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|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2023-11-07T15:38:40.404048Z",
"start_time": "2023-11-07T15:38:38.873539Z"
}
},
"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": "2023-11-07T15:38:40.423960Z",
"start_time": "2023-11-07T15:38:40.404555Z"
}
},
"id": "73b780e762c9de37"
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [],
"source": [
"# Create three centroids, one in the North End, one in the Financial District, and one in the Back Bay\n",
"centroids = [[42.364506, -71.054733], [42.358894, -71.056742], [42.3505, -71.0760]]\n",
"\n",
"northeastern_coordinate = \"-71.09033,42.33976\""
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T15:38:40.424218Z",
"start_time": "2023-11-07T15:38:40.420196Z"
}
},
"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": "2023-11-07T15:38:40.442314Z",
"start_time": "2023-11-07T15:38:40.425419Z"
}
},
"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": "2023-11-07T15:38:40.442798Z",
"start_time": "2023-11-07T15:38:40.430053Z"
}
},
"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": "2023-11-07T15:38:40.442952Z",
"start_time": "2023-11-07T15:38:40.432847Z"
}
},
"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 normalized_gps \n0 [0.7251058917247415, 0.7797482353989729] \n1 [0.6747391031099019, 0.7451825969538083] \n2 [1.0, 0.3993566550776867] \n3 [0.6697144722136962, 0.7283341725828262] \n4 [0.6458298305822171, 0.6498815915448888] \n.. ... \n33 [0.717298990038831, 0.8294124246148072] \n34 [0.7076956827824702, 0.8154190706511427] \n35 [0.7354428661210094, 0.8264787225922622] \n36 [0.8166178304491644, 0.8352672783369615] \n37 [0.7768384161061446, 0.972851090162032] \n\n[169 rows x 4 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 <th>normalized_gps</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 <td>[0.7251058917247415, 0.7797482353989729]</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 <td>[0.6747391031099019, 0.7451825969538083]</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 <td>[1.0, 0.3993566550776867]</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 <td>[0.6697144722136962, 0.7283341725828262]</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 <td>[0.6458298305822171, 0.6498815915448888]</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\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 <td>[0.717298990038831, 0.8294124246148072]</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 <td>[0.7076956827824702, 0.8154190706511427]</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 <td>[0.7354428661210094, 0.8264787225922622]</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 <td>[0.8166178304491644, 0.8352672783369615]</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 <td>[0.7768384161061446, 0.972851090162032]</td>\n </tr>\n </tbody>\n</table>\n<p>169 rows × 4 columns</p>\n</div>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a new column with normalized gps coordinates and centroids\n",
"TotalList['normalized_gps'], norm_centroids = utils.normalize_gps(TotalList['gps'].values.tolist(), centroids)\n",
"display(TotalList)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T15:38:40.443962Z",
"start_time": "2023-11-07T15:38:40.436165Z"
}
},
"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",
"norm_centroids_2 = norm_centroids[:2]\n",
"_, route_1_coordinates, route_2_coordinates = utils.cluster_and_minimize_2(TotalList, centroids, norm_centroids_2,\n",
" northeastern_coordinate, 0.5, minimize=False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T15:38:43.183771Z",
"start_time": "2023-11-07T15:38:40.443836Z"
}
},
"id": "ee9b3c1ecb360976"
},
{
"cell_type": "markdown",
"source": [
"## Create JSON"
],
"metadata": {
"collapsed": false
},
"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": "2023-11-07T15:38:43.192691Z",
"start_time": "2023-11-07T15:38:43.184382Z"
}
},
"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": "2023-11-07T15:38:45.665442Z",
"start_time": "2023-11-07T15:38:43.186783Z"
}
},
"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": "2023-11-07T15:38:45.678738Z",
"start_time": "2023-11-07T15:38:45.666570Z"
}
},
