{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f168804a-2c0d-4dd7-8b1c-5d909c889452",
   "metadata": {},
   "source": [
    "# NOGAPS Data\n",
    "\n",
    "This demonstrates how to use data from historical archive of NOGAPS from [NCEI](https://www.ncei.noaa.gov/products/weather-climate-models/navy-operational-global-atmospheric-prediction?msclkid=ee48a0e7cdb911eca49b9d0ed06548f8) for historical analyses from 1997 to 2008 at 0.5 and 1.0 degree grids. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b4cba677-7674-47d6-8dde-2d297b138c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "from paint.standard2 import cm_tmp\n",
    "from toolbox import EasyMap, pc\n",
    "\n",
    "from herbie import Herbie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6384df86-fc73-4496-a138-5f227535b6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=nogaps ┊ \u001b[3mproduct=058_240\u001b[0m ┊ \u001b[38;2;41;130;13m2008-Jul-28 12:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ ncei\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ ncei\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "H = Herbie(\"2008-07-28 12:00\", model=\"nogaps\", product=\"058_240\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "122440c6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ParserError",
     "evalue": "Error tokenizing data. C error: EOF inside string starting at row 74",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39m# TODO: Herbie cant read NOGAPS inventory files\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m H\u001b[39m.\u001b[39;49mread_idx(\u001b[39m\"\u001b[39;49m\u001b[39mTMP\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[1;32m~\\_GITHUB\\Herbie\\herbie\\archive.py:718\u001b[0m, in \u001b[0;36mHerbie.read_idx\u001b[1;34m(self, searchString)\u001b[0m\n\u001b[0;32m    697\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mread_idx\u001b[39m(\u001b[39mself\u001b[39m, searchString\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    698\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    699\u001b[0m \u001b[39m    Inspect the GRIB2 file contents by reading the index file.\u001b[39;00m\n\u001b[0;32m    700\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    716\u001b[0m \u001b[39m    A Pandas DataFrame of the index file.\u001b[39;00m\n\u001b[0;32m    717\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 718\u001b[0m     df \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex_as_dataframe\n\u001b[0;32m    720\u001b[0m     \u001b[39m# Filter DataFrame by searchString\u001b[39;00m\n\u001b[0;32m    721\u001b[0m     \u001b[39mif\u001b[39;00m searchString \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\u001b[39mNone\u001b[39;00m, \u001b[39m\"\u001b[39m\u001b[39m:\u001b[39m\u001b[39m\"\u001b[39m]:\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\functools.py:981\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[1;34m(self, instance, owner)\u001b[0m\n\u001b[0;32m    979\u001b[0m val \u001b[39m=\u001b[39m cache\u001b[39m.\u001b[39mget(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mattrname, _NOT_FOUND)\n\u001b[0;32m    980\u001b[0m \u001b[39mif\u001b[39;00m val \u001b[39mis\u001b[39;00m _NOT_FOUND:\n\u001b[1;32m--> 981\u001b[0m     val \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunc(instance)\n\u001b[0;32m    982\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    983\u001b[0m         cache[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mattrname] \u001b[39m=\u001b[39m val\n",
      "File \u001b[1;32m~\\_GITHUB\\Herbie\\herbie\\archive.py:575\u001b[0m, in \u001b[0;36mHerbie.index_as_dataframe\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    572\u001b[0m     read_this_idx \u001b[39m=\u001b[39m StringIO(response\u001b[39m.\u001b[39mtext)\n\u001b[0;32m    573\u001b[0m     response\u001b[39m.\u001b[39mclose()\n\u001b[1;32m--> 575\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(\n\u001b[0;32m    576\u001b[0m     read_this_idx,\n\u001b[0;32m    577\u001b[0m     sep\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m:\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    578\u001b[0m     names\u001b[39m=\u001b[39;49m[\n\u001b[0;32m    579\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mgrib_message\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    580\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mstart_byte\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    581\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mreference_time\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    582\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mvariable\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    583\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mlevel\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    584\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mforecast_time\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    585\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m?