{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is to decide $S_A = S_{ref}$ is a good approximation along our open boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "\n",
    "import os\n",
    "import subprocess as sp\n",
    "\n",
    "import sys\n",
    "sys.path.append('/data/nsoontie/MEOPAR/tools/I_ForcingFiles/OBC/')\n",
    "import gsw_calls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to check if, along our open boundary, $\\delta S ~=0$  by the gsw standards.  Right now, we are using reference salinity.\n",
    "\n",
    "Recall, $S_A = S_{ref} + \\delta S$\n",
    "\n",
    "The TEOS-10 primer says that in coastal areas where $\\delta S$ is unknown, it is appopriate to use $\\delta S=0$. That was also suggested to me in an email from Rich.\n",
    "\n",
    "**Note**: Matlab wrappers are linked in this directory. They are under version control in tools/I_ForcingFiles/OBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/open_boundaries/west/SalishSea2_Masson_corrected.nc')\n",
    "sal_pract = f.variables['vosaline'][:]\n",
    "temp_pot = f.variables['votemper'][:]\n",
    "dep = np.expand_dims(np.expand_dims(np.expand_dims(f.variables['deptht'][:],axis=0),axis=2),axis=3) \\\n",
    "         + np.zeros(sal_pract.shape)\n",
    "long = f.variables['nav_lon'][:] + np.zeros(sal_pract.shape)\n",
    "lat = f.variables['nav_lat'][:] + np.zeros(sal_pract.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/open_boundaries/west/SalishSea_west_TEOS10.nc')\n",
    "sal_ref = f.variables['vosaline'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = gsw_calls.call_p_from_z(-dep, lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sal_abs = gsw_calls.call_SA_from_SP(sal_pract, p, long, lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Absolute Salinity vs Practical Salinity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff2dd22da20>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7+vmh0OBNDrVw4cJXp2u1GrVarUHpmJkdGTo7O+ns7Cx1G4WeZyXpm8A/pPlPcPAzrg7H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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2dd1fc5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dS = sal_abs-sal_ref\n",
    "plt.hist(dS.flatten())\n",
    "plt.xlabel('$\\delta S$')\n",
    "plt.ylabel('number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff2da8b20f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2da8a75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(dS.flatten())\n",
    "plt.ylabel('$\\delta S$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is probably not very significant. We've decided to use $\\delta S = 0$. \n",
    "\n",
    "# Conservative Temperature vs Potential Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "CT = gsw_calls.call_CT_from_PT(sal_abs, temp_pot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff2da880160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4H++2LzMzayJFBiacABwXEfW68lbL/e7VVbrW1tbdy5VKhUqlUqN0zMyGhmq1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2da7374e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff=CT-temp_pot\n",
    "plt.hist(diff.flatten())\n",
    "plt.ylabel(('Conservative - Potential, deg C'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the differences aren't super big for boundary.\n",
    "\n",
    "# Matlab Reference Salinity vs ours\n",
    "\n",
    "Note: This comparison wes performed before the boundary files were overwritten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def call_SR_from_SP(SP):\n",
    "    \n",
    "    fname =\"'SRout'\"\n",
    "    SPfile= \"'SPfile'\"\n",
    "    for f, var in zip([SPfile,],\n",
    "                       [SP,]):\n",
    "        np.savetxt(f[1:-1],var.flatten(), delimiter=',')\n",
    "    shape = SP.shape\n",
    "    \n",
    "    functioncall = 'mw_gsw_SR_from_SP({},{});exit'.format(fname, SPfile)\n",
    "    cmd = [\"matlab\", \"-nodesktop\", \"-nodisplay\", \"-r\", functioncall]\n",
    "    sp.run(cmd)\n",
    "    SR = np.loadtxt(fname[1:-1], delimiter=',')\n",
    "    \n",
    "    for f in [fname, SPfile]:\n",
    "        os.remove(f[1:-1])\n",
    "    \n",
    "    return SR.reshape(shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7864b9c898>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MOZwVKy4CDq+kvbFjj2D9+jtp7mW+/sjtuT23N4TtVfX7TBIRUeoDbPsdQbVKIjIzs+Gl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\nmVktOUGZmVktOUGZmVkt/S8XfMeFqKjpQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7864bba198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sal_ref_matlab = call_SR_from_SP(sal_pract)\n",
    "diff = sal_ref_matlab - sal_ref\n",
    "plt.hist(diff.flatten())\n",
    "plt.ylabel('MAtlab ref Salinity - ours, g/kg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}