active_fires.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ffc77c6a",
   "metadata": {},
   "source": [
    "# Compare active fires to validation polys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6d97a2",
   "metadata": {},
   "source": [
    "#inputs: \n",
    "   mask: NDVI<.7 prior to harvest (clearest Aug/Sept Sentinel image)\n",
    "   valid_polys = shp / gpkg of validation polygons with attribute for day of (first collect polys and mask with urban/dark)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c9cac99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import osgeo  # needed only if running from Windows\n",
    "from pathlib import Path\n",
    "#from shapely.geometry import box\n",
    "import geopandas as gpd\n",
    "#import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc6d8b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append(r\"cropburn\")\n",
    "#from planet_obs_stats import *\n",
    "from timeseries import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ceabc4",
   "metadata": {},
   "source": [
    "## order and download active fire data from NASA FIRMS website\n",
    "###   Unzip folder and change link to active_fire_dir below. (current link is a sample dataset)\n",
    "### (see readme that comes with download for more info on product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5aaa12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "active_fire_dir = os.path.join(r'data','active_fires')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12262567",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grid_file = r\"data/Grid_Punjab_UTM.gpkg\"\n",
    "aoi_file = os.path.join(r'data','AOIs','cell31.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f114a75e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "viirs data are in: EPSG:4326\n",
      "original aoi file is in: EPSG:7773\n",
      "aoi file is now in: EPSG:4326\n"
     ]
    }
   ],
   "source": [
    "viirs_file = [f for f in os.listdir(active_fire_dir) if f.startswith('fire_archive_SV-C2') and f.endswith('.shp')]\n",
    "modis_file = [f for f in os.listdir(active_fire_dir) if f.startswith('fire_archive_M') and f.endswith('.shp')]\n",
    "\n",
    "## if aoi is single polygon shapefile:\n",
    "viirs = gpd.read_file(os.path.join(active_fire_dir,viirs_file[0]))\n",
    "modis = gpd.read_file(os.path.join(active_fire_dir,modis_file[0]))\n",
    "\n",
    "aoi = gpd.read_file(aoi_file)\n",
    "print(\"viirs data are in:\", viirs.crs)\n",
    "print(\"original aoi file is in:\", aoi.crs)\n",
    "if aoi.crs != viirs.crs:\n",
    "    aoi = aoi.to_crs(viirs.crs)\n",
    "print(\"aoi file is now in:\", aoi.crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c22b161",
   "metadata": {},
   "source": [
    "## crop active fire data to aoi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4fadac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#viirs_clip = get_pts_in_grid (grid_file, 31, viirs_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d67b51bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 166 active fire points in this period for this aoi\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>BRIGHTNESS</th>\n",
       "      <th>SCAN</th>\n",
       "      <th>TRACK</th>\n",
       "      <th>ACQ_DATE</th>\n",
       "      <th>ACQ_TIME</th>\n",
       "      <th>SATELLITE</th>\n",
       "      <th>INSTRUMENT</th>\n",
       "      <th>CONFIDENCE</th>\n",
       "      <th>VERSION</th>\n",
       "      <th>BRIGHT_T31</th>\n",
       "      <th>FRP</th>\n",
       "      <th>DAYNIGHT</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>day</th>\n",
       "      <th>mo</th>\n",
       "      <th>dos</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37417</th>\n",
       "      <td>30.471071</td>\n",
       "      <td>74.956268</td>\n",
       "      <td>342.60</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2019-11-04</td>\n",
       "      <td>0824</td>\n",
       "      <td>N</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>n</td>\n",
       "      <td>1</td>\n",
       "      <td>301.11</td>\n",
       "      <td>7.54</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>26.0</td>\n",
       "      <td>POINT (74.95627 30.47107)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32828</th>\n",
       "      <td>30.476761</td>\n",
       "      <td>74.970390</td>\n",
       "      <td>342.91</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.58</td>\n",
       "      <td>2019-11-03</td>\n",
       "      <td>0842</td>\n",
       "      <td>N</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>n</td>\n",
       "      <td>1</td>\n",
       "      <td>291.34</td>\n",
       "      <td>2.27</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>25.0</td>\n",
       "      <td>POINT (74.97039 30.47676)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48662</th>\n",
       "      <td>30.477396</td>\n",
       "      <td>74.971016</td>\n",
       "      <td>333.64</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2019-11-06</td>\n",
       "      <td>0748</td>\n",
       "      <td>N</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>n</td>\n",
       "      <td>1</td>\n",
       "      <td>301.65</td>\n",
       "      <td>6.58</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>28.0</td>\n",
       "      <td>POINT (74.97102 30.47740)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65827</th>\n",
       "      <td>30.471153</td>\n",
       "      <td>74.958527</td>\n",
       "      <td>331.68</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.56</td>\n",
       "      <td>2019-11-18</td>\n",
       "      <td>0724</td>\n",
       "      <td>N</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>n</td>\n",
       "      <td>1</td>\n",
       "      <td>300.64</td>\n",
       "      <td>1.58</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>40.0</td>\n",
       "      <td>POINT (74.95853 30.47115)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70468</th>\n",
       "      <td>30.475355</td>\n",
       "      <td>74.930885</td>\n",
       "      <td>327.66</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.36</td>\n",
       "      <td>2019-11-21</td>\n",
       "      <td>0806</td>\n",
       "      <td>N</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>n</td>\n",
       "      <td>1</td>\n",
       "      <td>295.15</td>\n",
       "      <td>1.89</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>43.0</td>\n",
       "      <td>POINT (74.93089 30.47536)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LATITUDE  LONGITUDE  BRIGHTNESS  SCAN  TRACK    ACQ_DATE ACQ_TIME  \\\n",
       "37417  30.471071  74.956268      342.60  0.39   0.44  2019-11-04     0824   \n",
       "32828  30.476761  74.970390      342.91  0.37   0.58  2019-11-03     0842   \n",
       "48662  30.477396  74.971016      333.64  0.46   0.39  2019-11-06     0748   \n",
       "65827  30.471153  74.958527      331.68  0.34   0.56  2019-11-18     0724   \n",
       "70468  30.475355  74.930885      327.66  0.39   0.36  2019-11-21     0806   \n",
       "\n",
       "      SATELLITE INSTRUMENT CONFIDENCE VERSION  BRIGHT_T31   FRP DAYNIGHT  \\\n",
       "37417         N      VIIRS          n       1      301.11  7.54        D   \n",
       "32828         N      VIIRS          n       1      291.34  2.27        D   \n",
       "48662         N      VIIRS          n       1      301.65  6.58        D   \n",
       "65827         N      VIIRS          n       1      300.64  1.58        D   \n",
       "70468         N      VIIRS          n       1      295.15  1.89        D   \n",
       "\n",
       "       TYPE  day  mo   dos                   geometry  \n",
       "37417     0    4  11  26.0  POINT (74.95627 30.47107)  \n",
       "32828     0    3  11  25.0  POINT (74.97039 30.47676)  \n",
       "48662     0    6  11  28.0  POINT (74.97102 30.47740)  \n",
       "65827     0   18  11  40.0  POINT (74.95853 30.47115)  \n",
       "70468     0   21  11  43.0  POINT (74.93089 30.47536)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viirs_clip = gpd.clip(viirs, aoi)\n",
    "print ('there are {} active fire points in this period for this aoi'.format(len(viirs_clip)))\n",
    "viirs_clip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2ee0a3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 28 active fire points in this period for this aoi\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>BRIGHTNESS</th>\n",
       "      <th>SCAN</th>\n",
       "      <th>TRACK</th>\n",
       "      <th>ACQ_DATE</th>\n",
       "      <th>ACQ_TIME</th>\n",
       "      <th>SATELLITE</th>\n",
       "      <th>INSTRUMENT</th>\n",
       "      <th>CONFIDENCE</th>\n",
       "      <th>VERSION</th>\n",
       "      <th>BRIGHT_T31</th>\n",
       "      <th>FRP</th>\n",
       "      <th>DAYNIGHT</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>30.4784</td>\n",
       "      <td>74.8745</td>\n",
       "      <td>320.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>0818</td>\n",
       "      <td>Aqua</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>67</td>\n",
       "      <td>6.03</td>\n",
       "      <td>276.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (74.87450 30.47840)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8390</th>\n",
       "      <td>30.4764</td>\n",
       "      <td>74.9720</td>\n",
       "      <td>320.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-11-04</td>\n",
       "      <td>0811</td>\n",
       "      <td>Aqua</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>33</td>\n",
       "      <td>6.03</td>\n",
       "      <td>300.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (74.97200 30.47640)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11400</th>\n",
       "      <td>30.4796</td>\n",
       "      <td>74.9489</td>\n",
       "      <td>316.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2019-11-09</td>\n",
       "      <td>0830</td>\n",
       "      <td>Aqua</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>65</td>\n",
       "      <td>6.03</td>\n",
       "      <td>304.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (74.94890 30.47960)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12974</th>\n",
       "      <td>30.4866</td>\n",
       "      <td>74.9236</td>\n",
       "      <td>318.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-11-18</td>\n",
       "      <td>0824</td>\n",
       "      <td>Aqua</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>70</td>\n",
       "      <td>6.03</td>\n",
       "      <td>301.4</td>\n",
       "      <td>9.7</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (74.92360 30.48660)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8426</th>\n",
       "      <td>30.4864</td>\n",
       "      <td>74.9157</td>\n",
       "      <td>320.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-11-04</td>\n",
       "      <td>0811</td>\n",
       "      <td>Aqua</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>22</td>\n",
       "      <td>6.03</td>\n",
       "      <td>301.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (74.91570 30.48640)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LATITUDE  LONGITUDE  BRIGHTNESS  SCAN  TRACK    ACQ_DATE ACQ_TIME  \\\n",
       "12011   30.4784    74.8745       320.5   1.0    1.0  2019-11-11     0818   \n",
       "8390    30.4764    74.9720       320.7   1.0    1.0  2019-11-04     0811   \n",
       "11400   30.4796    74.9489       316.1   1.3    1.1  2019-11-09     0830   \n",
       "12974   30.4866    74.9236       318.7   1.1    1.0  2019-11-18     0824   \n",
       "8426    30.4864    74.9157       320.1   1.0    1.0  2019-11-04     0811   \n",
       "\n",
       "      SATELLITE INSTRUMENT  CONFIDENCE VERSION  BRIGHT_T31   FRP DAYNIGHT  \\\n",
       "12011      Aqua      MODIS          67    6.03       276.2  17.3        D   \n",
       "8390       Aqua      MODIS          33    6.03       300.5   9.9        D   \n",
       "11400      Aqua      MODIS          65    6.03       304.1   8.8        D   \n",
       "12974      Aqua      MODIS          70    6.03       301.4   9.7        D   \n",
       "8426       Aqua      MODIS          22    6.03       301.0   9.3        D   \n",
       "\n",
       "       TYPE                   geometry  \n",
       "12011     0  POINT (74.87450 30.47840)  \n",
       "8390      0  POINT (74.97200 30.47640)  \n",
       "11400     0  POINT (74.94890 30.47960)  \n",
       "12974     0  POINT (74.92360 30.48660)  \n",
       "8426      0  POINT (74.91570 30.48640)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modis_clip = gpd.clip(modis, aoi)\n",
    "print ('there are {} active fire points in this period for this aoi'.format(len(modis_clip)))\n",
    "modis_clip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "071949d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "aoi.plot(ax=ax, alpha=.5)\n",
    "viirs_clip.plot(alpha=1, ax=ax)\n",
    "modis_clip.plot(alpha=1, ax=ax)\n",
    "plt.axis('equal')\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b428714c",
   "metadata": {},
   "source": [
    "## make histogram of fires by day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8327718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 15 burn days observed in this season\n",
      "the season is from 2019-10-23 00:00:00 to 2019-11-21 00:00:00\n",
      "the season is 29 days\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>ACQ_DATE</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obs_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-23</th>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-27</th>\n",
       "      <td>2019-10-27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-29</th>\n",
       "      <td>2019-10-29</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-03</th>\n",
       "      <td>2019-11-03</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ACQ_DATE  counts\n",
       "obs_date                      \n",
       "2019-10-23  2019-10-23       2\n",
       "2019-10-27  2019-10-27       1\n",
       "2019-10-29  2019-10-29       3\n",
       "2019-10-30  2019-10-30       4\n",
       "2019-11-03  2019-11-03      36"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viirs_bydate = viirs_clip.groupby(['ACQ_DATE'])['ACQ_DATE'].size().reset_index(name='counts')\n",
    "print ('there are {} burn days observed in this season'.format(len(viirs_bydate)))\n",
    "viirs_bydate[\"obs_date\"] = pd.to_datetime(viirs_bydate['ACQ_DATE'])\n",
    "viirs_bydate.set_index(\"obs_date\", inplace=True)\n",
    "\n",
    "season_first = viirs_bydate.index[0]\n",
    "season_last = viirs_bydate.index[-1]\n",
    "print('the season is from {} to {}'.format(season_first, season_last))\n",
    "print('the season is {} days'.format( (viirs_bydate.index[-1] - viirs_bydate.index[0] ).days))\n",
    "\n",
    "viirs_bydate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86f78f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range is 71 days, 0:00:00\n"
     ]
    }
   ],
   "source": [
    "viirs_doy = convert_timeseries_to_doy(viirs_bydate, 2019, start_day=283, end_day=354, season=True)\n",
    "\n",
    "from datetime import datetime as dt\n",
    "from datetime import timedelta as td\n",
    "\n",
    "start = dt.strptime(\"10-10-2019\", \"%d-%m-%Y\")\n",
    "end = dt.strptime(\"20-12-2019\", \"%d-%m-%Y\")\n",
    "print(f'range is {end-start}')\n",
    "date_labels = [((start - td(days=1)) + td(days=x)).strftime('%d-%m') for x in range(0,(end-start).days,2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "665996e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 9 burn days observed in this season\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>ACQ_DATE</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obs_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-10-26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-11-04</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-11-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ACQ_DATE  counts\n",
       "obs_date                    \n",
       "17        2019-10-26       1\n",
       "22        2019-10-31       3\n",
       "26        2019-11-04      15\n",
       "27        2019-11-05       1\n",
       "28        2019-11-06       1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modis_bydate = modis_clip.groupby(['ACQ_DATE'])['ACQ_DATE'].size().reset_index(name='counts')\n",
    "print ('there are {} burn days observed in this season'.format(len(modis_bydate)))\n",
    "modis_bydate[\"obs_date\"] = pd.to_datetime(modis_bydate['ACQ_DATE'])\n",
    "modis_bydate.set_index(\"obs_date\", inplace=True)\n",
    "modis_doy = convert_timeseries_to_doy(modis_bydate, 2019, start_day=283, end_day=354, season=True)\n",
    "modis_doy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d98a17d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "obs_date\n",
       "30    783.0\n",
       "26    692.0\n",
       "23    336.0\n",
       "28    299.0\n",
       "27    294.0\n",
       "Name: ha, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_ba_file = os.path.join(r'data','Walker2023RS','Img_Lists','ba_31_planet.csv')\n",
    "planet_ba = pd.read_csv(planet_ba_file)\n",
    "planet_ba.head()\n",
    "planet_ba[\"obs_date\"] = pd.to_datetime(planet_ba[\"burn_date\"])\n",
    "planet_ba.set_index(\"obs_date\",inplace=True)\n",
    "planet_doy = convert_timeseries_to_doy(planet_ba, 2019, start_day=283, end_day=354, season=True)\n",
    "planet_doy['ha'].sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bd6a46",
   "metadata": {},
   "source": [
    "## add PM2 data (for underlay):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca026870",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max PM2.5 value is 223.0695, Min value is 11.397333333333334\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "From Date\n",
       "1    27.234583\n",
       "2    70.958750\n",
       "3    58.074167\n",
       "4    35.095417\n",
       "5    38.424167\n",
       "Name: PM2.5, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pollution_data = pd.read_csv('./data/PM25/Punjab_Bhatinda.csv', usecols=['From Date', 'PM2.5'])\n",
    "# change 'None' to legit NaNs:\n",
    "pollution_data[\"PM2.5\"] = pd.to_numeric(pollution_data[\"PM2.5\"], errors='coerce')\n",
    "# convert to time series:\n",
    "pollution_data['From Date'] = pd.to_datetime(pollution_data['From Date'])\n",
    "pollution_ts = pollution_data[pollution_data['From Date'] > pd.Timestamp(2019,10,9)]\n",
    "pollution_daily = pollution_ts.set_index('From Date').resample('D')['PM2.5'].mean()\n",
    "#pollution_daily.dropna(axis=0, inplace=True)\n",
    "#pollution_daily = pollution_daily.astype(np.int64)\n",
    "print('max PM2.5 value is {}, Min value is {}'.format(pollution_daily.max(), pollution_daily.min()))\n",
    "pollution_doy = convert_timeseries_to_doy(pollution_daily, 2019, start_day=283, end_day=343, season=True)\n",
    "pollution_doy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "90b93043",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\klobw\\AppData\\Local\\Temp\\ipykernel_53980\\61993069.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(date_labels, rotation='vertical', fontname='Palatino Linotype')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(16,8))\n",
    "ax1.margins(x=0)\n",
    "\n",
    "ax1.xaxis.set_major_locator(ticker.MultipleLocator(2))\n",
    "ax1.xaxis.set_minor_locator(ticker.MultipleLocator(1))\n",
    "ax1.bar(viirs_doy.index, viirs_doy['counts'], color='royalblue', width=.9)\n",
    "ax1.bar(modis_doy.index, modis_doy['counts'], color='goldenrod', width=0.4)\n",
    "ax1.set_ylabel('active fire counts', fontsize = 24, fontname='Palatino Linotype') \n",
    "ax1.set_xlabel('observation date (day-month of 2019)', fontsize = 24, fontname='Palatino Linotype')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.margins(x=0)\n",
    "ax2.bar(planet_doy.index, planet_doy.ha, width=.7, color='none',edgecolor='black', linewidth=2)\n",
    "#plt.xticks(ticks=range(0, (viirs_bydate.index[-1] - viirs_bydate.index[0] ).days))\n",
    "ax1.set_xticklabels(date_labels, rotation='vertical', fontname='Palatino Linotype')\n",
    "plt.tick_params(axis='x', labelrotation=45, labelsize=18)\n",
    "ax2.set_ylabel('burned area (ha)', fontsize = 24, fontname='Palatino Linotype') \n",
    "\n",
    "viirs = mpatches.Patch(color='royalblue', label='VIIRS_active fire count')\n",
    "modis = mpatches.Patch(color='goldenrod', label='MODIS_active fire count')\n",
    "planet = mpatches.Patch(fc='none', ec='black', label='Planet burned area (ha)')\n",
    "ax1.legend(handles=[viirs,modis,planet], loc='upper right', fontsize=\"16\", frameon=False)\n",
    "\n",
    "ax1.tick_params(axis='x', labelsize=18)\n",
    "ax1.tick_params(axis='y', labelsize=18)\n",
    "ax2.tick_params(axis='x', labelsize=18)\n",
    "ax2.tick_params(axis='y', labelsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84b87284",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:venv.cropburn]",
   "language": "python",
   "name": "conda-env-venv.cropburn-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}