Scripts/Stephy/MLD_calc_Denisse.ipynb

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   "outputs": [],
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  },
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   "cell_type": "markdown",
   "id": "e268183f-9bd5-4aa9-a8e3-affc0446d7b8",
   "metadata": {},
   "source": [
    "# WL climatology for lat-min at -70"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b37674-7f90-40ea-8a72-9e638b0f9aed",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d74f7141-7d28-4ac1-a80e-fb67ab516cb8",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "# Monthly data and temporal variability\n",
    "\n",
    "#model\n",
    "expt    = '01deg_jra55v140_iaf'\n",
    "model   = 'access-om2-01'\n",
    "var1 = 'aice_m'\n",
    "var2 = 'temp'\n",
    "var3 = 'salt'\n",
    "var4 = 'pot_rho_0'\n",
    "\n",
    "#time domain\n",
    "yrst    = 1985 #1985\n",
    "yren    = 2015\n",
    "#start and end times\n",
    "st = str(yrst)+'-01-02'   #ACCESS OM dates are at 00:00Z, so a start date of 1st Jan would give you December of prev year\n",
    "en = str(yren)+'-12-30'\n",
    "\n",
    "#Common latitude and depth range\n",
    "\n",
    "# Latitude between 85 S and 55 S [85 S range for Weddell and Ross Gyre]\n",
    "latmin  = -75. # TESTING 75 S      # in the previous code it was -70\n",
    "latmax  = -50.\n",
    "\n",
    "# Depth [Upper 300m]\n",
    "zmin    = 0.\n",
    "zmax    = 500      #250  #400.\n",
    "\n",
    "# Set the longitude range for each sector in a list to read it in easier\n",
    "kh   =  [-15, 70, -55]         # 345,  70  # King Haakon\n",
    "ea   =  [70, -210, -57]        #  70 ,150  # East Antarctic\n",
    "rs   =  [-210, -135, -62]      # 150, 250  # Ross Sea\n",
    "am   =  [-135, -90, -63]       # 250, 290  # Amundsen sea\n",
    "bs   =  [-90, -60, -60]                    # Bellingshausen sea\n",
    "wl   =  [-60, -15, -58]        # 290, 345  # Weddell sea\n",
    "\n",
    "sectors = [kh, ea, rs, am, bs, wl]\n",
    "\n",
    "# winter ice extent and northern latitude bound for kh, ea, ra, ab, wl\n",
    "winter_sie = [55, 57, 62, 63, 58]\n",
    "\n",
    "# Put sector names in a list for plot titles\n",
    "\n",
    "sNam    = ['King Haakon', 'East Antarctic', 'Ross Sea', 'Amundsen Sea', 'Bellingshausen Sea', 'Weddell Sea']\n",
    "sNam2   = ['KH', 'EA', 'RS', 'AM', 'BS', 'WL']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ef297b-713d-44ca-a175-677e699dfa76",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64769707-c48b-4a4e-963a-9ca16b9661ab",
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   "outputs": [
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       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-c450a6cc-741d-11ec-aacd-40f2e9c7ecbe</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
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       "\n",
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       "                    <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
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       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
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       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">fcf956e3</p>\n",
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       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
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       "                    <strong>Workers:</strong> 4\n",
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       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 8\n",
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       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 120.00 GiB\n",
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       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
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       "\n",
       "            \n",
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       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
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       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
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       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-2e908016-cbc4-4d25-a553-2214f53e47c8</p>\n",
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       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:33283\n",
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       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
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       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
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       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 8\n",
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       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
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       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 120.00 GiB\n",
       "                    </td>\n",
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       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
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       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
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       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://10.6.121.14:35737\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38095/status\" target=\"_blank\">/proxy/38095/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 30.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46107\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/33747342.gadi-pbs/dask-worker-space/worker-bez8kyg1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
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       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://10.6.121.14:42337\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39517/status\" target=\"_blank\">/proxy/39517/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 30.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42453\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/33747342.gadi-pbs/dask-worker-space/worker-3tfa71rc\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
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       "            </details>\n",
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       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://10.6.121.14:36087\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41147/status\" target=\"_blank\">/proxy/41147/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 30.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37249\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/33747342.gadi-pbs/dask-worker-space/worker-rt_9k2ot\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://10.6.121.14:34261\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38839/status\" target=\"_blank\">/proxy/38839/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 30.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43903\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/33747342.gadi-pbs/dask-worker-space/worker-6nctjdjc\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:33283' processes=4 threads=8, memory=120.00 GiB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot destination\n",
    "%matplotlib inline\n",
    "\n",
    "#set up session\n",
    "\n",
    "import cosima_cookbook as cc\n",
    "\n",
    "import IPython.display\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import cmocean as cm\n",
    "import pandas as pd\n",
    "import cartopy.crs as crs\n",
    "import scipy.stats\n",
    "import scipy.signal\n",
    "import dask.array\n",
    "\n",
    "#connect to database\n",
    "session = cc.database.create_session(db='/g/data/ik11/databases/cosima_master.db')\n",
    "#db='/g/data/ik11/databases/cosima_master.db'\n",
    "#db='/g/data/hh5/tmp/cosima/database/access-om2.db'\n",
    "\n",
    "from dask.distributed import Client\n",
    "cl=Client(n_workers=4)\n",
    "cl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c756b08c-540a-40ec-bb36-1908ccd5c6d4",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef6fe3dc-8836-423c-840f-6c43efc9bda5",
   "metadata": {},
   "source": [
    "MLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89f54745-e7c6-4b91-8eaf-b0cb454b6d40",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1 µs, total: 4 µs\n",
      "Wall time: 6.2 µs\n"
     ]
    }
   ],
   "source": [
    "#Slice ocean variables while loading Depth range and to Southern Hemisphere\n",
    "\n",
    "#Since we are loading the climatologies of T, S, and Rho we dont need to load this again, but we need to load MLD \n",
    "\n",
    "#2 Temperature \n",
    "OM_temp = cc.querying.getvar(expt,var2,session,start_time=st,end_time=en, ncfile='ocean-3d-temp-1-monthly-mean-ym_%.nc').sel(yt_ocean= slice(latmin, latmax), st_ocean= slice(zmin, zmax))\n",
