Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  this notebook is for step 2 of tasks: Get the data in a form something like (longitude, latitude, time, fire/land/water/burned area/no data, FRP, confidence, source).  ml_tign should be doing something like that. We may add more such as pixel size or pixel ellipse later. Determine from literature the possible values delivered by the satellite products we use and check them out in real data. Make some pictures, keep good notes (git is best), store pictures\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODIS data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODIS Fire Data Features\n",
    "\n",
    "This notebook provides an overview of the key features found in MODIS fire detection datasets, with detailed explanations of each feature and its significance.\n",
    "https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_C.pdf#page=8.23\n",
    "\n",
    "## 1. Longitude and Latitude\n",
    "- **Description**: These represent the geographical position of each detected fire pixel.\n",
    "- **Format**: Degrees (°)\n",
    "- **Range**:\n",
    "  - Longitude: -180° to +180°\n",
    "  - Latitude: -90° to +90°\n",
    "  \n",
    "## 2. Time\n",
    "- **Description**: The time when each fire pixel was detected by the satellite.\n",
    "- **Format**: UTC (Coordinated Universal Time)\n",
    "- **Use**: Helps to timestamp fire occurrences for further temporal analysis.\n",
    "\n",
    "## 3. Fire Mask (Fire/Land/Water/Burned Area/No Data)\n",
    "- **Description**: The fire mask assigns pixels into different categories based on their type (fire, land, water, etc.).\n",
    "- **Categories**:\n",
    "  - `0`: Not processed (missing data)\n",
    "  - `1`: Not processed (obsolete, not used since Collection 1)\n",
    "  - `2`: Not processed (other reasons)\n",
    "  - `3`: Water (non-fire pixel)\n",
    "  - `4`: Cloud-covered (land or water)\n",
    "  - `5`: Non-fire land pixel\n",
    "  - `6`: Unknown (land or water)\n",
    "  - `7`: Fire (low confidence, land or water)\n",
    "  - `8`: Fire (nominal confidence, land or water)\n",
    "  - `9`: Fire (high confidence, land or water)\n",
    "  \n",
    "## 4. Fire Radiative Power (FRP)\n",
    "- **Description**: The amount of energy released by the fire, measured in megawatts (MW). FRP is useful for estimating fire intensity.\n",
    "- **Range**: Typically ranges from 0 MW to several thousand MW for large fires.\n",
    "- **Use**: FRP is an important metric for understanding fire behavior and estimating emissions.\n",
    "\n",
    "## 5. Confidence\n",
    "- **Description**: The confidence level indicates the reliability of the fire detection for each pixel.\n",
    "- **Categories**:\n",
    "  - Low confidence (7)\n",
    "  - Nominal confidence (8)\n",
    "  - High confidence (9)\n",
    "- **Use**: Confidence helps users filter detections based on their tolerance for false positives or negatives. Higher confidence values indicate more reliable fire detections.\n",
    "\n",
    "## 6. Source\n",
    "- **Description**: The source of the MODIS data, which refers to the satellite that provided the observation.\n",
    "- **Options**:\n",
    "  - **Terra (MOD14)**\n",
    "  - **Aqua (MYD14)**\n",
    "- **Use**: The source satellite helps track which observations came from Terra or Aqua.\n",
    "\n",
    "\n",
    "![How are the confidence values in the “FP confidence” SDS related to the confidence\n",
    "classes assigned to fire pixels?](fire_confidence.jpg)\n",
    "\n",
    "---\n",
    "\n",
    "This document provides a brief explanation of each feature. As we proceed with the analysis, we'll use this information to process and visualize the fire detection data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyhdf\n",
      "  Obtaining dependency information for pyhdf from https://files.pythonhosted.org/packages/33/6a/bbfb5b6a143b130e56e46ec95bcba94dce3c628e389051ff704d29bac6a6/pyhdf-0.11.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
      "  Downloading pyhdf-0.11.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.8 kB)\n",
      "Requirement already satisfied: numpy in /home/samiraalkaee/miniconda3/lib/python3.9/site-packages (from pyhdf) (1.21.5)\n",
      "Downloading pyhdf-0.11.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (771 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m771.5/771.5 kB\u001b[0m \u001b[31m38.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[33mDEPRECATION: neuralplot 0.0.8 has a non-standard dependency specifier matplotlib>=3.1numpy>=1.16. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of neuralplot or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling collected packages: pyhdf\n",
      "Successfully installed pyhdf-0.11.4\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pyhdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fire mask': (('number_of_scan_lines', 'pixels_per_scan_line'),\n",
       "  (2030, 1354),\n",
       "  21,\n",
       "  0),\n",
       " 'algorithm QA': (('number_of_scan_lines', 'pixels_per_scan_line'),\n",
       "  (2030, 1354),\n",
       "  25,\n",
       "  1),\n",
       " 'FP_line': (('number_of_active_fires',), (53,), 22, 2),\n",
       " 'FP_sample': (('number_of_active_fires',), (53,), 22, 3),\n",
       " 'FP_latitude': (('number_of_active_fires',), (53,), 5, 4),\n",
       " 'FP_longitude': (('number_of_active_fires',), (53,), 5, 5),\n",
       " 'FP_R2': (('number_of_active_fires',), (53,), 5, 6),\n",
       " 'FP_T21': (('number_of_active_fires',), (53,), 5, 7),\n",
       " 'FP_T31': (('number_of_active_fires',), (53,), 5, 8),\n",
       " 'FP_MeanT21': (('number_of_active_fires',), (53,), 5, 9),\n",
       " 'FP_MeanT31': (('number_of_active_fires',), (53,), 5, 10),\n",
       " 'FP_MeanDT': (('number_of_active_fires',), (53,), 5, 11),\n",
       " 'FP_MAD_T21': (('number_of_active_fires',), (53,), 5, 12),\n",
       " 'FP_MAD_T31': (('number_of_active_fires',), (53,), 5, 13),\n",
       " 'FP_MAD_DT': (('number_of_active_fires',), (53,), 5, 14),\n",
       " 'FP_power': (('number_of_active_fires',), (53,), 5, 15),\n",
       " 'FP_AdjCloud': (('number_of_active_fires',), (53,), 21, 16),\n",
       " 'FP_AdjWater': (('number_of_active_fires',), (53,), 21, 17),\n",
       " 'FP_WinSize': (('number_of_active_fires',), (53,), 21, 18),\n",
       " 'FP_NumValid': (('number_of_active_fires',), (53,), 22, 19),\n",
       " 'FP_confidence': (('number_of_active_fires',), (53,), 21, 20),\n",
       " 'FP_land': (('number_of_active_fires',), (53,), 21, 21),\n",
       " 'FP_MeanR2': (('number_of_active_fires',), (53,), 5, 22),\n",
       " 'FP_MAD_R2': (('number_of_active_fires',), (53,), 5, 23),\n",
       " 'FP_ViewZenAng': (('number_of_active_fires',), (53,), 5, 24),\n",
       " 'FP_SolZenAng': (('number_of_active_fires',), (53,), 5, 25),\n",
       " 'FP_RelAzAng': (('number_of_active_fires',), (53,), 5, 26),\n",
       " 'FP_CMG_row': (('number_of_active_fires',), (53,), 22, 27),\n",
       " 'FP_CMG_col': (('number_of_active_fires',), (53,), 22, 28),\n",
       " 'CMG_day': (('cmg_cells_day', 'cmg_values'), (8465, 8), 23, 29)}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhdf.SD import SD, SDC\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# Open the HDF5 file\n",
    "file = SD('MOD14.A2018312.1950.061.2021344091856.hdf', SDC.READ)\n",
    "\n",
    "# List all datasets in the file\n",
    "datasets = file.datasets()\n",
    "datasets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Access Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global Metadata in the HDF file:\n",
      "FirePix: 53\n",
      "LandFirePix: 53\n",
      "WaterFirePix: 0\n",
      "MissingPix: 0\n",
      "LandPix: 201994\n",
      "WaterPix: 2538848\n",
      "CoastPix: 7778\n",
      "WaterAdjacentFirePix: 13\n",
      "CloudAdjacentFirePix: 3\n",
      "UnknownLandPix: 0\n",
      "UnknownWaterPix: 9482\n",
      "LandCloudPix: 8394\n",
      "WaterCloudPix: 1026055\n",
      "GlintPix: 0\n",
      "GlintRejectedPix: 0\n",
      "CoastRejectedLandPix: 4\n",
      "HotSurfRejectedPix: 0\n",
      "ClearingRejectedPix: 0\n",
      "CoastRejectedWaterPix: 0\n",
      "DayPix: 2748620\n",
      "NightPix: 0\n",
      "MissingRadPix: 0\n",
      "MissingGeoPix: 0\n",
      "Satellite: Terra\n",
      "ProcessVersionNumber: 6.2.3\n",
      "MOD021KM input file: /MODAPSops7/archive/f7477/running/AM1M_C61_L1mc/14229088/MOD021KM.A2018312.1950.061.2018313074449.hdf\n",
      "MOD03 input file: /MODAPSops7/archive/f7477/running/AM1M_C61_L1mc/14229088/MOD03.A2018312.1950.061.2018313011438.hdf\n",
      "SystemID: Linux minion7477 3.10.0-1160.42.2.el7.x86_64 #1 SMP Tue Sep 7 14:49:57 UTC 2021 x86_64\n",
      "identifier_product_doi: 10.5067/MODIS/MOD14.061\n",
      "identifier_product_doi_authority: http://dx.doi.org\n",
      "CoreMetadata.0: \n",
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      "    OBJECT                 = DAYNIGHTFLAG\n",
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      "            OBJECT                 = GRINGPOINTLONGITUDE\n",
