{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33070195-2d5f-4ee3-b6cb-a658209b1f4d",
   "metadata": {},
   "source": [
    "# Basic Plotting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2a265a-5258-47df-9da2-86afb69cf660",
   "metadata": {},
   "source": [
    "**BEFORE BEGINNING THIS EXERCISE** -  Check that your kernel (upper right corner, above) is `NPL 2023b`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023b`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef5d835-37b7-4a4a-84c1-8053093f1500",
   "metadata": {},
   "source": [
    "_______________\n",
    "This activity was developed primarily by Cecile Hannay and Jesse Nusbaumer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35face4-1542-4ab0-8f04-8220a00b0086",
   "metadata": {},
   "source": [
    "_______________\n",
    "\n",
    "For the atmospheric data, we will look at common variables in the atmospheric diagnostics. This notebook covers 3 basic plotting examples:\n",
    "\n",
    "Exercise 1: Global lat/lon of surface temperature\n",
    "\n",
    "Exercise 2: Zonal mean of short wave cloud forcing\n",
    "\n",
    "Exercise 3: Temperature zonal mean with vertical levels\n",
    "\n",
    "Some of the plotting in these examples are based on the <a href=\"https://github.com/NCAR/ADF\">AMWG Diagnostics Framework (ADF)</a> and some are natively from the `xarray` functionality. `xarray` will be used for the data I/O, analysis, and some plotting, `matplotlib` and `cartopy` will aid in plotting, and `numpy` for calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72e6f251-c6ff-4478-adab-783ac1795e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import cftime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.path as mpath\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from matplotlib.lines import Line2D\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d56ba02-c652-4a97-a466-d3633e53aeb1",
   "metadata": {},
   "source": [
    "The first step is to grab an atmosphere (CAM) history file from your CESM model run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20d4eec8-5332-44a5-bd5c-910048c5d95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b1850.run_length/atm/hist/b1850.run_length.cam.h0.0003-07.nc'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set your username here:\n",
    "username = \"PUT_USER_NAME_HERE\"\n",
    "\n",
    "# Here we point to the archive directory from your b1850.run_length simulation\n",
    "monthly_output_path = f\"/glade/derecho/scratch/{username}/archive/b1850.run_length/atm/hist\"\n",
    "\n",
    "# If you were unable to successfully run the b1850.run_length simulation, then feel free to use\n",
    "# this provided simulation data instead:\n",
    "#monthly_output_path = \"/glade/campaign/cesm/tutorial/tutorial_2023_archive/b1850.run_length/atm/hist\"\n",
    "\n",
    "# Name of history file to plot\n",
    "file_name = \"b1850.run_length.cam.h0.0003-07.nc\"\n",
    "\n",
    "files = os.path.join(monthly_output_path, file_name)\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa978aa8-bf89-4971-a59b-ae2fafb9e0a5",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b1850.run_length/atm/hist/b1850.run_length.cam.h0.0003-07.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b1850.run_length/atm/hist/b1850.run_length.cam.h0.0003-07.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'da467520-dd8b-4048-a691-3445d0f0f93e']",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m ds\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/api.py:566\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    555\u001b[0m     decode_cf,\n\u001b[1;32m    556\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    562\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    563\u001b[0m )\n\u001b[1;32m    565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 566\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    567\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    568\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    570\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    571\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    572\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    573\u001b[0m     backend_ds,\n\u001b[1;32m    574\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    585\u001b[0m )\n\u001b[1;32m    586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:590\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]  # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    571\u001b[0m     filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    587\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    588\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m    589\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 590\u001b[0m     store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    598\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    599\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    600\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    