\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    586\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m??\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    587\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m???\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    588\u001b[0m     ],\n\u001b[0;32m    589\u001b[0m )\n\u001b[0;32m    591\u001b[0m \u001b[39m# Format the DataFrame\u001b[39;00m\n\u001b[0;32m    592\u001b[0m df[\u001b[39m\"\u001b[39m\u001b[39mreference_time\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(\n\u001b[0;32m    593\u001b[0m     df\u001b[39m.\u001b[39mreference_time, \u001b[39mformat\u001b[39m\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39md=\u001b[39m\u001b[39m%\u001b[39m\u001b[39mY\u001b[39m\u001b[39m%\u001b[39m\u001b[39mm\u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m%\u001b[39m\u001b[39mH\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    594\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[39m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mdelimiter\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n\u001b[0;32m    610\u001b[0m \u001b[39mwith\u001b[39;00m parser:\n\u001b[1;32m--> 611\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\u001b[39m.\u001b[39;49mread(nrows)\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1778\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   1771\u001b[0m nrows \u001b[39m=\u001b[39m validate_integer(\u001b[39m\"\u001b[39m\u001b[39mnrows\u001b[39m\u001b[39m\"\u001b[39m, nrows)\n\u001b[0;32m   1772\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1773\u001b[0m     \u001b[39m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[0;32m   1774\u001b[0m     (\n\u001b[0;32m   1775\u001b[0m         index,\n\u001b[0;32m   1776\u001b[0m         columns,\n\u001b[0;32m   1777\u001b[0m         col_dict,\n\u001b[1;32m-> 1778\u001b[0m     ) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mread(  \u001b[39m# type: ignore[attr-defined]\u001b[39;49;00m\n\u001b[0;32m   1779\u001b[0m         nrows\n\u001b[0;32m   1780\u001b[0m     )\n\u001b[0;32m   1781\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m   1782\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py:230\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m    228\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    229\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlow_memory:\n\u001b[1;32m--> 230\u001b[0m         chunks \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_reader\u001b[39m.\u001b[39;49mread_low_memory(nrows)\n\u001b[0;32m    231\u001b[0m         \u001b[39m# destructive to chunks\u001b[39;00m\n\u001b[0;32m    232\u001b[0m         data \u001b[39m=\u001b[39m _concatenate_chunks(chunks)\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:808\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:866\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:852\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\blaylock\\Miniconda3\\envs\\herbie-dev\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:1973\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mParserError\u001b[0m: Error tokenizing data. C error: EOF inside string starting at row 74"
     ]
    }
   ],
   "source": [
    "# TODO: Herbie cant read this NOGAPS inventory files\n",
    "\n",
    "H.inventory(\"TMP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8928077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=nogaps ┊ \u001b[3mproduct=008_240\u001b[0m ┊ \u001b[38;2;41;130;13m2008-Jul-28 12:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ ncei\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ ncei\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "H = Herbie(\"2008-07-28 12:00\", model=\"nogaps\", product=\"008_240\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4973897",
   "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>grib_message</th>\n",
       "      <th>start_byte</th>\n",
       "      <th>end_byte</th>\n",
       "      <th>range</th>\n",
       "      <th>reference_time</th>\n",
       "      <th>valid_time</th>\n",
       "      <th>variable</th>\n",
       "      <th>level</th>\n",
       "      <th>forecast_time</th>\n",
       "      <th>?</th>\n",
       "      <th>??</th>\n",
       "      <th>???</th>\n",
       "      <th>search_this</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>228297</td>\n",
       "      <td>21-228297</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>TMP</td>\n",
       "      <td>0 mb</td>\n",
       "      <td>kpds=11,100,0</td>\n",
       "      <td>anl</td>\n",
       "      <td>winds are N/S</td>\n",
       "      <td>Temp. [K]\\n2:89724:D=2008072812:HGT:0 mb:kpds=...</td>\n",
