Mask_1000_2000.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5be19a0f",
   "metadata": {},
   "source": [
    "Create the region mask for the Temperature offshore boxes 1000 to 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "192aa741",
   "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 as cm\n",
    "import glob\n",
    "import matplotlib.colors as col\n",
    "# need to install opencv-python for this:\n",
    "#import cv2\n",
    "\n",
    "import logging\n",
    "logging.captureWarnings(True)\n",
    "logging.getLogger('py.warnings').setLevel(logging.ERROR)\n",
    "\n",
    "from dask.distributed import Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74c87ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33237</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:40419/status' target='_blank'>http://127.0.0.1:40419/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>16</li>\n",
       "  <li><b>Memory: </b>44.92 GiB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:33237' processes=4 threads=16, memory=44.92 GiB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c97aa4e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "session = cc.database.create_session('/g/data/ik11/databases/cosima_master.db')\n",
    "exp = '01deg_jra55v140_iaf_cycle3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b512183",
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_range = slice(-90,-59)\n",
    "\n",
    "contour_depth = 1000\n",
    "contour_depth2 = 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4e6bb2",
   "metadata": {},
   "source": [
    "Import bathymetry and find depth contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c238b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "yt_ocean = cc.querying.getvar(exp,'yt_ocean',session,n=1)\n",
    "yt_ocean = yt_ocean.sel(yt_ocean=lat_range)\n",
    "xt_ocean = cc.querying.getvar(exp,'xt_ocean',session,n=1)\n",
    "\n",
    "ht = cc.querying.getvar(exp,'ht',session,n=1)\n",
    "ht = ht.sel(yt_ocean=lat_range)\n",
    "# fill in land with zeros:\n",
    "ht = ht.fillna(0)\n",
    "\n",
    "temp_mask = np.copy(ht)\n",
    "temp_mask[np.where((ht>=contour_depth)|(ht<contour_depth2))] = 0\n",
    "temp_mask[np.where((ht<contour_depth))] = 1\n",
    "temp_mask[np.where(ht>=contour_depth2)] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94d70f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in case you want to check what contour_mask0 looks like, e.g. is it continuous around Antarctica?\n",
    "\n",
    "plt.figure(1,figsize=(16,8))\n",
    "plt.pcolormesh(temp_mask)\n",
    "plt.colorbar()\n",
    "plt.clim((0,2))\n",
    "plt.xlim((400,800))\n",
    "plt.ylim((350,450));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dbc7849c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "save_dir =  '/g/data/jk72/ns3783/'\n",
    "outfile = os.path.join(save_dir,'Antarctic_slope_contour_'+str(contour_depth)+str(contour_depth2)+'m.npz')\n",
    "\n",
    "np.savez(outfile,temp_mask=temp_mask,xt_ocean=xt_ocean,yt_ocean=yt_ocean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f198390",
   "metadata": {},
   "outputs": [],
   "source": [
    "xu_ocean = cc.querying.getvar(exp,'xu_ocean',session,n=1)\n",
    "yu_ocean = cc.querying.getvar(exp,'yu_ocean',session,n=1)\n",
    "yu_ocean = yu_ocean.sel(yu_ocean=lat_range)\n",
    "\n",
    "#Make U\n",
    "hu = cc.querying.getvar(exp,'hu',session,n=1)\n",
    "hu = hu.sel(yu_ocean=lat_range)\n",
    "# fill in land with zeros:\n",
    "hu = hu.fillna(0)\n",
    "\n",
    "temp_maskU = np.copy(hu)\n",
    "temp_maskU[np.where((hu>=contour_depth)|(hu<contour_depth2))] = 0\n",
    "temp_maskU[np.where((hu<contour_depth))] = 1\n",
    "temp_maskU[np.where(hu>=contour_depth2)] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0289ef20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in case you want to check what contour_mask0 looks like, e.g. is it continuous around Antarctica?\n",
    "plt.figure(1,figsize=(16,8))\n",
    "plt.pcolormesh(temp_maskU)\n",
    "plt.colorbar()\n",
    "plt.clim((0,2))\n",
    "plt.xlim((400,800))\n",
    "plt.ylim((350,450));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc2d13c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "save_dir =  '/g/data/jk72/ns3783/'\n",
    "outfile = os.path.join(save_dir,'Antarctic_slope_contour_U'+str(contour_depth)+str(contour_depth2)+'m.npz')\n",
    "\n",
    "np.savez(outfile,temp_maskU=temp_maskU,xu_ocean=xu_ocean,yu_ocean=yu_ocean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27c22bce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis27-18.10]",
   "language": "python",
   "name": "conda-env-analysis27-18.10-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}