{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Smooth bathymetry  -grab from Susan's notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "from salishsea_tools import (nc_tools,viz_tools,tidetools,bathy_tools)\n",
    "from salishsea_tools.nowcast import figures\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!cp /ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea10.nc \\\n",
    "bathy_meter_SalishSea11.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.125"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bathy_11 = nc.Dataset('bathy_meter_SalishSea11.nc','r' )\n",
    "bathyy_11 = bathy_11.variables['Bathymetry'][:]\n",
    "bathyy_11[418,365]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = --,\n",
       "             mask = True,\n",
       "       fill_value = 0.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bathy_10 = nc.Dataset('bathy_meter_SalishSea10.nc','r+' )\n",
    "bathyy_10 = bathy_10.variables['Bathymetry']\n",
    "bathyy_10[427,308]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bathyy_11[:] = bathy_tools.smooth(bathyy_11[:], max_norm_depth_diff=0.8, smooth_factor=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_max(bathy):\n",
    "    i,j = np.unravel_index(bathy.argmax(), bathy.shape)\n",
    "    return i,j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_slope(bathy,di,dj):\n",
    "    imax, jmax = bathy.shape\n",
    "    Da = 0.5*(bathy[di:,dj:]+bathy[0:imax-di,0:jmax-dj])\n",
    "    Dd = bathy[di:,dj:]-bathy[0:imax-di,0:jmax-dj]\n",
    "    return Dd/Da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def smooth(gamma,bathy,i,j,di,dj):\n",
    "    a = 0.5*(bathy[i,j]+bathy[i+di,j+dj])\n",
    "    if bathy[i,j] < bathy[i+di,j+dj]:\n",
    "        change = gamma\n",
    "    else:\n",
    "        change = -gamma\n",
    "    bathy[i,j] = bathy[i,j] + gamma*a\n",
    "    bathy[i+di,j+dj] = bathy[i+di,j+dj] - gamma*a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "gamma = 0.2\n",
    "maxslope = 0.8\n",
    "\n",
    "slopei = find_slope(bathyy_8,1,0)\n",
    "i,j = find_max(slopei)\n",
    "slopej = find_slope(bathyy_8,0,1)\n",
    "k,l = find_max(slopej)\n",
    "while np.maximum(slopei[i,j],slopej[k,l]) > maxslope:\n",
    "    if slopei[i,j] > slopej[k,l]:\n",
    "        smooth(gamma,bathyy_8,i,j,1,0)\n",
    "    else:\n",
    "        smooth(gamma,bathyy_8,k,l,0,1)\n",
    "    slopei = find_slope(bathyy_8,1,0)\n",
    "    i,j = find_max(slopei)\n",
    "    slopej = find_slope(bathyy_8,0,1)\n",
    "    k,l = find_max(slopej)\n",
    "\n",
    "print (slopei[i,j])\n",
    "print (slopej[i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bathyy_10[:] = bathyy_11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = --,\n",
       "             mask = True,\n",
       "       fill_value = 0.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bathyy_10[428,306]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Salish Sea NEMO Bathymetry\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://bitbucket.org/salishsea/tools/src/tip/bathymetry/SalishSeaBathy.ipynb\n",
      "references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/grid/bathy_meter_SalishSea.nc\n",
      "comment: Based on 1_bathymetry_seagrid_WestCoast.nc file from 2-Oct-2013 WCSD_PREP tarball provided by J-P Paquin.\n",
      "history: \n",
      "    [2013-10-30 13:18] Created netCDF4 zlib=True dataset.\n",
      "    [2013-10-30 15:22] Set depths between 0 and 4m to 4m and those >428m to 428m.\n",
      "    [2013-10-31 17:10] Algorithmic smoothing.\n",
      "    [2013-11-21 19:53] Reverted to pre-smothing dataset (repo rev 3b301b5b9b6d).\n",
      "    [2013-11-21 20:14] Updated dataset and variable attributes to CF-1.6 conventions & project standards.\n",
      "    [2013-11-21 20:47] Removed east end of Jervis Inlet and Toba Inlet region due to deficient source bathymetry data in Cascadia dataset.\n",
      "    [2013-11-21 21:52] Algorithmic smoothing.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nc_tools.show_dataset_attrs(bathy_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bathy_10.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create TS for bathymetry6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from salishsea_tools import nc_tools\n",
    "from salishsea_tools import tidetools\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#old bathymetry inforamtion\n",
    "old_path = '../nemo-forcing/grid/bathy_meter_SalishSea2.nc'\n",
    "old_bathy = nc.Dataset(old_path, 'r')\n",
    "old_depth = old_bathy.variables['Bathymetry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#old initial file\n",
    "initial_path = '/data/dlatorne/MEOPAR/SalishSea/nowcast/14jun15/SalishSea_02401920_restart.nc'\n",
    "T_S = nc.Dataset(initial_path, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 14.77021985  14.34337293  14.20415199  13.84946759  13.67585241\n",
      "  13.59131301  13.64740562  13.5873905   13.43705051  13.34423781\n",
      "  13.15459748  13.03363     12.96472808  12.91797194  12.89496075\n",
      "  12.87349044  12.84061662  12.78115335  12.70208798  12.59582154\n",
      "  12.51719442  12.35120913  12.09723483   0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.           0.        ]\n",
      "[   0.5000003     1.5000031     2.50001144    3.50003052    4.50007057\n",
      "    5.50015068    6.50031042    7.50062323    8.50123596    9.50243282\n",
      "   10.50476551   11.50931168   12.51816654   13.53541183   14.56898212\n",
      "   15.63428783   16.76117325   18.00713539   19.48178482   21.38997841\n",
      "   24.10025597   28.22991562   34.68575668   44.51772308   58.48433304\n",
      "   76.58558655   98.06295776  121.86651611  147.08946228  173.11448669\n",
      "  199.57304382  226.26029968  253.06663513  279.93453979  306.834198\n",
      "  333.75018311  360.67453003  387.60321045  414.53408813  441.46609497]\n"
     ]
    }
   ],
   "source": [
    "#nc_tools.show_variables(T_S)\n",
    "old_T = T_S.variables['tb'][0] # omit the first dimension\n",
    "old_S = T_S.variables['sb'][0]\n",
    "depths = T_S.variables['nav_lev']\n",
    "lon = T_S.variables['nav_lon']\n",
    "lat = T_S.variables['nav_lat']\n",
    "print (old_T[:, 427, 292])\n",
    "print(depths[0:40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(898, 398)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fbaf2fa7200>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KyraMNu8Ta7Wl1bYsTSLk3DwlkBLVMUYg5GuFstKk9/okFPvpU8YE6tomJPng\nk7U6yJlmxxyA1wNyuz/mj0/5OADgM8FXbL/z06/8VbL8iTMq+QQROz2qlCGtHPTnHfHBfrbvG7wv\nK49mHToSlytbxobkyMm3zZhasG+YYY0+vzaqjt4ULPZIdzbmMO4wliyzufE4iz7gybjzQXPurqb8\nQRSrngGkRU6WccZoqdk49Ll5dzSmfc8rTL8c5z0m9lipPsX8Gmekfc/GdwEAl136FQDA4rNeg2LY\ncoj/buqf9t9D440mwf85pmMk4YhKMFot4YidE/Jub6U230oH5BNL9I6IGAUYwUxXlaHth2V4ToMR\nJpmSRmluYa+nMoVlb4lh0tq1ARh0aWtVeYPneFevdwZuz6WTPrM3ektuQqOXMB6u95N3913SnPTp\nW6STdlROWMXDln+j2xsDHJI5yUY41zyqdczXXnhfsnw7/OTptI0a1UJvQi0k4kytgYXqO7y3isO3\nX+L3PdTUsDsWDwFIv5OLXq2mbppSBPhvwL0/TsRFDAJjyDtiSHDQ315A+6v9n87mCq5VSYBkTF3Q\n6r6UKLiNJGw1RMoRTODDSZ7VuVn+/CvSRO68Ag2Nvs+HcD0AoGlma9LljlNOBgCsb9CQZkbxGv0z\nIQ3XogtWhmjGSEPi4fGuVJ/+yK3/DQDY81YfCn6pirnWb3v6Q0q+YWYzvU+J0XapxR/R671V6LVy\nee4LSZ8wMOcnfT495bT3vYgN4/aIxBsxNIiWcIDngTmvbgWQ74rEqDcGW3CCrS2vDHE+qOXa6rzd\ngQrPcGAGbdTMSZliv43eP2x6wiYeVmOmbr1+rnpMLNbj32Z22PSQLtiJuM+OShL5E7zHyJyVavKv\nzujEdYzJ12BCl2Fx1G/UXB0a63LlW9I0mt+8VAMw6JRi9d8nERExNKiQ6TRFw3Xwsx0OwFXOucvN\n9spz6AxuaIPH9Ke9RXXAIWkCmdTyzbeSs8KWQ9h8wr26zP1I1LSiH9IcBAAADZXmuZjkxyaYZ/jz\nhIme6LtbS021zodzC0c++X7VW8BHX5Lm7rhuWZCIdx2KI8z/oK5lYu6X6+nmt5tv9ni1l2j6TjE7\np+mkfUWOmIgnYjhQuRzxCoCvOOcWi8hk+LiIe5xzSwedQ0cRU1lGRESMfUws8xXAObfWObdYl7fC\nP+oyWxhz6FgkOXScc63wpcuPLjW06ljCTyGxpKbMS/NCNOVaAaTJYTqTPBFpH2rAzIgWuqwBqZQQ\nTtoxC5fLGK/OAAAgAElEQVTNS0DdmNby+zd6jWFJ435JH2rVDY3eN/au9/qosbzK8HcsTBZFljrA\n+gVXHzPxDwcAB8AHqky7yEszv0KaFyJRZBjpph51LY2vTbowS1qtqi5bFthEo/l6L/29b9RUbUlU\nYgPgrosTbRE7EEPAdJrM7DAAi4Yih84QDq0CPJ2euX6GqRSxwE+OTcnlV+bcZJLztKluXIqEi3lT\nUGteb5xZ2ZfRdasbPWHXGa8NTtYx2GP2sV4rvfo0UyLoLm17bIXnV5SMx1eFcOT/pUEWM96Yv+3+\ncT4h8NzHTcw0L5tKCt9v9J4KNjnSmXU+zPiAIzyZM7XmV+G13W9e/r30eKZkoLsEkp8QNSJiB6KI\nHNHytH/1B5UibgHwJQB9GIIcOkR1SHixWU7TAKO+1hPy3EO8icmJtFqThJ1WcXuef5hJII5sYraw\nbnG0mjkpSLc2GzY7rdebiM093lWrodZvq313Ohl45Q80LOwcNR/b01LysjdcUMO0eEXiIUBaJim9\nSVyMswCkE6GvelpzW5rgii2zvVW7KOefnv71Hz73ML5nfiYajb24SQMv1JX4n3Ec3PUQ98Uh+hAR\nEUOJIkzXvMC/iAtuKuwjIuMB/BrAL51zt4nIoRiCHDr9DG140bkOmMRH38Jsw9hvtrfO2hs9Ydhc\nwSH5EtYjoj8SrjO5G1iPrjbwsmjqSiWLXdarpq5XKzfLT/wxhy4ALDnD3zBaGNhwrRYDLTW5Ncx4\n8I1p7DXd/uZu8XMIrEPXXpteT9aL++wWTXhxhH7gfMcRuD5zA/koIiJGPir3jhAAVwNY4py7DBi6\nHDqDHFrEYCHvgwMrTxRUcQZsxHaSWQ35FZm7/6w+viwMrS68pxvnj4iICAzGO2IhgFMBPCkiTCj+\nNefcXaZPRTl0iKqQ8PPbgHkqSYy3pXPU4hKNTDv0NV5fXVc/03Qp7jPcH3KJ61phFjRa2Hfi7QCA\nrcZCfMNGLbOhE1bTZ3mrknIJkE7sHX2Gd/l66lSfHS7JQfFfSDHAZCJ/wjEunZzMeHQI8NNbPu8X\nDkrX/eMQrzt07OIlh8U5/wy22uhBTBnZ+Vsd8w8A96E4gRYxBlBhjTnn3IPox4us0hw6RFVIeBmQ\n+JXOt4FldB/Vj1SjpW7m1qduCKHvcOrdkOqznUX8imeoNlBXIv0lyc6Wgk/qzqkr8tyVXi5Zt1ea\nEvNIjWlmNra31mudH/V7XXJSStjP/V3Td/5Y+W26WsC2KoXWfrv/ABMHDOCqxk8ly90neTanfs0J\ntG1d/jfTW5N+vfQMqct5Pfw+LZC60shXL5ylrG3m1vAhRESMfsSw5Xy8gtS7eaYpTzOTJExiZk3L\nWWkfkuShGk41XbVOWyRzte5AMuY+1I2nmkm3N2k+XGrCnLSzfVj3LIkH0dnUBTOeSLqsrp2tXbLr\n4r0Vpvia3mRmX+LDzn6qBTatZf0fOBdA6lJW1+tvHDNyaQgxqw/TFY+ud0fV+hvC0iWHpeds9c0+\nJ/n46jUb/Xi7N5i0mv8XdNajRkSMeoxg4bUqQ9uIVP1sNesbdS5s/D90hbYzFpiac3pH42M0Cdbm\njuC65Vp+mNYzLUbr+cCoLubFPWqNMuw2pLD52IHEYt9lcRoEc8wxi/LOzZBrnttG4HGs81f4aMGG\nOX48x60w+r12f+I4722xOdeQ97mBlIRp1f7l71oI8/OSN04AyYV+YZlau5fpeqvuNBUsRESMDUQS\njoiIiKgiRjDT9VdZ4xoAbwew3jl3qK67BMA7AHTDF1L/uHPuZd1WVuKKdqQFgmwRoR6d+BrPlJGa\ntKx+/9RPuG5vDS7Qp2hahtb3d3aQcYZ6Ly1Qa01yP06sdc7yI2vmZBwAYdUI1bGTihrG0tz7JW+t\n7/2cWu1Md6mSa4/J517DubW988fwzJy0CnHLHG/dfm7dDwEAfS/6k+1zeJrjkRLKum519n1GLWDG\nX9iLS0nnZ9oyRXCD6UNpKE7FRYw1jGJN+FoAVwB5sU53AzjbOdcnIt8FcC6AcwaSuKKzyHKrSgDz\nqCyQ9NK6kKhRx+ppb/GuFHzctwEdW4NkPJy0C7OrAenEFCPxGBXXbWZTaxnQwMRqvLXYq0f5gqfg\nY/7Wwq7Elv29lkv9mbICkE6kzZ/pc+8+qdLDC9cblwemXyahtmpLYt3NnOwEbfm56Hq93fT5rbbO\nRNEl/m8REaMYo9USds49oPHSdt095u0iAO/R5SRxBYBWEWHiikyfKjpcWRJmXMM8GrL3a2srD6s+\nO/Uhv2fuNV7LXZ1LrdswoKMmyT1cCBJzdxAGvawuzR3xqonL83ciKdsoOJIvXe4WBn3Nld62YJyO\n2ZunDBix46Y1zwm+jpm+7f1Qektf/uSr/ALr2jUEbZZbTi7YZr+dVi4YDT6ScMRYwGitMVcGPgHg\nBl0eUOKKjHrFiaH5vPoL76lW2iQTWpvMv6nnVv16T62