nexrad_display_notebook_v1-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook will display any NEXRAD site Reflectivity file from the Level II data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
      "## library for working with weather radar data. Py-ART is partly\n",
      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
      "## Science user facility.\n",
      "##\n",
      "## If you use this software to prepare a publication, please cite:\n",
      "##\n",
      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/satdat/python/satx/lib/python3.6/site-packages/pyart/graph/cm.py:104: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if 'red' in spec:\n",
      "/satdat/python/satx/lib/python3.6/site-packages/pyart/graph/cm_colorblind.py:32: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if 'red' in spec:\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pyart\n",
    "\n",
    "import os\n",
    "import cartopy.crs as ccrs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "dadash='-------------------------------------'\n",
    "print(dadash+dadash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_lats = np.arange(0.0,90.0,1.0)\n",
    "#\n",
    "list_of_lons =np.arange(-179.0,181.0,1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PHMO'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ALL_call_signs= \\\n",
    "[\"PGUA\",\"RKSG\",\"RKJK\",\"RODN\",\"KABR\",\"KENX\",\"KABX\",\"KFDR\",\"KAMA\",\"PAHG\", \\\n",
    "\"KEWX\",\"KBBX\",\"PABC\",\"KBLX\",\"KBGM\",\"KBMX\",\"KBIS\",\"KCBX\",\"KBOX\",\"KBRO\", \\\n",
    "\"KBUF\",\"KCXX\",\"KFDX\",\"KICX\",\"KCLX\",\"KRLX\",\"KCYS\",\"KLOT\",\"KILN\",\"KCLE\", \\\n",
    "\"KCAE\",\"KGWX\",\"KCRP\",\"KFWS\",\"KDVN\",\"KFTG\",\"KDMX\",\"KDTX\",\"KDDC\",\"KDOX\", \\\n",
    "\"KDLH\",\"KDYX\",\"KEYX\",\"KEVX\",\"KEPZ\",\"KLRX\",\"KBHX\",\"PAPD\",\"KFSX\",\"KHPX\", \\\n",
    "\"KGRK\",\"KPOE\",\"KEOX\",\"KSRX\",\"KIWX\",\"KAPX\",\"KGGW\",\"KGLD\",\"KMVX\",\"KGJX\", \\\n",
    "\"KGRR\",\"KTFX\",\"KGRB\",\"KGSP\",\"KRMX\",\"KUEX\",\"KHDX\",\"KCBW\",\"KHGX\",\"KHTX\", \\\n",
    "\"KIND\",\"KJKL\",\"KJAN\",\"KJAX\",\"PHKN\",\"KEAX\",\"KBYX\",\"PAKC\",\"KMRX\",\"KARX\", \\\n",
    "\"LPLA\",\"KLCH\",\"KESX\",\"KDFX\",\"KILX\",\"KLZK\",\"KVTX\",\"KLVX\",\"KLBB\",\"KMQT\", \\\n",
    "\"KMXX\",\"KMAX\",\"KMLB\",\"KNQA\",\"KAMX\",\"PAIH\",\"KMAF\",\"KMKX\",\"KMPX\",\"KMBX\", \\\n",
    "\"KMSX\",\"KMOB\",\"PHMO\",\"KVAX\",\"KMHX\",\"KOHX\",\"KLIX\",\"KOKX\",\"PAEC\",\"KAKQ\", \\\n",
    "\"KLNX\",\"KTLX\",\"KOAX\",\"KPAH\",\"KPDT\",\"KDIX\",\"KIWA\",\"KPBZ\",\"KSFX\",\"KGYX\", \\\n",
    "\"KRTX\",\"KPUX\",\"KRAX\",\"KUDX\",\"KRGX\",\"KRIW\",\"KFCX\",\"KJGX\",\"KDAX\",\"KLSX\", \\\n",
