2020 CA Fires.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a program that will create a gif of all wildfires that occured in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sources that were helpful\n",
    "\n",
    "# https://registry.opendata.aws/noaa-goes/\n",
    "# https://github.com/blaylockbk/pyBKB_v3/blob/master/BB_GOES/mapping_GOES16_TrueColor.ipynb\n",
    "# https://github.com/blaylockbk/pyBKB_v3/blob/master/BB_GOES/mapping_GOES16_FireTemperature.ipynb\n",
    "# https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites\n",
    "# https://docs.opendata.aws/noaa-goes16/cics-readme.html\n",
    "# https://unidata.github.io/python-gallery/examples/mapping_GOES16_TrueColor.html\n",
    "\n",
    "# https://github.com/blaylockbk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "from pyproj import Proj\n",
    "import xarray\n",
    "import matplotlib.pyplot as plt\n",
    "import s3fs\n",
    "import metpy\n",
    "from datetime import datetime, timedelta\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import lxml\n",
    "from textwrap import wrap\n",
    "import imageio\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOAA has all of their GOES-17 and 16 data stored in an s3 bucket that anyone downloaded for free"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = s3fs.S3FileSystem(anon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting a list of files in directory\n",
    "file = fs.ls('s3://noaa-goes17/ABI-L2-MCMIPC/2020/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['noaa-goes17/ABI-L2-MCMIPC/2020/001',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/002',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/003',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/004',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/005',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/006',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/007',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/008',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/009',\n",
       " 'noaa-goes17/ABI-L2-MCMIPC/2020/010']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here I am grabbing a table from wikipedia that includes the start and containment date for wildfires in California\n",
    "#one issue discovered was that one fire was still in progress, so I deleated it\n",
    "\n",
    "#This data will be used later on to append a title to our images\n",
    "\n",
    "\n",
    "#https://stackoverflow.com/questions/50355577/scraping-wikipedia-tables-with-python-selectively\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "wiki_table = pd.DataFrame(columns = ['Name', 'County', 'Acres', 'Start', 'End', 'Notes'])\n",
    "\n",
    "URL = \"https://en.wikipedia.org/wiki/2020_California_wildfires\"\n",
    "\n",
    "res = requests.get(URL).text\n",
    "soup = BeautifulSoup(res)\n",
    "for items in soup.find('table', class_=\"wikitable\").find_all('tr')[1::1]:\n",
    "    data = items.find_all(['th','td'])\n",
    "    try:\n",
    "        Name = data[0].text.rstrip()\n",
    "        County = data[1].text.rstrip()\n",
    "        Acres = data[2].text.rstrip()\n",
    "        Start = data[3].text.rstrip() + \" 2020\"\n",
    "        End = data[4].text.rstrip() + \" 2020\"\n",
    "        Notes = data[5].text.rstrip()\n",
    "    except IndexError:pass\n",
    "    \n",
    "    wiki_dict = {'Name' : Name, 'County': County, 'Acres' : Acres, 'Start' : Start, 'End' : End, 'Notes' : Notes}\n",
    "    wiki_table = wiki_table.append(wiki_dict, ignore_index = True)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_table = wiki_table[wiki_table['Name'] != 'Dolan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>Name</th>\n",
       "      <th>County</th>\n",
       "      <th>Acres</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Interstate 5</td>\n",
       "      <td>Kings</td>\n",
       "      <td>2,060</td>\n",
       "      <td>May 3 2020</td>\n",
       "      <td>May 7 2020</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Range</td>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>5,000</td>\n",
       "      <td>May 27 2020</td>\n",
       "      <td>May 28 2020</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scorpion</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>1,395</td>\n",
       "      <td>May 31 2020</td>\n",
       "      <td>June 1 2020</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quail</td>\n",
       "      <td>Solano</td>\n",
       "      <td>1,837</td>\n",
       "      <td>June 6 2020</td>\n",
       "      <td>June 10 2020</td>\n",
       "      <td>3 structures destroyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wood</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>11,000</td>\n",
       "      <td>June 8 2020</td>\n",
       "      <td>June 12 2020</td>\n",
       "      <td>Burned on Camp Pendleton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>India</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>1,100</td>\n",
       "      <td>June 8 2020</td>\n",
       "      <td>June 14 2020</td>\n",
       "      <td>Burned on Camp Pendleton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Soda</td>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>1,672</td>\n",
       "      <td>June 10 2020</td>\n",
       "      <td>June 11 2020</td>\n",
       "      <td>2 structures destroyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grant</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>5,042</td>\n",
       "      <td>June 12 2020</td>\n",
       "      <td>June 17 2020</td>\n",
       "      <td>1 structure damaged</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Calaveras</td>\n",
       "      <td>1,455</td>\n",
       "      <td>June 16 2020</td>\n",
       "      <td>June 20 2020</td>\n",
       "      <td>2 structures destroyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Grade</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>1,050</td>\n",
       "      <td>June 22 2020</td>\n",
       "      <td>June 26 2020</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name           County   Acres         Start           End  \\\n",
       "0  Interstate 5            Kings   2,060    May 3 2020    May 7 2020   \n",
       "1         Range  San Luis Obispo   5,000   May 27 2020   May 28 2020   \n",
       "2      Scorpion    Santa Barbara   1,395   May 31 2020   June 1 2020   \n",
       "3         Quail           Solano   1,837   June 6 2020  June 10 2020   \n",
       "4          Wood        San Diego  11,000   June 8 2020  June 12 2020   \n",
       "5         India        San Diego   1,100   June 8 2020  June 14 2020   \n",
       "6          Soda  San Luis Obispo   1,672  June 10 2020  June 11 2020   \n",
       "7         Grant       Sacramento   5,042  June 12 2020  June 17 2020   \n",
       "8        Walker        Calaveras   1,455  June 16 2020  June 20 2020   \n",
       "9         Grade           Tulare   1,050  June 22 2020  June 26 2020   \n",
       "\n",
       "                      Notes  \n",
       "0                            \n",
       "1                            \n",
       "2                            \n",
       "3    3 structures destroyed  \n",
       "4  Burned on Camp Pendleton  \n",
       "5  Burned on Camp Pendleton  \n",
       "6    2 structures destroyed  \n",
       "7       1 structure damaged  \n",
       "8    2 structures destroyed  \n",
       "9                            "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_table.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to day of year\n",
    "wiki_table['Start'] = pd.to_datetime(wiki_table['Start']).dt.dayofyear\n",
    "wiki_table['End'] = pd.to_datetime(wiki_table['End']).dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>Name</th>\n",
       "      <th>County</th>\n",
       "      <th>Acres</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Interstate 5</td>\n",
       "      <td>Kings</td>\n",
       "      <td>2,060</td>\n",
       "      <td>124</td>\n",
       "      <td>128</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Range</td>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>5,000</td>\n",
       "      <td>148</td>\n",
       "      <td>149</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scorpion</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>1,395</td>\n",
       "      <td>152</td>\n",
       "      <td>153</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quail</td>\n",
       "      <td>Solano</td>\n",
       "      <td>1,837</td>\n",
       "      <td>158</td>\n",
       "      <td>162</td>\n",
       "      <td>3 structures destroyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wood</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>11,000</td>\n",
       "      <td>160</td>\n",
       "      <td>164</td>\n",
       "      <td>Burned on Camp Pendleton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>India</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>1,100</td>\n",
       "      <td>160</td>\n",
       "      <td>166</td>\n",
       "      <td>Burned on Camp Pendleton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Soda</td>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>1,672</td>\n",
       "      <td>162</td>\n",
       "      <td>163</td>\n",
       "      <td>2 structures destroyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Grant</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>5,042</td>\n",
       "      <td>164</td>\n",
       "      <td>169</td>\n",
       "      <td>1 structure damaged</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Calaveras</td>\n",
       "      <td>1,455</td>\n",
       "      <td>168</td>\n",
       "      <td>172</td>\n",
       "      <td>2 structures destroyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Grade</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>1,050</td>\n",
       "      <td>174</td>\n",
       "      <td>178</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name           County   Acres  Start  End                     Notes\n",
       "0  Interstate 5            Kings   2,060    124  128                          \n",
       "1         Range  San Luis Obispo   5,000    148  149                          \n",
       "2      Scorpion    Santa Barbara   1,395    152  153                          \n",
       "3         Quail           Solano   1,837    158  162    3 structures destroyed\n",
       "4          Wood        San Diego  11,000    160  164  Burned on Camp Pendleton\n",
       "5         India        San Diego   1,100    160  166  Burned on Camp Pendleton\n",
       "6          Soda  San Luis Obispo   1,672    162  163    2 structures destroyed\n",
       "7         Grant       Sacramento   5,042    164  169       1 structure damaged\n",
       "8        Walker        Calaveras   1,455    168  172    2 structures destroyed\n",
       "9         Grade           Tulare   1,050    174  178                          "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_table.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#generate a list of of all possible days within our list of files, this will ensure we only download days that had a fire\n",
    "\n",
    "\n",
    "start_list = wiki_table['Start'].tolist()\n",
    "end_list = wiki_table['End'].tolist()\n",
    "\n",
    "days_needed = []\n",
    "for i, j in zip(start_list, end_list):\n",
    "    for day in range(i, j):\n",
    "        days_needed.append(day)\n",
    "    \n",
    "\n",
    "#drop doubles\n",
    "possible_days = set(days_needed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected character after line continuation character (<ipython-input-1-b216009402af>, line 116)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-1-b216009402af>\"\u001b[1;36m, line \u001b[1;32m116\u001b[0m\n\u001b[1;33m    plt.title(f'Total Active fires: {len(names)}  + f'\\nDate: {time}')\u001b[0m\n\u001b[1;37m                                                                      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected character after line continuation character\n"
     ]
    }
   ],
   "source": [
    "errors = pd.DataFrame(columns = ['Day', 'Error'])\n",
    "\n",
    "for day in tqdm(possible_days):\n",
    "    try:\n",
    "        file = fs.glob(f's3://noaa-goes17/ABI-L2-MCMIPC/2020/{day}/03/')[0] #7:00 PM PST\n",
    "\n",
    "        item = str(file)\n",
    "        lst = item.split(\"/\")\n",
    "        name = lst[5]\n",
    "\n",
    "        path = \"X:/fires/\" + name\n",
    "\n",
    "        fs.download(file, path)\n",
    "\n",
    "        open_file = xarray.open_dataset(path)\n",
    "        \n",
    "        \n",
    "################################################################################################\n",
    "\n",
    "#code from https://github.com/blaylockbk/pyBKB_v3/blob/master/BB_GOES/mapping_GOES16_FireTemperature.ipynb\n",
    "\n",
    "        # Scan's start time, converted to datetime object\n",
    "        scan_start = datetime.strptime(open_file.time_coverage_start, '%Y-%m-%dT%H:%M:%S.%fZ')\n",
    "\n",
    "        # Scan's end time, converted to datetime object\n",
    "        scan_end = datetime.strptime(open_file.time_coverage_end, '%Y-%m-%dT%H:%M:%S.%fZ')\n",
    "\n",
    "        # File creation time, convert to datetime object\n",
    "        file_created = datetime.strptime(open_file.date_created, '%Y-%m-%dT%H:%M:%S.%fZ')\n",
    "        \n",
    "        midpoint = str(open_file['t'].data)[:-8]\n",
    "        scan_mid = datetime.strptime(midpoint, '%Y-%m-%dT%H:%M:%S.%f')\n",
    "\n",
    "\n",
    "        # Load the three channels into appropriate R, G, and B variables\n",
    "        R = open_file['CMI_C07'].data\n",
    "        G = open_file['CMI_C06'].data\n",
    "        B = open_file['CMI_C05'].data\n",
    "\n",
    "        # Normalize each channel by the appropriate range of values  e.g. R = (R-minimum)/(maximum-minimum)\n",
    "        R = (R-273)/(333-273)\n",
    "        G = (G-0)/(1-0)\n",
    "        B = (B-0)/(0.75-0)\n",
    "\n",
    "        # Apply range limits for each channel. RGB values must be between 0 and 1\n",
    "        R = np.clip(R, 0, 1)\n",
    "        G = np.clip(G, 0, 1)\n",
    "        B = np.clip(B, 0, 1)\n",
    "\n",
    "        # Apply the gamma correction to Red channel.\n",
    "        #   corrected_value = value^(1/gamma)\n",
    "        gamma = 0.4\n",
    "        R = np.power(R, 1/gamma)\n",
    "\n",
    "        # The final RGB array :)\n",
    "        RGB = np.dstack([R, G, B])\n",
    "\n",
    "        # Satellite height\n",
    "        sat_h = open_file['goes_imager_projection'].perspective_point_height\n",
    "\n",
    "        # Satellite longitude\n",
    "        sat_lon = open_file['goes_imager_projection'].longitude_of_projection_origin\n",
    "\n",
    "        # Satellite sweep\n",
    "        sat_sweep = open_file['goes_imager_projection'].sweep_angle_axis\n",
    "\n",
    "        # The projection x and y coordinates equals the scanning angle (in radians) multiplied by the satellite height\n",
    "        # See details here: https://proj4.org/operations/projections/geos.html?highlight=geostationary\n",
    "        x = open_file['x'][:] * sat_h\n",
    "        y = open_file['y'][:] * sat_h\n",
    "        \n",
    "################################################################################################\n",
    "\n",
    "\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "#code from https://unidata.github.io/python-gallery/examples/mapping_GOES16_TrueColor.html\n",
    "\n",
    "\n",
    "        # We'll use the `CMI_C02` variable as a 'hook' to get the CF metadata.\n",
    "        dat = open_file.metpy.parse_cf('CMI_C02')\n",
    "\n",
    "        geos = dat.metpy.cartopy_crs\n",
    "\n",
    "        # We also need the x (north/south) and y (east/west) axis sweep of the ABI data\n",
    "        x = dat.x\n",
    "        y = dat.y\n",
    "\n",
    "        fig = plt.figure(figsize=(8, 8))\n",
    "\n",
    "        pc = ccrs.PlateCarree()\n",
    "\n",
    "        ax = fig.add_subplot(1, 1, 1, projection=pc)\n",
    "        ax.set_extent([-125, -114, 32, 42.5], crs=pc)\n",
    "\n",
    "        ax.imshow(RGB, origin='upper',\n",
    "                  extent=(x.min(), x.max(), y.min(), y.max()),\n",
    "                  transform=geos,\n",
    "                  interpolation='none')\n",
    "\n",
    "        ax.coastlines(resolution='50m', color='white', linewidth=1)\n",
    "        ax.add_feature(ccrs.cartopy.feature.STATES, edgecolor = 'white')\n",
    "        \n",
    "################################################################################################\n",
    "\n",
    "        #add title to show name of fire and date\n",
    "        # Name of fire\n",
    "        \n",
    "        names = wiki_table[(wiki_table['Start'] <= day) & (wiki_table['End'] >= day)]['Name'].tolist()\n",
    "        #title = ', '.join(names)\n",
    "\n",
    "        #took a while to figure this title out\n",
    "        time = scan_start.strftime('%B %d %Y')\n",
    "        #plt.title(\"\\n\".join(wrap(f'Fire(s): {title}',60))  + f'\\nDate: {time}')\n",
    "        plt.title(f'Total Active fires: {len(names)}'  + f'\\nDate: {time}')\n",
    "\n",
    "        \n",
    "        plt.savefig(f'X:/geos17/{day}.png')\n",
    "        plt.close() #got warning, this lowers ram usage\n",
    "        \n",
    "    except Exception as e:\n",
    "        error_dict = {'Day' : day, 'Error': e}\n",
    "        errors = errors.append(error_dict, ignore_index = True)\n",
    "            \n",
    "errors.to_csv('error_log.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I ended up making a sperate file that used multirocessing to create the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = os.listdir('X:\\geos17')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#here we will create a gif\n",
    "\n",
    "images = []\n",
    "for filename in filenames:\n",
    "    images.append(imageio.imread(f'X:\\geos17\\{filename}'))\n",
    "imageio.mimsave('ca_fires.gif', images, duration=.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SegmentLocal](ca_fires.gif \"segment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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