vn/FA0uWpnLcaH8MRAFIrl/7BsxOtICU+\nehg8phU+bRj0Xgs929aTxjlRF6ZztOtUhnhijtchrMsbJ+02JZWjp+atB1J5hJOIrIW32riKbD5E\nK06P8zekVav1pCv1F7fIjIukS4803kCsJZyk7Ch04YuIGNUYrZZwKYjI1wF0O+euL9EtM1LkPqRc\ntRAAi95zMKSAVfqIv78tdcNOJGYl5foFqZ3bNLcVQCpHPAHvqkXpwZLdXE2FRp2YuSP+gtQ/tzbn\nj/3W47yb2dRaHaFN/EHrnXcSJcBZc/yGh00O42e1tBMLj1KOmGyyxYWh13xvM8odPc6fhDeZ3tm+\nT9unvBbSc1p6jANnek8M3myWX6NWdKs5Cfl9jb3rRUTsOIhIM4ajFM1YI2EROQ0+DOFNZnXZiSve\ngJRD48PuyIZPy2l99OhXnAbQOHdynMqLGBI451oAtPC9iJw3JAceSyQsIicCOAvA8c45+zA7oMQV\npR546UOchDuYeILE0qRsyU9gpIE9tns9o/cQbwnOVLXZWpEEU1gS9KCgLAEADfB5hOllMXVXnbiy\nIdccD+USvTIzN/rZxUMb0+KWlCFomYc5je04WF+PkoVNsUlwXZOatdMaN+hQ0q+Xssuq5zSohRxq\nv0FWpP9NE/I7lYZIqwP+mrx37t2RlCNGFLLKbo0U9OeidgOA4wFMF5GVAM6D94aYAOAeTeP2iHPu\njIEkrqhBtiY83mwHgCnUMW04L0mOZMwMX42mjxLy3is9M9bt5R/bGZFmyW5Wl5cLdlmpThya02LO\nLFN3h2TLcZBPbUBHqA/rPKHQw8OMb46SJWUEtjZbHGUDhiIzf7INNGEffh6+J/naHBnt3VoslSHY\n9JawNxKO9Sv6TVymfnW7mz6JnM4PVHg7FVlkEpocEwk5ouroHa2WsHPugxmrrynRv6zEFZOQcmhN\nsN6ikxNzxu13PHcgB9ApYqPZkeSo+0/f5pnn0IM8e9okQG21nvjqGj1hC63udO4urarMQfNGYInX\nVBnOg47XWuEkVFrAJE+2eX361IVurR/nuFxagHTqbp6QJ4xTEu7z+zeM26xDSq9ub49fntDkpYXu\nVfpBTUh44tL2krZf1naD6cOQ8ladaNxAEj4h6ODhCTn1tojSRUQ1MGpJeLjQiFRhKGURE53GEh5P\ny5eky09gLToKziRjJdGp2zxhTJ2R+sF2KR+37+oJrL4mw5GNFnl457CPOOHNwY4HodTgDxSGNncZ\nPxreKNpa9S7zkueuvhdNn+16F1Ajl9HGbZN1n62G7yarcbo9COiwwRpcxxsKXd+2mj78wqj+b9MV\niUFsv71XEBExEtBVO6H/TgCyHVmHFyP4/hAxFiHyY5f/2AI49/VoHUcMK3pzI1cUrgoJW+u3xyy/\nErRZ/RNQv6QkYA9EC5j/dVqnbUELoFavQO207vx9SiEMeAASWaRHM7+trtf8vTp6myGNSYMmqflI\nGcLWvtvQrSb6WuUnSsGt5py0UGmx8sI16D5zTN89A57jPnZiLjweZQmryRebqxP9lpx1b6MMwYFV\nscxIxE6N/qrtVBMjyhLu6b9LSggkDxLqP0wfmzMha1+r5YaETSKyVyaUIdjaiTn9jqk9kXSZye3J\nxBs6jc6jJsxAjtVb0qi/zlWaWJ1BFfycrRnjor8yJQHetZ41fZu0ZcbPDUFrwWvAz/eS2bY56Fsw\nL1efsZFfCF3dMm+rERHDhp5IwvmoR/bfkIMht4TaMIDUCg29JKy1FgZOcFuYAwJIrVmSem/Q2v7h\n5JvVfZXYc8GdhGHHW01I8p9wHABg1d81NLpHrdQWFKJVW5KfDRDpDPrwFCuD93a/Q4LjZ935aAmT\noC3x8jol/g8ZpZkS8Ftm4CS/4XDy7qd5vsjOnRnliYghRbEK7CMBVSfhcqZuOrcXrkvsLW6zn4Rk\nS/INn4LtEzNJiMYaJ5ws4YbZL0jc9pzaf3O9nyXjZFtSONSYsInP7qNqljKZetajfkvGOoKffZO2\niVWrM2ybjJm6SUlyhX5Qygc2n3Bo5ZZ8NOE3F/pe24vCb2l88N4eOJ+QIyKGA5XKESKyF3wCsxnw\npsdVzrnLRaQRwI0A9oE3g/7ZObdZ9ykrmyRRsnZSREQ1IHKFE7miZHHEiIiBoBe5sl4ZeAXAV5xz\nr4IPZ/qciMwDcA6Ae5xzBwD4o75HkE3yRABXikhJnq2KJTwFqW1kYyxot3FQtLWypIst+vRanyUf\ndAXrQhniZdOXeX51EuvFg7TWXG9q4dX/OXBboT5rv7MervInDaPsrJ/wsXgYAHDHvPf5FcyfbC1u\n+iYXWIpWhOUHY9Qfrxwf7a04zv00ktzp1d1gcyyF30qWNzfX8RylplFp+dYE20xy5UQz4WNAGunu\nZQrAzpY6929RqogYMLpQrotaPpxza6ECnXNuq4gshdfXToEPZAOAn8M/s56DAWaTBKroHTHeLCNY\nLvUUzMTvrGH5ChPBW8lie9CGnhQ2rV1bfp89XtYVtn4q+3cFrSV+3W3qSk92c/darl38QG0ieSYJ\nKpgUy3u/NGiztFdmYSfBkxh5Ba2rR0iovNo2vQdJmyRMgszKAB1qwByf/fYmBS2PZyfv9gz6cFsr\nIiKGCkOhCWta38Pg03PNdM5R6FyH9M8zoGySQJVIeDyA+lJn7inxVt+QfEnKjUFwBIB0dj/0ZrDc\nRH2XBL0yaIE0GIKXmRN/1qK2kWdIw4uZF9iScPLYc5Cy+Q+V5dst2XGQoaCdlXUjXVfM51bk39Wq\nJHmGrhR2mduyfjudQRveMq1lHEbUZD3TzAxa9rHPSE9m7BcRUT4G66ImIpMB/BrAl5xz7ZqyAQDg\nnHMiUko+KymtVa3acmLBlhhBOS5rSVV3awnTIAwj3UKLGEit3PA7sicnH+wSvLd9eBPQc+Rm9+Yd\nd7oxc5kT+IjZXod4/DOaAX6xIakVodVIQitMQgQAzp1X8jG9ODlfbH4g/ECUOUi0WdOnPUX62JsE\nv5z24L3Fi8E2zhRmySQREZWhGAk/1rINj7UUJsWyEJHx8AT8C+fcbbp6nYjs7pxbKyKzkDrFl51N\nkhi5fhsRERERQ4RifsILmuuxoDmVx666IF8jFG/yXg1giXPuMrPpdgAfA3CRtreZ9WVnkwSqRMKd\nMA/FGeZuaFtlxV5MyirdE4LWMY3IaRl9qO+G5U92zVimJawD6jIWdUedt2JzvZqCMuct2YYCv68U\nB2o0ResJTQCAthON9ffL+b5tDwtB2Uf6wVfAyJroErlUrWOe20oDxbRgtjb3cHnj698veF7pzRER\n/WAQmvBCAKcCeFJEOAN+LoDvArhJRE6HuqgBwECySRJVm5ij0pn1oMm/Lh/ILU8zveX4ciQeEnVI\novZTh8fhe0vKu+S32+q9x0lnbZLxONF8Sb5hnbtNJlPOTH1yWa7ha8yCZlIYp/fV9tBXZPhBUkx1\nZEum/FyhvtuTsT70pEi/yf7kk4iIoUSlmrBz7kEUd+U9IWtludkkiaqQ8P77Ap3KwqtM6C9FFf7N\nE3XQWLAFWnIu6AwUFt3k00ZAppnI8qDQ4zjdtrnWhxR3GsJh3l9GyDEkmesTjwikhM1trPC8ZrpJ\n9kDOXhN6DWRrwsMB6sgpGQOFXho1Rd4DhSE5KZmLXGByDkdCjhhedFfoorYj0F9S92sAvB3Aeufc\nobpu8JEis4BJKl3vb8KN91cWbv27b2eEFiwyyJew8gEJNEwruWuwHgD2DvaZGbwH0KP7tdd7UqHv\nb0da+yMh1LBqBhP4sM4dADzXd4A/zoMqP2giedxixpVMNIZ6TU2R5eGDndRLyTM8d5bnQ7iO73fc\njSQiAhjZuSP6i5i7Fj7qw2LIIkUiIiIidgR6UVPWqxror7LGA+qgbDH4SJHJSJ+u9zbr1f92/N+D\n/ln67MRg28SMPtOC9zOC9UBamUO3bZvh7xu9NemlWZ/Lrz4cSg9+6PkSA98zPeWS7nRy6eWntV7Q\nd3XFo9pusP6wtBYZkMHZRWsZl5V3bkgRSgepZVzKDY3jjEneI6qDsZbKcvCRIo0onCwzo9ljf992\nkpSzCDb0jrDHIcGHcsSM4L1Zxwoba2o9eWZJDQx9ZE04GwoZkvCz8JIDZYmX/2wKtT2o7V1c8ZC2\nNjCD+mkW+RLVTwlZjp5r9d+B7BcRMVQYayScoOJIkWmACSBLMTu/nTQtWA9kZ00DMgt9FmjBoYWM\nlHw31flO1HQ7DQkz3y89HrI0YZItty3t8/mEk/JE95rx3cAFVihmXSHr013Metzx1u9gEQk3otoY\nyZpwJSQ86EiR8xcDeM4vN88Hmg8KOvDJnZ4TNjw4lCHYzsjoE5Jw6C2BlHy3JgTrrVzrYhaSbmj1\nAqn3Awm77VF9CFiuHRKrF8Cy1mQvD16mrDojyNgWETE2ISLNAJqH+rjdBYEAIweVkPCgI0XOPw6p\nNZqV8yFiwAgf+aP1GTEa4ZxrgcmiLSLnDcVxR60cISI3wE/CTReRlQC+haGIFJlozmy1XCbBodEX\nVsiwCH1+p2VsCy1f6r8mBI+WL61aWsDWyqV1G9aCs9byMg28SKpl/FE3sKLFYzB4Kjly/4gWcETE\nYDFq5Qjn3AeLbBpcpEgNsgMnwvL13GYKcybBV2FARpafcH3+ti17exLdnEsJNpQaQjLOWpclR6x6\nUqtkkHR/ry0n4bDIDJAa8ODDjouBlnG0iCMiYnmjQtQiDbboytjOwDGSrx0lXdrCKsvWWyIkaJ0b\no6uZDSEO3c2ySJjLtIBJwuu6jRBNzfdv2jIAw9HtzMrjIflGF66IiOHEqJUjhg27Is3Ra70aSJ6U\nFkjGNv/vy0HfUiHISr5bZnsLmGS6NYNgQ/K1JMzUk5QuWjc2AQC6F5sZPsoNj/PA9O9lxYgs6aFk\n+voS2yIiIgaCSMIhdkGhtcr1ALbNyQ+Y6M6l/rhTOnw99p6c77PLM31+g80nrMch+VJ+yCLYcB3l\nCdtnvWrCiTb8ZyXfxUhB8l1BveQFbUm+paSHrKoUQ4OYoyEiIpJwRERERFXRNcZc1IYGYSgx0kQ5\nzFIWRp8BwOY6v41lg+a+2j/uz125CiFoQfMuyAg3+4XwHGG7KcM7YvU6dd+gwvCMOVlSmLM1aLMs\n4OpowNGNLWJnRbSEQ+yFRPdlrgYAaK/1EkCYBnKNCZkjOU4JMnFt2qswhJeVj1Pd1xN4m/FnC6Ph\n+N56Pqzf6O8Ufc/qnaNVN6yxZ6PsEBbmJEae/hs9KCJ2FkQSDrEr0KVeDm2105PV3UluBk+SiQVq\nSJjkm1Pi4rZJxuLc4yXvVrFhNz/7xy+Ax7UEG65bp6Y5CRsAultVA27VFbSE/wgDbuQsYjnuZ3Hy\nLSJiR2DU+gkPG0wi9w6ThIbWKN3FmDR9mnEUrlWfNpJkXQbZkXyXwOdv2BAQ7QZjCafr/M2Ak3Cb\nN6ZEncgOrdrS2HXPp30SF7Ri7mc7FsWznUVE7HyIfsIRERERVUSUIzLOmlMD0WYr4zKtYz5C5DKs\nSUoXWWHGXBdqy6lFnEoNtIDpC7x+tcY0bzCzqZuDlkl58gIwQj/gcMzVDcSIum/EzoxKSTirupCu\n/wKAM+CrCP3OOXe2ri+vupBB1TThXj2z9VSwqSGBVGqw9aFCbYcE+xiOSNbx0SP0+SX5rjcuGUkg\nRodq08t0PHbSjRowUxUlUXBZ+X+r4/kQSTYioji6Kq8xdy2AKwBcxxUi8gb44hbznXOviMhuut5W\nF9oDwL0icoBzrq/UCapGwu11+ZNmFqF+Y4m3Rj0e0r5+WyvmFKwLQ5JJvuuQZvBZs9FP7HWv0sk3\nEu5ac5IWbVeQdBmIkZmpswjiJFxERLVQqSZcpLrQZwH8h1YRgnPuJV1ffnUhg6qQsDO8mwtIFUhJ\nlBawlSxoObMPWxvhxnWUI3qC93meDyRfuhm3amst4Se4QIYu9EmOiIgYuRhiTXh/AMeJyIXwsbpf\ndc49hoFUFzKIE3NjBNHnNyKiOIqR8Isty/Biy/LMbSVQA2Cqc+41InIUgJsA7Fukb79eSVUj4Z7A\nks0CrV6rG9M6Tuu+1ea9t30oOySSRYeXLDraTWBHq7Y0bmns/h4o7ESXtFJRcOWuj4iI2FEo5ic8\ns/lAzGw+MHn/2AX9zqMBni1uBQDn3KMi0ici0zGA6kIWVSHhjl3GldRoQonCTsxR3w0T79iADnpX\n0Oe3o8Pvs3WZTr5tNQdnEh5qwHyY2GQlB9aCG778vxEREcOHIfYTvg3AGwHcLyIHAJjgnNsgImVX\nF7KoeGTqinEqgD74UhEfh88IcSOAfaBVN5xzm7P2J7FmT8zlW8nWa4KkS3ezzqDuGwC0bdFtrar9\nknRbtbWTbmGRixUkWpsYolgmtJFTEy7KEBERxTEIFzVWF5pmqgtdA+AaEXkKQDeAjwIDrC5kUBEJ\n62zhpwDMc851iciNAD4A4FUA7nHOXSwiZwM4R1956K2pSaxde3FIzLRkQ6sXsIl28pP8rFptngLo\n48vINj4Q0Oq1lvBTQR/Q/cxGww0kCi6Sb0TESEN3hS5qJaoLfaRI//KqCxmM679LJrbAm351IlID\noA6+ItwpAH6ufX4O4J0VHj8iIiJiyNCDXFmvaqAiS9g5t1FE/hPAP+DNxD845+4RkZnOOT67rwOM\nQ65Bd25CcmeyHzzNGUELeHJe6/t4K5mBGG19mgfCRrixzhuFkN9qS6t3gx0NZYTWoB25+m+0fiMi\nBoYxlztCRPYD8GUATfAFh24WkVNtH+ecE5FMPaQDdYnEYH2Ai026WTmCJYZIvm1rdbJtKVKs1PYn\n2q5gZrMObe2EJZfpMWFrKYUoR2rgJY1eERERIwVjMXfEkQAeds61AYCI3ArgtQDWisjuzrm1IjIL\nwPqsnb93fic6lOwam2fjoGZvMIdpJbdmkDC3tbXqRNxaNQqfNScIk+jP0UJ2K+jlYD0fmJeYlyLL\nAq7PWAfkT8YNP+k694VoAUeMaYhIM4DmoT7uWCThZwB8U0QmwUeMnADvirENwMcAXKTtbVk7/8v5\nMxLCXZahWND3l5bx1gwSxkvKR/TrtfXe6OO7u7Yr7tAFWsJZ1u5Acj2wb2Ei+XQdybw9o09EREQW\nnHMtSBMFQETOG4rjjrl8ws65v4nIdfA1hvvgHWmvAjAFwE0icjrURW2IxhkRERFRMcacJgwAzrmL\nAVwcrN4IbxX3i0lqlTZgU7KOSdyZuJ2w9d6SZOuUclu1XYZCrHhIF1SOSPx9rbwQVjrOuiTFqiFb\ni3q8trR8aQnTiq9crogyRETE4FCpi9qOQFVuD7XoypyY4yNDYZKedJjdW7U/YynY2om58QhAsqRU\nsMVsCzXgLE24MWMdkH/5QpINZYlJJfpGREQMJ8acHDFY5NCbJGq3F6czKGtEMu7sMwS2WYfMqDfO\nsfUYYu25SxdolVrSrQTF9OJyyJR3BNu3PA+KaAFHRAwNxqQcMRj0Ipd5UcKcEYmL2uZ0Yi4hX/r6\nJo4O1hSm9RmSbynJIUQW8Q6VBRuOI1rGERHDibHoHRGxAyByhaanjBZxRMRgEEk4QA9yiRxhqyWH\nlnCS3H2tmUij5ctouCThjg3A6C/arVTinf7673hEMo6IGBwiCWegO/EFTvVeXqiCXMOWA7nM0OQk\n0U6pybaBFN3ckYQbZYmIiB2BroIIrpGDKmnCNZnLYe6IBJZT6dG2Odw4WAKrJgHy3Eb7jkEeERFD\nhmgJB+jGhMQC7sywhIkkj/A2s3KWtsdo+0ddcJap6TQ8WK+IHQ3rd0y3uEjGERGDRSThAO2YkuQD\ntjowlyegGwBQxzDj6Wbne7VlPIcjcVkCG23kS1gHZ34e6uFbIHKpC52go04cEdE/op9wRMQYhMjF\nmiVwhq7hDbJUMqiIaiD6CQeYgG5M0cdsJvIB0kcGWsCTEkvYhDFPVoG9lSuyrF4+ypdKSznSwT80\n/8jZ8kT0nCgPIj/OSKtaSuoJJ0tH7p84on9EOaJMTFCNgReMuYOn7Z5mYW/L7eEXmnTFSpKwDS0e\nfeTr3BeEhJqN0tpw6X2rhSn9dykLxSzMrGsSer6E+w4l5mnbpi1zk6Tjign4RwYGUWPuGgBvB7De\nOXeorrsEwDvg68stB/Bx59zLuu1cAJ8A0Avgi865fss3Vy13BFHq4rBfT4/pQwm5SdtH5vu2Z9HQ\nDXAHIMtytetGJqmOHTj32bLIUeRnJb6HtmTJudMi2Y5gdHVXnMDnWgBXALjOrLsbwNnOuT4R+S6A\ncwGcIyIHA3g/gIPhqy3fKyIHOOf6Sp2g6pbwZGM1rNPcwpPU0mHb22OGyUk6rkrm9eyEFS2wLUHn\nnoy+rwR9EPStDkKSHp2k3D6sMkkxghxuQoyEO/qQxyEDgHPuAS1sbNfdY94uAvAeXf4nADc4514B\n0CoiywAcDeDPpc5RdRKOiBhqeHK2kkB5N4J83TgrYX/EaEVvz7Bpwp8AcIMuz0Y+4a6Ct4hLomok\nzF1BMQsAACAASURBVMm3miBU2W5jaHNDXRKZga17qik8OdzLzkQX00+zItNoFQ+kssboRjFSGm3W\nNi3S0pJB/9szcp+WPF/E6EMxEu578AH0PfRgRccUka8D6HbOXV+iW7//qYpJWEQaAPwUwKv0RB+H\njyG+EcA+0MoazrnN4b5dqE0ypGX574WRc3lJ3vfU9jXaMi7jrqPNEThBMhB/4R1LxtX0agjJlmMo\nZyyliLpaHhpZ5Ng/8WYh3/qNpDt20PNKEUv4mGb/Ii7+blnHE5HTAJwE4E1m9YsA9jLv90R+UptM\nDMYS/gGAO51z7xWRGgC7APg6gHuccxeLyNkAztFXHnzEXJ0uF8Z0k3QbNDZ5s6mskYDa8Ku0fcZY\nMyv4BMB1nEAZiNfEjnlIKJeMub3a1upocYWLBBph0dc7dP9nETkRwFkAjnfObTebbgdwvYhcCi9D\n7A9fe7MkKi15vyuA1zvnPgYAzrkeAC+LyCkAjtduP4cv2FdAwl2YkJQbsd4RjJgLW0bQAQAaVEpY\nFQz9ELO8Qj0mknqBodN81scOJ+1GpjwRPSgiIipAhZqwiNwAz2nTRWQlgPPgvSEmALhHRADgEefc\nGc65JSJyE4Al8IRyhnNu2OSIOQBeEpFrAbwawOMAvgxgpnOOWsA6IKOUckQmBkKoo8UajYgYMdhe\nsXfEBzNWX1Oi/4UALhzIOSol4RoAhwP4vHPuURG5DIHF65xzIpJJLNed/wI68RIAoKl5bxzY7GvT\n0yruUiuZsoTVhCdM9pN23btrToWpusHOtYlqe441R3+vLZMRDzScdGSmmoxkHDHWICLNAJqH/MAj\n66+bh0pJeBWAVc65R/X9LfAm+loR2d05t1ZEZgFYn7XzB84/IC1dVCKqirqxnbyb0qDhzg26357K\nQ68yO76k7eMqLfhAF6Qf15ZCKgfUkkfwNxkRMQbgnGtBqiNCRM4bkgOP4L9uRSSsJLtSo0Gegy9z\n/3/6+hiAi7S9LWv/LjMxl5XUvTbIolZXynJt0tbK4/TH+D9tO0mipbwlKrkUw/vNxpDXiIghwlgj\nYcUXAPxKRCZA46cB5ADcJCKnQ13UsnbsRm2SR9gmcO8NKmowgc8U4/c7eZxfbleLOCl9ZNNdMqT/\n7dr++nDfuqyP268HSURExGjHyJxnBzAIEnbO/Q3AURmbTshYFxEREVE9FMaEjRhUJWKuA3Vl1Xxi\nNF2S3B2pNNHQ6DWH9burNtxjntxbtd1N23doe8d8FIJSx+jLvBYREVEmxqgcUTF6kSso6mmXw6rL\neRNzKk1QS55AWWKrqcjMWA3qxGu1pUzxvCHjHs4ddgatRXWeZUQu0ECOqA1HRAwK2/vvUi1UrcZc\nUs7eWMTMfp9F0CFoJSfeEtuNZT2rlgf0YDrikJQBYBOrIqxCPmz+iZEdwBEREdEPoiWcj3WYWTAJ\nZ5dLkS+lCbq25cZpVN3E1Je4e7qS8FZdwZARfhFbkeJBtYoTj2a6r9k8ApmedhEREaMFkYQjIiIi\nqohIwvnowKTEkrUF+BgZR4mC7mu2T0/iS+z7JhrxxNTVrZvdme6ySduwbJvFYrWI22kBW9c1foOc\nvNuxsgS1YSJqxBERA8QIVhKrpAlbHTiVHlZjdt66joSEcwX7cvIuIWUjR3Tsug0A0LdpF7+CvEpS\nnpMxKBJz6/6+3WBJuNhlsutH8K02ImJnR3RRy0eHCdBgNjWgUBPuSjKt1RT0CbdNGGcyrRG7qQHZ\npYYjidbOlDJqel7w/r6FplOY9HvncmsLLfEQ0TKPGPEYwTZS1VzUSL52Eo6+w1meE4XH8EPnRJ0l\n9qm7eR/ijkmemDt7NMsPP63lVPJpOGn3etPpARIyGToryq5UBeDBIZJcRMQgEV3UIkYa+rNuKzlW\nvFlEjFhESzgfHZiUlzMiRG/gombfF8u6llcCaZxvumrUkq5Rvtldt9voOl6B3YL39ktjErZOnbxb\n1qQrTIBI4mdMK5kHGGjazBTDQWpDSb6DOXYk7IgdikjC+ehFTWbYMiWKcFtW9Y1w8s6is8/PxBWt\nsGoPz8Q/5MqDtLWFRJu0ZT3AqUq+7UY37mnVhae0ZUmlMCIvG6ONcAeLrLE5d54M5Zgj0UckiCSc\njwZsRhumFazvCQI4GJpsJ+ZCS5iEmwUGcPQytJlRdbulk4HYrP9THoazqLasHb/AQ4JtT5k+G5o4\nIuQfMHRvs32GByOZfEthqMdd6niRoHcyRBe1fPQil2nB2u2+zQ9jBoCcklpvGUPv3t5/kiBM1JaW\nL/nRHp7W8isZ24jHtG1neB7LQA8v4UZERJSBQbioici5AE4F0Adven0cvrBxv5Xly0HVckfM1LL0\nK02F6GLhyvmhzdlD7jKE2719Qt62ssiY1i0NV/ul8dLSuN1TW2vMk8TvIFMz81ujtuuQhWiRVQcx\nAGYnQ4XeESLSBOBTAOY557pE5EYAH4Cv5dNvZflyMK6yoUUMFUQucKNVPhhLiN/DGEdPma9CbIF/\nBq4TkRoAdQBWAzgFvqI8tH1npUMblCUsIjn4B/FVzrmTRaQRZZjoXajNnJgLrdwOFOq9YeKfHp18\ny5qESyxgbitV9joMdbYIJQv6EttPRr14s8oRDzBd5qLi5xwm0KqLpDJwFJswrMZYIoYQFWrCzrmN\nIvKfAP4Bry3+wTl3j4gMWWX5wcoRXwKwBGkUwzkow0S3UXJZEkRYa64jqwRSnwZ0KNFaEi6QH0Ly\n3W7+U+HdbyIKwXU1QZt3Dm1J1KIheK5VV1htOIwQiYiIGFZUqAmLyH4AvgzvI/UygJtF5FTbp1Rl\n+XJQMQmLyJ4ATgLw7wDO1NWnADhel38OXzU1UychmU5PXLkKc0eE3hJASr49paxaolifniLLQHrH\ntLtyuQH9gwnlqRuvpC9xGPocERGxw1DM3lnVArzYUmrPIwE87JxrAwARuRXAa1FmZflyMBhL+PsA\nzkJ+xEJZJnqPqaxhUUi+hd4Rg8LWEk+VFO6zvqzaoKW1m3X1OM+YkPA+umCTxrPq89CHOA8cMQlR\nMUQZYgyh2E9792b/Iv5yQdjjGQDfFJFJ8CxxAoC/ANiGMirLl4OKSFhE3gFgvXPuCRFpzupTykR/\n/vwb8Ypahg3N8zG1Oav2W0RExM4G5ZPmIT9w5Zrw30TkOvi5rz4AfwVwFbwE229l+XIgzg1cyhCR\nCwF8BP7+MhHeGr4VvvpyszHR73POHRTs697kfptkQctKa7lODegsC7ijO3+yrnNrob9xognTVY1h\nylnWLr8cE/VcFPQX5v52Ym6Nti9r+3ttf8MOD5nOS5Ml5z45bNZWeRNz0RK2iNbvyIKIOOfcoL4T\nEXH4SJk89wvBYM83UFRkCTvnvgbgawAgIscD+Kpz7iMicjHKMNF9FrX8jGmATU+ZrwVTB7YIybfn\nFdMn1IKL6b6VokEPaDXiBr2UDG3eb5DnqAAD84bI+uqzEmeMDkTyjCiJEfyTHqpgDf75v4syTPQO\n1BXkBQYKXdSyyJfI1WgOCSXcvl6zb3+TdnaOrBxCDpwtJkz2gRgsMgoAm2o9I/ftqYnkC67sFrO8\nB5x7W5VII8znWcrVo1S0X7hfdX7lkXwjysJYDlt2zt0P4H5d3ggvXPeLUgnbmSM4yTlcglRpAY/L\npSTQV1PELzjr04ahyFlc0hv0UUwaZ0hKs7C1zdwl/zh03mufm3HgaiD0vbbvw6rSWRel2K959FrR\nETsBYmWNiBAiDzn/sADYkGbnzoyWXUTEUGME2wZVyx2RlQMidFFjyaIeY62VlQeiGMr5Ijgsa0Tz\nLro9fwx1SX4IoJNjZEmlbcqln9cO/7N/erwVdpIuHyJXOB4xv00xuEdwSiilLFfmu+B1t650XEeL\nOCYoihgFiCScj2KJeor5B1viDcOTa8YXPmd09wbrJuo+2zO4i5xSikvCiLntzHuc6tmzxq0GALRN\nVBcKJpQ/UTvYcOhvaB5ix5PzwBX7e1eAnqC14wiTDWWJ6DODbTEKMGIEYyxrwpWdtDczECNcV2pi\njqAmnEXGBZioRqYlY16B4mmJM07q92/rmJ6smlrn/dXmzV4CAFiKg/2GtQEZA6kHxV2H6wKF42Wm\nEyt0FAZ2DC4nRPiVjy+xjcgi1pCoScp2AnIw1UVKu87FCbmIAaEcF9QqYVRownWT08f+9s35Sd1J\nvtZFjZN0fVwRTtBNNBzWE/yX+X8vxQE1hRy4QZ2IG7AJALDrdN++vEFrKm0t2MUcT6WKHiNZ4K5g\nIAOxNLO+1vAuk+UdManItiwSbQ+2ZfUNibnUZwjHl03ckXwjKsIIfkAbFSQcEUHEPMARFSHKEcVR\nKofEQJCtDevHm+gn+DJTWtKqpUVcKoaB0L5bN6TRGrV7+ued3Dh/y62pCcZjP9LrtH1UWxYZfcb0\ncUzJYVNzAKlMARS/vWdpK6Vc04j6YBvbRtNnY5GWY7HHDSULHicro1xlP8WYejKiLEQXteLIDfDq\nTFJpIitcOUQiS5CMQ2IEUkKeGPyXs3TjkLANmbetVX1Y1YeOcHz21CThp7Vl+PMups9jLPHcqm1I\nxkA6kRdKAaGsYPsQWdnd9gj6Zp0zzMlEouXNwZJw6OFh6+wVOx6li8rDqaO1HFGAESxHVJQ7YlAn\nFHFHu/szi3hyXRjSXGqCLtSIy0Gmm1tBqHMZ1ngGqTOablqjT9G55u9NfoPN4EZ9mD8M5pmwj0zf\nI3Ex1SdJvcV0IqmRuEiovCZZgRhEaPXa/cK+A5m1tOD4WoP31hLm2EMyHt7CqJGYRweGLHfEvDJ5\nbukoyR0xWFjrN2duUTVJOfuA3EwRJhJygaWZdZ4gtDk5nI2uUyt5XG1X3vuykGVZK5hoaM99lwMA\n1m+ckWzr5o1jrX7XzbrhMRgE1uxUfZTfNM/0YSKgkKRIYFZGIMmFoXxZBFuORFAOMbMPx0zCtZIK\nz8Ex806UdXOIbnARFSJqwhERIwtRstjJEF3U8lGLriQvhA14oIVcq1csuw6d5pfQx35axNbazQUW\navg+D8GEXndgGQPGWg41ZoMJE7tKnmtGYxqIsVknCmub/D7MjfHyY7unO0zl4NWCfbO+v2GPtE/B\n4304EWYt5FZtm4JtWcEa4fuhMiNo3do8GpRFmPQ+a1w8fzixFy3jiDIxgn8iVbOEJ2j9OIti5eyt\nfMFQ5u4+T94hGZdCSTLm8Sdm3DJDzwt9b4/HmwDXFXhHGDRoYAfDnldu13Icp5pfyna9FpQomrQ9\nzEyWPbEwODLJij685fzyXslYDsMIs6QH9i1Vtqkc8qZME56zzfQJ5ZVSkYUjoVpJxIhDlCPyMcX8\nUaw7Wi4gDVrEWWHOIRlngYSYq+kJ1qfve3s0Si8gUYti2zIJW5GXYQ2Fn82iqb4VAHBkfSoK/+LU\nT/mFZl2xVtsHkYIBexsORz5atbVfL8mbVqiNzgsRkm8pa7mcX3dWBEyxbVMy+lLP5rh4w30laAHg\nhWBdeSYQ5YkoS4xRRBe14rBJcAh6SaT0WphzmNZxbpxvp9SnxN7R4f+kIflmgX3CybssMg4n+qy1\ny3GELnelyJdSDKtK2xvSvAOeAAAsnazhz7tkhD8TDypZOhIP3dtezOj8lLaUNezNohzCCtNdhu8t\neoJtA0n6kyVHhMfLwh5BH16DEfw8GjH8GMFff9VJOCJiJCEGf4xRjDUSFpG9AFwHL+g5AFc55y4X\nkUYANwLYB1pZwzm3Ody/AZsTiaEThVpunVpK3SguNXDSLpe4tdmJufwrXkqfTfqotpyVQJ7HK3Wc\nUOOmBVybqX2zqoj/DB36NTyGI83+/lyUPLpn6bheZ76yTdryaf2WZl2gK1iWdkoLkXkqnjfbiskQ\nWT+TYhbxloy+oeWb9Y8IrdssCSTsU44UQsvYasXF04RmIUoVYwCD1IRFJAc/Q7PKOXdyuVxXDiq1\nhF8B8BXn3GIRmQzgcRG5B8DHAdzjnLtYRM4GcI6+8tCAzcmjeE2GWBN6RdhH+pwSWCkPimJkSckg\n/9gBiZdxRUK9F0hlFR4nJOV87ZvShf9cDE5px5SCPowQ7M4KSiFnz9GWPPh71X83HWo606c49A/O\nioojQv9coDhZso+dqNsS9Cl2jPAcxfoQpSYDwz5Zfsc1QRu9LMY8Bv/VfgnAEqR/inNQBteVg0oL\nfa6FThU557aKyFJ4k+MUAMdrt5/Dh3cVDCyHXkzJSCsWTsClSd7TP9BANNekT6DXlgyVHle4KiRU\nEq49zpRgVn6C3iSyUnZ2BN4GtJYtCbM/w7MnaD277q2GNFk9ulVbzrm9Q9vfmii0TSTh0HsgK8yY\n4LUdSFG+LEs43LdUuaRSE2qhpjxYzwwiK6l0JOQIDxHZE8BJAP4dwJm6uiyuKweD1oRFpAnAYQAW\nAZjpnKOz6joUxqICAOZiGVaoz1WnIQGSESWKCRke1iGB0ZK2FnXtuK5gn3zJo7MvPQat2lIEHZIu\nrXDbN5xgDEnZWuzsy3G1YVrRczc0+iec9i1K0BPNRi7Tp3ibtk3aHmL6PkB3Nk7MtWpro+rKyXBf\nrE8WaYUTaaUm1gYy+RbuUwqlyLSYpNLffhE7Gb4P4CzkPzaWxXXlYFAkrFLErwF8yTnXLpJKZs45\nJyKZAduPnH8vNuqfv7H5EOzT3AQAWKKJ0Gl55lA8f0MpvThEKFlMHpcSZOiZkEXGJFSuy7KEp2ND\n3jkmBbq27ct1zEHMbZvRgBC0jukP3Vlj+tTo9WbVDiYAelnbd5oDPZB+Gg+1WKUp7eOKEU9W1jNi\nsEQYopSXRTEM9GccEn05lnXEjoCINCN1zNwBaEF+PpZ8iMg7AKx3zj2hYytAKa4rBxWTsIiMhyfg\nXzjnbtPV60Rkd+fcWhGZhSJe9aeePwfLsR8AYDVmVzqEiIiIMQbnXAsMK4rIeUNz5GLGwkJ9EReE\nHY4FcIqInAT/7FkvIr9AmVxXDir1jhAAVwNY4py7zGy6HcDHAFyk7W0Zu6MdU/L0T2KaRknR0mQf\nq6fWBI/9EzJ8bMOCoaGnwkC0XNuHx5mKwknQBl3H8YQTh3Z8/FxzNWCC71dir6RPaL33jiuR1Y2y\nBC8pLWFrnL9WrbxH9tQVYYgzkEoT/OzUd60EVMzXt5zgiFJW80Cj+4BsyzU8TljVo9S5w30nIb0W\n7dFLYlSjMnnJOfc1AF8DABE5HsBXnXMfEZGLUQbXlYNKLeGFAE4F8KSIPKHrzgXwXQA3icjpULeN\nrJ3bMQVHaDzu5kTQBNZrCCv14gnB4zpQqO9muagRYeHQUlpuSJ4WIVFTarDnnKTHqQv+7CTTaSYM\nNzwecQCeTZbXq8TEcklr9ImhuymVYdq22jwSBlmFlF+j7SMkVCUn+xDFr2K7Sl/JR7GTbSHxhZ+l\nHHkiizxLkWR/P1M7hvD8lYYxV5q+M2JkYsjilvmPKYvrykGl3hEPItOPAABwQn/79yKH6UpK0w05\nHQxfJHM/tQhpIS7C0UkfkhEJNsw9DBRax7Rcs8KfSZq0cjnBN8mQQrgte/KuU9uOvM9CwrU3Dy5z\nfItwDACgwVj5nJycpp+Fn3PDuLS46IQ9PTl2b1bSpIFOy9g6oByJAJxjMD/O/ZQc+ZWsGB/0BTIK\n7gXHKeVJwW0zMtaxb6nQ5vB9lgtcoUUeWq7lFUoNxxPjmkY3Bp+T2jl3P4D7dXkjyuC6clCVX9ZT\nODR57J+L5cl6PtIfqjP4e6z0bHDYXk8kfZ7UkNynMB9AOpllLdhQAiBp0gvBIp0ELC5ZEDzHDJV/\nsiYHafHOwmodQ36Yddaxj8EiAPlEzeOsxiwdp2/3wsp0R1UPVk1XkqTLGlvLTZy8e722DzQVjB2P\na0sXt9YwHDoLocVoLU8SbEiiltTpOsfw4qzE8jx/WJkj28odGrkg9H+O3hKjGyM3g0+8vUdEDBDW\nko768GjByL2JVt0S7jYTULQsm+jDqobP3CTXLDBjtrdCp+e8pcjIuzakj+m0NCkF0BODPsbWp5ha\nLtdN1n3suGYGk1gLsBhAarkDhRFzBK1bazWzb0MwwTfNzKTR5Y2WPj9LflSd/2GNm+YdhPs2qI8a\n5YjJSMHf4He0/YC2200fer8xwRqTBd1lLdf+JuSyasOFP7NWs1wsB7K1egt9h537+g4iv5H7540Y\nCKIlnAfrHUHfYCAlT8oJ7a/2fSxZNfT65dn6uL9OJ7BmqgYLFE628RGe55pktNc5Sgjs83p1qLXJ\n5qdv8e4GG+p3BQAs19A069dLAq3r9SSyOjcr+axA/mRc6G/MbTbgY4qOi2OmK9/SvvR60d+572Ul\nX/IFL5f1jiCxLtD2+9r+IaMPtWTe+6wjSzvlh1ADZqesYp7skzVjGE7SkYSHT2rgMcrThiPGBkbu\nzbQqJPzEugWYMtP/yQ425MnoORIOiY3ECwDdOU+OHUFUXX5YcF1mn0/ipwDyNdkwhwXfrzH+y1vr\nPcHM6vLEP7PWW28bjPX9HA4EABya83o2vT5oAVuCDV3w+H6/3lQfb8/5bbzJcHKx7ebUI6LnXZpk\nmFWgGbxB69be/HkJf4t8WE84/hq4/0va5hm/Ya6J0ErNCoMu9QcIST0cjMdwWL6VknGUIEYjoiWc\nh75f74InPulNstyElBA5GUWrlJNatCrz1ilJZmUrSzKQBQSd5R0RJhCilWsf+0nm62o9Ie6nJqP1\n5eU5QmLljSVroq8n8N6w4H68Fn/AW/0Gw3EvP6jlkJqUQ6z84AeVgsRKXtyc0SccBreVjOYNSdQy\ndrEItKw/BPvu+Imw/sg4ku5YwNBX7B4qxIm5iFEBkX/XQIkdpQVHjC1EOSIf9wEv7+mtuGdPOTBZ\nXSxHQ1PeRE4+aBnbEIvawLe2Nwjo6M2wSsMJNTsZF0b3UaqwljAn0jj519Trxzwl1555fCC1gKl5\nT3k5tea7NXEPJZnDoJU2DlmQHmAV5QdtqeXSIrZ+wpyso9TaG7wH0t9pYYK7DARBH4klmyVHlIp0\nqw/6hhqz3RZz+0ZUiihH5ONFJJM+q+5MK+9OOckzAkkpTH4OpBNyoQ+wDTMmIWeVDcp6n4WssGqe\nK2tbKHW05bxPclZBU+rESahzlx9x+66moFOv32/vnJcjKEvss28aVffC1oP8AqUGygnkrKxvN8zX\nbvmQWdj4UUrmwg8DOUqlsCwnO1sYFFE6kU9/ZFxJhYxI7GMZ0RLOx2KkhSu/mq5u3dIEAKit96RE\nq9K6dzHgoiGwIu3EF3XicNKtZB7hAFlJ2LMyoqX9GZTRU7B/OD4GfUzZEoY4pz+Umt4+AMDqOm91\nk8xthOHWQ3yAQ9szOlln01wC2ZzHU9QG74HCibmyskqSjBlokvVjp7mdNaDw4Fl9SOKFHhMDmVSL\n/r07M6IlnI/OLcAK/bP9T5qGs3Oy9yhYeaoPW6YLlrU8D8RzAEoTali5guDEnyX1LKu22PHDNJc2\n33F6TN+G+SFyvSZfRVe2RUjiBYD2Oq8p0AJ+Vr0vLLlPG+c/T9tkvYY1+nWWqlBf2glBB6ttFp+S\nugqor9RPKUifmTmwciwVHieWtY8YKKIlHBExohC15Z0N0RIOsA4QLTRp3aqu8E1bjX+8fvA1/lF8\nwb6Lki58HOfEWalE6JwsC6ULa01yHY9TzDIeKJK0nB2aIGiAN+Iw6o9aOHNSAEAri8tt16+RcgRb\naxGH82ZUNbLGxb6FCeUyUMzP1x6IoH68MaPPQH6KWUEfERGlEF3UArwAOH2EfsY4/6/Q9hvaHuKN\nlMULX5N0WfIpHzHW3NgCADhQ0z9mVa6gr+0mJVgS48EbTVAEJ8OUlzeo5mz9hznpFmrLvRmXj32m\nvqRfuuqrPbsWdC1AV20qk/BGwWRB9E22PsVtStDjdtWw5bVB5JzlqDCzGu9D9iOEdS9LoUCWIJna\nckkkWxI0A03sjY6Je0LviEoLfUZijshCtITz4NwJRWa0W/xfeoV6TKxQor4j/dN13+9J++4fngwA\nWLmv149nGwuRLm1NyuqbAmu5rXFaYd+NawAAyxr3KxgXi5KS3KkFZ1WKTkKsNbG6U07KsoR7a/K3\n9eYKJwM7k8lJP5N2aFIjLg0i6es9pvDgfoApQmIuZe2WP39pyJgWsT1pY9C5lNUbzgKWyhFcCtFK\njsjCyP09DDkJi8iJAC6Dt7V+6py7qNx9nWsuQs5L02mgu9SKOtVXiFi64DDffv6wpMu0g7x1NWOc\ntyIPxZN5x7MhyQwL7mj0fZhaM8uNjVIFidumnqTHA13Lembk79trrjRJt6vWp2Tu3aXwa+gK0mSG\noc523YSJmgypJr8aR6aVW44HBftsz9gWXpYCwrY/9jCPBE+2JWNdOJtoB1+MhMMou/IRteCdDSPX\nEi6WmL0iiEgOwA/h828dDOCDIjJv4Mf5sfOvW53IrVVPsrKoJYuNRjiea6n2CAaIv1V7AANGscKP\nIxmjccxDg54yXzseQ20JHw1gmXOuFQBE5H8A/BPSzN0VItz9aOCRVr/Icj2/TF3d2t7htce2D/iW\nUWZP7etTUDL6DEh1YiaJf+/GOwrOft0f8P+3d24hVlZRHP/9GS/kSFomXZwBJRRKShqCLIykpKaI\nil60G5bQS0XWQxfrpfeI6sWXyohACUpiBB+cbhBFZaRhXlLBLiZjN83CUidXD3t/nn2+OWea0eP5\n9tT6weG7Hr7/Oefb6+xvrb3WZnEPjIseD4u93I4pDQq/H4kJIvGbLfy8HYO1H3gw9ib/Htf86y9c\nHbV58sJ2WlFuThyud/Sv2ANO76FdH8AVC2vbHaVlo+Bd4S8udxrSc8rVKU9k1/1ZWqbr5boS6Tnf\nxOWHwDRGVjtiNMPaapyG3u9ChpuqN08WMvY0t4B8e8KtNsIzIJ36gb1AE4flcBQGtScu08fX2mSU\nZl060VP+vTYFEmuim+CTeG4M8G3viq6LBTXXxaY7w/pHccbVo9E4TUg6vzoeXQid1HHmr0OzTvYo\nEAAABOhJREFU4Y501r9/4v5wjiWBucOdjR9AUhdE4QP+s1QRLk36aDqS4yDBlTCQ7JueHIPGgbmy\nG6KRb7ic2Fa42w80ynQrjwsezm1wiFqQrkxzY+tuBWdk/H98wi1yHRSNseiCJVM5qT4ybnZ7rID1\nRnLtaMSLwN6eGJW/rDQXG7D9YDDC2x4Ioy66J4X/kGkdtWK8GjyOjtRGOBzuHNq77TwUEi0K46si\nBTh+w6lPuDj3wPR6B2ujAveFoS2WaRp0WosZIKl9HzgnWf+5dKz4asuV12Co33i4WhLFRzgr/i4H\na08kWDHypajDURjldHbwoY3Di/Q4rSffIWoya53LVdJ84Bkz643bK4DjaXBOUuU+Xsdxxg5mdkp/\nyqO1Oad6vdHSaiM8DvgauA7YB3wG3GFmp+gTdhzH+W/SUneEmQ1KeogwaU4H8IobYMdxnOa0tCfs\nOI7jjI6WjhP+NyT1StohaZekJ9p57ZEgqVvS+5K2SvpK0sNx/9mS+iXtlLRB0tCCFRUjqUPSJknr\n4nbWmiVNlfSmpO2Stkm6ImfNklbE+2KLpNWSJuamV9IqSfslbUn2NdUYP9Ou2Cavz0jzs/G++FLS\nWklTkmOVa241bTPCrUrkOM0cAx41s7nAfODBqPFJoN/M5gDvxu3cWA5sozZCJXfNLwLrzewi4FJg\nB5lqljQTuB/oMbNLCK62JeSn91VC+0ppqFHSxcBiQlvsBVZKamunLNJI8wZgrpnNA3YCKyArzS2l\nnR/gRCKHmR0DikSObDCzATPbHNf/IGSJzABuAV6Lp70G3FaNwsZI6gJuAl6mVs0hW82xZ3O1ma2C\nEEsws9/IV/Mhwh/0pBh8nkQIPGel18w+BA6UdjfTeCuwxsyOxeSq3YQ22lYaaTazfjMrimt/CnTF\n9Sw0t5p2GuFGiRwzmpxbObH3cxnhJjjXzIrBrvupZZPkwvPAY8DxZF/OmmcBP0l6VdIXkl6S1Emm\nms3sV+A54DuC8T1oZv1kqrdEM40XUD+yPNf2uAxYH9fHiuZR0U4jPGYigJImA28By82sbhoHC5HM\nbD6LpJuBH81sE7VecB25aSaMyukBVppZD2F2u7pH+Zw0S7oQeASYSTAEkyXdnZ6Tk95mjEBjVvol\nPQ0cNbPVw5yWleaToZ1G+AegO9nuZmiOV+VIGk8wwK+b2dtx935J58Xj51Of8lU1VwG3SNoDrAGu\nlfQ6eWveC+w1s41x+02CUR7IVPPlwMdm9ouZDQJrgSvJV29Ks/ug3B67aJ433nYk3Utwsd2V7M5a\n88nSTiP8OTBb0kxJEwgO9r42Xv9fkSTgFWCbmb2QHOoDlsb1pcDb5fdWhZk9ZWbdZjaLECx6z8zu\nIW/NA8D3kubEXYuArcA68tS8A5gv6Yx4jywiBEFz1ZvS7D7oA5ZImiBpFjCbkFxVObEc7mPArWaW\nljDMVvMpYWZtewE3EjLqdgMr2nntEepbQPCrbgY2xVcvoWrQO4RI7QZgatVam+i/BuiL61lrBuYB\nGwk1LNcCU3LWDDxO+KPYQghwjc9NL+FJaB9wlBB/uW84jcBTsS3uAG7IRPMyYBfwbdIGV+akudUv\nT9ZwHMepkDE/xs5xHGcs40bYcRynQtwIO47jVIgbYcdxnApxI+w4jlMhboQdx3EqxI2w4zhOhbgR\ndhzHqZB/ANWOLy4b+Y5SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbaf312b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in new bathymetry\n",
