{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from salishsea_tools import geo_tools, tidetools, viz_tools, loadDataFRP\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base=nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/threemonthbase/testD/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "testa=nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/threemonthsa/test44/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "testb=nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/threemonthsb/testd/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Bathymetry = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n",
    "bathy, X, Y = tidetools.get_bathy_data(Bathymetry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')\n",
    "tmask = mesh.variables['tmask'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 898, 398) 415 337 1.0\n",
      "(40, 898, 398) 415 337 2.0\n",
      "(40, 898, 398) 442 259 3.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vdo/anaconda3/lib/python3.6/site-packages/numpy/ma/core.py:3883: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  check = self.filled(0).__eq__(other)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 898, 398) 438 268 4.0\n",
      "(40, 898, 398) 434 278 5.0\n",
      "(40, 898, 398) 432 281 6.0\n",
      "(40, 898, 398) 430 285 7.0\n",
      "(40, 898, 398) 428 289 8.0\n",
      "(40, 898, 398) 427 291 9.0\n",
      "(40, 898, 398) 412 291 10.0\n",
      "(40, 898, 398) 443 258 11.0\n",
      "(40, 898, 398) 438 268 12.0\n",
      "(40, 898, 398) 434 278 13.0\n",
      "(40, 898, 398) 432 281 14.1\n",
      "(40, 898, 398) 432 281 14.2\n",
      "(40, 898, 398) 432 287 15.0\n",
      "(40, 898, 398) 432 292 16.0\n",
      "(40, 898, 398) 427 291 17.0\n",
      "(40, 898, 398) 415 337 18.0\n"
     ]
    }
   ],
   "source": [
    "stationdata, casts = loadDataFRP.loadDataFRP_SSGrid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Date_UTC</th>\n",
       "      <th>Time_UTC_hhmmss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>17:54:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>18:05:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>19:44:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>20:25:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>21:05:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>21:40:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>21:58:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>22:30:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>20170410</td>\n",
       "      <td>22:45:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>17:19:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>18:13:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>18:51:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>19:24:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.1</td>\n",
       "      <td>20170531</td>\n",
       "      <td>19:50:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.2</td>\n",
       "      <td>20170531</td>\n",
       "      <td>19:53:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>20:12:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>20:41:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>21:01:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>20170531</td>\n",