      "              NUM_VAL              = 4\n",
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      "            OBJECT                 = GRINGPOINTLATITUDE\n",
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      "        NUM_VAL              = 1\n",
      "        VALUE                = \"MODIS\"\n",
      "      END_OBJECT             = ASSOCIATEDINSTRUMENTSHORTNAME\n",
      "\n",
      "    END_OBJECT             = ASSOCIATEDPLATFORMINSTRUMENTSENSORCONTAINER\n",
      "\n",
      "  END_GROUP              = ASSOCIATEDPLATFORMINSTRUMENTSENSOR\n",
      "\n",
      "  GROUP                  = ADDITIONALATTRIBUTES\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"1\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"1\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"QAPERCENTGOODQUALITY\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"1\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"1\"\n",
      "          VALUE                = \"100\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"2\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"2\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"QAPERCENTOTHERQUALITY\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"2\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"2\"\n",
      "          VALUE                = \"0\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"3\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"3\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"QAPERCENTNOTPRODUCEDCLOUD\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"3\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"3\"\n",
      "          VALUE                = \"37\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"4\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"4\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"QAPERCENTNOTPRODUCEDOTHER\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"4\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"4\"\n",
      "          VALUE                = \"0\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"5\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"5\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"FIREPIXELS\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"5\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"5\"\n",
      "          VALUE                = \"53\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"6\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"6\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"GRANULENUMBER\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"6\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"6\"\n",
      "          VALUE                = \"240\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"7\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"7\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"PROCESSVERSION\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"7\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"7\"\n",
      "          VALUE                = \"6.2.3\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"8\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"8\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"identifier_product_doi\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"8\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"8\"\n",
      "          VALUE                = \"10.5067/MODIS/MOD14.061\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "    OBJECT                 = ADDITIONALATTRIBUTESCONTAINER\n",
      "      CLASS                = \"9\"\n",
      "\n",
      "      OBJECT                 = ADDITIONALATTRIBUTENAME\n",
      "        CLASS                = \"9\"\n",
      "        NUM_VAL              = 1\n",
      "        VALUE                = \"identifier_product_doi_authority\"\n",
      "      END_OBJECT             = ADDITIONALATTRIBUTENAME\n",
      "\n",
      "      GROUP                  = INFORMATIONCONTENT\n",
      "        CLASS                = \"9\"\n",
      "\n",
      "        OBJECT                 = PARAMETERVALUE\n",
      "          NUM_VAL              = 1\n",
      "          CLASS                = \"9\"\n",
      "          VALUE                = \"http://dx.doi.org\"\n",
      "        END_OBJECT             = PARAMETERVALUE\n",
      "\n",
      "      END_GROUP              = INFORMATIONCONTENT\n",
      "\n",
      "    END_OBJECT             = ADDITIONALATTRIBUTESCONTAINER\n",
      "\n",
      "  END_GROUP              = ADDITIONALATTRIBUTES\n",
      "\n",
      "END_GROUP              = INVENTORYMETADATA\n",
      "\n",
      "END\n",
      "\n",
      "ArchiveMetadata.0: \n",
      "GROUP                  = ARCHIVEDMETADATA\n",
      "  GROUPTYPE            = MASTERGROUP\n",
      "\n",
      "  OBJECT                 = LONGNAME\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = \"MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km\"\n",
      "  END_OBJECT             = LONGNAME\n",
      "\n",
      "  OBJECT                 = PROCESSINGCENTER\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = \"MODAPS\"\n",
      "  END_OBJECT             = PROCESSINGCENTER\n",
      "\n",
      "  OBJECT                 = SPSOPARAMETERS\n",
      "    NUM_VAL              = 5\n",
      "    VALUE                = (\"2663\", \"2664\", \"2665\", \"2666\", \"2711\")\n",
      "  END_OBJECT             = SPSOPARAMETERS\n",
      "\n",
      "  OBJECT                 = NORTHBOUNDINGCOORDINATE\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 51.5588420771698\n",
      "  END_OBJECT             = NORTHBOUNDINGCOORDINATE\n",
      "\n",
      "  OBJECT                 = SOUTHBOUNDINGCOORDINATE\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 30.0324053501038\n",
      "  END_OBJECT             = SOUTHBOUNDINGCOORDINATE\n",
      "\n",
      "  OBJECT                 = EASTBOUNDINGCOORDINATE\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = -114.958684320693\n",
      "  END_OBJECT             = EASTBOUNDINGCOORDINATE\n",
      "\n",
      "  OBJECT                 = WESTBOUNDINGCOORDINATE\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = -148.365881710475\n",
      "  END_OBJECT             = WESTBOUNDINGCOORDINATE\n",
      "\n",
      "  OBJECT                 = INSTRUMENTSCANSPROCESSED\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 203\n",
      "  END_OBJECT             = INSTRUMENTSCANSPROCESSED\n",
      "\n",
      "  OBJECT                 = PERCENTLAND\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 7\n",
      "  END_OBJECT             = PERCENTLAND\n",
      "\n",
      "  OBJECT                 = CLOUDPIXELS\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 1034449\n",
      "  END_OBJECT             = CLOUDPIXELS\n",
      "\n",
      "  OBJECT                 = NOFIREPIXELS\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 1704636\n",
      "  END_OBJECT             = NOFIREPIXELS\n",
      "\n",
      "  OBJECT                 = GLINTPIXELS\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 0\n",
      "  END_OBJECT             = GLINTPIXELS\n",
      "\n",
      "  OBJECT                 = UNKNOWNPIXELS\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 9482\n",
      "  END_OBJECT             = UNKNOWNPIXELS\n",
      "\n",
      "  OBJECT                 = WATERPIXELS\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 2538848\n",
      "  END_OBJECT             = WATERPIXELS\n",
      "\n",
      "  OBJECT                 = CLOUDADJACENTFIREPIXELS\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 3\n",
      "  END_OBJECT             = CLOUDADJACENTFIREPIXELS\n",
      "\n",
      "  OBJECT                 = WATERADJACENTFIREPIXELS\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = 13\n",
      "  END_OBJECT             = WATERADJACENTFIREPIXELS\n",
      "\n",
      "  OBJECT                 = INTERNALCLOUDMASK\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = \"yes\"\n",
      "  END_OBJECT             = INTERNALCLOUDMASK\n",
      "\n",
      "  OBJECT                 = PROCESSINGENVIRONMENT\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = \"Linux minion7477 3.10.0-1160.42.2.el7.x86_64 #1 SMP Tue Sep 7 14:49:57 UTC 2021 x86_64\"\n",
      "  END_OBJECT             = PROCESSINGENVIRONMENT\n",
      "\n",
      "  OBJECT                 = DESCRREVISION\n",
      "    NUM_VAL              = 1\n",
      "    VALUE                = \"6.1\"\n",
      "  END_OBJECT             = DESCRREVISION\n",
      "\n",
      "END_GROUP              = ARCHIVEDMETADATA\n",
      "\n",
      "END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# File path to the MODIS HDF file\n",
    "mod14_file = 'MOD14.A2018312.1950.061.2021344091856.hdf'\n",
    "\n",
    "# Open the HDF file for reading\n",
    "hdf_file = SD(mod14_file, SDC.READ)\n",
    "\n",
    "# Retrieve global attributes (metadata) from the HDF file\n",
    "global_attrs = hdf_file.attributes()\n",
    "\n",
    "# Print metadata information\n",
    "print(\"Global Metadata in the HDF file:\")\n",
    "for key, value in global_attrs.items():\n",
