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{
"cells": [
{
"cell_type": "code",
"execution_count": 62,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:14.565659Z",
"start_time": "2023-11-06T00:26:14.551906Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import requests\n",
"import folium"
]
},
{
"cell_type": "code",
"execution_count": 63,
"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",
"coordinate_string = '-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;-71.0851891,42.3500031;-71.1123834,42.3360385;-71.2273649,42.3145041;-71.0834061,42.341987;-71.0992038,42.3306454;-71.0990577,42.3381442;-71.0949218,42.3419564;-71.0942861,42.3413301;-71.0908104,42.329969;-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;-71.3598149,42.3140229;-71.1126695,42.3836229;-71.119149,42.3884;-71.0872846,42.2961434;-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;-71.0945712,42.3253252;-71.1122037,42.4008442'\n",
"coordinates = requests.get('http://acetyl.net:5000/trip/v1/bike/' + northeastern_coordinate + coordinate_string)\n",
"\n",
"coordinates = coordinates.json()"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:15.268775Z",
"start_time": "2023-11-06T00:26:14.558083Z"
}
},
"id": "aa618161182b5b07"
},
{
"cell_type": "code",
"execution_count": 64,
"outputs": [],
"source": [
"# Create a dataframe from the JSON\n",
"df = pd.DataFrame(coordinates['waypoints'])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:15.278022Z",
"start_time": "2023-11-06T00:26:15.269158Z"
}
},
"id": "32c485788eedd94"
},
{
"cell_type": "code",
"execution_count": 65,
"outputs": [],
"source": [
"# Separate the location column into lon and lat columns\n",
"df['lat'] = df['location'].apply(lambda x: x[0])\n",
"df['lon'] = df['location'].apply(lambda x: x[1])\n",
"\n",
"df['waypoint_index'] = df['waypoint_index'].astype(int)\n",
"\n",
"# Map out the waypoints in order of the waypoint index\n",
"df = df.sort_values(by=['waypoint_index'])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:15.284313Z",
"start_time": "2023-11-06T00:26:15.275568Z"
}
},
"id": "535ab02638d20cb2"
},
{
"cell_type": "code",
"execution_count": 66,
"outputs": [
{
"data": {
"text/plain": " waypoint_index trips_index \\\n0 0 0 \n14 1 0 \n60 2 0 \n46 3 0 \n16 4 0 \n.. ... ... \n61 59 0 \n15 60 0 \n19 61 0 \n31 62 0 \n62 63 0 \n\n hint distance \\\n0 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n14 lhgDgIkYA4BkAAAAIgEAAFoBAAAaAAAAJyAzQWNrAEI8Ax... 7.134933 \n60 tg0igLoNIoAOAAAAAAAAADwAAADtAQAA8CrBQAAAAACcrM... 17.496916 \n46 k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e... 36.240351 \n16 rLwhgLG8IYAEAAAABQAAADAAAABsAAAA1X8CQHGb7D9Kz6... 24.054372 \n.. ... ... \n61 g38hgI1_IYBOAAAAfwAAAAAAAAAAAAAAZ4ECQsbEUkIAAA... 12.789906 \n15 cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC... 22.776295 \n19 5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc... 17.374491 \n31 WhEngAASJ4BcAAAAdwAAAHoBAAAFAAAAI3gkQQlZRUJLNi... 13.907079 \n62 s9QhgLbUIYAwAAAAkAAAAAAAAAAAAAAA2XmpQNgrgEEAAA... 4.111715 \n\n name location lat lon \n0 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n14 [-71.083465, 42.34194] -71.083465 42.341940 \n60 Public Alley 901 [-71.089677, 42.346361] -71.089677 42.346361 \n46 [-71.093834, 42.339096] -71.093834 42.339096 \n16 [-71.099284, 42.338007] -71.099284 42.338007 \n.. ... ... ... ... \n61 Tremont Street [-71.098267, 42.332009] -71.098267 42.332009 \n15 Alleghany Street [-71.099348, 42.33047] -71.099348 42.330470 \n19 Dudley Street [-71.090904, 42.329829] -71.090904 42.329829 \n31 Blue Hill Avenue [-71.087449, 42.296172] -71.087449 42.296172 \n62 [-71.09454, 42.325354] -71.094540 42.325354 \n\n[64 rows x 8 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 </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 </tr>\n <tr>\n <th>14</th>\n <td>1</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 </tr>\n <tr>\n <th>60</th>\n <td>2</td>\n <td>0</td>\n <td>tg0igLoNIoAOAAAAAAAAADwAAADtAQAA8CrBQAAAAACcrM...