       "      <td>22:05:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Station  Date_UTC Time_UTC_hhmmss\n",
       "0       1.0  20170410        17:54:17\n",
       "1       2.0  20170410        18:05:11\n",
       "2       3.0  20170410        19:44:22\n",
       "3       4.0  20170410        20:25:40\n",
       "4       5.0  20170410        21:05:12\n",
       "5       6.0  20170410        21:40:15\n",
       "6       7.0  20170410        21:58:48\n",
       "7       8.0  20170410        22:30:56\n",
       "8       9.0  20170410        22:45:20\n",
       "9      10.0  20170531        17:19:23\n",
       "10     11.0  20170531        18:13:05\n",
       "11     12.0  20170531        18:51:36\n",
       "12     13.0  20170531        19:24:38\n",
       "13     14.1  20170531        19:50:40\n",
       "14     14.2  20170531        19:53:25\n",
       "15     15.0  20170531        20:12:26\n",
       "16     16.0  20170531        20:41:47\n",
       "17     17.0  20170531        21:01:03\n",
       "18     18.0  20170531        22:05:25"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stationdata[['Station', 'Date_UTC', 'Time_UTC_hhmmss']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2.54296153e-01   2.37629285e-01   1.30744024e-01   6.30877904e-02\n",
      "   5.72300684e-01   5.66630045e-01   1.41610612e+00   8.58189202e-01\n",
      "   4.49425967e+00   3.62388824e+00   4.97102531e+00   3.80620648e+00\n",
      "   4.89693031e-01   1.48976142e+00   3.45346092e+00   8.78231892e-01\n",
      "   8.34433050e+00   6.37323388e+00   3.56028501e+00   3.12093266e+00\n",
      "   1.65439135e+00   1.04003063e+00   7.74946046e+00   5.05542847e+00\n",
      "   3.26381849e+00   3.47950290e+00   3.87288871e+00   3.30430298e+00\n",
      "   5.97097529e+00   4.55471703e+00   7.44537821e+00   6.21241326e+00\n",
      "   4.72306170e+00   5.00925346e+00   1.45078415e-05   9.14688210e-05]\n",
      "[  9.00183487   8.00092983   2.00000715   2.00000715   4.00005054\n",
      "   5.00011063   3.00002098   3.00002098   2.00000715   3.00002098\n",
      "   2.00000715   2.00000715   6.00023079   2.00000715   2.00000715\n",
      "   2.00000715   2.00000715   2.00000715   4.00005054   4.00005054\n",
      "   3.00002098   5.00011063   2.00000715   2.00000715   5.00011063\n",
      "   4.00005054   5.00011063   4.00005054   4.00005054   4.00005054\n",
      "   3.00002098   2.00000715   3.00002098   4.00005054  10.00359917\n",
      "   5.00011063]\n"
     ]
    }
   ],
   "source": [
    "max_dsdz = np.array([])\n",
    "depth_of_max_dsdz = np.array([])\n",
    "for n in range(1,19):\n",
    "    if n == 14:\n",
    "        depths = casts[14.1].dCast['depth_m']\n",
    "        dz = casts[14.1].dCast['depth_m'].values[1:] - casts[14.1].dCast['depth_m'].values[:-1]\n",
    "        ds = casts[14.1].dCast['gsw_srA0'].values[1:] - casts[14.1].dCast['gsw_srA0'].values[:-1]\n",
    "        depth_of_max_dsdz = np.append(depth_of_max_dsdz, \n",
    "                                      (depths[np.nanargmax(ds / dz)] \n",