    "    print(f\"{key}: {value}\")\n",
    "\n",
    "# Close the HDF file\n",
    "hdf_file.end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fire Mask Data Shape: (2030, 1354)\n",
      "Fire Mask Data (Sample): \n",
      "[[4 4 4 3 4 3 3 3 3 3]\n",
      " [4 4 4 4 4 3 3 3 3 3]\n",
      " [3 4 4 4 4 4 4 3 4 3]\n",
      " [3 3 3 3 3 3 3 3 4 3]\n",
      " [4 4 3 3 3 3 3 3 3 3]\n",
      " [4 4 3 3 3 4 3 3 3 3]\n",
      " [4 3 3 3 3 3 3 3 3 3]\n",
      " [4 3 3 3 4 3 3 3 3 3]\n",
      " [4 3 3 3 3 4 3 3 3 3]\n",
      " [3 3 3 3 3 3 3 3 3 3]]\n"
     ]
    }
   ],
   "source": [
    "from pyhdf.SD import SD, SDC\n",
    "import numpy as np\n",
    "\n",
    "# File path to the MODIS HDF file\n",
    "mod14_file = 'MOD14.A2018312.1950.061.2021344091856.hdf'\n",
    "\n",
    "# Open the HDF file for reading\n",
    "hdf_file = SD(mod14_file, SDC.READ)\n",
    "\n",
    "# Find the index of the \"fire mask\" SDS\n",
    "fire_mask_sds = hdf_file.select('fire mask')\n",
    "\n",
    "# Read the entire \"fire mask\" dataset\n",
    "fire_mask_data = fire_mask_sds.get()\n",
    "\n",
    "# Print the shape of the fire mask data and an example of its content\n",
    "print(f\"Fire Mask Data Shape: {fire_mask_data.shape}\")\n",
    "print(f\"Fire Mask Data (Sample): \\n{fire_mask_data[:10, :10]}\")\n",
    "\n",
    "# Closing the HDF file\n",
    "hdf_file.end()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "from pyhdf.SD import SD, SDC\n",
    "\n",
    "# Define class names with numbers\n",
    "class_names = {\n",
    "    0: '(0) Not processed (missing input data)',\n",
    "    1: '(1) Not processed',\n",
    "    2: '(2) Not processed',\n",
    "    3: '(3) Water',\n",
    "    4: '(4) Cloud',\n",
    "    5: '(5) Land ',\n",
    "    6: '(6) Unknown',\n",
    "    7: '(7) Fire (low confidence)',\n",
    "    8: '(8) Fire (nominal confidence)',\n",
    "    9: '(9) Fire (high confidence)'\n",
    "}\n",
    "\n",
    "# Define distinct colors for each class\n",
    "distinct_colors = [\n",
    "    '#000000',  # Black for not processed (missing input data)\n",
    "    '#4B0082',  # Indigo for not processed\n",
    "    '#8B4513',  # SaddleBrown for not processed\n",
    "    '#1E90FF',  # DodgerBlue for non-fire water pixel\n",
    "    '#FFFFFF',  # White for cloud\n",
    "    '#228B22',  # ForestGreen for non-fire land pixel\n",
    "    '#808080',  # Gray for unknown\n",
    "    '#FFFF00',  # Yellow for fire (low confidence)\n",
    "    '#FFA500',  # Orange for fire (nominal confidence)\n",
    "    '#FF0000'   # Red for fire (high confidence)\n",
    "]\n",
    "\n",
    "# File path to the MODIS HDF file\n",
    "mod14_file = 'MOD14.A2018312.1950.061.2021344091856.hdf'\n",
    "\n",
    "# Open the HDF file for reading\n",
    "hdf_file = SD(mod14_file, SDC.READ)\n",
    "\n",
    "# Find the index of the \"fire mask\" SDS\n",
    "fire_mask_sds = hdf_file.select('fire mask')\n",
    "\n",
    "# Read the entire \"fire mask\" dataset\n",
    "fire_mask_data = fire_mask_sds.get()\n",
    "\n",
    "# Closing the HDF file\n",
    "hdf_file.end()\n",
    "\n",
    "# Create a custom colormap\n",
    "cmap = ListedColormap(distinct_colors)\n",
    "\n",
    "# Create norm to map values to colors\n",
    "bounds = np.arange(11) - 0.5\n",
    "norm = BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "# Increase figure size to accommodate larger legend\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Plotting the Fire Mask Data with (0, 0) at the bottom-left corner\n",
    "img = plt.imshow(fire_mask_data, cmap=cmap, norm=norm, origin='lower')\n",
    "\n",
    "# Add colorbar with larger font size\n",
    "cbar = plt.colorbar(img, label='Fire Mask Classes', ticks=range(10))\n",
    "cbar.set_ticklabels([class_names[i] for i in range(10)])\n",
    "cbar.ax.tick_params(labelsize=12)  # Increase label size\n",
    "cbar.set_label('Fire Mask Classes', size=14)  # Increase colorbar title size\n",
    "\n",
    "plt.title('MODIS Fire Mask Visualization', fontsize=16)\n",
    "plt.xlabel('X Pixels', fontsize=14)\n",
    "plt.ylabel('Y Pixels', fontsize=14)\n",
    "\n",
    "# Adjust layout to prevent cutting off colorbar labels\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find and Print Indices with Fire Mask Values of 8 and 9:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indices of Nominal Confidence Fire (8):\n",
      "[(12, 1064), (118, 1241), (207, 1329), (211, 1330), (212, 1330), (486, 1181), (506, 1269), (510, 1269), (510, 1270), (641, 1183), (790, 1331), (925, 1316), (926, 1316), (927, 1315), (929, 1312), (930, 1315), (930, 1316), (931, 1314), (932, 1313), (933, 1317), (935, 1314), (937, 1315), (940, 1316)]\n",
      "\n",
      "Indices of High Confidence Fire (9):\n",
      "[(207, 1330), (785, 1331), (926, 1315), (928, 1313), (928, 1314), (928, 1315), (928, 1316), (929, 1313), (929, 1314), (929, 1316), (931, 1315), (932, 1314), (932, 1315), (932, 1316), (933, 1313), (933, 1314), (933, 1315), (933, 1316), (934, 1313), (934, 1316), (935, 1315), (935, 1316), (936, 1314), (936, 1315), (940, 1314), (940, 1315), (941, 1314), (941, 1315)]\n",
      "\n",
      "Total Nominal Confidence Fire Detections (8): 23\n",
      "Total High Confidence Fire Detections (9): 28\n"
     ]
    }
   ],
   "source": [
    "# File path to the MODIS HDF file\n",
    "mod14_file = 'MOD14.A2018312.1950.061.2021344091856.hdf'\n",
    "\n",
    "# Open the HDF file for reading\n",
    "hdf_file = SD(mod14_file, SDC.READ)\n",
    "# Find the index of the \"fire mask\" SDS\n",
    "fire_mask_sds = hdf_file.select('fire mask')\n",
    "\n",
    "# Read the entire \"fire mask\" dataset\n",
    "fire_mask_data = fire_mask_sds.get()\n",
    "\n",
    "# Closing the HDF file\n",
    "hdf_file.end()\n",
    "\n",
    "# Find indices where fire mask equals 8 or 9\n",
    "fire_8_indices = np.where(fire_mask_data == 8)  # Nominal confidence fire\n",
    "fire_9_indices = np.where(fire_mask_data == 9)  # High confidence fire\n",
    "\n",
    "# Combine and print the results\n",
    "print(\"Indices of Nominal Confidence Fire (8):\")\n",
    "print(list(zip(fire_8_indices[0], fire_8_indices[1])))\n",
    "\n",
    "print(\"\\nIndices of High Confidence Fire (9):\")\n",
    "print(list(zip(fire_9_indices[0], fire_9_indices[1])))\n",
    "\n",
    "# Optional: Count of detections\n",
    "print(f\"\\nTotal Nominal Confidence Fire Detections (8): {len(fire_8_indices[0])}\")\n",
    "print(f\"Total High Confidence Fire Detections (9): {len(fire_9_indices[0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "# File path to the MODIS HDF file\n",
    "mod14_file = 'MOD14.A2018312.1950.061.2021344091856.hdf'\n",
    "\n",
    "# Open the HDF file for reading\n",
    "hdf_file = SD(mod14_file, SDC.READ)\n",
    "\n",
    "\n",
    "\n",
    "# Find the index of the \"fire mask\" SDS\n",
    "fire_mask_sds = hdf_file.select('fire mask')\n",
    "\n",
    "# Read the entire \"fire mask\" dataset\n",
    "fire_mask_data = fire_mask_sds.get()\n",
    "\n",
    "# Closing the HDF file\n",
    "hdf_file.end()\n",
    "\n",
    "# Create separate masks for class 8 and 9\n",
    "fire_8_mask = fire_mask_data == 8\n",
    "fire_9_mask = fire_mask_data == 9\n",
    "\n",
    "# Plotting the Fire Mask Data (Only classes 8 and 9)\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Create a base image (all black)\n",
    "base_image = np.zeros(fire_mask_data.shape + (3,))\n",
    "\n",
    "# Set red color for class 8 (nominal confidence)\n",
    "base_image[fire_8_mask] = [1, 0, 0]  # Red\n",
    "\n",
    "# Set yellow color for class 9 (high confidence)\n",
    "base_image[fire_9_mask] = [1, 1, 0]  # Yellow\n",
    "\n",
    "# Plot the image\n",
    "plt.imshow(base_image, origin='lower', interpolation='none')\n",
    "\n",
    "# Create a custom colormap for the legend\n",
    "cmap = colors.ListedColormap(['red', 'yellow'])\n",
    "bounds = [8, 9, 10]\n",
    "norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "# Add a color bar\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
    "sm.set_array([])  # This line is necessary for the colorbar to work correctly\n",
    "cbar = plt.colorbar(sm, ticks=[8.5, 9.5], label='Fire Mask Classes')\n",
    "cbar.set_ticklabels(['8 (Nominal Confidence)', '9 (High Confidence)'])\n",
    "\n",
    "# Adding labels and title\n",
    "plt.title('MODIS Fire Mask Visualization (Classes 8 and 9)')\n",
    "plt.xlabel('X Pixels')\n",
    "plt.ylabel('Y Pixels')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pyhdf.SD import SD, SDC\n",
    "\n",
    "# Open the HDF5 file\n",
    "file = SD('MOD14.A2018312.1950.061.2021344091856.hdf', SDC.READ)\n",
    "\n",
    "# Read relevant datasets\n",
    "latitude = file.select('FP_latitude')[:]\n",
    "longitude = file.select('FP_longitude')[:]\n",
    "frp = file.select('FP_power')[:]  # Fire Radiative Power\n",
    "confidence = file.select('FP_confidence')[:]  # Confidence level\n",
    "\n",
    "# Plotting the data\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Scatter plot of fire locations colored by FRP\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.scatter(longitude, latitude, c=frp, cmap='hot', marker='o')\n",
