{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing equation(3.68) for SalishSeaLake as we vary different paramters (eg h, q, L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "from salishsea_tools import (nc_tools, viz_tools, gsw_calls, visualisations)\n",
    "import numpy.ma as ma\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mesh_mask = nc.Dataset('/home/vdo/MEOPAR/NEMO-forcing/grid/lake_meshmask201702.nc')\n",
    "lakebathy = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/lake_bathy201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tau = 0.2\n",
    "rho = 1022\n",
    "rhoprime = 1024\n",
    "h = 30\n",
    "hprime = 200 - h\n",
    "epsilon = (rhoprime - rho) / rhoprime\n",
    "H = h + hprime\n",
    "g = 9.8\n",
    "c1 = np.sqrt( g * H)\n",
    "c2 = np.sqrt( epsilon * g * h * hprime / (h + hprime))\n",
    "ustar = np.sqrt(tau / rho)\n",
    "f = 1e-4 \n",
    "R1 = c1 / f\n",
    "R2 = c2 / f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "delta = (np.square(ustar) * t / c1 * \n",
    "         (np.exp(x / R1) + np.sqrt( epsilon * np.square(H) / (h * hprime)) * np.exp(x / R2) )) \n",
    "deltaprime = -(np.square(ustar) * t * hprime / (c2 * (h + hprime) ) *\n",
    "               (np.exp(x / R2) + np.sqrt(epsilon*hprime/h)*np.exp(x/R2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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M4KBg2sa05a0eb3H6ydPM6jGL2HKxjFk/hpoTa5LyZgqzds7i/KXzTpcq4ggFvgSk4qHF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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1884b5160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-10000,0)\n",
    "delta = (np.square(ustar)  / c1 * \n",
    "         (np.exp(x / R1) + np.sqrt( epsilon * np.square(H) / (h * hprime)) * np.exp(x / R2) )) * 86400 \n",
    "deltaprime = -(np.square(ustar)  * hprime / (c2 * (h + hprime) ) * \n",
    "               (np.exp(x / R2) + np.sqrt(epsilon*hprime/h)*np.exp(x/R1))) *86400\n",
    "plt.plot(x,delta,'r-', x, deltaprime, 'g-')\n",
    "plt.grid('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "442718.87242357316"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6986.314747418699"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.850409226740055"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# January with vtau = 0.2, using max and min densities\n",
    "Q0 = Jan_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = Jan_den.max() - Jan_den.min()\n",
    "h1 = 76.58558654785156\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(14.2) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "deltae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.437888536719055"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# June with vtau = 0.2, using max and min densities\n",
    "Q0 = Jun_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = Jun_den.max() - Jun_den.min()\n",
    "h1 = 76.58558654785156\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(14.2) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "deltae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.426719354770988"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Jan with vtau = 0.4, using max and min densities\n",
    "Q0 = Jan_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = Jan_den.max() - Jan_den.min()\n",
    "h1 = 76.58558654785156\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "deltae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.754787813368489"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# June with vtau = 0.4, using max and min densities \n",
    "Q0 = Jun_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = Jun_den.max() - Jun_den.min()\n",
    "h1 = 76.58558654785156\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "deltae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe185a45080>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+w+tYxpgqsIJez43olsl7twwkPT6K6174hj9PWcrRwiKvYxljToEVdEOLtFje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\nGBMkrKAbY0yQ+H+qKD6femujpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe185a8a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q0 = Jun_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = np.linspace(5,20,100)\n",
    "h1 = 40\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "plt.plot(deltaq, deltae)\n",
    "plt.grid('on')\n",
    "plt.title('Varying delta Q from 5 to 20, h1 = 40, L = 200km, June')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe185aa09e8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mHaNe/JY9B456HcsYEwCs2AeZsDDnnvgPXNSVGat2csGT01llV+oYE/Ks2Aep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\njTEmBFixN8aYEGDF3hhjQsD/A+xT66fmt1AZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe185cd5278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q0 = Jan_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = np.linspace(5,20,100)\n",
    "h1 = 40\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "plt.plot(deltaq, deltae)\n",
    "plt.grid('on')\n",
    "plt.title('Varying delta Q from 5 to 20, h1 = 40, L = 200km, January')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe1883b1cf8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j3DGz+XTJdv1/t0p5ES32qkoEBAjD01P45s6+tIyrxz1TljFifJb+EEspL6HF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\n2CullB/4f6ouQkjcfTqqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1859a3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q0 = Jun_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = 12.5\n",
    "h1 = np.linspace(20,60,100)\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "plt.plot(h1, deltae)\n",
    "plt.grid('on')\n",
    "plt.title('Varying h1 from 20 to 60, delta Q = 12.5, L = 200km, June')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe18839ae80>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Ou+9KYn0ad2AE1tncGKbBvRqmUFqneq7aq2+yUVEqkiyf5Ncv7YN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\nmYLAy3gXmtAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe185c81550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q0 = Jan_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = 12.5\n",
    "h1 = np.linspace(20,60,100)\n",
    "L = 200000\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "plt.plot(h1, deltae)\n",
    "plt.grid('on')\n",
