training_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import requests\n",
    "from datetime import datetime, timezone, timedelta\n",
    "from google.cloud import bigquery\n",
    "from collections import deque\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "os.environ[\n",
    "    \"GOOGLE_APPLICATION_CREDENTIALS\"\n",
    "] = \"/Users/jeff-901/.config/gcloud/application_default_credentials.json\"\n",
    "client = bigquery.Client(\"cloud-city-cal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'city': 'Fremont',\n",
       "  'end_point': [37.45905179732368, -121.92400047999844],\n",
       "  'end_postmile': 10.782,\n",
       "  'id': 1,\n",
       "  'representative_point': [37.45677537, -121.9234573],\n",
       "  'representative_postmile': 10.634,\n",
       "  'start_point': [37.45449894221499, -121.92291417547628],\n",
       "  'start_postmile': 10.432,\n",
       "  'station_ids': {'401643': 0.34999999999999964}},\n",
       " {'city': 'Fremont',\n",
       "  'end_point': [37.484815553610595, -121.93810388857243],\n",
       "  'end_postmile': 12.648,\n",
       "  'id': 2,\n",
       "  'representative_point': [37.47193368, -121.9310522],\n",
       "  'representative_postmile': 11.678,\n",
       "  'start_point': [37.45905179732368, -121.92400047999844],\n",
       "  'start_postmile': 10.782,\n",
       "  'station_ids': {'401643': 0.04550000000000054,\n",
       "   '402789': 0.19950000000000045,\n",
       "   '402800': 0.3689999999999998,\n",
       "   '402802': 0.1144999999999996,\n",
       "   '402828': 0.4989999999999988,\n",
       "   '407219': 0.38950000000000173,\n",
       "   '408755': 0.2394999999999996,\n",
       "   '408756': 0.009499999999999176}},\n",
       " {'city': 'Fremont',\n",
       "  'end_point': [37.49336665307788, -121.94921944033166],\n",
       "  'end_postmile': 13.543,\n",
       "  'id': 3,\n",
       "  'representative_point': [37.4890911, -121.9436617],\n",
       "  'representative_postmile': 13.124,\n",
       "  'start_point': [37.484815553610595, -121.93810388857243],\n",
       "  'start_postmile': 12.648,\n",
       "  'station_ids': {'400189': 0.4435000000000002,\n",
       "   '408756': 0.45149999999999935}},\n",
       " {'city': 'Fremont',\n",
       "  'end_point': [37.50794166676078, -121.96793133086518],\n",
       "  'end_postmile': 14.99,\n",
       "  'id': 4,\n",
       "  'representative_point': [37.50065416, -121.9585754],\n",
       "  'representative_postmile': 14.264,\n",
       "  'start_point': [37.49336665307788, -121.94921944033166],\n",
       "  'start_postmile': 13.543,\n",
       "  'station_ids': {'400189': 0.06950000000000145,\n",
       "   '400249': 0.38400000000000034,\n",
       "   '400309': 0.37099999999999866,\n",
       "   '400417': 0.5914999999999999,\n",
       "   '401639': 0.031000000000000583}},\n",
       " {'city': 'Fremont',\n",
       "  'end_point': [37.52272046785407, -121.98796273601984],\n",
       "  'end_postmile': 16.488,\n",
       "  'id': 5,\n",
       "  'representative_point': [37.51533107, -121.977947],\n",
       "  'representative_postmile': 15.734,\n",
       "  'start_point': [37.50794166676078, -121.96793133086518],\n",
       "  'start_postmile': 14.99,\n",
       "  'station_ids': {'400662': 0.770999999999999, '401639': 0.7270000000000003}},\n",
       " {'city': 'Newark',\n",
       "  'end_point': [37.53005006161676, -122.00251784224919],\n",
       "  'end_postmile': 17.437,\n",
       "  'id': 6,\n",
       "  'representative_point': [37.52638526, -121.9952403],\n",
       "  'representative_postmile': 16.962,\n",
       "  'start_point': [37.52272046785407, -121.98796273601984],\n",
       "  'start_postmile': 16.488,\n",
       "  'station_ids': {'400141': 0.47449999999999903,\n",
       "   '400662': 0.01150000000000162,\n",
       "   '400761': 0.46300000000000097}},\n",
       " {'city': 'Fremont',\n",
       "  'end_point': [37.54724879283909, -122.02317353592868],\n",
       "  'end_postmile': 19.109,\n",
       "  'id': 7,\n",
       "  'representative_point': [37.53864943, -122.0128457],\n",
       "  'representative_postmile': 18.268,\n",
       "  'start_point': [37.53005006161676, -122.00251784224919],\n",
       "  'start_postmile': 17.437,\n",
       "  'station_ids': {'400137': 0.2585000000000015,\n",
       "   '400490': 0.6484999999999985,\n",
       "   '400761': 0.03599999999999781,\n",
       "   '401888': 0.7290000000000028}},\n",
       " {'city': 'Newark',\n",
       "  'end_point': [37.56400946106105, -122.03885611099612],\n",
       "  'end_postmile': 20.564,\n",
       "  'id': 8,\n",
       "  'representative_point': [37.55562913, -122.0310148],\n",
       "  'representative_postmile': 19.846,\n",
       "  'start_point': [37.54724879283909, -122.02317353592868],\n",
       "  'start_postmile': 19.109,\n",
       "  'station_ids': {'400137': 0.04099999999999682,\n",
       "   '400716': 0.5365000000000038,\n",
       "   '401011': 0.25049999999999883,\n",
       "   '401545': 0.6269999999999989}},\n",
       " {'city': 'Fremont',\n",
       "  'end_point': [37.58089773895889, -122.0488961778228],\n",
       "  'end_postmile': 21.831,\n",
       "  'id': 9,\n",
       "  'representative_point': [37.5724536, -122.0438761],\n",
       "  'representative_postmile': 21.221,\n",
       "  'start_point': [37.56400946106105, -122.03885611099612],\n",
       "  'start_postmile': 20.564,\n",
       "  'station_ids': {'400534': 0.27749999999999986,\n",
       "   '400539': 0.5649999999999977,\n",
       "   '400674': 0.3780000000000001,\n",
       "   '401011': 0.04650000000000176}},\n",
       " {'city': 'Union City',\n",
       "  'end_point': [37.60033977106464, -122.06195703014302],\n",
       "  'end_postmile': 23.4,\n",
       "  'id': 10,\n",
       "  'representative_point': [37.59061876, -122.0554266],\n",
       "  'representative_postmile': 22.636,\n",
       "  'start_point': [37.58089773895889, -122.0488961778228],\n",
       "  'start_postmile': 21.831,\n",
       "  'station_ids': {'400534': 0.20449999999999946,\n",
       "   '400536': 0.30849999999999866,\n",
       "   '401062': 0.3430000000000035,\n",
       "   '401529': 0.2829999999999977,\n",
       "   '401613': 0.4299999999999997}},\n",
       " {'city': 'Union City',\n",
       "  'end_point': [37.610269235705836, -122.06898751233689],\n",
       "  'end_postmile': 24.175,\n",
       "  'id': 11,\n",
       "  'representative_point': [37.6053045, -122.0654723],\n",
       "  'representative_postmile': 23.789,\n",
       "  'start_point': [37.60033977106464, -122.06195703014302],\n",
       "  'start_postmile': 23.4,\n",
       "  'station_ids': {'400488': 0.47949999999999804,\n",
       "   '400536': 0.2955000000000041}},\n",
       " {'city': 'Union City',\n",
       "  'end_point': [37.6195891771223, -122.07565862095001],\n",
       "  'end_postmile': 24.927,\n",
       "  'id': 12,\n",
       "  'representative_point': [37.61492921, -122.0723231],\n",
       "  'representative_postmile': 24.558,\n",
       "  'start_point': [37.610269235705836, -122.06898751233689],\n",
       "  'start_postmile': 24.175,\n",
       "  'station_ids': {'400488': 0.06550000000000011,\n",
       "   '400611': 0.22350000000000136,\n",
       "   '401561': 0.4629999999999974}},\n",
       " {'city': 'Hayward',\n",
       "  'end_point': [37.63427652471347, -122.08617069791671],\n",
       "  'end_postmile': 26.138,\n",
       "  'id': 13,\n",
       "  'representative_point': [37.62693285, -122.0809147],\n",
       "  'representative_postmile': 25.508,\n",
       "  'start_point': [37.6195891771223, -122.07565862095001],\n",
       "  'start_postmile': 24.927,\n",
       "  'station_ids': {'400041': 0.21950000000000003,\n",
       "   '400284': 0.44150000000000134,\n",
       "   '400611': 0.12650000000000006,\n",
       "   '400928': 0.42350000000000065}},\n",
       " {'city': 'Hayward',\n",
       "  'end_point': [37.64808873624996, -122.09598125792186],\n",
       "  'end_postmile': 27.176,\n",
       "  'id': 14,\n",
       "  'representative_point': [37.64118263, -122.091076],\n",
       "  'representative_postmile': 26.659,\n",
       "  'start_point': [37.63427652471347, -122.08617069791671],\n",
       "  'start_postmile': 26.138,\n",
       "  'station_ids': {'400041': 0.08349999999999724,\n",
       "   '408133': 0.240000000000002,\n",
       "   '408135': 0.20899999999999963,\n",
       "   '412637': 0.11250000000000071,\n",
       "   '417665': 0.22199999999999775,\n",
       "   '417666': 0.17099999999999937}},\n",
       " {'city': 'Hayward',\n",
       "  'end_point': [37.66005322330189, -122.10360271027861],\n",
       "  'end_postmile': 28.109,\n",
       "  'id': 15,\n",
       "  'representative_point': [37.65407098, -122.099792],\n",
       "  'representative_postmile': 27.642,\n",
       "  'start_point': [37.64808873624996, -122.09598125792186],\n",
       "  'start_postmile': 27.176,\n",
       "  'station_ids': {'400685': 0.2765000000000022,\n",
       "   '401003': 0.23350000000000293,\n",
       "   '408134': 0.3424999999999976,\n",
       "   '417666': 0.08050000000000068}},\n",
       " {'city': 'Hayward',\n",
       "  'end_point': [37.66870053748893, -122.10894520945567],\n",
       "  'end_postmile': 28.775,\n",
       "  'id': 16,\n",
       "  'representative_point': [37.66437688, -122.106274],\n",
       "  'representative_postmile': 28.444,\n",
       "  'start_point': [37.66005322330189, -122.10360271027861],\n",
       "  'start_postmile': 28.109,\n",
       "  'station_ids': {'400275': 0.2934999999999981,\n",
       "   '400898': 0.33000000000000185,\n",
       "   '401003': 0.042499999999996874}},\n",
       " {'city': 'San Lorenzo',\n",
       "  'end_point': [37.68660600577468, -122.13199519189834],\n",
       "  'end_postmile': 30.559,\n",
       "  'id': 17,\n",
       "  'representative_point': [37.67765327, -122.1204702],\n",
       "  'representative_postmile': 29.669,\n",
       "  'start_point': [37.66870053748893, -122.10894520945567],\n",
       "  'start_postmile': 28.775,\n",
       "  'station_ids': {'400180': 0.3260000000000005,\n",
       "   '400252': 0.137000000000004,\n",
       "   '400275': 0.0519999999999996,\n",
       "   '400515': 0.36999999999999744,\n",
       "   '400529': 0.32050000000000267,\n",
       "   '400939': 0.2815000000000012,\n",
       "   '400990': 0.29699999999999704}}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "segment_definition_query_job = client.query(\n",
    "    f\"\"\"\n",
    "            SELECT data\n",
    "            FROM `cloud-city-cal.cloud_city.metadata`\n",
    "            ORDER BY version DESC\n",
    "            LIMIT 1\n",
    "            \"\"\",\n",
    ")\n",
    "result = segment_definition_query_job.result()\n",
    "segment_definition = next(result)[\"data\"][\"segments\"]\n",
    "segment_definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "START = datetime.strptime(\"2024/01/01 00:00:00\", \"%Y/%m/%d %H:%M:%S\")\n",
