{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ariane is taking a vacation in Deep Bay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import matplotlib.patches as patches\n",
    "from salishsea_tools import viz_tools, geo_tools, tidetools\n",
    "from bathy_helpers import *\n",
    "import matplotlib.path as mpltPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 407 particles were released at various depthts at time=0 in the following domain marked by the black rectangle.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x7f16243e1ef0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FM3MrsBVgcnIyh7EzknrImbbhjcscAeRLLzfXjEpfZ2yZ+XT3+XvAF4B3Z+be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\nyjHYJJVjsEkqx2CTVI7BJqkcg01SOf8fymhk2TAQ3dkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1626a97d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(16,12))\n",
    "ax.pcolormesh(grid.variables['Bathymetry'][:])\n",
    "ax.set_ylim(590,635)\n",
    "ax.set_xlim(115,135)\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.add_patch(patches.Rectangle((120,598), 13, 10, fill=False, linewidth=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "July = nc.Dataset('/results/SalishSea/hindcast/01jul16/SalishSea_1h_20160701_20160701_grid_T.nc')\n",
    "ssh = July.variables['sossheig']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float32 sossheig(time_counter, y, x)\n",
       "    standard_name: sea_surface_height_above_geoid\n",
       "    long_name: sea surface height\n",
       "    units: m\n",
       "    online_operation: average\n",
       "    interval_operation: 40 s\n",
       "    interval_write: 1 h\n",
       "    cell_methods: time: mean (interval: 40 s)\n",
       "    cell_measures: area: area\n",
       "    _FillValue: 1e+20\n",
       "    missing_value: 1e+20\n",
       "    coordinates: time_centered nav_lat nav_lon\n",
       "unlimited dimensions: time_counter\n",
       "current shape = (24, 898, 398)\n",
       "filling on"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with nc.Dataset('/home/mdunphy/MEOPAR/NEMO-forcing/grid/coordinates_seagrid_SalishSea201702.nc', 'r') as cnc:\n",
    "    glamf = cnc.variables['glamf'][0,...]; gphif = cnc.variables['gphif'][0,...]\n",
    "    glamt = cnc.variables['glamt'][0,...]; gphit = cnc.variables['gphit'][0,...]\n",
    "NY, NX = glamt.shape[0], glamt.shape[1]\n",
    "glamfe, gphife = expandf(glamf, gphif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = nc.Dataset('/ocean/vdo/MEOPAR/ariane-runs/weeklong/ariane_trajectories_qualitative.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "latt = result.variables['traj_lat']\n",