    "\"KMTX\",\"KSJT\",\"KNKX\",\"KMUX\",\"KHNX\",\"TJUA\",\"KSOX\",\"KATX\",\"KSHV\",\"KFSD\", \\\n",
    "\"PACG\",\"PHKI\",\"PHWA\",\"KOTX\",\"KSGF\",\"KCCX\",\"KLWX\",\"KTLH\",\"KTBW\",\"KTWX\", \\\n",
    "\"KEMX\",\"KINX\",\"KVNX\",\"KVBX\",\"KICT\",\"KLTX\",\"KFFC\",\"KYUX\",\"KLGX\",\"PHMO\"]\n",
    "#\n",
    "CALL_SIGN=ALL_call_signs[-1]\n",
    "CALL_SIGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Molokai HI'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_call_signs={\"KABR\":\"Aberdeen_SD\", \"KABX\":\"Albuquerque_NM\", \"KAKQ\":\"Norfolk-VA\", \\\n",
    "\"KAMA\":\"Amarillo_TX\", \"KBBX\":\"Beale-AFB_CA\", \\\n",
    "\"KAMX\":\"Miami-FL\", \"KAPX\":\"Gaylord_MI\", \"KARX\":\"La-Crosse_WI\", \"KATX\":\"Seattle-Tacoma_WA\", \\\n",
    "\"KBGM\":\"Binghamton_NY\", \"KBHX\":\"Eureka_CA\", \"KBIS\":\"Bismarck_ND\", \"KBLX\":\"Billings_MT\", \\\n",
    "\"KBMX\":\"Birmingham_AL\", \"KCAE\":\"Columbia_SC\", \\\n",
    "\"KBOX\":\"Boston-MA\", \"KBRO\":\"Brownsville-TX\", \"KBUF\":\"Buffalo_NY\", \"KBYX\":\"Key-West-FL\", \\\n",
    "\"KCBW\":\"Houlton-Maine\", \"KCBX\":\"Boise_ID\", \"KCCX\":\"State-College_PA\", \"KCLE\":\"Cleveland_OH\", \\\n",
    "\"KCLX\":\"Charleston-SC\", \"KDDC\":\"Dodge-City_KS\", \\\n",
    "\"KCRP\":\"Corpus-Christi-TX\", \"KCXX\":\"Burlington_VT\", \"KCYS\":\"Cheyenne_WY\", \"KDAX\":\"Sacramento_CA\", \\\n",
    "\"KDFX\":\"Laughlin-AFB_TX\", \"KDIX\":\"Philadelphia-PA\", \"KDLH\":\"Duluth_MN\", \"KDMX\":\"Des-Moines_IA\", \\\n",
    "\"KDOX\":\"Dover-AFB-DE\", \"KEMX\":\"Tucson_AZ\", \\\n",
    "\"KDTX\":\"Detroit_MI\", \"KDVN\":\"Davenport_IA\", \"KDYX\":\"Dyess-AFB_TX\", \"KEAX\":\"Kansas-City_MO\", \\\n",
    "\"KENX\":\"Albany_NY\", \"KEOX\":\"Fort-Rucker_AL\", \"KEPZ\":\"El-Paso_TX\", \"KESX\":\"Las-Vegas_NV\", \\\n",
    "\"KEVX\":\"Eglin-AFB-FL\", \"KFDX\":\"Cannon-AFB_NM\", \\\n",
    "\"KEWX\":\"Austin-San-Antonio_TX\", \"KEYX\":\"Edwards-AFB_CA\", \"KFCX\":\"Roanoke_VA\", \"KFDR\":\"Altus-AFB_OK\", \\\n",
    "\"KFFC\":\"Atlanta_GA\", \"KFSD\":\"Sioux-Falls_SD\", \"KFSX\":\"Flagstaff_AZ\",\"KFTG\":\"Denver_CO\", \\\n",
    "\"KFWS\":\"Dallas-Ft.Worth_TX\", \"KGRK\":\"Fort-Hood_TX\", \\\n",
    "\"KGGW\":\"Glasgow_MT\", \"KGJX\":\"Grand-Junction_Co\", \"KGLD\":\"Goodland_KS\", \"KGRB\":\"Green-Bay_WI\", \\\n",
    "\"KGRR\":\"Grand-Rapids_MI\", \"KGSP\":\"Greer_SC\", \"KGWX\":\"Columbus-AFB,_ MS\", \"KGYX\":\"Portland-Maine\", \\\n",
    "\"KHDX\":\"Holloman-AFB_NM\", \"KHTX\":\"Huntsville_AL\",  \\\n",
    "\"KHGX\":\"Houston-Galveston-TX\", \"KHNX\":\"San-Joaquin-Valley_CA\", \"KHPX\":\"Fort-Campbell_KY\", \\\n",
    "\"KICT\":\"Wichita_KS\", \"KICX\":\"Cedar-City_UT\", \"KILN\":\"Cincinnati_OH\", \"KILX\":\"Lincoln_IL\", \\\n",
