{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at time-averaged fluxes through Boundary Pass\n",
    "To start, use ncra (on Salish) to average the files over 40 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.cm as cmx\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as NC\n",
    "import numpy as np\n",
    "\n",
    "from salishsea_tools import viz_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "U1 = NC.Dataset('/ocean/sallen/allen/research/MEOPAR/myResults/NEMO36_Tides/weaklog/SalishSea_40davg_20030421_20030510_grid_U.nc','r')\n",
    "T1 = NC.Dataset('/ocean/sallen/allen/research/MEOPAR/myResults/NEMO36_Tides/weaklog/SalishSea_40davg_20030421_20030510_grid_T.nc','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 40, 898, 398)\n",
      "44.5177\n"
     ]
    }
   ],
   "source": [
    "print (U1.variables['vozocrtx'].shape)\n",
    "uvel = U1.variables['vozocrtx'][0]\n",
    "sal = T1.variables['vosaline'][0]\n",
    "deptht = T1.variables['deptht'][:]\n",
    "print (deptht[23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read the bathymetry that we specify and send into NEMO\n",
    "sb_filepath = '../../NEMO-forcing/grid/bathy_meter_SalishSea2.nc'\n",
    "spec_bathy = NC.Dataset(sb_filepath, 'r')\n",
    "spec_depth = spec_bathy.variables['Bathymetry'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8a238d0d50>]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3rUAXsWNG3MjnYSFhTErG52Hx3yDCIsN4vTkjr5ENpqRKk0nrm52nT3UV1f9E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\nVhYnKgAAYGVxogIAAFYWJyoAAGBl/T+acT0gt3KuPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a24d97490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
    "viz_tools.set_aspect(ax)\n",
    "cmap = plt.get_cmap('winter_r')\n",
    "cmap.set_bad('burlywood')\n",
    "mesh = ax.pcolormesh(spec_depth, cmap=cmap)\n",
    "fig.colorbar(mesh)\n",
    "plt.axis((220, 340, 300, 380))\n",
    "jwanted = 283; imin = 300; imax = 380\n",
    "ax.plot((jwanted,jwanted),(imin,imax), 'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8a258e0390>]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rpXPP9V1JshHEijRkiDR+vEv/cccaYgC6QxBDpdaude+jvn19V5JsBLESXHZZ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\n4Mn/B5A2bkdYD1BrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a24d75410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imin = 320; imax = 350\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
    "ax.plot(np.arange(imin,imax), -spec_depth[imin:imax,jwanted])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 898, 398)\n"
     ]
    }
   ],
   "source": [
    "# read the bathymetry that NEMO puts out after one time step\n",
    "nc_filepath = '../../NEMO-forcing/grid/grid_bathy.nc'\n",
    "bathy = NC.Dataset(nc_filepath, 'r')\n",
    "depth = bathy.variables['grid_bathy'][:]\n",
    "dep_d = bathy.variables['deptht'][:]\n",
    "lon_d = bathy.variables['nav_lon'][:]\n",
    "lat_d = bathy.variables['nav_lat'][:]\n",
    "print (depth.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30,) (40, 30)\n"
     ]
    }
   ],
   "source": [
    "# depths are at the centre of the grid cells, find the bottom(floor) and \n",
    "# top(ceil) of the cells\n",
    "floor = np.empty_like(depth[:,imin:imax,jwanted])\n",
    "ceil = np.empty_like(depth[:,imin:imax,jwanted])\n",
    "ceil[0] = 0.\n",
    "floor[0] = 2*depth[0,imin:imax,jwanted]\n",
    "for k in range(1,40):\n",
    "    ceil[k] = floor[k-1]\n",
    "    floor[k] = 2*depth[k,imin:imax,jwanted] -floor[k-1]\n",
    "# find the actual bottom depth\n",
    "bottom = np.max(floor, axis=0)\n",
    "print (bottom.shape, floor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8a2524c990>]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UWrnS9YihNASxAlx8sfTcc9KxY74rKdyaNQQxAG1rXb7CWt+VIOlWr3bvp549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\ng5rTfwB8ywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a2349fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imin = 320; imax = 350\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
    "ax.plot(np.arange(imin,imax), - bottom[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.9959275363\n",
      "195.242652083 1268.90980244 -154.248570788 1381.46314836\n",
      "-97.6637393069 138.657820602\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zm9gHAOAoYzMAM2kzg+sbMvOLmfm+zNw5uu3xEXHHmp+5I1bf3QUANp+xGYCZVA6umXlt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\nAOja/wfa1w9LRz25hQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a26e0e350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the velocity\n",
