diff options
Diffstat (limited to 'ZestySalesman.ipynb')
| -rw-r--r-- | ZestySalesman.ipynb | 579 |
1 files changed, 512 insertions, 67 deletions
diff --git a/ZestySalesman.ipynb b/ZestySalesman.ipynb index 7e74f46..1d1fd59 100644 --- a/ZestySalesman.ipynb +++ b/ZestySalesman.ipynb @@ -2,65 +2,245 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2023-11-06T01:23:16.767323Z", - "start_time": "2023-11-06T01:23:16.761053Z" + "end_time": "2023-11-07T01:17:52.608101Z", + "start_time": "2023-11-07T01:17:52.539921Z" } }, "outputs": [], "source": [ "import pandas as pd\n", - "import numpy as np\n", - "import requests\n", "import folium\n", "import utils" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, + "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": "2023-11-07T01:17:52.672617Z", + "start_time": "2023-11-07T01:17:52.544450Z" + } + }, + "id": "73b780e762c9de37" + }, + { + "cell_type": "code", + "execution_count": 26, + "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": "2023-11-07T01:17:52.673868Z", + "start_time": "2023-11-07T01:17:52.558087Z" + } + }, + "id": "be4c8c1d77842ef7" + }, + { + "cell_type": "code", + "execution_count": 27, + "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": "2023-11-07T01:17:52.702176Z", + "start_time": "2023-11-07T01:17:52.568817Z" + } + }, + "id": "ffe4025e97a6c6b9" + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [], + "source": [ + "# Remove all columns but name and gps\n", + "TotalList = TotalList[['name', 'gps', 'list']]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-07T01:17:52.706405Z", + "start_time": "2023-11-07T01:17:52.577745Z" + } + }, + "id": "72657779b4484aae" + }, + { + "cell_type": "code", + "execution_count": 29, + "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": "2023-11-07T01:17:52.706689Z", + "start_time": "2023-11-07T01:17:52.581919Z" + } + }, + "id": "a157ffaec020a29a" + }, + { + "cell_type": "code", + "execution_count": 30, + "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": "2023-11-07T01:17:52.707232Z", + "start_time": "2023-11-07T01:17:52.597329Z" + } + }, + "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": 31, + "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=True)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-07T01:18:19.800168Z", + "start_time": "2023-11-07T01:17:52.606044Z" + } + }, + "id": "ee9b3c1ecb360976" + }, + { + "cell_type": "markdown", + "source": [ + "## Create JSON" + ], + "metadata": { + "collapsed": false + }, + "id": "c85b8ef869e35006" + }, + { + "cell_type": "code", + "execution_count": 32, "outputs": [], "source": [ "# Create a JSON request for the API\n", "# This is the data we want to get from the API\n", - "northeastern_coordinate = \"-71.09033,42.33976;\"\n", - "route_1 = '-71.2038948,42.4299758;-71.1060828,42.3511927;-71.0969274,42.3446263;-71.130887,42.35304;-71.1459593,42.3501823;-71.1460435,42.3495825;-71.1217152,42.3426377;-71.1258765,42.331864;-71.1095021,42.3364675;-71.133103,42.3890049;-71.1123834,42.3360385;-71.2273649,42.3145041;-71.0992038,42.3306454;-71.0990577,42.3381442;-71.0949218,42.3419564;-71.0942861,42.3413301;-71.0913583,42.3490205;-71.1000217,42.3323776;-71.1241295,42.3518397;-71.1618052,42.3245965;-71.1625829,42.340795;-71.167854,42.4107892;-71.155555,42.3317473;-71.1227278,42.3965778;-71.1126695,42.3836229;-71.119149,42.3884;-71.1427371,42.3433772;-71.1438455,42.3569102;-71.1313443,42.3525708;-71.1284677,42.3631904;-71.119301,42.388547;-71.097883,42.381008;-71.1107166,42.3741209;-71.1194344,42.3754427;-71.1013044,42.3627462;-71.1108423,42.3838224;-71.1026937,42.3820702;-71.1189467,42.373465;-71.1208817,42.3732344;-71.0968274,42.3799095;-71.094048,42.339381;-71.1854722,42.3621177;-71.1146697,42.3782386;-71.0935443,42.3817274;-71.0906355,42.3616095;-71.1161887,42.3766442;-71.0962734,42.3627993;-71.1155576,42.3784629;-71.0949101,42.3797674;-71.1087411,42.3640287;-71.09476,42.37736;-71.1014951,42.3614115;-71.1024769,42.3822934;-71.1011111,42.3636597;-71.0898829,42.3463992;-71.0983169,42.3319001'\n", - "route_2 = '-71.0553792,42.3688272;-71.0688746,42.3576234;-71.0754527,42.3565057;-71.0620802,42.3579151;-71.0586014,42.357357;-71.0572023,42.3587627;-71.0556268,42.36521;-71.0720926,42.3489004;-71.067859,42.3500079;-71.0632036,42.3556154;-71.0620134,42.3248471;-71.0851891,42.3500031;-71.066414,42.354296;-71.0834061,42.341987;-71.0569649,42.3604952;-71.0498714,42.3256817;-71.0908104,42.329969;-71.0616035,42.3537983;-71.0359433,42.3485465;-71.0638101,42.3587772;-71.0555003,42.3640137;-71.0712561,42.3407613;-71.0561781,42.3668968;-71.0664019,42.3554589;-71.059228,42.359349;-71.0668408,42.3524116;-71.0872846,42.2961434;-71.062146,42.366198;-71.0651214,42.3553972;-71.0596124,42.3509517;-71.0359354,42.3478381;-71.061757,42.3691906;-71.0609962,42.3803747;-71.0516339,42.3609921;-71.0809932,42.3675275;-71.0545357,42.3597994;-71.0342146,42.316274;-71.0756902,42.3695046;-71.0678704,42.3701829;-71.0656594,42.3718401;-71.0611749,42.3551807;-71.0554239,42.3739796;-71.0631664,42.3741694;-71.056823,42.361531;-71.0632852,42.2857047;-71.0637877,42.2845163;-71.0496839,42.3519736;-71.0454645,42.3162356;-71.0336324,42.3441918;-71.0487437,42.3508756;-71.0512911,42.3521821;-71.0013637,42.2075316;-71.0607764,42.3763541;-71.0374911,42.316031;-71.0125206,42.3378699;-71.0672898,42.3523158;-71.02832,42.2576602;-71.0502126,42.3516479;-71.0331956,42.3639107;-71.0432778,42.3528151;-71.0035279,42.2392354;-71.0470633,42.3537343;-71.0352443,42.3291218;-71.0240951,42.2743442;-71.0234949,42.3358743;-70.985881,42.420226;-71.0005483,42.2454086;-71.0096371,42.3367603;-71.0447796,42.3509709;-71.0092883,42.2763168;-71.0404428,42.3478375;-71.056908,42.361288;-71.0667744,42.3270498;-71.0119933,42.2806539'" + "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": "2023-11-06T01:23:16.774149Z", - "start_time": "2023-11-06T01:23:16.765417Z" + "end_time": "2023-11-07T01:18:19.807296Z", + "start_time": "2023-11-07T01:18:19.799849Z" } }, "id": "aa618161182b5b07" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "outputs": [], "source": [ "# Create a dataframe from the JSON\n", - "df1 = utils.create_json_df(northeastern_coordinate + route_1)\n", - "df2 = utils.create_json_df(northeastern_coordinate + route_2)" + "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": "2023-11-06T01:23:18.694403Z", - "start_time": "2023-11-06T01:23:16.768656Z" + "end_time": "2023-11-07T01:18:22.014184Z", + "start_time": "2023-11-07T01:18:19.803262Z" } }, "id": "32c485788eedd94" }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 34, "outputs": [], "source": [ "# Add columns for the route number\n", @@ -73,47 +253,57 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T01:23:18.698699Z", - "start_time": "2023-11-06T01:23:18.696008Z" + "end_time": "2023-11-07T01:18:22.024878Z", + "start_time": "2023-11-07T01:18:22.017438Z" } }, "id": "49dba1f17ca8337e" }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 35, "outputs": [ { "data": { - "text/plain": " waypoint_index trips_index \\\n0 0 0 \n17 1 0 \n22 2 0 \n73 3 0 \n11 4 0 \n.. ... ... \n3 70 0 \n12 71 0 \n9 72 0 \n8 73 0 \n14 74 0 \n\n hint distance \\\n0 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n17 5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc... 17.374491 \n22 XAAigHIAIoBKAAAASwAAAFUAAABDAQAARGUEQURlBEG2ZR... 11.054154 \n73 CdQhgB0OA4AYAAAAHgAAADkAAAAAAAAALdMlQdSMQ0Fd0r... 10.970598 \n11 43YhgPN2IYA1AAAAJAAAAAAAAAA5AAAAEha0QWgpbEEAAA... 18.896385 \n.. ... ... \n3 jt4hgJLeIYA7AAAALQAAAAAAAAAAAAAA4gPGQasVlUEAAA... 4.709088 \n12 0OEhgPvhIYADAAAABgAAAA8AAAA0AAAA2lq-PipQFD-Y-N... 2.009578 \n9 m8shgJ7LIYAOAAAAXgEAAAAAAAAAAAAAOFW-QDE5G0IAAA... 1.716409 \n8 YQ0DgBTPIYDvAAAAdAAAAAAAAAAAAAAAsgLVQbMxTUEAAA... 4.830022 \n14 lhgDgIkYA4BkAAAAIgEAAFoBAAAaAAAAJyAzQWNrAEI8Ax... 7.134933 \n\n name location lat lon \\\n0 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n17 Dudley Street [-71.090904, 42.329829] -71.090904 42.329829 \n22 [-71.071196, 42.34085] -71.071196 42.340850 \n73 [-71.066844, 42.327134] -71.066844 42.327134 \n11 Lucy Street [-71.06221, 42.324934] -71.062210 42.324934 \n.. ... ... ... ... \n3 [-71.075414, 42.356537] -71.075414 42.356537 \n12 [-71.085166, 42.349997] -71.085166 42.349997 \n9 Piedmont Street [-71.067854, 42.349993] -71.067854 42.349993 \n8 [-71.072038, 42.348915] -71.072038 42.348915 \n14 [-71.083465, 42.34194] -71.083465 42.341940 \n\n route \n0 2 \n17 2 \n22 2 \n73 2 \n11 2 \n.. ... \n3 2 \n12 2 \n9 2 \n8 2 \n14 2 \n\n[75 rows x 9 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>waypoint_index</th>\n <th>trips_index</th>\n <th>hint</th>\n <th>distance</th>\n <th>name</th>\n <th>location</th>\n <th>lat</th>\n <th>lon</th>\n <th>route</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA...</td>\n <td>0.236958</td>\n <td>Northeastern (Inbound)</td>\n <td>[-71.090331, 42.339762]</td>\n <td>-71.090331</td>\n <td>42.339762</td>\n <td>2</td>\n </tr>\n <tr>\n <th>17</th>\n <td>1</td>\n <td>0</td>\n <td>5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc...</td>\n <td>17.374491</td>\n <td>Dudley Street</td>\n <td>[-71.090904, 42.329829]</td>\n <td>-71.090904</td>\n <td>42.329829</td>\n <td>2</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2</td>\n <td>0</td>\n <td>XAAigHIAIoBKAAAASwAAAFUAAABDAQAARGUEQURlBEG2ZR...</td>\n <td>11.054154</td>\n <td></td>\n <td>[-71.071196, 42.34085]</td>\n <td>-71.071196</td>\n <td>42.340850</td>\n <td>2</td>\n </tr>\n <tr>\n <th>73</th>\n <td>3</td>\n <td>0</td>\n <td>CdQhgB0OA4AYAAAAHgAAADkAAAAAAAAALdMlQdSMQ0Fd0r...