    "lont = result.variables['traj_lon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bathy, lons, lats = tidetools.get_bathy_data(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 2min 46s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "number_of_particles = np.zeros(168)\n",
    "for n in range(168):\n",
    "    for m in range(407):\n",
    "        if (lont[:].mask[n,m]) == False: \n",
    "            y,x = geo_tools.find_closest_model_point(lont[n,m],latt[n,m],lons, lats, land_mask=bathy.mask)\n",
    "            if (598<y<608) and (120<x<133):\n",
    "                number_of_particles[n] = number_of_particles[n] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vdo/anaconda3/lib/python3.6/site-packages/numpy/ma/core.py:4185: UserWarning: Warning: converting a masked element to nan.\n",
      "  warnings.warn(\"Warning: converting a masked element to nan.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 6min 37s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "number_of_particles2=np.zeros(168)\n",
    "for l in range (168):\n",
    "    xarray, yarray = getboxij(glamfe, gphife, lont[l,:], latt[l,:])\n",
    "    a = np.array((xarray, yarray)).T\n",
    "    a = a[ (608>a[:,1]) & (a[:,1]>598) ]\n",
    "    a = a[ (133>a[:,0]) & (a[:,0]>120) ]\n",
    "    p,q = a.shape\n",
    "    number_of_particles2[l]=p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 401.,  396.])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def still_inside2(time):\n",
    "    number_of_particles2=np.zeros(time)\n",
    "    for l in range (time):\n",
    "        xarray, yarray = getboxij(glamfe, gphife, lont[l,:], latt[l,:])\n",
    "        a = np.array((xarray, yarray)).T\n",
    "        a = a[ (608>a[:,1]) & (a[:,1]>598) ]\n",
    "        a = a[ (133>a[:,0]) & (a[:,0]>120) ]\n",
    "        p,q = a.shape\n",
    "        number_of_particles2[l]=p\n",
    "    return number_of_particles2\n",
    "still_inside2(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 402.,  399.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = lont[:].mask\n",
    "def still_inside(time, number):\n",
    "    number_of_particles = np.zeros(time)\n",
    "    for n in range(time):\n",
    "        for m in range(number):\n",
    "            if (mask[n,m]) == False:\n",
    "                y,x = geo_tools.find_closest_model_point(lont[n,m],latt[n,m],lons, lats, land_mask=bathy.mask)\n",