    "maxdepth = 250.\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16,8))\n",
    "cmap = plt.get_cmap('bwr') \n",
    "cNorm  = colors.Normalize(vmin=-0.5, vmax=0.5)\n",
    "scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)\n",
    "\n",
    "waterfluxin = 0.\n",
    "waterfluxout = 0.\n",
    "weightin = 0.\n",
    "weightout = 0.\n",
    "surfaceflux = 0.\n",
    "deepflux = 0.\n",
    "for i in range(imax-imin):\n",
    "    for k in range(40):\n",
    "        rect = mpatches.Rectangle([i, -floor[k,i]], 1., floor[k,i]-ceil[k,i], \n",
    "                                  color=scalarMap.to_rgba(uvel[k,i+imin,jwanted]))\n",
    "        if uvel[k,i+imin,jwanted] > 0:\n",
    "            waterfluxin = waterfluxin + (uvel[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]))\n",
    "            weightin = weightin + floor[k,i]-ceil[k,i]\n",
    "        elif uvel[k,i+imin,jwanted] < 0:\n",
    "            waterfluxout = waterfluxout + (uvel[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]))\n",
    "            weightout = weightout + floor[k,i]-ceil[k,i]\n",
    "        if uvel[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]) == uvel[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]):\n",
    "            if k <= 23:\n",
    "                surfaceflux = surfaceflux + (uvel[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]))\n",
    "            else:\n",
    "                deepflux = deepflux + (uvel[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]))\n",
    "        ax[0].add_patch(rect)\n",
    "\n",
    "ax[0].set_xlim((0,imax-imin))\n",
    "ax[0].set_ylim((-maxdepth,0))\n",
    "\n",
    "cmap = plt.get_cmap('spectral') \n",
    "cNorm  = colors.Normalize(vmin=26, vmax=30.5)\n",
    "scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)\n",
    "\n",
    "salaverage = 0.\n",
    "depthtotal = 0.\n",
    "for i in range(imax-imin):\n",
    "    for k in range(40):\n",
    "        rect = mpatches.Rectangle([i, -floor[k,i]], 1., floor[k,i]-ceil[k,i], \n",
    "                                  color=scalarMap.to_rgba(sal[k,i+imin,jwanted]))\n",
    "        if sal[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]) == sal[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i]):\n",
    "            salaverage = salaverage + sal[k,i+imin,jwanted]*(floor[k,i]-ceil[k,i])\n",
    "            depthtotal = depthtotal + (floor[k,i]-ceil[k,i])\n",
    "        ax[1].add_patch(rect)\n",
    "\n",
    "ax[1].set_xlim((0,imax-imin))\n",
    "ax[1].set_ylim((-maxdepth,0))\n",
    "salaverage = salaverage/depthtotal\n",
    "print (salaverage)\n",
    "print (waterfluxin, weightin, waterfluxout, weightout)\n",
    "print (surfaceflux, deepflux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50.2378053186 1268.90980244 45.5935103197 1381.46314836\n",
      "0.0395913131273 0.0330037832525\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a228efc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the velocity\n",
    "maxdepth = 250.\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n",
    "cmap = plt.get_cmap('bwr') \n",
    "cNorm  = colors.Normalize(vmin=-0.5, vmax=0.5)\n",
    "scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)\n",
    "\n",
    "sumfluxin = 0.\n",
    "sumfluxout = 0.\n",
    "weightin = 0.\n",
    "weightout = 0.\n",
    "for i in range(imax-imin):\n",
    "    for k in range(40):\n",
    "        rect = mpatches.Rectangle([i, -floor[k,i]], 1., floor[k,i]-ceil[k,i], \n",
    "                                  color=scalarMap.to_rgba(uvel[k,i+imin,jwanted]*\n",
    "                                                          (sal[k,i+imin,jwanted]-salaverage)))\n",
    "        if uvel[k,i+imin,jwanted] > 0:\n",
    "            sumfluxin = sumfluxin + (uvel[k,i+imin,jwanted]*(sal[k,i+imin,jwanted]-salaverage)\n",
    "                                     * (floor[k,i]-ceil[k,i]))\n",
    "            weightin = weightin + floor[k,i]-ceil[k,i]\n",
    "        elif uvel[k,i+imin,jwanted] < 0:\n",
    "            sumfluxout = sumfluxout + (uvel[k,i+imin,jwanted]*(sal[k,i+imin,jwanted]-salaverage)\n",
    "                                       *(floor[k,i]-ceil[k,i]))\n",
    "            weightout = weightout + floor[k,i]-ceil[k,i]\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "ax.set_xlim((0,imax-imin))\n",
    "ax.set_ylim((-maxdepth,0))\n",
    "print (sumfluxin, weightin, sumfluxout, weightout)\n",
    "print (sumfluxin/weightin, sumfluxout/weightout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}