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{
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2023-11-07T00:07:54.400654Z",
"start_time": "2023-11-07T00:07:54.375821Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import folium\n",
"import utils"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": "2023-11-07T00:07:54.430515Z",
"start_time": "2023-11-07T00:07:54.381537Z"
}
},
"id": "73b780e762c9de37"
},
{
"cell_type": "code",
"execution_count": 18,
"outputs": [],
"source": [
"# Create two centroids, one in the North End and one in the Financial District\n",
"centroids = [[42.364506, -71.054733], [42.358894, -71.056742]]\n",
"\n",
"northeastern_coordinate = \"-71.09033,42.33976\""
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T00:07:54.431407Z",
"start_time": "2023-11-07T00:07:54.392677Z"
}
},
"id": "65e208650eb43b4"
},
{
"cell_type": "code",
"execution_count": 19,
"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])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T00:07:54.431829Z",
"start_time": "2023-11-07T00:07:54.397279Z"
}
},
"id": "ffe4025e97a6c6b9"
},
{
"cell_type": "code",
"execution_count": 20,
"outputs": [],
"source": [
"# Remove all columns but name and gps\n",
"TotalList = TotalList[['name', 'gps', 'list']]"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T00:07:54.432180Z",
"start_time": "2023-11-07T00:07:54.401907Z"
}
},
"id": "72657779b4484aae"
},
{
"cell_type": "code",
"execution_count": 21,
"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": "2023-11-07T00:07:54.432238Z",
"start_time": "2023-11-07T00:07:54.405216Z"
}
},
"id": "a157ffaec020a29a"
},
{
"cell_type": "code",
"execution_count": 22,
"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.. ... ... ... \n28 The Clam Box [42.2763168, -71.0092883] C \n29 The Partisans [42.3478375, -71.0404428] C \n30 Union Oyster House [42.361288, -71.056908] C \n31 Victoria's Diner [42.3270498, -71.0667744] C \n32 Wollaston Beach [42.2806539, -71.0119933] C \n\n normalized_gps \n0 [0.7251058917247415, 0.8141430878559053] \n1 [0.6747391031099019, 0.778052752104061] \n2 [1.0, 0.41697235794883575] \n3 [0.6697144722136962, 0.7604611403245493] \n4 [0.6458298305822171, 0.6785480000609988] \n.. ... \n28 [0.30922451563130937, 0.9374025730216268] \n29 [0.6307464973238023, 0.8540870458656248] \n30 [0.6912133469876947, 0.8100546647415456] \n31 [0.5372951958288665, 0.7836692527743693] \n32 [0.32872198960456106, 0.9301686741961767] \n\n[131 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, 0.8141430878559053]</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, 0.778052752104061]</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, 0.41697235794883575]</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, 0.7604611403245493]</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, 0.6785480000609988]</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>28</th>\n <td>The Clam Box</td>\n <td>[42.2763168, -71.0092883]</td>\n <td>C</td>\n <td>[0.30922451563130937, 0.9374025730216268]</td>\n </tr>\n <tr>\n <th>29</th>\n <td>The Partisans</td>\n <td>[42.3478375, -71.0404428]</td>\n <td>C</td>\n <td>[0.6307464973238023, 0.8540870458656248]</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Union Oyster House</td>\n <td>[42.361288, -71.056908]</td>\n <td>C</td>\n <td>[0.6912133469876947, 0.8100546647415456]</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Victoria's Diner</td>\n <td>[42.3270498, -71.0667744]</td>\n <td>C</td>\n <td>[0.5372951958288665, 0.7836692527743693]</td>\n </tr>\n <tr>\n <th>32</th>\n <td>Wollaston Beach</td>\n <td>[42.2806539, -71.0119933]</td>\n <td>C</td>\n <td>[0.32872198960456106, 0.9301686741961767]</td>\n </tr>\n </tbody>\n</table>\n<p>131 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": "2023-11-07T00:07:54.432731Z",