       "      <td>:TMP:0 mb:kpds=11,100,0:anl:winds are N/S:Temp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>407705</td>\n",
       "      <td>644127</td>\n",
       "      <td>407705-644127</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>TMP</td>\n",
       "      <td>1 mb</td>\n",
       "      <td>kpds=11,100,1</td>\n",
       "      <td>anl</td>\n",
       "      <td>winds are N/S</td>\n",
       "      <td>Temp. [K]\\n6:505554:D=2008072812:HGT:1 mb:kpds...</td>\n",
       "      <td>:TMP:1 mb:kpds=11,100,1:anl:winds are N/S:Temp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>823535</td>\n",
       "      <td>1068101</td>\n",
       "      <td>823535-1068101</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>TMP</td>\n",
       "      <td>2 mb</td>\n",
       "      <td>kpds=11,100,2</td>\n",
       "      <td>anl</td>\n",
       "      <td>winds are N/S</td>\n",
       "      <td>Temp. [K]\\n10:929528:D=2008072812:HGT:2 mb:kpd...</td>\n",
       "      <td>:TMP:2 mb:kpds=11,100,2:anl:winds are N/S:Temp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>1231217</td>\n",
       "      <td>1467639</td>\n",
       "      <td>1231217-1467639</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>2008-07-28 12:00:00</td>\n",
       "      <td>TMP</td>\n",
       "      <td>5 mb</td>\n",
       "      <td>kpds=11,100,5</td>\n",
       "      <td>anl</td>\n",
       "      <td>winds are N/S</td>\n",
       "      <td>Temp. [K]\\n14:1337210:D=2008072812:HGT:5 mb:kp...</td>\n",
       "      <td>:TMP:5 mb:kpds=11,100,5:anl:winds are N/S:Temp...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   grib_message  start_byte end_byte            range      reference_time  \\\n",
       "0             1          21   228297        21-228297 2008-07-28 12:00:00   \n",
       "2             5      407705   644127    407705-644127 2008-07-28 12:00:00   \n",
       "4             9      823535  1068101   823535-1068101 2008-07-28 12:00:00   \n",
       "6            13     1231217  1467639  1231217-1467639 2008-07-28 12:00:00   \n",
       "\n",
       "           valid_time variable level  forecast_time    ?             ??  \\\n",
       "0 2008-07-28 12:00:00      TMP  0 mb  kpds=11,100,0  anl  winds are N/S   \n",
       "2 2008-07-28 12:00:00      TMP  1 mb  kpds=11,100,1  anl  winds are N/S   \n",
       "4 2008-07-28 12:00:00      TMP  2 mb  kpds=11,100,2  anl  winds are N/S   \n",
       "6 2008-07-28 12:00:00      TMP  5 mb  kpds=11,100,5  anl  winds are N/S   \n",
       "\n",
       "                                                 ???  \\\n",
       "0  Temp. [K]\\n2:89724:D=2008072812:HGT:0 mb:kpds=...   \n",
       "2  Temp. [K]\\n6:505554:D=2008072812:HGT:1 mb:kpds...   \n",
       "4  Temp. [K]\\n10:929528:D=2008072812:HGT:2 mb:kpd...   \n",
       "6  Temp. [K]\\n14:1337210:D=2008072812:HGT:5 mb:kp...   \n",
       "\n",
       "                                         search_this  \n",
       "0  :TMP:0 mb:kpds=11,100,0:anl:winds are N/S:Temp...  \n",
       "2  :TMP:1 mb:kpds=11,100,1:anl:winds are N/S:Temp...  \n",
       "4  :TMP:2 mb:kpds=11,100,2:anl:winds are N/S:Temp...  \n",
       "6  :TMP:5 mb:kpds=11,100,5:anl:winds are N/S:Temp...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.inventory(\"TMP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e20e15b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "👨🏻‍🏭 Created directory: [C:\\Users\\blaylock\\data\\nogaps\\20080728]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\blaylock\\_GITHUB\\Herbie\\herbie\\archive.py:1041: UserWarning: sorry, on windows I couldn't remove the file.\n",
      "  warnings.warn(\"sorry, on windows I couldn't remove the file.\")\n"
     ]
    }
   ],
   "source": [
    "ds = H.xarray(\"TMP:5 mb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da66ba30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:              (latitude: 181, longitude: 360)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 2008-07-28T12:00:00\n",
       "    step                 timedelta64[ns] 00:00:00\n",
       "    isobaricInhPa        float64 5.0\n",
       "  * latitude             (latitude) float64 -90.0 -89.0 -88.0 ... 88.0 89.0 90.0\n",
       "  * longitude            (longitude) float64 -1.0 0.0 1.0 ... 356.0 357.0 358.0\n",
       "    valid_time           datetime64[ns] 2008-07-28T12:00:00\n",
       "Data variables:\n",
       "    t                    (latitude, longitude) float32 210.5 210.5 ... 252.4\n",
       "    gh                   (latitude, longitude) float32 3.048e+04 ... 3.679e+04\n",
       "    gribfile_projection  object None\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             fnmo\n",
       "    GRIB_centreDescription:  US Navy - Fleet Numerical Oceanography Center\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US Navy - Fleet Numerical Oceanography Center\n",
       "    model:                   nogaps\n",
       "    product:                 008_240\n",
