{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a = pd.read_csv('./DOT_Traffic_Speeds_NBE.csv')\n",
    "# save it as parquet\n",
    "# a.to_parquet('./DOT_Traffic_Speeds_NBE.parquet')\n",
    "# a = pd.read_parquet('./DOT_Traffic_Speeds_NBE.parquet')\n",
    "\n",
    "a = pd.read_parquet('./all_data.parquet')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = a.drop(columns=['ENCODED_POLY_LINE', 'ENCODED_POLY_LINE_LVLS', 'OWNER', 'TRANSCOM_ID', 'LINK_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "    ID  SPEED TRAVEL_TIME STATUS          DATA_AS_OF  \\\n",
       "0  378  15.53         172      0 2025-03-06 22:58:09   \n",
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       "2  126  54.05         139      0 2025-03-06 22:58:09   \n",
       "3  295      0           0   -101 2025-03-06 22:58:09   \n",
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       "\n",
       "                                         LINK_POINTS        BOROUGH  \\\n",
       "0  40.6210105,-74.168861 40.6207604,-74.168 40.61...  Staten Island   \n",
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       "\n",
       "                                   LINK_NAME  \n",
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       "2     BE N Castle Hill Avenue - Griswold Ave  \n",
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     "metadata": {},
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   "source": [
    "a.head()\n"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['DATA_AS_OF'] = pd.to_datetime(a['DATA_AS_OF'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%Y%m%d%H%M%S')"
   ]
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  {
   "cell_type": "code",
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       "    ID  SPEED TRAVEL_TIME STATUS      DATA_AS_OF  \\\n",
       "0  378  15.53         172      0  20250306225809   \n",
       "1  129  54.05          86      0  20250306225809   \n",
       "2  126  54.05         139      0  20250306225809   \n",
       "3  295      0           0   -101  20250306225809   \n",
       "4  159  36.66         151      0  20250306225809   \n",
       "\n",
       "                                         LINK_POINTS        BOROUGH  \\\n",
       "0  40.6210105,-74.168861 40.6207604,-74.168 40.61...  Staten Island   \n",
       "1  40.8240706,-73.874311 40.8247,-73.86959 40.825...          Bronx   \n",
       "2  40.8271606,-73.84993 40.82771,-73.84671 40.828...          Bronx   \n",
       "3  40.84064,-73.83831 40.83881,-73.83853 40.83463...          Bronx   \n",
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       "                                   LINK_NAME  \n",
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       "2     BE N Castle Hill Avenue - Griswold Ave  \n",
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   "source": [
    "---------------------------------"
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  {
   "cell_type": "code",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the unique values of ID\n",
    "id_list = a['ID'].unique().tolist()\n",
    "id_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
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         "rawType": "int64",
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        {
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        {
         "name": "BOROUGH",
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       "      <th>SPEED</th>\n",
       "      <th>TRAVEL_TIME</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>DATA_AS_OF</th>\n",
       "      <th>LINK_POINTS</th>\n",
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       "Empty DataFrame\n",
       "Columns: [ID, SPEED, TRAVEL_TIME, STATUS, DATA_AS_OF, LINK_POINTS, BOROUGH, LINK_NAME]\n",
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = a[a['ID'].apply(str).str.contains(\"444\", regex=False, na=False, case=False)]\n",
    "# convert DATA_AS_OF from %m/%d/%Y %I:%M:%S %p to %Y%m%d%H%M%S\n",
    "# df['DATA_AS_OF'] = pd.to_datetime(df['DATA_AS_OF'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%Y%m%d%H%M%S')\n",
    "df = df.sort_values(['DATA_AS_OF'])\n",
    "# reset the index\n",
    "df = df.reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check if all the previous id in this\n",
    "original_id = json.load(open('./id_info.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_id = original_id.keys()\n",
    "original_id = list(original_id)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "original_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "# find the id that is not in the original_id\n",
    "id_list = a['ID'].unique().tolist()\n",
    "id_list = [str(i) for i in id_list]\n",
    "id_list = set(id_list)\n",
    "id_list = list(id_list)\n",
    "id_list = [i for i in original_id if i not in id_list]\n",
    "id_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_list = a['ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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      "doing 379\n",
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      "doing 416\n",
      "doing 380\n"
     ]
    }
   ],
   "source": [
    "from multiprocessing import Pool\n",
    "\n",
    "os.makedirs('./2025_full_data', exist_ok=True)\n",
    "os.makedirs('./2025_slim_data', exist_ok=True)\n",
    "\n",
    "def process_id(id):\n",
    "    print(f'doing {id}')\n",
    "    # filter the data by id\n",
    "    df = a[a['ID'] == id]\n",
    "    \n",
    "    # convert DATA_AS_OF from %m/%d/%Y %I:%M:%S %p to %Y%m%d%H%M%S\n",
    "    df = df.sort_values(['DATA_AS_OF'])\n",
    "    # reset the index\n",
    "    df = df.reset_index(drop=True)\n",
    "    df.to_parquet('./2025_full_data/id_{}.parquet'.format(id))\n",
    "    # get the value of BOROUGH column of first raw\n",
    "    borough = df['BOROUGH'].iloc[0]\n",
    "    link = df['LINK_NAME'].iloc[0]\n",
    "    length = len(df)\n",
    "    id_info = {id: {'borough': borough, 'link': link, 'len': length}}\n",
    "    df = df.drop(columns=['BOROUGH', 'LINK_NAME', 'ID','LINK_POINTS', 'STATUS'])\n",
    "    # rearrange the columns\n",
    "    df = df[['DATA_AS_OF', 'SPEED', 'TRAVEL_TIME']]\n",
    "    df.to_parquet('./2025_slim_data/id_{}.parquet'.format(id))\n",
    "    return id_info\n",
    "\n",
    "with Pool() as pool:\n",
    "    results = pool.map(process_id, id_list)\n",
    "\n",
    "# Combine all the id_info dictionaries into one\n",
    "id_info = {}\n",
    "for result in results:\n",
    "    id_info.update(result)\n",
    "\n",
    "with open('./id_info_2025.json', 'w') as f:\n",
    "    json.dump(id_info, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
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        {
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         "rawType": "int64",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>TRAVEL_TIME</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>DATA_AS_OF</th>\n",
       "      <th>LINK_POINTS</th>\n",
       "      <th>BOROUGH</th>\n",
       "      <th>LINK_NAME</th>\n",
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       "Empty DataFrame\n",
       "Columns: [ID, SPEED, TRAVEL_TIME, STATUS, DATA_AS_OF, LINK_POINTS, BOROUGH, LINK_NAME]\n",
       "Index: []"
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "# convert the DATA_AS_OF to datetime\n",
    "\n",
    "df"
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  }
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