seal_data_comparison_from_adele.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temperature comparison with MEOP data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import cosima_cookbook as cc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import cartopy.crs as ccrs\n",
    "import xarray as xr\n",
    "import cmocean.cm as cmocean\n",
    "from glob import glob\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import matplotlib.colors as col\n",
    "from scipy import interpolate\n",
    "\n",
    "import logging\n",
    "logging.captureWarnings(True)\n",
    "logging.getLogger('py.warnings').setLevel(logging.ERROR)\n",
    "\n",
    "from dask.distributed import Client\n",
    "\n",
    "figdir = '/g/data/v45/akm157/figures/Weddell_connectivity/obs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-10099c9a-8ce0-11ee-ae28-000007e3fe80</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",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/proxy/39503/status\" target=\"_blank\">/proxy/39503/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">668867c8</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/proxy/39503/status\" target=\"_blank\">/proxy/39503/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 4\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 16\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 250.00 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-cbf5a3f8-3a55-4578-9ccc-3d14cd950bf4</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:35005\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"/proxy/39503/status\" target=\"_blank\">/proxy/39503/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 16\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 250.00 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\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: 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://127.0.0.1:36327\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46749/status\" target=\"_blank\">/proxy/46749/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38335\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/102553225.gadi-pbs/dask-worker-space/worker-kmiowo77\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: 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://127.0.0.1:41741\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41265/status\" target=\"_blank\">/proxy/41265/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33443\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/102553225.gadi-pbs/dask-worker-space/worker-f6nzxbd1\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: 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://127.0.0.1:46157\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38961/status\" target=\"_blank\">/proxy/38961/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44607\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/102553225.gadi-pbs/dask-worker-space/worker-t7rhjnjl\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://127.0.0.1:42825\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42649/status\" target=\"_blank\">/proxy/42649/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38453\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/102553225.gadi-pbs/dask-worker-space/worker-izmy1e28\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://127.0.0.1:35005' processes=4 threads=16, memory=250.00 GiB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_session = cc.database.create_session('/g/data/ik11/databases/cosima_master.db')\n",
    "ryf = '01deg_jra55v13_ryf9091'\n",
    "cycle2 = '01deg_jra55v140_iaf_cycle2'\n",
    "cycle3 = '01deg_jra55v140_iaf_cycle3'\n",
    "\n",
    "start_time='2150-01-01'\n",
    "end_time='2159-12-31'\n",
    "\n",
    "lon_slice  = slice(-120, -50)\n",
    "lat_slice  = slice(-75.5,-60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# topography data for plotting:\n",
    "ht = cc.querying.getvar(ryf,'ht' , master_session,n=1)\n",
