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|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2023-11-07T23:05:35.179983Z",
"start_time": "2023-11-07T23:05:34.039783Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import folium\n",
"import utils"
]
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [],
"source": [
"# Load the data\n",
"ListA = pd.read_csv('List A.csv')\n",
"ListB = pd.read_csv('List B.csv')\n",
"ListC = pd.read_csv('List C.csv')\n",
"ListD = pd.read_csv('List D.csv')"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:05:35.194166Z",
"start_time": "2023-11-07T23:05:35.181233Z"
}
},
"id": "73b780e762c9de37"
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [],
"source": [
"# Create two centroids, one in the North End and one in the Seaport District\n",
"centroids = [[42.365, -71.054], [42.351, -71.045]]\n",
"\n",
"northeastern_coordinate = \"-71.09033,42.33976\""
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:05:35.195314Z",
"start_time": "2023-11-07T23:05:35.193156Z"
}
},
"id": "be4c8c1d77842ef7"
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [],
"source": [
"# Combine the two lists and add a column to indicate the list\n",
"ListA['list'] = 'A'\n",
"ListB['list'] = 'B'\n",
"ListC['list'] = 'C'\n",
"ListD['list'] = 'D'\n",
"\n",
"TotalList = pd.concat([ListA, ListB, ListC, ListD])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:05:35.201800Z",
"start_time": "2023-11-07T23:05:35.197747Z"
}
},
"id": "ffe4025e97a6c6b9"
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [],
"source": [
"# Remove all columns but name and gps\n",
"TotalList = TotalList[['name', 'gps', 'list']]"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:05:35.215762Z",
"start_time": "2023-11-07T23:05:35.200811Z"
}
},
"id": "72657779b4484aae"
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [],
"source": [
"# Convert the gps column to a list of lists for k-means\n",
"TotalList['gps'] = TotalList['gps'].apply(lambda x: x.strip('[]').split(','))\n",
"TotalList['gps'] = TotalList['gps'].apply(lambda x: [float(i) for i in x])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:05:35.215916Z",
"start_time": "2023-11-07T23:05:35.204173Z"
}
},
"id": "a157ffaec020a29a"
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [
{
"data": {
"text/plain": " name gps list\n0 521 Commercial Street #525 [42.3688272, -71.0553792] A\n1 Acorn St [42.3576234, -71.0688746] A\n2 Arlington's Great Meadows [42.4299758, -71.2038948] A\n3 Arthur Fiedler Statue [42.3565057, -71.0754527] A\n4 BU Beach [42.3511927, -71.1060828] A\n.. ... ... ...\n33 The Quiet Few [42.3670906, -71.0359889] D\n34 The Tall Ship Boston [42.3649544, -71.0414523] D\n35 Toasted Flats [42.3711266, -71.0371343] D\n36 Vega Market [42.3891835, -71.033703] D\n37 Winthrop High School [42.3803348, -70.9799864] D\n\n[169 rows x 3 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>name</th>\n <th>gps</th>\n <th>list</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>521 Commercial Street #525</td>\n <td>[42.3688272, -71.0553792]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Acorn St</td>\n <td>[42.3576234, -71.0688746]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Arlington's Great Meadows</td>\n <td>[42.4299758, -71.2038948]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Arthur Fiedler Statue</td>\n <td>[42.3565057, -71.0754527]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>4</th>\n <td>BU Beach</td>\n <td>[42.3511927, -71.1060828]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>33</th>\n <td>The Quiet Few</td>\n <td>[42.3670906, -71.0359889]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>34</th>\n <td>The Tall Ship Boston</td>\n <td>[42.3649544, -71.0414523]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Toasted Flats</td>\n <td>[42.3711266, -71.0371343]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Vega Market</td>\n <td>[42.3891835, -71.033703]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Winthrop High School</td>\n <td>[42.3803348, -70.9799864]</td>\n <td>D</td>\n </tr>\n </tbody>\n</table>\n<p>169 rows × 3 columns</p>\n</div>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(TotalList)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:05:35.216384Z",
