)
df2 = utils.create_json_df(route_2, utils.list_to_string([centroids[1]]), northeastern_coordinate)


# In[11]:


# Add columns for the route number
df1['route'] = 1
df2['route'] = 2

# Concatenate the two dataframes
df = pd.concat([df1, df2], ignore_index=True)


# In[12]:


display(df)


# ## Map

# In[13]:


# Create a map
m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)

# Add the points and lines for the two routes with different colors
colors = ['red', 'blue']

for route in df['route'].unique():
    df_route = df[df['route'] == route]
    folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)
    for i in range(len(df_route)):
        folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(
            m)

# Display the map
m


# ## Results

# In[14]:


# Get the number of waypoints for each route
route_1_waypoints = len(route_1_coordinates)
route_2_waypoints = len(route_2_coordinates)
print("Route 1 has {} waypoints".format(route_1_waypoints))
print("Route 2 has {} waypoints".format(route_2_waypoints))


# In[15]:


trip_hrs_1 = utils.get_trip_time(route_1, route_1_waypoints, utils.list_to_string([centroids[0]]),
                                 northeastern_coordinate)
print("The trip will take {} hours".format(trip_hrs_1))
trip_hrs_2 = utils.get_trip_time(route_2, route_2_waypoints, utils.list_to_string([centroids[1]]),
                                 northeastern_coordinate)
print("The trip will take {} hours".format(trip_hrs_2))


# # 3 Routes

# In[16]:


# Cluster and minimize the data
# Add a third centroid in the Financial District
centroids.append([42.356, -71.055])
_, routes = utils.cluster_and_optimize(TotalList, centroids, northeastern_coordinate, time_diff=0.3, max_time=24)

route_1_coordinates = routes[0]
route_2_coordinates = routes[1]
route_3_coordinates = routes[2]


# ## Create JSON

# In[17]:


# Create a JSON request for the API
# This is the data we want to get from the API
route_1 = utils.list_to_string(route_1_coordinates)
route_2 = utils.list_to_string(route_2_coordinates)
route_3 = utils.list_to_string(route_3_coordinates)


# In[18]:


# Create a dataframe from the JSON
df1 = utils.create_json_df(route_1, utils.list_to_string([centroids[0]]), northeastern_coordinate)
df2 = utils.create_json_df(route_2, utils.list_to_string([centroids[1]]), northeastern_coordinate)
df3 = utils.create_json_df(route_3, utils.list_to_string([centroids[2]]), northeastern_coordinate)


# In[19]:


# Add columns for the route number
df1['route'] = 1
df2['route'] = 2
df3['route'] = 3

# Concatenate the three dataframes
df = pd.concat([df1, df2, df3], ignore_index=True)


# In[20]:


display(df)


# ## Map

# In[21]:


# Create a map
m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)

# Add the points and lines for the three routes with different colors
colors = ['red', 'blue', 'green']

for route in df['route'].unique():
    df_route = df[df['route'] == route]
    folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)
    for i in range(len(df_route)):
        folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(
            m)

# Display the map
m


# ## Results

# In[22]:


# Get the number of waypoints for each route
route_1_waypoints = len(route_1_coordinates)
route_2_waypoints = len(route_2_coordinates)
route_3_waypoints = len(route_3_coordinates)
print("Route 1 has {} waypoints".format(route_1_waypoints))
print("Route 2 has {} waypoints".format(route_2_waypoints))
print("Route 3 has {} waypoints".format(route_3_waypoints))


# In[23]:


# Get the trip time for each route
trip_hrs_1 = utils.get_trip_time(route_1, route_1_waypoints, utils.list_to_string([centroids[0]]),
                                 northeastern_coordinate)
print("The trip will take {} hours".format(trip_hrs_1))
trip_hrs_2 = utils.get_trip_time(route_2, route_2_waypoints, utils.list_to_string([centroids[1]]),
                                 northeastern_coordinate)
print("The trip will take {} hours".format(trip_hrs_2))
trip_hrs_3 = utils.get_trip_time(route_3, route_3_waypoints, utils.list_to_string([centroids[2]]),
                                 northeastern_coordinate)
print("The trip will take {} hours".format(trip_hrs_3))


# # 10 ROUTES (because I can)

# In[24]:


# Cluster and minimize the data
# Add seven more centroids around Boston with different latitudes and longitudes
for i in range(7):
    centroids.append([42.365 + i * 0.01, -71.054 + i * 0.01])

_, routes = utils.cluster_and_optimize(TotalList, centroids, northeastern_coordinate, time_diff=0.5, max_time=24)


# ## Create JSON

# In[25]:


# Create a JSON request for the API
# This is the data we want to get from the API
route_strings = []
for route in routes:
    route_strings.append(utils.list_to_string(route))


# In[26]:


# Create a dataframe from the JSON
dfs = []
for i in range(len(routes)):
    dfs.append(utils.create_json_df(route_strings[i], utils.list_to_string([centroids[i]]), northeastern_coordinate))
    
# Concatenate the dataframes
df = pd.concat(dfs, ignore_index=True)


# In[30]:


# Add columns for the route number
for i in range(len(routes)):
    df['route'].iloc[i * len(routes[i]):(i + 1) * len(routes[i])] = i + 1


# In[31]:


# Display the dataframe
display(df)


# ## Map

# In[37]:


# Create a map
m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)

# Add the points and lines for the three routes with different colors
colors = ['red', 'blue', 'green', 'orange', 'purple', 'pink', 'black', 'gray', 'brown', 'yellow']

for route in df['route'].unique():
    df_route = df[df['route'] == route]
    folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)
    for i in range(len(df_route)):
        folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(
            m)
        
# Display the map
m


# ## Results

# In[36]:


# Get the number of waypoints for each route
route_waypoints = []
for route in routes:
    route_waypoints.append(len(route))
for i in range(len(route_waypoints)):
    print("Route {} has {} waypoints".format(i + 1, route_waypoints[i]))


# In[34]:


# Get the trip time for each route
trip_hrs = []
for i in range(len(routes)):
    trip_hrs.append(utils.get_trip_time(route_strings[i], route_waypoints[i], utils.list_to_string([centroids[i]]),
                                        northeastern_coordinate))
for i in range(len(trip_hrs)):
    print("The trip will take {} hours".format(trip_hrs[i]))


# In[ ]: