{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6fb1386c",
   "metadata": {},
   "source": [
    "# Read Granules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d74b0c0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:09:15.850802Z",
     "iopub.status.busy": "2022-09-15T05:09:15.850545Z",
     "iopub.status.idle": "2022-09-15T05:09:17.462215Z",
     "shell.execute_reply": "2022-09-15T05:09:17.461677Z"
    }
   },
   "outputs": [],
   "source": [
    "import starepandas\n",
    "import netCDF4\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "import pandas\n",
    "import time\n",
    "import copy\n",
    "import geopandas\n",
    "import pystare\n",
    "import pyhdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6962199e",
   "metadata": {},
   "source": [
    "## Manually reading "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81aa6cf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:09:17.464789Z",
     "iopub.status.busy": "2022-09-15T05:09:17.464575Z",
     "iopub.status.idle": "2022-09-15T05:09:20.627222Z",
     "shell.execute_reply": "2022-09-15T05:09:20.626734Z"
    }
   },
   "outputs": [],
   "source": [
    "vnp03_name = '../tests/data/granules/VNP03DNB.A2020219.0742.001.2020219124651.nc'\n",
    "vnp03 = starepandas.io.granules.VNP03DNB(vnp03_name)\n",
    "vnp03.read_latlon()\n",
    "vnp03.read_data()\n",
    "vnp03 = vnp03.to_df(xy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c567ac5-758b-45ee-a71a-3f901220f011",
   "metadata": {},
   "source": [
    "## MOD09GA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a60b5bdf-a527-401e-bb4e-cb3dc368db92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:09:20.630008Z",
     "iopub.status.busy": "2022-09-15T05:09:20.629820Z",
     "iopub.status.idle": "2022-09-15T05:09:20.632493Z",
     "shell.execute_reply": "2022-09-15T05:09:20.632090Z"
    }
   },
   "outputs": [],
   "source": [
    "file_name = '../tests/data/granules/MOD09GA.A2020009.h00v08.006.2020011025435.hdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1eb575cb-43e2-4840-8c6d-20b653788d68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:09:20.634393Z",
     "iopub.status.busy": "2022-09-15T05:09:20.634170Z",
     "iopub.status.idle": "2022-09-15T05:09:24.356372Z",
     "shell.execute_reply": "2022-09-15T05:09:24.355792Z"
    }
   },
   "outputs": [
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    "starepandas.read_granule(file_name, xy=True)"
   ]
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       "      <td>0.2342</td>\n",
       "      <td>0.2225</td>\n",
       "      <td>0.2074</td>\n",
       "      <td>0.1464</td>\n",
       "      <td>0.0867</td>\n",
       "      <td>1073741824</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1849</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5759999</th>\n",
       "      <td>0.2174</td>\n",
       "      <td>0.2094</td>\n",
       "      <td>0.2342</td>\n",
       "      <td>0.2225</td>\n",
       "      <td>0.2074</td>\n",
       "      <td>0.1464</td>\n",
       "      <td>0.0867</td>\n",
       "      <td>1073741824</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1849</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5760000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sur_refl_b01_1  sur_refl_b02_1  sur_refl_b03_1  sur_refl_b04_1  \\\n",
       "0                   NaN             NaN             NaN             NaN   \n",
       "1                   NaN             NaN             NaN             NaN   \n",
       "2                   NaN             NaN             NaN             NaN   \n",
       "3                   NaN             NaN             NaN             NaN   \n",
       "4                   NaN             NaN             NaN             NaN   \n",
       "...                 ...             ...             ...             ...   \n",
       "5759995          0.2020          0.1939          0.2187          0.2053   \n",
       "5759996          0.2122          0.2043          0.2320          0.2174   \n",
       "5759997          0.2122          0.2043          0.2320          0.2174   \n",
       "5759998          0.2174          0.2094          0.2342          0.2225   \n",
       "5759999          0.2174          0.2094          0.2342          0.2225   \n",
       "\n",
       "         sur_refl_b05_1  sur_refl_b06_1  sur_refl_b07_1   QC_500m_1  \\\n",
       "0                   NaN             NaN             NaN        <NA>   \n",
       "1                   NaN             NaN             NaN        <NA>   \n",
       "2                   NaN             NaN             NaN        <NA>   \n",
       "3                   NaN             NaN             NaN        <NA>   \n",
       "4                   NaN             NaN             NaN        <NA>   \n",
       "...                 ...             ...             ...         ...   \n",
       "5759995          0.1872          0.1359          0.0817  1073741824   \n",
       "5759996          0.2015          0.1356          0.0900  1073741824   \n",
       "5759997          0.2015          0.1356          0.0900  1073741824   \n",
       "5759998          0.2074          0.1464          0.0867  1073741824   \n",
       "5759999          0.2074          0.1464          0.0867  1073741824   \n",
       "\n",
       "         obscov_500m_1  state_1km_1  cloud  cloud_shadow  cloud_internal  \\\n",
       "0                  NaN         <NA>   <NA>          True            True   \n",
       "1                  NaN         <NA>   <NA>          True            True   \n",
       "2                  NaN         <NA>   <NA>          True            True   \n",
       "3                  NaN         <NA>   <NA>          True            True   \n",
       "4                  NaN         <NA>   <NA>          True            True   \n",
       "...                ...          ...    ...           ...             ...   \n",
       "5759995           0.15         1849      1         False            True   \n",
       "5759996           0.19         1849      1         False            True   \n",
       "5759997           0.19         1849      1         False            True   \n",
       "5759998           0.11         1849      1         False            True   \n",
       "5759999           0.14         1849      1         False            True   \n",
       "\n",
       "         snow_mod35  snow_internal  \n",
       "0              True           True  \n",
       "1              True           True  \n",
       "2              True           True  \n",
       "3              True           True  \n",
       "4              True           True  \n",
       "...             ...            ...  \n",
       "5759995       False          False  \n",
       "5759996       False          False  \n",
       "5759997       False          False  \n",
       "5759998       False          False  \n",
       "5759999       False          False  \n",
       "\n",
       "[5760000 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod09ga = starepandas.io.granules.Mod09GA(file_name)\n",
    "mod09ga.read_data()\n",
    "\n",
    "ds_name = 'state_1km_1'\n",
    "mod09ga.read_dataset(ds_name, resample_factor=2)\n",
    "mod09ga.decode_state('state_1km_1')\n",
    "mod09ga.to_df()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfba25d6-1077-4c1f-90e6-43b28feb32ec",
   "metadata": {},
   "source": [
    "## MOD09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea8d3869-8e7d-4e0b-8ca6-32daa67eff44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:09:46.898376Z",
     "iopub.status.busy": "2022-09-15T05:09:46.898105Z",
     "iopub.status.idle": "2022-09-15T05:09:54.116509Z",
     "shell.execute_reply": "2022-09-15T05:09:54.115866Z"
    }
   },
   "outputs": [],
   "source": [
    "file_name = '../tests/data/granules/MOD09.A2002299.0710.006.2015151173939.hdf'\n",
    "mod09 = starepandas.read_granule(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "003a8e29-3416-4527-aaf0-516bb20139c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:09:54.119026Z",
     "iopub.status.busy": "2022-09-15T05:09:54.118826Z",
     "iopub.status.idle": "2022-09-15T05:10:00.853991Z",
     "shell.execute_reply": "2022-09-15T05:10:00.853487Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cloud</th>\n",
       "      <th>cloud_shadow</th>\n",
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       "      <th>2748615</th>\n",
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       "      <th>2748616</th>\n",
       "      <td>00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2748620 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cloud  cloud_shadow  cloud_internal  snow_mod35  snow_internal\n",
       "0          00         False           False       False          False\n",
       "1          00         False           False       False          False\n",
       "2          00         False           False       False          False\n",
       "3          00         False           False       False          False\n",
       "4          00         False           False       False          False\n",
       "...       ...           ...             ...         ...            ...\n",
       "2748615    00         False           False       False          False\n",
       "2748616    00         False           False       False          False\n",
       "2748617    00         False           False       False          False\n",
       "2748618    00         False           False       False          False\n",
       "2748619    00         False           False       False          False\n",
       "\n",
       "[2748620 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starepandas.io.granules.modis.decode_state(mod09['1km Reflectance Data State QA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f32d2eb",
   "metadata": {},
   "source": [
    "## MOD05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3f3faf4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:00.856967Z",
     "iopub.status.busy": "2022-09-15T05:10:00.856489Z",
     "iopub.status.idle": "2022-09-15T05:10:01.135265Z",
     "shell.execute_reply": "2022-09-15T05:10:01.134692Z"
    }
   },
   "outputs": [],
   "source": [
    "fname = '../tests/data/granules/MOD05_L2.A2019336.0000.061.2019336211522.hdf'\n",
    "mod05 = starepandas.read_granule(fname, sidecar=True, latlon=True, xy=True, read_timestamp=True)\n",
    "sids = mod05.to_array(mod05._sid_column_name)\n",
    "#mod05.to_sidecar('test.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a77e9851-5e55-4cfa-a9c0-89da5f449134",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:01.138268Z",
     "iopub.status.busy": "2022-09-15T05:10:01.137955Z",
     "iopub.status.idle": "2022-09-15T05:10:01.155440Z",
     "shell.execute_reply": "2022-09-15T05:10:01.154897Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
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       "      <td>2019-12-02</td>\n",
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       "      <td>64.109999</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.202934</td>\n",
       "      <td>-16.957928</td>\n",
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       "      <td>2019-12-02</td>\n",
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       "      <td>-24.639999</td>\n",
       "      <td>63.509999</td>\n",
       "      <td>-90.339998</td>\n",
       "      <td>NaN</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>2019-12-02</td>\n",
       "      <td>2019-12-02 00:05:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.493984e+08</td>\n",
       "      <td>146.759997</td>\n",
       "      <td>-25.159999</td>\n",
       "      <td>62.909999</td>\n",
       "      <td>-90.589998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>109615</th>\n",
       "      <td>64.084297</td>\n",
       "      <td>-66.281410</td>\n",
       "      <td>3727686638370615689</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>2019-12-02 00:05:00</td>\n",
       "      <td>265</td>\n",
       "      <td>405</td>\n",
       "      <td>8.493987e+08</td>\n",
       "      <td>121.569997</td>\n",
       "      <td>-74.679998</td>\n",
       "      <td>62.229999</td>\n",
       "      <td>47.999999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109616</th>\n",
       "      <td>63.960953</td>\n",
       "      <td>-66.591354</td>\n",
       "      <td>3727661374545790857</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>2019-12-02 00:05:00</td>\n",
       "      <td>266</td>\n",
       "      <td>405</td>\n",
       "      <td>8.493987e+08</td>\n",
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       "      <td>-75.049998</td>\n",
       "      <td>62.819999</td>\n",
       "      <td>47.719999</td>\n",
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       "    <tr>\n",
       "      <th>109617</th>\n",
       "      <td>63.831799</td>\n",
       "      <td>-66.911255</td>\n",
       "      <td>3727838256925064969</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>2019-12-02 00:05:00</td>\n",
       "      <td>267</td>\n",
       "      <td>405</td>\n",
       "      <td>8.493987e+08</td>\n",
       "      <td>121.369997</td>\n",
       "      <td>-75.439998</td>\n",
       "      <td>63.419999</td>\n",
       "      <td>47.429999</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>109618</th>\n",
       "      <td>63.698635</td>\n",
       "      <td>-67.236229</td>\n",
       "      <td>3727843063731949801</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>2019-12-02 00:05:00</td>\n",
       "      <td>268</td>\n",
       "      <td>405</td>\n",
       "      <td>8.493987e+08</td>\n",
       "      <td>121.259997</td>\n",
       "      <td>-75.829998</td>\n",
       "      <td>64.019999</td>\n",
       "      <td>47.139999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109619</th>\n",
       "      <td>63.551617</td>\n",
       "      <td>-67.589508</td>\n",
       "      <td>3727853163225616425</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>2019-12-02 00:05:00</td>\n",
       "      <td>269</td>\n",
       "      <td>405</td>\n",
       "      <td>8.493987e+08</td>\n",
       "      <td>121.149997</td>\n",
       "      <td>-76.249998</td>\n",
       "      <td>64.639999</td>\n",
       "      <td>46.819999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109620 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              lat        lon                 sids   ts_start  \\\n",
       "0       53.201778 -15.933996  4298473764500464809 2019-12-02   \n",
       "1       53.203171 -16.288101  4298458168380511209 2019-12-02   \n",
       "2       53.203518 -16.629105  4297394569014717897 2019-12-02   \n",
       "3       53.202934 -16.957928  4297300698872999369 2019-12-02   \n",
       "4       53.201508 -17.275316  4297290857922121161 2019-12-02   \n",
       "...           ...        ...                  ...        ...   \n",
       "109615  64.084297 -66.281410  3727686638370615689 2019-12-02   \n",
       "109616  63.960953 -66.591354  3727661374545790857 2019-12-02   \n",
       "109617  63.831799 -66.911255  3727838256925064969 2019-12-02   \n",
       "109618  63.698635 -67.236229  3727843063731949801 2019-12-02   \n",
       "109619  63.551617 -67.589508  3727853163225616425 2019-12-02   \n",
       "\n",
       "                    ts_end    x    y  Scan_Start_Time  Solar_Zenith  \\\n",
       "0      2019-12-02 00:05:00    0    0     8.493984e+08    147.089997   \n",
       "1      2019-12-02 00:05:00    1    0     8.493984e+08    146.999997   \n",
       "2      2019-12-02 00:05:00    2    0     8.493984e+08    146.919997   \n",
       "3      2019-12-02 00:05:00    3    0     8.493984e+08    146.839997   \n",
       "4      2019-12-02 00:05:00    4    0     8.493984e+08    146.759997   \n",
       "...                    ...  ...  ...              ...           ...   \n",
       "109615 2019-12-02 00:05:00  265  405     8.493987e+08    121.569997   \n",
       "109616 2019-12-02 00:05:00  266  405     8.493987e+08    121.469997   \n",
       "109617 2019-12-02 00:05:00  267  405     8.493987e+08    121.369997   \n",
       "109618 2019-12-02 00:05:00  268  405     8.493987e+08    121.259997   \n",
       "109619 2019-12-02 00:05:00  269  405     8.493987e+08    121.149997   \n",
       "\n",
       "        Solar_Azimuth  Sensor_Zenith  Sensor_Azimuth  Water_Vapor_Infrared  \n",
       "0          -22.959999      65.339999      -89.519998                   NaN  \n",
       "1          -23.539999      64.719999      -89.799998                   NaN  \n",
       "2          -24.099999      64.109999      -90.079998                   NaN  \n",
       "3          -24.639999      63.509999      -90.339998                   NaN  \n",
       "4          -25.159999      62.909999      -90.589998                   NaN  \n",
       "...               ...            ...             ...                   ...  \n",
       "109615     -74.679998      62.229999       47.999999                   NaN  \n",
       "109616     -75.049998      62.819999       47.719999                   NaN  \n",
       "109617     -75.439998      63.419999       47.429999                   NaN  \n",
       "109618     -75.829998      64.019999       47.139999                   NaN  \n",
       "109619     -76.249998      64.639999       46.819999                   NaN  \n",
       "\n",
       "[109620 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68aad75a-0e06-4f27-a438-6fa5cc9a4c21",
   "metadata": {},
   "source": [
    "### Subset Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a4da8f7-3279-4e94-9db6-22059770f66f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:01.157567Z",
     "iopub.status.busy": "2022-09-15T05:10:01.157225Z",
     "iopub.status.idle": "2022-09-15T05:10:01.200158Z",
     "shell.execute_reply": "2022-09-15T05:10:01.199635Z"
    }
   },
   "outputs": [],
   "source": [
    "import shapely\n",
    "roi = shapely.geometry.Polygon([[-120, 37],[-120, 38],[-119, 38],[-119, 37]])\n",
    "roi = geopandas.GeoDataFrame(geometry=[roi], crs=4326)\n",
    "roi = starepandas.STAREDataFrame(roi)\n",
    "roi['sids'] = roi.make_sids(level=10)\n",
