Datasets:
File size: 6,721 Bytes
10927b2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
{
"cells": [
{
"cell_type": "markdown",
"id": "eb4d98e6",
"metadata": {},
"source": [
"### In this notebook we will use Valhalla to snap the latitude/longitude points from VED to roads using OpenStreetMap data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d99459b1",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import requests\n",
"import json\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "markdown",
"id": "1b3e0b1f",
"metadata": {},
"source": [
"Create the headers and the meili request body."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "20178448",
"metadata": {},
"outputs": [],
"source": [
"def create_meili_request_body(df_trip):\n",
" \"\"\"Returns the body for meili for its request.\n",
" \"\"\"\n",
" coordinates = df_trip.to_json(orient='records')\n",
" head = '{\"shape\":'\n",
" tail = \"\"\",\"search_radius\": 5, \"shape_match\":\"map_snap\", \"costing\":\"auto\", \"format\":\"osrm\"}\"\"\"\n",
" body = head + coordinates + tail\n",
" return body"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "357a90c4",
"metadata": {},
"outputs": [],
"source": [
"headers = {'Content-type': 'application/json'}\n",
"trace_url = \"http://localhost:8002/trace_attributes\""
]
},
{
"cell_type": "markdown",
"id": "154c0f88",
"metadata": {},
"source": [
"Read all the file names"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f1097900",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "da818fc0e91c42fcb09ca4b0fadd0d45",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/54 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_path = \"../data/ved/dynamic/\"\n",
"files = [os.path.join(data_path, file) for file in tqdm(os.listdir(data_path)) if file.endswith(\".csv\")]\n",
"files.sort()"
]
},
{
"cell_type": "markdown",
"id": "2f9b0acf",
"metadata": {},
"source": [
"The functions that send the requests to the meili\n",
"Match types: {'matched': 0, 'interpolated': 1, 'unmatched': 2}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "91bffff4",
"metadata": {},
"outputs": [],
"source": [
"def process_matched_response(matched_points, group_df):\n",
" \"\"\" Returns a list of dictionaries for the matched coordinates.\n",
" Dictionary keys: matched_lat, matched_lon, and match_type. \n",
" If a points was not matched, returns the original coordinates. \"\"\"\n",
" \n",
" processed_matches = []\n",
" for (i, matched_point) in enumerate(matched_points):\n",
" match_type = {'matched': 0, 'interpolated': 1, 'unmatched': 2}\n",
" \n",
" matched_dict = {'matched_lat': group_df.iloc[i, 1] \n",
" if matched_point['type'] == 'unmatched' \n",
" else matched_point['lat'], \n",
" 'matched_lon': group_df.iloc[i, 0] \n",
" if matched_point['type'] == 'unmatched' \n",
" else matched_point['lon'], \n",
" 'match_type': match_type[matched_point['type']]}\n",
"\n",
" processed_matches.append(matched_dict)\n",
" \n",
" return processed_matches\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fb36bca3",
"metadata": {},
"outputs": [],
"source": [
"def map_match(file):\n",
" \"\"\" Takes a csv file and sends requests to Valhalla Meili service to snap the coordinates\n",
" in the file to roads. \"\"\"\n",
" signal_df = pd.read_csv(file)\n",
" dataframes = []\n",
" \n",
" # Group the dataframe by the columns VehId and Trip\n",
" for _, group_df in signal_df.groupby(['VehId', 'Trip']):\n",
" group = group_df.rename(columns={'Longitude[deg]':'lon','Latitude[deg]':'lat'})[['lon', 'lat']]\n",
" meili_request_body = create_meili_request_body(group)\n",
" r = requests.post(trace_url, data=str(meili_request_body), headers=headers)\n",
" \n",
" if r.status_code == 200:\n",
" res = r.json()\n",
" matched_dicts = process_matched_response(res['matched_points'], group)\n",
" matched_df = pd.DataFrame.from_dict(matched_dicts)\n",
" dataframes.append(matched_df.set_index(group_df.index.copy()))\n",
" \n",
" else:\n",
" print('Error', r.status_code)\n",
"\n",
" conc_dfs = pd.concat(dataframes)\n",
" signal_df = pd.merge(signal_df, conc_dfs, left_index=True, right_index=True)\n",
" return signal_df"
]
},
{
"cell_type": "markdown",
"id": "1d6be11d",
"metadata": {},
"source": [
"Process all files and save the results to another directory."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d1466e8d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18f1e3506e9d40eeb9f8966359fd6065",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/54 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def get_save_path(filename):\n",
" return f\"../data/ved-valhalla/{filename[len(data_path)-1:]}\"\n",
"\n",
"for file in tqdm(files):\n",
" matched_df = map_match(file)\n",
" matched_df.to_csv(get_save_path(file), na_rep=np.nan, index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7865645b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|