{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Forcing Fields\n",
    "\n",
    "`mom6_tools.MOM6grid` returns an object with MOM6 grid data.\n",
    "\n",
    "`mom6_tools.latlon_analysis` has a collection of tools used to perform spatial analysis  (e.g., time averages and spatial mean).\n",
    "\n",
    "The goal of this notebook is the following:\n",
    "\n",
    "1) serve as an example of how to post-process CESM/MOM6 output;\n",
    "\n",
    "2) create time averages of forcing fields;\n",
    "\n",
    "3) create time-series of globally-averaged forcing fields;\n",
    "\n",
    "4) compare model results vs. observations (TODO);\n",
    "\n",
    "5) compare model results vs. another model results (TODO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basemap module not found. Some regional plots may not function properly\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR 1: PROJ: proj_create_from_database: Open of /glade/work/gmarques/conda-envs/mom6-tools/share/proj failed\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import warnings, yaml, os\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from mom6_tools.MOM6grid import MOM6grid\n",
    "from mom6_tools.m6toolbox import genBasinMasks, cime_xmlquery\n",
    "from mom6_tools.latlon_analysis import time_mean_latlon\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the yaml file\n",
    "diag_config_yml_path = \"diag_config.yml\"\n",
    "diag_config_yml = yaml.load(open(diag_config_yml_path,'r'), Loader=yaml.Loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rundir directory is: /glade/derecho/scratch/gmarques/g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079/run\n",
      "Casename is: g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079\n"
     ]
    }
   ],
   "source": [
    "caseroot = diag_config_yml['Case']['CASEROOT']\n",
    "casename = cime_xmlquery(caseroot, 'CASE')\n",
    "DOUT_S = cime_xmlquery(caseroot, 'DOUT_S')\n",
    "rundir = cime_xmlquery(caseroot, 'RUNDIR')\n",
    "\n",
    "if DOUT_S:\n",
    "  OUTDIR = cime_xmlquery(caseroot, 'DOUT_S_ROOT')+'/ocn/hist/'\n",
    "else:\n",
    "  OUTDIR = cime_xmlquery(caseroot, 'RUNDIR')\n",
    "    \n",
    "print('Rundir directory is:', rundir)\n",
    "print('Casename is:', casename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an empty class object\n",
    "class args:\n",
    "  pass\n",
    "\n",
    "args.case_name = casename\n",
    "args.start_date = ''\n",
    "args.end_date = ''\n",
    "args.savefigs = False\n",
    "args.time_series = True\n",
    "# set avg dates\n",
    "avg = diag_config_yml['Avg']\n",
    "if not args.start_date : args.start_date = avg['start_date']\n",
    "if not args.end_date : args.end_date = avg['end_date']\n",
    "# array with 2D forcing variables to be processed\n",
    "args.variables = ['hfds','wfo', 'prlq']\n",
    "args.nw = 6 # number of workers\n",
    "args.static = casename+diag_config_yml['Fnames']['static']\n",
    "args.native = casename+diag_config_yml['Fnames']['native']\n",
    "args.geom =   casename+diag_config_yml['Fnames']['geom']\n",
    "args.infile = OUTDIR+args.native"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MOM6 grid successfully loaded... \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# read grid info\n",
    "geom_file = OUTDIR+'/'+args.geom\n",
    "if os.path.exists(geom_file):\n",
    "  grd = MOM6grid(OUTDIR+'/'+args.static, geom_file, xrformat=True)\n",
    "else:\n",
    "  grd = MOM6grid(OUTDIR+'/'+args.static, xrformat=True)\n",
    "      \n",
    "try:\n",
    "  depth = grd.depth_ocean.values\n",
    "except:\n",
    "  depth = grd.deptho.values\n",
    "    \n",
    "try:\n",
    "  area = grd.area_t.values\n",
    "except:\n",
    "  area = grd.areacello.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove Nan's, otherwise genBasinMasks won't work\n",
    "depth[np.isnan(depth)] = 0.0\n",
    "basin_code = genBasinMasks(grd.geolon.values, grd.geolat.values, depth, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requesting 6 workers... \n",
      "\n",
      "/proxy/43571/status\n",
      "About to plot time-average for Surface ocean heat flux from SW+LW+latent+sensible+masstransfer+frazil+seaice_melt_heat (hfds)... \n",
      "\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "Unlabeled multi-dimensional array cannot be used for indexing: yh",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/glade/derecho/scratch/gmarques/tmp/ipykernel_21702/3597705672.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# plot time averages. If variables is NOT specified, all 2D variables will be plotted.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#time_mean_latlon_plot(args,grd, variables=args.variables)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtime_mean_latlon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgrd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/mom6_tools/latlon_analysis.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(args, grd, variables)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'%s, [%s] averaged over years %s-%s'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_date\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0mextend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'both'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m             save=filename)\n\u001b[1;32m    174\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m           m6plot.xyplot( data , grd.geolon, grd.geolat, area=area,\n\u001b[0m\u001b[1;32m    176\u001b[0m             \u001b[0msuptitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcase_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'%s, [%s] averaged over years %s-%s'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_date\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mextend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'both'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/mom6_tools/m6plot.