{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Live AQI Monitoring\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vayuayan import CPCBLive\n",
    "import json\n",
    "import time\n",
    "from datetime import datetime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Get Your Location\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your Location:\n",
      "[\n",
      "  19.0748,\n",
      "  72.8856\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "# Initialize the Live AQI client\n",
    "client = CPCBLive()\n",
    "\n",
    "# Get your location\n",
    "location = client.get_system_location()\n",
    "print(\"Your Location:\")\n",
    "print(json.dumps(location, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Find Nearest Monitoring Station\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Monitoring Station:\n",
      "\n",
      "Monitoring Station ID: site_5104\n",
      "Station Name: Kurla, Mumbai - MPCB\n"
     ]
    }
   ],
   "source": [
    "# Find nearest station\n",
    "nearest_station = client.get_nearest_station()\n",
    "print(\"Nearest Monitoring Station:\")\n",
    "\n",
    "# Extract station ID for future use\n",
    "station_id = nearest_station[0]\n",
    "station_name = nearest_station[1]\n",
    "print(f\"\\nMonitoring Station ID: {station_id}\")\n",
    "print(f\"Station Name: {station_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Get Current Air Quality Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Air Quality at Kurla, Mumbai - MPCB:\n",
      "Pollutant   Avg   Min   Max   Period\n",
      "----------------------------------------\n",
      "PM2.5      62    51    83    Over the last 24 hours\n",
      "PM10       77    60    106   Over the last 24 hours\n",
      "NO2        5     4     7     Over the last 24 hours\n",
      "NH3        1     1     2     Over the last 24 hours\n",
      "SO2        37    36    37    Over the last 24 hours\n",
      "CO         9     4     16    Over the last 8 hours\n",
      "OZONE      28    22    29    Over the last 8 hours\n"
     ]
    }
   ],
   "source": [
    "# Get current AQI data\n",
    "aqi_data = client.get_live_aqi_data(station_id=station_id)\n",
    "\n",
    "if isinstance(aqi_data, Exception):\n",
    "    print(f\"❌ {aqi_data}\")\n",
    "print(f\"Current Air Quality at {station_name}:\")\n",
    "metrics = aqi_data.get(\"metrics\", [])\n",
    "if metrics:\n",
    "    print(\"Pollutant   Avg   Min   Max   Period\")\n",
    "    print(\"-\" * 40)\n",
    "    for m in metrics:\n",
    "        print(\n",
    "            f\"{m['name']:<10} {m['avg']:<5} {m['min']:<5} {m['max']:<5} {m['avgDesc']}\"\n",
    "        )\n",
    "else:\n",
    "    print(\"No data available, possibly due to station being offline.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Continuous Monitoring\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mahes\\AppData\\Local\\Temp\\ipykernel_22044\\2686430132.py:19: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  dates_dt = pd.to_datetime(dates)\n",
      "C:\\Users\\mahes\\AppData\\Local\\Temp\\ipykernel_22044\\2686430132.py:19: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  dates_dt = pd.to_datetime(dates)\n",
      "C:\\Users\\mahes\\AppData\\Local\\Temp\\ipykernel_22044\\2686430132.py:19: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  dates_dt = pd.to_datetime(dates)\n",
      "C:\\Users\\mahes\\AppData\\Local\\Temp\\ipykernel_22044\\2686430132.py:19: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  dates_dt = pd.to_datetime(dates)\n",
      "C:\\Users\\mahes\\AppData\\Local\\Temp\\ipykernel_22044\\2686430132.py:19: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  dates_dt = pd.to_datetime(dates)\n",
      "C:\\Users\\mahes\\AppData\\Local\\Temp\\ipykernel_22044\\2686430132.py:19: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  dates_dt = pd.to_datetime(dates)\n",