</td>\n <td>10.970598</td>\n <td></td>\n <td>[-71.066844, 42.327134]</td>\n <td>-71.066844</td>\n <td>42.327134</td>\n <td>2</td>\n </tr>\n <tr>\n <th>11</th>\n <td>4</td>\n <td>0</td>\n <td>43YhgPN2IYA1AAAAJAAAAAAAAAA5AAAAEha0QWgpbEEAAA...</td>\n <td>18.896385</td>\n <td>Lucy Street</td>\n <td>[-71.06221, 42.324934]</td>\n <td>-71.062210</td>\n <td>42.324934</td>\n <td>2</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>3</th>\n <td>70</td>\n <td>0</td>\n <td>jt4hgJLeIYA7AAAALQAAAAAAAAAAAAAA4gPGQasVlUEAAA...</td>\n <td>4.709088</td>\n <td></td>\n <td>[-71.075414, 42.356537]</td>\n <td>-71.075414</td>\n <td>42.356537</td>\n <td>2</td>\n </tr>\n <tr>\n <th>12</th>\n <td>71</td>\n <td>0</td>\n <td>0OEhgPvhIYADAAAABgAAAA8AAAA0AAAA2lq-PipQFD-Y-N...</td>\n <td>2.009578</td>\n <td></td>\n <td>[-71.085166, 42.349997]</td>\n <td>-71.085166</td>\n <td>42.349997</td>\n <td>2</td>\n </tr>\n <tr>\n <th>9</th>\n <td>72</td>\n <td>0</td>\n <td>m8shgJ7LIYAOAAAAXgEAAAAAAAAAAAAAOFW-QDE5G0IAAA...</td>\n <td>1.716409</td>\n <td>Piedmont Street</td>\n <td>[-71.067854, 42.349993]</td>\n <td>-71.067854</td>\n <td>42.349993</td>\n <td>2</td>\n </tr>\n <tr>\n <th>8</th>\n <td>73</td>\n <td>0</td>\n <td>YQ0DgBTPIYDvAAAAdAAAAAAAAAAAAAAAsgLVQbMxTUEAAA...</td>\n <td>4.830022</td>\n <td></td>\n <td>[-71.072038, 42.348915]</td>\n <td>-71.072038</td>\n <td>42.348915</td>\n <td>2</td>\n </tr>\n <tr>\n <th>14</th>\n <td>74</td>\n <td>0</td>\n <td>lhgDgIkYA4BkAAAAIgEAAFoBAAAaAAAAJyAzQWNrAEI8Ax...</td>\n <td>7.134933</td>\n <td></td>\n <td>[-71.083465, 42.34194]</td>\n <td>-71.083465</td>\n <td>42.341940</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n<p>75 rows × 9 columns</p>\n</div>" + "text/plain": " waypoint_index trips_index \\\n0 0 0 \n1 1 0 \n2 2 0 \n3 3 0 \n4 4 0 \n.. ... ... \n168 64 0 \n169 65 0 \n170 66 0 \n171 67 0 \n172 68 0 \n\n hint distance \\\n0 t4YsgAGHLIAAAAAAVQEAAAAAAAAwAAAAAAAAAHV0F0IAAA... 19.432511 \n1 IzYEgGw1BIASAAAArwAAADMAAACUAwAAynkIQGUkmkEXlL... 6.024489 \n2 G4gsgDiILICSAwAA5gAAAOkAAAAAAAAAQljLQnyXy0Fhy8... 2.602121 \n3 gIosgLaKLIDOAAAArgAAAFwBAAAAAAAAp3O3QafxmUEQiR... 15.458439 \n4 HpwsgCKcLIAAAAAAEgAAAAAAAAAAAAAAAAAAACg870AAAA... 39.201677 \n.. ... ... \n168 cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC... 22.776295 \n169 g38hgI1_IYBOAAAAfwAAAAAAAAAAAAAAZ4ECQsbEUkIAAA... 12.789906 \n170 e38hgIUAA4C6AgAAGQAAAAAAAAAAAAAA_DybQoNdJUEAAA... 6.310267 \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 [-71.056164, 42.366918] -71.056164 42.366918 \n3 [-71.055561, 42.368861] -71.055561 42.368861 \n4 [-71.062507, 42.365968] -71.062507 42.365968 \n.. ... ... ... ... \n168 Alleghany Street [-71.099348, 42.33047] -71.099348 42.330470 \n169 Tremont Street [-71.098267, 42.332009] -71.098267 42.332009 \n170 Carmel Street [-71.100092, 42.332401] -71.100092 42.332401 \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]", + "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>waypoint_index</th>\n <th>trips_index</th>\n <th>hint</th>\n <th>distance</th>\n <th>name</th>\n <th>location</th>\n <th>lat</th>\n <th>lon</th>\n <th>route</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>t4YsgAGHLIAAAAAAVQEAAAAAAAAwAAAAAAAAAHV0F0IAAA...