    "\"KIND\":\"Indianapolis_IN\", \"KJAX\":\"Jacksonville-FL\", \\\n",
    "\"KINX\":\"Tulsa_OK\", \"KIWA\":\"Phoenix_AZ\", \"KIWX\":\"Fort-Wayne_IN\", \"KJAN\":\"Jackson_MS\", \\\n",
    "\"KJGX\":\"Robins-AFB_GA\", \"KJKL\":\"Jackson_KY\", \"KLBB\":\"Lubbock_TX\", \"KLCH\":\"Lake-Charles-LA\", \\\n",
    "\"KLIX\":\"New-Orleans-LA\", \"KLTX\":\"Wilmington-NC\", \\\n",
    "\"KLNX\":\"North-Platte_NE\", \"KLOT\":\"Chicago_IL\", \"KLRX\":\"Elko_NV\", \"KLSX\":\"Saint-Louis_ MO\", \\\n",
    "\"KLVX\":\"Louisville_KY\", \"KLWX\":\"Sterling-VA\", \"KLZK\":\"Little-Rock_AR\", \"KMAF\":\"Midland-Odessa_TX\", \\\n",
    "\"KMAX\":\"Medford_OR\", \"KMOB\":\"Mobile-AL\", \\\n",
    "\"KMBX\":\"Minot-AFB_ND\", \"KMHX\":\"Morehead-City-NC\", \"KMKX\":\"Milwaukee_WI\", \"KMLB\":\"Melbourne-FL\", \\\n",
    "\"KMPX\":\"Minneapolis-St.Paul_MN\", \"KMQT\":\"Marquette_MI\", \"KMRX\":\"Knoxville-Tri-Cities_TN\", \\\n",
    "\"KMSX\":\"Missoula_MT\", \"KNKX\":\"San-Diego-CA\", \\\n",
    "\"KMTX\":\"Salt-Lake-City_UT\", \"KMUX\":\"San-Francisco_CA\", \"KMVX\":\"Grand-Forks_ND\", \"KMXX\":\"Maxwell-AFB_AL\", \\\n",
    "\"KNQA\":\"Memphis_TN\", \"KOAX\":\"Omaha_NE\", \"KOHX\":\"Nashville_TN\", \"KOKX\":\"New-York-City-NY\", \\\n",
    "\"KOTX\":\"Spokane_WA\", \"KPAH\":\"Paducah_KY\", \\\n",
    "\"KPBZ\":\"Pittsburgh_PA\", \"KPDT\":\"Pendleton_OR\", \"KPOE\":\"Fort-Polk_LA\", \"KPUX\":\"Pueblo_CO\", \\\n",
    "\"KRAX\":\"Raleigh-Durham_NC\", \"KRGX\":\"Reno-NV\", \"KSFX\":\"Pocatello-Idaho-Falls_ID\", \\\n",
    "\"KRIW\":\"Riverton_WY\", \"KRLX\":\"Charleston_WV\", \"KRMX\":\"Griffiss-AFB_NY\", \"KRTX\":\"Portland_OR\", \\\n",
    "\"KSGF\":\"Springfield_MO\", \"KSHV\":\"Shreveport_LA\", \"KSJT\":\"San-Angelo_TX\", \\\n",
    "\"KSOX\":\"Santa-Ana_Mountains_CA\", \"KTLX\":\"Oklahoma-City_OK\", \\\n",
    "\"KSRX\":\"Fort-Smith_AR\", \"KTBW\":\"Tampa-FL\", \"KTFX\":\"Great-Falls_MT\", \"KTLH\":\"Tallahassee-FL\", \\\n",
    "\"KTWX\":\"Topeka_KS\", \"KUDX\":\"Rapid-City_SD\", \"KUEX\":\"Hastings_NE\", \"KVAX\":\"Moody-AFB_GA\", \\\n",
    "\"KVBX\":\"Vandenberg-AFB_CA\", \"PABC\":\"Bethel_AK\", \\\n",
    "\"KVNX\":\"Vance-AFB_OK\", \"KVTX\":\"Los_Angeles_CA\", \"KYUX\":\"Yuma_AZ\", \"LPLA\":\"Lajes-AB_Azores\", \\\n",
    "\"PACG\":\"Sitka_AK\", \"PAEC\":\"Nome_AK\", \"PAHG\":\"Anchorage_AK\", \"PAIH\":\"Middleton-Island_AK\",\n",
    "\"PAKC\":\"King-Salmon_AK\", \"PHKM\":'Kamuela-Kohala-HI', \\\n",
    "\"PAPD\":\"Fairbanks_AK\", \"PGUA\":\"Anderson-AFB-Guam\", \"PHKI\":\"South-Kauai-HI\", \\\n",
    "\"PHKN\":\"Kamuela_HI\", \"PHMO\":\"Molokai-HI\", \"PHWA\":\"South-Shore-HI\", \"RKJK\":\"Kunsan-AB-Korea\", \\\n",
    "\"RKSG\":\"Camp-Humphreys-Korea\", \"RODN\":\"Kadena_Okinawa\", \"TJUA\":\"San-Juan-Puerto-Rico\", \\\n",