    "sids = roi.sids.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "935404ad-29c3-4f7b-9f64-930f552ed5e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:01.202790Z",
     "iopub.status.busy": "2022-09-15T05:10:01.202602Z",
     "iopub.status.idle": "2022-09-15T05:10:01.332101Z",
     "shell.execute_reply": "2022-09-15T05:10:01.331611Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roi.plot(trixels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35dc0658-b138-4276-a4a6-0c979cf33187",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:01.334424Z",
     "iopub.status.busy": "2022-09-15T05:10:01.334166Z",
     "iopub.status.idle": "2022-09-15T05:10:01.344313Z",
     "shell.execute_reply": "2022-09-15T05:10:01.343790Z"
    }
   },
   "outputs": [],
   "source": [
    "fname = '../tests/data/granules/MOD05_L2.A2019336.0000.061.2019336211522.hdf'\n",
    "mod = starepandas.io.granules.Mod05(fname)\n",
    "mod.read_sidecar_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0c3c7ea-a9ec-49bf-9084-1c97b4b1b595",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:01.347254Z",
     "iopub.status.busy": "2022-09-15T05:10:01.346688Z",
     "iopub.status.idle": "2022-09-15T05:10:01.443526Z",
     "shell.execute_reply": "2022-09-15T05:10:01.443036Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersects = pystare.intersects(sids, mod.sids.flatten())\n",
    "intersects = numpy.reshape(intersects, mod.sids.shape)\n",
    "intersects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16162b1b",
   "metadata": {},
   "source": [
    "## VNP02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "622c6ca1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:01.445739Z",
     "iopub.status.busy": "2022-09-15T05:10:01.445478Z",
     "iopub.status.idle": "2022-09-15T05:10:02.767568Z",
     "shell.execute_reply": "2022-09-15T05:10:02.766948Z"
    }
   },
   "outputs": [],
   "source": [
    "vnp02_name = '../tests/data/granules/VNP02DNB.A2020219.0742.001.2020219125654.nc'\n",
    "vnp02 = starepandas.read_granule(vnp02_name, sidecar=False, latlon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67315c2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:02.770174Z",
     "iopub.status.busy": "2022-09-15T05:10:02.769898Z",
     "iopub.status.idle": "2022-09-15T05:10:02.854563Z",
     "shell.execute_reply": "2022-09-15T05:10:02.853910Z"
    }
   },
   "outputs": [],
   "source": [
    "vnp02 = vnp03.join(vnp02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e93de612",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:02.857602Z",
     "iopub.status.busy": "2022-09-15T05:10:02.857270Z",
     "iopub.status.idle": "2022-09-15T05:10:03.326260Z",
     "shell.execute_reply": "2022-09-15T05:10:03.325796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>moon_illumination_fraction</th>\n",
       "      <th>land_water_mask</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>DNB_observations</th>\n",
       "      <th>DNB_quality_flags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>41.680256</td>\n",
       "      <td>-102.596710</td>\n",
       "      <td>267</td>\n",
       "      <td>0</td>\n",
       "      <td>92.790001</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.503916e-09</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>41.663277</td>\n",
       "      <td>-101.661072</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>92.799995</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.539059e-09</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>41.663055</td>\n",
       "      <td>-101.652069</td>\n",
       "      <td>369</td>\n",
       "      <td>0</td>\n",
       "      <td>92.799995</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7.083299e-09</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>41.654938</td>\n",
       "      <td>-101.342972</td>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>92.799995</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.231430e-09</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>41.460007</td>\n",
       "      <td>-96.951828</td>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>92.820000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.414932e-09</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134843</th>\n",
       "      <td>16.577330</td>\n",
       "      <td>-78.576134</td>\n",
       "      <td>4059</td>\n",
       "      <td>3231</td>\n",
       "      <td>92.879997</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.087984e-09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134844</th>\n",