"start_time": "2023-11-07T23:05:35.206794Z"
}
},
"id": "a03ebde91b87fa3b"
},
{
"cell_type": "markdown",
"source": [
"# 2 Routes"
],
"metadata": {
"collapsed": false
},
"id": "4bd41be9aca5094b"
},
{
"cell_type": "markdown",
"source": [
"## Cluster and Minimize"
],
"metadata": {
"collapsed": false
},
"id": "90d1d2f1a931597f"
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
" super()._check_params_vs_input(X, default_n_init=10)\n"
]
}
],
"source": [
"# Cluster and minimize the data\n",
"_, routes = utils.cluster_and_optimize(TotalList, centroids, northeastern_coordinate,\n",
" time_diff=0.25, max_time=24)\n",
"\n",
"route_1_coordinates = routes[0]\n",
"route_2_coordinates = routes[1]"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:04.963804Z",
"start_time": "2023-11-07T23:05:35.213646Z"
}
},
"id": "ee9b3c1ecb360976"
},
{
"cell_type": "markdown",
"source": [
"## Create JSON"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T17:33:02.697755Z",
"start_time": "2023-11-07T17:33:02.687460Z"
}
},
"id": "c85b8ef869e35006"
},
{
"cell_type": "code",
"execution_count": 9,
"outputs": [],
"source": [
"# Create a JSON request for the API\n",
"# This is the data we want to get from the API\n",
"route_1 = utils.list_to_string(route_1_coordinates)\n",
"route_2 = utils.list_to_string(route_2_coordinates)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:04.981278Z",
"start_time": "2023-11-07T23:06:04.967948Z"
}
},
"id": "aa618161182b5b07"
},
{
"cell_type": "code",
"execution_count": 10,
"outputs": [],
"source": [
"# Create a dataframe from the JSON\n",
"df1 = utils.create_json_df(route_1, utils.list_to_string([centroids[0]]), northeastern_coordinate)\n",
"df2 = utils.create_json_df(route_2, utils.list_to_string([centroids[1]]), northeastern_coordinate)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:07.185941Z",
"start_time": "2023-11-07T23:06:04.976840Z"
}
},
"id": "32c485788eedd94"
},
{
"cell_type": "code",
"execution_count": 11,
"outputs": [],
"source": [
"# Add columns for the route number\n",
"df1['route'] = 1\n",
"df2['route'] = 2\n",
"\n",
"# Concatenate the two dataframes\n",
"df = pd.concat([df1, df2], ignore_index=True)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:07.206851Z",
"start_time": "2023-11-07T23:06:07.193514Z"
}
},
"id": "49dba1f17ca8337e"
},
{
"cell_type": "code",
"execution_count": 12,
"outputs": [
{
"data": {
"text/plain": " waypoint_index trips_index \\\n0 0 0 \n1 1 0 \n2 2 0 \n3 3 0 \n4 4 0 \n.. ... ... \n168 61 0 \n169 62 0 \n170 63 0 \n171 64 0 \n172 65 0 \n\n hint distance \\\n0 1IwsgDuNLIBFAAAAWgEAAA8AAAAAAAAAFQP1QGa9GUI7qN... 8.262982 \n1 G4gsgDiILICSAwAA5gAAAOkAAAAAAAAAQljLQnyXy0Fhy8... 2.602121 \n2 gIosgLaKLIDOAAAArgAAAFwBAAAAAAAAp3O3QafxmUEQiR... 15.458439 \n3 HpwsgCKcLIAAAAAAEgAAAAAAAAAAAAAAAAAAACg870AAAA... 39.201677 \n4 qn8sgKt_LIAfAAAAAAAAAAAAAAAAAAAA2ElcQAAAAAAAAA... 39.331841 \n.. ... ... \n168 7hAigPYQIoA2AgAAYwEAAAAAAAAAAAAAnsd7Qq9XHUIAAA... 7.478611 \n169 bwwigH0MIoAFAAAAEAAAAFUAAAArAAAAag0xP3921D-BFx... 8.340476 \n170 MQwigFwMIoAoAAAANQAAABwAAAB-AAAAoidqQSAYl0GvUh... 11.504463 \n171 k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e... 36.240351 \n172 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n\n name location lat lon \\\n0 [-71.053931, 42.365054] -71.053931 42.365054 \n1 [-71.056164, 42.366918] -71.056164 42.366918 \n2 [-71.055561, 42.368861] -71.055561 42.368861 \n3 [-71.062507, 42.365968] -71.062507 42.365968 \n4 [-71.064277, 42.358851] -71.064277 42.358851 \n.. ... ... ... ... \n168 [-71.096959, 42.344689] -71.096959 42.344689 \n169 [-71.095003, 42.342001] -71.095003 42.342001 \n170 [-71.094327, 42.341231] -71.094327 42.341231 \n171 [-71.093834, 42.339096] -71.093834 42.339096 \n172 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n\n route \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 \n.. ... \n168 2 \n169 2 \n170 2 \n171 2 \n172 2 \n\n[173 rows x 9 columns]",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:07.219423Z",