    "plt.colorbar(label='Fire Radiative Power (FRP)')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Fire Locations Colored by FRP')\n",
    "\n",
    "# Scatter plot of fire locations colored by confidence\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.scatter(longitude, latitude, c=confidence, cmap='viridis', marker='o')\n",
    "plt.colorbar(label='Confidence Level')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Fire Locations Colored by Confidence')\n",
    "\n",
    "# Histogram of FRP values\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.hist(frp, bins=30, color='red')\n",
    "plt.xlabel('Fire Radiative Power (FRP)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of FRP Values')\n",
    "\n",
    "# Histogram of confidence levels\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.hist(confidence, bins=30, color='green')\n",
    "plt.xlabel('Confidence Level')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Confidence Levels')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique FRP values:\n",
      "[  17.673374   19.503208   22.523102   27.449251   30.935259   35.864765\n",
      "   41.33715    47.744495   60.081417   71.88807    85.88533    86.5471\n",
      "   94.02927   101.431244  117.72214   118.31452   132.65257   149.19609\n",
      "  151.51901   174.34921   225.30533   296.08765   324.10748   325.8158\n",
      "  351.4247    371.63098   425.24713   442.2041    461.38907   491.3017\n",
      "  574.7548    576.04395   616.5151    632.84686   653.1302    671.8993\n",
      "  702.475     752.5529    757.1603    820.87555   866.366     911.9679\n",
      "  932.34485   946.7112   1025.246    1103.601    1167.0106   1173.3677\n",
      " 1175.5682   1919.4377   2575.13     2619.3596   2948.6772  ]\n",
      "Unique Confidence values:\n",
      "[  0  28  37  42  44  50  52  53  57  61  63  64  65  66  67  68  70  72\n",
      "  73  76  78  79  80  87  90  94  96  97 100]\n"
     ]
    }
   ],
   "source": [
    "# Print unique values for FRP\n",
    "unique_frp = np.unique(frp)\n",
    "print(\"Unique FRP values:\")\n",
    "print(unique_frp)\n",
    "\n",
    "# Print unique values for Confidence\n",
    "unique_confidence = np.unique(confidence)\n",
    "print(\"Unique Confidence values:\")\n",
    "print(unique_confidence)"
   ]
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    "# VIIRS data\n"
   ]
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   "source": [
    "# VIIRS Fire Data Features\n",
    "\n",
    "This notebook provides an overview of the key features found in VIIRS fire detection datasets, based on the VIIRS Active Fire User Guide. Each feature is described with its significance and possible values.\n",
    "https://viirsland.gsfc.nasa.gov/PDF/VIIRS_activefire_User_Guide.pdf#page=8.92\n",
    "\n",
    "## 1. Longitude and Latitude\n",
    "- **Description**: Geographic coordinates indicating the position of each detected fire pixel.\n",
    "- **Format**: Degrees (°)\n",
    "- **Range**:\n",
    "  - Longitude: -180° to +180°\n",
    "  - Latitude: -90° to +90°\n",
    "  \n",
    "## 2. Time\n",
    "- **Description**: The timestamp of when the VIIRS sensor detected the fire pixel.\n",
    "- **Format**: UTC (Coordinated Universal Time)\n",
    "- **Use**: It is used to mark the exact time of fire detection, crucial for temporal analysis of fire events.\n",
    "\n",
    "## 3. Fire Mask (Fire/Land/Water/Burned Area/No Data)\n",
    "- **Description**: The fire mask assigns categories to each pixel based on whether it detected fire, land, water, or other conditions.\n",
    "- **Categories**:\n",
    "  - `0`: Not processed (missing data)\n",
    "  - `1`: Bowtie deletion (affected by scan geometry)\n",
    "  - `2`: Sun glint (solar reflection)\n",
    "  - `3`: Water\n",
    "  - `4`: Cloud-covered pixels\n",
    "  - `5`: Non-fire land pixel\n",
    "  - `6`: Unclassified (insufficient information)\n",
    "  - `7`: Low confidence fire pixel\n",
    "  - `8`: Nominal confidence fire pixel\n",
    "  - `9`: High confidence fire pixel\n",
    "\n",
    "## 4. FP_power (Fire Radiative Power - FRP)\n",
    "- **Description**: Fire Radiative Power (FRP) represents the amount of energy released by a fire, expressed in megawatts (MW). It provides a measure of fire intensity and can be used to estimate emissions.\n",
    "- **Format**: 32-bit floating point (float32)\n",
    "- **Range**: Typically ranges from >0 MW to around **5000 MW** for large fires.\n",
    "- **Use**: FRP is a critical metric for evaluating fire energy output and understanding fire behavior, intensity, and emissions.\n",
    "\n",
    "## 5. FP_confidence (Detection Confidence)\n",
    "- **Description**: Each fire detection is assigned a confidence level ranging from 0 to 100, representing the likelihood that the pixel represents a true fire.\n",
    "- **Format**: Unsigned 8-bit integer (uint8)\n",
    "- **Range**: \n",
    "  - **0**: Lowest confidence\n",
    "  - **100**: Highest confidence\n",
    "- **Use**: Detection confidence helps users filter out false positives or be more lenient depending on their tolerance for missing true fires. Higher values indicate greater confidence in the detection.\n",
    "\n",
    "## 6. Source\n",
    "- **Description**: Indicates the satellite and sensor responsible for detecting the fire pixel.\n",
    "- **Options**:\n",
    "  - **Suomi-NPP (VNP)**\n",
    "  - **NOAA-20**\n",
    "- **Use**: The source provides information on which satellite was used to collect the data.\n",
    "\n",
    "---\n",
    "\n",
    "This guide provides an overview of the main features of VIIRS fire data as outlined in the VIIRS Active Fire User Guide. Each feature is critical for interpreting and analyzing fire detection data, with updates to both **FP_power** and **FP_confidence** to reflect accurate details on fire radiative power and detection confidence.\n"
   ]
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   "cell_type": "code",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (phony_dim_0: 12149, phony_dim_1: 8, phony_dim_2: 8042,\n",
       "                    phony_dim_3: 269, phony_dim_4: 3216, phony_dim_5: 3200,\n",
       "                    phony_dim_6: 16)\n",
       "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
       "                                phony_dim_3, phony_dim_4, phony_dim_5,\n",
       "                                phony_dim_6\n",
       "Data variables: (12/36)\n",
       "    CMG_day        (phony_dim_0, phony_dim_1) uint16 288 160 1 0 0 ... 0 0 0 0\n",
       "    CMG_night      (phony_dim_2, phony_dim_1) uint16 288 160 12 0 0 ... 0 0 0 0\n",
       "    FP_AdjCloud    (phony_dim_3) uint8 0 0 0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 0 1\n",
       "    FP_AdjWater    (phony_dim_3) uint8 0 0 0 3 0 0 0 0 0 0 ... 0 1 0 0 0 1 1 0 0\n",
       "    FP_CMG_col     (phony_dim_3) int16 281 283 284 297 297 ... 252 252 258 254\n",
       "    FP_CMG_row     (phony_dim_3) int16 238 237 235 229 229 ... 165 165 163 164\n",
       "    ...             ...\n",
       "    fire mask      (phony_dim_4, phony_dim_5) uint8 ...\n",
       "    qhist07        (phony_dim_6) uint32 0 0 0 0 5620835 543022 ... 0 0 0 0 0 0\n",
       "    qhist11        (phony_dim_6) uint32 0 295 5 0 0 0 0 0 1324992 0 0 0 0 0 0 0\n",
       "    qhist13        (phony_dim_6) uint32 0 0 0 0 125 84 0 0 1324992 0 0 0 0 0 0 0\n",
       "    qhist15        (phony_dim_6) uint32 0 0 2 0 0 0 0 0 1324992 0 0 0 0 0 0 0\n",
       "    qhist16        (phony_dim_6) uint32 0 0 1 0 0 0 0 0 1324992 0 0 0 0 0 0 0\n",
       "Attributes: (12/81)\n",
       "    FirePix:                           269\n",
       "    LandFirePix:                       269\n",
       "    WaterFirePix:                      0\n",
       "    MissingPix:                        0\n",
       "    LandPix:                           5940961\n",
       "    WaterPix:                          3025247\n",
       "    ...                                ...\n",
       "    RangeBeginningDate:                2018-11-08\n",
       "    PGE_EndTime:                       2018-11-08 20:42:00.000\n",
       "    project:                           VIIRS Land SIPS Active Fire Project\n",
       "    SensorShortname:                   VIIRS\n",
       "    GRingPointSequenceNo:              [1 2 3 4]\n",