       "      <td>16.575632</td>\n",
       "      <td>-78.569000</td>\n",
       "      <td>4060</td>\n",
       "      <td>3231</td>\n",
       "      <td>92.879997</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.579183e-09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134845</th>\n",
       "      <td>16.573931</td>\n",
       "      <td>-78.561844</td>\n",
       "      <td>4061</td>\n",
       "      <td>3231</td>\n",
       "      <td>92.879997</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.579183e-09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134846</th>\n",
       "      <td>16.572226</td>\n",
       "      <td>-78.554688</td>\n",
       "      <td>4062</td>\n",
       "      <td>3231</td>\n",
       "      <td>92.879997</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.393264e-09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134847</th>\n",
       "      <td>16.570518</td>\n",
       "      <td>-78.547523</td>\n",
       "      <td>4063</td>\n",
       "      <td>3231</td>\n",
       "      <td>92.879997</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.986224e-09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4150456 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                lat         lon     x     y  moon_illumination_fraction  \\\n",
       "267       41.680256 -102.596710   267     0                   92.790001   \n",
       "368       41.663277 -101.661072   368     0                   92.799995   \n",
       "369       41.663055 -101.652069   369     0                   92.799995   \n",
       "403       41.654938 -101.342972   403     0                   92.799995   \n",
       "883       41.460007  -96.951828   883     0                   92.820000   \n",
       "...             ...         ...   ...   ...                         ...   \n",
       "13134843  16.577330  -78.576134  4059  3231                   92.879997   \n",
       "13134844  16.575632  -78.569000  4060  3231                   92.879997   \n",
       "13134845  16.573931  -78.561844  4061  3231                   92.879997   \n",
       "13134846  16.572226  -78.554688  4062  3231                   92.879997   \n",
       "13134847  16.570518  -78.547523  4063  3231                   92.879997   \n",
       "\n",
       "          land_water_mask  quality_flag  DNB_observations  DNB_quality_flags  \n",
       "267                     2             0      3.503916e-09                 16  \n",
       "368                     2             0      6.539059e-09                 16  \n",
       "369                     2             0      7.083299e-09                 16  \n",
       "403                     2             0      8.231430e-09                 16  \n",
       "883                     2             0      2.414932e-09                 16  \n",
       "...                   ...           ...               ...                ...  \n",
       "13134843                7             0      2.087984e-09                  0  \n",
       "13134844                7             0      1.579183e-09                  0  \n",
       "13134845                7             0      1.579183e-09                  0  \n",
       "13134846                7             0      2.393264e-09                  0  \n",
       "13134847                7             0      1.986224e-09                  0  \n",
       "\n",
       "[4150456 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnp02[(vnp02.land_water_mask==2) | (vnp02.land_water_mask==4) | (vnp02.land_water_mask==6) | (vnp02.land_water_mask==7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efbd5e95",
   "metadata": {},
   "source": [
    "## VNP CLDMSK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "424c9c08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:03.328495Z",
     "iopub.status.busy": "2022-09-15T05:10:03.328225Z",
     "iopub.status.idle": "2022-09-15T05:10:03.331044Z",
     "shell.execute_reply": "2022-09-15T05:10:03.330593Z"
    }
   },
   "outputs": [],
   "source": [
    "cldmsk_name = '../tests/data/granules/CLDMSK_L2_VIIRS_SNPP.A2020219.0742.001.2020219190616.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b00fe1b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:03.333843Z",
     "iopub.status.busy": "2022-09-15T05:10:03.333234Z",
     "iopub.status.idle": "2022-09-15T05:10:03.344561Z",
     "shell.execute_reply": "2022-09-15T05:10:03.343970Z"
    }
   },
   "outputs": [],
   "source": [
    "cldmsk = starepandas.io.granules.CLDMSKL2VIIRS(cldmsk_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "86eb7fae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:03.346796Z",