</td>\n <td>19.432511</td>\n <td></td>\n <td>[-71.054865, 42.364361]</td>\n <td>-71.054865</td>\n <td>42.364361</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>0</td>\n <td>IzYEgGw1BIASAAAArwAAADMAAACUAwAAynkIQGUkmkEXlL...</td>\n <td>6.024489</td>\n <td></td>\n <td>[-71.055569, 42.364032]</td>\n <td>-71.055569</td>\n <td>42.364032</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>0</td>\n <td>G4gsgDiILICSAwAA5gAAAOkAAAAAAAAAQljLQnyXy0Fhy8...</td>\n <td>2.602121</td>\n <td></td>\n <td>[-71.056164, 42.366918]</td>\n <td>-71.056164</td>\n <td>42.366918</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>0</td>\n <td>gIosgLaKLIDOAAAArgAAAFwBAAAAAAAAp3O3QafxmUEQiR...</td>\n <td>15.458439</td>\n <td></td>\n <td>[-71.055561, 42.368861]</td>\n <td>-71.055561</td>\n <td>42.368861</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4</td>\n <td>0</td>\n <td>HpwsgCKcLIAAAAAAEgAAAAAAAAAAAAAAAAAAACg870AAAA...</td>\n <td>39.201677</td>\n <td></td>\n <td>[-71.062507, 42.365968]</td>\n <td>-71.062507</td>\n <td>42.365968</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>168</th>\n <td>64</td>\n <td>0</td>\n <td>cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC...</td>\n <td>22.776295</td>\n <td>Alleghany Street</td>\n <td>[-71.099348, 42.33047]</td>\n <td>-71.099348</td>\n <td>42.330470</td>\n <td>2</td>\n </tr>\n <tr>\n <th>169</th>\n <td>65</td>\n <td>0</td>\n <td>g38hgI1_IYBOAAAAfwAAAAAAAAAAAAAAZ4ECQsbEUkIAAA...</td>\n <td>12.789906</td>\n <td>Tremont Street</td>\n <td>[-71.098267, 42.332009]</td>\n <td>-71.098267</td>\n <td>42.332009</td>\n <td>2</td>\n </tr>\n <tr>\n <th>170</th>\n <td>66</td>\n <td>0</td>\n <td>e38hgIUAA4C6AgAAGQAAAAAAAAAAAAAA_DybQoNdJUEAAA...</td>\n <td>6.310267</td>\n <td>Carmel Street</td>\n <td>[-71.100092, 42.332401]</td>\n <td>-71.100092</td>\n <td>42.332401</td>\n <td>2</td>\n </tr>\n <tr>\n <th>171</th>\n <td>67</td>\n <td>0</td>\n <td>k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e...</td>\n <td>36.240351</td>\n <td></td>\n <td>[-71.093834, 42.339096]</td>\n <td>-71.093834</td>\n <td>42.339096</td>\n <td>2</td>\n </tr>\n <tr>\n <th>172</th>\n <td>68</td>\n <td>0</td>\n <td>DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA...</td>\n <td>0.236958</td>\n <td>Northeastern (Inbound)</td>\n <td>[-71.090331, 42.339762]</td>\n <td>-71.090331</td>\n <td>42.339762</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n<p>173 rows × 9 columns</p>\n</div>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "display(df2)" + "display(df)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T01:23:18.708601Z", - "start_time": "2023-11-06T01:23:18.705324Z" + "end_time": "2023-11-07T01:18:22.033944Z", + "start_time": "2023-11-07T01:18:22.026906Z" } }, "id": "f231d9a35358988c" }, { + "cell_type": "markdown", + "source": [ + "## Map" + ], + "metadata": { + "collapsed": false + }, + "id": "75be92e34a36147f" + }, + { "cell_type": "code", - "execution_count": 15, + "execution_count": 36, "outputs": [ { "data": { - "text/plain": "<folium.folium.Map at 0x1277ac5d0>", - "text/html": "<div style=\"width:100%;\"><div 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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_aeeac5a1da4c82f72a6ebc2ad6ef0a4f {\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_aeeac5a1da4c82f72a6ebc2ad6ef0a4f" ></div>\n 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"execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -127,9 +317,10 @@ "\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", + " 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(m)\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" @@ -137,104 +328,358 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T01:23:18.781393Z", - "start_time": "2023-11-06T01:23:18.709803Z" + "end_time": "2023-11-07T01:18:22.118478Z", + "start_time": "2023-11-07T01:18:22.036338Z" } }, "id": "80fd847da2833913" }, { + "cell_type": "markdown", + "source": [ + "## Results" + ], + "metadata": { + "collapsed": false + }, + "id": "a7b562f75f7e0813" + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Route 1 has 102 waypoints\n", + "Route 2 has 67 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": "2023-11-07T01:18:22.120347Z", + "start_time": "2023-11-07T01:18:22.104950Z" + } + }, + "id": "f53c97acec1c2fc4" + }, + { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The trip will take 6.789722222222222 hours\n", - "The trip will take 9.955833333333333 hours\n" + "The trip will take 12.788333333333334 hours\n", + "The trip will take 13.1675 hours\n" ] } ], "source": [ - "trip_hrs_1 = utils.get_trip_time(route_1)\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)\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": "2023-11-06T01:23:20.448487Z", - "start_time": "2023-11-06T01:23:18.761079Z" + "end_time": "2023-11-07T01:18:24.705352Z", + "start_time": "2023-11-07T01:18:22.107540Z" } }, "id": "a3ec09dfb5cbb5b3" }, { + "cell_type": "markdown", + "source": [ + "# 3 Routes" + ], + "metadata": { + "collapsed": false + }, + "id": "de7b5856172d213c" + }, + { "cell_type": "code", - "execution_count": 17, + "execution_count": 47, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " waypoint_index trips_index \\\n", - "20 20 0 \n", - "\n", - " hint distance name \\\n", - "20 2M4pgNrOKYCCAQAADAAAAAAAAAAAAAAALKILQ27Ah0AAAA... 0.0 Echo Bridge \n", - "\n", - " location lat lon route \n", - "20 [-71.227365, 42.314504] -71.227365 42.314504 1 \n" + "/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": [ - "# Find the westmost point in Route 1\n", - "df1 = df[df['route'] == 1]\n", - "west = df1[df1['lon'] == df1['lon'].min()]\n", - "print(west)" + "# 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=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T01:23:20.455714Z", - "start_time": "2023-11-06T01:23:20.453647Z" + "end_time": "2023-11-07T01:22:14.864605Z", + "start_time": "2023-11-07T01:21:59.518900Z" } }, - "id": "be94c3708a1bd250" + "id": "bb6e00857e8175c0" + }, + { + "cell_type": "markdown", + "source": [ + "## Create JSON" + ], + "metadata": { + "collapsed": false + }, + "id": "19afb4f687b37383" }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 48, "outputs": [], "source": [ - "# Remove the westmost point from Route 1\n", - "df = df.drop(west.index)" + "# 