{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f168804a-2c0d-4dd7-8b1c-5d909c889452",
   "metadata": {},
   "source": [
    "# NOGAPS\n",
    "\n",
    "This demonstrates how to download historical NOGAPS data archived at [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. \n",
    "\n",
    "> For NOGAPS data from GODAE, use `model='navgem_godae`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4cba677-7674-47d6-8dde-2d297b138c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from herbie import Herbie\n",
    "\n",
    "from herbie import paint\n",
    "from herbie.toolbox import EasyMap, pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6384df86-fc73-4496-a138-5f227535b6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=nogaps_ncei ┊ \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_ncei\", product=\"058_240\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "122440c6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ParserError",
     "evalue": "Error tokenizing data. C error: EOF inside string starting at row 74",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: Herbie cant read this NOGAPS inventory files\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mH\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minventory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTMP\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/GITHUB/Herbie/herbie/core.py:791\u001b[0m, in \u001b[0;36mHerbie.inventory\u001b[0;34m(self, search, searchString, verbose)\u001b[0m\n\u001b[1;32m    764\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minventory\u001b[39m(\u001b[38;5;28mself\u001b[39m, search\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, searchString\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    765\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    766\u001b[0m \u001b[38;5;124;03m    Inspect the GRIB2 file contents by reading the index file.\u001b[39;00m\n\u001b[1;32m    767\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    789\u001b[0m \n\u001b[1;32m    790\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 791\u001b[0m     df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_as_dataframe\u001b[49m\n\u001b[1;32m    793\u001b[0m     \u001b[38;5;66;03m# TODO: Remove this eventually\u001b[39;00m\n\u001b[1;32m    794\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m searchString \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/herbie-dev/lib/python3.12/functools.py:995\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m    993\u001b[0m val \u001b[38;5;241m=\u001b[39m cache\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname, _NOT_FOUND)\n\u001b[1;32m    994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val \u001b[38;5;129;01mis\u001b[39;00m _NOT_FOUND:\n\u001b[0;32m--> 995\u001b[0m     val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstance\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    996\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    997\u001b[0m         cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname] \u001b[38;5;241m=\u001b[39m val\n",
      "File \u001b[0;32m~/GITHUB/Herbie/herbie/core.py:643\u001b[0m, in \u001b[0;36mHerbie.index_as_dataframe\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    640\u001b[0m     read_this_idx \u001b[38;5;241m=\u001b[39m StringIO(response\u001b[38;5;241m.\u001b[39mtext)\n\u001b[1;32m    641\u001b[0m     response\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m--> 643\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    644\u001b[0m \u001b[43m    \u001b[49m\u001b[43mread_this_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[43m    \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m:\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrib_message\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstart_byte\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mreference_time\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    650\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvariable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlevel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    652\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforecast_time\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    653\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m?\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    654\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m??\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    655\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m???\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    656\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    657\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    659\u001b[0m \u001b[38;5;66;03m# Format the DataFrame\u001b[39;00m\n\u001b[1;32m    660\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreference_time\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(\n\u001b[1;32m    661\u001b[0m     df\u001b[38;5;241m.\u001b[39mreference_time, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124md=\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    662\u001b[0m )\n",