    "END = datetime.strptime(\"2024/01/15 00:00:00\", \"%Y/%m/%d %H:%M:%S\")\n",
    "TIME_STEP = 5 * 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 17\u001b[0m\n\u001b[1;32m     15\u001b[0m station_t_to_speed \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m     16\u001b[0m i \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m     18\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstation_t_to_speed\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstation_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mspeed\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/api_core/page_iterator.py:208\u001b[0m, in \u001b[0;36mIterator._items_iter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_items_iter\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    207\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Iterator for each item returned.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 208\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpage\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_page_iter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mincrement\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpage\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_results\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/api_core/page_iterator.py:250\u001b[0m, in \u001b[0;36mIterator._page_iter\u001b[0;34m(self, increment)\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_results \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m page\u001b[38;5;241m.\u001b[39mnum_items\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m page\n\u001b[0;32m--> 250\u001b[0m page \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_page\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/api_core/page_iterator.py:373\u001b[0m, in \u001b[0;36mHTTPIterator._next_page\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Get the next page in the iterator.\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \n\u001b[1;32m    368\u001b[0m \u001b[38;5;124;03mReturns:\u001b[39;00m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;124;03m    Optional[Page]: The next page in the iterator or :data:`None` if\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[38;5;124;03m        there are no pages left.\u001b[39;00m\n\u001b[1;32m    371\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_next_page():\n\u001b[0;32m--> 373\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_next_page_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    374\u001b[0m     items \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_items_key, ())\n\u001b[1;32m    375\u001b[0m     page \u001b[38;5;241m=\u001b[39m Page(\u001b[38;5;28mself\u001b[39m, items, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem_to_value, raw_page\u001b[38;5;241m=\u001b[39mresponse)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/cloud/bigquery/table.py:1744\u001b[0m, in \u001b[0;36mRowIterator._get_next_page_response\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1742\u001b[0m         \u001b[38;5;28;01mdel\u001b[39;00m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstartIndex\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1743\u001b[0m     params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmaxResults\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_page_size\n\u001b[0;32m-> 1744\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapi_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1745\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_HTTP_METHOD\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\n\u001b[1;32m   1746\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/cloud/bigquery/client.py:829\u001b[0m, in \u001b[0;36mClient._call_api\u001b[0;34m(self, retry, span_name, span_attributes, job_ref, headers, **kwargs)\u001b[0m\n\u001b[1;32m    824\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m create_span(\n\u001b[1;32m    825\u001b[0m         name\u001b[38;5;241m=\u001b[39mspan_name, attributes\u001b[38;5;241m=\u001b[39mspan_attributes, client\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, job_ref\u001b[38;5;241m=\u001b[39mjob_ref\n\u001b[1;32m    826\u001b[0m     ):\n\u001b[1;32m    827\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m call()\n\u001b[0;32m--> 829\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:293\u001b[0m, in \u001b[0;36mRetry.__call__.<locals>.retry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m target \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    290\u001b[0m sleep_generator \u001b[38;5;241m=\u001b[39m exponential_sleep_generator(\n\u001b[1;32m    291\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maximum, multiplier\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multiplier\n\u001b[1;32m    292\u001b[0m )\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mretry_target\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43msleep_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mon_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:144\u001b[0m, in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sleep \u001b[38;5;129;01min\u001b[39;00m sleep_generator:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mtarget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39misawaitable(result):\n\u001b[1;32m    146\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(_ASYNC_RETRY_WARNING)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/cloud/_http/__init__.py:482\u001b[0m, in \u001b[0;36mJSONConnection.api_request\u001b[0;34m(self, method, path, query_params, data, content_type, headers, api_base_url, api_version, expect_json, _target_object, timeout, extra_api_info)\u001b[0m\n\u001b[1;32m    479\u001b[0m     data \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mdumps(data)\n\u001b[1;32m    480\u001b[0m     content_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 482\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    483\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    485\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    486\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontent_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_target_object\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[43mextra_api_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_api_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m:\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mfrom_http_response(response)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/cloud/_http/__init__.py:341\u001b[0m, in \u001b[0;36mJSONConnection._make_request\u001b[0;34m(self, method, url, data, content_type, headers, target_object, timeout, extra_api_info)\u001b[0m\n\u001b[1;32m    338\u001b[0m     headers[CLIENT_INFO_HEADER] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_agent\n\u001b[1;32m    339\u001b[0m headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUser-Agent\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_agent\n\u001b[0;32m--> 341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    342\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    343\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/cloud/_http/__init__.py:379\u001b[0m, in \u001b[0;36mJSONConnection._do_request\u001b[0;34m(self, method, url, headers, data, target_object, timeout)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_request\u001b[39m(\n\u001b[1;32m    346\u001b[0m     \u001b[38;5;28mself\u001b[39m, method, url, headers, data, target_object, timeout\u001b[38;5;241m=\u001b[39m_DEFAULT_TIMEOUT\n\u001b[1;32m    347\u001b[0m ):  \u001b[38;5;66;03m# pylint: disable=unused-argument\u001b[39;00m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Low-level helper:  perform the actual API request over HTTP.\u001b[39;00m\n\u001b[1;32m    349\u001b[0m \n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m    Allows batch context managers to override and defer a request.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;124;03m    :returns: The HTTP response.\u001b[39;00m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 379\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/google/auth/transport/requests.py:541\u001b[0m, in \u001b[0;36mAuthorizedSession.request\u001b[0;34m(self, method, url, data, headers, max_allowed_time, timeout, **kwargs)\u001b[0m\n\u001b[1;32m    538\u001b[0m remaining_time \u001b[38;5;241m=\u001b[39m guard\u001b[38;5;241m.\u001b[39mremaining_timeout\n\u001b[1;32m    540\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m TimeoutGuard(remaining_time) \u001b[38;5;28;01mas\u001b[39;00m guard:\n\u001b[0;32m--> 541\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mAuthorizedSession\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    542\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    543\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    545\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    546\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    547\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    548\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    549\u001b[0m remaining_time \u001b[38;5;241m=\u001b[39m guard\u001b[38;5;241m.\u001b[39mremaining_timeout\n\u001b[1;32m    551\u001b[0m \u001b[38;5;66;03m# If the response indicated that the credentials needed to be\u001b[39;00m\n\u001b[1;32m    552\u001b[0m \u001b[38;5;66;03m# refreshed, then refresh the credentials and re-attempt the\u001b[39;00m\n\u001b[1;32m    553\u001b[0m \u001b[38;5;66;03m# request.\u001b[39;00m\n\u001b[1;32m    554\u001b[0m \u001b[38;5;66;03m# A stored token may expire between the time it is retrieved and\u001b[39;00m\n\u001b[1;32m    555\u001b[0m \u001b[38;5;66;03m# the time the request is made, so we may need to try twice.