{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "215c6c96-6dde-4e63-b400-42e1733758b8",
   "metadata": {},
   "source": [
    "# User defined Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "spatial-tonight",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pystare\n",
    "import starepandas\n",
    "\n",
    "import geopandas\n",
    "import numpy\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "roman-recording",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "DEFAULT_DPI = mpl.rcParamsDefault['figure.dpi']\n",
    "mpl.rcParams['figure.dpi'] = 1.5 * DEFAULT_DPI\n",
    "mpl.rcParams['legend.fontsize'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "precious-newspaper",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MOD09GA|MYD09GA': starepandas.io.granules.modis.Mod09GA,\n",
       " 'MOD05|MYD05': starepandas.io.granules.modis.Mod05,\n",
       " 'MOD03|MYD03': starepandas.io.granules.modis.Mod03,\n",
       " 'MOD09|MYD09': starepandas.io.granules.modis.Mod09,\n",
       " 'VNP02DNB|VJ102DNB': starepandas.io.granules.viirsl2.VNP02DNB,\n",
       " 'VNP03DNB|VJ103DNB': starepandas.io.granules.viirsl2.VNP03DNB,\n",
       " 'VNP03MOD|VJ103MOD': starepandas.io.granules.viirsl2.VNP03MOD,\n",
       " 'VNP03IMG|VJ103IMG': starepandas.io.granules.viirsl2.VNP03IMG,\n",
       " 'VNP09': starepandas.io.granules.viirsl2.VNP09,\n",
       " 'CLDMSKL2VIIRS': starepandas.io.granules.viirsl2.CLDMSKL2VIIRS,\n",
       " 'SSMIS': starepandas.io.granules.ssmis.SSMIS,\n",
       " 'ATMS': starepandas.io.granules.atms.ATMS,\n",
       " 'MODXX': __main__.ModXX}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import starepandas.io.granules.modis\n",
    "\n",
    "class ModXX(starepandas.io.granules.modis.Modis):\n",
    "\n",
    "    def __init__(self, file_path, sidecar_path=None):\n",
    "        super(ModXX, self).__init__(file_path, sidecar_path)\n",
    "        self.nom_res = '5km'\n",
    "\n",
    "    def read_data(self):\n",
    "        dataset_names = ['Scan_Start_Time', 'Solar_Zenith', 'Solar_Azimuth',\n",
    "                         'Sensor_Zenith', 'Sensor_Azimuth', 'Water_Vapor_Infrared']\n",
    "\n",
    "        dataset_names2 = ['Cloud_Mask_QA', 'Water_Vapor_Near_Infrared',\n",
    "                          'Water_Vaport_Corretion_Factors', 'Quality_Assurance_Near_Infrared', 'Quality_Assurance_Infrared']\n",
    "        for dataset_name in dataset_names:\n",
    "            self.data[dataset_name] = self.hdf.select(dataset_name).get()\n",
    "            \n",
    "starepandas.io.granules.granule_factory_library['MODXX']=ModXX\n",
    "\n",
    "starepandas.io.granules.granule_factory_library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "transparent-boston",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "worldwide-calcium",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "granule_name=\"s3://eis-dh-fire/mod14/raw/MOD14.A2020214.0520.061.2020340090643.hdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "antique-cancer",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "granule_name=\"../starepandas//datasets/MODXX.hdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "anonymous-price",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod = starepandas.read_granule(granule_name, sidecar=False, latlon=False, add_stare=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "minimal-hepatitis",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod = starepandas.read_granule(granule_name, sidecar=True, latlon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "attractive-electricity",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod['stare_hex']=mod['sids'].apply(hex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "viral-windows",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod['wiv0']=mod['Water_Vapor_Infrared'].apply(lambda x: min(0,x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "incomplete-greeting",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod['pod']=mod['sids'].apply(lambda sid: pystare.spatial_clear_to_resolution(pystare.spatial_coerce_resolution(sid,7)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "chief-container",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod['pod_hex']=mod['pod'].apply(lambda sid: hex(sid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "exempt-article",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>sids</th>\n",
       "      <th>Scan_Start_Time</th>\n",