"start_time": "2023-11-07T00:07:54.412279Z"
}
},
"id": "a03ebde91b87fa3b"
},
{
"cell_type": "markdown",
"source": [
"# Cluster and Minimize"
],
"metadata": {
"collapsed": false
},
"id": "4bd41be9aca5094b"
},
{
"cell_type": "code",
"execution_count": 23,
"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",
"df, route_1_coordinates, route_2_coordinates = utils.cluster_and_minimize(TotalList, centroids, norm_centroids,\n",
" northeastern_coordinate, 0.5)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T00:08:20.577006Z",
"start_time": "2023-11-07T00:07:54.416349Z"
}
},
"id": "ee9b3c1ecb360976"
},
{
"cell_type": "code",
"execution_count": 24,
"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": "2023-11-07T00:08:20.591584Z",
"start_time": "2023-11-07T00:08:20.577492Z"
}
},
"id": "aa618161182b5b07"
},
{
"cell_type": "code",
"execution_count": 25,
"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": "2023-11-07T00:08:22.409355Z",
"start_time": "2023-11-07T00:08:20.579890Z"
}
},
"id": "32c485788eedd94"
},
{
"cell_type": "code",
"execution_count": 26,
"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": "2023-11-07T00:08:22.425179Z",
"start_time": "2023-11-07T00:08:22.412707Z"
}
},
"id": "49dba1f17ca8337e"
},
{
"cell_type": "code",
"execution_count": 27,
"outputs": [
{
"data": {
"text/plain": " waypoint_index trips_index \\\n0 0 0 \n13 1 0 \n68 2 0 \n40 3 0 \n22 4 0 \n.. ... ... \n14 67 0 \n11 68 0 \n69 69 0 \n19 70 0 \n71 71 0 \n\n hint distance \\\n0 dMQAgDTDAIAuAAAAEgAAAAAAAAAAAAAAiaamQKk960AAAA... 1.113855 \n13 oLwsgCS9LIBHAAAA2AAAAAAAAABgAQAAkQwAQdo1v0EAAA... 2.532529 \n68 CL0sgBS9LIAhAAAAagAAAAAAAAAAAAAAfoF0QPCwOkEAAA... 7.608103 \n40 YbwsgEO9LIBbAAAAEgAAAAAAAAAPAAAA5ua1QcswjkAAAA... 0.468602 \n22 UkAEgFxABIB8AAAAAAAAAAAAAAAYAgAAVjBdQQAAAAAAAA... 6.397300 \n.. ... ... \n14 -mUsgHZmLIATAAAAYgEAAL0AAADpAAAALf8HQHZ8HUK-9a... 55.355565 \n11 43YhgPN2IYA1AAAAJAAAAAAAAAA5AAAAEha0QWgpbEEAAA... 18.896385 \n69 CdQhgB0OA4AYAAAAHgAAADkAAAAAAAAALdMlQdSMQ0Fd0r... 10.970598 \n19 XAAigHIAIoBKAAAASwAAAFUAAABDAQAARGUEQURlBEG2ZR... 11.054154 \n71 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n\n name location lat lon \\\n0 State Street [-71.056741, 42.358884] -71.056741 42.358884 \n13 [-71.056995, 42.36049] -71.056995 42.360490 \n68 [-71.056994, 42.361263] -71.056994 42.361263 \n40 Creek Square [-71.056819, 42.361534] -71.056819 42.361534 \n22 [-71.059255, 42.359295] -71.059255 42.359295 \n.. ... ... ... ... \n14 [-71.049204, 42.325624] -71.049204 42.325624 \n11 Lucy Street [-71.06221, 42.324934] -71.062210 42.324934 \n69 [-71.066844, 42.327134] -71.066844 42.327134 \n19 [-71.071196, 42.34085] -71.071196 42.340850 \n71 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n\n route \n0 2 \n13 2 \n68 2 \n40 2 \n22 2 \n.. ... \n14 2 \n11 2 \n69 2 \n19 2 \n71 2 \n\n[72 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>dMQAgDTDAIAuAAAAEgAAAAAAAAAAAAAAiaamQKk960AAAA...