    "\n",
    "atr = OM_temp.attrs\n",
    "OM_temp = OM_temp-273.15 # Convert Kelvin to Celcius  # This calculation also removed the attributes from the array, so we fix that\n",
    "OM_temp.attrs = atr\n",
    "#OM_temp.coords   # Print the loaded file to check for year ranges etc\n",
    "\n",
    "#3 Salt\n",
    "OM_salt = cc.querying.getvar(expt,var3,session,start_time=st,end_time=en, ncfile='ocean-3d-salt-1-monthly-mean-ym_%.nc' ).sel(yt_ocean= slice(latmin, latmax), st_ocean= slice(zmin, zmax))\n",
    "OM_salt.data\n",
    "\n",
    "#4 Density\n",
    "OM_rho = cc.querying.getvar(expt,var4,session,start_time=st,end_time=en).sel(yt_ocean= slice(latmin, latmax), st_ocean= slice(zmin, zmax))\n",
    "\n",
    "%time\n",
    "\n",
    "#\n",
    "\n",
    "OM_mld = cc.querying.getvar(expt,'mld',session,start_time=st,end_time=en, ncfile='ocean-2d-mld-1-monthly-mean-ym_%.nc' ).sel(yt_ocean= slice(latmin, latmax))\n",
    "\n",
    "# Area\n",
    "A = cc.querying.getvar(expt,'area_t',session, n=1)\n",
    "\n",
    "#OM_temp.coords   # Print the loaded file to check for year ranges etc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2969fe41-4ab5-46a3-899a-3b9c91f7167b",
   "metadata": {},
   "source": [
    "### Functions to calculate WEIGHTED sector average from raw T, S, Rho and mld data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ecc7979-7921-40b9-8349-f9888d578d1f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def sector_mean(data, area, region):\n",
    "    \"\"\"\n",
    "    Create spatially averaged dataarray for the\n",
    "    data: Temperature or salinity dataset\n",
    "            OM_temp, OM_salt\n",
    "    region: Sea ice sectors\n",
    "            kh, ea, ra, ab, wl\"\"\"\n",
    "    \n",
    "    # Step one: slice to latitude ranges\n",
    "    \n",
    "    region_slice = data.sel(yt_ocean=slice(latmin, region[2]))\n",
    "    area_slice   = area.sel(yt_ocean=slice(latmin, region[2]))\n",
    "    \n",
    "    # Step two: create the weighted dataset\n",
    "    region_weighted = region_slice.weighted(area_slice.fillna(0)).mean('yt_ocean')\n",
    "    \n",
    "    # Step three: do the longitudinal selection and average\n",
    "    \n",
    "    \n",
    "    # Region slice\n",
    "    if region[0]>region[1]:\n",
    "        region_slice=region_weighted.isel(xt_ocean = ( data.xt_ocean > region[0] ) | (data.xt_ocean < region[1]) )\n",
    "        region_slice=region_slice.sel( st_ocean=slice(zmin, zmax))\n",
    "    else:\n",
    "        region_slice=region_weighted.sel(xt_ocean=slice(region[0],region[1]), st_ocean=slice(zmin, zmax))\n",
    "        \n",
    "          \n",
    "    spatial_mean=region_slice.mean('xt_ocean')\n",
    "   \n",
    "    \n",
    "    return spatial_mean\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def mld_sector_mean(data, area, region):\n",
    "    \"\"\"\n",
    "    Create spatially averaged dataarray for the\n",
    "    data: Temperature or salinity dataset\n",
    "            OM_temp, OM_salt\n",
    "    region: Sea ice sectors\n",
    "            kh, ea, ra, ab, wl\"\"\"\n",
    "    \n",
    "    # Step one: slice to latitude ranges\n",
    "    \n",
    "    region_slice = data.sel(yt_ocean=slice(latmin, region[2]))\n",
    "    area_slice   = area.sel(yt_ocean=slice(latmin, region[2]))\n",
    "    \n",
    "    # Step two: create the weighted dataset\n",
    "    region_weighted = region_slice.weighted(area_slice.fillna(0)).mean('yt_ocean')\n",
    "    \n",
    "    # Step three: do the longitudinal selection and average    \n",
    "    \n",
    "    if region[0]>region[1]:\n",
    "        region_slice=region_weighted.isel(xt_ocean = ( data.xt_ocean > region[0] ) | (data.xt_ocean < region[1]) )\n",
    "    else:\n",
    "        region_slice=region_weighted.sel(xt_ocean=slice(region[0],region[1]))\n",
    "             \n",
    "        \n",
    "    spatial_mean=region_slice.mean('xt_ocean')\n",
    "   \n",
    "    \n",
    "    return spatial_mean\n",
    "\n",
    "# Function to get the dT/dz max to overlay on plot\n",
    "\n",
    "def dTdz_max_depth(dTdz_dat, dummy_dat):\n",
    "    \n",
    "    ''' Input dTdz data (numpy array) along with respective sector climatology array\n",
    "        or any other array with similar dimension to get the metadata and depth values   \n",
    "    '''\n",
    "    \n",
    "    s_dat2 = dTdz_dat[1,...]                \n",
    "    s_dat = dummy_dat                \n",
    "\n",
    "    # Get the max and min from the dT/dz and dS/dz\n",
    "    ## # Need the min value only when it is negative and need the max for all months\n",
    "    \n",
    "    min_t = np.argmin(s_dat2, axis=1) # These give the index along the depth axis where the max of min values are encountered\n",
    "    max_t = np.argmax(s_dat2, axis=1)\n",
    "\n",
    "    # For now, let's only work with the dT/dz min\n",
    "    \n",
    "    # Empty list for dT/dz max and min values\n",
    "    dT_dz_max = []\n",
    "    # dT_dz_min = []\n",
    "    \n",
    "    # Empty list for depth at which the values occur\n",
    "    #dz_min = []\n",
    "    dz_max = []\n",
    "    \n",
    "    # Access and append the values and depth in loop\n",
    "    for i in range (len(max_t)):  # Range 12 or according to length of the input data\n",
    "\n",
    "        # get the values of dT/dz and dS/dz\n",
    "        val = s_dat.values\n",
    "     #   val_min = val[i, min_t[i]]\n",
    "        val_max = val[i, max_t[i]]\n",
    "      #  dT_dz_min.append(val_min)\n",
    "        dT_dz_max.append(val_max)\n",
    "\n",
    "        # get the depth\n",
    "        depth = s_dat.st_ocean.values \n",
    "       # d_min = depth[min_t[i]]\n",
    "        d_max = depth[max_t[i]]\n",
    "        #dz_min.append(d_min)\n",
    "        dz_max.append(d_max)\n",
    "\n",
    "    # Create the 24 indice dat for plotting \n",
    "\n",
    "    max_depth = dz_max\n",
    "    #max_depth.extend(dz_max)\n",
    "    dummy=dz_max\n",
    "    max_depth.extend(dummy)\n",
    "    max_val = dT_dz_max\n",
    "    max_val.extend(max_val)\n",
    "\n",
    "    dTdz_returns = [dz_max, dT_dz_max]\n",
    "#     print(shape(max_depth)\n",
    "    \n",
    "    return max_depth\n",
    "# to test: tt1 = dTdz_max_depth(kh_dTdz,  kh_Tc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc63d4f-206b-41e5-87a5-2a00da36caf3",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68b0406-3e3e-4b87-9801-07fee8ffb681",
   "metadata": {},
   "source": [
    "plotting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a241d606-4876-4ea6-b11c-f73a9a9a528d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def temp_climatology_plot_WL (data, data_dTdz, data_dDdz, data_mld):\n",
    "    \n",
    "    # colormap, levels \n",
    "    \n",
    "#     lev = np.arange(-2,1.5,0.1)\n",
    "#     cont_lev = [-1.5, 0 ]  #np.arange(-1.5, 1.5, 0.5)\n",
    "    \n",
    "    lev = np.arange(-1.8,1.2,0.1)\n",
    "    cont_lev = [-1.5, 0 ]  #np.arange(-1.5, 1.5, 0.5)\n",
    "    \n",
    "    \n",
    "    colmap = cm.cm.thermal    #'bwr'   \n",
    "    xlabels2=['J','F','M','A','M','J','J','A','S','O','N','D']\n",
    "    x=list(range(12))\n",
    "    \n",
    "    ax = plt.gca()\n",
    "    \n",
    "    # Plotting\n",
    "    # countour fill\n",
    "    p = plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False, extend='max')\n",
    "    \n",
    "    # Contour - thick + labels\n",
    "    cont1=plt.contour(x, data.st_ocean, data.transpose(), levels=[-1.5],   colors='darkgrey', linewidths=0.5) # linewidth = 1; default\n",
    "    cont2=plt.contour(x, data.st_ocean, data.transpose(), levels=[-0.5],   colors='lightgrey', linewidths=0.75) # linewidth = 1; default\n",
    "    cont3=plt.contour(x, data.st_ocean, data.transpose(), levels=[0.0],   colors='k', linewidths=0.75, alpha=0.5) # linewidth = 1; default\n",
    "    \n",
    "    \n",
    "    c_lab = ax.clabel(cont1, fmt='%1.1f', colors='lightgrey', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "    \n",
    "    c_lab = ax.clabel(cont3, fmt='%1.1f', colors='k', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "    \n",
    "    \n",
    "    \n",
    "#     c_lab = ax.clabel(cont, fmt='%1.2f', colors='black', fontsize=12)\n",
    "#     c_lab.set_alpha(.4)\n",
    "    \n",
    "    plt.gca().invert_yaxis()\n",
    "    #plt.colorbar(mappable = p)\n",
    "    ax.set_xticks(np.arange(len(x)))\n",
    "    ax.set_xticklabels(xlabels2,fontsize=10)\n",
    "    \n",
    "    # dT/dz\n",
    "    tt1 = dTdz_max_depth(data_dTdz,  data)\n",
    "    tt1 = tt1[0:12]\n",
    "    plt.plot(x, tt1, color='k', lw=2)\n",
    "    \n",
    "    # dRho/dz\n",
    "    tt2 = dTdz_max_depth(data_dDdz,  data)\n",
    "    tt2 = tt2[0:12]\n",
    "    plt.plot(x, tt2, color='lightgrey', lw=1.5, alpha=0.7)\n",
    "    plt.plot(x, tt2, color='k', lw=1.5, ls='--') #'teal'\n",
    "\n",
    "    \n",
    "    # MLD\n",