602\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    603\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:391\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    385\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    386\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m    387\u001b[0m )\n\u001b[1;32m    388\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    389\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m    390\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:338\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m    337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:400\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:394\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 394\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m    395\u001b[0m         ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/contextlib.py:135\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2464\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2027\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b1850.run_length/atm/hist/b1850.run_length.cam.h0.0003-07.nc'"
     ]
    }
   ],
   "source": [
    "ds = xr.open_dataset(files)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d93758-aedb-42ab-810c-257a37dae487",
   "metadata": {},
   "source": [
    "_______________\n",
    "## Exercise 1: Make a lat-lon plot of TS\n",
    "\n",
    "To highlight plotting the variables from the CESM atmosphere (CAM) file, the first example will plot a simple global lat/lon plot of surface temperature `TS`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6b83ad",
   "metadata": {},
   "source": [
    "### Grab data from first time stamp\n",
    "\n",
    "NOTE: This dataset has <em>only</em> one time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb6d347-cbcb-407c-bb4d-6d89c69fcf32",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_0 = ds.TS.sel({\"time\": ds.TS.time.values[0]}).squeeze()\n",
    "ts_0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1a965c",
   "metadata": {},
   "source": [
    "The next step is to set up the map. Since we are plotting a global lat/lon, we will use the \"Plate Carree\" projection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b183aba-6eae-48ad-9545-f3c918fb99ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the colormap\n",
    "cmap = mpl.colormaps[\"jet\"]\n",
    "\n",
    "# set up the figure with a Plate Carree projection\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "\n",
    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "\n",
    "# Plot the first timeslice of TS\n",
    "img = ax.pcolormesh(ds.lon, ds.lat, ts_0, cmap=cmap, transform=ccrs.PlateCarree())\n",
    "\n",
    "plt.title(\"Surface Temperature\", fontsize=20)\n",
    "\n",
    "# Set up colorbar\n",
    "plt.colorbar(img, orientation=\"vertical\", fraction=0.0242, pad=0.01)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd1f942-a16d-490b-98e6-5f57e92a1cce",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for the solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_1.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bedaea9-73cf-4011-b928-e8c2de752737",
   "metadata": {},
   "source": [
    "**Question:**\n",
    "\n",
    "The colorbar limits are set automatically by `pcolormesh`. How could you change the arguments for the `pcolormesh` function to set the plotting limits?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18f7699-44a7-46f9-9503-1d97fb9418f7",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "Choose a maximum temperature of 300K and minimum temperature of 225K.\n",
    "    \n",
    "```python\n",
    "img = ax.pcolormesh(ds.lon, ds.lat, ts_0, vmax=300, vmin=225, cmap=cmap, transform=ccrs.PlateCarree())\n",
    "\n",
    "```\n",
    "    \n",
    "</details>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20dc7be8",
   "metadata": {},
   "source": [
    "**Question:**\n",
    "\n",
    "How could we change the central longitude?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a14d27-a027-4774-87bf-63712f1f88b6",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "The default central longitude is 0. Try setting it to 180. Then try other values from 0-360.\n",
    "\n",
    "```python\n",
    "ax = fig.add_subplot(1,1,1, projection=ccrs.PlateCarree(central_longitude=180))\n",
    "```\n",
    "    \n",
    "</details>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c632aed7",
   "metadata": {},
   "source": [
    "A second quick example is for `xarray`'s built-in plotting which uses the `matplotlib` and `cartopy` in the background. `xarray` makes creating a basic plot fairly simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f48694b-1eae-4b88-87b3-9aec97ed7ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Xarray native plotting\n",
    "\n",
    "# Set up figure and axis\n",
    "fig, ax = plt.subplots(1, figsize=(20, 10))\n",
    "\n",
    "# Plot the data straight from the xarray dataset\n",