    "                                       + depths[np.nanargmax(ds / dz) + 1])/2) \n",
    "        max_dsdz = np.append(max_dsdz, np.nanmax(ds / dz))\n",
    "        depths = casts[14.2].uCast['depth_m']\n",
    "        dz = casts[14.2].uCast['depth_m'].values[1:] - casts[14.2].uCast['depth_m'].values[:-1]\n",
    "        ds = casts[14.2].uCast['gsw_srA0'].values[1:] - casts[14.2].uCast['gsw_srA0'].values[:-1]\n",
    "        depth_of_max_dsdz = np.append(depth_of_max_dsdz, \n",
    "                                      (depths[np.nanargmax(ds / dz)] \n",
    "                                       + depths[np.nanargmax(ds / dz) + 1])/2) \n",
    "        max_dsdz = np.append(max_dsdz, np.nanmax(ds / dz))\n",
    "    else:\n",
    "        depths = casts[n].dCast['depth_m']\n",
    "        dz = casts[n].dCast['depth_m'].values[1:] - casts[n].dCast['depth_m'].values[:-1]\n",
    "        ds = casts[n].dCast['gsw_srA0'].values[1:] - casts[n].dCast['gsw_srA0'].values[:-1]\n",
    "        depth_of_max_dsdz = np.append(depth_of_max_dsdz, \n",
    "                                      (depths[np.nanargmax(ds / dz)] \n",
    "                                       + depths[np.nanargmax(ds / dz) + 1])/2) \n",
    "        max_dsdz = np.append(max_dsdz, np.nanmax(ds / dz))\n",
    "        depths = casts[n].uCast['depth_m']\n",
    "        dz = casts[n].uCast['depth_m'].values[1:] - casts[n].uCast['depth_m'].values[:-1]\n",
    "        ds = casts[n].uCast['gsw_srA0'].values[1:] - casts[n].uCast['gsw_srA0'].values[:-1]\n",
    "        depth_of_max_dsdz = np.append(depth_of_max_dsdz, \n",
    "                                      (depths[np.nanargmax(ds / dz)] \n",
    "                                       + depths[np.nanargmax(ds / dz) + 1])/2) \n",
    "        max_dsdz = np.append(max_dsdz, np.nanmax(ds / dz))\n",
    "print(max_dsdz)\n",
    "print(depth_of_max_dsdz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "depths_base = np.array([])\n",
    "max_dsdz_base = np.array([])\n",
    "depths_a = np.array([])\n",
    "max_dsdz_a = np.array([])\n",
    "depths_b = np.array([])\n",
    "max_dsdz_b = np.array([])\n",
    "for n in range(19):\n",
    "    station = stationdata.iloc[[n]]\n",
    "    cast = casts[station['Station'].values[0]]\n",
    "    Yind, Xind = geo_tools.find_closest_model_point(station['LonDecDeg'].values[0], \n",
    "                                                    station['LatDecDeg'].values[0],\n",
    "                                                    X, Y, land_mask = bathy.mask)\n",
    "    if n == 13:\n",
    "        shape_depth = cast.dCast['depth_m'].values.shape[0]\n",
    "    else:\n",
    "        shape_depth = cast.uCast['depth_m'].values.shape[0]\n",
    "    if n < 9:\n",
    "        base=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthbase/testI/SalishSea_1h_20170526_20170604_grid_T_20170531-20170531.nc')\n",
    "        testa=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsa/test99/SalishSea_1h_20170526_20170604_grid_T_20170531-20170531.nc')\n",
    "        testb=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsb/testi/SalishSea_1h_20170526_20170604_grid_T_20170531-20170531.nc')\n",
    "    else:\n",