    "ht = ht.sel(yt_ocean=lat_slice).sel(xt_ocean=lon_slice)\n",
    "land_mask = np.squeeze(ht.values)*0\n",
    "land_mask[np.isnan(land_mask)] = 1\n",
    "land_mask = np.where(land_mask==1,land_mask,np.nan)\n",
    "\n",
    "# bottom cell indices:\n",
    "kmt = cc.querying.getvar(ryf,'kmt',master_session, n=-1)\n",
    "kmt = kmt.sel(yt_ocean=lat_slice).sel(xt_ocean=lon_slice)\n",
    "kmt = kmt.fillna(1.0).astype(int) - 1\n",
    "kmt.load()\n",
    "\n",
    "fontsize=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shelf mask to check if profiles are on shelf or not:\n",
    "\n",
    "# Note this is on the t grid\n",
    "contour_file = np.load('/g/data/ik11/grids/Antarctic_slope_contour_1000m.npz')\n",
    "shelf_mask = contour_file['contour_masked_above']\n",
    "yt_ocean = contour_file['yt_ocean']\n",
    "xt_ocean = contour_file['xt_ocean']\n",
    "shelf_mask[np.where(shelf_mask!=0)] = np.nan\n",
    "shelf_mask = shelf_mask+1\n",
    "shelf_map = np.nan_to_num(shelf_mask)\n",
    "shelf_mask = xr.DataArray(shelf_mask, coords = [('yt_ocean', yt_ocean), ('xt_ocean', xt_ocean)])\n",
    "shelf_map = xr.DataArray(shelf_map, coords = [('yt_ocean', yt_ocean), ('xt_ocean', xt_ocean)])\n",
    "shelf_map = shelf_map.sel(yt_ocean=lat_slice).sel(xt_ocean=lon_slice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select out obs profiles in correct region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39.4 s, sys: 8.06 s, total: 47.5 s\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# do some testing to see which seal data is in the right region:\n",
    "# Need to consider profiles from Brazil, Germany, UK, USA\n",
    "# I think the interpolated data is fine to use here, because I only want profiles < 1000m.\n",
    "\n",
    "path2files   = '/g/data/v45/akm157/data/meop_seal_data/'\n",
    "FileList = glob(path2files+'*/DATA_ncARGO/*')\n",
    "FileList.sort()\n",
    "\n",
    "WAP_FileList = []\n",
    "for file in FileList:\n",
    "    # open all profiles for this seal:\n",
    "    profile_data = xr.open_dataset(file)\n",
    "    lat = profile_data.LATITUDE\n",
    "    lon = profile_data.LONGITUDE\n",
    "    # check if in West Antarctic Peninsula box:\n",
    "    WAP_box=lat.where(lon>-120).where(lon<-50).where(lat<-60).dropna(dim='N_PROF')\n",
    "    if len(WAP_box)>0:\n",
    "        WAP_FileList += [file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "file 95/165\n",
      "file 96/165\n",
      "file 97/165\n",
      "file 98/165\n",
      "file 99/165\n",
      "file 100/165\n",
      "file 101/165\n",
      "file 102/165\n",
      "file 103/165\n",
      "file 104/165\n",
      "file 105/165\n",
      "file 106/165\n",
      "file 107/165\n",
      "file 108/165\n",
      "file 109/165\n",
      "file 110/165\n",
      "file 111/165\n",
      "file 112/165\n",
      "file 113/165\n",
      "file 114/165\n",
      "file 115/165\n",
      "file 116/165\n",
      "file 117/165\n",
      "file 118/165\n",
      "file 119/165\n",
      "file 120/165\n",
      "file 121/165\n",
      "file 122/165\n",
      "file 123/165\n",
      "file 124/165\n",
      "file 125/165\n",
      "file 126/165\n",
      "file 127/165\n",
      "file 128/165\n",
      "file 129/165\n",
      "file 130/165\n",
      "file 131/165\n",
      "file 132/165\n",
      "file 133/165\n",
      "file 134/165\n",
      "file 135/165\n",
      "file 136/165\n",
      "file 137/165\n",
      "file 138/165\n",
      "file 139/165\n",
      "file 140/165\n",
      "file 141/165\n",
      "file 142/165\n",
      "file 143/165\n",
      "file 144/165\n",
      "file 145/165\n",
      "file 146/165\n",
      "file 147/165\n",
      "file 148/165\n",
      "file 149/165\n",
      "file 150/165\n",
      "file 151/165\n",
      "file 152/165\n",
      "file 153/165\n",
      "file 154/165\n",
      "file 155/165\n",
      "file 156/165\n",
      "file 157/165\n",
      "file 158/165\n",
      "file 159/165\n",
      "file 160/165\n",
      "file 161/165\n",
      "file 162/165\n",
      "file 163/165\n",