      "File \u001b[0;32m~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, 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, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/pandas/io/parsers/readers.py:626\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[1;32m    625\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[0;32m--> 626\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1923\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1916\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[1;32m   1917\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1918\u001b[0m     \u001b[38;5;66;03m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[1;32m   1919\u001b[0m     (\n\u001b[1;32m   1920\u001b[0m         index,\n\u001b[1;32m   1921\u001b[0m         columns,\n\u001b[1;32m   1922\u001b[0m         col_dict,\n\u001b[0;32m-> 1923\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[attr-defined]\u001b[39;49;00m\n\u001b[1;32m   1924\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnrows\u001b[49m\n\u001b[1;32m   1925\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1926\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1927\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py:234\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[0;32m--> 234\u001b[0m         chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_low_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    235\u001b[0m         \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[1;32m    236\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks)\n",
      "File \u001b[0;32mparsers.pyx:838\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:905\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:874\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:891\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:2061\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;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": 6,
   "id": "c8928077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found ┊ model=nogaps_ncei ┊ \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_ncei\", product=\"008_240\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a4973897",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "      <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>228296.0</td>\n",
       "      <td>21-228296</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>644126.0</td>\n",
       "      <td>407705-644126</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>1068100.0</td>\n",
       "      <td>823535-1068100</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>1467638.0</td>\n",
       "      <td>1231217-1467638</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   228296.0        21-228296 2008-07-28 12:00:00   \n",
       "2             5      407705   644126.0    407705-644126 2008-07-28 12:00:00   \n",
       "4             9      823535  1068100.0   823535-1068100 2008-07-28 12:00:00   \n",
       "6            13     1231217  1467638.0  1231217-1467638 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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.inventory(\"TMP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e20e15b2",
   "metadata": {},
   "outputs": [
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       "    description:             Navy Operational Global Atmospheric Prediction S...\n",
       "    remote_grib:             https://www.ncei.noaa.gov/data/navy-operational-...\n",
       "    local_grib:              /home/blaylock/data/nogaps_ncei/20080728/subset_...\n",
       "    search:                  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-00d48734-6abe-434a-a8f0-2c256ac354a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-00d48734-6abe-434a-a8f0-2c256ac354a2' 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-dbd8ac98-fd60-4c0b-ae22-894f99a5563c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dbd8ac98-fd60-4c0b-ae22-894f99a5563c' class='xr-section-summary' >Coordinates: <span>(7)</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-0c6abe2d-5b89-43af-b870-7152ee9fb4f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c6abe2d-5b89-43af-b870-7152ee9fb4f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-facd1112-04f5-4195-8c56-0c554ff0d6dd' class='xr-var-data-in' type='checkbox'><label for='data-facd1112-04f5-4195-8c56-0c554ff0d6dd' 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-32ca4500-5421-4236-91be-7b24dba8d0d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32ca4500-5421-4236-91be-7b24dba8d0d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b70482e4-bf45-46e2-a0ec-869b2d388744' class='xr-var-data-in' type='checkbox'><label for='data-b70482e4-bf45-46e2-a0ec-869b2d388744' 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-ac4eb98b-a79a-4b37-936a-d1be57f954cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac4eb98b-a79a-4b37-936a-d1be57f954cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382f62d8-8939-4e9e-bf9a-04e5c8087408' class='xr-var-data-in' type='checkbox'><label for='data-382f62d8-8939-4e9e-bf9a-04e5c8087408' 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-16326424-f1c4-403e-83df-b108271b5a61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16326424-f1c4-403e-83df-b108271b5a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc86e344-4aa8-4586-876b-34005301cf67' class='xr-var-data-in' type='checkbox'><label for='data-dc86e344-4aa8-4586-876b-34005301cf67' 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-62e271a2-e5bb-4fd6-9e31-9de10ec1cedc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62e271a2-e5bb-4fd6-9e31-9de10ec1cedc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f8d5f25-e599-42ca-819f-e261ba44c620' class='xr-var-data-in' type='checkbox'><label for='data-8f8d5f25-e599-42ca-819f-e261ba44c620' 