    "\"KLGX\":\"Langley-Hill_WA\", \"PHMO\":'Molokai HI'}\n",
    "\n",
    "PlaceName = dict_call_signs[CALL_SIGN]\n",
    "PlaceName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "#CALL_SIGN=\"PHMO\"\n",
    "data_dir='/u/curr/etc/dynamic/obs_data/met/cqc/radar/'+CALL_SIGN\n",
    "\n",
    "data_dir='/satdat/curr/m4b/radar/nexrad/'+CALL_SIGN+'/'\n",
    "\n",
    "\n",
    "list_of_files= []\n",
    "list_of_files=os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next \n",
    "list_of_files.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "derivedatafrom =list_of_files[-1]\n",
    "#derivedatafrom =list_of_files[15]\n",
    "\n",
    "#derivedatafrom ='20161223135600.compress.raw'\n",
    "#\n",
    "#\n",
    "filename = data_dir+derivedatafrom\n",
    "#filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCESS: Read in Level II RADAR  datafile: /satdat/curr/m4b/radar/nexrad/PHMO/20210720180054.compress.raw\n"
     ]
    }
   ],
   "source": [
    "read_radar_ok = 1\n",
    "\n",
    "#\n",
    "\n",
    "try:\n",
    "    radar = pyart.io.read(filename)\n",
    "except:\n",
    "    read_radar_ok = 0\n",
    "    print(\"WARNING: Error reading LevelII  datafile: \"+filename)\n",
    "    #=================================================================#\n",
    "\n",
    "if (read_radar_ok  == 1):\n",
    "    print(\"SUCCESS: Read in Level II RADAR  datafile: \"+filename)\n",
    "    #\n",
    "    #====================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "SITE_latitude = radar.latitude['data'][0]\n",
    "\n",
    "SITE_longitude  = radar.longitude['data'][0]\n",
    "\n",
    "minlon = SITE_longitude - 4.5\n",
    "maxlon = SITE_longitude + 4.5\n",
    "#\n",
    "minlat = SITE_latitude - 3.5\n",
    "maxlat = SITE_latitude + 3.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of lats for grid plotting:\n",
      "[18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0]\n"
     ]
    }
   ],
   "source": [
    "local_lats=[]\n",
    "local_lons=[]\n",
    "\n",
    "for item in list_of_lats:\n",
    "    #\n",
    "    if (item > minlat ) & (item < maxlat):\n",
    "        local_lats.append(item)\n",
    "        #---------------------------------\n",
    "        # END OF IF LOOP\n",
    "        #---------------------------------\n",
    "    #---------------------------------\n",
    "    # END OF FOR LOOP\n",
    "    #---------------------------------\n",
    "\n",
    "print('List of lats for grid plotting:')\n",
    "print(local_lats)\n",
    "\n",
    "for item in list_of_lons:\n",
    "    #\n",
    "    if (item > minlon ) & (item < maxlon):\n",
    "        local_lons.append(item)\n",
    "        #---------------------------------\n",