    "                if (598<y<658) and (118<x<134):\n",
    "                    number_of_particles[n] = number_of_particles[n] + 1\n",
    "    return number_of_particles\n",
    "\n",
    "still_inside(2,407)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vdo/anaconda3/lib/python3.6/site-packages/numpy/ma/core.py:4185: UserWarning: Warning: converting a masked element to nan.\n",
      "  warnings.warn(\"Warning: converting a masked element to nan.\")\n"
     ]
    }
   ],
   "source": [
    "b = still_inside2(168)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wzuGhBMSTUrHPVxAREUlX8d7K2AI4z8y+TnBnwZbYQne/rLoDmFkBcAmwA1gR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\neLHkwBhjjDFeLDkwxhhjjJf/A8FwevYj9LEHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28b84bd048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(8,6))\n",
    "time = range(168)\n",
    "ax.plot(time, number_of_particles, 'r-')\n",
    "ax.plot(time, b, 'b-')\n",
    "ax.grid('on')\n",
    "ax.set_title('Number of Particles in Domain', fontsize=16)\n",
    "ax.set_ylabel('Number of Particles', fontsize=14)\n",
    "ax.set_xlabel('Time (h)', fontsize=14)\n",
    "ax.tick_params(labelsize=12)\n",
    "#plt.savefig('Arianeresult.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "index_deep_particles=[]\n",
    "for n in range(407):\n",
    "    if result.variables['init_z'][n] > 6:\n",
    "        index_deep_particles.append(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "number_of_deep_particles = np.zeros(168)\n",
    "for n in range(168):\n",
    "    for m in index_deep_particles:\n",
    "        if (lont[:].mask[n,m]) == False: \n",
    "            y,x = geo_tools.find_closest_model_point(lont[n,m],latt[n,m],lons, lats, land_mask=bathy.mask)\n",
    "            if (598<y<608) and (120<x<133):\n",
    "                number_of_deep_particles[n] = number_of_deep_particles[n] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3a4f5c4198>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Od+V/RPd4lm4L0D09jh+N8arv11QGeH+N18UvzuCE3glkJzff5OK2GXtYUxng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\n4v+TZFiY6zYEawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a51e716d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(8,6))\n",
    "time = range(168)\n",
    "ax.plot(time, number_of_deep_particles)\n",
    "ax.grid('on')\n",
    "ax.set_title('Number of Particles (init depth > 6) in Domain at time = x')\n",
    "ax.set_ylabel('Number of Particles')\n",