    "new_path = '/ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea6.nc'\n",
    "Fraser = nc.Dataset(new_path, 'r')\n",
    "bathy = Fraser.variables['Bathymetry'][:]\n",
    "print(bathy.shape)\n",
    "plt.pcolormesh(bathy[350: 520, 280 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "floor = np.empty_like(depths)\n",
    "ceil = np.empty_like(depths)\n",
    "ceil[0] = 0.\n",
    "floor[0] = 2*depths[0]\n",
    "for k in range(1,40):\n",
    "    ceil[k] = floor[k-1]\n",
    "    floor[k] = 2*depths[k] -floor[k-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S = np.empty_like(old_S)\n",
    "T = np.empty_like(old_T)\n",
    "#for every cell with top of cell depth < bathymetry, use old TS, if old TS=0, find closest point and use that.\n",
    "for k in range(40):\n",
    "    for j in range(398):\n",
    "        for i in range(898):\n",
    "            if ceil[k] < bathy[i,j]:\n",
    "                if old_S[k,i,j] <> 0:\n",
    "                    S[k,i,j] = old_S[k, i, j]\n",
    "                    T[k,i,j] = old_T[k, i, j]\n",
    "                else:\n",
    "                    # closest neighbour thing\n",
    "                    masked_array = np.ma.array(old_S[k], mask = old_S[k] == 0)\n",
    "                    X, Y = tidetools.find_closest_model_point(lon[i,j], lat[i,j], lon, lat, masked_array)\n",
    "                    S[k, i, j] = old_S[k, X, Y]\n",
    "                    T[k, i, j] = old_T[k, X, Y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Salinity Temperature Initial Conditions based on Nowcast June 14, 2015 for north-extended but not     to Howe Sound, and widen at beginning Fraser river channel\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: REQUIRED\n",
      "references: REQUIRED\n",
      "history: [2015-08-06 21:12:14] Created netCDF4 zlib=True dataset.\n",
      "comment: Salinity and Temperature conditions from nowcast June 14, 2015 onto north extended Fraser bathymetry\n",
      "<type 'netCDF4.Dimension'>: name = 'y', size = 898\n",
      "\n",
      "<type 'netCDF4.Dimension'>: name = 'x', size = 398\n",
      "\n",
      "<type 'netCDF4.Dimension'>: name = 'deptht', size = 40\n",
      "\n",
      "<type 'netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# build nc file\n",
    "new_TS = nc.Dataset('TSnorless6.nc', 'w')\n",
    "nc_tools.init_dataset_attrs(\n",
    "    new_TS, \n",
    "    title='Salinity Temperature Initial Conditions based on Nowcast June 14, 2015 for north-extended but not \\\n",
    "    to Howe Sound, and widen at beginning Fraser river channel', \n",
    "    notebook_name='Smooth bathymetry & Create New TS file', \n",
    "    nc_filepath='/ocean/jieliu/research/meopar/nemo-forcing/initial_strat/TSnorless6.nc',\n",
    "    comment='Salinity and Temperature conditions from nowcast June 14, 2015 onto north extended Fraser bathymetry')\n",
    "new_TS.createDimension('y', 898)\n",
    "new_TS.createDimension('x', 398)\n",
    "new_TS.createDimension('deptht',size = len(depths))\n",
    "new_TS.createDimension('time_counter', None)\n",
    "nc_tools.show_dimensions(new_TS)\n",
    "# show variables\n",
    "nav_lat = new_TS.createVariable('nav_lat', 'float32', ('y','x'))\n",
    "nav_lat.long_name = 'Latitude'\n",
    "nav_lat.units = 'degrees_north'\n",
    "nav_lat = lat\n",
    "nav_lon = new_TS.createVariable('nav_lon', 'float32', ('y','x'))\n",
    "nav_lon.long_name = 'Longitude'\n",
    "nav_lon.units = 'degrees_east'\n",
    "nav_lon = lon\n",
    "deptht = new_TS.createVariable('deptht', 'float32', ('deptht'))\n",
    "deptht.long_name = 'Depth'\n",
    "deptht.units = 'm'\n",
    "deptht.positive = 'down'\n",
    "deptht.valid_range = np.array((4., 428.))##minimum depth 4m\n",
    "deptht = depths\n",
    "time_counter = new_TS.createVariable('time_counter', 'float32', ('time_counter'))\n",
    "time_counter.units = 'seconds since 2015-06-14 0:00:00'\n",
    "time_counter.long_name = 'Time axis'\n",
    "vosaline = new_TS.createVariable('vosaline', 'float32', \n",
    "                               ('time_counter','deptht','y','x'))\n",
    "vosaline.units = 'none'\n",
    "vosaline.long_name = 'Practical Salinity'  \n",
    "vosaline.coordinates = 'nav_lon nav_lat deptht time_counter'\n",
    "vosaline.grid = 'SalishSea6, north extended Fraser'\n",
    "vosaline[0] = S\n",
    "votemper = new_TS.createVariable('votemper', 'float32', \n",
    "                               ('time_counter','deptht','y','x'))\n",
    "votemper.units = 'degC'\n",
    "votemper.long_name = 'Temperature' \n",
    "votemper.coordinates = 'nav_lon nav_lat deptht time_counter'\n",
    "votemper[0] = T\n",
    "new_TS.history = \"\"\"[2015-06-14] Created\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fbaf2e93cf8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CdST/KT0vw8hDVefJyiEBICJnj6TjabSQVfU2glIlIvIz4EWq+rCIbAQuE5ELyFwV64Ab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\ngmwYhlETTJANwzBqggmyYRhGTfj/AREcG3PtFKJTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbaf2f7b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## temperature before\n",
    "plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## modify temperature for some grid cells\n",
    "k = 0 \n",
    "i = 413\n",
    "j = 351\n",
    "votemper[0,  : , i, j : j + 5] = 14. ## for(413, 351:355) from 0 to 4m\n",
    "votemper[0,  : , i + 4, j + 4: j +13] = 14. ## for (417, 355:363).. ..\n",
    "votemper[0,  : , i + 1, j + 4: j + 7] = 14. ## for (414, 355:357).. ..\n",
    "votemper[0,  : , i + 2, j + 6: j + 8] = 14. ## for (415, 357:358).. ..\n",
    "votemper[0,  : , i + 3, j + 7: j + 10] = 14. ## for (416, 358:360).. ..\n",
    "votemper[0,  : , i + 5, j + 9: j + 15] = 14. ## for (418, 360:365).. ..\n",
    "## plus north ones(all depth):\n",
    "votemper[0, k : , i + 5 : i + 88,  j + 14] = 14. ## for (418-500, 365).. ..\n",
    "votemper[0, k : ,  i + 87,  j+ 14 : j + 45] = 14. ## for (500, 365-395)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fbaf2d085a8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IyM+BF6nqQyKyEbhERD5N5qpYB1w/kpGLarp5AUj4pdtFOktUBGiT2JVW1aVQSxo5t/F9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\nwskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbaf2f85f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## temperature after\n",
    "plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fbaf2b6ea28>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ThZaIrgdwLIBZRLQawPnM7Gd+qbgGmHk5Ed0IYDmSn/jpzBx2HWyEN1rteR4q4pssKxMY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbaf2e52fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## salinity before\n",
    "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## set salinity after New Westminster as 1,before as 0,river source cell as 0\n",
    "k = 0 \n",
    "i = 418\n",
    "j = 365\n",
    "vosaline[0,  :, i-2, j-30: j-13] = 0.#16,335:351)(416 ,358:360)\n",
    "vosaline[0,  :, i -2, j -7: j -4] = 0.\n",
    "vosaline[0,  :, i -1,j -14:j -1] = 0.#(417, 351:363)..  ..\n",
    "vosaline[0,  :, i - 4, j - 18: j - 15] = 0.## for (414, 347:349)( 414, 355:357).. ..\n",
    "vosaline[0,  :, i - 4, j - 10: j - 7] = 0.\n",
    "vosaline[0,  :, i - 5,j - 16:j- 9]=0.## for (413, 349:355)\n",
    "vosaline[0,  :, i - 3, j  - 19:j-17] =0.#(415, 346, 347, 357, 358).. ..\n",
    "vosaline[0,  :, i - 3, j  - 8: j - 6] = 0.\n",
    "vosaline[0,  :, i , j -5 : j +1] = 0.#for(418,360:365) .. ..New Westminster\n",
    "## plus north ones(all depth):\n",
    "vosaline[0,  : , i: i+ 83 ,  j]  = 0. ## for (418-500, 365).. ..\n",
    "vosaline[0,  : ,  i + 82,  j : j + 30] = 0. ## for (500, 365-394)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## make salinity(500, 395)==0(source) for all depth\n",
    "vosaline[0,  : ,  500,  395] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## make original salinity of freshwater source point as 4\n",
    "vosaline[0, 0:4 , 416, 334] = 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## modify damp salinity values but not for the widen part\n",
    "k = 0; i = 425; j = 302; d = 1.\n",
    "vosaline[0, k: k +4, i, j+1] = d\n",
    "vosaline[0, k: k +4, i-1, j:j+3] = d\n",
    "vosaline[0, k: k +4, i-2, j+1:j+5] = d\n",
    "vosaline[0, k: k +4, i-3, j+3:j+7] = d\n",
    "vosaline[0, k: k +4, i-4, j+5:j+9] = d\n",
    "vosaline[0, k: k +4, i-5, j+7:j+11] = d\n",
    "vosaline[0, k: k +4, i-6, j+9:j+13] = d\n",
    "vosaline[0, k: k +4, i-7, j+11:j+14] = d\n",
    "vosaline[0, k: k +4, i-8, j+12:j+16] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## modify salinity values of straight channel connect damp and further extended channel\n",
    "k = 0; i = 416; j = 317; d= 1.\n",
    "vosaline[0, k : k +4, i, j-1:j+18] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fbaf29f0170>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n3Mv3oBosn0o5QjsH8ciCtBAuoD5nalp+BCA916x/F99yrdMCu7Nb+hauTVgw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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbaf2b1b910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## salinity after\n",