</td>\n <td>1.113855</td>\n <td>State Street</td>\n <td>[-71.056741, 42.358884]</td>\n <td>-71.056741</td>\n <td>42.358884</td>\n <td>2</td>\n </tr>\n <tr>\n <th>13</th>\n <td>1</td>\n <td>0</td>\n <td>oLwsgCS9LIBHAAAA2AAAAAAAAABgAQAAkQwAQdo1v0EAAA...</td>\n <td>2.532529</td>\n <td></td>\n <td>[-71.056995, 42.36049]</td>\n <td>-71.056995</td>\n <td>42.360490</td>\n <td>2</td>\n </tr>\n <tr>\n <th>68</th>\n <td>2</td>\n <td>0</td>\n <td>CL0sgBS9LIAhAAAAagAAAAAAAAAAAAAAfoF0QPCwOkEAAA...</td>\n <td>7.608103</td>\n <td></td>\n <td>[-71.056994, 42.361263]</td>\n <td>-71.056994</td>\n <td>42.361263</td>\n <td>2</td>\n </tr>\n <tr>\n <th>40</th>\n <td>3</td>\n <td>0</td>\n <td>YbwsgEO9LIBbAAAAEgAAAAAAAAAPAAAA5ua1QcswjkAAAA...</td>\n <td>0.468602</td>\n <td>Creek Square</td>\n <td>[-71.056819, 42.361534]</td>\n <td>-71.056819</td>\n <td>42.361534</td>\n <td>2</td>\n </tr>\n <tr>\n <th>22</th>\n <td>4</td>\n <td>0</td>\n <td>UkAEgFxABIB8AAAAAAAAAAAAAAAYAgAAVjBdQQAAAAAAAA...</td>\n <td>6.397300</td>\n <td></td>\n <td>[-71.059255, 42.359295]</td>\n <td>-71.059255</td>\n <td>42.359295</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>14</th>\n <td>67</td>\n <td>0</td>\n <td>-mUsgHZmLIATAAAAYgEAAL0AAADpAAAALf8HQHZ8HUK-9a...</td>\n <td>55.355565</td>\n <td></td>\n <td>[-71.049204, 42.325624]</td>\n <td>-71.049204</td>\n <td>42.325624</td>\n <td>2</td>\n </tr>\n <tr>\n <th>11</th>\n <td>68</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>69</th>\n <td>69</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>19</th>\n <td>70</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>71</th>\n <td>71</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>72 rows × 9 columns</p>\n</div>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df2)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T00:08:22.440853Z",
"start_time": "2023-11-07T00:08:22.424158Z"
}
},
"id": "f231d9a35358988c"
},
{
"cell_type": "code",
"execution_count": 28,
"outputs": [
{
"data": {
"text/plain": "<folium.folium.Map at 0x13df5cf10>",
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},
"execution_count": 28,
"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": "2023-11-07T00:08:22.513542Z",
"start_time": "2023-11-07T00:08:22.430363Z"
}
},
"id": "80fd847da2833913"
},
{
"cell_type": "code",
"execution_count": 29,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Route 1 has 61 waypoints\n",
"Route 2 has 70 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": "2023-11-07T00:08:22.513689Z",
"start_time": "2023-11-07T00:08:22.488854Z"
}
},
"id": "f53c97acec1c2fc4"
},
{
"cell_type": "code",
"execution_count": 30,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The trip will take 10.36111111111111 hours\n",
"The trip will take 10.586666666666666 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": "2023-11-07T00:08:24.460727Z",
"start_time": "2023-11-07T00:08:22.491469Z"
}
},
"id": "a3ec09dfb5cbb5b3"
},
{
"cell_type": "code",
"execution_count": 30,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-07T00:08:24.471189Z",
"start_time": "2023-11-07T00:08:24.460431Z"
}
},
"id": "eafe5678c44e94fd"
}
],
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"language": "python",
"name": "python3"
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|