    "ts_0.plot.contourf(cmap=\"jet\", levels=np.arange(220, 321, 5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47469f3-3b57-41c9-9bb2-f56a295bf027",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for the solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_2.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d12294c-691b-491f-bf88-be4c267a5c73",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b295a0-4675-4d4e-814f-5aac53b90973",
   "metadata": {},
   "source": [
    "## Exercise 2: Zonal plot of SWCF\n",
    "\n",
    "The second example will plot the short wave cloud forcing (`SWCF`) zonally."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765089ca",
   "metadata": {},
   "source": [
    "Grab the variable data and mean over the the lon value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "801cf760-f576-4cea-9dd6-0dadfc1788a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_swcf = ds.SWCF\n",
    "\n",
    "# Get all the dataset dimensions\n",
    "d = ds_swcf.dims\n",
    "\n",
    "# Grab all dimensions to mean the lon values from\n",
    "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n",
    "\n",
    "# Make new dataset of zonal mean values\n",
    "ds_swcf_zonal = ds_swcf.mean(dim=davgovr)\n",
    "\n",
    "# print some values of new zonally-averaged SWCF variable\n",
    "ds_swcf_zonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b92a7bdd-b2dc-4a2c-a6e9-9b75e361492a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure and axis\n",
    "fig, ax = plt.subplots(1, figsize=(12, 7))\n",
    "\n",
    "# Set Main title for subplots:\n",
    "plt.title(\"Short Wave Cloud Forcing\", fontsize=20)\n",
    "\n",
    "# Plot value on y-axis and latitude on the x-axis\n",
    "ax.plot(ds_swcf_zonal.lat, ds_swcf_zonal, c=\"goldenrod\")\n",
    "\n",
    "ax.set_xlim(\n",
    "    [max([ds_swcf_zonal.lat.min(), -90.0]), min([ds_swcf_zonal.lat.max(), 90.0])]\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Latitude\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ccd4c3-3c40-4b61-9ac1-3df14249eed1",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for the solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_3.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cd24f4",
   "metadata": {},
   "source": [
    "**Question**\n",
    "\n",
    "What code could you add to set a legend for the plot line?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afcc9ee3-0312-4440-b720-a547382a960a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "enter the following lines after the `ax.set_xlabel` command and before the `plt.show()` command. \n",
    "    \n",
    "```python\n",
    "label = ds_swcf.time.values[0].strftime() # -> '0001-02-01 00:00:00'\n",
    "line = Line2D([0], [0], label=label,\n",
    "                        color=\"blue\")\n",
    "                        \n",
    "fig.legend(handles=[line],bbox_to_anchor=(-0.15, 0.15, 1.05, .102),loc=\"right\",\n",
    "                   borderaxespad=0.0,fontsize=16,frameon=False)\n",
    "```\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe1163c-0ec0-4f9e-94df-4163078c8803",
   "metadata": {},
   "source": [
    "**Question**\n",
    "\n",
    "What else could you label the line legend, if anything?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5cc83e2-3385-472f-864a-88307c6195c2",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "Try setting the label value to something else like your simulation run name, the season or month, etc.\n",
    "    \n",
    "```\n",
    "label = 'Season '\n",
    "```\n",
    "\n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7018dc2-c95b-4643-b078-d5033a6f236e",
   "metadata": {},
   "source": [
    "**Question**\n",
    "\n",
    "How can you change the color on the plot and the legend?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfc85e8-831b-4ead-a5e9-aa25ce2194ff",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "To change colors, try a different named color (e.g. \"red\" or \"goldenrod\"). You should make sure both the plot and the legend match or your plot won't make sense.\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a42e67-2609-4f07-8485-bc8f5035e14a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for a solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_4.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d50620e-407d-44ef-bce5-e76c076bcbcd",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49402bdd-648e-4021-af0e-9280067b7d76",
   "metadata": {},
   "source": [
    "## Exercise 3: Plot of zonal T\n",
    "\n",
    "This example will plot the 3D zonal mean of temperature `T` with the pressure as the y-variable and latitude as the x-variable. We are showing you four different ways to make the same plot since each is valuable for plots with pressure on the y-axis and how to format those to get more information from the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "619d661b-84c9-4a5f-8366-36d8023f082e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all dimensions of length one\n",
    "ds_t = ds.T.squeeze()\n",