    "    plt.plot(data_mld, color='r' )\n",
    "    \n",
    "#     plt.colorbar(p)\n",
    "\n",
    "\n",
    "    \n",
    "def salt_climatology_plot_WL (data, data_dTdz, data_dDdz, data_mld):\n",
    "    \n",
    "    # colormap, levels \n",
    "    \n",
    "#     lev = np.arange(33,35,0.1)\n",
    "#     cont_lev = [33.5, 34, 34.5]    #np.arange(33, 35, 0.25)\n",
    "#     cont_lev2 = np.arange(33.5, 34.5, 0.1)\n",
    "    \n",
    "    lev = np.arange(33.5,34.5,0.05)\n",
    "    cont_lev = [ 34, 34.5]    #np.arange(33, 35, 0.25)\n",
    "    cont_lev2 = np.arange(33.5, 34.5, 0.1)\n",
    "    \n",
    "    colmap = cm.cm.haline  # cm.cm.matter         #'gnuplot_r'     #'jet'  #cm.cm.haline, ice, matter\n",
    "    xlabels2=['J','F','M','A','M','J','J','A','S','O','N','D']\n",
    "    x=list(range(12))\n",
    "    \n",
    "    ax = plt.gca()\n",
    "    \n",
    "    # Plotting\n",
    "    # countour fill\n",
    "    p = plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False, extend='both')\n",
    "    \n",
    "    # Contour - thick + labels\n",
    "    cont=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev,   colors='darkgrey', linewidths=0.5) # linewidth = 1; default\n",
    "    cont2=plt.contour(x, data.st_ocean, data.transpose(), levels=[33.5],   colors='lightgrey', linewidths=0.75) # linewidth = 1; default\n",
    "    \n",
    "\n",
    "    \n",
    "    c_lab = ax.clabel(cont, fmt='%1.1f', colors='black', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "    \n",
    "    c_lab = ax.clabel(cont2, fmt='%1.1f', colors='lightgrey', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "    \n",
    "    \n",
    "    plt.gca().invert_yaxis()\n",
    "    #plt.colorbar(mappable = p)\n",
    "    ax.set_xticks(np.arange(len(x)))\n",
    "    ax.set_xticklabels(xlabels2,fontsize=10)\n",
    "    \n",
    "    # dT/dz\n",
    "    tt1 = dTdz_max_depth(data_dTdz,  data)\n",
    "    tt1 = tt1[0:12]\n",
    "    plt.plot(x, tt1, color='k', lw=2)\n",
    "    \n",
    "    # dRho/dz\n",
    "    tt2 = dTdz_max_depth(data_dDdz,  data)\n",
    "    tt2 = tt2[0:12]\n",
    "    plt.plot(x, tt2, color='lightgrey', lw=1.5, alpha=0.7)\n",
    "    plt.plot(x, tt2, color='k', lw=1.5, ls='--') #'teal'\n",
    "\n",
    "    \n",
    "    # MLD\n",
    "    plt.plot(data_mld, color='r', lw=1.5 ) \n",
    "    \n",
    "#     plt.colorbar(p)\n",
    "\n",
    " \n",
    "    \n",
    " \n",
    "    \n",
    "def dTdz_climatology_plot_WL (data, data_dTdz, data_dDdz, data_mld):\n",
    "    # Use dTdz_1 set for data\n",
    "    data = data*10\n",
    "\n",
    "#     from matplotlib import colors\n",
    "#     divnorm=colors.TwoSlopeNorm(vmin=-0.6, vcenter=0., vmax=0.5)\n",
    "    \n",
    "    #colormap, levels \n",
    "    mnlev = -0.4\n",
    "    mxlev = 0.3\n",
    "    nlev = 10\n",
    "    lev = np.arange(mnlev, mxlev, mxlev/nlev)\n",
    "    cont_lev = [ 0.075, 0.25]   \n",
    "    cont_lev2 = [-0.01]\n",
    "    \n",
    "    from matplotlib import colors\n",
    "    divnorm=colors.TwoSlopeNorm(vmin=mnlev, vcenter=0., vmax=mxlev)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #[ -0.2, 0, 0.2]\n",
    "    \n",
    "#     lev = np.arange(-0.1, 0.5, 0.02)\n",
    "#     cont_lev = [ 0.075, 0.25]   \n",
    "#     cont_lev2 = [-0.1]#[ -0.2, 0, 0.2]\n",
    "    \n",
    "    colmap = cm.cm.balance           #'gnuplot_r'     #'jet'  #cm.cm.haline, ice, matter\n",
    "    xlabels2=['J','F','M','A','M','J','J','A','S','O','N','D']\n",
    "    x=list(range(12))\n",
    "    \n",
    "    ax = plt.gca()\n",
    "    \n",
    "    # Plotting\n",
    "    # countour fill\n",
    "    p = plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False, extend='both', norm=divnorm)\n",
    "    \n",
    "    # Contour - thick + labels\n",
    "    cont=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev,   colors='w', linewidths=1) # linewidth = 1; default\n",
    "\n",
    "    cont2=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev2,   colors='lightgrey', linewidths=1.25) # linewidth = 1; default\n",
    "\n",
    "    dTdz_cont3 = plt.contour( x, data.st_ocean, data.transpose(), colors='w', levels = [0.19], linewidths=1.5 ) #0.20 ***\n",
    "\n",
    "    \n",
    "    c_lab = ax.clabel(cont, fmt='%1.2f', colors='black', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "   \n",
    "    c_lab2 = ax.clabel(cont2, fmt='%1.2f', colors='black', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab2] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "    \n",
    "    \n",
    "    plt.gca().invert_yaxis()\n",
    "    ax.set_xticks(np.arange(len(x)))\n",
    "    ax.set_xticklabels(xlabels2,fontsize=10)\n",
    "    \n",
    "    # dT/dz\n",
    "    tt1 = dTdz_max_depth(data_dTdz,  data)\n",
    "    tt1 = tt1[0:12]\n",
    "    plt.plot(x, tt1, color='k', lw=2)\n",
    "    \n",
    "    # dRho/dz\n",
    "    tt2 = dTdz_max_depth(data_dDdz,  data)\n",
    "    tt2 = tt2[0:12]\n",
    "    plt.plot(x, tt2, color='lightgrey', lw=1.5, alpha=0.7)\n",
    "    plt.plot(x, tt2, color='k', lw=1.5, ls='--') #'teal'\n",
    "    \n",
    "    # MLD\n",
    "    plt.plot(data_mld, color='r', lw=2 ) \n",
    "    \n",
    "#     plt.colorbar(p)\n",
    "  \n",
    "    \n",
    "def dDdz_climatology_plot_WL (data, data_dTdz, data_dDdz, data_mld):\n",
    "    # Use dTdz_1 set for data\n",
    "    data = data*100 \n",
    "    \n",
    "#     # colormap, levels \n",
    "#     lev = np.arange(0.25, 3.25, 0.1) #(0, 3, 0.25)\n",
    "#     cont_lev = [0.25]   #[0.25]\n",
    "#     cont_lev2 = [0.375]\n",
    "#     cont_lev3 = [0.45]\n",
    "\n",
    "    # colormap, levels \n",
    "    lev = np.arange(0.25, 0.75, 0.025) #(0, 3, 0.25)\n",
    "    cont_lev = [0.25]   #[0.25]\n",
    "    cont_lev2 = [0.375]\n",
    "    cont_lev3 = [0.45]\n",
    "\n",
    "\n",
    "    colmap = cm.cm.matter  #deep    #cm.cm.rain        #'gnuplot_r'     #'jet'  #cm.cm.haline, ice, matter\n",
    "    xlabels2=['J','F','M','A','M','J','J','A','S','O','N','D']\n",
    "    x=list(range(12))\n",
    "    \n",
    "    ax = plt.gca()\n",
    "    \n",
    "    # Plotting\n",
    "    # countour fill\n",
    "    p = plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False, extend='both') #levels=lev,\n",
    "    \n",
    "    # Contour - thick + labels\n",
    "    cont=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev, colors='darkgrey', linewidths=0.5) # linewidth = 1; default  levels=cont_lev,\n",
    "    cont2=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev2, colors='darkgrey', linewidths=0.5) # linewidth = 1; default levels=cont_lev2,\n",
    "    cont3=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev3, colors='darkgrey', linewidths=0.75) # linewidth = 1; default levels=cont_lev2,\n",
    "    cont4=plt.contour(x, data.st_ocean, data.transpose(), levels=[0.75], colors='lightgrey', linewidths=0.75) # linewidth = 1; default levels=cont_lev2,\n",
    "    cont5=plt.contour(x, data.st_ocean, data.transpose(), levels=[0.5], colors='grey', linewidths=0.75) # linewidth = 1; default levels=cont_lev2,\n",
    "    \n",
    "    c_lab = ax.clabel(cont4, fmt='%1.2f', colors='w', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "    \n",
    "    c_lab = ax.clabel(cont, fmt='%1.2f', colors='black', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "\n",
    "    \n",
    "    plt.gca().invert_yaxis()\n",
    "#     plt.colorbar(mappable = p)\n",
    "    ax.set_xticks(np.arange(len(x)))\n",
    "    ax.set_xticklabels(xlabels2,fontsize=10)\n",
    "    \n",
    "    # dT/dz\n",
    "    tt1 = dTdz_max_depth(data_dTdz,  data)\n",
    "    tt1 = tt1[0:12]\n",
    "    plt.plot(x, tt1, color='k', lw=2)\n",
    "    \n",
    "    # dRho/dz\n",
    "    tt2 = dTdz_max_depth(data_dDdz,  data)\n",
    "    tt2 = tt2[0:12]\n",
    "    plt.plot(x, tt2, color='lightgrey', lw=1.5, alpha=0.7)\n",
    "    plt.plot(x, tt2, color='k', lw=1.5, ls='--') #'teal'\n",
    "\n",
    "    \n",
    "    # MLD\n",
    "    plt.plot(data_mld, color='r', lw=1.5 )    \n",
    "    \n",
    "#     plt.colorbar(p)   \n",
    "\n",
    "  \n",
    "\n",
    "   \n",
    "    \n",
    "    \n",
    "def dSdz_climatology_plot_WL (data, data_dTdz, data_dDdz, data_mld):\n",
    "    # Use dTdz_1 set for data\n",
    "    data = data*100 \n",
    "    \n",
    "    # colormap, levels \n",
    "    lev = np.arange(0, 4.5, 0.2) #(0, 3, 0.25)\n",
    "    cont_lev = [0.5]   \n",
    "    cont_lev2 = [0.65]\n",
    "    colmap = cm.cm.rain           #'gnuplot_r'     #'jet'  #cm.cm.haline, ice, matter\n",
    "    xlabels2=['J','F','M','A','M','J','J','A','S','O','N','D']\n",
    "    x=list(range(12))\n",
    "    \n",
    "    ax = plt.gca()\n",
    "    \n",
    "    # Plotting\n",
    "    # countour fill\n",
    "    p = plt.contourf(x, data.st_ocean, data.transpose(),  cmap = colmap, levels=lev, yincrease=False, add_colorbar=False, extend='both') #levels=lev,\n",
    "    \n",