\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    483\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    501\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/urllib3/connectionpool.py:793\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    790\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    792\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 793\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    805\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    806\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    808\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m    809\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/urllib3/connectionpool.py:537\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m    536\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 537\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    539\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/urllib3/connection.py:466\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresponse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTTPResponse\n\u001b[1;32m    465\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 466\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    469\u001b[0m     assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py:1419\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1417\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1418\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1419\u001b[0m         \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1420\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1421\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py:331\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    329\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 331\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    332\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    333\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py:292\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 292\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    293\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    294\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/socket.py:707\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 707\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    708\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    709\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/ssl.py:1253\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1249\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1250\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1251\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1252\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1253\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1254\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1255\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/ssl.py:1105\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1104\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1105\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1106\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1107\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "speed_query_job = client.query(\n",
    "    f\"\"\"\n",
    "            SELECT station_id, \n",
    "                    UNIX_SECONDS(PARSE_TIMESTAMP('%m/%d/%Y %H:%M:%S', time)) + 28800 AS t, \n",
    "                    lanes[0].average_speed AS speed\n",
    "            FROM `cloud-city-cal.cloud_city.pems_partitioned`\n",
    "            WHERE UNIX_SECONDS(PARSE_TIMESTAMP('%m/%d/%Y %H:%M:%S', time)) >= {int(START.replace(tzinfo=timezone.utc).timestamp())}\n",
    "            AND UNIX_SECONDS(PARSE_TIMESTAMP('%m/%d/%Y %H:%M:%S', time)) < {int(END.replace(tzinfo=timezone.utc).timestamp())}\n",
    "            AND publish_time >= {int((START-timedelta(days=1)).replace(tzinfo=timezone.utc).timestamp())*(10**6)}\n",
    "            AND publish_time <= {int((END + timedelta(days=1)).replace(tzinfo=timezone.utc).timestamp())*(10**6)}\n",
    "            ORDER BY station_id, t ASC\n",
    "            \"\"\",\n",
    ")\n",
    "result = speed_query_job.result()\n",
    "station_t_to_speed = {}\n",
    "i = 1\n",
    "for row in result:\n",
    "    row = dict(row.items())\n",
    "    station_t_to_speed[(row[\"station_id\"], row[\"t\"])] = row[\"speed\"]\n",
    "\n",
    "data = []\n",
    "for t in range(int(START.timestamp()), int(END.timestamp()), TIME_STEP):\n",
    "    for segment in segment_definition:\n",
    "        agg_speed = (segment[\"end_postmile\"] - segment[\"start_postmile\"]) / sum(\n",
    "            [\n",
    "                segment[\"station_ids\"][station_id]\n",
    "                / station_t_to_speed[(int(station_id), t)]\n",
    "                for station_id in segment[\"station_ids\"]\n",
    "            ]\n",
    "        )\n",
    "        data.append({\"segment_id\": segment[\"id\"], \"t\": t, \"speed\": agg_speed})\n",
    "speed_df = pd.DataFrame(data)\n",
    "speed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_df.to_csv(\"speed_preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_df = pd.read_csv(\"speed_preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>segment_id</th>\n",
       "      <th>t</th>\n",
       "      <th>Thunderstorm</th>\n",
       "      <th>Drizzle</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Mist</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>Haze</th>\n",
       "      <th>Dust</th>\n",
       "      <th>Fog</th>\n",
       "      <th>Sand</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Squall</th>\n",
       "      <th>Tornado</th>\n",
       "      <th>Clear</th>\n",
       "      <th>Clouds</th>\n",
       "      <th>visibility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>1704096000</td>\n",
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       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
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       "      <td>1704096000</td>\n",
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       "      <td>1704096000</td>\n",
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       "      <td>10000</td>\n",
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       "      <td>1704096000</td>\n",
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       "      <td>10000</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",
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       "    <tr>\n",
       "      <th>68539</th>\n",
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       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68540</th>\n",
       "      <td>17</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68541</th>\n",
       "      <td>17</td>\n",
       "      <td>1705304700</td>\n",
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       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68542</th>\n",
       "      <td>17</td>\n",
       "      <td>1705305000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68543</th>\n",
       "      <td>17</td>\n",
       "      <td>1705305300</td>\n",
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       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68544 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       segment_id           t Thunderstorm Drizzle Rain Snow Mist Smoke Haze  \\\n",
       "0               1  1704096000            0       0    0    0    0     0    0   \n",
       "1               2  1704096000            0       0    0    0    0     0    0   \n",
       "2               3  1704096000            0       0    0    0    0     0    0   \n",
       "3               4  1704096000            0       0    0    0    0     0    0   \n",
       "4               5  1704096000            0       0    0    0    0     0    0   \n",
       "...           ...         ...          ...     ...  ...  ...  ...   ...  ...   \n",
       "68539          17  1705304100            0       0    0    0    0     0    0   \n",
       "68540          17  1705304400            0       0    0    0    0     0    0   \n",
       "68541          17  1705304700            0       0    0    0    0     0    0   \n",
       "68542          17  1705305000            0       0    0    0    0     0    0   \n",
       "68543          17  1705305300            0       0    0    0    0     0    0   \n",
       "\n",
       "      Dust Fog Sand Ash Squall Tornado Clear Clouds  visibility  \n",
       "0        0   0    0   0      0       0     0      1       10000  \n",
       "1        0   0    0   0      0       0     0      1       10000  \n",
       "2        0   0    0   0      0       0     0      1       10000  \n",
       "3        0   0    0   0      0       0     0      1       10000  \n",
       "4        0   0    0   0      0       0     0      1       10000  \n",
       "...    ...  ..  ...  ..    ...     ...   ...    ...         ...  \n",
       "68539    0   0    0   0      0       0     0      1       10000  \n",
       "68540    0   0    0   0      0       0     0      1       10000  \n",
       "68541    0   0    0   0      0       0     0      1       10000  \n",
       "68542    0   0    0   0      0       0     0      1       10000  \n",
       "68543    0   0    0   0      0       0     0      1       10000  \n",
       "\n",
       "[68544 rows x 18 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = {}\n",
    "for segment in segment_definition:\n",
    "    if segment[\"city\"] not in cities:\n",
    "        cities[segment[\"city\"]] = [segment[\"id\"]]\n",
    "    else:\n",
    "        cities[segment[\"city\"]].append(segment[\"id\"])\n",
    "WEATHER_CONDITION = {\n",
    "    \"Thunderstorm\": 0,\n",
    "    \"Drizzle\": 1,\n",
    "    \"Rain\": 2,\n",
    "    \"Snow\": 3,\n",
    "    \"Mist\": 4,\n",
    "    \"Smoke\": 5,\n",
    "    \"Haze\": 6,\n",
    "    \"Dust\": 7,\n",
    "    \"Fog\": 8,\n",
    "    \"Sand\": 9,\n",
    "    \"Ash\": 10,\n",
    "    \"Squall\": 11,\n",
    "    \"Tornado\": 12,\n",
    "    \"Clear\": 13,\n",
    "    \"Clouds\": 14,\n",
    "}\n",
    "data = []\n",
    "for city in cities:\n",
    "    query_job = client.query(\n",
    "        f\"\"\"\n",
    "        SELECT dt AS t, weather[0].main, visibility\n",
    "        FROM `cloud-city-cal.cloud_city.weather_partitioned`\n",
    "        WHERE name LIKE \"{city}\"\n",
    "        AND dt >= {int(START.timestamp())}\n",
    "        ANd dt <= {int(END.timestamp()) + 60}\n",
    "        AND publish_time >= {int((START-timedelta(days=1)).replace(tzinfo=timezone.utc).timestamp())*(10**6)}\n",
    "        AND publish_time <= {int((END + timedelta(days=1)).replace(tzinfo=timezone.utc).timestamp())*(10**6)}\n",
    "        ORDER BY dt ASC\n",
    "        \"\"\",\n",
    "    )\n",
    "    result = query_job.result()\n",
    "    result = [row for row in result]\n",
    "\n",
    "    idx = 0\n",
    "    for t in range(int(START.timestamp()), int(END.timestamp()), TIME_STEP):\n",
    "        while idx + 1 < len(result) and result[idx + 1][0] <= t + 60:\n",
    "            idx += 1\n",
    "        weather_encoding = [\"0\"] * len(WEATHER_CONDITION)\n",
    "        weather_encoding[WEATHER_CONDITION[result[idx][1]]] = \"1\"\n",
    "        for segment_id in cities[city]:\n",
    "            data.append([segment_id, t] + weather_encoding + [result[idx][2]])\n",
    "weather_df = pd.DataFrame(\n",
    "    data, columns=[\"segment_id\", \"t\"] + list(WEATHER_CONDITION.keys()) + [\"visibility\"]\n",
    ")\n",
    "weather_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coordinate_to_postmile(x: float, y: float) -> float:\n",
    "    payload = {\"callType\": \"getPostmileForPoint\", \"content\": {\"x\": x, \"y\": y}}\n",
    "    response = requests.post(\n",
    "        \"https://postmile.dot.ca.gov/PMQT/proxy.php\",\n",
    "        headers={\n",
    "            \"Content-Type\": \"application/x-www-form-urlencoded\",\n",
    "        },\n",
    "        data=\"request=\" + json.dumps(payload),\n",
    "    )\n",
    "    response.raise_for_status()\n",
    "    data = json.loads(response.text)\n",
