From 6b9d2b694821e63da8a9db47a97ce9cc6d807a4b Mon Sep 17 00:00:00 2001
From: itsGarrin <garrin.shieh@gmail.com>
Date: Tue, 7 Nov 2023 18:36:29 -0500
Subject: pleb.

---
 ZestySalesman.py | 368 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 368 insertions(+)
 create mode 100644 ZestySalesman.py

(limited to 'ZestySalesman.py')

diff --git a/ZestySalesman.py b/ZestySalesman.py
new file mode 100644
index 0000000..1901a0e
--- /dev/null
+++ b/ZestySalesman.py
@@ -0,0 +1,368 @@
+#!/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[ ]:
+
+
+
+
-- 
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