      "C:\\Users\\mahes\\AppData\\Local\\Temp\\ipykernel_22044\\2686430132.py:19: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  dates_dt = pd.to_datetime(dates)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PM2.5 Analysis:\n",
      "  Min: 51\n",
      "  Max: 83\n",
      "  Mean: 62.08\n",
      "\n",
      "PM10 Analysis:\n",
      "  Min: 60\n",
      "  Max: 106\n",
      "  Mean: 76.75\n",
      "\n",
      "NO2 Analysis:\n",
      "  Min: 4\n",
      "  Max: 7\n",
      "  Mean: 4.75\n",
      "\n",
      "NH3 Analysis:\n",
      "  Min: 1\n",
      "  Max: 2\n",
      "  Mean: 1.21\n",
      "\n",
      "SO2 Analysis:\n",
      "  Min: 36\n",
      "  Max: 37\n",
      "  Mean: 36.71\n",
      "\n",
      "CO Analysis:\n",
      "  Min: 4\n",
      "  Max: 16\n",
      "  Mean: 8.21\n",
      "\n",
      "OZONE Analysis:\n",
      "  Min: 22\n",
      "  Max: 29\n",
      "  Mean: 27.96\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Analyze and plot time series for each pollutant in last_hours\n",
    "# Collect all pollutants' time series\n",
    "pollutant_names = []\n",
    "all_values = []\n",
    "all_dates = None\n",
    "\n",
    "for pollutant in aqi_data[\"last_hours\"]:\n",
    "    name = pollutant[\"name\"]\n",
    "    data = pollutant[\"data\"]\n",
    "    if not data:\n",
    "        continue\n",
    "\n",
    "    dates = [entry[\"date\"] for entry in data]\n",
    "    values = [entry[\"val\"] for entry in data]\n",
    "\n",
    "    # Convert dates to datetime\n",
    "    dates_dt = pd.to_datetime(dates)\n",
    "    pollutant_names.append(name)\n",
    "    all_values.append(values)\n",
    "    if all_dates is None:\n",
    "        all_dates = dates_dt\n",
    "\n",
    "# Format x-axis labels as \"HourAM/PM, DD/MM/YY\"\n",
    "x_labels = [\n",
    "    dt.strftime(\"%I%p, %d/%m/%y\").replace(\"AM\", \"AM\").replace(\"PM\", \"PM\")\n",
    "    for dt in all_dates\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(14, 6))\n",
    "for i, values in enumerate(all_values):\n",
    "    plt.plot(all_dates, values, marker=\"o\", label=pollutant_names[i])\n",
    "\n",
    "plt.title(f\"Pollutant Levels - Last {len(all_dates)} Hours\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.xticks(all_dates, x_labels, rotation=45)\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Show min, max, mean for each pollutant\n",
    "for i, values in enumerate(all_values):\n",
    "    print(f\"\\n{pollutant_names[i]} Analysis:\")\n",
    "    print(f\"  Min: {min(values)}\")\n",
    "    print(f\"  Max: {max(values)}\")\n",
    "    print(f\"  Mean: {sum(values)/len(values):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Set Up Alerts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'param': 'PM10', 'value': 77, 'remark': 'Satisfactory', 'color': '#009933'}\n",
      "\n",
      "Current AQI: {'param': 'PM10', 'value': 77, 'remark': 'Satisfactory', 'color': '#009933'}\n",
      "Alert Level: 🟢 Satisfactory\n",
      "Recommendation: Air quality is acceptable.\n"
     ]
    }
   ],
   "source": [
    "def check_air_quality_alert(aqi_value):\n",
    "    \"\"\"\n",
    "    Check AQI and return alert level.\n",
    "\n",
    "    AQI Categories:\n",
    "    - 0-50: Good\n",
    "    - 51-100: Satisfactory\n",
    "    - 101-200: Moderate\n",
    "    - 201-300: Poor\n",
    "    - 301-400: Very Poor\n",
    "    - 401+: Severe\n",
    "    \"\"\"\n",
    "    if aqi_value <= 50:\n",
    "        return \"✅ Good\", \"Air quality is good. Enjoy outdoor activities!\"\n",
    "    elif aqi_value <= 100:\n",
    "        return \"🟢 Satisfactory\", \"Air quality is acceptable.\"\n",
    "    elif aqi_value <= 200:\n",
    "        return (\n",
    "            \"🟡 Moderate\",\n",
    "            \"Sensitive individuals should limit prolonged outdoor exposure.\",\n",
    "        )\n",
    "    elif aqi_value <= 300:\n",
    "        return (\n",
    "            \"🟠 Poor\",\n",
    "            \"Everyone may begin to experience health effects. Reduce outdoor activities.\",\n",
    "        )\n",