    "    if \"locations\" not in data:\n",
    "        print(data, x, y)\n",
    "    postmile_data = data[\"locations\"][0][\"results\"][0][\"postmile\"]\n",
    "    payload = {\n",
    "        \"callType\": \"getOdometerForPostmile\",\n",
    "        \"content\": {\n",
    "            \"req\": {\n",
    "                \"routeNumber\": postmile_data[\"routeNumber\"],\n",
    "                \"routeSuffixCode\": postmile_data[\"routeSuffix\"],\n",
    "                \"countyCode\": postmile_data[\"county\"],\n",
    "                \"postmilePrefixCode\": postmile_data[\"postmilePrefix\"],\n",
    "                \"postmileSuffixCode\": postmile_data[\"postmileSuffix\"],\n",
    "                \"postmileValue\": postmile_data[\"postmileValue\"],\n",
    "                \"alignmentCode\": \"\",\n",
    "            }\n",
    "        },\n",
    "    }\n",
    "    response = requests.post(\n",
    "        \"https://postmile.dot.ca.gov/PMQT/proxy.php\",\n",
    "        headers={\n",
    "            \"Content-Type\": \"application/x-www-form-urlencoded\",\n",
    "        },\n",
    "        data=\"request=\" + json.dumps(payload),\n",
    "    )\n",
    "    response.raise_for_status()\n",
    "    return float(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " {'id': '511.org/1134291',\n",
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       " {'id': '511.org/1134303',\n",
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       " {'id': '511.org/1134389',\n",
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       " {'id': '511.org/1134404',\n",
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       " {'id': '511.org/1134872',\n",
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       " {'id': '511.org/1134946',\n",
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       "  'f1_': 37.77438735961914},\n",
       " {'id': '511.org/1134971',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Minor',\n",
       "  'created_time': 1704289560,\n",
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       " {'id': '511.org/1134997',\n",
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       " {'id': '511.org/1135210',\n",
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       " {'id': '511.org/1135287',\n",
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       " {'id': '511.org/1135309',\n",
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       " {'id': '511.org/1135325',\n",
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       " {'id': '511.org/1135348',\n",
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       " {'id': '511.org/1135361',\n",
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       " {'id': '511.org/1135395',\n",
       "  'event_type': 'INCIDENT',\n",
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       " {'id': '511.org/1135421',\n",
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       "  'severity': 'Moderate',\n",
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       " {'id': '511.org/1135422',\n",
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       " {'id': '511.org/1135530',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Moderate',\n",
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       " {'id': '511.org/1135542',\n",
       "  'event_type': 'CONSTRUCTION',\n",
       "  'severity': 'Unknown',\n",
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       "  'updated_time': 1704425880,\n",
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       " {'id': '511.org/1135581',\n",
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       " {'id': '511.org/1135695',\n",
       "  'event_type': 'CONSTRUCTION',\n",
       "  'severity': 'Unknown',\n",
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       " {'id': '511.org/1135864',\n",
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       " {'id': '511.org/1135868',\n",
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       " {'id': '511.org/1135909',\n",
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       " {'id': '511.org/1135947',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Minor',\n",
       "  'created_time': 1704523380,\n",
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       " {'id': '511.org/1135973',\n",
       "  'event_type': 'INCIDENT',\n",
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       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Minor',\n",
       "  'created_time': 1705251780,\n",
       "  'updated_time': 1705251780,\n",
       "  'f0_': -122.1173095703125,\n",
       "  'f1_': 37.676570892333984},\n",
       " {'id': '511.org/1139130',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Minor',\n",
       "  'created_time': 1705254000,\n",
       "  'updated_time': 1705254000,\n",
       "  'f0_': -122.19332885742188,\n",
       "  'f1_': 37.737335205078125},\n",
       " {'id': '511.org/1139141',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Moderate',\n",
       "  'created_time': 1705257420,\n",
       "  'updated_time': 1705257540,\n",
       "  'f0_': -122.16366577148438,\n",
       "  'f1_': 37.70854949951172},\n",
       " {'id': '511.org/1139145',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Moderate',\n",
       "  'created_time': 1705259220,\n",
       "  'updated_time': 1705259220,\n",
       "  'f0_': -122.23131561279297,\n",
       "  'f1_': 37.77438735961914},\n",
       " {'id': '511.org/1139149',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Major',\n",
       "  'created_time': 1705260720,\n",
       "  'updated_time': 1705260720,\n",
       "  'f0_': -122.09683990478516,\n",
       "  'f1_': 37.648841857910156},\n",
       " {'id': '511.org/1139170',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Moderate',\n",
       "  'created_time': 1705269600,\n",
       "  'updated_time': 1705269660,\n",
       "  'f0_': -122.16079711914062,\n",
       "  'f1_': 37.70658874511719},\n",
       " {'id': '511.org/1139184',\n",
       "  'event_type': 'INCIDENT',\n",
       "  'severity': 'Moderate',\n",
       "  'created_time': 1705274400,\n",
       "  'updated_time': 1705274400,\n",
       "  'f0_': -121.91110229492188,\n",
       "  'f1_': 37.41029357910156}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEVERITY_TO_SCORE = {\"MINOR\": 1, \"MODERATE\": 2, \"MAJOR\": 3, \"SEVERE\": 4, \"UNKNOWN\": 1}\n",
    "EVENT_TYPE_TO_IDX = {\n",
    "    \"CONSTRUCTION\": 0,\n",
    "    \"SPECIAL_EVENT\": 1,\n",
    "    \"INCIDENT\": 2,\n",
    "    \"WEATHER_CONDITION\": 3,\n",
    "    \"ROAD_CONDITION\": 4,\n",
    "    \"None\": 5,\n",
    "}\n",
    "event_query_job = client.query(\n",
    "    f\"\"\"\n",
    "    SELECT id, \n",
    "        event_type, \n",
    "        severity, \n",
    "        UNIX_SECONDS(PARSE_TIMESTAMP('%Y-%m-%dT%H:%MZ', created)) + 28800 AS created_time, \n",
    "        UNIX_SECONDS(PARSE_TIMESTAMP('%Y-%m-%dT%H:%MZ', updated)) + 28800 AS updated_time, \n",
    "    geography_point.coordinates[0], geography_point.coordinates[1] \n",
    "    FROM cloud-city-cal.cloud_city.bay_area_511_event_partitioned\n",
    "    WHERE \"I-880 N\" in UNNEST(roads.name)\n",
    "    AND UNIX_SECONDS(PARSE_TIMESTAMP('%Y-%m-%dT%H:%MZ', created)) + 28800 >= {int(START.replace(tzinfo=timezone.utc).timestamp()) - 60 * 60}\n",
    "    AND UNIX_SECONDS(PARSE_TIMESTAMP('%Y-%m-%dT%H:%MZ', created)) + 28800 < {int(END.replace(tzinfo=timezone.utc).timestamp())}\n",
    "    AND publish_time >= {int((START-timedelta(days=1)).replace(tzinfo=timezone.utc).timestamp())*(10**6)}\n",
    "    AND publish_time <= {int((END + timedelta(days=1)).replace(tzinfo=timezone.utc).timestamp())*(10**6)}\n",
    "    ORDER BY created_time ASC, updated_time ASC\n",
    "    \"\"\"\n",
    ")\n",
    "result = event_query_job.result()\n",
    "events = []\n",
    "event_ids = set()\n",
    "i = 0\n",
    "import time\n",
    "\n",
    "for row in result:\n",
    "    row = dict(row.items())\n",
    "    if row[\"id\"] in event_ids:\n",
    "        continue\n",
    "    event_ids.add(row[\"id\"])\n",
    "    i += 1\n",
    "    # row[\"postmile\"] = coordinate_to_postmile(row[\"f0_\"], row[\"f1_\"])\n",
    "    events.append(row)\n",
    "\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deque([{'id': '511.org/1134404',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704111060,\n",
       "        'updated_time': 1704111060,\n",
       "        'f0_': -122.1280746459961,\n",
       "        'f1_': 37.68397903442383,\n",
       "        'postmile': 30.27},\n",
       "       {'id': '511.org/1134427',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704116160,\n",
       "        'updated_time': 1704116160,\n",
       "        'f0_': -122.1270980834961,\n",
       "        'f1_': 37.68330383300781,\n",
       "        'postmile': 30.198},\n",
       "       {'id': '511.org/1134508',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704150660,\n",
       "        'updated_time': 1704150660,\n",
       "        'f0_': -121.93550109863281,\n",
       "        'f1_': 37.48200225830078,\n",
       "        'postmile': 12.382},\n",
       "       {'id': '511.org/1134607',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704189540,\n",
       "        'updated_time': 1704189600,\n",
       "        'f0_': -121.94078826904297,\n",
       "        'f1_': 37.319488525390625,\n",
       "        'postmile': 0.161},\n",
       "       {'id': '511.org/1134641',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704210120,\n",
       "        'updated_time': 1704210240,\n",
       "        'f0_': -122.26669311523438,\n",
       "        'f1_': 37.79535675048828,\n",
       "        'postmile': 41.334},\n",
       "       {'id': '511.org/1134690',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704244860,\n",
       "        'updated_time': 1704244980,\n",
       "        'f0_': -121.93254852294922,\n",
       "        'f1_': 37.47624969482422,\n",
       "        'postmile': 11.96},\n",
       "       {'id': '511.org/1134849',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704275460,\n",
       "        'updated_time': 1704275460,\n",
       "        'f0_': -122.27696990966797,\n",
       "        'f1_': 37.79944610595703,\n",
       "        'postmile': 41.955},\n",
       "       {'id': '511.org/1134857',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704275940,\n",
       "        'updated_time': 1704275940,\n",
       "        'f0_': -122.2831802368164,\n",
       "        'f1_': 37.8018913269043,\n",
       "        'postmile': 42.338},\n",
       "       {'id': '511.org/1134863',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Major',\n",
       "        'created_time': 1704276480,\n",
       "        'updated_time': 1704276480,\n",
       "        'f0_': -122.27696990966797,\n",
       "        'f1_': 37.79944610595703,\n",
       "        'postmile': 41.955},\n",
       "       {'id': '511.org/1134872',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704277320,\n",