     "iopub.status.busy": "2022-09-15T05:10:03.346512Z",
     "iopub.status.idle": "2022-09-15T05:10:05.332082Z",
     "shell.execute_reply": "2022-09-15T05:10:05.331560Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Integer_Cloud_Mask</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.747662</td>\n",
       "      <td>-104.969498</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.747696</td>\n",
       "      <td>-104.950218</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.747730</td>\n",
       "      <td>-104.930237</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.747761</td>\n",
       "      <td>-104.910843</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.747784</td>\n",
       "      <td>-104.891747</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10342395</th>\n",
       "      <td>16.522247</td>\n",
       "      <td>-78.595093</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10342396</th>\n",
       "      <td>16.518646</td>\n",
       "      <td>-78.580162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10342397</th>\n",
       "      <td>16.515034</td>\n",
       "      <td>-78.565208</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10342398</th>\n",
       "      <td>16.511414</td>\n",
       "      <td>-78.550224</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10342399</th>\n",
       "      <td>16.507788</td>\n",
       "      <td>-78.535187</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10342400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                lat         lon  Integer_Cloud_Mask\n",
       "0         41.747662 -104.969498                   3\n",
       "1         41.747696 -104.950218                   3\n",
       "2         41.747730 -104.930237                   3\n",
       "3         41.747761 -104.910843                   3\n",
       "4         41.747784 -104.891747                   3\n",
       "...             ...         ...                 ...\n",
       "10342395  16.522247  -78.595093                   2\n",
       "10342396  16.518646  -78.580162                   1\n",
       "10342397  16.515034  -78.565208                   0\n",
       "10342398  16.511414  -78.550224                   1\n",
       "10342399  16.507788  -78.535187                   1\n",
       "\n",
       "[10342400 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cldmsk.read_latlon()\n",
    "cldmsk.read_data()\n",
    "cldmsk.to_df()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ccc701d",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e50b81d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:05.335364Z",
     "iopub.status.busy": "2022-09-15T05:10:05.334706Z",
     "iopub.status.idle": "2022-09-15T05:10:06.903877Z",
     "shell.execute_reply": "2022-09-15T05:10:06.903198Z"
    }
   },
   "outputs": [],
   "source": [
    "path = '../tests/data/granules/VNP03DNB.A2020219.0742.001.2020219124651.nc'\n",
    "vnp03dnb = starepandas.io.granules.VNP03DNB(path)\n",
    "vnp03dnb.read_latlon()\n",
    "\n",
    "path = '../tests/data/granules/VNP03MOD.A2020219.0742.001.2020219124651.nc'\n",
    "vnp03mod = starepandas.io.granules.VNP03MOD(path)\n",
    "vnp03mod.read_latlon()\n",
    "\n",
    "path = '../tests/data/granules/CLDMSK_L2_VIIRS_SNPP.A2020219.0742.001.2020219190616.nc'\n",
    "cldmsk = starepandas.io.granules.CLDMSKL2VIIRS(path)\n",
    "cldmsk.read_latlon()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "023a29c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-15T05:10:06.906302Z",
     "iopub.status.busy": "2022-09-15T05:10:06.906067Z",
     "iopub.status.idle": "2022-09-15T05:10:07.017975Z",
     "shell.execute_reply": "2022-09-15T05:10:07.017464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f62d8da2470>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5,5), dpi=100)\n",
    "\n",
    "n = 20\n",
    "ns = 0\n",
    "nt = 0\n",
    "\n",
    "ax.grid('on')\n",
    "\n",
    "ax.plot(cldmsk.lon[ns:ns+n, nt:nt+n].flatten(), cldmsk.lat[ns:ns+n, nt:nt+n].flatten(), \n",
    "        color='r', lw=0, marker='.')\n",
    "\n",
    "ax.plot(vnp03mod.lon[ns:ns+n, nt:nt+n].flatten(), vnp03mod.lat[ns:ns+n, nt:nt+n].flatten(), \n",
    "        color='g',lw=0, marker='.')\n",
    "\n",
    "ax.plot(vnp03dnb.lon[ns:ns+n, nt:nt+n].flatten(), vnp03dnb.lat[ns:ns+n, nt:nt+n].flatten(), \n",
    "        color='b',lw=0, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4421e5ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}