    "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fbaf277a680>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q0z5NJil39wnAKvoKyTeHuPL3HDiyynUfa6N2q87eRpRNby1wo/PZVkxF90RM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\nYM+QIUOGEUMm2DNkyJBhxPD/5qByBXzXhToAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbaf2b36110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_TS.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Create TS file for bathymetry 6 for sep25-oct7 spin up, then hindcast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#old initial file\n",
    "initial_path = '/data/dlatorne/MEOPAR/SalishSea/nowcast/early-days/24-26sep14/SalishSea_00129600_restart.nc'\n",
    "T_S = nc.Dataset(initial_path, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 16.07758754  16.06995039  15.95650878  15.80249028  15.40679218\n",
      "  14.96464942  14.64882925  14.41677323  14.10704308  13.88312656\n",
      "  13.75059047  13.63027657  13.53098886  13.36371394  13.24643531\n",
      "  13.12736562  13.0202796   12.94171459  12.8280736   12.70510011\n",
      "  12.52621711  12.28949568  12.09501551   0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.           0.        ]\n",
      "[   0.5000003     1.5000031     2.50001144    3.50003052    4.50007057\n",
      "    5.50015068    6.50031042    7.50062323    8.50123596    9.50243282\n",
      "   10.50476551   11.50931168   12.51816654   13.53541183   14.56898212\n",
      "   15.63428783   16.76117325   18.00713539   19.48178482   21.38997841\n",
      "   24.10025597   28.22991562   34.68575668   44.51772308   58.48433304\n",
      "   76.58558655   98.06295776  121.86651611  147.08946228  173.11448669\n",
      "  199.57304382  226.26029968  253.06663513  279.93453979  306.834198\n",
      "  333.75018311  360.67453003  387.60321045  414.53408813  441.46609497]\n"
     ]
    }
   ],
   "source": [
    "#nc_tools.show_variables(T_S)\n",
    "old_T = T_S.variables['tb'][0] # omit the first dimension\n",
    "old_S = T_S.variables['sb'][0]\n",
    "depths = T_S.variables['nav_lev']\n",
    "lon = T_S.variables['nav_lon']\n",
    "lat = T_S.variables['nav_lat']\n",
    "print (old_T[:, 427, 292])\n",
    "print(depths[0:40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(898, 398)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f744fb09e60>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JDwVUaCnJ+ENCxovUwS3SPinW78ZMtMuY2F/L2qaBG/BQoVkts+wQHzortS6ebPnnVIWo\nVqhf3U8aXJqzhX+WjK/iHfGbhSdFfRiWveA1iaqjPs42dhzBvHtfBQDUnyQPoDgILmBnRl3fXZJg\nrV0HeTe31m4zxiyDI9e1ABhb34jYcik+aE1QMaWEeQ/qVJQSk3YzrLR1nntF3aBDcDli5sP+ebNr\nu4dhGBMjszn5po2/eDJfQmJLkFw+Qu+SUgfEe7i+YK+yIYbsSUfFTr9nbLoVALB1oZug5aTdvYhJ\nOC4j5MEnYyD6Z4uqWrzVrxEHjBQMANOJu+4COEJ9DcAiY8wVcEFv75Ru05FNuH0GrVWxXD2yYLRh\nLkUWdKLxss7JnDTHF9lfVB27Vfskh+oOATsB+sl0IkXcCeBrYhHfDaf3/s4Ycwqc19h78xxecOKt\nIiTcjVSUSDulzDVmt+I+FnjMzGuL+qwYLzkAJsu7d0Z2PByXkqkmmAP67hNZclp2UckfjJkCQBj0\n0wWitlhv8sNyT+9UX+9Fakx6AjAL7D8bwIb81ykT1nmG4Q3EeaSNJEZ/4mg3KPrwTlcyFTOYLRjn\nyRFJkI/wg60XAQD+55KPqZ3753QP2EmQJyQ5/Zb7KwRjzGi48J1fWWuZReRway3NnzsRZ255AxAH\ndIcZ0JMsCagICfegJtKEoYohNsokHSsc0HOiUU3eTZnlpIoNb4pEwUmtNyqbMdRw/ofPFP/O6io+\nu3n7mqWdpLZtZBrqAg8XCVIYdZZjpdlTV8gR7qG1/pzYyl39sMzw8VbO3Sd7HQBWsf+LGEzs/lJc\n6PPvR7tQpqVwTytKUbWq1BARVcsgPzOwcpzqJHLEdxouAgC8/kdFvCf2Z9T9A8PvA7IxZJGweZiu\nZZL7Iy5elb3fGGMA3AhgqbVWJw1Ybow5RiKHjwPwqmwvOmitj6ENLrpQm1UnjKAlTJB8pyiNMtPb\n7Bb4P0qO2pZ9Lr5uF+2bUgJMKTkaJGggi3hrvJY8oV9mnpYsc1uy99VeFbvrTWly1urxklOR94vu\nfxs1q4seGwWEMO2STmL3AyHhJwdnUtP80LVWpQrZ80H3Gc6Z5czk7Xu4nFJL6uLQ5kdkNvb4MX8C\nANRRuk55LYAXjnOf/Yd/Ex+0/S3srOEyYxkwaCif6RYCOBXAC8YYzt58C8DnAfzUGFMH54b0eaC0\noLX+D60f2IhJ6BQLuF65UcUJVTqz1ieq4iTMARuRSUbateoC3SoHw1CBUX7yeo3HhCkyQmjvV30X\nIBtScTgiSiDWjcXSHzXVWbstTemoC+/Pu2UWbwOcVclabF1qSnjq2Y7slmxyJySBr75/dtQn9kXW\nzKxLUgwMPrXwxmj51/e6UvfPzXbX+TVcFvprNsVlKT7W5PyCWW2jboNM5iYoM5Qzrpt1Jr6Am3M7\nVAF2hjwoVYcymc5auwj5s00ekeeYkoLWykllOaxgzGIYs7jvjgXPkXF/owFTzXVSAgICklFX5F8F\nUBFL+FkcGllvk1T+xhpxW/OlivGIJ+YOgEv+kprn+q79sETcaS3nYWqbcbWGbB9a7Qv2erRk7cKo\n5FI2PGuQxS00IfNORnJqU1a1hazrs5rTN5ENrW3u6g5+zyxX4/5QyaWwbyQ9xZZwI6QEkrwxNGNl\n1n4gto7/stHVo1t9vJiRz72dNQRrR2Ogrd8oCOQjzQCA3239SLyzzlnC8ze572ppk0u0/p6muGzF\nEXAP0YbfeRaw3K9fLPznqO8Xb4+t3y/oObmAnRtV7AdWkaGtx9Qog9VUpfeSNMaKNkyNuE1NTjV7\nNcZa3+V0z67V6t20VZaXy9tCG4pCHGnmo9k13TLW5SSubEfcQhF7SapQFDEnk3ZTzvl7tI9+1Py8\nfBDNRRy0sV+PI+SelHtoTRWPBpbmSSHOVsb7LXwNXMeJrv6Z9pGnxZPxtrwKmBTL6Dg+rpd35dlO\nYD/nWacJf2q7yyH5KZVLcvMeIkcxMk4UnjcWuu/+9Ftvh0Y11tULqDAqlLC9GFTIOyIVTSKtUt4c\neyB7apLBG9qiW47ZWX0Ob3JWUubsOAfF6hOkQFtazE+GyXKeasWMqC+e4zIJmPVMtYtWWtqx3j7/\n9jWjFFhJmWtk1n7D/DgopeU4F6dMV62ZQsaHbnop6mM4OSkuWxN3OFI+YE/XZhriz0nt/fAj4sQN\n5tqjs0siUWfnYXTA0cnm+ZELRKIZ31tyQbNrj5T1lfGu2+HM1XOWXJv3fBNTMm8g0wG8/T+Dqyrt\nT8oGBOQgWMLZWHvXTNw431lA75t1b7R9ivx30Xc4JY8vXfuLliBfuVcIKddE/6HAAbOcBbhqliP4\nzEebAQAdy8UCy6jB0Df3YWEezhNqoiY5baYFTOtRn6h8WKc4oG5T7PlAC/goPAEglmGMfjbw26Of\no1iKNRKvM1vJLrP3cct3znR6yY3XfmlAxo5fidn7L/k9EMYvcize0+2+z/0aXon2/QDfzu5Mi0X/\nMhkc2pq974qtoudc5F0w5AQO8FHFJFyRLGpYYKN/nL1Ofjnax0TeE8S0oQWcKhB/u1Yc+bVuzG2r\nsnym49d0jeXrndXc+78iMCZ9WSThl71WW2Ac4r2xt4e1Y1EKPou47A/zJbSIFc5E6A0b4gdSRL4c\ns5Dw9n3cfOu4TSpnCMlbfGyP/YDTvteriLllf3OeE+N3c2x3V73Tbn+pEhjTMmeWswfE7eOn7Y7U\nUzXxd9VY6952Mm+KxiwuapuviO/LxBvlfvnkG7/YxBAvkisPcQ/wf/1jfL9sBX2Ai4H2Ew7eEcVj\nwLKonV5k358PfWWNEe8dUUmY692EHP/y9rvE/QUEBAwSaor8q9DQ8sIYcxOADwLYwBpzat+/Argc\nwGRrXbnJovNoXgXMP9q9M+touA7JolYTJfdxometOpTbVor+mpZS6Hsr31Zqy3yVf1F0Xk70aXlj\nwVQ3G7/q885q3vCC6LL/oy46XtrT3bVnTHfnX32P0qeXSLtarLwv5H5sc5csiJ76rqPpZuFE1Nnq\nM/gTkL9LuaiPzLTYRJwwrS3r89FKZb7d5inxOXqmOVNzYpOzPH+MrwEA5t0Xe1u88QEnmu/+oLz3\ni4b+3h0qnloUk85jXLu43k1+Hlv/CIDsZDvdNG//IBvcC0ps/QLJ8gOA7YfG9sGWuolZn/NaVjTV\nbsBVbgkHVBhj+u5SKfTF/TcDuAZeCQNJdPxeKP+ukvJoNgJL7nGzNIef/Oec3fznjVJbKlCaIGEz\ngm7NqOlRH+acoG5MUu4UR8BWFUmWXipREZx8osSQVheltPord/zqDzryrb0g1nC7OsQjg0l5eL6f\nSVtgIuusb7nXaj3pSOmEwQesSqyT4P/lr87dLMo1LPORl8BVw5z2sXgGbO2D7rjPv88lmLj++46E\n8fF4HD+RtG5fggRT8MGyS9yHl2+rd08mhhsfLm5kR7XHbhJ1f5IFPsQo22tXPB+iIP2lLvaD5++B\n92LF1e9wO/6AgIDiMFy9I6y1j0n6Nh9XAvg3AP+rthWfR7MGkEhbLHlXnNH8qKYnZHe2v3ASGUcT\nV6PcMRsVy7WLFwNLmFNb3iLi7l9ePTo+EUuWMS3Hd6VVLleI3MLEN2qZm7Trela5xe3wWka8cVg6\nbHmys6hZRYJ4GO+Jlnvkq1n9VUfMr54gqSx1deo+qm2Yw5SwKpe6Hl+D3QT8LLGyhHOVMLu59p/e\n50pi3/ngqXEXIWRqySfBTazyraMuLogRky7vSdI/An+BdJQXT4+xKpKS9yWqoMGw50WAPTjpcwQE\neKjiibmSh2aM+QcAq621L5hsobPoPJonHfAb3HvCKQCArpdjIltxlDPlpngZvLSLGgmWk3eHwCUD\n36KqZDJ8lxYUrbW1vWIt61HSTZmEkVhHkpNNDB2XiIxHAGtd5IUxTCIucoSoLLUnOGs5VRN7b3Rs\ndGP9+S2iWZCkmtUlldNAFsGW8NpdbhkkVosyJ3/Kted/Ktr3gdlOU6HkcdqmO1wfehfqvDvd3jax\ncl9+315RF4ZWz3tQZBGxkpuVHxsfVt994gq34TPqGhWIUA8YhhgpJGyMqYdLXqE9QQvNJCa6Xrxy\n0R3Aa//nVt7ZAhzcUsowAgICRijS6TTS6fTAn7iK5Yg+XdREjrjXWjvXGDMXTkhgujPmyjwCwBkA\nYK29VI67H8CF1trF3vns6fY6/PyvYgVqS+bdzmTaa7qzghiuqn2AOZHHqDq+ttcpE4yWNLXgJZIF\nh5riM+2HRn3b25zl2nuumGB8Lb5BV5eIc/ta+wEYI1rw2AQ5Ql6Vx9/vROFtd4se8R3kglqpCtSz\nw6RACL05vvstZ7J//yXZ8LLqJPeE7mO/a3JvDf/05z9GXc4++koAwE9/969ug8j1y4+O/bQ/KOLv\nq6fE1UVsXJxjWCC4qJWHAXNRK7Kwq/n+0LuolWQJW2tfhCrFYIxZCeAQa+0mY0zReTT3wyuYc7B7\ntV82WTldbHMMuGq9e29lflwd2kzZge1Yr5glEE+8LRD5gK/OJOV96+NggcfqnT7cfosj49evZl5J\nXXEiJltj1gNI8P8VDmGRk21pIV9WjF6pK0ZrX+LhVdnCnCMLMgHJNJqdEoKdlQNFJIpRq+VB/4N4\nl72SS3LCfeQGiu+z9po5jw7GvwHOxK/7MfqAnRbDVY4wxtwG4BgAk4wxqwB8z1qrHYMiM7qUPJp7\nY0UUmJHZ85loO93OSLDMn5BSljDBSbu2yJyMEbuxuYkpBnIw6ky7f7HaM8unvz5GSDg2loFnKG0L\n+U4WUtapivmcWCctPbWWx0mErGVinALVMaoQ5lu528Z+1CWh2Fv02lq+CaiQ5ANOkjeIc9SBKQDv\nBsw9bvW6k88AAHzB+4r1d37WtTHxnllKLueAAKJCGdKKQV/eEZ/oY/8sb72oPJr1aI9crnQZG5Ij\nJ9+2YGLOsX6GNfr86qg6elOw2CPd2ZjDuF1ZsszmxvMs/rgj445F6tqdzdmDyFc9A4hzMSynzrJM\n7Rz43LxDjUlXuEotvxrlPCZ2XyU+xfwap8R9z8OlAICrrvwGAGDJuUciH7Ye6L6bhpfc99B0u0rw\nf77qGEg4oBwMV0s4YOeE+UfXttxFB+QT8vYNCBgWqGKmq8jQ9sZyvCrBCGNVSaM4t7DTU5nCsqfA\nMGnt6gAMurRlRN7gNT7S45yB21Jxaszpm5wlV9vkJIwnGtzk3SOXt0R9ehfLpB2VE1bx0OXf6PbG\nAIeotl8TrG3BcMY7L3kkWr4HrpjepE0S1UJvQplQtHGMBRaK7/CeIg7fc7k7dq6qYXcUHgcQfyc/\nPEhMXT3B99+ADbmBA/qDKvaOqAgJ7//862g7yP3T6VzBdSIJkIypC2rdlxIF95GEtYZIOYIJfDjJ\nsyY1zV1/ZZzInXegscn1+SRuBQA0T81EXe49+SQAwIZGCWlmFK/SPyPSsGlZ0DJEC6oNkYfHR2J9\n+tN3/TcAYMZdLhT8ShFztd/25MeFfP3MZvKcMkrbpRZ/SI/z5aZP99Wpr0R9/MCcn/W69JSTTnkD\nG0clupkHBJSOYAl7eA2YeVAGALBNkTCj3hhswQm21qwyxNmglqur83Z5KjzDgRm0UTMzZoq9Nzn/\nsMkRmzhojZm69YbZ4smwRM5/tzpg8+OyoCfivojhiD/DeYzMXCUm/5qETtzGmHwJJrQJFkfDJsnV\nIbEu174vTqP53SslAINOKVr/fQEBAQODMplOUjTcAjfbYQFcb629Wu0vP4dO/4bWf0x+yVlU+x4Y\nJ5CJLd9sKzkpbNmH9iXukWUeR6KmFf04jooPlFBpXotJfnSCeYY/145xRN+VKTTVOg/WLuxzvBXH\nN50FfPjlce6OW5Z7iXjXIz/8/A/iWmbU83ID3fx2dc3uBzmJpvdkdXCcTtpV5AiJeAIGA+XLEW8D\n+Ia1dokxZjyAZ40xD1lrl/U7h44gpLIMCAgY+RhT5J8Ha+06a+0SWd4G96rLbGHMoaMR5dCx1mbg\nSpcfXmholbGEX0RkSU2YE+eFaE5lAMTJYTqiPBFxH2rAzIjmu6wBsZTgT9oxYk7nJaBuTGv5Y5uc\nxrC0ae+oD7XqxibnG3vfR8V9QFeGvze2fo1xkkTsF1x5TIWrX7cvXKDKpB86aebXiPNCRIqMKDEc\nfrrpnVGteI2SAAAgAElEQVQXZkmrE9Vl63ydaDRb76W/9+2Sqi2KSmwEbFZevoCAQcYAMJ1EDy8A\nsHggcugM4NDKwEvxlRumqEoR893k2IRUdmXOzSo5T6voxoVIOJ83BbXmDcqZlX0ZXbemyRF2vfLa\n4GQdgz2mH+W00htPVyWCWKS5W1d4duWQXAXjoYf5f/HylOOy9z06yiUEnv2sipnmbRNJ4UdNzlNB\nJ0c6p96Fuu17iCNzptb8Jpy2+92rr4jPp0oG2svhJUQNCBhC5JEj0i+5v74gUsSdAL4GoBcDkEOH\nqAwJL1HLcRpgNNQ5Qp59oDMxOZFWp5Kw0ypuy/IPUwnEkUzMGtotjlYzJwXp1qbDZif1OBOxpdu5\najXWuX11/xhPBl77YwkLO1/Mx7a4lLzZE/BqmOavSDwAiMskxQ+Jy3AugHgi9B0vSW5LFVyxdbqz\nahen3NvTv/7d5R7GFepnItHYS5ol8EJcif8ZR8PeCtivDsxnCAgYUORhupb57o+4+I7cPsaY0QB+\nC+BX1tq7JYdOM4DnxQqeAacVHwHnm6rrqjG/TqlDG1x0rAfG8tU3N9sw9p7urLO2JkcYOlewT76E\n9ojoi4TrVe4G1qOr87wsmjtjyWLcBtHU5W6lprmJP+bQBYClZ7sHRpqBDTeL9VtocmuQsei4OPaa\nbn+zt7o5BNaha6uL7yfrxX1xqyS8OEQ+cLbjCLKmGE5DQED1o3zvCAPgRgBLrbVXAQOXQ6efQwvo\nL8wpAFh5IqeKM6AjtnVmNV2Ruesp8fFlwjFx4T1LOX8EBASgP94RCwGcCuAFYwwTin/LWnuf6lNW\nDh2iIiT82nZgjkgSo3XpHLG4jESmzT3S6avrG6aqLvl9hvtCKnJdy82CRgv7j/ggAGCbshCP3SRl\nNmTCavI0Z1VSLgHiib3Dz3YuXy+e6rLDRTko/gsxSkwm8mccoSYnE14dPNxw55fdwv7xtr8f6HSH\n9nFOcliScu9ga5QedCEudmP+vYz5x4D9ZGljDQioSpRZY85auwh9eJGVm0OHqAgJLwciv9J5OrCM\n7qPykWqk1M3shtgNwfcdjr0bYn22I49f8RTRBuoLpL8k2elS8FHdOXFFnr3KySXr94hTYh4qMc3M\nxvb+BqnzI36vS0+MCfvVv0n6zutEy58sFrCuSiG13x7dV8UBA7i+6XPRcteJjs2pX3MCbXun+830\n1MRfLz1D6lNOD39ECqSuUvLV6+cKa6u5NQQSDhgJCGHL2XgbsXfzVJXUfSpJmMTMEmnT4j4kybkS\nTjVZtE5dJHONHEAy5jHUjSeqSbf3SD5casKctNN9WPcsigeR2dT5U56Luqypmy5dkuvivR+q+Jo8\nZKZf7sLObpACm9qy/g9cACB2KavvcQ+OKak4hJjVh+mKR9e7w+rcA2HZ0gXxNTOu2etEF1+9dpMb\nb9dGlVbz/7zOctaAgGGPKhZeKzK0TYjVz4za3iRzYaP/LhuknTJf1ZyTJxpfo0mwOncEt62Q8sO0\nnmkxas8HRnUxL+5ha4VhtyOGzscORBb7uCXxDNURRyzOujZDrnltHYHHsc5b6aIFG2e68Ry9Uun3\n0v25o523xZZUY9bnBmISplX7l79JIcwvm6xxAohu9OvLxdq9SrZrdac5ZyEgYGQgkHBAQEBABVHF\nTNdXZY2bAHwQwAZr7VzZdjmADwHogiukfoa19i