       "    description:             Navy Operational Global Atmospheric Prediction S...\n",
       "    remote_grib:             https://www.ncei.noaa.gov/data/navy-operational-...\n",
       "    local_grib:              C:\\Users\\blaylock\\data\\nogaps\\20080728\\subset_b9...\n",
       "    searchString:            TMP:5 mb</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-683a8dbd-d043-4dee-9856-f92604739ca6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-683a8dbd-d043-4dee-9856-f92604739ca6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 181</li><li><span class='xr-has-index'>longitude</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-58f0fff7-5dac-4a92-90cd-4621696baf58' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58f0fff7-5dac-4a92-90cd-4621696baf58' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2008-07-28T12:00:00</div><input id='attrs-6f3935c9-aa9c-43c2-993a-5efa8c9cea20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f3935c9-aa9c-43c2-993a-5efa8c9cea20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5e3c04-bb48-4c6a-b297-ab065d10d2f3' class='xr-var-data-in' type='checkbox'><label for='data-7c5e3c04-bb48-4c6a-b297-ab065d10d2f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2008-07-28T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-1a5f6757-caa5-4f80-b65a-73f042ef371f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a5f6757-caa5-4f80-b65a-73f042ef371f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be7638c-eac0-4131-8106-892788952eab' class='xr-var-data-in' type='checkbox'><label for='data-7be7638c-eac0-4131-8106-892788952eab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isobaricInhPa</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0</div><input id='attrs-f1b2e1d5-2dd7-4ddd-8f44-fb2d504d67e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1b2e1d5-2dd7-4ddd-8f44-fb2d504d67e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e763213-063a-468e-ab1f-8bddcf01bf94' class='xr-var-data-in' type='checkbox'><label for='data-0e763213-063a-468e-ab1f-8bddcf01bf94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array(5.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.0 -88.0 ... 89.0 90.0</div><input id='attrs-fc0bd8f3-8508-4755-9c6e-96a7987c6c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc0bd8f3-8508-4755-9c6e-96a7987c6c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a2c27f-64f3-4c0e-93d0-1ba95aa22630' class='xr-var-data-in' type='checkbox'><label for='data-11a2c27f-64f3-4c0e-93d0-1ba95aa22630' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90., -89., -88., -87., -86., -85., -84., -83., -82., -81., -80., -79.,\n",
       "       -78., -77., -76., -75., -74., -73., -72., -71., -70., -69., -68., -67.,\n",
       "       -66., -65., -64., -63., -62., -61., -60., -59., -58., -57., -56., -55.,\n",
       "       -54., -53., -52., -51., -50., -49., -48., -47., -46., -45., -44., -43.,\n",
       "       -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31.,\n",
       "       -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19.,\n",
       "       -18., -17., -16., -15., -14., -13., -12., -11., -10.,  -9.,  -8.,  -7.,\n",
       "        -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,   2.,   3.,   4.,   5.,\n",
       "         6.,   7.,   8.,   9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,\n",
       "        18.,  19.,  20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,\n",
       "        30.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,\n",
       "        42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,\n",
       "        54.,  55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
       "        66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,  77.,\n",
       "        78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,  88.,  89.,\n",
       "        90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 0.0 1.0 ... 356.0 357.0 358.0</div><input id='attrs-22fe906e-c057-4524-8845-5b9bb8466b3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22fe906e-c057-4524-8845-5b9bb8466b3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aa56dcb-7bb6-4864-ba17-363be0fb1ff4' class='xr-var-data-in' type='checkbox'><label for='data-0aa56dcb-7bb6-4864-ba17-363be0fb1ff4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ -1.,   0.,   1., ..., 356., 357., 358.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2008-07-28T12:00:00</div><input id='attrs-f82e5fc3-7a1b-41af-bffc-3bd858d2e946' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f82e5fc3-7a1b-41af-bffc-3bd858d2e946' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0507c93a-d428-412a-bc0e-fab977272835' class='xr-var-data-in' type='checkbox'><label for='data-0507c93a-d428-412a-bc0e-fab977272835' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2008-07-28T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8849ee7b-eec8-4a47-b51d-a69b36483a7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8849ee7b-eec8-4a47-b51d-a69b36483a7c' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>210.5 210.5 210.5 ... 