      "file 164/165\n",
      "file 165/165\n",
      "CPU times: user 5min 19s, sys: 17.8 s, total: 5min 37s\n",
      "Wall time: 5min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now go through the profiles in the WAP box and select out all profiles on the shelf:\n",
    "# We also need salinity, to convert in situ temp to conservative temp.\n",
    "\n",
    "# I have checked TEMP_ADJUSTED_QC,PSAL_ADJUSTED_QC,PRES_ADJUSTED_QC=1 for all profiles used.\n",
    "\n",
    "nprofiles = 22312 # 47636\n",
    "ndepth = 22\n",
    "temp = np.zeros((nprofiles,ndepth))\n",
    "#saltQC = np.zeros((nprofiles,ndepth))\n",
    "#presQC = np.zeros((nprofiles,ndepth))\n",
    "salt = np.zeros((nprofiles,ndepth))\n",
    "pres = np.zeros((nprofiles,ndepth))\n",
    "lon_obs = np.zeros((nprofiles))\n",
    "lat_obs = np.zeros((nprofiles))\n",
    "date = []\n",
    "profile_count = 0\n",
    "\n",
    "file_count = 1\n",
    "for file in WAP_FileList:\n",
    "    print('file '+str(file_count)+'/'+str(len(WAP_FileList)))\n",
    "    # open all profiles for this seal:\n",
    "    profile_data = xr.open_dataset(file)\n",
    "    lat = profile_data.LATITUDE\n",
    "    lon = profile_data.LONGITUDE\n",
    "    # go through each profile and find nearest model grid point:\n",
    "    for profile in range(len(lat)):\n",
    "        # NOTE THAT LONGITUDE IS NOT ADJUSTED FOR THE MODEL RANGE HERE, BECAUSE RANGE ON PENINSULA IS OK\n",
    "        # BUT THIS WILL NOT WORK ELSEWHERE!!\n",
    "        on_shelf = shelf_map.sel(xt_ocean=lon[profile],method='nearest').sel(yt_ocean=lat[profile],method='nearest')\n",
    "        if on_shelf>0:\n",
    "            # check temp has at least 2 data points:\n",
    "            temp_profile = profile_data.TEMP_ADJUSTED[profile]\n",
    "            if len(np.where((temp_profile!=0.)&(~np.isnan(temp_profile)))[0]) > 2:\n",
    "                # check the dive reached at least 200 dbar:\n",
    "                pres_profile = profile_data.PRES_ADJUSTED[profile]\n",
    "                if pres_profile.max() >= 450:\n",
    "                    date = np.append(date,profile_data.JULD[profile])\n",
    "                    # save profile:\n",
    "                    lat_obs[profile_count] = lat[profile]\n",
    "                    lon_obs[profile_count] = lon[profile]\n",
    "                    # find depth of profile:\n",
    "                    nlevels = len(profile_data.PRES_ADJUSTED[profile])\n",
    "                    temp[profile_count,:nlevels] = temp_profile\n",
    "                    salt_profile = profile_data.PSAL_ADJUSTED[profile]\n",
    "                    salt[profile_count,:nlevels] = salt_profile\n",
    "                    #saltQC_profile = profile_data.PSAL_ADJUSTED_QC[profile]\n",
    "                    #saltQC[profile_count,:nlevels] = saltQC_profile\n",
    "                    #presQC_profile = profile_data.PRES_ADJUSTED_QC[profile]\n",
    "                    #presQC[profile_count,:nlevels] = presQC_profile\n",
    "                    pres[profile_count,:nlevels] = pres_profile\n",
    "                    profile_count += 1\n",
    "    file_count += 1\n",
    "# Wall time: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolate MEOP profiles onto model depth grid and convert to conservative temp:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to absolute salinity:\n",
    "from gsw import SA_from_SP\n",
    "salt_abs = SA_from_SP(salt,pres,np.tile(lon_obs,(ndepth,1)).swapaxes(0,1),np.tile(lat_obs,(ndepth,1)).swapaxes(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to conservative temp:\n",
    "from gsw import CT_from_t\n",
    "temp_conservative = CT_from_t(salt_abs,temp,pres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert pressure to depth:\n",
    "from gsw import z_from_p\n",
    "z = -z_from_p(pres,np.tile(lat_obs,(ndepth,1)).swapaxes(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.9 s, sys: 1.04 s, total: 11.9 s\n",