    "    # Contour - thick + labels\n",
    "    cont=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev,   colors='darkgrey', linewidths=0.5) # linewidth = 1; default\n",
    "    cont2=plt.contour(x, data.st_ocean, data.transpose(), levels=cont_lev2,   colors='darkgrey', linewidths=0.5) # linewidth = 1; default\n",
    "    \n",
    "    c_lab = ax.clabel(cont, fmt='%1.2f', colors='black', fontsize=10)\n",
    "    [(txt.set_alpha(0.4), txt.set_rotation(0) )for txt in c_lab] #https://stackoverflow.com/questions/15957774/how-to-set-the-background-color-of-contour-labels-in-matplotlib\n",
    "\n",
    "    \n",
    "    plt.gca().invert_yaxis()\n",
    "#     plt.colorbar(mappable = p)\n",
    "    ax.set_xticks(np.arange(len(x)))\n",
    "    ax.set_xticklabels(xlabels2,fontsize=10)\n",
    "    \n",
    "    # dT/dz\n",
    "    tt1 = dTdz_max_depth(data_dTdz,  data)\n",
    "    tt1 = tt1[0:12]\n",
    "    plt.plot(x, tt1, color='k', lw=2)\n",
    "    \n",
    "    # dRho/dz\n",
    "    tt2 = dTdz_max_depth(data_dDdz,  data)\n",
    "    tt2 = tt2[0:12]\n",
    "    #plt.plot(x, tt2, color='w', lw=2.5, alpha=0.7)\n",
    "    plt.plot(x, tt2, color='k', lw=1.5, ls='--') #'teal'\n",
    "    \n",
    "    # MLD\n",
    "    plt.plot(data_mld, color='r', lw=1.5 )\n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44e9e7a8-ab15-4d60-b4ac-a050ad9846f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b09d477f-efa3-4574-96c3-bed69c0dc6d2",
   "metadata": {},
   "source": [
    "## Calculating sector wise averages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca96112-1a1d-46b9-ba6d-1714ccf5c7b2",
   "metadata": {
    "tags": []
   },
   "source": [
    "temp_monthly = OM_temp.groupby('time.month').mean('time')\n",
    "#WL\n",
    "print ('Calculating WL T Clim')\n",
    "wl_Tc = sector_mean(temp_monthly, A, wl)\n",
    "wl_Tc.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_T_test70S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_T.nc ')\n",
    "\n",
    "salt_monthly = OM_salt.groupby('time.month').mean('time')\n",
    "#WL\n",
    "print ('Calculating WL S Clim')\n",
    "wl_Sc = sector_mean(salt_monthly, A, wl)\n",
    "wl_Sc.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_S_test70S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_S.nc ')\n",
    "\n",
    "\n",
    "rho_monthly = OM_rho.groupby('time.month').mean('time')\n",
    "#RS\n",
    "print ('Calculating WL D Clim')\n",
    "wl_Dc = sector_mean(rho_monthly, A, wl)\n",
    "wl_Dc.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_D_test70S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_D.nc ')\n",
    "\n",
    "mld_monthly = OM_mld.groupby('time.month').mean('time')\n",
    "#RS\n",
    "print ('Calculating WL mld Clim')\n",
    "wl_mld = mld_sector_mean(mld_monthly, A, wl)\n",
    "wl_mld.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_mld_test70S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_mld.nc ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74383387-e1e3-45c8-a6cd-75c752e2258d",
   "metadata": {},
   "source": [
    "### Load the calculated data from Gadi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ab11f6c-a46e-4704-bfbe-221fa3280195",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# loading the data back\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_T_test70S_04012022.nc') \n",
    "wl_Tc = da.temp\n",
    "\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_S_test70S_04012022.nc') \n",
    "wl_Sc = da.salt\n",
    "\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_D_test70S_04012022.nc') \n",
    "wl_Dc = da.pot_rho_0\n",
    "\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_mld_test70S_04012022.nc') \n",
    "wl_mld = da.mld\n",
    "\n",
    "wl_dTdz = np.gradient(wl_Tc)/np.gradient(wl_Tc.st_ocean)\n",
    "wl_dSdz = np.gradient(wl_Sc)/np.gradient(wl_Sc.st_ocean)\n",
    "wl_dDdz = np.gradient(wl_Dc)/np.gradient(wl_Sc.st_ocean)\n",
    "\n",
    "new_dims=wl_Tc.dims[:] \n",
    "new_coords=wl_Tc.coords\n",
    "\n",
    "wl_dTdz_1 = xr.DataArray(wl_dTdz[1,...].squeeze(),dims=new_dims,coords=new_coords)\n",
    "wl_dSdz_1 = xr.DataArray(wl_dSdz[1,...].squeeze(),dims=new_dims,coords=new_coords)\n",
    "wl_dDdz_1 = xr.DataArray(wl_dDdz[1,...].squeeze(),dims=new_dims,coords=new_coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4193f807-4b02-4d15-a7b3-96570cd12454",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         -1.02561887e-01, -6.93003409e-02, -4.23071886e-02,\n",
       "         -2.22909013e-02, -8.51460742e-03,  1.56041106e-04,\n",
       "          4.82889472e-03,  6.58405957e-03,  6.03203537e-03,\n",
       "          4.10479954e-03,  2.23282352e-03,  1.07773942e-03,\n",
       "          5.33942762e-04,  2.83999029e-04,  1.55834961e-04,\n",
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       "          3.54703876e-08, -1.29280628e-05, -1.99312243e-05,\n",
       "         -2.30495817e-05, -2.38810732e-05, -2.46457904e-05],\n",
       "        [-4.41469323e-01, -4.19748649e-01, -3.79894382e-01,\n",
       "         -3.43765029e-01, -3.11050265e-01, -2.81443824e-01,\n",
       "         -2.54662856e-01, -2.30451012e-01, -2.08610587e-01,\n",
       "         -1.89013639e-01, -1.71625540e-01, -1.56509077e-01,\n",
       "         -1.43463391e-01, -1.31798271e-01, -1.19897329e-01,\n",
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       "         -5.06535474e-02, -3.41413420e-02, -2.02387839e-02,\n",
       "         -9.56201875e-03, -2.80693527e-03,  3.14736880e-04,\n",
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       "         -6.94437116e-05, -5.53936416e-05, -4.80221626e-05],\n",
       "        [ 3.78250324e-02,  3.55030371e-02,  3.18690803e-02,\n",
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       "         -3.09633417e-03, -6.76986783e-03, -9.27566288e-03,\n",
       "         -8.46772484e-03, -5.51570014e-03, -2.98237928e-03,\n",
       "         -1.60594877e-03, -9.57665452e-04, -6.56842283e-04,\n",
       "         -4.93923819e-04, -3.78258959e-04, -2.92594073e-04,\n",
       "         -2.25106625e-04, -1.73805372e-04, -1.34744760e-04,\n",
       "         -1.04680440e-04, -8.33573362e-05, -7.20522202e-05],\n",
       "        [ 1.86863337e-01,  1.75978721e-01,  1.58325768e-01,\n",
       "          1.42405397e-01,  1.27931654e-01,  1.14713946e-01,\n",
       "          1.02612957e-01,  9.14755571e-02,  8.13083774e-02,\n",
       "          7.20711711e-02,  6.37164109e-02,  5.61966479e-02,\n",
       "          4.94207230e-02,  4.33105634e-02,  3.77789894e-02,\n",
       "          3.27771413e-02,  2.82434655e-02,  2.40771260e-02,\n",
       "          2.01978699e-02,  1.65411955e-02,  1.29846859e-02,\n",
       "          9.43707672e-03,  5.85134372e-03,  2.36590207e-03,\n",
       "          5.23574079e-06, -8.19122463e-04, -7.37596832e-04,\n",
       "         -5.48292833e-04, -4.21634271e-04, -3.35223367e-04,\n",
       "         -2.80259172e-04, -2.32631493e-04, -1.92477921e-04,\n",
       "         -1.57770488e-04, -1.28103540e-04, -1.04068078e-04,\n",
       "         -8.49046485e-05, -6.89350728e-05, -5.91165053e-05],\n",
       "        [ 5.10402125e-01,  4.82360128e-01,  4.34758944e-01,\n",
       "          3.91359416e-01,  3.51497094e-01,  3.14600512e-01,\n",
       "          2.80146042e-01,  2.47704732e-01,  2.17409916e-01,\n",
       "          1.89351051e-01,  1.63376333e-01,  1.39711792e-01,\n",
       "          1.18192469e-01,  9.87226943e-02,  8.13001193e-02,\n",
       "          6.60779333e-02,  5.30209297e-02,  4.21077026e-02,\n",
       "          3.32759926e-02,  2.62857669e-02,  2.08267103e-02,\n",
       "          1.66294364e-02,  1.33620227e-02,  1.02694151e-02,\n",
       "          6.73607168e-03,  3.48465163e-03,  1.49181846e-03,\n",
       "          5.67588232e-04,  2.08732253e-04,  8.80619919e-05,\n",
       "          3.56280071e-05,  8.80350079e-06, -5.70093415e-06,\n",
       "         -1.31591289e-05, -1.52718807e-05, -1.50371126e-05,\n",
       "         -1.38464398e-05, -1.13431436e-05, -7.58621396e-06],\n",
       "        [ 7.24334546e-01,  6.85546935e-01,  6.18349039e-01,\n",
       "          5.56847377e-01,  5.00176570e-01,  4.47500904e-01,\n",
       "          3.97989251e-01,  3.51023586e-01,  3.06834026e-01,\n",
       "          2.65637947e-01,  2.27187069e-01,  1.91985040e-01,\n",
       "          1.59860209e-01,  1.30752552e-01,  1.04791492e-01,\n",
       "          8.23384345e-02,  6.34080089e-02,  4.80851478e-02,\n",
       "          3.63199323e-02,  2.76539194e-02,  2.14920875e-02,\n",
       "          1.73200247e-02,  1.43995244e-02,  1.17602891e-02,\n",
       "          8.46361123e-03,  4.89050892e-03,  2.32848710e-03,\n",
       "          1.02084140e-03,  4.82578525e-04,  2.85082419e-04,\n",
       "          1.85290097e-04,  1.27324396e-04,  8.96236705e-05,\n",
       "          6.25509060e-05,  4.47297892e-05,  3.40182854e-05,\n",
       "          2.75326865e-05,  2.33530796e-05,  2.41742114e-05]],\n",