"start_time": "2023-11-07T23:06:07.214064Z"
}
},
"id": "f231d9a35358988c"
},
{
"cell_type": "markdown",
"source": [
"## Map"
],
"metadata": {
"collapsed": false
},
"id": "75be92e34a36147f"
},
{
"cell_type": "code",
"execution_count": 13,
"outputs": [
{
"data": {
"text/plain": "<folium.folium.Map at 0x14fd8be10>",
"text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"<!DOCTYPE html>\n<html>\n<head>\n \n <meta http-equiv="content-type" content="text/html; charset=UTF-8" />\n \n <script>\n L_NO_TOUCH = false;\n L_DISABLE_3D = false;\n </script>\n \n <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css"/>\n <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css"/>\n <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>\n \n <meta name="viewport" content="width=device-width,\n initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n <style>\n #map_5a1967516626ecbf1f03b634f55bb869 {\n position: relative;\n width: 100.0%;\n height: 100.0%;\n left: 0.0%;\n top: 0.0%;\n }\n .leaflet-container { font-size: 1rem; }\n </style>\n \n</head>\n<body>\n \n \n <div class="folium-map" id="map_5a1967516626ecbf1f03b634f55bb869" ></div>\n \n</body>\n<script>\n \n \n var map_5a1967516626ecbf1f03b634f55bb869 = L.map(\n "map_5a1967516626ecbf1f03b634f55bb869",\n {\n center: [42.35900213872833, -71.07218852023122],\n crs: L.CRS.EPSG3857,\n zoom: 11,\n zoomControl: true,\n preferCanvas: false,\n }\n );\n\n \n\n \n \n var tile_layer_1c94bb14aece3a0b64db6974539ef8b7 = L.tileLayer(\n "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n ).addTo(map_5a1967516626ecbf1f03b634f55bb869);\n \n \n var poly_line_92ffa92892583897807a1ed2e4fa6fdc = L.polyline(\n 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).addTo(map_5a1967516626ecbf1f03b634f55bb869);\n \n \n var circle_marker_b15de6f1803021c5823f9f1aca6825f0 = L.circleMarker(\n [42.365054, -71.053931],\n {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 3, "stroke": true, "weight": 3}\n ).addTo(map_5a1967516626ecbf1f03b634f55bb869);\n \n \n var circle_marker_72099332ad8c2e78d697336f52f90238 = L.circleMarker(\n [42.366918, -71.056164],\n {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 3, "stroke": true, "weight": 3}\n ).addTo(map_5a1967516626ecbf1f03b634f55bb869);\n \n \n var circle_marker_313d7beb606873db7c507d07cb36f67d = L.circleMarker(\n [42.368861, -71.055561],\n {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 3, "stroke": true, "weight": 3}\n ).addTo(map_5a1967516626ecbf1f03b634f55bb869);\n \n \n var circle_marker_0dd9242f067518621d6756a5d61fe657 = L.circleMarker(\n [42.365968, -71.062507],\n {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 3, "stroke": true, "weight": 3}\n ).addTo(map_5a1967516626ecbf1f03b634f55bb869);\n \n \n var circle_marker_2b86d619f00bd033f279585cfb6a1b15 = L.circleMarker(\n [42.358851, -71.064277],\n {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", 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style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a map\n",
"m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)\n",
"\n",
"# Add the points and lines for the two routes with different colors\n",
"colors = ['red', 'blue']\n",
"\n",
"for route in df['route'].unique():\n",
" df_route = df[df['route'] == route]\n",
" folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)\n",
" for i in range(len(df_route)):\n",
" folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(\n",
" m)\n",
"\n",
"# Display the map\n",
"m"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:07.316817Z",
"start_time": "2023-11-07T23:06:07.221200Z"
}
},
"id": "80fd847da2833913"
},
{
"cell_type": "markdown",
"source": [
"## Results"
],
"metadata": {
"collapsed": false
},
"id": "a7b562f75f7e0813"
},
{
"cell_type": "code",
"execution_count": 14,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Route 1 has 105 waypoints\n",
"Route 2 has 64 waypoints\n"
]
}
],
"source": [
"# Get the number of waypoints for each route\n",
"route_1_waypoints = len(route_1_coordinates)\n",
"route_2_waypoints = len(route_2_coordinates)\n",
"print(\"Route 1 has {} waypoints\".format(route_1_waypoints))\n",
"print(\"Route 2 has {} waypoints\".format(route_2_waypoints))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:07.318988Z",
"start_time": "2023-11-07T23:06:07.297230Z"
}
},
"id": "f53c97acec1c2fc4"
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The trip will take 13.1925 hours\n",
"The trip will take 13.017777777777777 hours\n"
]
}
],
"source": [
"trip_hrs_1 = utils.get_trip_time(route_1, route_1_waypoints, utils.list_to_string([centroids[0]]),\n",
" northeastern_coordinate)\n",
"print(\"The trip will take {} hours\".format(trip_hrs_1))\n",
"trip_hrs_2 = utils.get_trip_time(route_2, route_2_waypoints, utils.list_to_string([centroids[1]]),\n",
" northeastern_coordinate)\n",
"print(\"The trip will take {} hours\".format(trip_hrs_2))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:06:09.735945Z",
"start_time": "2023-11-07T23:06:07.299647Z"
}
},
"id": "a3ec09dfb5cbb5b3"
},
{
"cell_type": "markdown",
"source": [
"# 3 Routes"
],
"metadata": {
"collapsed": false
},
"id": "de7b5856172d213c"
},
{
"cell_type": "code",
"execution_count": 16,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
" super()._check_params_vs_input(X, default_n_init=10)\n"
]
}
],
"source": [
"# Cluster and minimize the data\n",
"# Add a third centroid in the Financial District\n",
"centroids.append([42.356, -71.055])\n",
"_, routes = utils.cluster_and_optimize(TotalList, centroids, northeastern_coordinate, time_diff=0.3, max_time=24)\n",
"\n",
"route_1_coordinates = routes[0]\n",
"route_2_coordinates = routes[1]\n",
"route_3_coordinates = routes[2]"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:36.195528Z",
"start_time": "2023-11-07T23:06:09.732162Z"
}
},
"id": "bb6e00857e8175c0"
},
{
"cell_type": "markdown",
"source": [
"## Create JSON"
],
"metadata": {
"collapsed": false
},
"id": "19afb4f687b37383"
},
{
"cell_type": "code",
"execution_count": 17,
"outputs": [],
"source": [
"# Create a JSON request for the API\n",
"# This is the data we want to get from the API\n",
"route_1 = utils.list_to_string(route_1_coordinates)\n",
"route_2 = utils.list_to_string(route_2_coordinates)\n",
"route_3 = utils.list_to_string(route_3_coordinates)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:36.198703Z",
"start_time": "2023-11-07T23:07:36.194798Z"
}
},
"id": "e886e061f86a2118"
},
{
"cell_type": "code",
"execution_count": 18,
"outputs": [],
"source": [
"# Create a dataframe from the JSON\n",
"df1 = utils.create_json_df(route_1, utils.list_to_string([centroids[0]]), northeastern_coordinate)\n",
"df2 = utils.create_json_df(route_2, utils.list_to_string([centroids[1]]), northeastern_coordinate)\n",
"df3 = utils.create_json_df(route_3, utils.list_to_string([centroids[2]]), northeastern_coordinate)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:38.315154Z",
"start_time": "2023-11-07T23:07:36.199174Z"
}
},
"id": "23e4682fe9e30631"
},
{
"cell_type": "code",
"execution_count": 19,
"outputs": [],
"source": [
"# Add columns for the route number\n",
"df1['route'] = 1\n",
"df2['route'] = 2\n",
"df3['route'] = 3\n",
"\n",
"# Concatenate the three dataframes\n",
"df = pd.concat([df1, df2, df3], ignore_index=True)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:38.325567Z",
"start_time": "2023-11-07T23:07:38.320173Z"
}
},
"id": "c3a5c5d6f3ac46c0"
},
{
"cell_type": "code",
"execution_count": 20,
"outputs": [
{
"data": {
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:38.333653Z",