       "        'updated_time': 1704277440,\n",
       "        'f0_': -122.07596588134766,\n",
       "        'f1_': 37.61997604370117,\n",
       "        'postmile': 24.959},\n",
       "       {'id': '511.org/1134946',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Major',\n",
       "        'created_time': 1704285180,\n",
       "        'updated_time': 1704285240,\n",
       "        'f0_': -122.23131561279297,\n",
       "        'f1_': 37.77438735961914,\n",
       "        'postmile': 38.798},\n",
       "       {'id': '511.org/1134971',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Minor',\n",
       "        'created_time': 1704289560,\n",
       "        'updated_time': 1704289620,\n",
       "        'f0_': -122.09046173095703,\n",
       "        'f1_': 37.640254974365234,\n",
       "        'postmile': 26.595},\n",
       "       {'id': '511.org/1134997',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704297000,\n",
       "        'updated_time': 1704297000,\n",
       "        'f0_': -122.18600463867188,\n",
       "        'f1_': 37.72895431518555,\n",
       "        'postmile': 34.731},\n",
       "       {'id': '511.org/1135002',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704298560,\n",
       "        'updated_time': 1704298560,\n",
       "        'f0_': -122.2009506225586,\n",
       "        'f1_': 37.745941162109375,\n",
       "        'postmile': 36.167},\n",
       "       {'id': '511.org/1135005',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Major',\n",
       "        'created_time': 1704299100,\n",
       "        'updated_time': 1704299160,\n",
       "        'f0_': -122.21214294433594,\n",
       "        'f1_': 37.758419036865234,\n",
       "        'postmile': 37.212},\n",
       "       {'id': '511.org/1135023',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704306180,\n",
       "        'updated_time': 1704306240,\n",
       "        'f0_': -121.90946960449219,\n",
       "        'f1_': 37.355892181396484,\n",
       "        'postmile': 3.369},\n",
       "       {'id': '511.org/1135097',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704324480,\n",
       "        'updated_time': 1704324540,\n",
       "        'f0_': -122.04744720458984,\n",
       "        'f1_': 37.575950622558594,\n",
       "        'postmile': 21.466},\n",
       "       {'id': '511.org/1135151',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704330900,\n",
       "        'updated_time': 1704331020,\n",
       "        'f0_': -121.90504455566406,\n",
       "        'f1_': 37.36029052734375,\n",
       "        'postmile': 3.758},\n",
       "       {'id': '511.org/1135210',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704342660,\n",
       "        'updated_time': 1704342720,\n",
       "        'f0_': -122.1408462524414,\n",
       "        'f1_': 37.69281005859375,\n",
       "        'postmile': 31.204},\n",
       "       {'id': '511.org/1135234',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704347520,\n",
       "        'updated_time': 1704347640,\n",
       "        'f0_': -121.95176696777344,\n",
       "        'f1_': 37.49528884887695,\n",
       "        'postmile': 13.735},\n",
       "       {'id': '511.org/1135240',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704348900,\n",
       "        'updated_time': 1704349020,\n",
       "        'f0_': -122.05827331542969,\n",
       "        'f1_': 37.595184326171875,\n",
       "        'postmile': 22.987},\n",
       "       {'id': '511.org/1135241',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Major',\n",
       "        'created_time': 1704349140,\n",
       "        'updated_time': 1704349140,\n",
       "        'f0_': -122.06414031982422,\n",
       "        'f1_': 37.60342788696289,\n",
       "        'postmile': 23.642},\n",
       "       {'id': '511.org/1135273',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704355320,\n",
       "        'updated_time': 1704355320,\n",
       "        'f0_': -122.06414031982422,\n",
       "        'f1_': 37.60342788696289,\n",
       "        'postmile': 23.642},\n",
       "       {'id': '511.org/1135282',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704357240,\n",
       "        'updated_time': 1704357240,\n",
       "        'f0_': -122.16931915283203,\n",
       "        'f1_': 37.71245574951172,\n",
       "        'postmile': 33.264},\n",
       "       {'id': '511.org/1135287',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704358140,\n",
       "        'updated_time': 1704358140,\n",
       "        'f0_': -122.16931915283203,\n",
       "        'f1_': 37.71245574951172,\n",
       "        'postmile': 33.264},\n",
       "       {'id': '511.org/1135309',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704361200,\n",
       "        'updated_time': 1704361320,\n",
       "        'f0_': -122.10562133789062,\n",
       "        'f1_': 37.6633415222168,\n",
       "        'postmile': 28.363},\n",
       "       {'id': '511.org/1135325',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704363900,\n",
       "        'updated_time': 1704364020,\n",
       "        'f0_': -122.09046173095703,\n",
       "        'f1_': 37.640254974365234,\n",
       "        'postmile': 26.595},\n",
       "       {'id': '511.org/1135348',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704366900,\n",
       "        'updated_time': 1704366960,\n",
       "        'f0_': -122.08124542236328,\n",
       "        'f1_': 37.62737274169922,\n",
       "        'postmile': 25.543},\n",
       "       {'id': '511.org/1135361',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704369780,\n",
       "        'updated_time': 1704369840,\n",
       "        'f0_': -122.09872436523438,\n",
       "        'f1_': 37.652137756347656,\n",
       "        'postmile': 27.496},\n",
       "       {'id': '511.org/1135362',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704370320,\n",
       "        'updated_time': 1704370380,\n",
       "        'f0_': -122.25428009033203,\n",
       "        'f1_': 37.789337158203125,\n",
       "        'postmile': 40.484},\n",
       "       {'id': '511.org/1135395',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704375300,\n",
       "        'updated_time': 1704375300,\n",
       "        'f0_': -122.27603149414062,\n",
       "        'f1_': 37.79904556274414,\n",
       "        'postmile': 41.898},\n",
       "       {'id': '511.org/1135421',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704392160,\n",
       "        'updated_time': 1704392220,\n",
       "        'f0_': -122.30197143554688,\n",
       "        'f1_': 37.80388641357422,\n",
       "        'postmile': 43.422},\n",
       "       {'id': '511.org/1135422',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704393540,\n",
       "        'updated_time': 1704393540,\n",
       "        'f0_': -122.27603149414062,\n",
       "        'f1_': 37.79904556274414,\n",
       "        'postmile': 41.898},\n",
       "       {'id': '511.org/1135462',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704411780,\n",
       "        'updated_time': 1704411780,\n",
       "        'f0_': -122.08124542236328,\n",
       "        'f1_': 37.62737274169922,\n",
       "        'postmile': 25.543},\n",
       "       {'id': '511.org/1135530',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704423000,\n",
       "        'updated_time': 1704423060,\n",
       "        'f0_': -122.08124542236328,\n",
       "        'f1_': 37.62737274169922,\n",
       "        'postmile': 25.543},\n",
       "       {'id': '511.org/1135542',\n",
       "        'event_type': 'CONSTRUCTION',\n",
       "        'severity': 'Unknown',\n",
       "        'created_time': 1704425880,\n",
       "        'updated_time': 1704425880,\n",
       "        'f0_': -121.9144287109375,\n",
       "        'f1_': 37.41876983642578,\n",
       "        'postmile': 7.928},\n",
       "       {'id': '511.org/1135581',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704434460,\n",
       "        'updated_time': 1704434640,\n",
       "        'f0_': -122.2746810913086,\n",
       "        'f1_': 37.79854202270508,\n",
       "        'postmile': 41.815},\n",
       "       {'id': '511.org/1135584',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704434940,\n",
       "        'updated_time': 1704434940,\n",
       "        'f0_': -122.09872436523438,\n",
       "        'f1_': 37.652137756347656,\n",
       "        'postmile': 27.496},\n",
       "       {'id': '511.org/1135636',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704443460,\n",
       "        'updated_time': 1704443880,\n",
       "        'f0_': -122.09027862548828,\n",
       "        'f1_': 37.651336669921875,\n",
       "        'postmile': 26.348},\n",
       "       {'id': '511.org/1135640',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Major',\n",
       "        'created_time': 1704443940,\n",
       "        'updated_time': 1704443940,\n",
       "        'f0_': -121.90504455566406,\n",
       "        'f1_': 37.36029052734375,\n",
       "        'postmile': 3.758},\n",
       "       {'id': '511.org/1135657',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704447300,\n",
       "        'updated_time': 1704447300,\n",
       "        'f0_': -122.08124542236328,\n",
       "        'f1_': 37.62737274169922,\n",
       "        'postmile': 25.543},\n",
       "       {'id': '511.org/1135681',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704450600,\n",
       "        'updated_time': 1704450720,\n",
       "        'f0_': -121.93254852294922,\n",
       "        'f1_': 37.47624969482422,\n",
       "        'postmile': 11.96},\n",
       "       {'id': '511.org/1135695',\n",
       "        'event_type': 'CONSTRUCTION',\n",
       "        'severity': 'Unknown',\n",
       "        'created_time': 1704452940,\n",
       "        'updated_time': 1704452940,\n",
       "        'f0_': -122.0793228149414,\n",
       "        'f1_': 37.631446838378906,\n",
       "        'postmile': 25.736},\n",
       "       {'id': '511.org/1135725',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1704458940,\n",
       "        'updated_time': 1704458940,\n",
       "        'f0_': -122.09683990478516,\n",
       "        'f1_': 37.648841857910156,\n",
       "        'postmile': 27.251},\n",
       "       {'id': '511.org/1135779',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
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       "        'f0_': -122.16366577148438,\n",
       "        'f1_': 37.70854949951172,\n",
       "        'postmile': 32.85},\n",
       "       {'id': '511.org/1138736',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705180920,\n",
       "        'updated_time': 1705180980,\n",
       "        'f0_': -122.23613739013672,\n",
       "        'f1_': 37.776798248291016,\n",
       "        'postmile': 39.107},\n",