    "        # END OF IF LOOP\n",
    "        #---------------------------------\n",
    "    #---------------------------------\n",
    "    # END OF FOR LOOP\n",
    "    #---------------------------------\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of lons for grid plotting:\n",
      "[-161.0, -160.0, -159.0, -158.0, -157.0, -156.0, -155.0, -154.0, -153.0]\n"
     ]
    }
   ],
   "source": [
    "print('List of lons for grid plotting:')\n",
    "print(local_lons)\n",
    "\n",
    "CallSign =CALL_SIGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PlaceName = 'Molokai HI'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The year-month-day     is: d20210720\n",
      "The hour-minute-second is: s180054\n",
      "The year-month-day -2-     is: 2021-07-20\n",
      "The hour-minute-second -2- is: 18:00:54\n",
      "Formatted Date-time String:\n",
      "2021-07-20---18:00:54 UTC\n"
     ]
    }
   ],
   "source": [
    "yymmdd='d'+derivedatafrom[0:8]\n",
    "hhmmss='s'+derivedatafrom[8:14]\n",
    "#\n",
    "print('The year-month-day     is: '+yymmdd)\n",
    "print('The hour-minute-second is: '+hhmmss)\n",
    "#\n",
    "#\n",
    "#yyyy_mm_dd = derivedatafrom[0:4]+'_'+derivedatafrom[4:2]+'_'+derivedatafrom[6:2]\n",
    "yyyy_mm_dd = derivedatafrom[0:4]+'-'+derivedatafrom[4:6]+'-'+derivedatafrom[6:8]\n",
    "#\n",
    "#hh_MM_sec  = derivedatafrom[8:2]+':'+derivedatafrom[10:2]+':'+derivedatafrom[12:2]\n",
    "hh_MM_sec  = derivedatafrom[8:10]+':'+derivedatafrom[10:12]+':'+derivedatafrom[12:14]\n",
    "#\n",
    "print('The year-month-day -2-     is: '+yyyy_mm_dd)\n",
    "#\n",
    "print('The hour-minute-second -2- is: '+hh_MM_sec)\n",
    "#\n",
    "ymdhms_formatted = yyyy_mm_dd+'---'+hh_MM_sec+' UTC'\n",
    "#\n",
    "print('Formatted Date-time String:')\n",
    "\n",
    "print(ymdhms_formatted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "display = pyart.graph.RadarMapDisplay(radar)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "GIS_shapefile = '/satdat/m4b/NEXRAD/python/shape/cb_2015_us_state_500k'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "projection = ccrs.LambertConformal(central_latitude=radar.latitude['data'][0], \\\n",
    "    central_longitude=radar.longitude['data'][0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display.plot_ppi_map('reflectivity', 1, vmin=-32, vmax=64, min_lon=minlon, max_lon=maxlon, \\\n",
    "    min_lat=minlat, max_lat=maxlat, \\\n",
    "    lat_lines = local_lats, lon_lines = local_lons, \\\n",
    "    #shapefile=GIS_shapefile, \\\n",
    "    projection=projection, \\\n",
    "    lat_0=radar.latitude['data'][0],lon_0=radar.longitude['data'][0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}