    "        base=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthbase/testD/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "        testa=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsa/test44/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "        testb=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsb/testd/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "    if int(station['Time_UTC_hhmmss'].values[0][3:5]) < 30:\n",
    "        delta = (30 + int(station['Time_UTC_hhmmss'].values[0][3:5])) / 60\n",
    "        before = int(station['Time_UTC_hhmmss'].values[0][:2]) - 1\n",
    "    else:\n",
    "        delta = (int(station['Time_UTC_hhmmss'].values[0][3:5]) - 30) / 60\n",
    "        before = int(station['Time_UTC_hhmmss'].values[0][:2])\n",
    "    pt_mask = tmask[0,:shape_depth,Yind,Xind]\n",
    "    deptht = base.variables['deptht'][:shape_depth]\n",
    "    base_sal = np.ma.masked_array(delta * base.variables['vosaline'][before,:shape_depth,Yind, Xind]\n",
    "                                  + (1-delta)*base.variables['vosaline'][before+1,:shape_depth,Yind, Xind],\n",
    "                                  mask = 1-pt_mask)\n",
    "    a_sal = np.ma.masked_array(delta*testa.variables['vosaline'][before,:shape_depth,Yind, Xind]\n",
    "                               +(1-delta)*testa.variables['vosaline'][before+1,:shape_depth,Yind,Xind], \n",
    "                               mask = 1-pt_mask)\n",
    "    b_sal = np.ma.masked_array(delta*testb.variables['vosaline'][before,:shape_depth,Yind, Xind]\n",
    "                               +(1-delta)*testb.variables['vosaline'][before+1,:shape_depth,Yind,Xind], \n",
    "                               mask = 1-pt_mask)\n",
    "    dz = deptht[1:] - deptht[:-1]\n",
    "    ds_base = base_sal[1:] - base_sal[:-1]\n",
    "    ds_a = a_sal[1:] - a_sal[:-1]\n",
    "    ds_b = b_sal[1:] - b_sal[:-1]\n",
    "    if n == 13:\n",
    "        depths_base = np.append(depths_base, (deptht[np.nanargmax(ds_base / dz)] \n",
    "                                            + deptht[np.nanargmax(ds_base / dz) + 1])/2) \n",
    "        max_dsdz_base = np.append(max_dsdz_base, np.nanmax(ds_base / dz))\n",
    "        depths_a = np.append(depths_a, (deptht[np.nanargmax(ds_a / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_a / dz) + 1])/2)\n",
    "        max_dsdz_a = np.append(max_dsdz_a, np.nanmax(ds_a / dz))\n",
    "        depths_b = np.append(depths_b, (deptht[np.nanargmax(ds_b / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_b / dz) + 1])/2)\n",
    "        max_dsdz_b = np.append(max_dsdz_b, np.nanmax(ds_b / dz))\n",
    "    elif n == 14:\n",
    "        depths_base = np.append(depths_base, (deptht[np.nanargmax(ds_base / dz)] \n",
    "                                            + deptht[np.nanargmax(ds_base / dz) + 1])/2) \n",
    "        max_dsdz_base = np.append(max_dsdz_base, np.nanmax(ds_base / dz))\n",
    "        depths_a = np.append(depths_a, (deptht[np.nanargmax(ds_a / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_a / dz) + 1])/2)\n",
    "        max_dsdz_a = np.append(max_dsdz_a, np.nanmax(ds_a / dz))\n",