       "       {'id': '511.org/1138737',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705182240,\n",
       "        'updated_time': 1705182240,\n",
       "        'f0_': -122.2635269165039,\n",
       "        'f1_': 37.793338775634766,\n",
       "        'postmile': 41.113},\n",
       "       {'id': '511.org/1138740',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705188480,\n",
       "        'updated_time': 1705188480,\n",
       "        'f0_': -121.897216796875,\n",
       "        'f1_': 37.36344528198242,\n",
       "        'postmile': 387.645},\n",
       "       {'id': '511.org/1138755',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705192740,\n",
       "        'updated_time': 1705192740,\n",
       "        'f0_': -122.22392272949219,\n",
       "        'f1_': 37.77189636230469,\n",
       "        'postmile': 38.362},\n",
       "       {'id': '511.org/1138759',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705194900,\n",
       "        'updated_time': 1705194900,\n",
       "        'f0_': -122.2635269165039,\n",
       "        'f1_': 37.793338775634766,\n",
       "        'postmile': 41.113},\n",
       "       {'id': '511.org/1138787',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705202700,\n",
       "        'updated_time': 1705202700,\n",
       "        'f0_': -122.1356430053711,\n",
       "        'f1_': 37.6891975402832,\n",
       "        'postmile': 30.819},\n",
       "       {'id': '511.org/1138805',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Major',\n",
       "        'created_time': 1705205040,\n",
       "        'updated_time': 1705205520,\n",
       "        'f0_': -122.27696990966797,\n",
       "        'f1_': 37.79944610595703,\n",
       "        'postmile': 41.955},\n",
       "       {'id': '511.org/1138820',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705208280,\n",
       "        'updated_time': 1705208280,\n",
       "        'f0_': -122.02040100097656,\n",
       "        'f1_': 37.5435676574707,\n",
       "        'postmile': 18.815},\n",
       "       {'id': '511.org/1138890',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705222620,\n",
       "        'updated_time': 1705222680,\n",
       "        'f0_': -122.26669311523438,\n",
       "        'f1_': 37.79535675048828,\n",
       "        'postmile': 41.334},\n",
       "       {'id': '511.org/1138932',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705230000,\n",
       "        'updated_time': 1705230180,\n",
       "        'f0_': -122.3001708984375,\n",
       "        'f1_': 37.81456756591797,\n",
       "        'postmile': 44.303},\n",
       "       {'id': '511.org/1138944',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705231380,\n",
       "        'updated_time': 1705231440,\n",
       "        'f0_': -122.17821502685547,\n",
       "        'f1_': 37.720703125,\n",
       "        'postmile': 34.019},\n",
       "       {'id': '511.org/1138989',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705237020,\n",
       "        'updated_time': 1705237020,\n",
       "        'f0_': -122.1726303100586,\n",
       "        'f1_': 37.715206146240234,\n",
       "        'postmile': 33.531},\n",
       "       {'id': '511.org/1138990',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705237080,\n",
       "        'updated_time': 1705237200,\n",
       "        'f0_': -122.2798080444336,\n",
       "        'f1_': 37.80051040649414,\n",
       "        'postmile': 42.123},\n",
       "       {'id': '511.org/1138991',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705237200,\n",
       "        'updated_time': 1705237200,\n",
       "        'f0_': -122.30197143554688,\n",
       "        'f1_': 37.80388641357422,\n",
       "        'postmile': 43.422},\n",
       "       {'id': '511.org/1139009',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705238580,\n",
       "        'updated_time': 1705238580,\n",
       "        'f0_': -122.16366577148438,\n",
       "        'f1_': 37.70854949951172,\n",
       "        'postmile': 32.85},\n",
       "       {'id': '511.org/1139097',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705247340,\n",
       "        'updated_time': 1705247340,\n",
       "        'f0_': -122.20948028564453,\n",
       "        'f1_': 37.75568389892578,\n",
       "        'postmile': 36.985},\n",
       "       {'id': '511.org/1139098',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705247580,\n",
       "        'updated_time': 1705247580,\n",
       "        'f0_': -121.93728637695312,\n",
       "        'f1_': 37.333641052246094,\n",
       "        'postmile': 1.195},\n",
       "       {'id': '511.org/1139107',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705249140,\n",
       "        'updated_time': 1705249140,\n",
       "        'f0_': -122.06414031982422,\n",
       "        'f1_': 37.60342788696289,\n",
       "        'postmile': 23.642},\n",
       "       {'id': '511.org/1139113',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705249680,\n",
       "        'updated_time': 1705249680,\n",
       "        'f0_': -122.08171081542969,\n",
       "        'f1_': 37.62764358520508,\n",
       "        'postmile': 25.563},\n",
       "       {'id': '511.org/1139111',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705249680,\n",
       "        'updated_time': 1705249800,\n",
       "        'f0_': -122.07596588134766,\n",
       "        'f1_': 37.61997604370117,\n",
       "        'postmile': 24.959},\n",
       "       {'id': '511.org/1139117',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705250400,\n",
       "        'updated_time': 1705250400,\n",
       "        'f0_': -122.23812866210938,\n",
       "        'f1_': 37.780052185058594,\n",
       "        'postmile': 39.353},\n",
       "       {'id': '511.org/1139120',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705251060,\n",
       "        'updated_time': 1705251120,\n",
       "        'f0_': -122.27482604980469,\n",
       "        'f1_': 37.7985725402832,\n",
       "        'postmile': 41.823},\n",
       "       {'id': '511.org/1139124',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Minor',\n",
       "        'created_time': 1705251780,\n",
       "        'updated_time': 1705251780,\n",
       "        'f0_': -122.1173095703125,\n",
       "        'f1_': 37.676570892333984,\n",
       "        'postmile': 29.488},\n",
       "       {'id': '511.org/1139130',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Minor',\n",
       "        'created_time': 1705254000,\n",
       "        'updated_time': 1705254000,\n",
       "        'f0_': -122.19332885742188,\n",
       "        'f1_': 37.737335205078125,\n",
       "        'postmile': 35.442},\n",
       "       {'id': '511.org/1139141',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705257420,\n",
       "        'updated_time': 1705257540,\n",
       "        'f0_': -122.16366577148438,\n",
       "        'f1_': 37.70854949951172,\n",
       "        'postmile': 32.85},\n",
       "       {'id': '511.org/1139145',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705259220,\n",
       "        'updated_time': 1705259220,\n",
       "        'f0_': -122.23131561279297,\n",
       "        'f1_': 37.77438735961914,\n",
       "        'postmile': 38.798},\n",
       "       {'id': '511.org/1139149',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Major',\n",
       "        'created_time': 1705260720,\n",
       "        'updated_time': 1705260720,\n",
       "        'f0_': -122.09683990478516,\n",
       "        'f1_': 37.648841857910156,\n",
       "        'postmile': 27.251},\n",
       "       {'id': '511.org/1139170',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705269600,\n",
       "        'updated_time': 1705269660,\n",
       "        'f0_': -122.16079711914062,\n",
       "        'f1_': 37.70658874511719,\n",
       "        'postmile': 32.646},\n",
       "       {'id': '511.org/1139184',\n",
       "        'event_type': 'INCIDENT',\n",
       "        'severity': 'Moderate',\n",
       "        'created_time': 1705274400,\n",
       "        'updated_time': 1705274400,\n",
       "        'f0_': -121.91110229492188,\n",
       "        'f1_': 37.41029357910156,\n",
       "        'postmile': 7.313}])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"event.json\", \"r\") as f:\n",
    "    tmp = json.load(f)\n",
    "    d = {}\n",
    "    for row in tmp:\n",
    "        d[row[0]] = row[-1]\n",
    "    del tmp\n",
    "    for event in events:\n",
    "        event[\"postmile\"] = d[event[\"id\"]]\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>segment_id</th>\n",
       "      <th>t</th>\n",
       "      <th>CONSTRUCTION</th>\n",
       "      <th>SPECIAL_EVENT</th>\n",
       "      <th>INCIDENT</th>\n",
       "      <th>WEATHER_CONDITION</th>\n",
       "      <th>ROAD_CONDITION</th>\n",
       "      <th>None</th>\n",
       "      <th>event_score</th>\n",
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       "    <tr>\n",
       "      <th>68540</th>\n",
       "      <td>14</td>\n",
       "      <td>1705305300</td>\n",
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       "    <tr>\n",
       "      <th>68541</th>\n",
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       "      <td>1705305300</td>\n",
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       "    <tr>\n",
       "      <th>68542</th>\n",
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       "      <td>1705305300</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>68544 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       segment_id           t CONSTRUCTION SPECIAL_EVENT INCIDENT  \\\n",
       "0               1  1704096000            0             0        0   \n",
       "1               2  1704096000            0             0        0   \n",
       "2               3  1704096000            0             0        0   \n",
       "3               4  1704096000            0             0        0   \n",
       "4               5  1704096000            0             0        0   \n",
       "...           ...         ...          ...           ...      ...   \n",
       "68539          13  1705305300            0             0        0   \n",
       "68540          14  1705305300            0             0        0   \n",
       "68541          15  1705305300            0             0        0   \n",
       "68542          16  1705305300            0             0        0   \n",
       "68543          17  1705305300            0             0        0   \n",
       "\n",
       "      WEATHER_CONDITION ROAD_CONDITION None  event_score  \n",
       "0                     0              0    1          0.0  \n",
       "1                     0              0    1          0.0  \n",
       "2                     0              0    1          0.0  \n",
       "3                     0              0    1          0.0  \n",