       "\n",
       "       [[-3.16097870e-03, -3.03366774e-03, -3.41041981e-03,\n",
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       "         -2.65438527e-02, -1.77796309e-02, -7.77136913e-03,\n",
       "          2.94956418e-03,  1.27004018e-02,  1.85905692e-02,\n",
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       "          1.40526648e-03,  1.00779865e-03,  7.29943248e-04,\n",
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       "          4.15762694e-04,  5.13539501e-04,  6.05905686e-04],\n",
       "        [ 2.42754251e-03,  1.62234281e-03,  6.51813791e-04,\n",
       "          3.31067077e-04,  1.90820974e-04,  1.37335101e-04,\n",
       "          1.64437124e-04,  2.60749032e-04,  5.06531142e-04,\n",
       "          1.08034547e-03,  2.15571968e-03,  3.57112749e-03,\n",
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       "         -8.53870431e-03, -1.53160437e-02, -1.83320058e-02,\n",
       "         -1.69993400e-02, -1.25574075e-02, -5.68371519e-03,\n",
       "          3.23322648e-03,  1.19837699e-02,  1.70643805e-02,\n",
       "          1.65050456e-02,  1.19934695e-02,  7.27332587e-03,\n",
       "          4.31337939e-03,  2.77292470e-03,  1.90591752e-03,\n",
       "          1.35141359e-03,  9.71401180e-04,  7.02339015e-04,\n",
       "          5.08428558e-04,  4.05153797e-04,  3.98665255e-04,\n",
       "          4.10309797e-04,  5.09453479e-04,  6.02210290e-04],\n",
       "        [ 2.10201841e-03,  1.44656084e-03,  6.47451576e-04,\n",
       "          3.69624764e-04,  2.38029096e-04,  1.75938211e-04,\n",
       "          1.86373130e-04,  2.09385033e-04,  2.45074608e-04,\n",
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       "          1.56770671e-03,  2.61030343e-03,  3.99346807e-03,\n",
       "          5.05346160e-03,  4.90213320e-03,  3.60245213e-03,\n",
       "          2.33494554e-03,  2.56608631e-03,  5.27003832e-03,\n",
       "          1.00827723e-02,  1.49939760e-02,  1.71657565e-02,\n",
       "          1.52489938e-02,  1.08101912e-02,  6.63506097e-03,\n",
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       "          5.20306317e-04,  8.61446875e-04,  1.51140252e-03,\n",
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       "          7.85225454e-03,  1.09215137e-02,  1.46500428e-02,\n",
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       "          4.05934881e-03,  2.64363466e-03,  1.83335792e-03,\n",
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       "        [ 1.56312249e-03,  1.10200433e-03,  5.34843481e-04,\n",
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       "          4.63346528e-03,  2.84384683e-03,  1.91574635e-03,\n",
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       "          4.88824210e-04,  3.86866757e-04,  3.83210416e-04,\n",
       "          3.97005041e-04,  4.97930385e-04,  5.91160891e-04],\n",
       "        [ 1.50819716e-03,  1.06274288e-03,  5.14876231e-04,\n",
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       "          9.58033144e-05,  9.59121994e-05,  1.06916479e-04,\n",
       "          1.29232249e-04,  1.71432437e-04,  2.55009520e-04,\n",
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       "          1.73877631e-02,  2.46900617e-02,  2.78957681e-02,\n",
       "          2.46753291e-02,  1.74457691e-02,  1.03897271e-02,\n",
       "          5.72216513e-03,  3.28848431e-03,  2.10850333e-03,\n",
       "          1.44680293e-03,  1.01353141e-03,  7.28145060e-04,\n",
       "          5.23099637e-04,  4.07761601e-04,  3.94892194e-04,\n",
       "          4.03367776e-04,  5.01830615e-04,  5.94609095e-04],\n",
       "        [ 1.24350940e-03,  8.62111894e-04,  3.90710516e-04,\n",
       "          2.16859007e-04,  1.29435685e-04,  8.07716330e-05,\n",
       "          6.15112741e-05,  4.80488720e-05,  3.29366841e-05,\n",
       "          2.74138682e-05,  2.34523928e-05,  2.55948245e-05,\n",
       "          3.22666734e-05,  4.86596659e-05,  9.10496512e-05,\n",
       "          1.76504945e-04,  3.69949274e-04,  8.21675168e-04,\n",
       "          1.76857576e-03,  3.64505778e-03,  7.13535250e-03,\n",
       "          1.29630411e-02,  2.08085796e-02,  2.71482384e-02,\n",
       "          2.72002007e-02,  2.07313878e-02,  1.25957983e-02,\n",
       "          6.83023225e-03,  3.77925961e-03,  2.32469500e-03,\n",
       "          1.56626082e-03,  1.09262343e-03,  7.86600309e-04,\n",
       "          5.67810349e-04,  4.40045567e-04,  4.18353237e-04,\n",
       "          4.19137877e-04,  5.10743210e-04,  6.00877451e-04],\n",
       "        [ 5.95809020e-04,  3.45003791e-04,  2.69377991e-05,\n",
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       "         -2.23611573e-04, -2.37161156e-04, -2.43766029e-04,\n",
       "         -2.43552222e-04, -2.49444502e-04, -2.52234342e-04,\n",
       "         -2.47637199e-04, -2.41800187e-04, -2.30810052e-04,\n",
       "         -2.00415986e-04, -1.07225559e-04,  1.68822086e-04,\n",
       "          8.38752807e-04,  2.25474533e-03,  5.06229133e-03,\n",
       "          9.99121759e-03,  1.72447981e-02,  2.46689076e-02,\n",
       "          2.69787426e-02,  2.18171484e-02,  1.36097284e-02,\n",
       "          7.36504946e-03,  4.01758540e-03,  2.43066530e-03,\n",
       "          1.62440269e-03,  1.13828707e-03,  8.22741760e-04,\n",
       "          5.96807423e-04,  4.63503995e-04,  4.37379615e-04,\n",
       "          4.34030464e-04,  5.21497940e-04,  6.09655513e-04],\n",
       "        [-2.02920312e-03, -1.70449367e-03, -1.47784634e-03,\n",
       "         -1.67301699e-03, -1.96149379e-03, -2.28616377e-03,\n",
       "         -2.65315614e-03, -2.95261819e-03, -3.09797414e-03,\n",
       "         -3.13653080e-03, -3.09223842e-03, -3.01835978e-03,\n",
       "         -2.95979265e-03, -2.92518301e-03, -2.88163469e-03,\n",
       "         -2.80117409e-03, -2.68610129e-03, -2.41907994e-03,\n",
       "         -1.74322597e-03, -2.86761869e-04,  2.59216054e-03,\n",
       "          7.67603906e-03,  1.48809678e-02,  2.18577251e-02,\n",
       "          2.41537493e-02,  1.99119395e-02,  1.27318455e-02,\n",
       "          7.03249407e-03,  3.90566596e-03,  2.39721430e-03,\n",
       "          1.61321470e-03,  1.13422367e-03,  8.23461176e-04,\n",
       "          6.01119544e-04,  4.68504086e-04,  4.41135566e-04,\n",
       "          4.37807855e-04,  5.25986845e-04,  6.14259443e-04],\n",
       "        [-4.78907001e-03, -4.11835230e-03, -3.96289203e-03,\n",
       "         -5.06888092e-03, -6.63785965e-03, -8.68045143e-03,\n",
       "         -1.11661808e-02, -1.34818696e-02, -1.51413196e-02,\n",
       "         -1.64331729e-02, -1.72012485e-02, -1.74814298e-02,\n",
       "         -1.75796171e-02, -1.73552309e-02, -1.65485791e-02,\n",
       "         -1.51821059e-02, -1.33747399e-02, -1.10433737e-02,\n",
       "         -8.22290896e-03, -4.84783822e-03, -3.53153500e-04,\n",
       "          5.90494511e-03,  1.35392049e-02,  2.00159215e-02,\n",
       "          2.15321952e-02,  1.73669865e-02,  1.10817520e-02,\n",
       "          6.24477435e-03,  3.60081831e-03,  2.28093886e-03,\n",
       "          1.55434161e-03,  1.09971367e-03,  8.00249558e-04,\n",
       "          5.85141385e-04,  4.58873439e-04,  4.35994621e-04,\n",
       "          4.35207068e-04,  5.26136193e-04,  6.16140100e-04]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing from numpy to xarray\n",
    "wl_dTdz = xr.DataArray(wl_dTdz)\n",
    "#Change dimension names\n",
    "wl_dTdz = wl_dTdz.rename({'dim_0': 'test'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82781228-25e5-4245-84bd-9f4204f6ecf9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn colorpalette : https://medium.com/@morganjonesartist/color-guide-to-seaborn-palettes-da849406d44f\n",
    "import matplotlib\n",
    "matplotlib.rc('axes',edgecolor='lightgrey') #---> Sets the color of the frame, once this is run, it set that default for everything in the session\n",
    "\n",
    "\n",
    "x=list(range(12))\n",
    "\n",
    "from matplotlib import ticker\n",
    "\n",
    "import matplotlib.gridspec as gridspec # GRIDSPEC !\n",
    "from matplotlib.colorbar import Colorbar # For dealing with Colorbars the proper way - TBD in a separate PyCoffee ?\n",
    "\n",
    "fig = plt.figure(1, figsize=(15,8));\n",
    "\n",
    "\n",
    "# Now, create the gridspec structure, as required\n",
    "gs = gridspec.GridSpec(2,5, height_ratios=[1.1,0.12], width_ratios=[.9, .9, .9, .9, .9])  # Make it 9 rows to adjust the length of colorbar\n",
    "\n",
    "# 3 rows, 4 columns, each with the required size ratios. \n",
    "# Also make sure the margins and spacing are apropriate\n",
    "\n",
    "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.1, hspace=0.15)\n",
    "\n",
    "#################################################################################################################################\n",
    "\n",
    "# T - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,0]) # place it where it should be.\n",
