{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = a.drop(columns=['ENCODED_POLY_LINE', 'ENCODED_POLY_LINE_LVLS', 'OWNER', 'TRANSCOM_ID', 'LINK_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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       "    ID  SPEED  TRAVEL_TIME  STATUS              DATA_AS_OF  \\\n",
       "0  262  34.80          359       0  06/02/2017 11:41:59 PM   \n",
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       "2  106  39.77          159       0  06/02/2017 11:41:59 PM   \n",
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       "4    3  14.91          422       0  06/02/2017 11:41:59 PM   \n",
       "\n",
       "                                         LINK_POINTS    BOROUGH  \\\n",
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       "\n",
       "                                           LINK_NAME  \n",
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       "1  CIP N TNB - Whitestone Expwy S Exit 14 (Linden...  \n",
       "2                       12th Ave S 57th St - 45th St  \n",
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   "source": [
    "a.head()\n"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "    ID  SPEED  TRAVEL_TIME  STATUS      DATA_AS_OF  \\\n",
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       "4    3  14.91          422       0  20170602234159   \n",
       "\n",
       "                                         LINK_POINTS    BOROUGH  \\\n",
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       "                                           LINK_NAME  \n",
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   "metadata": {},
   "source": [
    "---------------------------------"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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       " 433,\n",
       " 412,\n",
       " 413,\n",
       " 258,\n",
       " 259,\n",
       " 394,\n",
       " 261,\n",
       " 154,\n",
       " 155,\n",
       " 153,\n",
       " 453,\n",
       " 428,\n",
       " 129,\n",
       " 126,\n",
       " 295,\n",
       " 159,\n",
       " 338,\n",
       " 427,\n",
       " 142,\n",
       " 160,\n",
       " 185,\n",
       " 426,\n",
       " 425,\n",
       " 110,\n",
       " 369,\n",
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       " 423,\n",
       " 424,\n",
       " 165,\n",
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       " 349,\n",
       " 205,\n",
       " 439,\n",
       " 206,\n",
       " 405,\n",
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       " 375,\n",
       " 381,\n",
       " 377,\n",
       " 350,\n",
       " 378,\n",
       " 435,\n",
       " 384,\n",
       " 441,\n",
       " 436,\n",
       " 437,\n",
       " 382,\n",
       " 316,\n",
       " 148,\n",
       " 157,\n",
       " 339,\n",
       " 432,\n",
       " 389,\n",
       " 108,\n",
       " 107,\n",
       " 225,\n",
       " 446,\n",
       " 444,\n",
       " 224,\n",
       " 442,\n",
       " 447,\n",
       " 443,\n",
       " 122,\n",
       " 123,\n",
       " 313]"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "ID",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "SPEED",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TRAVEL_TIME",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "STATUS",
         "rawType": "int64",
         "type": "integer"
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        {
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        {
         "name": "LINK_POINTS",
         "rawType": "object",
         "type": "string"
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        {
         "name": "BOROUGH",
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         "name": "LINK_NAME",
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       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <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",
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      "text/plain": [
       "    ID  SPEED  TRAVEL_TIME  STATUS      DATA_AS_OF  \\\n",
       "0  444   0.00            0    -101  19780101000000   \n",
       "1  444  13.05          105       0  20151208152435   \n",
       "\n",
       "                                         LINK_POINTS    BOROUGH  \\\n",
       "0  40.7051105,-74.01663 40.70432,-74.01667 40.703...  Manhattan   \n",
       "1  40.7051105,-74.01663 40.70432,-74.01667 40.703...  Manhattan   \n",
       "\n",
       "                                     LINK_NAME  \n",
       "0  West St S Battery Pl - FDR N Catherine Slip  \n",
       "1  West St S Battery Pl - FDR N Catherine Slip  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
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    }
   ],
   "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": 19,
   "metadata": {},
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    {
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      "doing 447\n",
      "doing 443\n",
      "doing 122\n",
      "doing 123\n",
      "doing 313\n"
     ]
    }
   ],
   "source": [
    "from multiprocessing import Pool\n",
    "\n",
    "os.makedirs('./full_data', exist_ok=True)\n",
    "os.makedirs('./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('./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('./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.json', 'w') as f:\n",
    "    json.dump(id_info, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
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        {
         "name": "index",
         "rawType": "int64",
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        },
        {
         "name": "ID",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "SPEED",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TRAVEL_TIME",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "STATUS",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "DATA_AS_OF",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "LINK_POINTS",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "BOROUGH",
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         "type": "string"
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        {
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         "type": "string"
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      "text/plain": [
       "    ID  SPEED  TRAVEL_TIME  STATUS      DATA_AS_OF  \\\n",
       "0  444   0.00            0    -101  19780101000000   \n",
       "1  444  13.05          105       0  20151208152435   \n",
       "\n",
       "                                         LINK_POINTS    BOROUGH  \\\n",
       "0  40.7051105,-74.01663 40.70432,-74.01667 40.703...  Manhattan   \n",
       "1  40.7051105,-74.01663 40.70432,-74.01667 40.703...  Manhattan   \n",
       "\n",
       "                                     LINK_NAME  \n",
       "0  West St S Battery Pl - FDR N Catherine Slip  \n",
       "1  West St S Battery Pl - FDR N Catherine Slip  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the DATA_AS_OF to datetime\n",
    "\n",
    "df"
   ]
  }
 ],
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