"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 84 0 \n169 85 0 \n170 86 0 \n171 87 0 \n172 88 0 \n\n hint distance \\\n0 t4YsgAGHLIAAAAAAVQEAAAAAAAAwAAAAAAAAAHV0F0IAAA... 19.432511 \n1 IzYEgGw1BIASAAAArwAAADMAAACUAwAAynkIQGUkmkEXlL... 6.024489 \n2 5IgsgAqJLID7AAAAHgAAAAwAAAAIAAAAz5ffQcMBVEDFYK... 5.871835 \n3 G4gsgDiILICSAwAA5gAAAOkAAAAAAAAAQljLQnyXy0Fhy8... 2.602121 \n4 gIosgLaKLIDOAAAArgAAAFwBAAAAAAAAp3O3QafxmUEQiR... 15.458439 \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 [-71.054865, 42.364361] -71.054865 42.364361 \n1 [-71.055569, 42.364032] -71.055569 42.364032 \n2 [-71.055582, 42.365251] -71.055582 42.365251 \n3 [-71.056164, 42.366918] -71.056164 42.366918 \n4 [-71.055561, 42.368861] -71.055561 42.368861 \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": "2023-11-07T15:38:45.679592Z",
"start_time": "2023-11-07T15:38:45.669550Z"
}
},
"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 0x146a39e50>",
"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_38c32a82e4c47f408c385fba9746ea22 {\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_38c32a82e4c47f408c385fba9746ea22" ></div>\n \n</body>\n<script>\n \n \n var map_38c32a82e4c47f408c385fba9746ea22 = L.map(\n "map_38c32a82e4c47f408c385fba9746ea22",\n {\n center: [42.35904369942197, -71.07226178034684],\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_0ee8f4b3658203e2ef1afa76a0d600b5 = 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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var poly_line_d30ea21b6098c54619a2ef7537c68f84 = L.polyline(\n 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[42.421213, -71.027113], [42.412279, -71.031525], [42.411785, -71.01537], [42.398247, -71.028327], [42.397588, -71.035674], [42.391309, -71.036726], [42.390284, -71.038526], [42.385546, -71.039316], [42.386461, -71.032794], [42.389192, -71.033749], [42.393843, -71.041015], [42.402568, -71.051453], [42.422264, -71.043219], [42.418307, -71.050739], [42.42069, -71.055953], [42.407436, -71.062128], [42.403792, -71.058992], [42.398809, -71.061206], [42.397236, -71.072007], [42.400829, -71.112241], [42.396589, -71.122704], [42.429978, -71.203921], [42.410941, -71.168458], [42.388907, -71.133098], [42.388412, -71.119219], [42.38859, -71.119303], [42.383988, -71.110771], [42.383573, -71.112746], [42.375457, -71.119379], [42.373491, -71.118959], [42.373266, -71.120839], [42.378275, -71.114496], [42.378452, -71.115739], [42.376696, -71.115952], [42.374259, -71.110851], [42.382131, -71.102659], [42.382238, -71.102512], [42.380957, -71.097894], [42.380072, -71.096887], [42.381759, -71.093444], 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"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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_c4e88e24a4b364d68347ff5f7ce52f26 = L.circleMarker(\n [42.365251, -71.055582],\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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_7e36d30fa5738d730aa74b78d965cb2b = 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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_cda2d20cd72f9815158b61dc0845cd77 = 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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_d66c2c41b67c9eedb446b1fc35ec96a3 = 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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_0704199c74d52b10f4a1e7bf823285bb = L.circleMarker(\n [42.369195, -71.061735],\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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_ae967bd63b5ef3c936c48600ecbb0cce = L.circleMarker(\n [42.371832, -71.065634],\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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_1b10fbbd4e83912029eb1bc067ccf943 = L.circleMarker(\n [42.369868, -71.06828],\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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_a08ab415a4f6e6f43959c226222b4974 = L.circleMarker(\n [42.369473, -71.075628],\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_38c32a82e4c47f408c385fba9746ea22);\n \n \n var circle_marker_70e39db769e1392526b3830521a2b327 = L.circleMarker(\n [42.374142, -71.063105],\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 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},
"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": "2023-11-07T15:38:45.781790Z",
"start_time": "2023-11-07T15:38:45.677705Z"
}
},
"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 82 waypoints\n",
