{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from salishsea_tools import geo_tools, nc_tools, tidetools\n",
    "import xarray as xr\n",
    "import glob\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import LogNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n",
    "ferry_data = 'https://salishsea.eos.ubc.ca/erddap/tabledap/ubcONCTWDP1mV1'\n",
    "bathy, X, Y = tidetools.get_bathy_data(grid)\n",
    "ferry = nc.Dataset(ferry_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "threemonthsa = sorted(glob.glob('/ocean/vdo/MEOPAR/completed-runs/threemonthsa/test*/*1h*grid_T*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threemonthsb = sorted(glob.glob('/ocean/vdo/MEOPAR/completed-runs/threemonthsb/test*/*1h*grid_T*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threemonthsbase = sorted(glob.glob('/ocean/vdo/MEOPAR/completed-runs/threemonthbase/test*/*1h*grid_T*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mesh_mask = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')\n",
    "tmask = mesh_mask.variables['tmask'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salinity is done\n",
      "temperature is done\n",
      "time is done\n"
     ]
    }
   ],
   "source": [
    "with nc_tools.scDataset(threemonthsa) as f:\n",
    "    threemonthsa_sal = f.variables['vosaline'][:,1,...]\n",
    "    print('salinity is done')\n",
    "    threemonthsa_temp = f.variables['votemper'][:,1,...]\n",
    "    print('temperature is done')\n",
    "    timesa = f.variables['time_counter'][:]\n",
    "    print('time is done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 6, 3, 18, 0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.num2date(ferry.variables['s.time'][135000], ferry.variables['s.time'].units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salinity is done\n",
      "temperature is done\n",
      "time is done\n"
     ]
    }
   ],
   "source": [
    "with nc_tools.scDataset(threemonthsb) as f:\n",
    "    threemonthsb_sal = f.variables['vosaline'][:,1,...]\n",
    "    print('salinity is done')\n",
    "    threemonthsb_temp = f.variables['votemper'][:,1,...]\n",
    "    print('temperature is done')\n",
    "    timesb = f.variables['time_counter'][:]\n",
    "    print('time is done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salinity is done\n",
      "temperature is done\n",
      "time is done\n"
     ]
    }
   ],
   "source": [
    "with nc_tools.scDataset(threemonthsbase) as f:\n",
    "    threemonthsbase_sal = f.variables['vosaline'][:,1,...]\n",
    "    print('salinity is done')\n",
    "    threemonthsbase_temp = f.variables['votemper'][:,1,...]\n",
    "    print('temperature is done')\n",
    "    timesbase = f.variables['time_counter'][:]\n",
    "    print('time is done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/threemonthbase/testA/SalishSea_1h_20170307_20170316_grid_U_20170313-20170313.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "converted_timesa = nc.num2date(timesa, h.variables['time_counter'].units)\n",
    "converted_timesb = nc.num2date(timesb, h.variables['time_counter'].units)\n",
    "converted_timesbase = nc.num2date(timesbase, h.variables['time_counter'].units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "list_of_model_sals = np.array([])\n",
    "list_of_model_temps = np.array([])\n",
    "list_of_ferry_sals = np.array([])\n",
    "list_of_ferry_temps = np.array([])\n",
    "unit = ferry.variables['s.time'].units\n",
    "for n in range(14500,135000):\n",
    "    if ((ferry.variables['s.latitude'][n].mask == False) \n",
    "        and (ferry.variables['s.salinity'][n].mask == False)):\n",
    "        Yind, Xind = geo_tools.find_closest_model_point(ferry.variables['s.longitude'][n], \n",
    "                                                        ferry.variables['s.latitude'][n], \n",
    "                                                        X, Y, land_mask = bathy.mask)\n",
    "        if tmask[0,1,Yind, Xind] == 1:\n",
    "            date = nc.num2date(ferry.variables['s.time'][n], unit)\n",
    "            if date.minute <= 30:\n",
    "                before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                                   hour = (date.hour), minute = 30) - datetime.timedelta(hours=1)\n",
    "                index = np.argmin(np.abs(converted_timesa - date))\n",
    "                delta = (date - before).seconds / 3600\n",
    "                s_val = ((delta * (threemonthsa_sal[index-1, Yind, Xind])) + \n",
    "                       (1- delta)*(threemonthsa_sal[index, Yind, Xind]))\n",
    "                t_val = ((delta * (threemonthsa_temp[index-1, Yind, Xind])) + \n",
    "                       (1- delta)*(threemonthsa_temp[index, Yind, Xind]))\n",
    "            if date.minute > 30:\n",
    "                before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                                       hour = (date.hour), minute = 30)\n",
    "                index = np.argmin(np.abs(converted_timesa - date))\n",