       "      <th>Solar_Zenith</th>\n",
       "      <th>Solar_Azimuth</th>\n",
       "      <th>Sensor_Zenith</th>\n",
       "      <th>Sensor_Azimuth</th>\n",
       "      <th>Water_Vapor_Infrared</th>\n",
       "      <th>stare_hex</th>\n",
       "      <th>wiv0</th>\n",
       "      <th>pod</th>\n",
       "      <th>pod_hex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>53.201778</td>\n",
       "      <td>-15.933996</td>\n",
       "      <td>4298473764500464809</td>\n",
       "      <td>8.493984e+08</td>\n",
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       "      <td>53.203171</td>\n",
       "      <td>-16.288101</td>\n",
       "      <td>4298458168380511209</td>\n",
       "      <td>8.493984e+08</td>\n",
       "      <td>14700</td>\n",
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       "      <td>53.203518</td>\n",
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       "      <td>53.202934</td>\n",
       "      <td>-16.957928</td>\n",
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       "      <td>8.493984e+08</td>\n",
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       "      <td>0x3ba31437b15cedc9</td>\n",
       "      <td>-9999</td>\n",
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       "      <td>0x3ba3000000000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.201508</td>\n",
       "      <td>-17.275316</td>\n",
       "      <td>4297290857922121161</td>\n",
       "      <td>8.493984e+08</td>\n",
       "      <td>14676</td>\n",
       "      <td>-2516</td>\n",
       "      <td>6291</td>\n",
       "      <td>-9059</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0x3ba30b446afb4dc9</td>\n",
       "      <td>-9999</td>\n",
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       "      <td>0x33bbe00000000007</td>\n",
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       "    <tr>\n",
       "      <th>109619</th>\n",
       "      <td>63.551617</td>\n",
       "      <td>-67.589508</td>\n",
       "      <td>3727853163225616425</td>\n",
       "      <td>8.493987e+08</td>\n",
       "      <td>12115</td>\n",
       "      <td>-7625</td>\n",
       "      <td>6464</td>\n",
       "      <td>4682</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0x33bbfeb370e11029</td>\n",
       "      <td>-9999</td>\n",
       "      <td>3727819407183839239</td>\n",
       "      <td>0x33bbe00000000007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109620 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              lat        lon                 sids  Scan_Start_Time   \n",
       "0       53.201778 -15.933996  4298473764500464809     8.493984e+08  \\\n",
       "1       53.203171 -16.288101  4298458168380511209     8.493984e+08   \n",
       "2       53.203518 -16.629105  4297394569014717897     8.493984e+08   \n",
       "3       53.202934 -16.957928  4297300698872999369     8.493984e+08   \n",
       "4       53.201508 -17.275316  4297290857922121161     8.493984e+08   \n",
       "...           ...        ...                  ...              ...   \n",
       "109615  64.084297 -66.281410  3727686638370615689     8.493987e+08   \n",
       "109616  63.960953 -66.591354  3727661374545790857     8.493987e+08   \n",
       "109617  63.831799 -66.911255  3727838256925064969     8.493987e+08   \n",
       "109618  63.698635 -67.236229  3727843063731949801     8.493987e+08   \n",
       "109619  63.551617 -67.589508  3727853163225616425     8.493987e+08   \n",
       "\n",
       "        Solar_Zenith  Solar_Azimuth  Sensor_Zenith  Sensor_Azimuth   \n",
       "0              14709          -2296           6534           -8952  \\\n",
       "1              14700          -2354           6472           -8980   \n",
       "2              14692          -2410           6411           -9008   \n",
       "3              14684          -2464           6351           -9034   \n",
       "4              14676          -2516           6291           -9059   \n",
       "...              ...            ...            ...             ...   \n",
       "109615         12157          -7468           6223            4800   \n",
       "109616         12147          -7505           6282            4772   \n",
       "109617         12137          -7544           6342            4743   \n",
       "109618         12126          -7583           6402            4714   \n",
       "109619         12115          -7625           6464            4682   \n",
       "\n",
       "        Water_Vapor_Infrared           stare_hex  wiv0                  pod   \n",
       "0                      -9999  0x3ba73f1d5162e4a9 -9999  4298439553720516615  \\\n",
       "1                      -9999  0x3ba730ee103253e9 -9999  4298439553720516615   \n",
       "2                      -9999  0x3ba3699789d0adc9 -9999  4297384022557851655   \n",
       "3                      -9999  0x3ba31437b15cedc9 -9999  4297278469441585159   \n",
       "4                      -9999  0x3ba30b446afb4dc9 -9999  4297278469441585159   \n",
       "...                      ...                 ...   ...                  ...   \n",
       "109615                 -9999  0x33bb673f5a0cf189 -9999  3727678669695483911   \n",
       "109616                 -9999  0x33bb504528bf0789 -9999  3727643485323395079   \n",
       "109617                 -9999  0x33bbf124cc213f09 -9999  3727819407183839239   \n",
       "109618                 -9999  0x33bbf583f82590e9 -9999  3727819407183839239   \n",
       "109619                 -9999  0x33bbfeb370e11029 -9999  3727819407183839239   \n",
       "\n",
       "                   pod_hex  \n",
       "0       0x3ba7200000000007  \n",
       "1       0x3ba7200000000007  \n",
       "2       0x3ba3600000000007  \n",
       "3       0x3ba3000000000007  \n",
       "4       0x3ba3000000000007  \n",
       "...                    ...  \n",
       "109615  0x33bb600000000007  \n",
       "109616  0x33bb400000000007  \n",
       "109617  0x33bbe00000000007  \n",
       "109618  0x33bbe00000000007  \n",
       "109619  0x33bbe00000000007  \n",
       "\n",
       "[109620 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "social-dynamics",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "granule_name=\"../starepandas/datasets/MOD05_L2.A2019336.0000.061.2019336211522.hdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "square-dodge",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mod = starepandas.read_granule(granule_name, sidecar=True, latlon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "invalid-energy",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "mod.set_trixels(mod.make_trixels(), inplace=True)\n",
    "mod.plot(ax=ax, trixels=True\n",
    "         ,boundary=False, column='Water_Vapor_Infrared', linewidth=0.2\n",
    "         ,cmap='rainbow')\n",
    "\n",
    "del ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "resident-optimum",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MOD09GA|MYD09GA': starepandas.io.granules.modis.Mod09GA,\n",
       " 'MOD05|MYD05': starepandas.io.granules.modis.Mod05,\n",
       " 'MOD03|MYD03': starepandas.io.granules.modis.Mod03,\n",
       " 'MOD09|MYD09': starepandas.io.granules.modis.Mod09,\n",
       " 'VNP02DNB|VJ102DNB': starepandas.io.granules.viirsl2.VNP02DNB,\n",
       " 'VNP03DNB|VJ103DNB': starepandas.io.granules.viirsl2.VNP03DNB,\n",
       " 'VNP03MOD|VJ103MOD': starepandas.io.granules.viirsl2.VNP03MOD,\n",
       " 'VNP03IMG|VJ103IMG': starepandas.io.granules.viirsl2.VNP03IMG,\n",
       " 'VNP09': starepandas.io.granules.viirsl2.VNP09,\n",
       " 'CLDMSKL2VIIRS': starepandas.io.granules.viirsl2.CLDMSKL2VIIRS,\n",
       " 'SSMIS': starepandas.io.granules.ssmis.SSMIS,\n",
       " 'ATMS': starepandas.io.granules.atms.ATMS,\n",
       " 'MODXX': __main__.ModXX,\n",
       " 'VNP02IMG': __main__.VNP02IMG,\n",
       " 'VNP03IMG': __main__.VNP03IMG}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from starepandas.io.granules.viirsl2 import VIIRSL2\n",
    "\n",
    "class VNP03IMG(VIIRSL2):\n",
    "\n",
    "    def __init__(self, file_path, sidecar_path=None):\n",
    "        super(VNP03IMG,self).__init__(file_path, sidecar_path)\n",
    "\n",
    "    def read_data(self):\n",
    "        \"\"\"                                                                                                                                                                    \n",
    "        reads the data from a VNP03IMG granule into the self.data dictionary.                                                                                                  \n",
    "        Two variables are read:                                                                                                                                              \n",
    "                                                                                                                                                                               \n",