      "Wall time: 11.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Interpolate to model point nearest 450m:\n",
    "st_ocean = cc.querying.getvar(ryf,'st_ocean' , master_session,n=1)\n",
    "model_depth = st_ocean.sel(st_ocean=450,method='nearest')\n",
    "\n",
    "temp_interp = np.zeros((nprofiles))\n",
    "\n",
    "max_profile_depth = z.max(axis=1)\n",
    "\n",
    "for profile in range(nprofiles):\n",
    "    # select out available pressure range of data:\n",
    "    z_profile = z[profile,:][z[profile,:]>0]\n",
    "    temp_profile = temp_conservative[profile,:][z[profile,:]>0]\n",
    "    # check profile is deep enough:\n",
    "    # keep if within 70m of model depth (468.4475154m)\n",
    "    if max_profile_depth[profile] < model_depth-70:\n",
    "        # else set all to nans:\n",
    "        temp_profile = temp_profile*np.nan\n",
    "    \n",
    "    f = interpolate.interp1d(z_profile,temp_profile,bounds_error=False,fill_value=\"extrapolate\")\n",
    "    temp_interp[profile] = f(model_depth)\n",
    "# Wall time: 10.2 s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon_obs = lon_obs[~np.isnan(temp_interp)]\n",
    "lat_obs = lat_obs[~np.isnan(temp_interp)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date = date[~np.isnan(temp_interp)]\n",
    "meop_temp = temp_interp[~np.isnan(temp_interp)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Longitude')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 14\n",
    "depth_slice = 450\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sc = plt.scatter(lon_obs,lat_obs,c=meop_temp,s=.5,cmap='RdBu_r',vmin=-2,vmax=2)\n",
    "cbar = plt.colorbar(sc, orientation = 'vertical',extend = 'neither',pad=.02)\n",
    "cbar.set_label(str(depth_slice)+'m temperature from seal profiles ($^\\circ$C)',fontsize=fontsize,labelpad=5)\n",
    "cbar.ax.tick_params(labelsize=fontsize)\n",
    "\n",
    "plt.contour(ht.xt_ocean,ht.yt_ocean,ht,levels=[1000],colors='k')\n",
    "plt.contourf(ht.xt_ocean,ht.yt_ocean,land_mask,colors='grey')\n",
    "plt.xlim(-120,-50)\n",
    "plt.ylim(-75.5,-60)\n",
    "plt.tick_params(labelsize=fontsize)\n",
    "plt.ylabel('Latitude',fontsize=fontsize)\n",
    "plt.xlabel('Longitude',fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save interpolated seal data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[20138, 22312]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(meop_temp), len(date)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_nr = len(meop_temp) # length of all profiles -> 20138 = I have that many different profiles\n",
    "meop_temp = xr.DataArray(meop_temp, coords = [np.arange(data_nr)], dims = ['profile_number'], name = 'seal profile cons. temp. interpolated onto model depth grid closest to 200m')\n",
    "\n",
    "lon_obs   = xr.DataArray(lon_obs, coords = [np.arange(data_nr)], dims = ['profile_number'], name = 'longitude of seal profiles')\n",
    "lat_obs   = xr.DataArray(lat_obs, coords = [np.arange(data_nr)], dims = ['profile_number'], name = 'latitude of seal profiles')\n",
    "date      = xr.DataArray(date, coords = [np.arange(nprofiles)], dims = ['profile_number'], name = 'date of seal profiles')\n",
    "\n",
    "ds = xr.Dataset({'meop_temp': meop_temp,'lon_obs':lon_obs,'lat_obs':lat_obs,'date':date})\n",
    "outpath = '/g/data/e14/mv7494/seal_data_comparison/' + 'WAP_seal_profiles_'+str(depth_slice)+'m_depth_ACCESS-OM2-01.nc'\n",
    "ds.to_netcdf(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ----------------------------------------------------------------------------------------------------------------------- #\n",
    "#                                                   Notes down below                                                      #\n",
    "# ----------------------------------------------------------------------------------------------------------------------- #"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3-22.04]",
   "language": "python",
   "name": "conda-env-analysis3-22.04-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}