    "                delta = (date - before).seconds / 3600\n",
    "                s_val = ((delta * (threemonthsa_sal[index, Yind, Xind])) + \n",
    "                       (1- delta)*(threemonthsa_sal[index+1, Yind, Xind]))\n",
    "                t_val = ((delta * (threemonthsa_temp[index, Yind, Xind])) + \n",
    "                       (1- delta)*(threemonthsa_temp[index+1, Yind, Xind]))\n",
    "            list_of_ferry_sals = np.append(list_of_ferry_sals, ferry.variables['s.salinity'][n])\n",
    "            list_of_ferry_temps = np.append(list_of_ferry_temps, ferry.variables['s.temperature'][n])\n",
    "            list_of_model_sals = np.append(list_of_model_sals, s_val)\n",
    "            list_of_model_temps = np.append(list_of_model_temps, t_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7efeeecdc470>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAJcCAYAAAAWzMXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efeeee7b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,10))\n",
    "c, xedge, yedge, im = ax.hist2d(list_of_ferry_sals, list_of_model_sals, bins = 100, norm=LogNorm())\n",
    "im\n",
    "fig.colorbar(im, ax=ax)\n",
    "ax.set_xlabel('Ferry Data')\n",
    "ax.set_ylabel('Model')\n",
    "ax.set_title('Test A salinity, rn_crban = 150, rn_charn = 20000, nn_z0_met = 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bias =  1.94161838071\n",
      "RMSE = 5.56267806197\n",
      "Willmott = 0.743195687801\n"
     ]
    }
   ],
   "source": [
    "print('bias =  ' + str(-np.mean(list_of_ferry_sals) + np.mean(list_of_model_sals)))\n",
    "print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_sals - list_of_ferry_sals)**2) / len(list_of_model_sals))))\n",
    "xbar = np.mean(list_of_ferry_sals)\n",
    "print('Willmott = ' + str(1-(np.sum((list_of_model_sals - list_of_ferry_sals)**2)  / \n",
    "                             np.sum((np.abs(list_of_model_sals - xbar) + np.abs(list_of_ferry_sals - xbar))**2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_of_modelb_sals = np.array([])\n",
    "list_of_modelb_temps = np.array([])\n",
    "list_of_ferryb_sals = np.array([])\n",
    "list_of_ferryb_temps = np.array([])\n",
    "unit = ferry.variables['s.time'].units\n",
    "for n in range(14500,135000):\n",
    "    if ((ferry.variables['s.latitude'][n].mask == False) \n",
    "        and (ferry.variables['s.salinity'][n].mask == False)):\n",
    "        Yind, Xind = geo_tools.find_closest_model_point(ferry.variables['s.longitude'][n], \n",
    "                                                        ferry.variables['s.latitude'][n], \n",
    "                                                        X, Y, land_mask = bathy.mask)\n",
    "        date = nc.num2date(ferry.variables['s.time'][n], unit)\n",
    "        if date.minute <= 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                               hour = (date.hour), minute = 30) - datetime.timedelta(hours=1)\n",
    "            index = np.argmin(np.abs(converted_timesb - date))\n",
    "            delta = (date - before).seconds / 3600\n",
    "            s_val = ((delta * (threemonthsb_sal[index-1, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsb_sal[index, Yind, Xind]))\n",
    "            t_val = ((delta * (threemonthsb_temp[index-1, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsb_temp[index, Yind, Xind]))\n",
    "        if date.minute > 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                                   hour = (date.hour), minute = 30)\n",
    "            index = np.argmin(np.abs(converted_timesb - date))\n",
    "            delta = (date - before).seconds / 3600\n",
    "            s_val = ((delta * (threemonthsb_sal[index, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsb_sal[index+1, Yind, Xind]))\n",
    "            t_val = ((delta * (threemonthsb_temp[index, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsb_temp[index+1, Yind, Xind]))\n",
    "        list_of_ferryb_sals = np.append(list_of_ferryb_sals, \n",
    "                                        ferry.variables['s.salinity'][n])\n",
    "        list_of_ferryb_temps = np.append(list_of_ferryb_temps, \n",
    "                                         ferry.variables['s.temperature'][n])\n",
    "        list_of_modelb_sals = np.append(list_of_modelb_sals, s_val)\n",
    "        list_of_modelb_temps = np.append(list_of_modelb_temps, t_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7efeeeeb5710>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAJcCAYAAAAWzMXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efeefcf5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,10))\n",
    "c, xedge, yedge, im = ax.hist2d(list_of_ferryb_sals, list_of_modelb_sals, bins = 100, norm=LogNorm())\n",
    "im\n",
    "fig.colorbar(im, ax=ax)\n",
    "ax.set_xlabel('Ferry Data')\n",
    "ax.set_ylabel('Model')\n",
    "ax.set_title('Test B salinity, rn_crban = 100, rn_charn = 70000, nn_z0_met = 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bias =  1.79176312479\n",