       "4                     0              0    1          0.0  \n",
       "...                 ...            ...  ...          ...  \n",
       "68539                 0              0    1          0.0  \n",
       "68540                 0              0    1          0.0  \n",
       "68541                 0              0    1          0.0  \n",
       "68542                 0              0    1          0.0  \n",
       "68543                 0              0    1          0.0  \n",
       "\n",
       "[68544 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = deque(events)\n",
    "queue = deque()\n",
    "data = []\n",
    "for t in range(int(START.timestamp()), int(END.timestamp()), TIME_STEP):\n",
    "    while events and events[0][\"created_time\"] <= t:\n",
    "        queue.append(events.popleft())\n",
    "    while queue and queue[0][\"created_time\"] < t - 60 * 60:\n",
    "        queue.popleft()\n",
    "    for segment in segment_definition:\n",
    "        score = 0\n",
    "        event_type = 5\n",
    "        for event in queue:\n",
    "            if (\n",
    "                event[\"postmile\"] < segment[\"representative_postmile\"]\n",
    "                and event[\"postmile\"] >= segment[\"representative_postmile\"] - 10\n",
    "            ):\n",
    "                new_score = (\n",
    "                    SEVERITY_TO_SCORE[event[\"severity\"].upper()]\n",
    "                    * math.exp(\n",
    "                        -float(segment[\"representative_postmile\"] - event[\"postmile\"])\n",
    "                        / 7\n",
    "                    )\n",
    "                    * math.exp(-float(t - event[\"created_time\"]) / 600)\n",
    "                )\n",
    "\n",
    "                if new_score > score:\n",
    "                    score = new_score\n",
    "                    event_type = EVENT_TYPE_TO_IDX[event[\"event_type\"]]\n",
    "        event_encoding = [\"0\"] * len(EVENT_TYPE_TO_IDX)\n",
    "        event_encoding[event_type] = \"1\"\n",
    "        data.append([segment[\"id\"], t] + event_encoding + [score])\n",
    "event_df = pd.DataFrame(\n",
    "    data, columns=[\"segment_id\", \"t\"] + list(EVENT_TYPE_TO_IDX.keys()) + [\"event_score\"]\n",
    ")\n",
    "event_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>segment_id</th>\n",
       "      <th>t</th>\n",
       "      <th>speed</th>\n",
       "      <th>Thunderstorm</th>\n",
       "      <th>Drizzle</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Mist</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>...</th>\n",
       "      <th>Clear</th>\n",
       "      <th>Clouds</th>\n",
       "      <th>visibility</th>\n",
       "      <th>CONSTRUCTION</th>\n",
       "      <th>SPECIAL_EVENT</th>\n",
       "      <th>INCIDENT</th>\n",
       "      <th>WEATHER_CONDITION</th>\n",
       "      <th>ROAD_CONDITION</th>\n",
       "      <th>None</th>\n",
       "      <th>event_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>1704096000</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>68539</th>\n",
       "      <td>68539</td>\n",
       "      <td>13</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>68540</th>\n",
       "      <td>68540</td>\n",
       "      <td>14</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>72.252537</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>10000</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68541</th>\n",
       "      <td>68541</td>\n",
       "      <td>15</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>67.500198</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68542</th>\n",
       "      <td>68542</td>\n",
       "      <td>16</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>72.808780</td>\n",
       "      <td>0</td>\n",
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       "      <td>10000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68543</th>\n",
       "      <td>68543</td>\n",
       "      <td>17</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>73.968130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>10000</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68544 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  segment_id           t      speed Thunderstorm Drizzle  \\\n",
       "0               0           1  1704096000  76.500000            0       0   \n",
       "1               1           2  1704096000  75.105919            0       0   \n",
       "2               2           3  1704096000  74.800000            0       0   \n",
       "3               3           4  1704096000  74.800000            0       0   \n",
       "4               4           5  1704096000  74.800000            0       0   \n",
       "...           ...         ...         ...        ...          ...     ...   \n",
       "68539       68539          13  1705305300  71.663103            0       0   \n",
       "68540       68540          14  1705305300  72.252537            0       0   \n",
       "68541       68541          15  1705305300  67.500198            0       0   \n",
       "68542       68542          16  1705305300  72.808780            0       0   \n",
       "68543       68543          17  1705305300  73.968130            0       0   \n",
       "\n",
       "      Rain Snow Mist Smoke  ... Clear Clouds visibility CONSTRUCTION  \\\n",
       "0        0    0    0     0  ...     0      1      10000            0   \n",
       "1        0    0    0     0  ...     0      1      10000            0   \n",
       "2        0    0    0     0  ...     0      1      10000            0   \n",
       "3        0    0    0     0  ...     0      1      10000            0   \n",
       "4        0    0    0     0  ...     0      1      10000            0   \n",
       "...    ...  ...  ...   ...  ...   ...    ...        ...          ...   \n",
       "68539    0    0    0     0  ...     0      1      10000            0   \n",
       "68540    0    0    0     0  ...     0      1      10000            0   \n",
       "68541    0    0    0     0  ...     0      1      10000            0   \n",
       "68542    0    0    0     0  ...     0      1      10000            0   \n",
       "68543    0    0    0     0  ...     0      1      10000            0   \n",
       "\n",
       "      SPECIAL_EVENT INCIDENT WEATHER_CONDITION ROAD_CONDITION None  \\\n",
       "0                 0        0                 0              0    1   \n",
       "1                 0        0                 0              0    1   \n",
       "2                 0        0                 0              0    1   \n",
       "3                 0        0                 0              0    1   \n",
       "4                 0        0                 0              0    1   \n",
       "...             ...      ...               ...            ...  ...   \n",
       "68539             0        0                 0              0    1   \n",
       "68540             0        0                 0              0    1   \n",
       "68541             0        0                 0              0    1   \n",
       "68542             0        0                 0              0    1   \n",
       "68543             0        0                 0              0    1   \n",
       "\n",
       "       event_score  \n",
       "0              0.0  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              0.0  \n",
       "...            ...  \n",
       "68539          0.0  \n",
       "68540          0.0  \n",
       "68541          0.0  \n",
       "68542          0.0  \n",
       "68543          0.0  \n",
       "\n",
       "[68544 rows x 27 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df = pd.concat((df, speed_df))\n",
    "df = df.merge(weather_df, on=[\"segment_id\", \"t\"])\n",
    "df = df.merge(event_df, on=[\"segment_id\", \"t\"])\n",
    "df_cp = df.__deepcopy__()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68527</th>\n",
       "      <td>1</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>76.100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68542</th>\n",
       "      <td>16</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>72.808780</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68534</th>\n",
       "      <td>8</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>72.061583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68543</th>\n",
       "      <td>17</td>\n",
       "      <td>1705305300</td>\n",
       "      <td>73.968130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68544 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       segment_id           t      speed Thunderstorm Drizzle Rain Snow Mist  \\\n",
       "0               1  1704096000  76.500000            0       0    0    0    0   \n",
       "16             17  1704096000  74.656162            0       0    0    0    0   \n",
       "15             16  1704096000  76.500000            0       0    0    0    0   \n",
       "14             15  1704096000  75.263771            0       0    0    0    0   \n",
       "13             14  1704096000  72.831002            0       0    0    0    0   \n",
       "...           ...         ...        ...          ...     ...  ...  ...  ...   \n",
       "68528           2  1705305300  74.889263            0       0    0    0    0   \n",
       "68527           1  1705305300  76.100000            0       0    0    0    0   \n",
       "68542          16  1705305300  72.808780            0       0    0    0    0   \n",
       "68534           8  1705305300  72.061583            0       0    0    0    0   \n",
       "68543          17  1705305300  73.968130            0       0    0    0    0   \n",
       "\n",
       "      Smoke Haze  ... hour_21 hour_22 hour_23 day_0 day_1 day_2 day_3 day_4  \\\n",
       "0         0    0  ...       0       0       0     1     0     0     0     0   \n",
       "16        0    0  ...       0       0       0     1     0     0     0     0   \n",
       "15        0    0  ...       0       0       0     1     0     0     0     0   \n",
       "14        0    0  ...       0       0       0     1     0     0     0     0   \n",
       "13        0    0  ...       0       0       0     1     0     0     0     0   \n",
       "...     ...  ...  ...     ...     ...     ...   ...   ...   ...   ...   ...   \n",
       "68528     0    0  ...       0       0       1     0     0     0     0     0   \n",
       "68527     0    0  ...       0       0       1     0     0     0     0     0   \n",
       "68542     0    0  ...       0       0       1     0     0     0     0     0   \n",
       "68534     0    0  ...       0       0       1     0     0     0     0     0   \n",
       "68543     0    0  ...       0       0       1     0     0     0     0     0   \n",
       "\n",
       "       day_5 day_6  \n",
       "0          0     0  \n",
       "16         0     0  \n",
       "15         0     0  \n",
       "14         0     0  \n",
       "13         0     0  \n",