252.4 252.4</div><input id='attrs-dfa94cc0-c328-4362-baba-b5c48a60375f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfa94cc0-c328-4362-baba-b5c48a60375f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b60ba088-d805-49ea-a7ba-7d86558efeb2' class='xr-var-data-in' type='checkbox'><label for='data-b60ba088-d805-49ea-a7ba-7d86558efeb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>65160</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>360</dd><dt><span>GRIB_Ny :</span></dt><dd>181</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-1.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>T Temperature K</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>unknown</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[210.48495, 210.48495, 210.48495, ..., 210.48495, 210.48495,\n",
       "        210.48495],\n",
       "       [210.27496, 210.28496, 210.29497, ..., 210.25496, 210.26495,\n",
       "        210.27496],\n",
       "       [210.07497, 210.09496, 210.10497, ..., 210.03496, 210.05496,\n",
       "        210.06496],\n",
       "       ...,\n",
       "       [252.30496, 252.29497, 252.29497, ..., 252.30496, 252.30496,\n",
       "        252.30496],\n",
       "       [252.33496, 252.33496, 252.32497, ..., 252.33496, 252.33496,\n",
       "        252.33496],\n",
       "       [252.35497, 252.35497, 252.35497, ..., 252.35497, 252.35497,\n",
       "        252.35497]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gh</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.048e+04 3.048e+04 ... 3.679e+04</div><input id='attrs-40b25dea-2c09-47f3-8b97-197a44886b9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40b25dea-2c09-47f3-8b97-197a44886b9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b84cd34-81cb-4248-a4a4-a0aff3b27393' class='xr-var-data-in' type='checkbox'><label for='data-2b84cd34-81cb-4248-a4a4-a0aff3b27393' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>156</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>65160</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>360</dd><dt><span>GRIB_Ny :</span></dt><dd>181</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential_height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gh</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-1.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential height</dd><dt><span>GRIB_parameterName :</span></dt><dd>GH Geopotential height gpm</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>unknown</dd><dt><span>GRIB_shortName :</span></dt><dd>gh</dd><dt><span>GRIB_stepRange :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>gpm</dd><dt><span>long_name :</span></dt><dd>Geopotential height</dd><dt><span>units :</span></dt><dd>gpm</dd><dt><span>standard_name :</span></dt><dd>geopotential_height</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[30475.318, 30475.318, 30475.318, ..., 30475.318, 30475.318,\n",
       "        30475.318],\n",
       "       [30489.219, 30489.318, 30489.318, ..., 30490.02 , 30490.219,\n",
       "        30489.02 ],\n",
       "       [30505.818, 30506.02 , 30506.02 , ..., 30507.52 , 30507.719,\n",
       "        30505.62 ],\n",
       "       ...,\n",
       "       [36787.82 , 36787.418, 36787.117, ..., 36787.418, 36787.02 ,\n",
       "        36788.22 ],\n",
       "       [36788.32 , 36788.117, 36787.918, ..., 36788.02 , 36787.82 ,\n",
       "        36788.617],\n",
       "       [36786.82 , 36786.82 , 36786.82 , ..., 36786.82 , 36786.82 ,\n",
       "        36786.82 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-4f9b88ad-ee82-4f74-9e33-8d3795ab108b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f9b88ad-ee82-4f74-9e33-8d3795ab108b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13a28670-cea5-4fd1-abf6-b5ae3487d513' class='xr-var-data-in' type='checkbox'><label for='data-13a28670-cea5-4fd1-abf6-b5ae3487d513' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCRS[&quot;unknown&quot;,DATUM[&quot;unknown&quot;,ELLIPSOID[&quot;unknown&quot;,6367470,0,LENGTHUNIT[&quot;metre&quot;,1,ID[&quot;EPSG&quot;,9001]]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8901]],CS[ellipsoidal,2],AXIS[&quot;longitude&quot;,east,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433,ID[&quot;EPSG&quot;,9122]]],AXIS[&quot;latitude&quot;,north,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433,ID[&quot;EPSG&quot;,9122]]]]</dd><dt><span>semi_major_axis :</span></dt><dd>6367470.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6367470.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>unknown</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>long_name :</span></dt><dd>NOGAPS model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0312d634-0bef-40f3-8121-6b7804869a6c' class='xr-section-summary-in' type='checkbox'  ><label for='section-0312d634-0bef-40f3-8121-6b7804869a6c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95a97a86-8742-4f37-a7e4-e198a0cd35e5' class='xr-index-data-in' type='checkbox'/><label for='index-95a97a86-8742-4f37-a7e4-e198a0cd35e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-90.0, -89.0, -88.0, -87.0, -86.0, -85.0, -84.0, -83.0, -82.0,\n",