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": "2023-11-07T01:22:16.725390Z", + "start_time": "2023-11-07T01:22:16.722334Z" + } + }, + "id": "e886e061f86a2118" + }, + { + "cell_type": "code", + "execution_count": 49, + "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": "2023-11-07T01:22:19.351381Z", + "start_time": "2023-11-07T01:22:17.034813Z" + } + }, + "id": "23e4682fe9e30631" + }, + { + "cell_type": "code", + "execution_count": 50, + "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": "2023-11-07T01:22:19.360710Z", + "start_time": "2023-11-07T01:22:19.355746Z" + } + }, + "id": "c3a5c5d6f3ac46c0" + }, + { + "cell_type": "code", + "execution_count": 51, + "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 29 0 \n171 30 0 \n172 31 0 \n173 32 0 \n174 33 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 gLshgIS7IYAAAAAAPAAAAAAAAAAAAAAAAAAAAPGU1UAAAA... 10.782119 \n171 e38hgIUAA4C6AgAAGQAAAAAAAAAAAAAA_DybQoNdJUEAAA... 6.310267 \n172 cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC... 22.776295 \n173 s9QhgLbUIYAwAAAAkAAAAAAAAAAAAAAA2XmpQNgrgEEAAA... 4.111715 \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 [-71.10963, 42.336448] -71.109630 42.336448 \n171 Carmel Street [-71.100092, 42.332401] -71.100092 42.332401 \n172 Alleghany Street [-71.099348, 42.33047] -71.099348 42.330470 \n173 [-71.09454, 42.325354] -71.094540 42.325354 \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]", + "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>waypoint_index</th>\n <th>trips_index</th>\n <th>hint</th>\n <th>distance</th>\n <th>name</th>\n <th>location</th>\n <th>lat</th>\n <th>lon</th>\n <th>route</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>t4YsgAGHLIAAAAAAVQEAAAAAAAAwAAAAAAAAAHV0F0IAAA...</td>\n <td>19.432511</td>\n <td></td>\n <td>[-71.054865, 42.364361]</td>\n <td>-71.054865</td>\n <td>42.364361</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>0</td>\n <td>e1kugJlZLoBmAAAA6QAAAAAAAAAAAAAAZ6M2QSewzkEAAA...</td>\n <td>4.756158</td>\n <td></td>\n <td>[-71.060933, 42.376178]</td>\n <td>-71.060933</td>\n <td>42.376178</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>0</td>\n <td>tFkugHVaLoAOAAAAAAAAABgAAAAAAAAAwMG2QAAAAAB6ii...</td>\n <td>4.525535</td>\n <td></td>\n <td>[-71.060753, 42.376391]</td>\n <td>-71.060753</td>\n <td>42.376391</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>0</td>\n <td>sJAugLOQLoBuAQAAlAEAAAAAAAAAAAAAHFcjQvEZM0IAAA...</td>\n <td>7.844897</td>\n <td></td>\n <td>[-71.060948, 42.380436]</td>\n <td>-71.060948</td>\n <td>42.380436</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4</td>\n <td>0</td>\n <td>VREtgNlJBIBCAAAAYAAAAAAAAAARAAAAOOzeQU7vHkIAAA...</td>\n <td>22.681980</td>\n <td>Factory Street</td>\n <td>[-71.061206, 42.398809]</td>\n <td>-71.061206</td>\n <td>42.398809</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>170</th>\n <td>29</td>\n <td>0</td>\n <td>gLshgIS7IYAAAAAAPAAAAAAAAAAAAAAAAAAAAPGU1UAAAA...</td>\n <td>10.782119</td>\n <td></td>\n <td>[-71.10963, 42.336448]</td>\n <td>-71.109630</td>\n <td>42.336448</td>\n <td>3</td>\n </tr>\n <tr>\n <th>171</th>\n <td>30</td>\n <td>0</td>\n <td>e38hgIUAA4C6AgAAGQAAAAAAAAAAAAAA_DybQoNdJUEAAA...