    "\n",
    "tt = temp_climatology_plot_WL(wl_Tc, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='lightgrey', alpha=0.5, lw=0.25 ) # vertical lines\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(a)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='darkgrey', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,0]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "lev = lev = np.arange(-1.8,1.2,0.1) # Make sure to check this is the same level used in the temperature plot!!!!\n",
    "colmap =  cm.cm.thermal  #cm.cm.thermal #'bwr'\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "cb.set_label('Temperature (°C)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "# S - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,1]) # place it where it should be.\n",
    "\n",
    "tt = salt_climatology_plot_WL(wl_Sc, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='grey', alpha=0.3, lw=0.25 ) # vertical lines\n",
    "#ax.axes.yaxis.set_visible(False)\n",
    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(b)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='w', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,1]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "lev = np.arange(33.5,34.5,0.05) # Make sure to check this is the same level used in the temperature plot!!!!\n",
    "colmap = cm.cm.haline  #cm.cm.matter\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "\n",
    "# while using mathematical eqtn, that have default fontweight, so we need to change that explicitly\n",
    "# #########matplotlib.rcParams['text.latex.preamble'] = [r'\\lightmath']\n",
    "\n",
    "# ######params = {'mathtext.default': 'regular' }          \n",
    "# ######plt.rcParams.update(params)\n",
    "cb.set_label('Salinity (psu)', labelpad=10, fontsize=15, fontweight='light')\n",
    "#cb.set_label('Salinity (kgm-3)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "# dT/dz - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,2]) # place it where it should be.\n",
    "\n",
    "tt = dTdz_climatology_plot_WL (wl_dTdz_1, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='grey', alpha=0.25, lw=0.25 ) # vertical lines\n",
    "#ax.axes.yaxis.set_visible(False)\n",
    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(c)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='w', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,2]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "\n",
    "#colormap, levels \n",
    "mnlev = -0.4\n",
    "mxlev = 0.3\n",
    "nlev = 10\n",
    "lev = np.arange(mnlev, mxlev, mxlev/nlev)\n",
    "cont_lev = [ 0.075, 0.25]   \n",
    "cont_lev2 = [-0.01]\n",
    "\n",
    "colmap = cm.cm.balance\n",
    "\n",
    "from matplotlib import colors\n",
    "divnorm=colors.TwoSlopeNorm(vmin=mnlev, vcenter=0., vmax=mxlev)\n",
    "\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False, norm=divnorm);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "\n",
    "# while using mathematical eqtn, that have default fontweight, so we need to change that explicitly\n",
    "\n",
    "cb.set_label('dT/dz (°C/m)x10', labelpad=10, fontsize=15, fontweight='light')\n",
    "#cb.set_label('Salinity (kgm-3)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "# dD/dz - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,3]) # place it where it should be.\n",
    "\n",
    "tt = dDdz_climatology_plot_WL (wl_dDdz_1, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='grey', alpha=0.25, lw=0.25 ) # vertical lines\n",
    "#ax.axes.yaxis.set_visible(False)\n",
    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(d)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='w', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "t = plt.text( 12, 130,   'Depth (m)',color='k', fontsize=14, fontweight='light', rotation=90)\n",
    "\n",
    "\n",
    "\n",
    "t = plt.text( 6.57, 290,   '  4',color='k', fontsize=10, fontweight='light')\n",
    "t = plt.text( 6.3, 289,   ' -',color='k', fontsize=12, fontweight='light')\n",
    "\n",
    "#t = plt.text( 8.87, 290,   '  2',color='k', fontsize=10, fontweight='light')\n",
    "\n",
    "\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,3]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "\n",
    "lev = np.arange(0.25, 0.75, 0.025) # Make sure to check this is the same level used in the temperature plot!!!!\n",
    "colmap = cm.cm.matter  #cm.cm.rain\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "\n",
    "# https://stackoverflow.com/questions/27698377/how-do-i-make-sans-serif-superscript-or-subscript-text-in-matplotlib\n",
    "\n",
    "# while using mathematical eqtn, that have default fontweight, so we need to change that explicitly\n",
    "# #########matplotlib.rcParams['text.latex.preamble'] = [r'\\lightmath']\n",
    "\n",
    "# ######params = {'mathtext.default': 'regular' }          \n",
    "# ######plt.rcParams.update(params)\n",
    "cb.set_label('d\\u03C1/dz (kg/m )x100', labelpad=10, fontsize=15, fontweight='light')\n",
    "#cb.set_label('Salinity (kgm-3)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "#fig.savefig('WL_Clim_23_23.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e21fef36-8523-4fb4-a21c-d74556dc4d28",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fa7a760b-a3ce-46b9-b670-8109b08066d8",
   "metadata": {},
   "source": [
    "# WL climatology for lat-min at -90"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd434b3-77b8-4db5-8de9-5445e4704829",
   "metadata": {},
   "source": [
    "latmin  = -90. # TESTING 80 S"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18efa547-e682-4ca5-b68f-830240cc7ee1",
   "metadata": {
    "tags": []
   },
   "source": [
    "temp_monthly = OM_temp.groupby('time.month').mean('time')\n",
    "#WL\n",
    "print ('Calculating WL T Clim')\n",
    "wl_Tc = sector_mean(temp_monthly, A, wl)\n",
    "wl_Tc.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_T_test90S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_T.nc ')\n",
    "\n",
    "salt_monthly = OM_salt.groupby('time.month').mean('time')\n",
    "#WL\n",
    "print ('Calculating WL S Clim')\n",
    "wl_Sc = sector_mean(salt_monthly, A, wl)\n",
    "wl_Sc.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_S_test90S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_S.nc ')\n",
    "\n",
    "\n",
    "rho_monthly = OM_rho.groupby('time.month').mean('time')\n",
    "#RS\n",
    "print ('Calculating WL D Clim')\n",
    "wl_Dc = sector_mean(rho_monthly, A, wl)\n",
    "wl_Dc.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_D_test90S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_D.nc ')\n",
    "\n",
    "mld_monthly = OM_mld.groupby('time.month').mean('time')\n",
    "#RS\n",
    "print ('Calculating WL mld Clim')\n",
    "wl_mld = mld_sector_mean(mld_monthly, A, wl)\n",
    "wl_mld.to_netcdf('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_mld_test90S_04012022.nc')\n",
    "print ('Saved to 6_AreaWeighted_WL_Clim_mld.nc ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7399d087-ec6b-4b4d-a10a-4ee9da32a8dd",
   "metadata": {},
   "source": [
    "### Load the calculated data from Gadi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e492ea7-0395-4d2c-9e8e-761050681852",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# loading the data back\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_T_test90S_04012022.nc') \n",
    "wl_Tc = da.temp\n",
    "\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_S_test90S_04012022.nc') \n",
    "wl_Sc = da.salt\n",
    "\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_D_test90S_04012022.nc') \n",
    "wl_Dc = da.pot_rho_0\n",
    "\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_mld_test90S_04012022.nc') \n",
    "wl_mld = da.mld\n",
    "\n",
    "wl_dTdz = np.gradient(wl_Tc)/np.gradient(wl_Tc.st_ocean)\n",
    "wl_dSdz = np.gradient(wl_Sc)/np.gradient(wl_Sc.st_ocean)\n",
    "wl_dDdz = np.gradient(wl_Dc)/np.gradient(wl_Sc.st_ocean)\n",
    "\n",
    "new_dims=wl_Tc.dims[:] \n",
    "new_coords=wl_Tc.coords\n",
    "\n",
    "wl_dTdz_1 = xr.DataArray(wl_dTdz[1,...].squeeze(),dims=new_dims,coords=new_coords)\n",
    "wl_dSdz_1 = xr.DataArray(wl_dSdz[1,...].squeeze(),dims=new_dims,coords=new_coords)\n",
    "wl_dDdz_1 = xr.DataArray(wl_dDdz[1,...].squeeze(),dims=new_dims,coords=new_coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27cf9845-60e3-423e-a9fb-4314061996d7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn colorpalette : https://medium.com/@morganjonesartist/color-guide-to-seaborn-palettes-da849406d44f\n",
    "import matplotlib\n",
    "matplotlib.rc('axes',edgecolor='lightgrey') #---> Sets the color of the frame, once this is run, it set that default for everything in the session\n",
    "\n",
    "\n",
    "x=list(range(12))\n",
    "\n",
    "from matplotlib import ticker\n",
    "\n",
    "import matplotlib.gridspec as gridspec # GRIDSPEC !\n",
    "from matplotlib.colorbar import Colorbar # For dealing with Colorbars the proper way - TBD in a separate PyCoffee ?\n",