"start_time": "2023-11-07T23:07:38.322616Z"
}
},
"id": "17a8cc8fed5450a6"
},
{
"cell_type": "markdown",
"source": [
"## Map"
],
"metadata": {
"collapsed": false
},
"id": "b20a57aa09792c39"
},
{
"cell_type": "code",
"execution_count": 21,
"outputs": [
{
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},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a map\n",
"m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)\n",
"\n",
"# Add the points and lines for the three routes with different colors\n",
"colors = ['red', 'blue', 'green']\n",
"\n",
"for route in df['route'].unique():\n",
" df_route = df[df['route'] == route]\n",
" folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)\n",
" for i in range(len(df_route)):\n",
" folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(\n",
" m)\n",
"\n",
"# Display the map\n",
"m"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:38.444061Z",
"start_time": "2023-11-07T23:07:38.336503Z"
}
},
"id": "702adaec008a6ec8"
},
{
"cell_type": "markdown",
"source": [
"## Results"
],
"metadata": {
"collapsed": false
},
"id": "a947e49e27c734e9"
},
{
"cell_type": "code",
"execution_count": 22,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Route 1 has 61 waypoints\n",
"Route 2 has 56 waypoints\n",
"Route 3 has 52 waypoints\n"
]
}
],
"source": [
"# Get the number of waypoints for each route\n",
"route_1_waypoints = len(route_1_coordinates)\n",
"route_2_waypoints = len(route_2_coordinates)\n",
"route_3_waypoints = len(route_3_coordinates)\n",
"print(\"Route 1 has {} waypoints\".format(route_1_waypoints))\n",
"print(\"Route 2 has {} waypoints\".format(route_2_waypoints))\n",
"print(\"Route 3 has {} waypoints\".format(route_3_waypoints))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:38.445284Z",
"start_time": "2023-11-07T23:07:38.401884Z"
}
},
"id": "4106acf2adad01d7"
},
{
"cell_type": "code",
"execution_count": 23,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The trip will take 9.394444444444444 hours\n",
"The trip will take 8.852777777777778 hours\n",
"The trip will take 9.325555555555555 hours\n"
]
}
],
"source": [
"# Get the trip time for each route\n",
"trip_hrs_1 = utils.get_trip_time(route_1, route_1_waypoints, utils.list_to_string([centroids[0]]),\n",
" northeastern_coordinate)\n",
"print(\"The trip will take {} hours\".format(trip_hrs_1))\n",
"trip_hrs_2 = utils.get_trip_time(route_2, route_2_waypoints, utils.list_to_string([centroids[1]]),\n",
" northeastern_coordinate)\n",
"print(\"The trip will take {} hours\".format(trip_hrs_2))\n",
"trip_hrs_3 = utils.get_trip_time(route_3, route_3_waypoints, utils.list_to_string([centroids[2]]),\n",
" northeastern_coordinate)\n",
"print(\"The trip will take {} hours\".format(trip_hrs_3))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:07:40.521741Z",
"start_time": "2023-11-07T23:07:38.405889Z"
}
},
"id": "c58106faf0fc7f4e"
},
{
"cell_type": "markdown",
"source": [
"# 10 ROUTES (because I can)"
],
"metadata": {
"collapsed": false
},
"id": "4068a0b6460f19ab"
},
{
"cell_type": "code",
"execution_count": 24,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
" super()._check_params_vs_input(X, default_n_init=10)\n"
]
}
],
"source": [
"# Cluster and minimize the data\n",
"# Add seven more centroids around Boston with different latitudes and longitudes\n",
"for i in range(7):\n",
" centroids.append([42.365 + i * 0.01, -71.054 + i * 0.01])\n",
"\n",
"_, routes = utils.cluster_and_optimize(TotalList, centroids, northeastern_coordinate, time_diff=0.5, max_time=24)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:09:18.376367Z",
"start_time": "2023-11-07T23:07:40.529888Z"
}
},
"id": "5995d6556f940e67"
},
{
"cell_type": "markdown",
"source": [
"## Create JSON"
],
"metadata": {
"collapsed": false
},
"id": "8c6f5aeb5e6c2832"
},
{
"cell_type": "code",
"execution_count": 25,
"outputs": [],
"source": [
"# Create a JSON request for the API\n",