       "              -81.0,\n",
       "              ...\n",
       "               81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0,\n",
       "               90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=181))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e717096-4459-42b1-956a-3a6b47aaafe0' class='xr-index-data-in' type='checkbox'/><label for='index-7e717096-4459-42b1-956a-3a6b47aaafe0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -1.0,   0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,\n",
       "                8.0,\n",
       "              ...\n",
       "              349.0, 350.0, 351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0,\n",
       "              358.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9028d06-2768-4c23-acc4-24d39c8b7aa9' class='xr-section-summary-in' type='checkbox'  ><label for='section-f9028d06-2768-4c23-acc4-24d39c8b7aa9' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>fnmo</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US Navy - Fleet Numerical Oceanography Center</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US Navy - Fleet Numerical Oceanography Center</dd><dt><span>model :</span></dt><dd>nogaps</dd><dt><span>product :</span></dt><dd>008_240</dd><dt><span>description :</span></dt><dd>Navy Operational Global Atmospheric Prediction System (1997-2008; GRIB1)</dd><dt><span>remote_grib :</span></dt><dd>https://www.ncei.noaa.gov/data/navy-operational-atmostpheric-prediction-system/access/200807/20080728/nogaps-008_240_20080728_1200_000.grb</dd><dt><span>local_grib :</span></dt><dd>C:\\Users\\blaylock\\data\\nogaps\\20080728\\subset_b9ef68bf__nogaps-008_240_20080728_1200_000.grb</dd><dt><span>searchString :</span></dt><dd>TMP:5 mb</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:              (latitude: 181, longitude: 360)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 2008-07-28T12:00:00\n",
       "    step                 timedelta64[ns] 00:00:00\n",
       "    isobaricInhPa        float64 5.0\n",
       "  * latitude             (latitude) float64 -90.0 -89.0 -88.0 ... 88.0 89.0 90.0\n",
       "  * longitude            (longitude) float64 -1.0 0.0 1.0 ... 356.0 357.0 358.0\n",
       "    valid_time           datetime64[ns] 2008-07-28T12:00:00\n",
       "Data variables:\n",
       "    t                    (latitude, longitude) float32 210.5 210.5 ... 252.4\n",
       "    gh                   (latitude, longitude) float32 3.048e+04 ... 3.679e+04\n",
       "    gribfile_projection  object None\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             fnmo\n",
       "    GRIB_centreDescription:  US Navy - Fleet Numerical Oceanography Center\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             US Navy - Fleet Numerical Oceanography Center\n",
       "    model:                   nogaps\n",
       "    product:                 008_240\n",
       "    description:             Navy Operational Global Atmospheric Prediction S...\n",
       "    remote_grib:             https://www.ncei.noaa.gov/data/navy-operational-...\n",
       "    local_grib:              C:\\Users\\blaylock\\data\\nogaps\\20080728\\subset_b9...\n",
       "    searchString:            TMP:5 mb"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "119c9742-4514-4abf-bba5-b74354352537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.0, 1.0, 'NOGAPS: ?')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = EasyMap(crs=ds.herbie.crs, figsize=[8, 8]).ax\n",
    "p = ax.pcolormesh(\n",
    "    ds.longitude, ds.latitude, ds.t, transform=pc, **cm_tmp(units=\"K\").cmap_kwargs\n",
    ")\n",
    "plt.colorbar(\n",
    "    p, ax=ax, orientation=\"horizontal\", pad=0.01, **cm_tmp(units=\"K\").cbar_kwargs\n",
    ")\n",
    "\n",
    "ax.set_title(f\"{ds.t.GRIB_name} {ds.isobaricInhPa.item()} hPa\", loc=\"right\")\n",
    "ax.set_title(f\"{H.model.upper()}: {H.product_description}\", loc=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f93e016-4ec8-4410-923d-c071d7faebad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e43318f7-4993-4687-8cf9-3d962d8a48e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "herbie-dev",
   "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": "3.10.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "ac511961bc6976a3d289cde8fc247e6b0e9298cf513cb762aa0a7df646d192ed"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}