    "\n",
    "fig = plt.figure(1, figsize=(15,8));\n",
    "\n",
    "\n",
    "# Now, create the gridspec structure, as required\n",
    "gs = gridspec.GridSpec(2,5, height_ratios=[1.1,0.12], width_ratios=[.9, .9, .9, .9, .9])  # Make it 9 rows to adjust the length of colorbar\n",
    "\n",
    "# 3 rows, 4 columns, each with the required size ratios. \n",
    "# Also make sure the margins and spacing are apropriate\n",
    "\n",
    "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.1, hspace=0.15)\n",
    "\n",
    "#################################################################################################################################\n",
    "\n",
    "# T - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,0]) # place it where it should be.\n",
    "\n",
    "tt = temp_climatology_plot_WL(wl_Tc, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='lightgrey', alpha=0.5, lw=0.25 ) # vertical lines\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(a)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='darkgrey', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,0]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "lev = lev = np.arange(-1.8,1.2,0.1) # Make sure to check this is the same level used in the temperature plot!!!!\n",
    "colmap =  cm.cm.thermal  #cm.cm.thermal #'bwr'\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "cb.set_label('Temperature (°C)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "# S - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,1]) # place it where it should be.\n",
    "\n",
    "tt = salt_climatology_plot_WL(wl_Sc, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='grey', alpha=0.3, lw=0.25 ) # vertical lines\n",
    "#ax.axes.yaxis.set_visible(False)\n",
    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(b)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='w', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,1]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "lev = np.arange(33.5,34.5,0.05) # Make sure to check this is the same level used in the temperature plot!!!!\n",
    "colmap = cm.cm.haline  #cm.cm.matter\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "\n",
    "# while using mathematical eqtn, that have default fontweight, so we need to change that explicitly\n",
    "# #########matplotlib.rcParams['text.latex.preamble'] = [r'\\lightmath']\n",
    "\n",
    "# ######params = {'mathtext.default': 'regular' }          \n",
    "# ######plt.rcParams.update(params)\n",
    "cb.set_label('Salinity (psu)', labelpad=10, fontsize=15, fontweight='light')\n",
    "#cb.set_label('Salinity (kgm-3)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "# dT/dz - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,2]) # place it where it should be.\n",
    "\n",
    "tt = dTdz_climatology_plot_WL (wl_dTdz_1, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='grey', alpha=0.25, lw=0.25 ) # vertical lines\n",
    "#ax.axes.yaxis.set_visible(False)\n",
    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(c)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='w', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,2]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "\n",
    "#colormap, levels \n",
    "mnlev = -0.4\n",
    "mxlev = 0.3\n",
    "nlev = 10\n",
    "lev = np.arange(mnlev, mxlev, mxlev/nlev)\n",
    "cont_lev = [ 0.075, 0.25]   \n",
    "cont_lev2 = [-0.01]\n",
    "\n",
    "colmap = cm.cm.balance\n",
    "\n",
    "from matplotlib import colors\n",
    "divnorm=colors.TwoSlopeNorm(vmin=mnlev, vcenter=0., vmax=mxlev)\n",
    "\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False, norm=divnorm);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "\n",
    "# while using mathematical eqtn, that have default fontweight, so we need to change that explicitly\n",
    "\n",
    "cb.set_label('dT/dz (°C/m)x10', labelpad=10, fontsize=15, fontweight='light')\n",
    "#cb.set_label('Salinity (kgm-3)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "\n",
    "# dD/dz - Climatology\n",
    "\n",
    "ax1 = plt.subplot(gs[0,3]) # place it where it should be.\n",
    "\n",
    "tt = dDdz_climatology_plot_WL (wl_dDdz_1, wl_dTdz, wl_dDdz, wl_mld)\n",
    "plt.axvline(x=6, color='navy', linestyle='--', linewidth=2, alpha=0.3)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "plt.setp(ax.get_yticklabels(), fontsize=15, fontweight='ultralight')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, fontweight='ultralight')\n",
    "plt.gca().xaxis.grid(color='grey', alpha=0.25, lw=0.25 ) # vertical lines\n",
    "#ax.axes.yaxis.set_visible(False)\n",
    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "t = plt.text( 7.5, 10,   '(d)',color='k', fontsize=14, fontweight='light')\n",
    "t.set_bbox(dict(facecolor='w', alpha=0.4, edgecolor='none'))\n",
    "\n",
    "t = plt.text( 12, 130,   'Depth (m)',color='k', fontsize=14, fontweight='light', rotation=90)\n",
    "\n",
    "\n",
    "\n",
    "t = plt.text( 6.57, 290,   '  4',color='k', fontsize=10, fontweight='light')\n",
    "t = plt.text( 6.3, 289,   ' -',color='k', fontsize=12, fontweight='light')\n",
    "\n",
    "#t = plt.text( 8.87, 290,   '  2',color='k', fontsize=10, fontweight='light')\n",
    "\n",
    "\n",
    "\n",
    "# Colorbar\n",
    "cbax = plt.subplot(gs[1,3]) # Place it where it should be.\n",
    "# # --------------------------------------------------------\n",
    "data = wl_Tc \n",
    "\n",
    "lev = np.arange(0.25, 0.75, 0.025) # Make sure to check this is the same level used in the temperature plot!!!!\n",
    "colmap = cm.cm.matter  #cm.cm.rain\n",
    "im=plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False);#--> T-colorbar\n",
    "\n",
    "cb = Colorbar(ax = cbax, mappable = im, orientation = 'horizontal')\n",
    "\n",
    "# https://stackoverflow.com/questions/27698377/how-do-i-make-sans-serif-superscript-or-subscript-text-in-matplotlib\n",
    "\n",
    "# while using mathematical eqtn, that have default fontweight, so we need to change that explicitly\n",
    "# #########matplotlib.rcParams['text.latex.preamble'] = [r'\\lightmath']\n",
    "\n",
    "# ######params = {'mathtext.default': 'regular' }          \n",
    "# ######plt.rcParams.update(params)\n",
    "cb.set_label('d\\u03C1/dz (kg/m )x100', labelpad=10, fontsize=15, fontweight='light')\n",
    "#cb.set_label('Salinity (kgm-3)', labelpad=10, fontsize=15, fontweight='light')\n",
    "plt.setp(cb.ax.xaxis.get_ticklabels(), weight='light', fontsize=10);\n",
    "cb.ax.locator_params(nbins=6)\n",
    "\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "#fig.savefig('WL_Clim_23_23.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "422b6735-9feb-4f12-b76c-4d3eb99c4936",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c968f761-06fa-4609-8541-98e15fe6dcc6",
   "metadata": {},
   "source": [
    "# Testing the MLD"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2738b1-70f6-4298-a82a-7b6f4782f226",
   "metadata": {},
   "source": [
    "MLD from the density climatology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0acb719e-b155-4a6b-8d81-490c87a18658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.93995261,  9.44688511, 13.81592751, 22.24687195, 29.44745636,\n",
       "       38.24056625, 55.20640182, 69.69342041, 78.0960083 , 78.0960083 ,\n",
       "       29.44745636,  1.68073463])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the MLD from model density\n",
    "\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_D_test90S_04012022.nc') \n",
    "wl_Dc90 = da.pot_rho_0 \n",
    "\n",
    "test_depth2 = (wl_Dc90.st_ocean).where(wl_Dc90 <= wl_Dc90[:,0]+0.03) # This will be using the density climatology bounded at 90S\n",
    "mld_depth = test_depth2.max('st_ocean')\n",
    "mld_depth.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d1b3d219-5669-480c-a993-8ab45ec9a6db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test = xr.where(wl_Dc90 <= wl_Dc90[:,0]+0.03, 1, np.nan)\n",
    "ind = test.sum('st_ocean').values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "29221306-d790-4579-a5c8-516e88f296b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ind =ind-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f2ef222e-e621-45b7-85cc-0b3a14d6601c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the model MLD using 70S as boundary for comparison\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_mld_test70S_04012022.nc') \n",
    "wl_mld70S = da.mld\n",
    "\n",
    "# Loading the model MLD using 90S as boundary for comparison\n",
    "da = xr.open_dataset('/g/data/v45/sl0603/code_and_data/T_S_climatology/6_AreaWeighted_WL_Clim_mld_test90S_04012022.nc') \n",
    "wl_mld90S = da.mld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3132f0d7-84cd-46a3-922c-cd0d7e529485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1528005c38b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 7))\n",
    "\n",
    "x = list(range(12))\n",
    "data = wl_Dc90-1000\n",