    "        a) land_water_mask                                                                                                                                                     \n",
    "        1: Shallow_Ocean 2: Land 3: Coastline 4: Shallow_Inland 5: Ephemeral 6: Deep_Inland 7: Continental 8: Deep_Ocean                                                       \n",
    "        b) quality_flag                                                                                                                                                        \n",
    "        1: Input_invalid 2: Pointing_bad 3: Terrain_bad                                                                                                                        \n",
    "        :return:                                                                                                                                                               \n",
    "        \"\"\"\n",
    "\n",
    "        group = self.netcdf.groups['geolocation_data']\n",
    "        self.data['land_water_mask'] = group['land_water_mask'][:].data\n",
    "        self.data['quality_flag'] = group['quality_flag'][:].data\n",
    "\n",
    "\n",
    "class VNP02IMG(VIIRSL2):\n",
    "\n",
    "    def __init__(self, file_path, sidecar_path=None):\n",
    "        super(VNP02IMG, self).__init__(file_path, sidecar_path)\n",
    "        self.companion_prefix = 'VNP03IMG'\n",
    "\n",
    "    def read_data(self):\n",
    "        for band in ['I04','I05']:\n",
    "            IMG = self.netcdf.groups['observation_data'][band][:].data\n",
    "            quality_flags = self.netcdf.groups['observation_data'][band+'_quality_flags'][:].data\n",
    "            \n",
    "            # TODO Do we need to allocate these first?\n",
    "            #\n",
    "            self.data[band+'_observations']  = IMG\n",
    "            self.data[band+'_quality_flags'] = quality_flags\n",
    "\n",
    "    def latlon(self):\n",
    "        pass\n",
    "\n",
    "    def read_sidecar_cover(self, sidecar_path=None):\n",
    "        pass\n",
    "\n",
    "    def read_sidecar_index(self, sidecar_path=None):\n",
    "        pass\n",
    "\n",
    "starepandas.io.granules.granule_factory_library['VNP02IMG']=VNP02IMG\n",
    "starepandas.io.granules.granule_factory_library['VNP03IMG']=VNP03IMG\n",
    "\n",
    "starepandas.io.granules.granule_factory_library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "greenhouse-pharmacology",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "granule_name = \"../tests/data/granules/viirs/VNP03DNB.A2022308.1930.002.2022309041547.nc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "found-hormone",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: OMP_NUM_THREADS=8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'8'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%env OMP_NUM_THREADS=8\n",
    "%env OMP_NUM_THREADS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "structural-encounter",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: OMP_NUM_THREADS=4\n",
      "CPU times: user 1.79 s, sys: 2.02 s, total: 3.82 s\n",
      "Wall time: 4.14 s\n"
     ]
    }
   ],
   "source": [
    "%env OMP_NUM_THREADS=4\n",
    "vnp=None\n",
    "del vnp\n",
    "%time vnp = starepandas.read_granule(granule_name, sidecar=True, latlon=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00200680-3414-4be8-a970-513897790e04",
   "metadata": {},
   "source": [
    "# Creating PODs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a1819fd-be19-40ef-a78a-9335a962f595",
   "metadata": {},
   "outputs": [],
   "source": [
    "vnp = vnp[0:100_000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "environmental-application",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vnp['stare_hex']=vnp['sids'].apply(hex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "unable-texture",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vnp['pod']=vnp['sids'].apply(lambda sid: pystare.spatial_clear_to_resolution(pystare.spatial_coerce_resolution(sid, 7)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ethical-member",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vnp['pod_hex']=vnp['pod'].apply(lambda sid: hex(sid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e231bdf-9b96-43d2-be36-13a005c418d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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