       "...      ...   ...  \n",
       "68528      0     1  \n",
       "68527      0     1  \n",
       "68542      0     1  \n",
       "68534      0     1  \n",
       "68543      0     1  \n",
       "\n",
       "[68544 rows x 57 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"day\"] = df[\"t\"].apply(lambda x: datetime.fromtimestamp(x).weekday())\n",
    "df.loc[:, \"hour\"] = df[\"t\"].apply(lambda x: datetime.fromtimestamp(x).hour)\n",
    "df = pd.get_dummies(df, columns=[\"hour\", \"day\"], dtype=int)\n",
    "df = df.sort_values(by=\"t\", ascending=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['segment_id', 't', 'speed', 'Thunderstorm', 'Drizzle', 'Rain', 'Snow',\n",
       "       'Mist', 'Smoke', 'Haze', 'Dust', 'Fog', 'Sand', 'Ash', 'Squall',\n",
       "       'Tornado', 'Clear', 'Clouds', 'visibility', 'CONSTRUCTION',\n",
       "       'SPECIAL_EVENT', 'INCIDENT', 'WEATHER_CONDITION', 'ROAD_CONDITION',\n",
       "       'None', 'event_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>segment_id</th>\n",
       "      <th>t</th>\n",
       "      <th>speed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1704096000</td>\n",
       "      <td>76.5</td>\n",
       "      <td>76.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>1704096300</td>\n",
       "      <td>76.5</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>1704096600</td>\n",
       "      <td>76.3</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>1704096900</td>\n",
       "      <td>75.9</td>\n",
       "      <td>76.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>1704097200</td>\n",
       "      <td>77.2</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "      <td>1704097500</td>\n",
       "      <td>76.1</td>\n",
       "      <td>75.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>1704097800</td>\n",
       "      <td>76.0</td>\n",
       "      <td>74.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1</td>\n",
       "      <td>1704098100</td>\n",
       "      <td>75.8</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1</td>\n",
       "      <td>1704098400</td>\n",
       "      <td>74.3</td>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1</td>\n",
       "      <td>1704098700</td>\n",
       "      <td>73.7</td>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     segment_id           t  speed     y\n",
       "0             1  1704096000   76.5  76.3\n",
       "17            1  1704096300   76.5  75.9\n",
       "34            1  1704096600   76.3  77.2\n",
       "51            1  1704096900   75.9  76.1\n",
       "68            1  1704097200   77.2  76.0\n",
       "85            1  1704097500   76.1  75.8\n",
       "102           1  1704097800   76.0  74.3\n",
       "119           1  1704098100   75.8  73.7\n",
       "136           1  1704098400   74.3  74.6\n",
       "153           1  1704098700   73.7  74.6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame()\n",
    "TIMES = 2  # how many timesteps (5-minute) after the current time\n",
    "for i in range(len(df[\"segment_id\"].unique())):\n",
    "    df1 = df[df[\"segment_id\"] == i + 1].copy()\n",
    "    df1.loc[:, \"y\"] = df1[\"speed\"].shift(-TIMES)\n",
    "    df1 = df1.iloc[:-TIMES, :]\n",
    "    data = pd.concat([data, df1], axis=0)\n",
    "data[[\"segment_id\", \"t\", \"speed\", \"y\"]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data = data.drop(columns=[\"segment_id\", \"t\"])\n",
    "X = train_data.iloc[:, :-1]\n",
    "Y = train_data[\"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def rmse(y, y_predict):\n",
    "    return np.sqrt(np.mean((y - y_predict) ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train rmse: 2.750284673364142\n",
      "test rmse: 2.7918855674762493\n",
      "baseline train rmse: 2.774126366843409\n",
      "baseline test rmse: 2.8177787594760106\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression as lm\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.5, shuffle=True)\n",
    "model = lm()\n",
    "columns = X_train.columns\n",
    "model.fit(X_train[columns], y_train)\n",
    "y_train_predict = model.predict(X_train[columns])\n",
    "y_test_predict = model.predict(X_test[columns])\n",
    "\n",
    "print(\"train rmse:\", rmse(y_train, y_train_predict))\n",
    "print(\"test rmse:\", rmse(y_test, y_test_predict))\n",
    "print(\"baseline train rmse:\", rmse(X_train[\"speed\"], y_train))\n",
    "print(\"baseline test rmse:\", rmse(X_test[\"speed\"], y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.65610744e-01, -4.81468299e-14,  3.02162176e-01, -3.90137394e-02,\n",
       "        4.85722573e-17, -1.26732792e-01,  0.00000000e+00,  1.80227474e-01,\n",
       "        5.55111512e-17, -7.44727058e-02, -2.77555756e-17,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00, -7.13043637e-02, -1.70866048e-01,\n",
       "        1.13302253e-05,  8.71959281e-02,  0.00000000e+00, -4.19873584e-02,\n",
       "        0.00000000e+00,  0.00000000e+00, -4.52085697e-02, -8.63940222e-02])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "draw_data = data[data[\"segment_id\"] == 1].sort_values(by=\"t\", ascending=True)\n",
    "train_data = draw_data.drop(columns=[\"segment_id\", \"t\"])\n",
    "X = train_data.iloc[:, :-1]\n",
    "Y = train_data[\"y\"]\n",
    "Y_predict = model.predict(X)\n",
    "start = datetime(2024, 1, 2, 8, 0, 0, 0)\n",
    "end = datetime(2024, 1, 2, 20, 0, 0, 0)\n",
    "idx = (draw_data[\"t\"] >= start.timestamp()) & (draw_data[\"t\"] < end.timestamp())\n",
    "x_axis = mdates.drange(start, end, timedelta(minutes=5))\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d %H:00:00\"))\n",
    "plt.gca().xaxis.set_major_locator(mdates.HourLocator(interval=2))\n",
    "plt.plot(x_axis, Y_predict[idx], label=\"Prediction\")  # [400:500]\n",
    "plt.plot(x_axis, Y[idx], label=\"Actual\")  # [400:500]\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Speed (miles/hr)\")\n",
    "plt.title(\"Prediction of Speed vs Actual Speed\")\n",
    "plt.savefig(\"Prediction.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nozomu MLP code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Category Encoding Function\n",
    "def categorize_data(X):\n",
    "    # Categories for bad weather conditions\n",
    "    bad_weather_conditions = [\n",
    "        'Thunderstorm', 'Drizzle', 'Rain', 'Snow', 'Mist', \n",
    "        'Smoke', 'Haze', 'Dust', 'Fog', 'Sand', 'Ash', \n",
    "        'Squall', 'Tornado'\n",
    "    ]\n",
    "    X['Bad_Weather'] = X[bad_weather_conditions].any(axis=1).astype(int) \n",
    "\n",
    "    # Categories for good weather conditions\n",
    "    good_weather_conditions = ['Clear', 'Clouds']\n",
    "    X['Good_Weather'] = X[good_weather_conditions].any(axis=1).astype(int) \n",
    "\n",
    "    # Categories for event conditions\n",
    "    event_conditions = [\n",
    "        'CONSTRUCTION', 'SPECIAL_EVENT', 'INCIDENT', \n",
    "        'WEATHER_CONDITION', 'ROAD_CONDITION', 'None'\n",
    "    ]\n",
    "    X['Event_Present'] = X[event_conditions].any(axis=1).astype(int) \n",
    "\n",
    "    # Drop the original columns\n",
    "    X = X.drop(columns=bad_weather_conditions + good_weather_conditions + event_conditions)\n",
    "    \n",
    "    return X\n",
    "\n",
    "# copy the data and categorize it\n",
    "X_categorized = categorize_data(X.copy())\n",
    "\n",
    "X_categorized.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "# Numerical features\n",
    "numerical_features= [\"speed\", \"visibility\", \"event_score\",'speed_5min_ago']\n",
    "\n",
    "# Constants\n",
    "N_SPLITS = 5\n",
    "PCA_COMPONENTS = 33\n",
    "\n",
    "# Cross-validation with PCA\n",
    "def cross_validate_model(X, y, numerical_features, kf):\n",
    "    train_rmse_list = []\n",
    "    test_rmse_list = []\n",
    "\n",
    "    for train_index, test_index in kf.split(X):\n",
    "        # Split data\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "        # Standardization of numerical features data sets\n",
    "        scaler = StandardScaler()\n",
    "        X_train_scaled = standardize_features(X_train, numerical_features, scaler)\n",
    "        X_test_scaled = standardize_features(X_test, numerical_features, scaler, fit=False)\n",
    "\n",
    "        # PCA application\n",
    "        X_train_pca, X_test_pca = apply_pca(X_train_scaled, X_test_scaled, PCA_COMPONENTS)\n",
    "\n",
    "        # Train and evaluate model\n",
    "        model, train_rmse, test_rmse = train_and_evaluate_model(X_train_pca, X_test_pca, y_train, y_test)\n",
    "\n",
    "        # Results\n",
    "        train_rmse_list.append(train_rmse)\n",
    "        test_rmse_list.append(test_rmse)\n",
    "\n",
    "    return train_rmse_list, test_rmse_list\n",
    "\n",
    "# Standardize numerical features\n",
    "def standardize_features(X, numerical_features, scaler, fit=True):\n",
    "    X_numerical = X[numerical_features]\n",
    "    X_scaled_numerical = scaler.fit_transform(X_numerical) if fit else scaler.transform(X_numerical)\n",
    "    X_scaled = pd.concat([pd.DataFrame(X_scaled_numerical, columns=numerical_features, index=X.index),\n",
    "                          X.drop(columns=numerical_features)], axis=1)\n",
    "    return X_scaled\n",
    "\n",
    "# Apply PCA to the data\n",
    "def apply_pca(X_train, X_test, n_components):\n",
    "    pca = PCA(n_components=n_components)\n",
    "    X_train_pca = pca.fit_transform(X_train)\n",
    "    X_test_pca = pca.transform(X_test)\n",
    "    return X_train_pca, X_test_pca\n",
    "\n",
    "# Train and evaluate the model\n",
    "def train_and_evaluate_model(X_train, X_test, y_train, y_test):\n",
    "    model = MLPRegressor(hidden_layer_sizes=(100, 50), activation='relu', solver='sgd',\n",
    "                         learning_rate='adaptive', alpha=0.0038, max_iter=1000)\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    y_train_predict = model.predict(X_train)\n",
    "    y_test_predict = model.predict(X_test)\n",
    "\n",
    "    train_rmse = rmse(y_train, y_train_predict)\n",
    "    test_rmse = rmse(y_test, y_test_predict)\n",
    "\n",
    "    return model, train_rmse, test_rmse\n",
    "\n",
    "# Load the data\n",
    "X_categorized = pd.DataFrame(train_data.iloc[:, :-1])  \n",
    "Y = pd.Series(train_data[\"y\"])  \n",
    "\n",
    "# Reset the index\n",
    "X_categorized = X_categorized.reset_index(drop=True)\n",
    "Y = Y.reset_index(drop=True)\n",
    "\n",
    "kf = KFold(n_splits=N_SPLITS, shuffle=True)\n",
    "train_rmse_list, test_rmse_list = cross_validate_model(X_categorized, Y, numerical_features, kf)\n",
    "\n",
    "print(\"\\n___Average RMSE over 5-Fold Cross-Validation with MLP after PCA___\")\n",
    "print(\"Average train RMSE:\", np.mean(train_rmse_list))\n",
    "print(\"Average test RMSE:\", np.mean(test_rmse_list))\n"
   ]
  }
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