       "    creator_name:                      VIIRS Land SIPS Processing Group</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9e5ba296-cd70-4b99-ba14-3dde79fef2b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9e5ba296-cd70-4b99-ba14-3dde79fef2b5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 12149</li><li><span>phony_dim_1</span>: 8</li><li><span>phony_dim_2</span>: 8042</li><li><span>phony_dim_3</span>: 269</li><li><span>phony_dim_4</span>: 3216</li><li><span>phony_dim_5</span>: 3200</li><li><span>phony_dim_6</span>: 16</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1149f2f5-0fae-41ad-acd7-99f842b86990' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1149f2f5-0fae-41ad-acd7-99f842b86990' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-be5f0a62-3d3a-4cea-9327-e1412f1d39ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-be5f0a62-3d3a-4cea-9327-e1412f1d39ff' class='xr-section-summary' >Data variables: <span>(36)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CMG_day</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6e3dbac-c226-4aca-b56e-6ecc39afffc2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6e3dbac-c226-4aca-b56e-6ecc39afffc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4170398d-c3ed-401c-9d9b-86292f8f6319' class='xr-var-data-in' type='checkbox'><label for='data-4170398d-c3ed-401c-9d9b-86292f8f6319' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[288, 160,   1, ...,   1,   0,   0],\n",
       "       [289, 160,  18, ...,  16,   0,   0],\n",
       "       [290, 160,  36, ...,  36,   0,   0],\n",
       "       ...,\n",
       "       [211, 261, 142, ...,   0,   0,   0],\n",
       "       [212, 261, 106, ...,   0,   0,   0],\n",
       "       [213, 261,  28, ...,   0,   0,   0]], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CMG_night</span></div><div class='xr-var-dims'>(phony_dim_2, phony_dim_1)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6fcef7d6-fc6c-4f7b-ab70-63fe7a5e1216' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fcef7d6-fc6c-4f7b-ab70-63fe7a5e1216' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6428f90-d589-469f-b125-7084c2ef46ef' class='xr-var-data-in' type='checkbox'><label for='data-a6428f90-d589-469f-b125-7084c2ef46ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[288, 160,  12, ...,   0,   0,   0],\n",
       "       [289, 160,  14, ...,   0,   0,   0],\n",
       "       [290, 160,  16, ...,   0,   0,   0],\n",
       "       ...,\n",
       "       [212, 261,  49, ...,   0,   0,   0],\n",
       "       [213, 261,  35, ...,   0,   0,   0],\n",
       "       [214, 261,   3, ...,   0,   0,   0]], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_AdjCloud</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cdf0ce96-9367-4bf5-90c0-a7fefb1bb47b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdf0ce96-9367-4bf5-90c0-a7fefb1bb47b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac865878-976f-471e-b61e-494dda15989f' class='xr-var-data-in' type='checkbox'><label for='data-ac865878-976f-471e-b61e-494dda15989f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of adjacent cloud pixels</dd><dt><span>valid_range :</span></dt><dd>[0 8]</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_AdjWater</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40891f95-95f6-4156-804c-074c9f0f665c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40891f95-95f6-4156-804c-074c9f0f665c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3b97c84-05b4-4458-be2f-240763715c95' class='xr-var-data-in' type='checkbox'><label for='data-a3b97c84-05b4-4458-be2f-240763715c95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of adjacent water pixels</dd><dt><span>valid_range :</span></dt><dd>[0 8]</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 1, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_CMG_col</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4985076-d3f1-48f3-afeb-f4c9cb7e1609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4985076-d3f1-48f3-afeb-f4c9cb7e1609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88c4c5bd-2d45-4376-89be-332c81195ede' class='xr-var-data-in' type='checkbox'><label for='data-88c4c5bd-2d45-4376-89be-332c81195ede' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>CMG column</dd></dl></div><div class='xr-var-data'><pre>array([281, 283, 284, ..., 252, 258, 254], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_CMG_row</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea06e41c-7aad-4a4b-9e43-4783b9bf3d40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea06e41c-7aad-4a4b-9e43-4783b9bf3d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-176fe3c6-ac5c-452d-869f-0ce1da81d0f3' class='xr-var-data-in' type='checkbox'><label for='data-176fe3c6-ac5c-452d-869f-0ce1da81d0f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>CMG row</dd></dl></div><div class='xr-var-data'><pre>array([238, 237, 235, ..., 165, 163, 164], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MAD_DT</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-562122fc-6ad7-4c8e-90e4-a38144e5225f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-562122fc-6ad7-4c8e-90e4-a38144e5225f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec67cde8-4631-4994-a276-f6953ec930f3' class='xr-var-data-in' type='checkbox'><label for='data-ec67cde8-4631-4994-a276-f6953ec930f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>background brightness temperature difference mean absolute deviation</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([0.276512, 0.199287, 1.087764, ..., 2.797724, 5.046178, 2.892559],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MAD_R7</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d194e96e-d3f9-4dd5-95b6-687b01737ea6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d194e96e-d3f9-4dd5-95b6-687b01737ea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-356ba357-7c95-4920-8e58-e6b5e0b43854' class='xr-var-data-in' type='checkbox'><label for='data-356ba357-7c95-4920-8e58-e6b5e0b43854' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>background M7 reflectance mean absolute deviationM\f��U</dd></dl></div><div class='xr-var-data'><pre>array([0.006939, 0.008414, 0.004396, ..., 0.017168, 0.037178, 0.044064],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MAD_T13</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a87f17e7-7cb2-40e6-b503-e15ee8d799e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a87f17e7-7cb2-40e6-b503-e15ee8d799e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c96badbc-f80e-4bf4-b300-843392ad91b8' class='xr-var-data-in' type='checkbox'><label for='data-c96badbc-f80e-4bf4-b300-843392ad91b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>background M13 brightness temperature mean absolute deviation</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([0.863387, 1.357348, 1.348509, ..., 2.795641, 4.947053, 1.423857],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MAD_T15</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7354473c-c635-423a-b788-60224067cc56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7354473c-c635-423a-b788-60224067cc56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e451f216-232a-434d-b78e-4080c68dc58a' class='xr-var-data-in' type='checkbox'><label for='data-e451f216-232a-434d-b78e-4080c68dc58a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>background M15 brightness temperature mean absolute deviation</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([0.732451, 1.231622, 0.900626, ..., 0.828212, 0.792411, 2.149637],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MeanDT</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c9ce6809-2dcf-46ab-8309-72ddb6ab1623' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9ce6809-2dcf-46ab-8309-72ddb6ab1623' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-106326d5-7bd1-4781-806d-f672bb844352' class='xr-var-data-in' type='checkbox'><label for='data-106326d5-7bd1-4781-806d-f672bb844352' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>mean background brightness temperature difference</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([-1.014592, -1.482863, -0.573016, ...,  0.308001,  5.603833,  1.195482],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MeanR7</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e5cea57a-3df4-47a1-bea9-ea7d6c97dd93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5cea57a-3df4-47a1-bea9-ea7d6c97dd93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ebd2289-8155-4bd8-8be1-2ef67f6102c5' class='xr-var-data-in' type='checkbox'><label for='data-8ebd2289-8155-4bd8-8be1-2ef67f6102c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>background M7 reflectance\u0007\t��U</dd></dl></div><div class='xr-var-data'><pre>array([0.137789, 0.155112, 0.132448, ..., 0.079878, 0.130234, 0.11165 ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MeanT13</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b6fa5cfd-03cf-458d-b0d9-d1d540636480' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6fa5cfd-03cf-458d-b0d9-d1d540636480' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd851883-aba8-40c7-b1cc-c5ece314668f' class='xr-var-data-in' type='checkbox'><label for='data-cd851883-aba8-40c7-b1cc-c5ece314668f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>M13 brightness temperature of backgroundA\u0001</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([299.31506, 303.6114 , 298.8648 , ..., 273.66418, 273.0543 , 272.63843],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_MeanT15</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-689e3a42-bc72-4452-be73-ed3966c50d79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-689e3a42-bc72-4452-be73-ed3966c50d79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb02efbd-d4c5-4f7d-8c54-419e57da176c' class='xr-var-data-in' type='checkbox'><label for='data-eb02efbd-d4c5-4f7d-8c54-419e57da176c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>M15 brightness temperature of background!