"Route 2 has 87 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": "2023-11-07T15:38:45.782104Z",
"start_time": "2023-11-07T15:38:45.736109Z"
}
},
"id": "f53c97acec1c2fc4"
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The trip will take 11.154166666666667 hours\n",
"The trip will take 14.8425 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": "2023-11-07T15:38:48.049877Z",
"start_time": "2023-11-07T15:38:45.738475Z"
}
},
"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",
"_, route_1_coordinates, route_2_coordinates, route_3_coordinates = utils.cluster_and_minimize_3(TotalList, centroids,\n",
" norm_centroids,\n",
" northeastern_coordinate,\n",
" 0.2, minimize=False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T15:38:50.390169Z",
"start_time": "2023-11-07T15:38:48.048739Z"
}
},
"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": "2023-11-07T15:38:50.404500Z",
"start_time": "2023-11-07T15:38:50.392611Z"
}
},
"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": "2023-11-07T15:38:52.713204Z",
"start_time": "2023-11-07T15:38:50.400202Z"
}
},
"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": "2023-11-07T15:38:52.724854Z",
"start_time": "2023-11-07T15:38:52.718223Z"
}
},
"id": "c3a5c5d6f3ac46c0"
},
{
"cell_type": "code",
"execution_count": 20,
"outputs": [
{
"data": {
"text/plain": " waypoint_index trips_index \\\n0 0 0 \n1 1 0 \n2 2 0 \n3 3 0 \n4 4 0 \n.. ... ... \n170 39 0 \n171 40 0 \n172 41 0 \n173 42 0 \n174 43 0 \n\n hint distance \\\n0 t4YsgAGHLIAAAAAAVQEAAAAAAAAwAAAAAAAAAHV0F0IAAA... 19.432511 \n1 e1kugJlZLoBmAAAA6QAAAAAAAAAAAAAAZ6M2QSewzkEAAA... 4.756158 \n2 tFkugHVaLoAOAAAAAAAAABgAAAAAAAAAwMG2QAAAAAB6ii... 4.525535 \n3 sJAugLOQLoBuAQAAlAEAAAAAAAAAAAAAHFcjQvEZM0IAAA... 7.844897 \n4 VREtgNlJBIBCAAAAYAAAAAAAAAARAAAAOOzeQU7vHkIAAA... 22.681980 \n.. ... ... \n170 cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC... 22.776295 \n171 s9QhgLbUIYAwAAAAkAAAAAAAAAAAAAAA2XmpQNgrgEEAAA... 4.111715 \n172 5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc... 17.374491 \n173 k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e... 36.240351 \n174 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n\n name location lat lon \\\n0 [-71.054865, 42.364361] -71.054865 42.364361 \n1 [-71.060933, 42.376178] -71.060933 42.376178 \n2 [-71.060753, 42.376391] -71.060753 42.376391 \n3 [-71.060948, 42.380436] -71.060948 42.380436 \n4 Factory Street [-71.061206, 42.398809] -71.061206 42.398809 \n.. ... ... ... ... \n170 Alleghany Street [-71.099348, 42.33047] -71.099348 42.330470 \n171 [-71.09454, 42.325354] -71.094540 42.325354 \n172 Dudley Street [-71.090904, 42.329829] -71.090904 42.329829 \n173 [-71.093834, 42.339096] -71.093834 42.339096 \n174 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n\n route \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 \n.. ... \n170 3 \n171 3 \n172 3 \n173 3 \n174 3 \n\n[175 rows x 9 columns]",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T15:38:52.735139Z",
"start_time": "2023-11-07T15:38:52.730797Z"
}
},
"id": "17a8cc8fed5450a6"
},
{
"cell_type": "markdown",
"source": [
"## Map"
],
"metadata": {
"collapsed": false
},
"id": "b20a57aa09792c39"
},
{
"cell_type": "code",
"execution_count": 21,
"outputs": [
{
"data": {
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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": "2023-11-07T15:38:52.827726Z",
"start_time": "2023-11-07T15:38:52.736404Z"
}
},
"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 55 waypoints\n",
"Route 2 has 72 waypoints\n",
"Route 3 has 42 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": "2023-11-07T15:38:52.840867Z",
"start_time": "2023-11-07T15:38:52.802540Z"
}
},
"id": "4106acf2adad01d7"
},
{
"cell_type": "code",
"execution_count": 23,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The trip will take 8.883333333333333 hours\n",
"The trip will take 8.458055555555555 hours\n",
"The trip will take 10.230555555555556 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": "2023-11-07T15:38:54.879411Z",
"start_time": "2023-11-07T15:38:52.805867Z"
}
},
"id": "c58106faf0fc7f4e"
},
{
"cell_type": "code",
"execution_count": 23,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T15:38:54.879657Z",
"start_time": "2023-11-07T15:38:54.867101Z"
}
},
"id": "a2f10e3152b95a69"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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