"# This is the data we want to get from the API\n",
"route_strings = []\n",
"for route in routes:\n",
" route_strings.append(utils.list_to_string(route))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:09:18.395941Z",
"start_time": "2023-11-07T23:09:18.378652Z"
}
},
"id": "375b090921cab03e"
},
{
"cell_type": "code",
"execution_count": 26,
"outputs": [],
"source": [
"# Create a dataframe from the JSON\n",
"dfs = []\n",
"for i in range(len(routes)):\n",
" dfs.append(utils.create_json_df(route_strings[i], utils.list_to_string([centroids[i]]), northeastern_coordinate))\n",
" \n",
"# Concatenate the dataframes\n",
"df = pd.concat(dfs, ignore_index=True)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:09:21.149586Z",
"start_time": "2023-11-07T23:09:18.384347Z"
}
},
"id": "74f619c6df3bd6c4"
},
{
"cell_type": "code",
"execution_count": 30,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/8b/9mzlkxlx3zx9nmpy8prjpmm80000gn/T/ipykernel_42497/2886398449.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['route'].iloc[i * len(routes[i]):(i + 1) * len(routes[i])] = i + 1\n"
]
}
],
"source": [
"# Add columns for the route number\n",
"for i in range(len(routes)):\n",
" df['route'].iloc[i * len(routes[i]):(i + 1) * len(routes[i])] = i + 1"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:10:17.312994Z",
"start_time": "2023-11-07T23:10:17.307208Z"
}
},
"id": "488924ebe78c61aa"
},
{
"cell_type": "code",
"execution_count": 31,
"outputs": [
{
"data": {
"text/plain": " waypoint_index trips_index \\\n0 0 0 \n1 1 0 \n2 2 0 \n3 3 0 \n4 4 0 \n.. ... ... \n184 11 0 \n185 12 0 \n186 13 0 \n187 14 0 \n188 15 0 \n\n hint distance \\\n0 1IwsgDuNLIBFAAAAWgEAAA8AAAAAAAAAFQP1QGa9GUI7qN... 8.262982 \n1 LRUugHAVLoA1AAAA7wEAAKAAAADqAAAAYZa9QBEBXEIOWo... 1.865658 \n2 lM4AgM3LAIAEAAAAHAAAAJEAAAC_AgAAyLv6PxJ7NEGyPn... 2.242639 \n3 ZQ0fgPINH4AgAAAAEQAAAFEAAAAqAAAArYRYQRHu20BfWQ... 48.627645 \n4 HR8ugIJiBICVAQAARwAAAAAAAACLAAAAQ1M0Qu3l-EAAAA... 0.645763 \n.. ... ... \n184 -2EugABiLoCcAQAAigAAAAAAAAAAAAAAMQI3QqZ0dUEAAA... 7.363621 \n185 VSIfgAYjH4AUAAAAAAAAACUBAADDAAAAaIcPQAAAAADYBw... 18.888832 \n186 0OEhgPvhIYADAAAABgAAAA8AAAA0AAAA2lq-PipQFD-Y-N... 2.009578 \n187 C-AhgGbgIYBZAAAAMQAAAAAAAABqAAAAj5QfQS1zq0AAAA... 4.887502 \n188 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n\n name location lat \\\n0 [-71.053931, 42.365054] -71.053931 \n1 [-71.061735, 42.369195] -71.061735 \n2 Miller's River Littoral Way [-71.065634, 42.371832] -71.065634 \n3 [-71.06828, 42.369868] -71.068280 \n4 [-71.094764, 42.377355] -71.094764 \n.. ... ... ... \n184 [-71.102659, 42.382131] -71.102659 \n185 [-71.110851, 42.374259] -71.110851 \n186 [-71.085166, 42.349997] -71.085166 \n187 [-71.091358, 42.348977] -71.091358 \n188 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 \n\n lon route \n0 42.365054 1 \n1 42.369195 1 \n2 42.371832 1 \n3 42.369868 4 \n4 42.377355 1 \n.. ... ... \n184 42.382131 10 \n185 42.374259 6 \n186 42.349997 6 \n187 42.348977 6 \n188 42.339762 6 \n\n[189 rows x 9 columns]",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Display the dataframe\n",
"display(df)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:10:19.393773Z",
"start_time": "2023-11-07T23:10:19.390813Z"
}
},
"id": "8e436ba5d3949420"
},
{
"cell_type": "markdown",
"source": [
"## Map"
],
"metadata": {
"collapsed": false
},
"id": "1552586cb84a48c5"
},
{
"cell_type": "code",
"execution_count": 37,
"outputs": [
{
"data": {
"text/plain": "<folium.folium.Map at 0x16c5df950>",