</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([300.32965, 305.09424, 299.43784, ..., 273.35617, 267.45044, 271.44293],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_NumValid</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b16ff1fb-9d16-45e9-8239-3968e6b7a279' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b16ff1fb-9d16-45e9-8239-3968e6b7a279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9916657c-70af-4135-ae31-bde47e277e6e' class='xr-var-data-in' type='checkbox'><label for='data-9916657c-70af-4135-ae31-bde47e277e6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of valid background pixels</dd></dl></div><div class='xr-var-data'><pre>array([22, 22, 22, ..., 14, 22, 21], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_R7</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-405b3a14-6c74-42d8-805f-0fbd752cf2a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-405b3a14-6c74-42d8-805f-0fbd752cf2a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6de98b57-d562-41a9-93f8-e9f2328f0008' class='xr-var-data-in' type='checkbox'><label for='data-6de98b57-d562-41a9-93f8-e9f2328f0008' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>M7 reflectance of fire pixel�U</dd></dl></div><div class='xr-var-data'><pre>array([0.125168, 0.149778, 0.125508, ..., 0.108595, 0.12163 , 0.140062],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_RelAzAng</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e438f90d-c31e-4c5a-833a-951de3eb26ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e438f90d-c31e-4c5a-833a-951de3eb26ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc00a608-6d60-4ff3-b50c-5197909bbb6c' class='xr-var-data-in' type='checkbox'><label for='data-bc00a608-6d60-4ff3-b50c-5197909bbb6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative azimuth angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd></dl></div><div class='xr-var-data'><pre>array([-53.17    , -52.159996, -52.799995, ..., -60.729996, -60.699997,\n",
       "       -60.159996], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_SolZenAng</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d7d6ec06-23a3-4be3-a919-8d997848ad80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7d6ec06-23a3-4be3-a919-8d997848ad80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad782d78-c10f-4878-833b-f96f04d56c56' class='xr-var-data-in' type='checkbox'><label for='data-ad782d78-c10f-4878-833b-f96f04d56c56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>solar zenith angle��</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[  0. 180.]</dd></dl></div><div class='xr-var-data'><pre>array([52.41, 52.85, 53.34, ..., 67.24, 67.93, 67.63], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_T13</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f38318a8-4a46-4e44-be4a-4582d889693a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f38318a8-4a46-4e44-be4a-4582d889693a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41de9a60-309f-44c6-970e-f56b4d12f194' class='xr-var-data-in' type='checkbox'><label for='data-41de9a60-309f-44c6-970e-f56b4d12f194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>M13 brightness temperature of fire pixel!</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([315.84286, 319.15652, 313.23077, ..., 310.1062 , 305.7835 , 315.4683 ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_T15</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49a16248-4ece-4910-9050-a8a21a316ce2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49a16248-4ece-4910-9050-a8a21a316ce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de3092d8-dc6f-4461-9169-e8d0d089af41' class='xr-var-data-in' type='checkbox'><label for='data-de3092d8-dc6f-4461-9169-e8d0d089af41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>M15 brightness temperature of fire pixel!</dd><dt><span>units :</span></dt><dd>kelvins</dd></dl></div><div class='xr-var-data'><pre>array([303.4636 , 306.49136, 299.33234, ..., 275.6495 , 269.09027, 273.1455 ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_ViewZenAng</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22a0e5c4-15f7-49fc-b34f-77c46219c620' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22a0e5c4-15f7-49fc-b34f-77c46219c620' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fb64ee8-49ed-433b-82a2-a16e4dd1d9ea' class='xr-var-data-in' type='checkbox'><label for='data-8fb64ee8-49ed-433b-82a2-a16e4dd1d9ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>view zenith angle</dd><dt><span>valid_range :</span></dt><dd>[ 0. 70.]</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([25.09    , 28.869999, 29.99    , ..., 13.849999, 22.199999, 17.07    ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_WinSize</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e71741fb-ebc4-46ee-80ad-086364d33dcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e71741fb-ebc4-46ee-80ad-086364d33dcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9e20d89-7fc6-41e5-9797-ed843ad23705' class='xr-var-data-in' type='checkbox'><label for='data-e9e20d89-7fc6-41e5-9797-ed843ad23705' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>background window size</dd><dt><span>valid_range :</span></dt><dd>[ 5 21]</dd></dl></div><div class='xr-var-data'><pre>array([5, 5, 5, ..., 5, 5, 5], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_confidence</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b07412f-194a-4c66-a54e-862080fac8c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b07412f-194a-4c66-a54e-862080fac8c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cde42563-6caa-425f-84f5-3b638f05846a' class='xr-var-data-in' type='checkbox'><label for='data-cde42563-6caa-425f-84f5-3b638f05846a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>detection confidence</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_range :</span></dt><dd>[  0 100]</dd></dl></div><div class='xr-var-data'><pre>array([72, 75, 68, ..., 66, 62, 71], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_land</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e42ab5ff-0491-45e9-81cd-dd18f874156b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e42ab5ff-0491-45e9-81cd-dd18f874156b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59043680-cae1-40d6-8fa9-b3059402d16f' class='xr-var-data-in' type='checkbox'><label for='data-59043680-cae1-40d6-8fa9-b3059402d16f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land pixel flag</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, ..., 1, 1, 1], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_latitude</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3650b48b-90e2-44c9-9d02-32a23c4563a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3650b48b-90e2-44c9-9d02-32a23c4563a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10673c96-995d-4a76-8aef-f80cacad84b8' class='xr-var-data-in' type='checkbox'><label for='data-10673c96-995d-4a76-8aef-f80cacad84b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of fire pixel</dd><dt><span>valid_range :</span></dt><dd>[-90.  90.]</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([30.49449 , 30.716946, 31.232765, ..., 48.63014 , 49.00946 , 48.92631 ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_line</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-018bc63b-fe48-4aff-9888-3148c3a59a9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-018bc63b-fe48-4aff-9888-3148c3a59a9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f8c1b66-7189-4775-8686-aeb5f71e3236' class='xr-var-data-in' type='checkbox'><label for='data-5f8c1b66-7189-4775-8686-aeb5f71e3236' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>granule line of fire pixel˵�U</dd></dl></div><div class='xr-var-data'><pre>array([ 355,  377,  453, ..., 3172, 3201, 3206], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_longitude</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4d6193ff-360d-407e-959d-842ef3b6fc4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d6193ff-360d-407e-959d-842ef3b6fc4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-698e095a-c2d2-4968-9168-f8e988cf861e' class='xr-var-data-in' type='checkbox'><label for='data-698e095a-c2d2-4968-9168-f8e988cf861e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of fire pixel</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd></dl></div><div class='xr-var-data'><pre>array([-109.627945, -109.05372 , -108.98867 , ..., -116.83572 , -115.34224 ,\n",