</td>\n <td>17.496916</td>\n <td>Public Alley 901</td>\n <td>[-71.089677, 42.346361]</td>\n <td>-71.089677</td>\n <td>42.346361</td>\n </tr>\n <tr>\n <th>46</th>\n <td>3</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 </tr>\n <tr>\n <th>16</th>\n <td>4</td>\n <td>0</td>\n <td>rLwhgLG8IYAEAAAABQAAADAAAABsAAAA1X8CQHGb7D9Kz6...</td>\n <td>24.054372</td>\n <td></td>\n <td>[-71.099284, 42.338007]</td>\n <td>-71.099284</td>\n <td>42.338007</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 </tr>\n <tr>\n <th>61</th>\n <td>59</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 </tr>\n <tr>\n <th>15</th>\n <td>60</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 </tr>\n <tr>\n <th>19</th>\n <td>61</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 </tr>\n <tr>\n <th>31</th>\n <td>62</td>\n <td>0</td>\n <td>WhEngAASJ4BcAAAAdwAAAHoBAAAFAAAAI3gkQQlZRUJLNi...</td>\n <td>13.907079</td>\n <td>Blue Hill Avenue</td>\n <td>[-71.087449, 42.296172]</td>\n <td>-71.087449</td>\n <td>42.296172</td>\n </tr>\n <tr>\n <th>62</th>\n <td>63</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 </tr>\n </tbody>\n</table>\n<p>64 rows × 8 columns</p>\n</div>"
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:15.297169Z",
"start_time": "2023-11-06T00:26:15.287354Z"
}
},
"id": "49dba1f17ca8337e"
},
{
"cell_type": "code",
"execution_count": 67,
"outputs": [
{
"data": {
"text/plain": "<folium.folium.Map at 0x12d416a90>",
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height: 100.0%;"></div>`)[0];\n popup_f3884ec1eed10cc6a041bec9224e46ed.setContent(html_42fc8bb0411473af5a79f82add8156dd);\n \n \n\n marker_ae2fd5398f53e13750199c7b8c37368e.bindPopup(popup_f3884ec1eed10cc6a041bec9224e46ed)\n ;\n\n \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": 67,
"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 a marker for each waypoint and connect with a line as the salesman would travel\n",
"for i in range(len(df)):\n",
" folium.Marker([df.iloc[i]['lon'], df.iloc[i]['lat']], popup=df.iloc[i]['name']).add_to(m)\n",
" if i < len(df) - 1:\n",
" folium.PolyLine([[df.iloc[i]['lon'], df.iloc[i]['lat']], [df.iloc[i + 1]['lon'], df.iloc[i + 1]['lat']]],\n",
" color=\"red\", weight=2.5, opacity=1).add_to(m)\n",
"\n",
"# Display the map\n",
"m"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:15.360267Z",
"start_time": "2023-11-06T00:26:15.298760Z"
}
},
"id": "80fd847da2833913"
},
{
"cell_type": "code",
"execution_count": 68,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The trip will take 10.198055555555555 hours\n"
]
}
],
"source": [
"trip_hrs = int(coordinates['trips'][0]['duration'])/3600\n",
"print(\"The trip will take {} hours\".format(trip_hrs))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:15.360383Z",
"start_time": "2023-11-06T00:26:15.353591Z"
}
},
"id": "a3ec09dfb5cbb5b3"
},
{
"cell_type": "code",
"execution_count": 68,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-11-06T00:26:15.360410Z",
"start_time": "2023-11-06T00:26:15.355568Z"
}
},
"id": "be94c3708a1bd250"
}
],
"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",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