    "        depths_b = np.append(depths_b, (deptht[np.nanargmax(ds_b / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_b / dz) + 1])/2)\n",
    "        max_dsdz_b = np.append(max_dsdz_b, np.nanmax(ds_b / dz))\n",
    "    else:    \n",
    "        depths_base = np.append(depths_base, (deptht[np.nanargmax(ds_base / dz)] \n",
    "                                            + deptht[np.nanargmax(ds_base / dz) + 1])/2) \n",
    "        max_dsdz_base = np.append(max_dsdz_base, np.nanmax(ds_base / dz))\n",
    "        depths_base = np.append(depths_base, (deptht[np.nanargmax(ds_base / dz)] \n",
    "                                            + deptht[np.nanargmax(ds_base / dz) + 1])/2) \n",
    "        max_dsdz_base = np.append(max_dsdz_base, np.nanmax(ds_base / dz))\n",
    "        depths_a = np.append(depths_a, (deptht[np.nanargmax(ds_a / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_a / dz) + 1])/2)\n",
    "        max_dsdz_a = np.append(max_dsdz_a, np.nanmax(ds_a / dz))\n",
    "        depths_a = np.append(depths_a, (deptht[np.nanargmax(ds_a / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_a / dz) + 1])/2)\n",
    "        max_dsdz_a = np.append(max_dsdz_a, np.nanmax(ds_a / dz))\n",
    "        depths_b = np.append(depths_b, (deptht[np.nanargmax(ds_b / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_b / dz) + 1])/2)\n",
    "        max_dsdz_b = np.append(max_dsdz_b, np.nanmax(ds_b / dz))\n",
    "        depths_b = np.append(depths_b, (deptht[np.nanargmax(ds_b / dz)] \n",
    "                                        + deptht[np.nanargmax(ds_b / dz) + 1])/2)\n",
    "        max_dsdz_b = np.append(max_dsdz_b, np.nanmax(ds_b / dz))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  -0.029609549186\n",
      "test a bias =  0.0927777573718\n",
      "test b bias =  -0.0380832070854\n"
     ]
    },
    {
     "data": {
      "image/png": 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bW6VIJDNNFzcyAQAAAKQdzR4AAAAAAAAPodkDAAAAAADgITR7AABwWigkBQJa\nWVcnBQKJay9muqCls1crNrfqhp+9qhWbW9XS2ev2kgAAAFxX5PYCAADwtFBIamiQYjEZSeruTlxL\nzs1FciPTBS2dvdq0bZ/6B4YkSb19/dq0bZ8kqb623M2lAQAAuIqdPQAAOKmxUYrFkmuxWKLupUwX\nbNkeHmv0jOofGNKW7WGXVgQAAJAdaPYAAOCknp6p1XM10wUv9PVPqQ4AAJAvaPYAAOCkioqp1XM1\n0wXzZ5dOqQ4AwGSYBQevoNkDAICTmpokny+55vMl6l7KdMHGNdUqLS5MqpUWF2rjmmqXVgQAyGWj\ns+B6R3aIjs6Co+GDXESzBwAAJwWDUnOzVFkpa4xUWZm4dnJQshuZLqivLdftaxerfGQnT/nsUt2+\ndjHDmQEAZ4RZcPASTuMCAMBpwaAUDGpne7tWrVrl3UwX1NeWq762XO0e/z4BAM5jFhy8hJ09AAAA\nAIC8xyw4eElKzR5jzGxjzAPGmGeMMU8bY5Y7vTAAQB44EpIOBLTSXycdCCSuvZgJAACyHrPg4CWp\n7uz5sqSfWWsvkLRU0tPOLQnAGeFNM3LNkZB0sEEa7JYxVhrsTlw7+fvIjUwAAJATmAUHL5l0Zo8x\npkzS5ZJukCRr7XFJx51dFoApGX0Da2MyRq+9gZWkMocGsrqRCW851CjZWHLNxhJ1p34PuZEJAECa\ntXT2asv2sHr7+lX+aKs2rqmmIZEmzIKDV6Sys+c8SYckfdsY02mM+aYx5myH1wVgKk73BtZLmfCW\nwZ6p1XM1EwCANOJ4cACpMNba0z/AmIslPSpphbX2V8aYL0v6k7X2syc9rkFSgyT5/f5lW7dudWjJ\nmXP06FHNnDmTTDKzPnOlvy5xS8pJrDXaGW31TOaJvPzrmS+Zf/6GazWjKHpK/digX4++5MzfIW5k\nnsjLv55kkkkmmWRmJvP/tMd0+Nipr8HmzDD6f6t8jud7+eeWTDJzwerVq/dYay+e9IHW2tN+SJon\nKXLC9WWSfny6r1m2bJn1gra2NjLJzI3M31Va+7RO/fhdpbcyT+DpX898yey719pnfMm/f57xJepe\nyjyBp389ySSTTDLJzEhO4NM/spXjfAQ+/aOM5Hv555ZMMnOBpMftJH0ca+3kt3FZaw9Kes4YMzqC\n/O2SfnOGTSgATpjbJJmT/iXH+BJ1L2XCW8qC0rxmqahS1hqpqDJx7eTsHDcyJQaoAwDShuPBAaQi\n1dO4/kYKeJlwAAAgAElEQVRSyBjzlKQaSbc5tyQAU5ZPb5rhLWVBaWEkcevfwkhmfv9kOpNTxwAA\nacTx4ABSkVKzx1q711p7sbV2ibW23lr7stMLAzBF+fCmGchFDFAHAKQRx4MDSMWkR68DAIBp4NQx\nAECacTw4gMmkehsXAAA4E0UVU6vnaiYAAACyBs0eAACcxAB1AAAAZBjNHgAAnJRPA9Q5dQwAACAr\n0OwBAMBp+TBAnVPHAAAAsgbNHgAAMH2cOgYAAJA1aPYAAIDp49QxAACArEGzBwAATB+njgEAAGQN\nmj0AAGD6OHUMAAAga9DsAQAA05dPp44BAABkOZo9AAAgPfLh1DEAAIAcQLMHAAAAAADAQ2j2AAAA\nAACyTjQaUkdHQFKdOjoCikZDnswEnECzBwAAAABySSgkBQJaWVcnBQKJa49lRqMhhcMNise7JVnF\n490Khxscbb64kQk4hWYPAAAAAOSKUEhqaJC6u2Wslbq7E9dONl9cyOzqatTwcCypNjwcU1dXo6cy\nAafQ7AEAAACAXNHYKMWSGxKKxRJ1D2XG4z1TqudqJuAUmj0AAAAAkCt6Jmg8TFTP0cySkoop1XM1\nE3AKzR4AAAAgg1o6e7Vic6tu+NmrWrG5VS2dvW4vCbmkYoLGw0T1HM2sqmpSQYEvqVZQ4FNVVZOn\nMgGn0OwBAAAAMqSls1ebtu1Tb1+/JKm3r1+btu2j4YPUNTVJvuSGhHy+RN1DmX5/UNXVzSopqZRk\nVFJSqerqZvn9QU9lAk6h2QMAAABkyJbtYfUPDCXV+geGtGV72KUVIecEg1Jzs1RZKWuMVFmZuA46\n2JBwI1OJ5svy5RFJrVq+PJKRposbmW5gh6H30ewBgCzEX8AA4E0vjOzoSbUOjCsYlCIR7WxtlSIR\nx5surmXCEewwzA80ewDkllBICgS0sq5OCgScPWbUpUz+AgYA75o/u3RKdQBIN3YY5geaPQByRygk\nNTRI3d0y1krd3YlrJ5svLmTyFzAAeNfGNdUqLS5MqpUWF2rjmmqXVgQg37DDMD/Q7AGQOxobpVgs\nuRaLJeoeyuQvYADwrvract2+drHKR3bylM8u1e1rF6u+ttzllQHIF+wwzA80ewDkjp6eqdVzNJO/\ngAHA2+pry7Xr5jrdc9XZ2nVzHY0eABnFDsP8QLMHQO6oqJhaPUcz+QsYAABA0pGQdCCglf466UAg\nce3FzAxjh2F+oNkDIHc0NUk+X3LN50vUPZTJX8AAACDvHQlJBxukwW4ZY6XB7sS1k80XNzLlzims\n7DD0Ppo9AHJHMCg1N0uVlbLGSJWViWsnj/50I1P8BQwAAPLcoUbJnjQ30cYSdQ9lcgornEKzB0Bu\nCQalSEQ7W1ulSMTxpotrmQAAABPJh9ubBieYjzhRPUczOYUVTqHZAwAAAAC5Il9ubyqaYD7iRPUc\nzeQUVjiFZg8AAAAA5Io8ub1Jc5skc9LcRONL1D2UySmscArNHgAAAADIFXlye5PKgtK8ZqmoUtYa\nqagycV3m4O30LmRyCiucQrMHAAAAAHKFC7caxYbnT6meNmVBaWFEO6Ot0sKIs40elzI5hRVOodkD\nIKe4cTQlAABA1pjbpGGdlVQa1lmO3mr0f/dcr/hQUVItPlSk/7vnescy8wmnsMIJNHsA5IyWzl5t\n2/VlfXLptfr2mnfqk0uv1bZdX3a84RONhtTREZBUp46OgKLRDJx4AQAAMI7oMSl8xOrYoGStdGww\ncR095lxm+I/H9UxfcuYzfVbhPx53LlSSQiEpENDKujopEEhcO82NTMABRZM/BACyw88f/5qCF9yh\nkqK4JOkNpYcUvOAOff/xItXX3uZIZjQaUjjcoOHhxFDCeLxb4XCDJMnv5wh2AACQWV1djYrHBxRN\nOqxpQH1djY69Nll3wX/o5YEhPfrSidUhrbvgPyR90ZFMhUJSQ4MUi8lIUnd34lqSgg69BnMjE3AI\nO3sA5Iy6c7451ugZVVIUV90533Qss6urcazRM2p4OKauLgdPnwAAAJhAPD7+UOSJ6ukwu+TQlOpp\n0dgoxU46ASwWS9S9lAk4hGYPgJwxZ8ZLU6qngxsvqAAAACZSUjL+IOaJ6ukwY4LnnqieFj0TvNaa\nqJ6rmYBDaPYAyBnDBeOf+DBRPR3ceEEFAAAwkaqqJhUU+JJqBQU+VVU5N6DZjUxVTPBaa6J6rmYC\nDqHZAyBnLLrgC7IqTapZlWrRBV9wLNOVFzcAAAAT8PuDqq5uVklJpSSjkpJKVVc3OzpL0I1MNTVJ\nvuTXYPL5EnUvZQIOodkDIGf4/UFddOFdSS80LrrwLu+9uAEAADgNvz+o5csjklq1fHkkI69LMp4Z\nDErNzVJlpawxUmVl4trJQcluZAIOodkDIKfkxYsbAAAAJJoskYh2trZKkUhmmi5uZAIOoNkDAAAA\nAADgITR7AAAAAAAAPIRmDwAAAAAAgIek3OwxxhQaYzqNMT9yckEAAAAAAAA4c1PZ2fMJSU87tRAA\nAAAAAABMX0rNHmPMOZL+QtI3nV0OAAAAAAAApiPVnT3/KunvJQ07uBYAAAAAAABMk7HWnv4Bxvyl\npKuttTcZY1ZJ+pS19i/HeVyDpAZJ8vv9y7Zu3erAcjPr6NGjmjlzJplkkkkmmWSSSSaZZJKZA5mP\nvDCgB387oMPHhjVnRoHefX6x/uf84oxke/3nlkwyycwOq1ev3mOtvXjSB1prT/sh6XZJz0uKSDoo\nKSbp3tN9zbJly6wXtLW1kUlmzmQePHivfeSRStvWZuwjj1Tagwfv9WTmKK//epJJJplkkkkmmVPz\n0BPP2ws+81Nb+ekfjX1c8Jmf2oeeeD4j+V7+uSWTTDKzh6TH7SR9HGvt5LdxWWs3WWvPsdYGJF0r\nqdVae90Zt6EApF00GlI43KB4vFuSVTzerXC4QdFoyFOZcFZLZ69WbG7VDT97VSs2t6qls9ftJQEA\nkLIt28PqHxhKqvUPDGnL9rBLKwIA90zlNC4AWaqrq1HDw7Gk2vBwTF1djZ7KhHNaOnu1ads+9fb1\nS5J6+/q1ads+Gj7AeI6EpAMBrfTXSQcCiWsvZgI55oWRv8