</td>\n <td>6.310267</td>\n <td>Carmel Street</td>\n <td>[-71.100092, 42.332401]</td>\n <td>-71.100092</td>\n <td>42.332401</td>\n <td>3</td>\n </tr>\n <tr>\n <th>172</th>\n <td>31</td>\n <td>0</td>\n <td>cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC...</td>\n <td>22.776295</td>\n <td>Alleghany Street</td>\n <td>[-71.099348, 42.33047]</td>\n <td>-71.099348</td>\n <td>42.330470</td>\n <td>3</td>\n </tr>\n <tr>\n <th>173</th>\n <td>32</td>\n <td>0</td>\n <td>s9QhgLbUIYAwAAAAkAAAAAAAAAAAAAAA2XmpQNgrgEEAAA...</td>\n <td>4.111715</td>\n <td></td>\n <td>[-71.09454, 42.325354]</td>\n <td>-71.094540</td>\n <td>42.325354</td>\n <td>3</td>\n </tr>\n <tr>\n <th>174</th>\n <td>33</td>\n <td>0</td>\n <td>DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA...</td>\n <td>0.236958</td>\n <td>Northeastern (Inbound)</td>\n <td>[-71.090331, 42.339762]</td>\n <td>-71.090331</td>\n <td>42.339762</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n<p>175 rows × 9 columns</p>\n</div>" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-07T01:22:19.375055Z", + "start_time": "2023-11-07T01:22:19.364517Z" + } + }, + "id": "17a8cc8fed5450a6" + }, + { + "cell_type": "markdown", + "source": [ + "## Map" + ], + "metadata": { + "collapsed": false + }, + "id": "b20a57aa09792c39" + }, + { + "cell_type": "code", + "execution_count": 52, + "outputs": [ + { + "data": { + "text/plain": "<folium.folium.Map at 0x12109f6d0>", + "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 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).addTo(map_893e4460b48d10a1df1dd6cc61e39573);\n \n</script>\n</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" + }, + "execution_count": 52, + "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": "2023-11-07T01:22:20.246243Z", + "start_time": "2023-11-07T01:22:20.167900Z" + } + }, + "id": "702adaec008a6ec8" + }, + { + "cell_type": "markdown", + "source": [ + "## Results" + ], + "metadata": { + "collapsed": false + }, + "id": "a947e49e27c734e9" + }, + { + "cell_type": "code", + "execution_count": 53, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Route 1 has 57 waypoints\n", + "Route 2 has 80 waypoints\n", + "Route 3 has 32 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": "2023-11-07T01:22:21.994911Z", + "start_time": "2023-11-07T01:22:21.992304Z" + } + }, + "id": "4106acf2adad01d7" + }, + { + "cell_type": "code", + "execution_count": 54, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The trip will take 9.175 hours\n", + "The trip will take 9.341111111111111 hours\n", + "The trip will take 9.398333333333333 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": "2023-11-06T01:23:20.460791Z", - "start_time": "2023-11-06T01:23:20.456599Z" + "end_time": "2023-11-07T01:22:25.544575Z", + "start_time": "2023-11-07T01:22:23.206069Z" } }, - "id": "21fef07e5b2a03a0" + "id": "c58106faf0fc7f4e" }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 46, "outputs": [], "source": [], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T01:23:20.460900Z", - "start_time": "2023-11-06T01:23:20.458522Z" + "end_time": "2023-11-07T01:18:42.067793Z", + "start_time": "2023-11-07T01:18:42.056069Z" } }, - "id": "eafe5678c44e94fd" + "id": "a2f10e3152b95a69" } ], "metadata": { |