3ZV1TiijbEBYJ0cZ9umfgazZSRkrSsYZ/YT7h+\nTwkukLdoWoba93e6l3GGei8tUG1N8jhOrHVMcyNr4WQcAMOqEaJjRxU1lKW555vOWt/zVbHame5S\nJNduFTxXw7m1PbPH8PLMuApxeqazbr+0/icAgN433MX2OjjO8UgJZX2XOPu+LBYw4y/0zaWk83Np\nmSK4UfWhNDSkFbYCAoYAw1gTvhnANciOdXoQwHnW2l5jzKUALgBwfimJKzryLGdEAphDZYGkF9eF\nRI04Vk96n3Ol4Ou+DujY5iXj4aSdn10NiCemGInHqLguNZtax4AGJlbjo0XfPcoXvARf87fldiW2\n7uO0XOrPlBWAeCJt3lSXe/cFkR5ev1W5PDD9Mgk1Iy2JdVd1seOl5eei6/UO1ef30loVRYcZCAgY\n9hiulrC19jGJl9bbHlKriwH8kyxHiSsAZIwxTFyR6FNFhytNwoxrmEND9lFp36c6iT478XF3ZOpI\np+WuScXWrR/QURPlHs4FibnLC4NeXh/njnjHmBXZB5GUdRQcyZcudwu9vupOb58/SsbszFMGjOhx\n05rnBF/7VNf2fDJ+pK944R1ugXXtGr02yS0n5e3T306GC0qDDyQcMBIwXGvMFYEzAdwmyyUlrkio\nVxwZmq+Jv/AMsdLGqtDaaP5NPLcaNjhqffeUx6IuL6ac1fgMDgEQW7n0D54eaQUx8dHD4Bmp8KnD\noPdY6Ni2gTTOiTo/naPeJjLEczOdDqFd3jhptzmqHD0xazsQyyOcRGQtvDXKVWTLgVJxepR7IK1e\nIxddJb+4xWpcJF16pPEBoi3hKGVHrgtfQMCwxnC1hAvBGPNtAF3W2lsLdEuMFHkEMVctBMCi9xwM\nKWC1vOLvo0vdsBOJWUi5YX5s5zbPzgCI5Yjn4Fy1KD1ospstqdCoEzN3xF8Q++fWpdy533+0czOb\nWCcj1Ik/aL3zSSIEOG2m2/GEymH8ipR2YuFRyhHjVbY4P/Sa6zqj3OGj3EX4kOmZ7vq0fs5pId2n\nx+fYb6rzxODDZsVNYkVn1EXI72v1Uy8gYOiQTqeRTqcH/sQjjYSNMafDhSG8R20uOnHFsYg5NLzs\nVjdcWk7to0e/4jiAxtqThnJIASMYLS0taGlpidYvvvjigTnxSCJhY8wJAM4FcIy1Vr/MlpS4otAL\nL32Io3AHFU8QWZqULfkJlDSw+w6nZ/Qc6CzBqaI2ayuSYApLgh4UlCUAoBEujzC9LCbuIhNXOuSa\n46FcIndm6iY3uzi3KS5uSRmClrmf01iPg/X1KFnoFJsEtzWLWTupaaMMJf56KbusflWCWsih+htk\nRfrfNSO7U2EYkwHw12jd2n8s6riAgKFCUtmtakFfLmq3ATgGwGRjzCoAF8J5Q9QCeEjSuD1prT27\nlMQVNUjWhEer/QAwgTqmDuclyZGMmeGrSfURQt5zlWPG+j3cazsj0jTZTet0csG4VeLEITktZk5T\ndXdIthwH+VQHdPj6sMwTGnp4qPHNFLKkjMBWZ4ujbMBQZOZP1oEm7MPPw3WSr86R0dYlxVIZgk1v\nCf0g4Vi/Id/EVeJXt5vqE8np/EC5j1NjYjHa2iNy9gcEDDV6hqslbK39RMLmmwr0LypxxVjEHFrj\nbdfo4MSccvsdzQPIAXSKUJnGInKU4ydvd8wzd3/HnjoJUGudI776JkfYhlZ3PHcXV1XmoPkg0MSr\nqgxnQcarrXASKi1gkifbrD694kK3zo1zVCouQDpxV0fItaOEhHvd8Y2jtsiQ4rvb0+2Wa5udtNC1\nWj6oCgmPXNrelPbr0m5UfRhSnpGJxo0k4eO9Dg6OkGNviyBdBFQCw5aEBwtNiBWGQhYx0aEs4dG0\nfEm6/ATaoqPgTDIWEp243RHGxCmxH2yn8HHbLo7AGmoSHNlokftPDv2K4z8c9HjgSw3uRH5oc6fy\no+GDojUjT5k3XQRF7xuqzw55CoiRy2jj1vFyzDYVdTFeXkp2eAEdOliD2/hAoevbNtWHXxjV/+2y\nITKI9bf3NgICqgGddbV9dwKQ7Mg6uKji50PASIQx1yH7tQWw9tuVGUzAToOeVPWKwhUhYW39dqvl\nt702qX8E6peUBPSJaAHzf53WaavXAqiTO1A3qSv7mELwAx6ASBbplsxvaxokf6+MXmdIY9KgsWI+\nUobQte82domJvk4sV0rBGXVNWqi0WHnjGuWYmarvDC8WmcfoiTn/fJQltCafb67OyLdktXsbZQgO\nrIJlRgJ2avRVbaeSqCpLuLvvLjEhkDxIqH9XfXTOhKRjtZbrEzaJSN8ZX4Zgqyfm5Dum9kTSZSa3\nFyJv6Dg6j5owAznWbI2j/jpWS2J1BlXwc2YSxkV/ZUoCfGq9ovo2S8uMnxu9VoP3gJ/vTbVvi9c3\nZ16uIWEnvxC6uiU+VgMCBg3dgYSz0YDkf0MOhtySWKOY99L3ktDWmh84wX1+DgggtmZJ6j1eq/v7\nk29a9xViT3lPEoYdb1MhyX/G0QCA1X+T0OhusVLTyEVGWpKfDhDp8PrwEqu8dX3cgd75k558tIRJ\n0Jp4eZ8iv5eE0kwR+C0zcJLfsD95dwO0L7K15yScKyCgfOSrwF4NqDgJFzN107Ejd1tkb3Gf/iQk\nW5Kv/xas35hJQjTWOOGkCdfPfkHi1teU/lsa3CwZJ9uiwqHKhI18dp8Ws5TJ1JNe9dMJ2wh+9s3S\nRlatzLBtVmbqZiHJlfJBKR/ofMK+lVvw1YTfnO97rW8Kv6XR3ro+cTYhBwQMBsqVI4wxe8AlMJsC\nZ3pcb6292hjTBOB2AHvBmUH/bK3dIscUlU2SKFg7KSCgEjDmGhhzTaWHETCC0INUUX8JeBvAN6y1\n74ALZ/qSMWYOgPMBPGSt3ReuZO/5AOBlkzwBwLXGmII8WxFLeAJi20jHWNBu46BoayVJF1vl7bUh\nST7o9Lb5MsRbqi/z/Mok1hv7S625ntjCa3jKc1uhPqu/s25uchf1o+y0n/BReAIAcO+cU9wG5k/W\nFjd9k3MsRS3C8oMx6o93jq/2WhzncRJJbuXubtQ5lvxvJcmbm9t4jULTqLR8a7x9KrlypJnwNSCO\ndHcyBaBnS639NwQElIpOFOuilg1r7TqIQGet3WZcHP/uAE6GC2QDgF/AvbOejxKzSQIV9I4YrZbh\nLRd6C2bid9awfJuJ4LVkscNrfU8KndauNbvP7m/JBl0/lf07vVYTvxw2cZUju9l7rJAubqA6kTyT\nBOVMimWtL/PaJO2VWdhJ8CRG3kHt6uETKu+2Tu9B0iYJkyCTMkD7GjDHp7+9sV7L8+nJuxleH+7L\nICBgoDAQmrCk9V0Al55rqrWWQud6xP88JWWTBCpEwqMBNBS6cneBVVkh+ZKUm7zgCADx7L7vzaC5\nifouCXqV1wJxMARvMyf+tEWtI88QhxczL7Am4ei1Z39h858Iy7dpsuMgfUE7KetGvC2fz60x/y5L\nJE/flUIvc1/Sb6fDa/1HpraM/YiapHeaqV7LPvod6YWE4wICikd/XdSMMeMB/BbA16y1bZKyAQBg\nrbXGmMQUDexS6NwVq7YcWbAFRlCMy1pU1V1bwjQI/Ug33yIGYivX/470xckH47x13YcPAblGanpP\n1nknKzOXOYEPme50iGe/IBnglyiSWulbjSS03CREAGDthYnb4/35yPkytcYPRJmDRJs0fdqdp49+\nSPDLafPWNd7w9nGmMEkmCQgoD/lI+Jn0djyTzk2KpWGMGQ1HwL+01t4tm9cbY3az1q4zxkxD7BRf\ndDZJonr9NgICAgIGCPn8hOe3NGB+SyyPXX9xtkZonMl7I4Cl1tqr1K57AHwGwA+lvVttLzqbJFAh\nEu6AeilOMHd92yop9mJsUukeH7SOaUROSuhDfdcvf7JLwjItYRlQp7Ko2+udFZvqkRSUKWfJNub4\nfcXYT6IpMsc3AwBaT1DW36/mubbNLwSlX+n7XwEjaaLL/X6A2ILV0kA+LZitzj1c3Pj69gue08f+\ngIDC6IcmvBDAqQBeMMZwBvwCAJcCuMMYcxbERQ0ASskmSVRsYo5KZ9KLJv91+UKueZrpLUcXI/GQ\nqH0S1Z/aPw/XNSmPy263NziPk466KONxpPmSfP06d5tVppyp8uayQsLXmAVNpTCOn6ttvq/I4IOk\nGOvImkz5uXx9tzthu+9JEX+TfcknAQEDiXI1YWvtIuR35T0+aWOx2SSJipDwPrOADmHh1Sr0l6IK\n/80jdVBZsDlacsrrDOQW3eTbhkemiUjyoJDzWNm3pc6FFHcowmHeX0bIMSSZ2yOPCMSEzX2s8Lx2\nskr2QM5e63sNJGvCgwHqyDEZA7leGjV51oHckJyYzI2JKyYEQg4YbHSV6aI2FOgrqftNAD4IYIO1\ndq5s63+kyDRgrEjX+6hw432EhTN/c+0U34JFAvkSWj4ggfppJXfxtgPAnt4xU711AN1yXFuDIxX6\n/rbHtT8iQvWrZjCBD+vcAcCrvfu68ywS+UESyeNONa5ootHXa2ryLA8e9KReTJ7+tZM8H/xtXB+6\nB0lAAFDduSP6ipi7GS7qQ2PAIkUCAgIChgI9qCnqrxLoq7LGY+KgrNH/SJHxiN+u91Tbxf929N+8\n/kn67Bhv35iEPpO89SnediCuzCH7tk9xz42emvjWbEhlVx/2pQc39GyJgetMT7m0K55ceuslqRd0\nqWx4WtqN2h+W1iIDMji7qC3jovLODSh86SC2jAu5oXGcIcl7QGUw0lJZ9j9SpAm5k2VqNLvv49oO\nknISwfreEfo8JHhfjpjirattrLCxts6RZ5LUwNBH1oTToZA+Cb8CJzlQlnjrKVWobZG093HD49Lq\nwAzqp0nkS1Q+JWQxeq7Wf0s5LiBgoDDSSDhC2ZEikwAVQBZjenY7dpK3HUjOmgYkFvrM0YJ9Cxkx\n+W6ud52o6XYoEma+X3o8JGnCJFvuW9br8glH5YkeVuO7jQusUMy6QtqnO5/1OPTWb38RCDeg0qhm\nTbgcEu53pMhFSwC86pZb5gEt+3sd+OZOzwkdHuzLEGynJPTxSdj3lkBMvtsignVWrnYx80nXt3qB\n2PuBhN36tLwErJAOkdULYHkmOsqBtympzggS9gUEjEyk02mk0+kBP29XTiBA9aAcEu53pMhFRyO2\nRpNyPgSUDP+VP1ifAcMRLS0taGlpidYvvjhXyioHw1aOMMbcBjcJN9kYswrA9zAQkSJj1JW1lssk\nODT6/AoZGr7P76SEfb7lS/1XheDR8qVVSwtYW7m0bv1acNpaXi6BF1G1jD/JDla0eAYKL0Zn7hvB\nAg4I6C+GrRxhrf1Enl39ixSpQXLghF++nvtUYc4o+MoPyEjyE27I3rd1T0eiW1IxwfpSg0/GSduS\n5IjVL0iVDJLu/dJyEg6L1QCpAfc/7DgfaBkHizggIJQ3ykUd4mCLzoT9DBwj+epR0qXNr7KsvSV8\ngpa5Mbqa6RBi390siYS5TAuYJLy+SwnR1Hyfl5YBGJZuZ1oe98k3uHAFBAwmhq0cMWjYBXGOXu3V\nQPKktEAy1vl/3/L6FgpBFvLdOt1ZwCTTbQkE65OvJmGmnqR0kdnUDADoWqJm+Cg3PMsT07+XFSOS\npIeC6esL7AsICCgFgYR9jEOutcrtALbPzA6Y6ErF/rgT2l099u6U6zPu5V63Q+cTlvOQfCk/JBGs\nv43yhO6zQTThSBt+Ssh3CWKQfFdSL3ldWpJvIekhqSrFwCDkaAgICCQcEBAQUFF0jjAXtYGBH0qM\nOFEOs5T50WcAsKXe7WPZoNkHudf92atWwwctaD4FGeGmvxBew283J3hHrFkv7htUGF5WF4sKc2a8\nNskCrowGHNzYAnZWBEvYxx6IdF/magCAtjonAfhpINeqkDmS4wQvE9fmPXJDeFn5ONZ9HYG3Kn82\nPxqO69rzYcMm96TofUWeHBnZsVZfjbKDX5iTqD79N3hQBOwsCCTsYxegU7wcWusmR5u7otwMjiQj\nC1SRMMk3JcTFfWOVxbn7m86tYuOubvaPXwDPqwnW37ZeTHMSNgB0ZUQDzsgGWsJ/ggJ3chaxGPez\nMPkWEDAUGLZ+woMGlci9XSWhoTVKdzEmTZ+kHIXrxKeNJFmfQHYk36Vw+Rs2ekS7UVnC8Tb3MOAk\n3JZNMVFHskNGWhq79rW4T+SCls/9bGiRP9tZQMDOh+AnHBAQEFBBBDki4aopMRB1tjIu0zrmK0Qq\nwZqkdJEUZsxtvrYcW8Sx1EALmL7AG9ZITPNGNZu6xWuZlCcrAMP3A/bHXNlAjKD7BuzMKJeEk6oL\nyfavADgbrorQH6y158n24qoLKVRME+6RK2tPBZ0aEoilBl0fytd2SLDP4JBoG189fJ9fku8G5ZIR\nBWK0iza9XMajJ92oATNVURQFl5T/tzKeD4FkAwLyo7P8GnM3A7gGwC3cYIw5Fq64xTxr7dvGmF1l\nu64utDuAh40x+1prewtdoGIk3FafPWmm4es3mnhrxOMh7uv2ZTAzZ5sfkkzyXY84g8/aTW5ir2u1\nTL6RcNepi6SlXUnSZSBGYqbOPAiTcAEBlUK5mnCe6kJfBPAfUkUI1to3ZXvx1YUUKkLCVvFuyiNV\nICZRWsBasqDlzD5sdYQbt1GO6PbWszwfSL50M85Iqy3h57hAhs71SQ4ICKheDLAmvA+Ao40xl8DF\n6n7TWvsMSqkupBAm5kYIgs9vQEB+5CPhN9LL8UZ6ReK+AqgBMNFae6Qx5jAAdwCYladvocpD0ckq\ngm7Pkk0CrV6tG9M6juu+1WWt6z6UHSLJot1JFu1tKrAjIy2NWxq79wO5neiSVigKrtjtAQEBQ4V8\nfsJTW/bD1Jb9ovVnLu5zHg1wbHEXAFhrnzbG9BpjJqOE6kIaFSHh9nGjCmo0vkShJ+ao7/qJd3RA\nB70r6PPb3u6O2bZcJt+2qZMzCQ81YL5MbNaSA2vBDV7+34CAgMHDAPsJ3w3gOACPGmP2BVBrrd1o\njCm6upBG2SMTV4xTAfTClYo4Ay4jxO0A9oJU3bDWbkk6nsSaPDGXbSVrrwmSLt3NOry6bwDQulX2\nZUT7JelmpNWTbn6Ri5UkWp0YIl8mtOqpCRdkiICA/OiHixqrC01S1YVuAnCTMeZFAF0ATgNKrC6k\nUBYJy2zh5wDMsdZ2GmNuB/BxAO8A8JC19jJjzHkAzpe/LPTU1ETWrr45JGZasr7VC+hEO9lJflav\nUW8B9PFlZBtfCGj1akv4Ra8P6H6mo+FKiYIL5BsQUG3oKtNFrUB1oU/n6V9cdSGFUX13ScRWONOv\n3hhTA6AeriLcyQB+IX1+AeDDZZ4/ICAgYMDQjVRRf5VAWZawtXaTMeY/Afwdzkx8wFr7kDFmqrWW\n7+7rAeWQq9CVqo2eTPqDxzkjaAGPz2pdH2clMxCjtVfyQOgIN9Z5oxDye2lp9W7Uo6GMkPHa6tV/\ng/UbEFAaRlzuCGPM3gC+DqAZruDQb4wxp+o+1lprjEnUQ9pRH0kM2gc436SbliNYYojk27pOJtuW\nIcYqaX8m7UpmNmuXVk9YcpkeE7qWko9ipAbe0uAVERBQLRiJuSMOBfCEtbYVAIwxdwF4J4B1xpjd\nrLXrjDHTAGxIOviKizrQLmTX1DId+7c4g9lPK7ktgYS5rzUjE3HrjGtfURfwk+iPlUJ2HczGphmb\neYl5K5Is4IaEbUD2ZNzgk661Xxn0awQEVBLpdBrpdHrAzzsSSfhlAN81xoyFixg5Hs4VYzuAzwD4\nobR3Jx38LxdNiQh3eYJiQd9fWsbbEkgYbwr50q9X13ujj2/Eq3Qx42RbkrVbSq4H9s1NJB9vI5m3\nJfQJCAhIQktLC1paWqL1iy8emBSsIy6fsLX2eWPMLXA1hnvhHGmvBzABwB3GmLMgLmoDNM6AgICA\nsjHiNGEAsNZeBuAyb/MmOKu4T4wVfbYRm6NtTOLOxO2ErvcWJVunlJuRdjly0fa4v0FaLS/4lY6T\nbkm+asjaoh7tXYOWMK348uWKIEMEBPQP5bqoDQUq8nioQ2fixBxfGXKT9MTD7Nom/RlLwVbLvDl8\nR7KkVLBV7fM14CRNuClhG5B9+/yL+rLE2AJ9AwICBhMjTo7oL1LoiRK165vT4ZU1Ihl39CoC2yJD\nZtQbo4u7NbFSA6Z5rPeVg3x6cTFkSgtZ9y3OgyJYwAEBA4MRKUf0Bz1IJd4UP2dE5KK2JZ6Yi8iX\nvr5RigdtCmek9cm3kOTgI4l4B8qC9ccRLOOAgMHESPSOCBgCGHMNgGARBwT0F4GEPXQjFckRulqy\nbwlHyd3XqYk0Wr6MhosS7ugAjL6i3Qol3umr/9AjkHFAQP8QSDgBXZEvcKz38kbl5BrWHMhlhiZH\nvr+FJttKKbo5lIQbZImAgKFAZ04EV/WgQppwTeKynzsiguZUerRt8Xf2l8AqSYC8ttK+Q5BHQMCA\nIVjCHrpQG1nAHQmWMBHlEd6uNk6T9ghp/yQLVjP1QHlFDDW03zHd4gIZBwT0F4GEPbRhQpQPWOvA\nXK5FFwCgngl3JquDH5aW8RyWxKUJbLiRLzFaLfPzUA/fCpewf3TWEUEnDgjoG8FPOCBgBIITprGM\nxAdkKXlIAoYCwU/YQy26MEFes5nIB4hfGWgBj40sYRXGPF4E9gw3JFm9fJUvlJay2sF/aMosyfJE\n8JwoDsZcl7C1kNTjT5Ym/auMTtgWUI0IckSRqBWNgTeMuYMn7RZnYW9N7e4WmmXDKpKwDi0efuRr\n7VeUZZWEwtpw4WMrhQl9dykKJDs/3WjSPfGtUP/YgcQUbzysZxBP8lr7b4Nw3YBS0Y8aczcB+CCA\nDdbaubLtcgAfgqsvtwLAGdbat2TfBQDOBNAD4KvW2j7LN1csdwRR6OawX3e36kMJuVnaJ+e5tnvx\nwA1wCJBkuept1UmqIwfWfrGofsb8vMDeeDLY2tP7NZ6AwUVnV9kJfG4GcA2AW9S2BwGcZ63tNcZc\nCuACAOcbYw4A8DEAB8BVW37YGLOvtba30AUqbgmPV9bMesktPFZ+3Gx7utUwOUnHTdG8nn41pAW2\n1evcndD3ba8PvL6VgU/Sw5OU2wZVJslHkINNiIFwhx+yOKQEWGsfk8LGettDanUxgH+S5X8AcJu1\n9m0AGWPMcgCHA3iq0DUqTsIBAQMNR87xw73YB0G2bpyUsD9guKKne9A04TMB3CbL05FNuKvhLOKC\nqBgJc/KtxgtV1vsY2txYH0VmYNsMMYXH+0dpP+F8+mlSZNrON6Odj5SGm7VNi7SwZND3/mIn2IIF\nPLJyDfsAACAASURBVHyRj4R7Fz2G3scXlXVOY8y3AXRZa28t0C2xzqZG2SRsjGkEcAOAd8iFzoCL\nIb4dwF6QyhrW2i3+sZ2oizKkJfnv+ZFzWUneZ0h7pLSMy7jvcHUGTpCU4i88tGRcSa8Gn2w5hmLG\nUoioK+WhkUSOfRNvErKt30C6Iwfdb+exhI9ocX/EZZcWdT5jzOkATgTwHrX5DQB7qPUZyE5qk4j+\nWMI/BvBHa+1HjTE1AMYB+DaAh6y1lxljzgNwvvxlwUXM1ctybkw3SbdRYpO3qMoaEagNv0Pal5U1\ns5JvANzGAp+leE0MzUtCsWTM/ZW2VoeLK1wg0ACN3p6B+382xpwA4FwAx1hrd6hd9wC41bioqt0B\n7ANXe7Mgyi15vwuAd1trPwMA1tpuAG8ZY04GcIx0+wWANBJIuBO1UbkR7R3BiDm/ZQQdAKBRpITV\n3tAPVMsrxWMCaWn9hD5JH9uftKtOeSJ4UAQElIEyNWFjzG1wnDbZGLMKwIVw3hC1AB4yxgDAk9ba\ns621S40xdwBYCkcoZ1trB02OmAngTWPMzQAOAvAsgK8DmGqtpRawHkgopRyQiFIIdbhYowEBVYMd\nZXtHfCJh800F+l8C4JJSrlEuCdcAOBjAl621TxtjroJn8VprrTEm8Slwy0WvowNvAgCaW/bEfi27\nAYit4k6xkilLaE24drybtOvaTXIqTJQdeq7NiLZnWXP0fmmZjLivfMM+qjPVZCDjgJGGdDqNdDo9\n8Ceurn/dLJgirOXcg4zZDc4Enynr74Iz0WcBONZau84YMw3AI9ba/b1j7e/te+LSRSqqapVo2usl\nEmkFZmetA8Da3ukAgNaMa7HIuPZFdZG0tM9KazOysMxrNYrJOVwd36S1F1Z6CAEBQwJjDKy1pp/n\nsHi+SJ47qP/XKxVlWcJCsqskGuRVuDL3/yd/nwHwQ2nvTjq+U03MJSV1r/OyqNUXslybpdXyOP0x\n/k/aDk7IFfKWKOdWDC4pB7INCBggVIf9lIj+TBl+BcCvjTG1kPhpACkAdxhjzoK4qCUd2IW6KI+w\nTuDe41XUYAKfCcrvd/wot9zW6Nqo9JFOdzlH2g9K+9uDXWuTPm6fHiQBAQHDHdU5zw6gHyRsrX0e\nwGEJu45P2BYQEBBQOeTGhFUNKhIx1476omo+MZouSu6OWJpobHKaw4bdRFPuVjJORtpdpf2QtPfO\nQy4odQy/zGsBAQFFYoTKEWWjB6mcop562a+6rPtQmqCWXEtZYpuqyMxYDerE66SlTPGaIuPuDbLQ\n4bUalXmXMeZiAEEbDgjoN3b03aVSqFiNuaicvbKImf0+iaB90EqeICTcukNZ1tPqeEIHpiP2SRkA\nNtPzYjWyofNPVHcAR0BAQB8IlnA21mNqziScXi5EvpQm6NqWGiVRdWNiX+KuyULC22QDQ0b4RWxD\njEViFUceLHRf03kENiAgIGAYI5BwQEBAQAURSDgb7RgbWbK6AB8j4yhR0H1N9+mOfIld30gjHhO7\nunWxO9NdNkvrl23TWCIWcRstYO26xm+Qk3dDK0tQGyaCRhwQUCKqWEmskCasdeBYeliD6Vnb2iMS\nTuUcy8m7iJSVHNG+y3YAQO/mcW4DeZWkPDNhUCTmzD6u3ahJON9t0tur+FEbELCzI7ioZaNdBWgw\nmxqQqwl3RpnWanL6+PtqR6lMa8SuIvR2ivsaiVbPlDJqeo63/shC1clP+r1zubX5lriPYJkHVD2q\n2EaqmIsayVdPwtF3OMlzIvccbuicqNPEPnFX50PcPtYRc0e3ZPnhp9WcSj71J+3erTo9RkImQydF\n2RWqANw/BJILCOgngotaQLWhL+u2nHOFh0VA1SJYwtlox9isnBE+ejwXNb2us65pZJVAGuWazhqx\npGtElthN9uvoOt6BXb11/aXNlbZDJu+WN8sGFSAS+RnTSuYJSk2bGWMwSG0gybc/5w6EHTCkCCSc\njR7UJIYtU6Lw9yVV3/An7zQ6et1MXN4Kq/r0TPxDrmTiTV1ItFla1gOcKOTbpnTj7owsMKcmSyr5\nEXnJGG6E218kjc3aCwd0zIHoAyIEEs5GI7agFZNytnd7ARwMTdYTc74lTMJNAgM4ehjazKi6XePJ\nQGwRq5in4SyqLmvHL/BAb5/OYbyxmSNC9gl99zbdZ3BQzeRbCAM97kLnCwS9kyG4qGWjB6lEC1bv\nd212GDMApITUeooYeteOvpMEYYy0tHzJj/r0tJbfTthHPCNtG8PzWAZ6cAk3ICCgCPTDRc0YcwGA\nUwH0wpleZ8AVNu6zsnwxqFjuiKlSln6VqhCdL1w5O7Q5ecidinC7dtRm7SuKjGnd0nDVXxpvLY3b\nGdJqY54kfi+ZmpnfmqRdjyQEi6wyCAEwOxnK9I4wxjQD+ByAOdbaTmPM7QA+Dlfnvc/K8sVgVHlD\nCxgoGHPxsJUPRhLC9zDC0V3kXy62wr0D1xtjagDUA1gD4GS4ivKQ9sPlDq1flrAxJgX3Ir7aWnuS\nMaYJRZjonahLnJjzrdx25Oq9fuKfbpl8S5qEiyxg7itU9toPddbwJQv6EutPRr14i8gRjzFd5uL8\n1xwk0KoLpFI68k0YBgxzlKkJW2s3GWP+E8Df4bTFB6y1DxljBqyyfH/liK8BWIo4iuF8FGGi6yi5\nJAnCrzXXnlQCqVcCOoRoNQnnyA8++e5QLmr+028McsFtNV6bdQ1pSdRGQvCiIqNaG/YjRAICAgYV\nZWrCxpi9AXwdzkfqLQC/McacqvsUqixfDMomYWPMDAAnAvh3AOfI5pMBHCPLv4Cre5yok5BMJ0eu\nXLm5I3xvCSAm3+5CVi2Rr093nmUgfmLqQ7nciL7BhPLUjVfRl9gPfQ4ICBgy5LN3VqeBN9KFjjwU\nwBPW2lYAMMbcBeCdANYZY3ZTleXLznfbH0v4RwDORXbEQlEmereqrKGRS7653hH9wrYClawp3Cd9\nWXVeS2s36e5xnjEi4b1kQSeNZ9XngQ9xLh0hCVE+BBliBCHfT3u3FvdH/CVHjnoZwHeNMWPhWOJ4\nAH8BsB1FVJYvBmWRsDHmQwA2WGufM8a0JPUpZKK/dtHteFssw8aWeZjYklT7LSAgYGdDOp1GOp0e\n+BOXrwk/b4y5BW7uqxfAXwFcDyfB9llZvhgYa0uXMowxlwD4NNzzZQycNXwXXPXlFmWiP2Kt3d87\n1r7H/j7KgpaU1nK9GNBJFnB7V/ZkXce2XH/jSBOmqxrDlJOsXX45Kuo5L+gvzOP1xNxaad+S9n5p\nf8cOj6vOy6Ilaz9bxIXLQ3ETc8ES1gjWb3XBGANrbYFX2KLOYfHpInnul/2/XqkoyxK21n4LwLcA\nwBhzDIBvWms/bYy5DEWY6C6LWnbGNECnp8zWgqkDa/jk2/226uNrwfl033LRKCfUGnGj3EqGNu/d\nz2uUgdK8IZK++qTEGcMDgTwDCqKKf9IDFazBx8ylKMJEb0d9Tl5gINdFLYl8iVSN5JAQwu3tUcf2\nNWmn58iKIWTP2aJ2vAvEYJFRANhc5xi5d4Ykks+5s1vV8u6w9gNFXHgw4OfzLOTqUSjazz+uMr/y\nQL4BRWEkhy1bax8F8Kgsb4ITrvtEoYTtzBEc5RwuQKq0gEelYhLorcnjF5z0af1Q5CQu6fH6CMaO\nUiQlWdhap47LPg+d99pmJ5y4EvB9r/W6X1U66abk+zUPXys6YCdAqKwR4MOYx+FeFgAd0mztOUnd\nAwIC+oMqtg0qljsiKQeE76LGkkXdylorKg9EPhTzRXBY2ojmU3RH9hjqo/wQQAfHyJJK20Xb/7J0\n+J994vOt1JN02TDmmuiM2W2M/r2CU0IpZLky3wXvu3al4zZaxCFBUcAwQCDhbORL1JPPP1gTrx+e\nXDM69z2jq8fbNkaO2ZEw6UlOKcQlfsTcDuY9jvXsaaPWAABax4gLBRPKnyAddDj0dyQPseXFeeKy\n/b3LQLfX6nH4yYaSRPSp3r4QBRhQxRjJmnB5F+1JDMTwtxWamCOoCSeRcQ7GiJWqyZh3IH9a4oSL\nuuNb2ydHmybWO3+1OdOXAgCW4QC3Y51HxkDsQXHfwbJA4Xi56sQKHbmBHf3LCeF/5aML7COSiNUn\napKynoDsT3WRwq5zYUIuoCQU44JaIQwLTbh+fPza37YlO6k7yVe7qHGSrpcb/Am6McpnsNuzjvn/\nXogDanJ9DjeKE3EjNgMAdpns2rc2Sk2lbTmHqPOJVNGtJAvc5w2kFEsz6Wv1nzJJ3hFj8+xLItE2\nb19SX5+YC30Gf3zJxB3IN6AsVPEL2rAg4YAAIuQBDigLQY7Ij0I5JEpBsjYsH2+Mm+BLTGlJq5YW\ncaEYBkL6btsYR2vUzXDvO6lR7pFbU+ONR3+kd0n7tLQsMvqy6mOZkkOn5gBimQLI/3hP0lYKuaYR\nDd4+tk2qz6Y8Lceiz+tLFjxPUka58n6KIfVkQFEILmr5kSrx7owVaSIpXNlHJEuQjH1iBGJCHuNJ\nDEm6sU/Yisxb14k+LOpDuz8+fWmS8EvSMvx5nOrzDEs8Z6T1yRiIJ/J8KcCXFXQfIim72+5e36Rr\n+jmZSLR8OGgS9j08dJ29fOejdFF+OHWwlgNyUMVyRFm5I/p1QWPs4fbRxCKe3OaHNBeaoPM14mKQ\n6OaWE+pchDWeQOqMppvU5FJ0rv1bs9uhM7hRH+YPg3km9CvTFSQupvokqadVJ5IaiYuEynuSFIhB\n+FavPs7vW8qspQbHl/HWtSXMsftkPLiFUQMxDw8MWO6IOUXy3LJhkjuiv9DWb0o9omqicvYeuaki\nTCTkHEsz6TpeaHN0Oh1dJ1byqLrOrPWikGRZC5hoaMasFQCADZumRPu6+OBYJ991i+x4BgqeNTtR\nXuU3z1F9mAjIJykSmJYRSHJ+KF8SwRYjERRDzOzDMZNwtaTCa3DMfBIlPRyCG1xAmQiacEBAdSFI\nFjsZgotaNurQGeWF0AEPtJDr5I4l16GT/BLy2k+LWFu7Kc9C9dez4E3odXmWMaCsZV9jVqgd01nw\nWlOa4kCMLTJRWNfsjmFujLee2S0+YCIHLxbse2X9tt3jPjmv9/5EmLaQM9I2e/uSgjX89YEyI2jd\n6jwalEWY9D5pXLy+P7EXLOOAIlHFP5GKWcK1Uj9OI185ey1fMJS5q9eRt0/GhVCQjHn+MQmPTN/z\nQtb1+fgQ4LYc7wiFRgnsYNjzqh1SjuNU9UvZIfeCEkWztAvUZNlzC70zk6zow1vML+/thGU/jDBJ\nemDfQmWbiiFvyjT+NVtVH19eKRRZWA3VSgKqDkGOyMYE9Y+i3dFSHmnQIk4Kc/bJOAkkxFRNt7c9\nXu/plig9j0Q18u1LJGxBVoY15H42jeaGDADg0IZYFP7lqZ9zCy2yYZ20ixCDAXsbD0Y2MtLqr5fk\nTStUR+f58Mm3kLVczK87KQIm374JCX2pZ3NcfOC+7bUA8Lq3rTgTiPJEkCVGKIKLWn7oJDgEvSRi\nes3NOUzrODXKtRMaYmJvb3f/pD75JoF9/Mm7JDL2J/q0tctx+C53hciXUgyrSusH0px9nwMALBsv\n4c/jEsKfiUVClpbEQ/e2NxI6vygtZQ39sCiGsPx0l/66Rre3r5SkP0lyhH++JOzu9eE9qOL30YDB\nRxV//RUn4YCAakII/hihGGkkbIzZA8AtcIKeBXC9tfZqY0wTgNsB7AWprGGt3eIf34gtkcTQgVwt\nt14spS7klxo4aZeK3Nr0xFz2HS+kz0Z9RFtOSiDP8xU6j69x0wKuS9S+WVXEfYZ2+RqewaHqeHct\nSh5d02Rc71Jf2WZp+bZ+Z4ss0BUsSTulhcg8Fa+pfflkiKSfST6LeGtCX9/yTfqP8K3bJAnE71OM\nFELLWGvF+dOEJiFIFSMA/dSEjTEpuBma1dbak4rlumJQriX8NoBvWGuXGGPGA3jWGPMQgDMAPGSt\nvcwYcx6A8+UvC43YEr2K1ySINb5XhH6lTwmBFfKgyEeWlAyyz+2ReBF3xNd7gVhW4Xl8Us7Wvild\nuM/F4JQ2TMjpwwjBrqSgFHL2TGnJg/eL/rt5rupMn2LfPzgpKo7w/XOB/GTJPnqibqvXJ985/Gvk\n60MUmgz0+yT5Hdd4bfCyGPHo/1f7NQBLEf9TnI8iuK4YlFvocx1kqshau80YswzO5DgZwDHS7Rdw\n4V05A0uhBxMS0or5E3Bxkvf4H6gUzTXq4+m1BUOlR+Vu8gmVhKvPM8Gbla+Vh0RSys52z9uA1rIm\nYfZneHat1LPr2qZIk9WjM9Jyzu1D0v5eRaFtJgn73gNJYcYE720pRfmSLGH/2ELlkgpNqPmacn89\nM4ikpNKBkAMcjDEzAJwI4N8BsPRNUVxXDPqtCRtjmgEsALAYwFRrLZ1V1yM3FhUAMBvLsVJ8rjoU\nCZCMKFHUJnhY+wRGS1pb1HWjOr1jsiWPjt74HLRqCxG0T7q0wnVff4LRJ2VtsbMvx9WKSXmv3djk\n3nDatgpBj1E7uUyf4u3SNkt7oOr7GN3ZODGXkVZH1RWT4T5fnyTS8ifSCk2slTL55h9TCIXINJ+k\n0tdxATsZfgTgXGS/NhbFdcWgXyQsUsRvAXzNWttmTBxyba21xpjEgO0nL3oYm+Sfv6nlQOzV0gwA\nWCqJ0Gl5ppA/f0MhvdiHL1mMHxUTpO+ZkETGJFRuS7KEJ2Nj1jXGerq27sttzEHMfVvQCB+0jukP\n3VGj+tTI/WbVDiYAekvaD6sTPRZ/GgexWE1z3MfmI56krGdEf4nQRyEvi3wo9WfsE30xlnXAUCCd\nTiOdTg/lFZGdjyUbxpgPAdhgrX3OGNOS1KcQ1xWDsknYGDMajoB/aa29WzavN8bsZq1dZ4yZhjxe\n9adeNBMrsDcAYA2mlzuEgICAEYaWlha0tLRE6xdf3J8qMhr5jIWF8hdd0e9wFICTjTEnwr17Nhhj\nfokiua4YlOsdYQDcCGCptfYqteseAJ8B8ENp7044HG2YkKV/EpMkSoqWJvtoPbXGe+2vTfCx9QuG\n+p4KpWi5ug/PMxG5k6CNso3j8ScO9fj4uWZLwATXV2GPqI9vvfeMKpDVjbIEbyktYW2cv1OsvCdn\nyAY/xBmIpQl+duq7WgLK5+tbTHBEIau51Og+INly9c/jV/UodG3/2LGI70Vb8JIY1ihPXrLWfgvA\ntwDAGHMMgG9aaz9tjLkMRXBdMSjXEl4I4FQALxhjnpNtFwC4FMAdxpizIG4bSQe3YQIOkXjcLZGg\nCWyQEFbqxbXe6zqQq+8muagRfuHQQlquT54aPlFTatDXHCvnqff+2Ummk1QYrn8+Yl+8Ei1vEImJ\n5ZLWyhtDV3Msw7Ru03kkFJIKKR8p7ZMkVCEn/RLFr2KHSF/RR9GTbT7x+Z+lGHkiiTwLkWRfP1M9\nBv/65YYxl5u+M6A6MWBxy/yPKYrrikG53hGLkOhHAAA4vq/je5DCZCGlyYqcDoArkrm3WIS0EBfj\n8KgPyYgE6+ceBnKtY1quSeHPJE1auZzgG6tIwd+XPHnXIW171mch4eqHB5c5vsU4AgDQqKx8Tk5O\nks/Cz7lxVFxctHaGI8euLUKaNNBpGWsHlEPhgXMM6se5t5Ajv5KVo72+QELBPe88hTwpuG9Kwjb2\nLRTa7K8nucDlWuS+5VpcoVR/PCGuaXij/zmprbWPAnhUljehCK4rBhX5Zb2IudFr/2ysiLbzlX6u\nzODvvsqxwYI9nov6vCAhuS9iHoB4MktbsL4EQNKkF4JGPAmYX7IgeI0pIv8kTQ7S4p2GNTKG7DDr\npHMfgcUAsoma51mDaTJO1+6BVfGBoh6sniwkSZc1tpqbOHn3bmkfa84ZO56Vli5uGT8cOgm+xagt\nTxKsT6Ka1Ok6x/DipMTyvL5fmSPZyh0YucD3fw7eEsMb1ZvBJzzeAwJKhLakgz48XFC9D9GKW8Jd\nagKKlmUzfVjF8Jkd5ZoFpkx3VujklLMUGXnXivg1nZYmpQB6YtDHWPsUU8vltvFyjB7XVG8Saz6W\nAIgtdyA3Yo6gdautZvZt9Cb4JqmZNLq80dLnZ8mOqnM/rFGTnINw70bxUaMcMR4x+Bv8gbQfl3aH\n6kPvNyZYY7Kg+7Tl2teEXFJtOP9nllHL+XIga6s313fY2m9jaFC9/7wBpSBYwlnQ3hH0DQZi8qSc\n0HaQ66PJqrHHLU+X1/31MoE1VTRYIHeyja/wvNZYpb3OFEJgn3eLQ61ONj95q3M32NiwCwBghYSm\nab9eEmh9jyORNalp0WcFsifjfH9j7tMBHxNkXBwzXfmW9cb3i/7OvW8J+ZIveLu0dwSJdb60P5L2\ngYQ+1JL57NOOLG2UH3wNmJ2SinmyT9KMoT9JRxIePKmB5yhOGw4YGajeh2lFSPi59fMxYar7JztA\nkSej50g4JDYSLwB0pRw5tntRddlhwfWJfT6LGwBka7J+Dguur1X+y9saHMFM63TEP7XOWW8blfX9\nKvYDAMxNOT2bXh+0gDXB+i54XN+7J9bH21JuHx8ynFxs/U3sEdH9EUkyzCrQDN6gdasf/ryFv0c2\ntCccfw08/k1ps4xfP9eEb6UmhUEX+gfwSd0fjMNgWL7lknGQIIYjgiWchd7fjsNzn3UmWao2JkRO\nRtEq5aQWrcqsbUKSSdnKogxkHkEneUf4CYRo5erXfpL5+jpHiHuLyah9eXkNn1j5YEma6Ov2vDc0\neBzvxQN4v9uhOO6tRVIOqVlIWMsPblAxSKzkxS0JffxhcF/BaF6fRDVj54tAS/qHYN+hnwjri4wD\n6Y4EDHzF7oFCmJgLGBYw5t8BDKUWHDCyEOSIbDwCvDXDWXGvnLxftDlfjobmrImcbNAy1iEWdZ5v\nbY8X0NGTYJX6E2p6Ms6P7qNUoS1hTqRx8q+5x415Qqot8fxAbAFT857wVmzNd0niHkoyCyCVNg6c\nH59gNeUHaanl0iLWfsKcrKPU2uOtA/HvNDfBXQK8oI/Ikk2SIwpFujV4fX2NWe8LuX0DykWQI7Lx\nBqJJn9V/jCvvTjjRMQJJyU9+DsQTcr4PsA4zJiEnlQ1KWk9CUlg1r5W0z5c6WlPOJzmpoCl14ijU\nudONuG0XVdCpxx23Z8rJEZQl9poVR9W9vm1/t0CpgXICOSvp2/XztWs+ZBY2fpSCufD9QI5CKSyL\nyc7mB0UUTuTTFxmXUyEjEPtIRrCEs7EEceHKb8abM1ubAQB1DY6UaFVq9y4GXDR6VqSe+KJO7E+6\nFcwj7CEpCXtSRrS4P4MyunOO98fHoI8JW/0Q5/iHUtPTCwBYU++sbpK5jjDcdqALcGh9WSbrdJpL\nIJnzeIk6bx3InZgrKqskyZiBJkk/dprbSQPyT57UhySe6zFRyqRa8O/dmREs4Wx0bAVWyj/b/8Rp\nODvGO4+CVae6sGW6YGnLcz+8CqAwofqVKwhO/GlST7Jq853fT3Op8x3H53Stnx8i1aPyVXQmW4Qk\nXgBoq3eaAi3gV8T7QpP7pFHu87SOl3tYI19noQr1hZ0QZLDSJvEps5XmJO4r9FPy0mcmDqwYS4Xn\nCWXtA0pFsIQDAqoKQVve2RAsYQ/rASOFJrVb1TWuaa1xr9eLjnSv4vNnLY668HWcE2eFEqFzssyX\nLrQ1yW08Tz7LuFREaTnbJUFQiQ9iP+qPWjhzUgBAhsXldsjXSDmCrbaI/XkzqhpJ42Lf3IRyCcjn\n56tPRFA/3pTQp5SfYlLQR0BAIQQXNQ+vA1ZeoV9Wzv8rpf2OtAe6d98lC4+Muiz9nIsYa2lKAwD2\nk/SPSZUr6Gu7WQiWxHjAJhUUwckw4eWNojlr/2FOuvnack/C7WOfiW/Kly76avcuOV1z0FkXyyR8\nUDBZEH2TtU9xqxD0qF0kbHmdFzmnOcrPrMbnkP4Ift3LQsiRJUimulwSyZYEzUAT/aBj4h7fO6Lc\nQp+BmAOSECzhLFibnAHOmLRbWCkeEyuFqO+N/+m6HnWk/eBPTgIArJrl9OPpykKkS1uzsPpmz1pu\nbZqU23fTWgDA8qa9c8bFoqQkd2rBSZWioxBrSaxuhZOSLOGemux9PancycCOaHLSzaTNjWrExUEk\nvT1H5J7cDTCGT8yFrN3i5y8VGdMi1hdt8joXsnr9WcBCOYILIVjJAUmo3t/DgJOwMeYEAFfB2Vo3\nWGt/WOyx1rbkOeeyeOU+saJOdRUils1f4NovL4i6TNrfWVdTRjkrci5eyDqfDklmWHB7k+vD1JpJ\nbmyUKkjcOvUkPR7oWtY9JfvYHnWnSbqddS4lc8+43K+h00uT6Yc66221YyQZUk12NY5EK7cYDwr2\n2ZGwz78tOYStf+x+HglebGvCNn82UQ8+Hwn7UXbFI2jBOxuq1xLOl5i9LBhjUgB+Apd/6wAAnzDG\nzCn9PNfJ310w5q6BHGJZWJxOYqMqx6vpSo+gRDxf6QGUjKEtSDkwGI5jHhh0F/k39BhoS/hwAMut\ntRkAMMb8D4B/QJy5u0z4hx8OPJlxiyzX86vY1a31Q057bP24axll9uIsl4KS0WdArBMzSfxHN92b\nc/VbHgA+djBQI4qHFSs3tUtC4vdOCRCRO0udN9Udf8HdYk321OS//ZQ64jp5bl1nlNtX3PW6dogF\nrH9Dr6WBI1ri9ZTXJk3eUS/2jQbdx89OGUXXdXitXvbzSug+GWkfA/7/9s4uxKoqiuO/P6aSI2mZ\n9OEMKKFQUuIQZGEEJTVFaPSSfWEJvVRkPViNvfQeUb34UioiKIGJjOCD04cQRWU0hjlOKkhlMtOH\nThaGH8zqYe/TPffOveOMnu7ZY+sHlzlnn3M5/3vn7HX3WWuvtZnB6GpHjGVaW4WiR7+7d++uWpRy\nPDAeNRdDuiPhoo3wLMgv/cBRoIHDciQyg9oe/+YfXyuLUZq1VkbKf1aWQGJLdBN8Ec+NAb4DrdF1\nsbjiuuh5LGx/FldcPRON06Tc4FdD0YXQQhVXHB+eDXe6pfr9kwfCOZYLzJ1qqf8AkndBZD7gBl9Z\nBwAABM9JREFUv2sqwuWTPhrO5BgkuBL6c20zc8egfmCu1g1Rzzdcm9iWudtP1Mt0q50XPJLb4CSV\nIF0tjY2tuxWc0fH/8QkPm8J/YWSdMRuC5QJ5qo6Mmz0cmvV+rjUa8SywdyRG5RfWrMUGHBgMRrj3\n2TDrom1K+A2ZMaFSjFfnhtDpygyHUy3DR7ctJ0OiRWZ8laUAx2847xPOzj0xs9rBWq/AfWZos7/5\nNOh8LWaAXO37wNW57d9qjmVfbW3lNRjuNx6plkT2Ea6M/5fByhMJls18yepwZEY5vzr48M7hRXqc\n4kl3iprMCrKbgKRFwOtm1hH3O4GhfHBOUnEXdBznksfMdP6zGjNWm3Ox1xsrRRvhy4DvgXuAY8BX\nwKNmdpE+YcdxnEuTQt0RZnZO0vOERXMmAOvcADuO4zSm0JGw4ziOMzYKnSd8PiR1SOqTdEjSK828\n9miQ1CbpE0n7JX0n6YXYfpWkbkkHJe2SNLxgRclImiCpR9KOuJ+0ZknTJW2VdEBSr6TbUtYsqTPe\nF/skbZY0OTW9ktZLGpC0L9fWUGP8TIdin7w3Ic1vxPviW0nbJE3LHStdc9E0zQgXlcjxH3MWeMnM\n5gOLgOeixleBbjObB3wU91NjFdBLZYZK6prfAXaa2Y3ALUAfiWqWNBt4Bmg3s5sJrrblpKd3A6F/\n5amrUdJNwCOEvtgBrJXU1EFZpJ7mXcB8M1sAHAQ6ISnNhdLMD/BvIoeZnQWyRI5kMLN+M9sbt/8i\nZInMApYCG+NpG4GHylFYH0mtwAPAe1SqOSSrOY5s7jSz9RBiCWb2B+lqPkn4gZ4Sg89TCIHnpPSa\n2afAiZrmRhqXAVvM7GxMrjpM6KNNpZ5mM+s2s6y49pdAa9xOQnPRNNMI10vkmNXg3NKJo5+FhJvg\nGjPLJrsOUMkmSYW3gNXAUK4tZc1zgF8lbZD0jaR3JbWQqGYzOw68CfxIML6DZtZNonpraKTxeqpn\nlqfaH1cCO+P2eNE8JppphMdNBFDSVOADYJWZVS3jYCGSmcxnkfQg8IuZ9VAZBVeRmmbCrJx2YK2Z\ntRNWt6t6lE9Js6QbgBeB2QRDMFXSE/lzUtLbiFFoTEq/pNeAM2a2eYTTktJ8ITTTCP8MtOX22xie\n41U6kiYSDPAmM9semwckXRuPX0d1ylfZ3AEslXQE2ALcLWkTaWs+Chw1sz1xfyvBKPcnqvlW4HMz\n+93MzgHbgNtJV2+eRvdBbX9spXHeeNOR9BTBxfZ4rjlpzRdKM43w18BcSbMlTSI42LuaeP3zIknA\nOqDXzN7OHeoCVsTtFcD22veWhZmtMbM2M5tDCBZ9bGZPkrbmfuAnSfNi0xJgP7CDNDX3AYskXR7v\nkSWEIGiqevM0ug+6gOWSJkmaA8wlJFeVTiyHuxpYZmb5EobJar4ozKxpL+B+QkbdYaCzmdcepb7F\nBL/qXqAnvjoIVYM+JERqdwHTy9baQP9dQFfcTlozsADYQ6hhuQ2YlrJm4GXCD8U+QoBrYmp6CU9C\nx4AzhPjL0yNpBNbEvtgH3JeI5pXAIeCHXB9cm5Lmol+erOE4jlMi436OneM4znjGjbDjOE6JuBF2\nHMcpETfCjuM4JeJG2HEcp0TcCDuO45SIG2HHcZwScSPsOI5TIv8AhTTApGZX4IQAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f746e2ea510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in new bathymetry\n",
    "new_path = '/ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea6.nc'\n",
    "Fraser = nc.Dataset(new_path, 'r')\n",
    "bathy = Fraser.variables['Bathymetry'][:]\n",
    "print(bathy.shape)\n",
    "plt.pcolormesh(bathy[350: 520, 280 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "floor = np.empty_like(depths)\n",
    "ceil = np.empty_like(depths)\n",
    "ceil[0] = 0.\n",
    "floor[0] = 2*depths[0]\n",
    "for k in range(1,40):\n",
    "    ceil[k] = floor[k-1]\n",
    "    floor[k] = 2*depths[k] -floor[k-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "S = np.empty_like(old_S)\n",
    "T = np.empty_like(old_T)\n",
    "#for every cell with top of cell depth < bathymetry, use old TS, if old TS=0, find closest point and use that.\n",
    "for k in range(40):\n",
    "    for j in range(398):\n",
    "        for i in range(898):\n",
    "            if ceil[k] < bathy[i,j]:\n",
    "                if old_S[k,i,j] <> 0:\n",
    "                    S[k,i,j] = old_S[k, i, j]\n",
    "                    T[k,i,j] = old_T[k, i, j]\n",
    "                else:\n",
    "                    # closest neighbour thing\n",
    "                    masked_array = np.ma.array(old_S[k], mask = old_S[k] == 0)\n",
    "                    X, Y = tidetools.find_closest_model_point(lon[i,j], lat[i,j], lon, lat, masked_array)\n",
    "                    S[k, i, j] = old_S[k, X, Y]\n",
    "                    T[k, i, j] = old_T[k, X, Y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Salinity Temperature Initial Conditions based on Nowcast Sep 24, 2014 for north-extended but not     to Howe Sound, and widen at beginning Fraser river channel\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: REQUIRED\n",
      "references: REQUIRED\n",
      "history: [2015-10-05 14:34:52] Created netCDF4 zlib=True dataset.\n",
      "comment: Salinity and Temperature conditions from nowcast Sep 24, 2014  onto north extended Fraser bathymetry\n",
      "<type 'netCDF4._netCDF4.Dimension'>: name = 'y', size = 898\n",
      "\n",
      "<type 'netCDF4._netCDF4.Dimension'>: name = 'x', size = 398\n",
      "\n",
      "<type 'netCDF4._netCDF4.Dimension'>: name = 'deptht', size = 40\n",
      "\n",
      "<type 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# build nc file\n",
    "new_TS = nc.Dataset('TSnorlessSep.nc', 'w')\n",
    "nc_tools.init_dataset_attrs(\n",
    "    new_TS, \n",
    "    title='Salinity Temperature Initial Conditions based on Nowcast Sep 24, 2014 for north-extended but not \\\n",
    "    to Howe Sound, and widen at beginning Fraser river channel', \n",
    "    notebook_name='Smooth bathymetry & Create New TS file', \n",
    "    nc_filepath='/ocean/jieliu/research/meopar/nemo-forcing/initial_strat/TSnorless6.nc',\n",
    "    comment='Salinity and Temperature conditions from nowcast Sep 24, 2014  onto north extended Fraser bathymetry')\n",
    "new_TS.createDimension('y', 898)\n",
    "new_TS.createDimension('x', 398)\n",
    "new_TS.createDimension('deptht',size = len(depths))\n",
    "new_TS.createDimension('time_counter', None)\n",
    "nc_tools.show_dimensions(new_TS)\n",
    "# show variables\n",
    "nav_lat = new_TS.createVariable('nav_lat', 'float32', ('y','x'))\n",
    "nav_lat.long_name = 'Latitude'\n",
    "nav_lat.units = 'degrees_north'\n",
    "nav_lat = lat\n",
    "nav_lon = new_TS.createVariable('nav_lon', 'float32', ('y','x'))\n",
    "nav_lon.long_name = 'Longitude'\n",
    "nav_lon.units = 'degrees_east'\n",
    "nav_lon = lon\n",
    "deptht = new_TS.createVariable('deptht', 'float32', ('deptht'))\n",
    "deptht.long_name = 'Depth'\n",
    "deptht.units = 'm'\n",
    "deptht.positive = 'down'\n",
    "deptht.valid_range = np.array((4., 428.))##minimum depth 4m\n",
    "deptht = depths\n",
    "time_counter = new_TS.createVariable('time_counter', 'float32', ('time_counter'))\n",
    "time_counter.units = 'seconds since 2015-06-14 0:00:00'\n",
    "time_counter.long_name = 'Time axis'\n",
    "vosaline = new_TS.createVariable('vosaline', 'float32', \n",
    "                               ('time_counter','deptht','y','x'))\n",
    "vosaline.units = 'none'\n",
    "vosaline.long_name = 'Practical Salinity'  \n",
    "vosaline.coordinates = 'nav_lon nav_lat deptht time_counter'\n",
    "vosaline.grid = 'SalishSea6, north extended Fraser'\n",
    "vosaline[0] = S\n",
    "votemper = new_TS.createVariable('votemper', 'float32', \n",
    "                               ('time_counter','deptht','y','x'))\n",
    "votemper.units = 'degC'\n",
    "votemper.long_name = 'Temperature' \n",
    "votemper.coordinates = 'nav_lon nav_lat deptht time_counter'\n",
    "votemper[0] = T\n",
    "new_TS.history = \"\"\"[2014-09-24] Created\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f743d702c20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JUZTG8enNKxaKh+FTW/oOB7QuiVHiaH8bwF8S0UoA/w9FeNcKANcR0btRhncN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\n0mZgof6kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7450bd6790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## temperature and I don't modify it further\n",
    "plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f743d564830>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DAXjIbX0b/62WWvW1FFVIKJXIZn/BggUf0uVt+n97frutn6v63GDeRIZhFDPW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\nMIaMCa1hGMaQ+f/M9RpK3L7IXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f743d846c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## salinity before and I need to modify it further\n",
    "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## set salinity after New Westminster as 1,before as 0,river source cell as 0\n",
    "k = 0 \n",
    "i = 418\n",
    "j = 365\n",
    "vosaline[0,  :, i-2, j-30: j-13] = 0.#16,335:351)(416 ,358:360)\n",
    "vosaline[0,  :, i -2, j -7: j -4] = 0.\n",
    "vosaline[0,  :, i -1,j -14:j -1] = 0.#(417, 351:363)..  ..\n",
    "vosaline[0,  :, i - 4, j - 18: j - 15] = 0.## for (414, 347:349)( 414, 355:357).. ..\n",
    "vosaline[0,  :, i - 4, j - 10: j - 7] = 0.\n",
    "vosaline[0,  :, i - 5,j - 16:j- 9]=0.## for (413, 349:355)\n",
    "vosaline[0,  :, i - 3, j  - 19:j-17] =0.#(415, 346, 347, 357, 358).. ..\n",
    "vosaline[0,  :, i - 3, j  - 8: j - 6] = 0.\n",
    "vosaline[0,  :, i , j -5 : j +1] = 0.#for(418,360:365) .. ..New Westminster\n",
    "## plus north ones(all depth):\n",
    "vosaline[0,  : , i: i+ 83 ,  j]  = 0. ## for (418-500, 365).. ..\n",
    "vosaline[0,  : ,  i + 82,  j : j + 30] = 0. ## for (500, 365-394)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## make salinity(500, 395)==0(source) for all depth\n",
    "vosaline[0,  : ,  500,  395] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## make original salinity of freshwater source point as 4\n",
    "vosaline[0, : , 414:417, 334] = 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## modify damp salinity values but not for the widen part\n",
    "k = 0; i = 425; j = 302; d = 1.\n",
    "vosaline[0, k: k +4, i, j+1] = d\n",
    "vosaline[0, k: k +4, i-1, j:j+3] = d\n",
    "vosaline[0, k: k +4, i-2, j+1:j+5] = d\n",
    "vosaline[0, k: k +4, i-3, j+3:j+7] = d\n",
    "vosaline[0, k: k +4, i-4, j+5:j+9] = d\n",
    "vosaline[0, k: k +4, i-5, j+7:j+11] = d\n",
    "vosaline[0, k: k +4, i-6, j+9:j+13] = d\n",
    "vosaline[0, k: k +4, i-7, j+11:j+14] = d\n",
    "vosaline[0, k: k +4, i-8, j+12:j+16] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## modify salinity values of straight channel connect damp and further extended channel\n",
    "k = 0; i = 416; j = 317; d= 1.\n",
    "vosaline[0, k : k +4, i, j-1:j+18] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f743d459560>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WcIWWAIrNAaEmmZePNqTOopqnjFBNcbL8OtB6X/FnE96EFiKTi3/f823afb6fcNynM5mM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\n2O3c8HW8eBUnAgf25Iy3Jtz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5Gm2R6aDIHSvv+sv5D+uXcPYCt6h6kT/mBa8JXKO/rHaN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f743d5f77d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## salinity after\n",
    "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7f743d1649e0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yPq57y9RH9M7wpPKz6GOnDfXOia7rxf+OTtnv4ghrzLPICjU63dGn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f743d531590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
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