    "ds_t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eee4a65-9094-41e6-b905-b6623d62bde4",
   "metadata": {},
   "source": [
    "### Plot 1: Natively via xarray\n",
    "\n",
    "This plot uses the xarray native grids for the plot\n",
    "\n",
    " - y-axis is <strong>increasing</strong> with height\n",
    " - y-axis is <strong>not</strong> in log pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d7c3b5c-96ec-4da6-af39-7c4460827f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average over all dimensions except `lev` and `lat`.\n",
    "d = ds_t.dims\n",
    "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n",
    "\n",
    "DS = ds_t.mean(dim=davgovr)\n",
    "DS.transpose(\"lat\", \"lev\", ...)\n",
    "fig, ax = plt.subplots(1, figsize=(20, 10))\n",
    "DS.plot.contourf(ax=ax, y=\"lev\", cmap=\"Spectral_r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5324e5a3-c6b4-4213-bf11-6184338a63e2",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for a solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_5.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8a57a1-0e26-4740-8182-bf4da973c911",
   "metadata": {},
   "source": [
    "### Plot 2: Quick ADF style\n",
    "\n",
    "This plot uses the quick style of the AMWG Diagnostics Framework (ADF) packages.\n",
    "\n",
    " - y-axis is <strong>increasing</strong> with height\n",
    " - y-axis is <strong>not</strong> in log pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2440adfe-60ab-4feb-832c-496423057404",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average over all dimensions except `lev` and `lat`.\n",
    "d = ds_t.dims\n",
    "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n",
    "\n",
    "DS = ds_t.mean(dim=davgovr)\n",
    "\n",
    "lev = DS[\"lev\"]\n",
    "lat = DS[\"lat\"]\n",
    "mlev, mlat = np.meshgrid(lev, lat)\n",
    "\n",
    "# Generate zonal plot:\n",
    "fig, ax = plt.subplots(1, figsize=(15, 7))\n",
    "\n",
    "# Create zonal plot with vertical levels\n",
    "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n",
    "\n",
    "# Format axis and ticks\n",
    "ax.set_xlabel(\"Latitude\")\n",
    "fig.colorbar(img, ax=ax, location=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0ac9e7-4a8e-498a-be2b-3a7ddd8cca25",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for a solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_6.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f31b9b-db5d-4e2a-81f9-df146980dec0",
   "metadata": {},
   "source": [
    "**Questions**\n",
    "\n",
    "- What differences do you see between the ADF quick plot and the xarray native plot? \n",
    "- Why might you want to use one or the other?\n",
    "- Where, vertically, do high pressures occur and what does this mean for where the ground would be located on this plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e8b966-f242-4e43-bb51-9deaa77a6bc6",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "Notice that the colorbar and axes labels are different between the two plots.  Also note that the y-axis is going from lower to higher pressure values, but in the real atmosphere the highest pressures are near the surface (so the plot is inverted relative to the actual height of the layers above the Earth's surface). \n",
    "    \n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e866346a-2d31-4df9-955c-b7a2ae00b888",
   "metadata": {},
   "source": [
    "### Plot 3: ADF style with reversed y-axis\n",
    "\n",
    " - y-axis is <strong>decreasing</strong> with height\n",
    " - y-axis is <strong>not</strong> in log pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c08d64d9-40d9-4064-9b06-2b8489d916a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average over all dimensions except `lev` and `lat`.\n",
    "d = ds_t.dims\n",
    "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n",
    "\n",
    "DS = ds_t.mean(dim=davgovr)\n",
    "\n",
    "# print(DS.lev.min(),DS.lev.max())\n",
    "\n",
    "lev = DS[\"lev\"]\n",
    "lat = DS[\"lat\"]\n",
    "mlev, mlat = np.meshgrid(lev, lat)\n",
    "\n",
    "# Generate zonal plot:\n",
    "fig, ax = plt.subplots(1, figsize=(15, 7))\n",
    "\n",
    "# Create zonal plot with vertical levels\n",
    "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n",
    "\n",
    "# Format axis and ticks\n",
    "plt.gca().invert_yaxis()\n",
    "ax.tick_params(which=\"minor\", length=4, color=\"r\")\n",
    "\n",
    "# Set up colorbar\n",
    "cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])\n",
    "posn = ax.get_position()\n",
    "\n",
    "# Set position and size of colorbar position: [left, bottom, width, height]\n",
    "cbar_ax.set_position([posn.x0 + posn.width + 0.005, posn.y0, 0.02, posn.height])\n",
    "\n",
    "ax.set_xlabel(\"Latitude\")\n",
    "fig.colorbar(img, cax=cbar_ax, orientation=\"vertical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0aa084-f95e-4951-81b0-3951e8ed0b29",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for a solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_7.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0beeca6-4697-4bc8-b42d-e43ccceecbef",
