{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f168804a-2c0d-4dd7-8b1c-5d909c889452",
   "metadata": {},
   "source": [
    "**Brian Blaylock**  \n",
    "*May 6, 2022*\n",
    "\n",
    "# Historical NOGAPS Data\n",
    "\n",
    "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": 1,
   "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.cartopy_tools import common_features, pc\n",
    "\n",
    "from herbie import Herbie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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)",
      "\u001b[1;32mc:\\Users\\blayl_depgywe\\BB_python\\Herbie\\docs\\user_guide\\notebooks\\data_nogaps.ipynb Cell 4'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/blayl_depgywe/BB_python/Herbie/docs/user_guide/notebooks/data_nogaps.ipynb#ch0000009?line=0'>1</a>\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~\\BB_python\\Herbie\\herbie\\archive.py:661\u001b[0m, in \u001b[0;36mHerbie.read_idx\u001b[1;34m(self, searchString)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=639'>640</a>\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    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=640'>641</a>\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=641'>642</a>\u001b[0m \u001b[39m    Inspect the GRIB2 file contents by reading the index file.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=642'>643</a>\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=658'>659</a>\u001b[0m \u001b[39m    A Pandas DataFrame of the index file.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=659'>660</a>\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=660'>661</a>\u001b[0m     df \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex_as_dataframe\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=662'>663</a>\u001b[0m     \u001b[39m# Filter DataFrame by searchString\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=663'>664</a>\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;32m~\\miniconda3\\envs\\herbie\\lib\\functools.py:993\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[1;34m(self, instance, owner)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/functools.py?line=990'>991</a>\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    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/functools.py?line=991'>992</a>\u001b[0m \u001b[39mif\u001b[39;00m val \u001b[39mis\u001b[39;00m _NOT_FOUND:\n\u001b[1;32m--> <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/functools.py?line=992'>993</a>\u001b[0m     val \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunc(instance)\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/functools.py?line=993'>994</a>\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/functools.py?line=994'>995</a>\u001b[0m         cache[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mattrname] \u001b[39m=\u001b[39m val\n",
      "File \u001b[1;32m~\\BB_python\\Herbie\\herbie\\archive.py:519\u001b[0m, in \u001b[0;36mHerbie.index_as_dataframe\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=509'>510</a>\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39midx \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNo index file found for \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgrib\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=511'>512</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mIDX_STYLE \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mwgrib2\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=512'>513</a>\u001b[0m     \u001b[39m# Sometimes idx end in ':', other times it doesn't (in some Pando files).\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=513'>514</a>\u001b[0m     \u001b[39m# https://pando-rgw01.chpc.utah.edu/hrrr/sfc/20180101/hrrr.t00z.wrfsfcf00.grib2.idx\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=514'>515</a>\u001b[0m     \u001b[39m# https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20210101/conus/hrrr.t00z.wrfsfcf00.grib2.idx\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=515'>516</a>\u001b[0m     \u001b[39m# Sometimes idx has more than the standard messages\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=516'>517</a>\u001b[0m     \u001b[39m# https://noaa-nbm-grib2-pds.s3.amazonaws.com/blend.20210711/13/core/blend.t13z.core.f001.co.grib2.idx\u001b[39;00m\n\u001b[1;32m--> <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=518'>519</a>\u001b[0m     df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=519'>520</a>\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49midx,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=520'>521</a>\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    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=521'>522</a>\u001b[0m         names\u001b[39m=\u001b[39;49m[\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=522'>523</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39mgrib_message\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=523'>524</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39mstart_byte\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=524'>525</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39mreference_time\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=525'>526</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39mvariable\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=526'>527</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39mlevel\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=527'>528</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39mforecast_time\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=528'>529</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39m?\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=529'>530</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39m??\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=530'>531</a>\u001b[0m             \u001b[39m\"\u001b[39;49m\u001b[39m???\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=531'>532</a>\u001b[0m         ],\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=532'>533</a>\u001b[0m     )\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=534'>535</a>\u001b[0m     \u001b[39m# Format the DataFrame\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=535'>536</a>\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    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=536'>537</a>\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    <a href='file:///c%3A/Users/blayl_depgywe/BB_python/Herbie/herbie/archive.py?line=537'>538</a>\u001b[0m     )\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/util/_decorators.py?line=304'>305</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/util/_decorators.py?line=305'>306</a>\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/util/_decorators.py?line=306'>307</a>\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39marguments),\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/util/_decorators.py?line=307'>308</a>\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/util/_decorators.py?line=308'>309</a>\u001b[0m         stacklevel\u001b[39m=\u001b[39mstacklevel,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/util/_decorators.py?line=309'>310</a>\u001b[0m     )\n\u001b[1;32m--> <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/util/_decorators.py?line=310'>311</a>\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:680\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    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=664'>665</a>\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=665'>666</a>\u001b[0m     dialect,\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=666'>667</a>\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=675'>676</a>\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    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=676'>677</a>\u001b[0m )\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=677'>678</a>\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=679'>680</a>\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:581\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=577'>578</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=579'>580</a>\u001b[0m \u001b[39mwith\u001b[39;00m parser:\n\u001b[1;32m--> <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=580'>581</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\u001b[39m.\u001b[39;49mread(nrows)\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1254\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=1251'>1252</a>\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   <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=1252'>1253</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=1253'>1254</a>\u001b[0m     index, columns, col_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mread(nrows)\n\u001b[0;32m   <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=1254'>1255</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m   <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/readers.py?line=1255'>1256</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py:225\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/c_parser_wrapper.py?line=222'>223</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/c_parser_wrapper.py?line=223'>224</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlow_memory:\n\u001b[1;32m--> <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/c_parser_wrapper.py?line=224'>225</a>\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    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/c_parser_wrapper.py?line=225'>226</a>\u001b[0m         \u001b[39m# destructive to chunks\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/blayl_depgywe/miniconda3/envs/herbie/lib/site-packages/pandas/io/parsers/c_parser_wrapper.py?line=226'>227</a>\u001b[0m         data \u001b[39m=\u001b[39m _concatenate_chunks(chunks)\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:805\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:861\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:847\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\herbie\\lib\\site-packages\\pandas\\_libs\\parsers.pyx:1960\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 NOGAPS inventory files\n",
    "\n",
    "H.read_idx(\"TMP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 25,
   "id": "a4973897",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>grib_message</th>\n",
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       "      <th>level</th>\n",
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       "      <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",
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       "    <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",
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       "    <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",
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      ],
      "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.read_idx(\"TMP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e20e15b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\blayl_depgywe\\BB_python\\Herbie\\herbie\\archive.py:983: 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": 27,
   "id": "da66ba30",
   "metadata": {},
   "outputs": [
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       "\n",
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       "</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 0.0 1.0 2.0 ... 357.0 358.0 359.0\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\\blayl_depgywe\\data\\nogaps\\20080728\\subs...\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-5e0844c8-3300-44a2-bb1c-9d68032074f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e0844c8-3300-44a2-bb1c-9d68032074f1' 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-d56fb56e-4a35-4aac-a747-a22193524843' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d56fb56e-4a35-4aac-a747-a22193524843' 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-f18b41ca-c31f-4b80-8ca6-b5d79be30f03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f18b41ca-c31f-4b80-8ca6-b5d79be30f03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea57e546-3ccc-41b8-abc3-ae36927d7c2a' class='xr-var-data-in' type='checkbox'><label for='data-ea57e546-3ccc-41b8-abc3-ae36927d7c2a' 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-af0004f4-5d7c-418e-9411-52191caf33b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af0004f4-5d7c-418e-9411-52191caf33b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30cf5f90-74a4-48b1-82e3-ea6a634e8962' class='xr-var-data-in' type='checkbox'><label for='data-30cf5f90-74a4-48b1-82e3-ea6a634e8962' 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-2c079ce2-d36f-4412-84b6-ca5fa81ccb95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c079ce2-d36f-4412-84b6-ca5fa81ccb95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d3a1e06-b904-4faa-9862-90b5de8ba347' class='xr-var-data-in' type='checkbox'><label for='data-3d3a1e06-b904-4faa-9862-90b5de8ba347' 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-70b2a293-0cf3-4cf3-a0cd-9a515c8a3e21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70b2a293-0cf3-4cf3-a0cd-9a515c8a3e21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db754acb-28ed-42f1-a776-bf909561c8e1' class='xr-var-data-in' type='checkbox'><label for='data-db754acb-28ed-42f1-a776-bf909561c8e1' 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'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-2867be0c-41d4-4e24-9845-b8e22aebb46c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2867be0c-41d4-4e24-9845-b8e22aebb46c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ea8a264-5625-4bdf-ae8e-9d436191c4bb' class='xr-var-data-in' type='checkbox'><label for='data-4ea8a264-5625-4bdf-ae8e-9d436191c4bb' 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([  0.,   1.,   2., ..., 357., 358., 359.])</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-c2fc6e85-2401-483e-8958-6b5ebcc4c8ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2fc6e85-2401-483e-8958-6b5ebcc4c8ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0c66d56-8c2f-46cd-a116-60796c25d2f2' class='xr-var-data-in' type='checkbox'><label for='data-b0c66d56-8c2f-46cd-a116-60796c25d2f2' 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-d28bc61a-7881-4ff6-9847-f36ad5967abf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d28bc61a-7881-4ff6-9847-f36ad5967abf' 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-4a669ec8-e1b6-43af-9717-5bfbea2995b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a669ec8-e1b6-43af-9717-5bfbea2995b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bdabaf9-69e0-4d61-9ebc-2479c3090ebb' class='xr-var-data-in' type='checkbox'><label for='data-4bdabaf9-69e0-4d61-9ebc-2479c3090ebb' 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>9999</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-f160ccda-49e7-4a08-93a7-4e993f9a4f6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f160ccda-49e7-4a08-93a7-4e993f9a4f6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-274c59ba-6f6a-4bc5-b7b4-d2965a95f0c8' class='xr-var-data-in' type='checkbox'><label for='data-274c59ba-6f6a-4bc5-b7b4-d2965a95f0c8' 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>9999</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-c76dfb94-c739-441d-bfe9-19f5961ea65c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c76dfb94-c739-441d-bfe9-19f5961ea65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e406064c-1895-4178-a7b0-4936d62d4011' class='xr-var-data-in' type='checkbox'><label for='data-e406064c-1895-4178-a7b0-4936d62d4011' 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-0d986a63-8e96-4715-b81a-16f0dcd417cd' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d986a63-8e96-4715-b81a-16f0dcd417cd' 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\\blayl_depgywe\\data\\nogaps\\20080728\\subset_c1dfd96eea8cc2b62785275bca38ac261256e278__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 0.0 1.0 2.0 ... 357.0 358.0 359.0\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\\blayl_depgywe\\data\\nogaps\\20080728\\subs...\n",
       "    searchString:            TMP:5 mb"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "119c9742-4514-4abf-bba5-b74354352537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.0, 1.0, 'NOGAPS: set1')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = common_features(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=0.05, **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": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