"text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"<!DOCTYPE html>\n<html>\n<head>\n \n <meta http-equiv="content-type" content="text/html; charset=UTF-8" />\n \n <script>\n L_NO_TOUCH = false;\n L_DISABLE_3D = false;\n </script>\n \n <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css"/>\n <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css"/>\n <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>\n \n <meta name="viewport" content="width=device-width,\n initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n <style>\n #map_a113ca2999b944715e9a3600543bd417 {\n position: relative;\n width: 100.0%;\n height: 100.0%;\n left: 0.0%;\n top: 0.0%;\n }\n .leaflet-container { font-size: 1rem; }\n </style>\n \n</head>\n<body>\n \n \n 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[42.344315, -71.033918],\n {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 3, "stroke": true, "weight": 3}\n ).addTo(map_a113ca2999b944715e9a3600543bd417);\n \n \n var poly_line_a0f1037bde39a4d4395a360f65bcbd36 = L.polyline(\n [[42.369868, -71.06828]],\n {"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "noClip": false, "opacity": 1.0, "smoothFactor": 1.0, "stroke": true, "weight": 3}\n ).addTo(map_a113ca2999b944715e9a3600543bd417);\n \n \n var circle_marker_b7d7743431fd67c9861f8cdd168c3079 = L.circleMarker(\n [42.369868, -71.06828],\n {"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": 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},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a map\n",
"m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)\n",
"\n",
"# Add the points and lines for the three routes with different colors\n",
"colors = ['red', 'blue', 'green', 'orange', 'purple', 'pink', 'black', 'gray', 'brown', 'yellow']\n",
"\n",
"for route in df['route'].unique():\n",
" df_route = df[df['route'] == route]\n",
" folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)\n",
" for i in range(len(df_route)):\n",
" folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(\n",
" m)\n",
" \n",
"# Display the map\n",
"m"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:13:22.619199Z",
"start_time": "2023-11-07T23:13:22.527566Z"
}
},
"id": "4305c6981e48e87f"
},
{
"cell_type": "markdown",
"source": [
"## Results"
],
"metadata": {
"collapsed": false
},
"id": "4723f2f26efe49d3"
},
{
"cell_type": "code",
"execution_count": 36,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Route 1 has 20 waypoints\n",
"Route 2 has 10 waypoints\n",
"Route 3 has 28 waypoints\n",
"Route 4 has 1 waypoints\n",
"Route 5 has 15 waypoints\n",
"Route 6 has 37 waypoints\n",
"Route 7 has 18 waypoints\n",
"Route 8 has 16 waypoints\n",
"Route 9 has 10 waypoints\n",
"Route 10 has 14 waypoints\n"
]
}
],
"source": [
"# Get the number of waypoints for each route\n",
"route_waypoints = []\n",
"for route in routes:\n",
" route_waypoints.append(len(route))\n",
"for i in range(len(route_waypoints)):\n",
" print(\"Route {} has {} waypoints\".format(i + 1, route_waypoints[i]))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:12:32.990935Z",
"start_time": "2023-11-07T23:12:32.987254Z"
}
},
"id": "5887f93dd890bc77"
},
{
"cell_type": "code",
"execution_count": 34,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The trip will take 3.1816666666666666 hours\n",
"The trip will take 3.8113888888888887 hours\n",
"The trip will take 3.9852777777777777 hours\n",
"The trip will take 3.8975 hours\n",
"The trip will take 4.088611111111111 hours\n",
"The trip will take 4.039444444444444 hours\n",
"The trip will take 3.17 hours\n",
"The trip will take 3.209722222222222 hours\n",
"The trip will take 4.1275 hours\n",
"The trip will take 3.2069444444444444 hours\n"
]
}
],
"source": [
"# Get the trip time for each route\n",
"trip_hrs = []\n",
"for i in range(len(routes)):\n",
" trip_hrs.append(utils.get_trip_time(route_strings[i], route_waypoints[i], utils.list_to_string([centroids[i]]),\n",
" northeastern_coordinate))\n",
"for i in range(len(trip_hrs)):\n",
" print(\"The trip will take {} hours\".format(trip_hrs[i]))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T23:11:11.477373Z",
"start_time": "2023-11-07T23:11:08.172393Z"
}
},
"id": "3a4b529fc3f2b336"
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
},
"id": "a4cf5509f890423a"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
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|