{
 "cells": [
  {
   "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 *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2694 particles were released at various depthts at time=0 in the following domain marked by the black rectangle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x7fb680545be0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w9BwCS88hsPQcAkvPnWXWV4cufaG2rLRKjWbqFztnTWE6ltVjTapV5JbA0nMI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\neg6BpecQWHoOgaXnEFh6DoGl9/8BNUMwjKXSGukAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6805f6390>"
      ]
     },
     "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(590,670)\n",
    "ax.set_xlim(115,135)\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.add_patch(patches.Rectangle((118,598), 15, 59, fill=False, linewidth=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = nc.Dataset('/ocean/vdo/MEOPAR/ariane-runs/monthlong/ariane_trajectories_qualitative.nc')\n",
    "latt = result.variables['traj_lat']\n",
    "lont = result.variables['traj_lon']\n",
    "bathy, lons, lats = tidetools.get_bathy_data(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49.530588150024414, 49.530588150024414, 49.530588150024414]\n",
      "[49.532856563657987, 49.531638623788915, 49.530687912675347]\n"
     ]
    }
   ],
   "source": [
    "sample_index=[4,7,8]\n",
    "time0 = latt[0,:]\n",
    "for l in range(time):\n",
    "    deep_latt = [latt[l,m] for m in sample_index]\n",
    "    deep_lont = [lont[l,m] for m in sample_index]\n",
    "    pts = np.array([deep_lont,deep_latt]).T\n",
    "    test = poly.contains_points(pts)\n",
    "    number_of_deep_particles[l]= sum(test) / length_of_deep_particles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = still_inside(744,2694)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GRrv7peH2eIKWgLd4u2XgK+6+MyzfA7zL3V8It+cAC9y9wMzeB3zX3WdEXf8n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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb680da7550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(8,6))\n",
    "time = range(744)\n",
    "ax.plot(time, 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "index_deep_particles=[]\n",
    "d = result.variables['init_z']\n",
    "for n in range(2694):\n",
    "    if d[n] > 6:\n",
    "        index_deep_particles.append(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1689"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(index_deep_particles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask = lont[:].mask\n",
    "def deep_particles(time, index):\n",
    "    number_of_deep_particles = np.zeros(time)\n",
    "    for n in range(time):\n",
    "        for m in index:\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_deep_particles[n] = number_of_deep_particles[n] + 1\n",
    "    return number_of_deep_particles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sa = deep_particles(744,index_deep_particles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb68089dba8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fx1tBz/5u0QYjIiKdUmvu47/DzBYAF4RFt7v7P5MbljSmNDzj37FPZ/wiItI2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb680837908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(8,6))\n",
    "time = range(744)\n",
    "ax.plot(time, sa)\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": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index_shallow_particles=[]\n",
    "for n in range(2694):\n",
    "    if result.variables['init_z'][n] < 6:\n",
    "        index_shallow_particles.append(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1005"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(index_shallow_particles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask=lont[:].mask\n",
    "def shallow_particles(time, index):\n",
    "    number_of_shallow_particles = np.zeros(time)\n",
    "    for n in range(time):\n",
    "        for m in index:\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_shallow_particles[n] = number_of_shallow_particles[n] + 1\n",
    "    return number_of_shallow_particles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sb = shallow_particles(744, index_shallow_particles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb680636b00>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+GVxpKsmBlo6owxARERlUJtfxyyDKUgnduU9ERGJBiX8U6Dp+ERGJi4Gu4384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\n/CIiImOIEr+IiMgY8v8DjQxVvxU4fUsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6808cd048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(8,6))\n",
    "time = range(744)\n",
    "ax.plot(time, sb)\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": []
  }
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