      "RMSE = 5.34840517666\n",
      "Willmott = 0.767047886945\n"
     ]
    }
   ],
   "source": [
    "print('bias =  ' + str(-np.mean(list_of_ferryb_sals) + np.mean(list_of_modelb_sals)))\n",
    "print('RMSE = ' + str(np.sqrt(np.sum((list_of_modelb_sals - list_of_ferryb_sals)**2) / len(list_of_modelb_sals))))\n",
    "xbar = np.mean(list_of_ferryb_sals)\n",
    "print('Willmott = ' + str(1-(np.sum((list_of_modelb_sals - list_of_ferryb_sals)**2)  / \n",
    "                             np.sum((np.abs(list_of_modelb_sals - xbar) + np.abs(list_of_ferryb_sals - xbar))**2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_of_modelbase_sals = np.array([])\n",
    "list_of_modelbase_temps = np.array([])\n",
    "list_of_ferrybase_sals = np.array([])\n",
    "list_of_ferrybase_temps = np.array([])\n",
    "unit = ferry.variables['s.time'].units\n",
    "for n in range(14500,135000):\n",
    "    if ((ferry.variables['s.latitude'][n].mask == False) \n",
    "        and (ferry.variables['s.salinity'][n].mask == False)):\n",
    "        Yind, Xind = geo_tools.find_closest_model_point(ferry.variables['s.longitude'][n], \n",
    "                                                        ferry.variables['s.latitude'][n], \n",
    "                                                        X, Y, land_mask = bathy.mask)\n",
    "        date = nc.num2date(ferry.variables['s.time'][n], unit)\n",
    "        if date.minute <= 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                               hour = (date.hour), minute = 30) - datetime.timedelta(hours=1)\n",
    "            index = np.argmin(np.abs(converted_timesbase - date))\n",
    "            delta = (date - before).seconds / 3600\n",
    "            s_val = ((delta * (threemonthsbase_sal[index-1, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsbase_sal[index, Yind, Xind]))\n",
    "            t_val = ((delta * (threemonthsbase_temp[index-1, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsbase_temp[index, Yind, Xind]))\n",
    "        if date.minute > 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                                   hour = (date.hour), minute = 30)\n",
    "            index = np.argmin(np.abs(converted_timesbase - date))\n",
    "            delta = (date - before).seconds / 3600\n",
    "            s_val = ((delta * (threemonthsbase_sal[index, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsbase_sal[index+1, Yind, Xind]))\n",
    "            t_val = ((delta * (threemonthsbase_temp[index, Yind, Xind])) + \n",
    "                   (1- delta)*(threemonthsbase_temp[index+1, Yind, Xind]))\n",
    "        list_of_ferrybase_sals = np.append(list_of_ferrybase_sals, \n",
    "                                           ferry.variables['s.salinity'][n])\n",
    "        list_of_ferrybase_temps = np.append(list_of_ferrybase_temps, \n",
    "                                            ferry.variables['s.temperature'][n])\n",
    "        list_of_modelbase_sals = np.append(list_of_modelbase_sals, s_val)\n",
    "        list_of_modelbase_temps = np.append(list_of_modelbase_temps, t_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7efef109ecc0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAJcCAYAAAAWzMXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACATvn/qMAQQnukh4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efef1435588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,10))\n",
    "c, xedge, yedge, im = ax.hist2d(list_of_ferrybase_sals, \n",
    "                                list_of_modelbase_sals, bins = 100, norm=LogNorm())\n",
    "im\n",
    "fig.colorbar(im, ax=ax)\n",
    "ax.set_xlabel('Ferry Data')\n",
    "ax.set_ylabel('Model')\n",
    "ax.set_title('Base Case Salinity, rn_crban = 100, rn_charn = 70000, nn_z0_met = 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bias =  1.52707575253\n",
      "RMSE = 5.24464144716\n",
      "Willmott = 0.777509231541\n"
     ]
    }
   ],
   "source": [
    "print('bias =  ' + str(-np.mean(list_of_ferrybase_sals) + np.mean(list_of_modelbase_sals)))\n",
    "print('RMSE = ' + str(np.sqrt(np.sum((list_of_modelbase_sals - list_of_ferrybase_sals)**2) \n",
    "                              / len(list_of_modelbase_sals))))\n",
    "xbar = np.mean(list_of_ferrybase_sals)\n",
    "print('Willmott = ' + str(1-(np.sum((list_of_modelbase_sals - list_of_ferrybase_sals)**2)  / \n",
    "                             np.sum((np.abs(list_of_modelbase_sals - xbar) \n",
    "                                     + np.abs(list_of_ferrybase_sals - xbar))**2))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  Salinity         | Bias          | RMSE          | WS             |\n",
    "|-----------|---------------|---------------|----------------|\n",
    "| Base Case | 1.52707575253 | 5.24464144716 | 0.777509231541 |\n",
    "| Test A    | 1.94161838071 | 5.56267806197 | 0.743195687801 |\n",
    "| Test B    | 1.79176312479 | 5.34840517666 | 0.767047886945 |"
   ]
  },
  {
   "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
}