    "lev = np.arange(26.6, 27.6, 0.025) #(0, 3, 0.25)\n",
    "colmap = cm.cm.matter_r\n",
    "\n",
    "# Fill plot using the density climatology\n",
    "p = plt.contourf(x, data.st_ocean, data.transpose(), levels=lev, cmap = colmap, yincrease=False, add_colorbar=False, extend='both') #levels=lev,\n",
    "\n",
    "\n",
    "#Coparing the MLD claculated vs the model derived\n",
    "plt.plot(x, mld_depth, 'green') #---> calculated mld\n",
    "plt.plot(x, wl_mld90S, 'r') #----> mld from model averaged at 90S\n",
    "plt.plot(x, wl_mld70S, 'r--') #----> mld from model averaged at 70S\n",
    "\n",
    "# dT/dz\n",
    "tt1 = dTdz_max_depth(wl_dTdz,  wl_Tc)\n",
    "tt1 = tt1[0:12]\n",
    "plt.plot(x, tt1, color='k', lw=2)\n",
    "\n",
    "# dRho/dz\n",
    "tt2 = dTdz_max_depth(wl_dDdz,  wl_Tc)\n",
    "tt2 = tt2[0:12]\n",
    "plt.plot(x, tt2, color='lightgrey', lw=1.5, alpha=0.7)\n",
    "plt.plot(x, tt2, color='k', lw=1.5, ls='--') #'teal'\n",
    "\n",
    "\n",
    "\n",
    "plt.ylim((0,350))\n",
    "plt.gca().invert_yaxis()\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ccf68b-c37d-4bab-8787-3a287929fb7a",
   "metadata": {},
   "source": [
    "## Calculating the actual difference between the surface density and the density at the calculated MLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b3f1603c-4f2b-4a71-aafd-02939681c18b",
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   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pot_rho_0&#x27; (month: 12)&gt;\n",
       "array([0.0189209 , 0.0300293 , 0.0144043 , 0.0246582 , 0.02087402,\n",
       "       0.01855469, 0.02709961, 0.02661133, 0.02368164, 0.02270508,\n",
       "       0.02966309, 0.03027344], dtype=float32)\n",
       "Coordinates:\n",
       "    st_ocean  float64 0.5413\n",
       "  * month     (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pot_rho_0'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6dbb329b-b26e-4e5e-938e-4450735b91a8' class='xr-array-in' type='checkbox' checked><label for='section-6dbb329b-b26e-4e5e-938e-4450735b91a8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.01892 0.03003 0.0144 0.02466 ... 0.02368 0.02271 0.02966 0.03027</span></div><div class='xr-array-data'><pre>array([0.0189209 , 0.0300293 , 0.0144043 , 0.0246582 , 0.02087402,\n",
       "       0.01855469, 0.02709961, 0.02661133, 0.02368164, 0.02270508,\n",
       "       0.02966309, 0.03027344], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-90b9b17a-4039-48d2-9f7c-6ae276b02c5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90b9b17a-4039-48d2-9f7c-6ae276b02c5d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>st_ocean</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413</div><input id='attrs-ac24b99f-c3d7-485a-9a57-a0286f169ffb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac24b99f-c3d7-485a-9a57-a0286f169ffb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17d17b6d-a64a-4196-a4cf-7f80189fbafb' class='xr-var-data-in' type='checkbox'><label for='data-17d17b6d-a64a-4196-a4cf-7f80189fbafb' 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'></dl></div><div class='xr-var-data'><pre>array(0.54128075)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-09c77263-facd-4717-8ce0-74e083652027' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09c77263-facd-4717-8ce0-74e083652027' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f92d022-3d1c-4a45-b649-26d2cab20d72' class='xr-var-data-in' type='checkbox'><label for='data-0f92d022-3d1c-4a45-b649-26d2cab20d72' 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'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c53b5b44-c213-48fe-b44c-da48d3882cd1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c53b5b44-c213-48fe-b44c-da48d3882cd1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pot_rho_0' (month: 12)>\n",
       "array([0.0189209 , 0.0300293 , 0.0144043 , 0.0246582 , 0.02087402,\n",
       "       0.01855469, 0.02709961, 0.02661133, 0.02368164, 0.02270508,\n",
       "       0.02966309, 0.03027344], dtype=float32)\n",
       "Coordinates:\n",
       "    st_ocean  float64 0.5413\n",
       "  * month     (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 35,
     "metadata": {},
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    }
   ],
   "source": [
    "# Get the surface density values\n",
    "test_surface_values = wl_Dc90[:,0]\n",
    "#test_surface_values\n",
    "\n",
    "# Get the density at the  calculated MLD\n",
    "\n",
    "# From the st_ocean values get the index for MLD\n",
    "indx = []\n",
    "for i in range (12):\n",
    "    ind = np.where(wl_Dc90.st_ocean == mld_depth[i])    \n",
    "    indx.append(ind)    \n",
    "#indx  #--> This index is within a list and the elements are tuples containing 1x1 arrays eg. [(array[4])]\n",
    "\n",
    "# get the value of rho at the mld \n",
    "test_mld_values_n = []\n",
    "for i in range (12):\n",
    "    ind = np.asscalar(np.asarray(indx[i]))\n",
    "    x = wl_Dc90[i, ind]\n",
    "    test_mld_values_n.append(x)    \n",
    "#test_mld_values_n    \n",
    "\n",
    "# Get the difference\n",
    "diff = test_mld_values_n - test_surface_values\n",
    "diff"
   ]
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  {
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       "[1026.6732177734375,\n",
       " 1026.646240234375,\n",
       " 1026.8623046875,\n",
       " 1027.1324462890625,\n",
       " 1027.318115234375,\n",
       " 1027.4423828125,\n",
       " 1027.533935546875,\n",
       " 1027.593017578125,\n",
       " 1027.626220703125,\n",
       " 1027.6334228515625,\n",
       " 1027.5797119140625,\n",
       " 1027.2003173828125]"
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     "execution_count": 116,
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   "source": [
    "mld = []\n",
    "for i, j in zip(range(1,13), ind):\n",
    "    mld.append(wl_Dc90.sel(month = i)[j].values.tolist())\n",
    "    \n",
    "mld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ced07aad-909f-46bd-a407-68f88c58501f",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pot_rho_0&#x27; (month: 12)&gt;\n",
       "array([0.0189209 , 0.0300293 , 0.0144043 , 0.0246582 , 0.02087402,\n",
       "       0.01855469, 0.02709961, 0.02661133, 0.02368164, 0.02270508,\n",
       "       0.02966309, 0.02001953])\n",
       "Coordinates:\n",
       "    st_ocean  float64 0.5413\n",
       "  * month     (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pot_rho_0'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7e75834a-d121-46f0-ad17-1346bcf5a88b' class='xr-array-in' type='checkbox' checked><label for='section-7e75834a-d121-46f0-ad17-1346bcf5a88b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.01892 0.03003 0.0144 0.02466 ... 0.02368 0.02271 0.02966 0.02002</span></div><div class='xr-array-data'><pre>array([0.0189209 , 0.0300293 , 0.0144043 , 0.0246582 , 0.02087402,\n",
       "       0.01855469, 0.02709961, 0.02661133, 0.02368164, 0.02270508,\n",
       "       0.02966309, 0.02001953])</pre></div></div></li><li class='xr-section-item'><input id='section-62a1f250-cd4e-4bae-b299-afa9e3cac943' class='xr-section-summary-in' type='checkbox'  checked><label for='section-62a1f250-cd4e-4bae-b299-afa9e3cac943' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>st_ocean</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413</div><input id='attrs-3ebe45ae-9e4e-4da6-8f96-6511adde0dae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ebe45ae-9e4e-4da6-8f96-6511adde0dae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad6b7193-4abc-46d5-99b7-37b421269390' class='xr-var-data-in' type='checkbox'><label for='data-ad6b7193-4abc-46d5-99b7-37b421269390' 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'></dl></div><div class='xr-var-data'><pre>array(0.54128075)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-4a4db094-4d03-42b2-a26f-ac2bc786bf83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a4db094-4d03-42b2-a26f-ac2bc786bf83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a395c5b4-742b-4d5f-bc1e-8c80d8c7c4d5' class='xr-var-data-in' type='checkbox'><label for='data-a395c5b4-742b-4d5f-bc1e-8c80d8c7c4d5' 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'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66e1c286-1e6b-4bfc-aef1-cb3759281f25' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66e1c286-1e6b-4bfc-aef1-cb3759281f25' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pot_rho_0' (month: 12)>\n",
       "array([0.0189209 , 0.0300293 , 0.0144043 , 0.0246582 , 0.02087402,\n",
       "       0.01855469, 0.02709961, 0.02661133, 0.02368164, 0.02270508,\n",
       "       0.02966309, 0.02001953])\n",
       "Coordinates:\n",
       "    st_ocean  float64 0.5413\n",
       "  * month     (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 117,
     "metadata": {},
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   "source": [
    "mld - wl_Dc90[:,0]"
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