       "       -116.32882 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_power</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6bf38692-601c-4041-8f14-8132ee401489' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bf38692-601c-4041-8f14-8132ee401489' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fea5d2bc-2bdc-4d68-9c74-84ccbfdd207b' class='xr-var-data-in' type='checkbox'><label for='data-fea5d2bc-2bdc-4d68-9c74-84ccbfdd207b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>fire radiative power</dd><dt><span>units :</span></dt><dd>MWn��U</dd></dl></div><div class='xr-var-data'><pre>array([ 9.679773, 11.169374,  9.01126 , ..., 10.933623, 10.104868, 14.806291],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FP_sample</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4efe086-1ca4-4f2f-9dfc-bc902ba2d274' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4efe086-1ca4-4f2f-9dfc-bc902ba2d274' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e3db74e-4de7-4948-8380-35a8d5d4c456' class='xr-var-data-in' type='checkbox'><label for='data-3e3db74e-4de7-4948-8380-35a8d5d4c456' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>granule sample of fire pixel</dd><dt><span>valid_range :</span></dt><dd>[   0 3199]</dd></dl></div><div class='xr-var-data'><pre>array([1188, 1127, 1109, ..., 1372, 1235, 1319], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>algorithm QA</span></div><div class='xr-var-dims'>(phony_dim_4, phony_dim_5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5603b9fd-995a-40cc-b708-ed5bb675ca59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5603b9fd-995a-40cc-b708-ed5bb675ca59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6feb67d-134d-447e-ad0e-cb09199472e4' class='xr-var-data-in' type='checkbox'><label for='data-c6feb67d-134d-447e-ad0e-cb09199472e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>bit field</dd></dl></div><div class='xr-var-data'><pre>[10291200 values with dtype=uint32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fire mask</span></div><div class='xr-var-dims'>(phony_dim_4, phony_dim_5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd2e62b2-18db-413e-adc7-e2841de2f543' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd2e62b2-18db-413e-adc7-e2841de2f543' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-777e4415-82c2-4941-aff4-647d6bb40515' class='xr-var-data-in' type='checkbox'><label for='data-777e4415-82c2-4941-aff4-647d6bb40515' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_range :</span></dt><dd>[0 9]</dd><dt><span>legend :</span></dt><dd>Classes:\n",
       "0 missing input data\n",
       "1 not processed (trim)\n",
       "2 not processed (unused)\n",
       "3 non-fire water\n",
       "4 cloud\n",
       "5 non-fire land\n",
       "6 unknown\n",
       "7 fire (low confidence)\n",
       "8 fire (nominal confidence)\n",
       "9 fire (high confidence)�ė���</dd></dl></div><div class='xr-var-data'><pre>[10291200 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhist07</span></div><div class='xr-var-dims'>(phony_dim_6)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a4b2d8b-9ec5-472e-b68b-ff6ed7e5045b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a4b2d8b-9ec5-472e-b68b-ff6ed7e5045b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5665eb0-7fda-47ff-b032-2d6a4b16a8c6' class='xr-var-data-in' type='checkbox'><label for='data-c5665eb0-7fda-47ff-b032-2d6a4b16a8c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([      0,       0,       0,       0, 5620835,  543022,  143186,       0,\n",
       "       1324992,       0,       0,       0,       0,       0,       0,       0],\n",
       "      dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhist11</span></div><div class='xr-var-dims'>(phony_dim_6)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c141886d-adfa-4f94-bb25-265d3e5975ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c141886d-adfa-4f94-bb25-265d3e5975ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6fe8f9f-dbe8-41f5-8a6e-39d79659fa9b' class='xr-var-data-in' type='checkbox'><label for='data-f6fe8f9f-dbe8-41f5-8a6e-39d79659fa9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([      0,     295,       5,       0,       0,       0,       0,       0,\n",
       "       1324992,       0,       0,       0,       0,       0,       0,       0],\n",
       "      dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhist13</span></div><div class='xr-var-dims'>(phony_dim_6)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-610d5c53-5046-4bc6-b71f-6d58bba77be6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-610d5c53-5046-4bc6-b71f-6d58bba77be6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65d03619-2ca3-4844-b4be-7db6615df9a9' class='xr-var-data-in' type='checkbox'><label for='data-65d03619-2ca3-4844-b4be-7db6615df9a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([      0,       0,       0,       0,     125,      84,       0,       0,\n",
       "       1324992,       0,       0,       0,       0,       0,       0,       0],\n",
       "      dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhist15</span></div><div class='xr-var-dims'>(phony_dim_6)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e0c0921-0a80-46ab-898c-418592572e43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e0c0921-0a80-46ab-898c-418592572e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c416158a-1cf7-4b0f-92aa-9f1c6cc44d49' class='xr-var-data-in' type='checkbox'><label for='data-c416158a-1cf7-4b0f-92aa-9f1c6cc44d49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([      0,       0,       2,       0,       0,       0,       0,       0,\n",
       "       1324992,       0,       0,       0,       0,       0,       0,       0],\n",
       "      dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhist16</span></div><div class='xr-var-dims'>(phony_dim_6)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9abaeb1e-b379-411d-8e6f-ed155c0e0de8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9abaeb1e-b379-411d-8e6f-ed155c0e0de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fc0a181-35bf-49e9-8d6c-51ad928c83a7' class='xr-var-data-in' type='checkbox'><label for='data-8fc0a181-35bf-49e9-8d6c-51ad928c83a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([      0,       0,       1,       0,       0,       0,       0,       0,\n",
       "       1324992,       0,       0,       0,       0,       0,       0,       0],\n",
       "      dtype=uint32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3aeb7de8-00e8-401b-84e8-6766d3cd3b0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-3aeb7de8-00e8-401b-84e8-6766d3cd3b0f' class='xr-section-summary' >Attributes: <span>(81)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>FirePix :</span></dt><dd>269</dd><dt><span>LandFirePix :</span></dt><dd>269</dd><dt><span>WaterFirePix :</span></dt><dd>0</dd><dt><span>MissingPix :</span></dt><dd>0</dd><dt><span>LandPix :</span></dt><dd>5940961</dd><dt><span>WaterPix :</span></dt><dd>3025247</dd><dt><span>WaterAdjacentFirePix :</span></dt><dd>19</dd><dt><span>CloudAdjacentFirePix :</span></dt><dd>3</dd><dt><span>UnknownLandPix :</span></dt><dd>0</dd><dt><span>UnknownWaterPix :</span></dt><dd>77309</dd><dt><span>LandCloudPix :</span></dt><dd>1381291</dd><dt><span>WaterCloudPix :</span></dt><dd>293443</dd><dt><span>GlintPix :</span></dt><dd>0</dd><dt><span>GlintRejectedPix :</span></dt><dd>0</dd><dt><span>CoastRejectedLandPix :</span></dt><dd>20</dd><dt><span>HotSurfRejectedPix :</span></dt><dd>0</dd><dt><span>ClearingRejectedPix :</span></dt><dd>0</dd><dt><span>CoastRejectedWaterPix :</span></dt><dd>0</dd><dt><span>DayPix :</span></dt><dd>8966208</dd><dt><span>NightPix :</span></dt><dd>0</dd><dt><span>MissingRadPix :</span></dt><dd>0</dd><dt><span>MissingRefPix :</span></dt><dd>0</dd><dt><span>MissingGeoPix :</span></dt><dd>0</dd><dt><span>TrimmedPix :</span></dt><dd>1324992</dd><dt><span>Satellite :</span></dt><dd>JPSS-1</dd><dt><span>ProcessVersionNumber :</span></dt><dd>1.6.1</dd><dt><span>InputFileL1B :</span></dt><dd>/MODAPSops7/archive/f20067/running/VJ1_L1glu/31963231/VJ102CCMOD.A2018312.2036.021.2022294180356.nc</dd><dt><span>InputFileGeo :</span></dt><dd>/MODAPSops7/archive/f20067/running/VJ1_L1glu/31963231/VJ103MOD.A2018312.2036.021.2021034154726.nc\u0015$sF</dd><dt><span>SystemID :</span></dt><dd>Linux minion20067 5.4.0-1062-fips #70-Ubuntu SMP Mon Sep 26 18:37:48 UTC 2022 x86_64</dd><dt><span>Unagg_GRingLatitude :</span></dt><dd>43.95516,49.88363,29.09851,24.56887</dd><dt><span>Unagg_GRingLongitude :</span></dt><dd>-137.86559,-98.39822,-97.05113,-127.12839</dd><dt><span>publisher_name :</span></dt><dd>LP DAAC</dd><dt><span>institution :</span></dt><dd>NASA Goddard Space Flight Center</dd><dt><span>RangeBeginningTime :</span></dt><dd>20:36:00.000000</dd><dt><span>PlatformShortName :</span></dt><dd>JPSS-1</dd><dt><span>publisher_email :</span></dt><dd>lpdaac@usgs.gov</dd><dt><span>license :</span></dt><dd>http://science.nasa.gov/earth-science/earth-science-data/data-information-policy/</dd><dt><span>cdm_data_type :</span></dt><dd>swath</dd><dt><span>PGENumber :</span></dt><dd>530</dd><dt><span>EndTime :</span></dt><dd>2018-11-08 20:42:00.000</dd><dt><span>creator_url :</span></dt><dd>https://ladsweb.modaps.eosdis.nasa.gov</dd><dt><span>PGE_StartTime :</span></dt><dd>2018-11-08 20:36:00.000</dd><dt><span>LocalGranuleID :</span></dt><dd>VJ114.A2018312.2036.002.2022294181925.nc</dd><dt><span>LongName :</span></dt><dd>VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 750m</dd><dt><span>stdname_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>AlgorithmType :</span></dt><dd>SCI</dd><dt><span>RangeEndingDate :</span></dt><dd>2018-11-08</dd><dt><span>ShortName :</span></dt><dd>VJ114</dd><dt><span>PGE_Name :</span></dt><dd>PGE530</dd><dt><span>title :</span></dt><dd>VIIRS Active Fires</dd><dt><span>PGEVersion :</span></dt><dd>2.0.4</dd><dt><span>ProductionTime :</span></dt><dd>2022-10-21 18:19:25.000</dd><dt><span>ProcessingEnvironment :</span></dt><dd>Linux minion20067 5.4.0-1062-fips #70-Ubuntu SMP Mon Sep 26 18:37:48 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux</dd><dt><span>GRingPointLatitude :</span></dt><dd>[43.9552 49.8836 29.0985 24.5689]</dd><dt><span>SatelliteInstrument :</span></dt><dd>JP1VIIRS</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.gsfc.VIIRSland</dd><dt><span>SouthBoundingCoordinate :</span></dt><dd>24.5689</dd><dt><span>ProcessingCenter :</span></dt><dd>LandSIPS</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>https://doi.org</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>StartTime :</span></dt><dd>2018-11-08 20:36:00.000</dd><dt><span>AlgorithmVersion :</span></dt><dd>NPP_PR14 1.6.1</dd><dt><span>GRingPointLongitude :</span></dt><dd>[-137.866   -98.3982  -97.0511 -127.128 ]</dd><dt><span>VersionID :</span></dt><dd>002</dd><dt><span>processing_level :</span></dt><dd>Level 2</dd><dt><span>WestBoundingCoordinate :</span></dt><dd>-137.866</dd><dt><span>NorthBoundingCoordinate :</span></dt><dd>49.9252</dd><dt><span>publisher_url :</span></dt><dd>https://lpdaac.usgs.gov</dd><dt><span>InputPointer :</span></dt><dd>VJ102CCMOD.A2018312.2036.021.2022294180356.nc, VJ103MOD.A2018312.2036.021.2021034154726.nc</dd><dt><span>EastBoundingCoordinate :</span></dt><dd>-97.0511</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/VIIRS/VJ114.002</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>DayNightFlag :</span></dt><dd>Day</dd><dt><span>RangeEndingTime :</span></dt><dd>20:42:00.000000</dd><dt><span>creator_email :</span></dt><dd>modis-ops@lists.nasa.gov</dd><dt><span>RangeBeginningDate :</span></dt><dd>2018-11-08</dd><dt><span>PGE_EndTime :</span></dt><dd>2018-11-08 20:42:00.000</dd><dt><span>project :</span></dt><dd>VIIRS Land SIPS Active Fire Project</dd><dt><span>SensorShortname :</span></dt><dd>VIIRS</dd><dt><span>GRingPointSequenceNo :</span></dt><dd>[1 2 3 4]</dd><dt><span>creator_name :</span></dt><dd>VIIRS Land SIPS Processing Group</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (phony_dim_0: 12149, phony_dim_1: 8, phony_dim_2: 8042,\n",
       "                    phony_dim_3: 269, phony_dim_4: 3216, phony_dim_5: 3200,\n",
       "                    phony_dim_6: 16)\n",
       "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
       "                                phony_dim_3, phony_dim_4, phony_dim_5,\n",
       "                                phony_dim_6\n",
       "Data variables: (12/36)\n",
       "    CMG_day        (phony_dim_0, phony_dim_1) uint16 ...\n",
       "    CMG_night      (phony_dim_2, phony_dim_1) uint16 ...\n",
       "    FP_AdjCloud    (phony_dim_3) uint8 ...\n",
       "    FP_AdjWater    (phony_dim_3) uint8 ...\n",
       "    FP_CMG_col     (phony_dim_3) int16 ...\n",
       "    FP_CMG_row     (phony_dim_3) int16 ...\n",
       "    ...             ...\n",
       "    fire mask      (phony_dim_4, phony_dim_5) uint8 ...\n",
       "    qhist07        (phony_dim_6) uint32 ...\n",
       "    qhist11        (phony_dim_6) uint32 ...\n",
       "    qhist13        (phony_dim_6) uint32 ...\n",
       "    qhist15        (phony_dim_6) uint32 ...\n",
       "    qhist16        (phony_dim_6) uint32 ...\n",
       "Attributes: (12/81)\n",
       "    FirePix:                           269\n",
       "    LandFirePix:                       269\n",
       "    WaterFirePix:                      0\n",
       "    MissingPix:                        0\n",
       "    LandPix:                           5940961\n",
       "    WaterPix:                          3025247\n",
       "    ...                                ...\n",
       "    RangeBeginningDate:                2018-11-08\n",
       "    PGE_EndTime:                       2018-11-08 20:42:00.000\n",
       "    project:                           VIIRS Land SIPS Active Fire Project\n",
       "    SensorShortname:                   VIIRS\n",
       "    GRingPointSequenceNo:              [1 2 3 4]\n",
       "    creator_name:                      VIIRS Land SIPS Processing Group"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read .nc file\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "file_path = 'VJ114.A2018312.2036.002.2022294181925.nc'\n",
    "dataset = xr.open_dataset(file_path)\n",
    "dataset \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "import xarray as xr\n",
    "\n",
    "# Define class names with numbers\n",
    "class_names = {\n",
    "    0: '(0) Not processed (missing data)',\n",
    "    1: '(1) Bowtie deletion',\n",
    "    2: '(2) Sun glint',\n",
    "    3: '(3) Water',\n",
    "    4: '(4) Cloud-covered pixels',\n",
    "    5: '(5) Land pixel',\n",
    "    6: '(6) Unclassified',\n",
    "    7: '(7) Low confidence fire pixel',\n",
    "    8: '(8) Nominal confidence fire pixel',\n",
    "    9: '(9) High confidence fire pixel'\n",
    "}\n",
    "\n",
    "# Define distinct colors for each class\n",
    "distinct_colors = [\n",
    "    '#000000',  # Black for not processed (missing data)\n",
    "    '#4B0082',  # Indigo for bowtie deletion\n",
    "    '#FF00FF',  # Magenta for sun glint\n",
    "    '#1E90FF',  # DodgerBlue for water\n",
    "    '#FFFFFF',  # White for cloud-covered pixels\n",
    "    '#228B22',  # ForestGreen for non-fire land pixel\n",
    "    '#808080',  # Gray for unclassified\n",
    "    '#FFFF00',  # Yellow for low confidence fire pixel\n",
    "    '#FFA500',  # Orange for nominal confidence fire pixel\n",
    "    '#FF0000'   # Red for high confidence fire pixel\n",
    "]\n",
    "\n",
    "# File path to the NetCDF file\n",
    "file_path = 'VJ114.A2018312.2036.002.2022294181925.nc'\n",
    "\n",
    "# Open the NetCDF file\n",
    "dataset = xr.open_dataset(file_path)\n",
    "\n",
    "# Extract the fire mask data and flip it vertically\n",
    "fire_mask_data = np.flipud(dataset['fire mask'].values)\n",
    "\n",
    "# Create a custom colormap\n",
    "cmap = ListedColormap(distinct_colors)\n",
    "\n",
    "# Create norm to map values to colors\n",
    "bounds = np.arange(11) - 0.5\n",
    "norm = BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "# Increase figure size to accommodate larger legend\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Plotting the Fire Mask Data\n",
    "img = plt.imshow(fire_mask_data, cmap=cmap, norm=norm, origin='lower')\n",
    "\n",
    "# Add colorbar with larger font size\n",
    "cbar = plt.colorbar(img, label='Fire Mask Classes', ticks=range(10))\n",
    "cbar.set_ticklabels([class_names[i] for i in range(10)])\n",
    "cbar.ax.tick_params(labelsize=12)  # Increase label size\n",
    "cbar.set_label('Fire Mask Classes', size=14)  # Increase colorbar title size\n",
    "\n",
    "plt.title('VIIRS Fire Mask Visualization', fontsize=16)\n",
    "plt.xlabel('X Pixels', fontsize=14)\n",
    "plt.ylabel('Y Pixels', fontsize=14)\n",
    "\n",
    "# Adjust layout to prevent cutting off colorbar labels\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Close the dataset\n",
    "dataset.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "# Load the VIIRS data\n",
    "file_path = 'VJ114.A2018312.2036.002.2022294181925.nc'\n",
    "dataset = xr.open_dataset(file_path)\n",
    "\n",
    "# Extract relevant data\n",
    "latitude = dataset['FP_latitude'].values\n",
    "longitude = dataset['FP_longitude'].values\n",
    "frp = dataset['FP_power'].values  # Fire Radiative Power\n",
    "confidence = dataset['FP_confidence'].values  # Confidence level\n",
    "\n",
    "# Plotting the data\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Scatter plot of fire locations colored by FRP\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.scatter(longitude, latitude, c=frp, cmap='hot', marker='o')\n",
    "plt.colorbar(label='Fire Radiative Power (FRP)')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Fire Locations Colored by FRP')\n",
    "\n",
    "# Scatter plot of fire locations colored by confidence\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.scatter(longitude, latitude, c=confidence, cmap='viridis', marker='o')\n",
    "plt.colorbar(label='Confidence Level')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Fire Locations Colored by Confidence')\n",
    "\n",
    "# Histogram of FRP values\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.hist(frp, bins=30, color='red')\n",
    "plt.xlabel('Fire Radiative Power (FRP)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of FRP Values')\n",
    "\n",
    "# Histogram of confidence levels\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.hist(confidence, bins=30, color='green')\n",
    "plt.xlabel('Confidence Level')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Confidence Levels')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}