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(field, x, y, area, xlabel, xunits, ylabel, yunits, title, suptitle, clim, colormap, extend, centerlabels, nbins, landcolor, axis, add_cbar, aspect, resolution, sigma, annotate, ignore, save, debug, show, interactive, logscale)\u001b[0m\n\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m   \u001b[0;31m# Diagnose statistics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmaskedField\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmasked_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mignore\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmaskedField\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 604\u001b[0;31m   \u001b[0msMin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msMax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msMean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msStd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msRMS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmyStats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaskedField\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marea\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m   \u001b[0mxLims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mboundaryStats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxCoord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m   \u001b[0myLims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mboundaryStats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myCoord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/mom6_tools/m6plot.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(s, area, debug)\u001b[0m\n\u001b[1;32m   1307\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'myStats: max(s) ='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msMax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0marea\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msMin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msMax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1309\u001b[0m   \u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marea\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1310\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'myStats: sum(area) ='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1311\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnomask\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1312\u001b[0m   \u001b[0msumArea\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'myStats: sum(area) ='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msumArea\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'after masking'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1314\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'myStats: sum(s) ='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    881\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m             \u001b[0;31m# Coordinates in key, value and self[key] should be consistent.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    883\u001b[0m             \u001b[0;31m# TODO Coordinate consistency in key is checked here, but it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m             \u001b[0;31m# causes unnecessary indexing. It should be optimized.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m                 \u001b[0massert_coordinate_consistent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    872\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_coord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    875\u001b[0m             \u001b[0;31m# xarray-style array indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 876\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_key_to_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, indexers, drop, missing_dims, **indexers_kwargs)\u001b[0m\n\u001b[1;32m   1497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1498\u001b[0m         \u001b[0mindexers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meither_dict_or_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"isel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_fancy_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1501\u001b[0;31m             ds = self._to_temp_dataset()._isel_fancy(\n\u001b[0m\u001b[1;32m   1502\u001b[0m                 \u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmissing_dims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m             )\n\u001b[1;32m   1504\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, indexers, drop, missing_dims)\u001b[0m\n\u001b[1;32m   3012\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3013\u001b[0m         \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3014\u001b[0m         \u001b[0mmissing_dims\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mErrorOptionsWithWarn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3015\u001b[0m     ) -> Self:\n\u001b[0;32m-> 3016\u001b[0;31m         \u001b[0mvalid_indexers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_indexers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing_dims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3017\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3018\u001b[0m         \u001b[0mvariables\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3019\u001b[0m         \u001b[0mindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_variables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misel_indexes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_indexers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/glade/work/gmarques/conda-envs/mom6-tools/lib/python3.11/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, indexers, missing_dims)\u001b[0m\n\u001b[1;32m   2784\u001b[0m                     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2785\u001b[0m                         \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parse_array_of_cftime_strings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2787\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2788\u001b[0;31m                     raise IndexError(\n\u001b[0m\u001b[1;32m   2789\u001b[0m                         \u001b[0;34m\"Unlabeled multi-dimensional array cannot be \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2790\u001b[0m                         \u001b[0;34mf\"used for indexing: {k}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2791\u001b[0m                     )\n",
      "\u001b[0;31mIndexError\u001b[0m: Unlabeled multi-dimensional array cannot be used for indexing: yh"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# plot time averages. If variables is NOT specified, all 2D variables will be plotted.\n",
    "#time_mean_latlon_plot(args,grd, variables=args.variables)\n",
    "time_mean_latlon(args,grd,args.variables)"
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   "execution_count": null,
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   "source": []
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