   "metadata": {},
   "source": [
    "**Question:**\n",
    "\n",
    "- What differences do you see on the y-axis between the result from Plot 2 and Plot 3? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd69767-20ac-49ff-9c1f-ade44886de56",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "The plots are mirror images of each other because the y-axis has been flipped. Plot 2 has the highest pressures at the top of the y-axis while Plot 3 has the highest pressures at the bottom of the y-axis.\n",
    "    \n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd94a75-58cb-4b5d-a806-45b64c26e727",
   "metadata": {},
   "source": [
    "**Question:**\n",
    "\n",
    "- Where, vertically, do high pressures occur and what does this mean for where the ground would be located on these two figures ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1b7ab1-20ff-4745-bad7-cf50c9b807b8",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "The highest pressures in the atmosphere occur at the ground where the most atmospheric mass is pressing downward. This means that Plot 2 is oriented so the location where the ground is is the top of the plot, or up. In Plot 3 the orientation is so that the ground would be at the bottom of the plot, or down. Thus Plot 3 is often more intuitive to read because it's oriented with the way we perceive the atmosphere.\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84fc1ae8-74f3-4c4f-9171-18b5625f2d96",
   "metadata": {},
   "source": [
    "### Plot 4: Complex ADF style\n",
    "\n",
    " - y-axis is <strong>decreasing</strong> with height\n",
    " - y-axis <strong>is</strong> in log pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50564cf7-948e-4b77-9537-707b7358bf71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average over all dimensions except `lev` and `lat`.\n",
    "d = ds_t.dims\n",
    "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n",
    "\n",
    "DS = ds_t.mean(dim=davgovr)\n",
    "\n",
    "# print(DS.lev.min(),DS.lev.max())\n",
    "\n",
    "lev = DS[\"lev\"]\n",
    "lat = DS[\"lat\"]\n",
    "mlev, mlat = np.meshgrid(lev, lat)\n",
    "\n",
    "# Generate zonal plot:\n",
    "fig, ax = plt.subplots(1, figsize=(15, 7))\n",
    "\n",
    "# Create zonal plot with vertical levels\n",
    "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n",
    "\n",
    "# Format axis and ticks\n",
    "plt.yscale(\"log\")\n",
    "plt.gca().invert_yaxis()\n",
    "ax.tick_params(which=\"minor\", length=4, color=\"r\")\n",
    "\n",
    "# Set up colorbar\n",
    "cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])\n",
    "posn = ax.get_position()\n",
    "\n",
    "# Set position and size of colorbar position: [left, bottom, width, height]\n",
    "cbar_ax.set_position([posn.x0 + posn.width + 0.005, posn.y0, 0.02, posn.height])\n",
    "\n",
    "ax.set_xlabel(\"Latitude\")\n",
    "fig.colorbar(img, cax=cbar_ax, orientation=\"vertical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a8fb095-89fd-4112-9d4f-de5f4ee9f143",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">   \n",
    "<details>\n",
    "<summary><font face=\"Times New Roman\" color='blue'>Click here for a solution</font></summary><br>\n",
    "\n",
    "![plot example](../../../images/diagnostics/cam/basics_plot_8.png)\n",
    "\n",
    "*<p style=\"text-align: center;\"> Figure: Plotting solution. </p>*\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d7c9ee8-9445-489f-9b07-3ab67e45398d",
   "metadata": {},
   "source": [
    "**Question:**\n",
    "\n",
    "- What differences do you see on the y-axis between the result from Plot 3 and Plot 4? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f931e791-2f0a-423f-b44b-644686657767",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "The plots both have the y-axis oriented so that the highest pressures, or ground, is at the bottom of the plot. But the scale of the y-axis has changed so that in Plot 3 it is logarithmic.\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a6af568-90cf-4f8c-a3c8-5dcda30359fd",
   "metadata": {},
   "source": [
    "**Question:**\n",
    "\n",
    "- What does that mean in terms of which part of the atmosphere are emphasized?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa7131d-4337-4da2-8b8d-db9a69432142",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">  \n",
    "<details>\n",
    "\n",
    "<summary> <font face=\"Times New Roman\" color='blue'>Click here for hints</font> </summary>\n",
    "\n",
    "With the logarithmic y-axis more of the upper atmosphere is shown on the figure. If you wanted to emphasize the boundary layer you might want to change the limits of the figure.\n",
    "    \n",
    "</details>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c7c4e8c-4a4a-4f3e-a6d7-4b5e7725a2f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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