    "    elif aqi_value <= 400:\n",
    "        return \"🔴 Very Poor\", \"Health alert! Everyone should avoid outdoor activities.\"\n",
    "    else:\n",
    "        return (\n",
    "            \"🆘 Severe\",\n",
    "            \"Health emergency! Stay indoors and avoid any outdoor exposure.\",\n",
    "        )\n",
    "\n",
    "\n",
    "# Check current AQI\n",
    "if aqi_data and \"aqi\" in aqi_data:\n",
    "    print(aqi_data[\"aqi\"])\n",
    "    current_aqi = aqi_data[\"aqi\"]\n",
    "    alert_level, message = check_air_quality_alert(current_aqi[\"value\"])\n",
    "\n",
    "    print(f\"\\nCurrent AQI: {current_aqi}\")\n",
    "    print(f\"Alert Level: {alert_level}\")\n",
    "    print(f\"Recommendation: {message}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Get AQI for Specific Coordinates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest station to Mumbai:\n",
      "[\n",
      "  \"site_5104\",\n",
      "  \"Kurla, Mumbai - MPCB\"\n",
      "]\n",
      "\n",
      "Current AQI in Mumbai:\n",
      "{\n",
      "  \"title\": \"Kurla, Mumbai - MPCB\",\n",
      "  \"nOfCom\": 100,\n",
      "  \"down\": \"false\",\n",
      "  \"downmessage\": \"\",\n",
      "  \"date\": \"Wednesday, 08 Oct 2025 12:00 PM\",\n",
      "  \"temp\": \"\",\n",
      "  \"aqi\": {\n",
      "    \"param\": \"PM10\",\n",
      "    \"value\": 77,\n",
      "    \"remark\": \"Satisfactory\",\n",
      "    \"color\": \"#009933\"\n",
      "  },\n",
      "  \"metrics\": [\n",
      "    {\n",
      "      \"name\": \"PM2.5\",\n",
      "      \"avg\": 62,\n",
      "      \"avgDesc\": \"Over the last 24 hours\",\n",
      "      \"min\": 51,\n",
      "      \"max\": 83,\n",
      "      \"pollutantName\": \"PM2.5\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"PM10\",\n",
      "      \"avg\": 77,\n",
      "      \"avgDesc\": \"Over the last 24 hours\",\n",
      "      \"min\": 60,\n",
      "      \"max\": 106,\n",
      "      \"pollutantName\": \"PM10\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"NO2\",\n",
      "      \"avg\": 5,\n",
      "      \"avgDesc\": \"Over the last 24 hours\",\n",
      "      \"min\": 4,\n",
      "      \"max\": 7,\n",
      "      \"pollutantName\": \"NO2\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"NH3\",\n",
      "      \"avg\": 1,\n",
      "      \"avgDesc\": \"Over the last 24 hours\",\n",
      "      \"min\": 1,\n",
      "      \"max\": 2,\n",
      "      \"pollutantName\": \"NH3\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"SO2\",\n",
      "      \"avg\": 37,\n",
      "      \"avgDesc\": \"Over the last 24 hours\",\n",
      "      \"min\": 36,\n",
      "      \"max\": 37,\n",
      "      \"pollutantName\": \"SO2\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"CO\",\n",
      "      \"avg\": 9,\n",
      "      \"avgDesc\": \"Over the last 8 hours\",\n",
      "      \"min\": 4,\n",
      "      \"max\": 16,\n",
      "      \"pollutantName\": \"CO\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"OZONE\",\n",
      "      \"avg\": 28,\n",
      "      \"avgDesc\": \"Over the last 8 hours\",\n",
      "      \"min\": 22,\n",
      "      \"max\": 29,\n",
      "      \"pollutantName\": \"OZONE\"\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Example: Get AQI for Mumbai coordinates\n",
    "mumbai_lat = 19.0760\n",
    "mumbai_lon = 72.8777\n",
    "\n",
    "coords = (mumbai_lat, mumbai_lon)\n",
    "\n",
    "# Find nearest station to these coordinates\n",
    "mumbai_station = client.get_nearest_station(coords=coords)\n",
    "print(\"Nearest station to Mumbai:\")\n",
    "print(json.dumps(mumbai_station, indent=2))\n",
    "\n",
    "# Get AQI data for that location\n",
    "mumbai_aqi = client.get_live_aqi_data(coords=coords)\n",
    "mumbai_aqi.pop(\"last_hours\", None)  # Remove last_hours for brevity\n",
    "print(\"\\nCurrent AQI in Mumbai:\")\n",
    "print(json.dumps(mumbai_aqi, indent=2))"
   ]
  }
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