    "plt.title('Varying h1 from 20 to 60, delta Q = 12.5, L = 200km, January')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe1858e2d68>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+38zD762kpFS5e3Q/rjoxyQZIM2HFir4xAVi/K5fJc5fy/ca9nNwzgYcuHEjn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\nTJPMgFnPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe18591e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q0 = Jun_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = 12.5\n",
    "h1 = 40\n",
    "L = np.linspace(150000,250000,1000)\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "plt.plot(L, deltae)\n",
    "plt.grid('on')\n",
    "plt.title('Varying L from 150K to 250K, delta Q = 12.5, h1 = 40, June')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe185880c18>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9vWY342dnsHnvIa45qQPjzu9JQ+uQz0SgYB+BfRNIARJEZAswQVVfBq6knKYm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe185a669e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q0 = Jan_den.max()\n",
    "C =  10**(-3)\n",
    "Qa = 1\n",
    "g = 9.8\n",
    "deltaq = 12.5\n",
    "h1 = 40\n",
    "L = np.linspace(150000,250000,1000)\n",
    "\n",
    "k = 1\n",
    "gprime = deltaq * g / Q0\n",
    "ustar = np.sqrt(np.square(20) * Qa * C / Q0) \n",
    "W = gprime * np.square(h1) / (np.square(ustar) * L)\n",
    "deltae = k*h1/W \n",
    "plt.plot(L, deltae)\n",
    "plt.grid('on')\n",
    "plt.title('Varying L from 150K to 250K, delta Q = 12.5, h1 = 40, January')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "June = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/initial_strat/summer2016_201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    coordinates: time_counter\n",
       "    dimensions(sizes): deptht(40), y(898), x(398)\n",
       "    variables(dimensions): float64 \u001b[4mvosaline\u001b[0m(deptht,y,x), float64 \u001b[4mvotemper\u001b[0m(deptht,y,x), float32 \u001b[4mdeptht\u001b[0m(deptht), float32 \u001b[4mnav_lat\u001b[0m(y,x), float32 \u001b[4mnav_lon\u001b[0m(y,x), int64 \u001b[4mtime_counter\u001b[0m()\n",
       "    groups: "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "June"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mesh_mask = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bathy = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f7ecc558e80>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MOk63CBJjSS9nIsRfNLOvRslPSVpmZgclLQMOR+kHgJWx3VdEaY7TGGmeDV0X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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ecc9299e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(np.ma.masked_array(June.variables['votemper'][0,...],\n",
    "                               mask = bathy.variables['Bathymetry'][:].mask))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Jan = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/initial_strat/winter2017_201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f7ecc37ccc0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tykoqp0xcjJ1JpAYxMrMDwKOSzgx3XQjsJFjDalW4bxVwfbi9FVgp6URJS4FlwF2l1trJ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\njtMyXJgdx3Fahguz4zhOy/j/CkjxDtZVwlkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ecc453a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(np.ma.masked_array(Jan.variables['votemper'][0,...],\n",
    "                               mask = bathy.variables['Bathymetry'][:].mask))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_rho(Sal, TempC, P):\n",
    "    sqrSal = np.sqrt(Sal)\n",
    "    R1 = ((((6.536332e-9 * TempC - 1.120083e-6) * TempC + 1.001685e-4)\n",
    "           * TempC - 9.095290e-3) * TempC + 6.793952e-2) * TempC - 28.263737\n",
    "    R2 = (((5.3875e-9 * TempC - 8.2467e-7) * TempC + 7.6438e-5)\n",
    "          * TempC - 4.0899e-3) * TempC + 8.24493e-1\n",
    "    R3 = (-1.6546e-6 * TempC + 1.0227e-4) * TempC - 5.72466e-3\n",
    "    SIG = (4.8314e-4 * Sal + R3 * sqrSal + R2) * Sal + R1\n",
    "    V350P = 1.0 / 1028.1063\n",
    "    SVA   = -SIG * V350P / (1028.1063 + SIG)\n",
    "    rho = 28.106331 - SVA / (V350P * (V350P + SVA)) + 1000\n",
    "    return rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Jan_den = np.ma.masked_array(calc_rho(Jan.variables['vosaline'][:], \n",
    "                                      Jan.variables['votemper'][:],\n",
    "                                      Jan.variables['deptht'][:][:, np.newaxis, np.newaxis]),\n",
    "                             mask = 1-mesh_mask.variables['tmask'][0,...])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ecb512550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(np.arange(0,398), Jan.variables['deptht'][:],Jan_den[:,500,:])\n",
    "plt.ylim(100,0)\n",
    "plt.xlim(200,350)\n",
    "plt.grid('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Jun_den = np.ma.masked_array(calc_rho(June.variables['vosaline'][:], \n",
    "                                      June.variables['votemper'][:],\n",
    "                                      June.variables['deptht'][:][:, np.newaxis, np.newaxis]),\n",
    "                             mask = 1-mesh_mask.variables['tmask'][0,...])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wHYCIOBgRL1HjYZ5mZpYvRUTjWtUaSmcA/cBjlD59rwOuAp6NiImpjoAXB9cr2i8CFgFM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\noZr2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ecba84630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(np.arange(0,398), June.variables['deptht'][:], Jun_den[:,500,:])\n",
    "plt.colorbar()\n",
    "plt.ylim(100,1)\n",
    "plt.xlim(200,350)\n",
    "plt.grid('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024.3840748161324"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jun_den.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1017.159972304221"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jun_den.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024.3937942281859"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jan_den.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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    "Equations show we are limited in the displacement range. Our tests stay in the range ish. "
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