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-0292b43d-553d-48b2-9c1d-00fc09142fe9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0292b43d-553d-48b2-9c1d-00fc09142fe9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bba8c40-46a7-4a07-a1e7-ef78beb56b67' class='xr-var-data-in' type='checkbox'><label for='data-4bba8c40-46a7-4a07-a1e7-ef78beb56b67' 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><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-492c0be3-95c5-4e44-a6e0-1afc56e43c55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-492c0be3-95c5-4e44-a6e0-1afc56e43c55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48b9ba84-f076-44c4-a60f-044eea398fa5' class='xr-var-data-in' type='checkbox'><label for='data-48b9ba84-f076-44c4-a60f-044eea398fa5' 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>horizontal_datum_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_NCEI 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-d8ea841e-b9a7-4143-9b30-6db19408f34a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8ea841e-b9a7-4143-9b30-6db19408f34a' class='xr-section-summary' >Data variables: <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-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-2be47218-768d-40ed-8357-ab0226f71c11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2be47218-768d-40ed-8357-ab0226f71c11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4013230d-f366-4d9c-9871-29d8bb3c29ac' class='xr-var-data-in' type='checkbox'><label for='data-4013230d-f366-4d9c-9871-29d8bb3c29ac' 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>GRIB_uvRelativeToGrid :</span></dt><dd>0</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-430a604e-8581-46e8-86a8-277fb8ced5f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-430a604e-8581-46e8-86a8-277fb8ced5f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17b8ab25-cd36-496e-95fc-06a0c4eb1b3d' class='xr-var-data-in' type='checkbox'><label for='data-17b8ab25-cd36-496e-95fc-06a0c4eb1b3d' 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>GRIB_uvRelativeToGrid :</span></dt><dd>0</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></ul></div></li><li class='xr-section-item'><input id='section-5c47a557-baf0-4bf1-b05c-71583477af91' class='xr-section-summary-in' type='checkbox'  ><label for='section-5c47a557-baf0-4bf1-b05c-71583477af91' 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-fc360ab9-3eb9-4871-a9ae-5b3d9290c7ec' class='xr-index-data-in' type='checkbox'/><label for='index-fc360ab9-3eb9-4871-a9ae-5b3d9290c7ec' 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(Index([-90.0, -89.0, -88.0, -87.0, -86.0, -85.0, -84.0, -83.0, -82.0, -81.0,\n",
       "       ...\n",
       "        81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0,  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-8cd4116c-878d-49fe-89f4-45183217f015' class='xr-index-data-in' type='checkbox'/><label for='index-8cd4116c-878d-49fe-89f4-45183217f015' 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(Index([ -1.0,   0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,\n",
       "       ...\n",
       "       349.0, 350.0, 351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 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-f25ac544-8a67-485e-a905-8900a3d9b65f' class='xr-section-summary-in' type='checkbox'  ><label for='section-f25ac544-8a67-485e-a905-8900a3d9b65f' 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_ncei</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>/home/blaylock/data/nogaps_ncei/20080728/subset_b9ef68bf__nogaps-008_240_20080728_1200_000.grb</dd><dt><span>search :</span></dt><dd>TMP:5 mb</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 526kB\n",
       "Dimensions:              (latitude: 181, longitude: 360)\n",
       "Coordinates:\n",
       "    time                 datetime64[ns] 8B 2008-07-28T12:00:00\n",
       "    step                 timedelta64[ns] 8B 00:00:00\n",
       "    isobaricInhPa        float64 8B 5.0\n",
       "  * latitude             (latitude) float64 1kB -90.0 -89.0 -88.0 ... 89.0 90.0\n",
       "  * longitude            (longitude) float64 3kB -1.0 0.0 1.0 ... 357.0 358.0\n",
       "    valid_time           datetime64[ns] 8B 2008-07-28T12:00:00\n",
       "    gribfile_projection  object 8B None\n",
       "Data variables:\n",
       "    t                    (latitude, longitude) float32 261kB 210.5 ... 252.4\n",
       "    gh                   (latitude, longitude) float32 261kB 3.048e+04 ... 3....\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_ncei\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:              /home/blaylock/data/nogaps_ncei/20080728/subset_...\n",
       "    search:                  TMP:5 mb"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = H.xarray(\"TMP:5 mb\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "119c9742-4514-4abf-bba5-b74354352537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.0, 1.0, 'NOGAPS_NCEI: ?')"
      ]
     },
     "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,\n",
    "    ds.latitude,\n",
    "    ds.t - 273.15,\n",
    "    transform=pc,\n",
    "    **paint.NWSTemperature.kwargs2,\n",
    ")\n",
    "plt.colorbar(\n",
    "    p, ax=ax, orientation=\"horizontal\", pad=0.01, **paint.NWSTemperature.cbar_kwargs2\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\")"
   ]
  }
 ],
 "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": "3.12.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "ac511961bc6976a3d289cde8fc247e6b0e9298cf513cb762aa0a7df646d192ed"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}