    "ax.set_xlabel('Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index_shallow_particles=[]\n",
    "for n in range(407):\n",
    "    if result.variables['init_z'][n] < 6:\n",
    "        index_shallow_particles.append(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "number_of_shallow_particles = np.zeros(168)\n",
    "for n in range(168):\n",
    "    for m in index_shallow_particles:\n",
    "        if (lont[:].mask[n,m]) == False: \n",
    "            y,x = geo_tools.find_closest_model_point(lont[n,m],latt[n,m],lons, lats, land_mask=bathy.mask)\n",
    "            if (598<y<608) and (120<x<133):\n",
    "                number_of_shallow_particles[n] = number_of_shallow_particles[n] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3a4f20ebe0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zO+BUYIOZDXP3dWY2DNjY1cLuPguYBTB16lSvr69PQUhvaWhooLfrbF+8AV58\nnqOPP5HjRvU/9AJZoC/lzVUqczzErcxxKy/Et8x90dN9+u8EjnP3NjOrAP5G0JO/r1YCp4Tr3Etw\nN8DzBP0ELgO+F/7NuV/yKwub9/WjOyIiko16SvrN7t4G4O57zMxSsUF3n2dm9wMLgFbgBYKaeyVw\nn5l9AlgBXJSK7WVSuZK+iIhksZ6S/iQzeyl8b8CEcLjPz95392uBazuNbiKo9ees8rD3/j713hcR\nkSzUU9J/W8aiyBMdNf3dSvoiIpKFuk36Hc/cl+T1rygBYPue5ogjEREROVhu3FeWI6rLiigpLGBz\no5K+iIhkHyX9FDIzBlWWsKWxKepQREREDtJt0jezx8K/389cOLlvUGUJW3arpi8iItmnp458w8zs\nVOACM5tN0Gt/P3dfkNbIctSgfqVsVk1fRESyUE9J/xvA14GRwI86TXPgzHQFlcsGV5aydGNj1GGI\niIgcpKfe+/cD95vZ11P0+N1YGFxZwubGJtydFD3PSEREJCV6qukD4O7fMrMLCB7LC9Dg7g+mN6zc\nNaiyhKbWdhqbWqkqK446HBERkf0O2XvfzL4LfAFYFL6+YGbfSXdguWpwZSkAW3TbnoiIZJlD1vSB\n9wHHu3s7gJndQfC8/K+mM7BcNagj6e9uYuzgfhFHIyIi8pZk79NP/J3YmnQEki8G9Queyrdpl2r6\nIiKSXZKp6X8XeMHM/kpw2947gavTGlUOq616q6YvIiKSTZLpyHePmTUAJ4ejvuLu69MaVQ4bED5/\nX9f0RUQk2yRT08fd1wFz0hxLXigpKqCmvFgP6BERkayjZ++nweDKEtX0RUQk6yjpp8GgSj2KV0RE\nsk+PSd/MCs1scaaCyRcdT+UTERHJJj0mfXdvA14zs9EZiicvDK4s1S/tiYhI1kmmI98A4BUzexbY\n3THS3S9IW1Q5blC/UrbvaaGlrZ3iQl1BERGR7JBM0v962qPIM4Mqg9v2tu5upq66LOJoREREAsnc\np/+4mY0BJrr7o2ZWARSmP7Tc1fH8/c2NTUr6IiKSNZL5wZ1PAfcDPw9HjQD+kM6gct3gSj2gR0RE\nsk8yF5yvBE4DdgK4+xJgSDqDynWDEmr6IiIi2SKZpN/k7vurrGZWBHj6Qsp9qumLiEg2SibpP25m\nXwXKzewc4DfA/6Y3rNxWWVpESVEBm/WjOyIikkWSSfpXA5uAl4FPAw8B/5HOoHKdmTG4nx7FKyIi\n2SWZ3vvR4YDxAAAdKElEQVTtZnYHMI+gWf81d1fz/iEMrtKjeEVEJLscMumb2fuAnwFvAAaMM7NP\nu/sf0x1cLhtcWcqGnfuiDkNERGS/ZB7O80PgDHdfCmBmE4D/A5T0ezCifznPv7k16jBERET2S+aa\n/q6OhB9aBuxKUzx5Y8ygCnbua2X7Hl3XFxGR7NBtTd/MPhC+fd7MHgLuI7im/2Hgub5s1Mz6A7cA\nR4fr/DjwGnAvMBZ4E7jI3bf1ZTtRGj2wAoCVW/fQv6Ik4mhERER6runPCF9lwAbgXUA9QU/+8j5u\n9wbgT+4+CTgOeJXgLoHH3H0i8Fg4nLPGDOoHwIoteyKOREREJNBtTd/dL0/HBs2sBngn8LFwO81A\ns5nNJDipALgDaAC+ko4YMmHUwOC8aOVWJX0REckOyfTeHwd8jqDZff/8ffhp3XEErQW/NLPjgPnA\nF4A6d18XzrMeqOvl+rNCRUkRtVWlrNiy+9Azi4iIZIAd6pZ7M/s7cCvBw3naO8a7++O92qDZVGAu\ncJq7zzOzGwie6/85d++fMN82dx/QxfJXAFcA1NXVnTR79uzehNGtxsZGKisrU7Ku6+fupbAArp7W\n16sh6ZPK8uYKlTke4lbmuJUX4lvmGTNmzHf3qb1ZPplb9va5+096s/JurAZWu/u8cPh+guv3G8xs\nmLuvM7NhwMauFnb3WcAsgKlTp3p9fX0KQ4OGhgZStc45G15k7rItKVtfOqSyvLlCZY6HuJU5buWF\n+Ja5L5K5Ze8GM7vWzN5uZid2vHq7QXdfD6wys6PCUWcBi4A5wGXhuMuAB3q7jWwxelAF63buo6m1\nLepQREREkqrpHwN8FDiTt5r3PRzurc8Bd5lZCcF9/5cTnIDcZ2afAFYAF/Vh/VlhzKAK3GH1tr1M\nqI1XE5SIiGSfZJL+h4HxiT+v21fu/iLQ1fWIs1K1jWwwemBw297KLXuU9EVEJHLJNO8vBPofci45\nSMcDetSDX0REskEyNf3+wGIzew7Y/7NxfbhlLzYGV5ZQUVLICt2rLyIiWSCZpH9t2qPIU2bG6IEV\nrFLSFxGRLHDIpN/b+/ElMHpgBcs3q3lfRESid8hr+ma2y8x2hq99ZtZmZjszEVw+GDOogpVb99De\n3vNDkERERNItmZp+Vcd7MzNgJnBKOoPKJ6MH9aOptZ2Nu5oYWlMWdTgiIhJjyfTe388DfwDenaZ4\n8s7YQUEPfjXxi4hI1JL5wZ0PJAwWENxfvy9tEeWZiUOChpIlG3fx9gmDIo5GRETiLJne+zMS3rcC\nbxI08UsS6qpLqSkvZvH6XVGHIiIiMZfMNf3LMxFIvjIzjqqr4nUlfRERiVi3Sd/MvtHDcu7u30pD\nPHnpqKFV/OHFNbg7QV9IERGRzOupI9/uLl4AnwC+kua48sqRQ6vYta+VtTvUFUJERKLTbU3f3X/Y\n8d7MqoAvEPwa3mzgh90tJwebNDTozPf6+l2M6F8ecTQiIhJXPd6yZ2YDzezbwEsEJwgnuvtX3H1j\nRqLLE0fWBUlfnflERCRKPV3T/wHwAWAWcIy7N2YsqjxTU17MsJoyXluvBxmKiEh0eqrpfxEYDvwH\nsDbhUby79Bjew3fU0Cpe26DzJhERiU5P1/QP62l90rOj6qp4eukWWtraKS7URysiIpmn7JMhRw2t\normtnTf1OF4REYmIkn6GHBX24H9tgzrziYhINJJ5DK+kwITaSgoLjEcWbaC0qJDBlSWcMHpA1GGJ\niEiMKOlnSFlxIZOGVvHAi2t54MW1mMG8r57FkCr93K6IiGSGkn4G3fXJ6azetpf5K7Zx7ZxXWL1t\nr5K+iIhkjJJ+BvWvKKF/RQkF4fP31+uxvCIikkHqyBeB4f2D2v3a7XsjjkREROJEST8CNeXFlBUX\nqKYvIiIZpaQfATNjeE0565T0RUQkg5T0IzK0pox1O9S8LyIimaOkH5GhNWVq3hcRkYxS0o/I8Jpy\nNuxqoq3dow5FRERiQkk/IkNrymhrdzbtaoo6FBERiQkl/Yjsv21P1/VFRCRDIkv6ZlZoZi+Y2YPh\n8EAze8TMloR/8/rB9EOry4HUPKBn4ZodbN/T3Of1iIhIfouypv8F4NWE4auBx9x9IvBYOJy3htUE\nNf2+3ra3t7mND978NDc8tiQVYYmISB6LJOmb2UjgfcAtCaNnAneE7+8ALsx0XJnUvyJ4QM+6Hp7K\nt3rbHpZu7PmneBes3EZTazsvrtqe6hBFRCTPmHvme4+b2f3Ad4Eq4Evufr6ZbXf3/uF0A7Z1DHda\n9grgCoC6urqTZs+endLYGhsbqaysTOk6u/OVJ/YwprqAfzm+6x/d+cFze1m1y/lRfTlFBdblPL9f\n0swDb7RQXAA3n13R7XzdyWR5s4XKHA9xK3PcygvxLfOMGTPmu/vU3iyf8R/cMbPzgY3uPt/M6rua\nx93dzLo8G3H3WcAsgKlTp3p9fZer6LWGhgZSvc7uTFgyl30tbdTXn3bQtObWdt547GH2tThNgydx\n9rHDulzHz15/BthKSzsMm3QiU4bXHFYMmSxvtlCZ4yFuZY5beSG+Ze6LKJr3TwMuMLM3gdnAmWb2\na2CDmQ0DCP9ujCC2jOrpAT0vr9nOvpZ2Cgx+PXdFl/M0tbbxwsrtnP22IcEyq3ekLVYREcl9GU/6\n7n6Nu49097HAxcBf3P0jwBzgsnC2y4AHMh1bpvX0gJ55y7cCcPlp43hm2RaWbmw8aJ6XVu+gqbWd\nD08dRVVZES+tUdIXEZHuZdN9+t8DzjGzJcDZ4XBe6+kBPfOWbWXikEo+864JFBUY9zy7sot5tgAw\nfdxAjhlRo5q+iIj0KNKk7+4N7n5++H6Lu5/l7hPd/Wx33xplbJnw1m17B/bgb21rZ/6KbUwfP5Da\nqlLeffRQ7p+/mj3NrQfMN2/5ViYNraJ/RQnHjKxh8fqdNLW2ZSx+ERHJLdlU04+dYTXBA3o636u/\naN1OGptamT5uEAAfO3UsO/a2cOkt89i4M5i3pePEYNxAAI4d0Z+WNue19T3f4iciIvGlpB+h7h7Q\nM29Z0MjRkdBPHjuQmy89kcXrdnHBjU9x25PL+cljS9jT3Ma08MTg2JFBr/2X1MQvIiLdUNKPUP+K\nYgZXlvDwwvUkPi9h3vKtjBvcjyHVb92//55jhvHbfz6V4iLjugcX8T9/WUp5cSGnjA9ODEYOKGdA\nRbGu64uISLcyfp++vMXM+OK5R3HN717mdwvW8MGTRtLW7jz35lbOmzL0oPknD6/mL1+sZ8feFgAq\nSgqpKCnav65jRvZXD34REemWavoR+4epozhhdH++89CrrNq6h0/e8Rw79rZwxqQhXc5fXFjA4MpS\nBleW7k/4HY4bWcPrG3bR2NTa5bIiIhJvSvoRKygwvn3h0Wzb08xZP3qcvy3ZzLcvPJrzjj64pn8o\nJ48dSFu7M3/FtjREKiIiuU5JPwtMGV7DZ941geqyYn79yel85JQxvVrPSWMGUFhgPLt8S4ojFBGR\nfKBr+lniy+dN4kvnHkXBYf5gTqJ+pUUcM6Jmf+9/ERGRRKrpZ5G+JPwO08cN5O+rt7OvRQ/pERGR\nAynp55np4wfS0uYsWKnr+iIiciAl/Txz0piBmMGzy9XELyIiB1LSzzM15cVMHlat6/oiInIQJf08\nNG3cQBas3EZza3vUoYiISBZR0s9D08cNoqm1nZdWb486FBERySJK+nlo+riBFBUYf1q4PupQREQk\niyjp56EB/Uo4d0od9y9YrVv3RERkPyX9PPWR6WPYvqeFh15eF3UoIiKSJZT089TbJwxi/OB+3DVv\nZdShiIhIllDSz1Nmxj9OH838Fdt4dd3OqMMREZEsoKSfxz544khKigq4a96KqEMREZEsoKSfxwb0\nK+HC44dz3/OrWb55d9ThiIhIxJT089yXzj2K0sICvvHAQtw96nBERCRCSvp5bkh1GV8890j+tmQz\nD72s+/ZFROJMST8GPnLKGKYMr+a6B19h2+7mqMMREZGIKOnHQFFhAde//xi27W5h5k+f4vUNu6IO\nSUREIqCkHxPHj+rP7E+fwt6WNt7/06d4/PVNUYckIiIZpqQfIyeOHsCcz57GqIEVXDX7BTX1i4jE\njJJ+zAyrKee//+F4du5r5b8eXpzUMgtWbmPO39eq97+ISI5T0o+htw2r5vJTx3LPs6tYur3nH+TZ\nsaeFT93xPJ+/5wWu/u3LNLXqB3xERHKVkn5MXXXOkQytLuOOV5rZ09za7Xw/+PNitu1p5h+mjuLe\n51dx6S/msWlXUwYjFRGRVFHSj6nK0iKumzmF1bva+dDNz7B6256D5vn7qu3cNW8ll506lu9/6Fj+\n55ITWLh2BzNvfJKFa3ZEELWIiPRFUaY3aGajgDuBOsCBWe5+g5kNBO4FxgJvAhe5+7ZMxxcn504Z\nylUnlXLLwj3MvPEpZhw3/IDpTyzZRG1lKf92zpEAzDhuOOMG9+OKO5/nQz97mpsvPYkzJg2JInQR\nEemFKGr6rcAX3X0ycApwpZlNBq4GHnP3icBj4bCk2XG1Rfz+ytMY1r+M3y1YfcBr175WvvuBY6gq\nK94//9Ejanjgs6czvH85P3rk9QgjFxGRw5Xxmr67rwPWhe93mdmrwAhgJlAfznYH0AB8JdPxxdER\nQyp58HPvSHr+2qpSzj9mGDf+dSm79rUccFIgIiLZy6K8DcvMxgJPAEcDK929fzjegG0dw52WuQK4\nAqCuru6k2bNnpzSmxsZGKisrU7rObNbb8i7a0sZ/PbePfzuplGNrM37u2Cdx28egMsdB3MoL8S3z\njBkz5rv71N4sH9m3tZlVAr8FrnL3nUGeD7i7m1mXZyPuPguYBTB16lSvr69PaVwNDQ2kep3ZrLfl\nndbcyo/m/5m9VSOpr5+U+sDSKG77GFTmOIhbeSG+Ze6LSHrvm1kxQcK/y91/F47eYGbDwunDgI1R\nxCbJqSgp4tiRNcxbtiXqUEREJEkZT/ph0/2twKvu/qOESXOAy8L3lwEPZDo2OTzTxg3ipdU72Nus\nB/aIiOSCKGr6pwEfBc40sxfD13uB7wHnmNkS4OxwWLLY9PEDaW13FqzUnZUiIrkgit77TwLWzeSz\nMhmL9M3UMQMoMJi3fCunHTE46nBEROQQ9EQ+6bWqsmKmDNd1fRGRXKGkL30ybdxAXli1nX0tuq4v\nIpLtlPSlT86ZXEdzazu3Prk86lBEROQQlPSlT04ZP4j3HD2Unzy2hFVbD/7RHhERyR5K+tJn35gx\nmcIC49o5rxDlEx5FRKRnSvrSZ8NqyvnXs4/kL4s38siiDVGHIyIi3VDSl5T42GljOaquiv/830Xs\naW6NOhwREemCkr6kRHFhAd9+/9Gs2b6Xnzy2NOpwRESkC0r6kjInjx3Ih08ayS1/W8brG3ZFHY6I\niHSipC8pdfV7JtGvtIhrfveynskvIpJllPQlpQZVlnLdzCksWLmND/3sadZs3xt1SCIiElLSl5Sb\nefwIbr1sKiu37GHmjU+ydKOa+kVEsoGSvqTFmZPq+P2Vp9LY1Mqdz6yIOhwREUFJX9LoiCFVvGNi\nLY8u2qCH9oiIZAElfUmrc95Wx9od+1i0bmfUoYiIxJ6SvqTVGZOGYAaPLtqY8nW3tTuznniDP7yw\nJuXrFhHJR0VRByD5rbaqlBNG9efRVzfwhbMnpmy9u/a1cNXsF3ls8UZKCgs4blR/xg3ul7L1i4jk\nIyV9SbtzJg/l+39azLodexlWU97jvD/882u8msSlgNc3NLJm+16+dO6R/PzxZXzjgYXc+fFpmFmq\nwhYRyTtq3pe0O2fyEAAefbXnJv7F63fyP39ZyusbGlm3Y1+Pr9qqUn718Wl89syJfPHcI/nbks38\n38vrMlEcEZGcpZq+pN2E2krGDqrgpr8u5S+vbqCsuJD/vGAKQ6rLDpjvrrkrKSkq4IErT2NAv5Kk\n1/+RU8bwm/mrue5/F3HmpCFUlOiwFhHpimr6knZmxr/UH0FtVSlbdjfz8Cvruf3pNw+YZ3dTK79/\nYQ3nHzPssBI+QFFhAd84fzIbdzUx58W1KYxcRCS/KOlLRlx08ijmfPZ05nz2dM6cVMd9z6+iubV9\n//Q5f19LY1Mrl54yulfrnzZuIJOGVvHreSv0TAARkW4o6UvGXXrKaDY3NvPnResBcHd+PXcFk4ZW\nceLoAb1ap5lx6fTRLFyzk5dW70hluCIieUNJXzLunRNrGTmgnLvmrgSg4fVNvLJ2J5eeMqZPve8v\nPGEEFSWF3DXvwMf+btrVxL//5u/89bXUPytARCSXKOlLxhUWGJdMG80zy7bwg4cX86k7nmdCbT/e\nf8KIPq23qqyYmccPZ87f17JjbwsAC9fs4IIbn+Q381fz8duf42ePv6HmfxGJLXVzlkhcNHUUP370\ndX761zeoP6qWn1xyApWlfT8cL50+hnueXcUls+bSv6KYBSu3MahfKb/957fzy6fe5Ht/XMzY6gJu\neWMulaVF/Mf7JjNqYEUKSiQikv2U9CUStVWlfPndk9jX0sa/nHEEhQWpeajO0SNquOztY3hl7U6a\nW9s5Z/JQrp0xmcGVpZw4egDHjezPfU+/RlNLOwtWbOZrLQu54/KT9VAfEYkFJX2JzKfeOT4t6/3P\nmUd3Od7M+NQ7xzOxfSX19ady+1PL+eb/LuKhl9fzvmOH7Z+vsamV7z70Km9sakx5bIUFxkdPGct5\nRw9N+bpFRA5F1/Qltj5yyhimDK/mugdfobGpFYCVW/bwwZueZvZzq2hrd9qdlL7WbNvLZ349n/9+\n5HXa29W3QEQySzV9ia2iwgK+feHRfODmp7nwp08xsF8Jr63fBcAdl0/j9ImDU77NfS1t/McfFnLD\nY0t4+JX1VJcX92l9E2or+dr73paS/hDpMH/FNm59chmfP2sik4ZWRx2OSOyppi+xdsLoAVx7/mQG\nV5ZQYHDK+IE8cOVpaUn4AGXFhfzgQ8dy3cwp9K8opsDo9Qvg3udW8sGbnmbV1j1pibcvfvN80KHy\noZfX84GbnubhV9ZHHZJI7GVd9cDMzgNuAAqBW9z9exGHJHnuY6eN42OnjcvY9syMf3r7WP7p7WP7\nvK6/LdnEZ+9+gQtufJKfXnoip05I3cnKc29u5cePvk5TS/uhZ+6kua2dl1bv4NQJg/jGjMl85f6X\n+PSv5nPC6P4U9qLTZEVpEV857yimDK857GVF5C1ZVdM3s0Lgp8B7gMnAJWY2OdqoRLLXOybW8ocr\nT2NQZSkfvfVZfvXMmyl5DsE9z67kH38xl+WbdlNaXHDYr6qyIj535hHc+fFpTBpazb2ffjufOH0c\nFSWFvVrforU7+dDNz/B/L+mXFEX6Ittq+tOApe6+DMDMZgMzgUWRRiWSxcYN7sfv/+VUvjD7Rb7+\nwCvc/ewqyooPPJ/fuWMvNyx6Kqn1Nbe288ranbzzyFr+5+ITqKnoW78DCC5rfP383p+/b9y1j8/8\naj5X3r2Anz9Rk9QtnodT5nwQt/JC7pT5Rxcdz7jB/aIOAwDLpqeTmdmHgPPc/ZPh8EeB6e7+2YR5\nrgCuAKirqztp9uzZKY2hsbGRysrKlK4zm8WtvJC/ZW535/+WtfDa1oOb41vbWikqTP4c/4gBBcwY\nX5yy5yekQku787slLazamdzlhsMtc66LW3khd8r8T1NKGFKRmob1xsZGZsyYMd/dp/Zm+ez/tDpx\n91nALICpU6d6fX19Stff0NBAqteZzeJWXsjvMp95Rtfj86XM55yZ/Lz5UuZkxa28EN8y90VWXdMH\n1gCjEoZHhuNERESkj7It6T8HTDSzcWZWAlwMzIk4JhERkbyQVc377t5qZp8FHia4Ze82d38l4rBE\nRETyQlYlfQB3fwh4KOo4RERE8k22Ne+LiIhImijpi4iIxISSvoiISEwo6YuIiMSEkr6IiEhMKOmL\niIjEhJK+iIhITCjpi4iIxISSvoiISExk1U/rHi4z2wSsSPFqBwObU7zObBa38oLKHBdxK3Pcygvx\nLXM/d6/tzcI5nfTTwcye7+3vFOeiuJUXVOa4iFuZ41ZeUJl7Q837IiIiMaGkLyIiEhNK+gebFXUA\nGRa38oLKHBdxK3Pcygsq82HTNX0REZGYUE1fREQkJpT0Q2Z2npm9ZmZLzezqqONJBzMbZWZ/NbNF\nZvaKmX0hHP9NM1tjZi+Gr/dGHWsqmdmbZvZyWLbnw3EDzewRM1sS/h0QdZypYGZHJezHF81sp5ld\nlW/72MxuM7ONZrYwYVy3+9TMrgn/t18zs3dHE3XfdFPmH5jZYjN7ycx+b2b9w/FjzWxvwv7+WXSR\n9143Ze72WM71/dxNee9NKOubZvZiOL5X+1jN+4CZFQKvA+cAq4HngEvcfVGkgaWYmQ0Dhrn7AjOr\nAuYDFwIXAY3u/v8iDTBNzOxNYKq7b04Y91/AVnf/XniSN8DdvxJVjOkQHtdrgOnA5eTRPjazdwKN\nwJ3ufnQ4rst9amaTgXuAacBw4FHgSHdviyj8XummzOcCf3H3VjP7PkBY5rHAgx3z5apuyvxNujiW\n82E/d1XeTtN/COxw9+t6u49V0w9MA5a6+zJ3bwZmAzMjjinl3H2duy8I3+8CXgVGRBtVZGYCd4Tv\n7yA4+ck3ZwFvuHuqH2AVOXd/AtjaaXR3+3QmMNvdm9x9ObCU4H8+p3RVZnf/s7u3hoNzgZEZDyyN\nutnP3cn5/dxTec3MCCpo9/RlG0r6gRHAqoTh1eR5MgzPEk8A5oWjPhc2Ed6WL03dCRx41Mzmm9kV\n4bg6d18Xvl8P1EUTWlpdzIFfEPm8j6H7fRqX/++PA39MGB4XNvs+bmbviCqoNOnqWM73/fwOYIO7\nL0kYd9j7WEk/hsysEvgtcJW77wRuBsYDxwPrgB9GGF46nO7uxwPvAa4Mm9D28+AaV15d5zKzEuAC\n4DfhqHzfxwfIx33aEzP7GtAK3BWOWgeMDo/7fwPuNrPqqOJLsVgdywku4cCT+F7tYyX9wBpgVMLw\nyHBc3jGzYoKEf5e7/w7A3Te4e5u7twO/IMeaxA7F3deEfzcCvyco34awj0NHX4eN0UWYFu8BFrj7\nBsj/fRzqbp/m9f+3mX0MOB+4NDzZIWzi3hK+nw+8ARwZWZAp1MOxnLf72cyKgA8A93aM6+0+VtIP\nPAdMNLNxYQ3pYmBOxDGlXHhN6FbgVXf/UcL4YQmzvR9Y2HnZXGVm/cJOi5hZP+BcgvLNAS4LZ7sM\neCCaCNPmgFpBPu/jBN3t0znAxWZWambjgInAsxHEl3Jmdh7wZeACd9+TML427MiJmY0nKPOyaKJM\nrR6O5bzdz8DZwGJ3X90xorf7uChtIeaQsOfrZ4GHgULgNnd/JeKw0uE04KPAyx23fQBfBS4xs+MJ\nmkPfBD4dTXhpUQf8PjjfoQi4293/ZGbPAfeZ2ScIfqnxoghjTKnw5OYcDtyP/5VP+9jM7gHqgcFm\nthq4FvgeXexTd3/FzO4DFhE0gV+ZSz26O3RT5muAUuCR8Bif6+6fAd4JXGdmLUA78Bl3T7ZDXNbo\npsz1XR3L+bCfuyqvu9/Kwf1zoJf7WLfsiYiIxISa90VERGJCSV9ERCQmlPRFRERiQklfREQkJpT0\nRUREYkK37IkIZjYIeCwcHAq0AZvC4T3ufmokgYlISumWPRE5QHe/YiYiuU/N+yLSIzNrDP/Whz/s\n8YCZLTOz75nZpWb2rJm9bGYTwvlqzey3ZvZc+Dot2hKISAclfRE5HMcBnwHeRvB0xyPdfRpwC/C5\ncJ4bgP9295OBD4bTRCQL6Jq+iByO5zp+vtbM3gD+HI5/GTgjfH82MDl8LCxAtZlVuntjRiMVkYMo\n6YvI4WhKeN+eMNzOW98nBcAp7r4vk4GJyKGpeV9EUu3PvNXUT/jjKCKSBZT0RSTVPg9MNbOXzGwR\nQR8AEckCumVPREQkJlTTFxERiQklfRERkZhQ0hcREYkJJX0REZGYUNIXERGJCSV9ERGRmFDSFxER\niQklfRERkZj4/3f/CkJqadlSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a4f63cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(8,6))\n",
    "time = range(168)\n",
    "ax.plot(time, number_of_shallow_particles)\n",
    "ax.grid('on')\n",
    "ax.set_title('Number of Particles (init depth < 6) in Domain at time = x')\n",
    "ax.set_ylabel('Number of Particles')\n",
    "ax.set_xlabel('Time')"
   ]
  },
  {
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}