NSrQOAl02p2WOtbbfjzOsB4K54vGdK\n9VzNhHP411AgRUdC0sEGabBbxlhpsDtx7WTzxY1MIAfNn106pToAeBk7ewAPKCmpmFI9VzPhHP41\nFEjRoUbJJu9qlI0l6l7KBHLQxjXVKi0uTKqVFhdq45pql1YEAO6h2QN4QFVVkwoKfEm1ggKfqqqa\nPJUJ5/CvoUCKBifYvThRPVczgRxUX1uu29cuVvnI313ls0t1+9rFqq8td3llAJB5NHsAD/D7g6qu\nblZJSaUko5KSSlVXN8vvD3oq0y35MLiYfw0FUlQ0we7Fieq5mgnkqPracu26uU73XHW2dt1cR6MH\nQN6i2QN4hN8f1PLlEUmtWr48kpGmixuZmZYvg4v511AgRXObJJO8q1HGl6h7KRMAAOQ0mj0AcBr5\nNLiYfw0FUlAW1O74F3Qw9kYNW6ODsTdqd/wLUpmDze6yoDSvWSqqlLVGKqpMXDuZCQAAclqR2wsA\ngGzG4GIAJ0rs9luo/oG7x2qlxYW6fajX2QZpWVAqC2pne7tWrVrlXA4AAPAEdvYAwGkwuBjAifJp\ntx8wXfkw8w4AshXNHgA4DQYXIx14w+Md7PYDUpMvM+8AIFvR7AGA02BwMaaLNzzewm4/IDXsggMA\nd9HsAYBJMLgY08EbHm9htx+QGnbBAYC7aPYAAOAg3vB4C7v9gNSwCw4A3EWzBwAAB/GGx3vY7QdM\nLp92wTGXDUA2otkDILccCUkHAlrpr5MOBBLXXsyEZ+TTGx4A2SvTDYl82QXHXDYA2arI7QUAQMqO\nhKSDDZKNyRhJg92Ja0kqC3onE54y+sZmy/awevv6VT67VBvXVHvuDQ+A7DXakBidHzbakJDk6P+L\n6mvLVV9brvb2dq1atcqxHDedbi4b/58H4CZ29gDIHYcaJRtLrtlYou6lTHgOt/0AcBOD4p3DXDYA\n2YpmD4DcMdgztXquZgLASZgJgumgIeEc5rIByFY0ewDkjqKKqdVzNRMATsBMEEwXDQnnMJcNQLai\n2QMgd8xtkowvuWZ8ibqXMgHgBNyCg+miIeGcfBlEDSD3MKAZQO4YHYh8qFF2oEemuCLRdHFyULIb\nmQBwAm7BwXQxKN5Z+TCIGkDuodkDILeUBaWyoHZm8gWVG5kAMGL+7NKxW7hOrgOpoiEBAPmF27gA\nAACyGLfgAACAqaLZA3hFKCQFAlpZVycFAolrL2YCQJ5hJggAAJgqbuMCvCAUkhoapFhMRpK6uxPX\nkhR0aLaMG5kAkKe4BQcAAEwFO3sAL2hslGKx5Foslqh7KRMAAAAAMCmaPYAX9PRMrZ6rmQAAAACA\nSdHsAbygomJq9VzNBAAAOI1oNKSOjoCkOnV0BBSNOj9P0I1MAJgMzR7AC5qaJJ8vuebzJepeygQA\nAJhANBpSONygeLxbklU83q1wuMHR5osbmQCQCpo9gBcEg1Jzs1RZKWuMVFmZuHZyULIbmQAAABPo\n6mrU8HDyPMHh4Zi6upybJ+hGJgCkgmYP4BXBoBSJaGdrqxSJZKbp4kYmAADAOOLx8ecGTlTP1UwA\nSAXNHgAAAAA5r6Rk/LmBE9VzNRMAUkGzBwAAAEDOq6pqUkFB8jzBggKfqqqcmyfoRiYApIJmDwAA\nAICc5/cHVV3drJKSSklGJSWVqq5ult/v3G3mbmQCQCpo9gAAAADwBL8/qOXLI5JatXx5JCNNFzcy\nAWAyNHsAAAAAAAA8hGYPAAAAAACAh9DsAQAAAAAA8BCaPQAAAAC84UhIOhDQSn+ddCCQuAayXSgk\nBQJaWVcnBQKJay9mIqOK3F4AAAAAAEzbkZB0sEGyMRkjabA7cS1JZQxNRpYKhaRt66VvDci8SdIf\nuqWvr098LujQ71s3MpFx7OwBAAAAkPsONUo2llyzsUTdSewmwnT84hPSZwakciXenZcrcf2LT3gr\nExnHzh4AAAAAuW+wZ2r1dGA3EabrA4el0pNqpSN1L2Ui49jZAwAAACD3FVVMrZ4Obu0mgne8aYr1\nXM1ExtHsAQAAAJD75jZJxpdcM75E3Slu7CaCtwzMmVo9VzORcTR7AAAAAOS+sqA0r1kqqpS1Riqq\nTFw7eTuVG7uJ4C2BL0tDZyXXhs5K1L2UiYyj2QMAAADAG8qC0sKIdkZbpYUR5+fmuLGbCN5SFpTO\nuTu5SXnO3c7+3nUjExlHswcAAAAAzoQbu4ngPZluUrqViYyatNljjDnXGNNmjPmNMebXxhjOYwMA\nAAAAiTfNALJSKkevD0r6P9baJ4wxsyTtMcb8wlr7G4fXBgAAAAAAgCmadGePtfYP1tonRv77FUlP\nSyp3emEAAADZKBoNqaMjIKlOHR0BRaMhT2YCAIDclcrOnjHGmICkWkm/cmIxAAAA2SwaDSkcbtDw\ncEySFI93KxxukCT5/c7cuuFGJgAAyG3GWpvaA42ZKWmnpCZr7bZxPt8gqUGS/H7/sq1bt6Zzna44\nevSoZs6cSWaOZ75xxg5VzfymSgpfVHzojeo6+hG9eOwKz2WO8vqvJ5lkkkmmu5nXSoqOU/dLcuq1\njxuZr/H2ryeZZJJJJplk5pbVq1fvsdZePOkDrbWTfkgqlrRd0t+l8vhly5ZZL2hrayMz1zP77rX2\nGZ+1T+u1j2d8ibqXMk/g6V9PMskkk0yXM9vajG1r0zgfxlOZyfltGckhk0wyySSTTC9lOkXS4zaF\nvkwqp3EZSd+S9LS19l+m14MCMuxQo2RjyTUbS9S9lAkAyIiSkoop1XM1EwAA5LZJmz2SVkj6gKQ6\nY8zekY+rHV4XkB6DPVOr52omACAjqqqaVFDgS6oVFPhUVdXkqUwAAJDbJh3QbK19WJLJwFqA9Cuq\nkAa7x697KRMAkBGjA5G7uhoVj/eopKRCVVVNjg5KdiMTAADktlR29gC5a26TZJL/NVTGl6h7KRMA\nkDF+f1DLl0cktWr58khGmi5uZAIAgNxFswfeVhaU5jVLRZWy1khFlYnrMgdfJLuRCQBAmrV09mrF\n5lbd8LNXtWJzq1o6e91eEjC5UEgKBLSyrk4KBBLXAJCHaPbA+8qC0sKIdkZbpYWRzDRd3MgEACBN\nWjp7tWnbPvX29UuSevv6tWnbPho+yG6hkNTQIHV3y1grdXcnrp1u+NBgApCFaPYAAAAgyZbtYfUP\nDCXV+geGtGV72KUVASlobJRiJ52IGosl6k5xq8EEAJOg2QMAAIAkL4zs6Em1DmSFnglOPp2ong5u\nNJgAIAU0ewAAeYU5JMDk5s8unVIdyAoVE5x8OlE9HdxoMAFACmj2AADyBnNIgNRsXFOt0uLCpFpp\ncaE2rql2aUVACpqaJN9JJ6L6fIm6U9xoMAFACmj2AB7BbgVgcswhAVJTX1uu29cuVvnITp7y2aW6\nfe1i1deWu7wy4DSCQam5WaqslDVGqqxMXAcdPCjDjQYTPCcaDamjIyCpTh0dAUWjzs98ciMTmVXk\n9gIATN/oboXRN7GjuxUk8cIcOAFzSIDU1deWq762XO3t7Vq1apXbywFSEwxKwaB2Zur37WgjqbFR\ntqdHpqIi0ehxssEET4lGQwqHGzQ8nJj9FI93KxxukCT5/c78PnIjE5nHzh7AA9itAKSGOSQAgLQL\nBqVIRDtbW6VIhEYPpqSrq3Gs6TJqeDimri7nhny7kYnMo9kDeAC7FYDUMIcEaREKSYGAVtbVSYFA\nZo5YdiMTAOC4eHz8Yd4T1XM1E5lHswfwAHYrAKlhDgmmLRSSGhqk7m4Za6Xu7sS1k80XNzIBABlR\nUjL+MO+J6rmaicyj2QN4ALsVgNTV15Zr1811uueqs7Xr5joaPZiaxkYplrz1XbFYou6lTABARlRV\nNamgIHnId0GBT1VVzg35diMTmceAZsADRt+sbtkeVm9fv8pnl2rjmmrexAJAuvVMsMV9onquZgIA\nMmJ0IHJXV6Pi8R6VlFSoqqrJ0UHJbmQi89jZA+/Lk9kK7FYAgAyomGCL+0T1XM0EAGSM3x/U8uUR\nSa1avjySkaaLG5nILJo98DZmKwAA0qmpSfIlb32Xz5eoeykTAADkNJo98DZmKwAA0ikYlJqbpcpK\nWWOkysrEtZNHLbuRCQAAchrNHngbsxUAAOkWDEqRiHa2tkqRSGaaLm5kuqCls1crNrfqhp+9qhWb\nW9XS2ev2kgAAyEk0e+BtzFYAACAntHT2atO2fert65ck9fb1a9O2fTR80iVPZhgCABJo9sDbmK0A\nAEBO2LI9rP6BoaRa/8CQtmwPu7QiD2GGIQDkHZo98DZmKwAAkBNeGNnRk2odU8AMQwDIOzR74H3M\nVgAAIOvNn106pTqmgBmGAJB3aPYAOGMM0gSyF38+kWs2rqlWaXFhUq20uFAb11S7tCIPYYYhAOQd\nmj0AzgiDNIHsxZ9P5KL62nLdvnaxykd28pTPLtXtaxervrbc5ZV5ADMMASDv0OwBcEYYpAlkL/58\nIlfV15Zr1811uueqs7Xr5joaPenCDEMAyDs0ewCcEQZpAtmLP58ATsEMQwDIKzR7AJwRBmkC2Ys/\nnwAAAPmNZg88LxoNqaMjIKlOHR0BRaMhT2ZmGoM0gezFn08AAID8RrMHnhaNhhQONyge75ZkFY93\nKxxucLT54kamGxikCWQv/nwCOMWRkHQgoJX+OulAIHHtxUwAgCSpyO0FAE7q6mrU8HAsqTY8HFNX\nV6P8fmfuG3cj0y31teWqry1Xe3u7Vq1a5fZyAJyAP58AxhwJSQcbJBuTMZIGuxPXklTm0GsTNzIB\nAGPY2QNPi8d7plTP1UwAAIAJHWqUbPI/RMnGEnUvZQIAxtDsgacNmflTqudqJgAAwIQGJ/gHp4nq\nuZoJABhDswee9uBvr1d8sCSpFh8s0YO/vd5TmUCuauns1YrNrbrhZ69qxeZWtXT2ur0kAPCeooqp\n1XM1EwAwhmYPPO2nz67Qt3/9cb3UP1fWGr3UP1ff/vXH9dNnV3gqE8hFLZ292rRtn3r7+iVJvX39\n2rRtHw0fAEi3uU2S8SXXjC9R91ImAGAMA5rhafNnl+rRP6zWo39YnVQfPaHGK5lALtqyPaz+gaGk\nWv/AkLZsD3NqFACk0+hA5EONsgM9MsUViaaLk4OS3cgEAIxhZw88beOaapUWFybVSosLtXFNtacy\ngVz0wsiOnlTrAOCIUEgKBLSyrk4KBBLXXswsC0oLI9oZbZUWRjLTdHEjEwAgiZ098LjR3QFbtofV\n29ev8tml2rim2tFdA25kArlo/uzSsVu4Tq4DQEaEQlJDgxSLyUhSd3fiWpKCDjUm3MgEAOQddvbA\n8+pry7Xr5jrdc9XZ2nVzXUaaLm5kArmGXXAAXNfYKMVOOh48FkvUvZQJAMg77OwBALiCXXAAXNcz\nwTHgE9VzNRMAkHdo9oyjpbP3tTcfj7Zm5M2HG5kA4Lb62nLV15arvb1dq1atcns5APJNRYW0qFv6\npKQ3SfqDpC9J2u/g8eBuZAIA8g63cZ3EjaOAOX4YAABkm+i2m9TxQJE0vFodDxQpuu0m72V+/Wrp\nnyWVK/GquFyJ669f7a1MSdFoSB0dAUl16ugIKBp1fii0G5kAgASaPSc53VHAXsoEAACYSHTbTfrj\n3G+o9oIhrXyTVHvBkP449xuONl/cyNT5P5FOnglfOlL3UGY0GlI43KB4vFuSVTzerXC4wdHmixuZ\nAIDX0Ow5iRtHAbt1/HBLZ69WbG7VDT97VSs2t7KTCAAASJKOzLpT58+RZhRJxiR+PH9Oou6lTA1O\nMCdnonqOZnZ1NWp4OHko9PBwTF1dzg2FdiMTAPAamj0nmejIXyePAnYjk1vHAADARCreZFV40qvE\nwoJE3UuZKppgTs5E9RzNjMfHbyRNVM/VTADAa2j2nMSNo4DdyHTr1jF2EwEAkP1KCqdWz9XMI2dd\nraGTeklDNlH3UmZJyfiNpInquZoJAHhNSqdxGWOukvRlSYWSvmmt3ezoqlxUX1uui35/rSoWPqyS\nYik+IPUceJvOr/1vT2W+0Nevr1Q26soVT45l/mLXUm3obnIss6WzV8W/+kv919V7xzL/65EatehH\nnDwGAEAWGR6YqcKzjo5f91Dmb3p/ojIjVc1MNJXiQ1LXUelI30+03KGehBuZVVVNCocbkm6rKijw\nqarKudd9bmQCyG6/3XaZKhY+rJV+6dhTI+951zr3ntetzGwx6c4eY0yhpK9J+l+SLpL0V8aYi5xe\nmFuO7LhCCy54WDPOGrlf/CxpwQUP68iOKzyV+W8L/lFXr3wyKfPqlU/q3xb8o2OZvsev0VWX703K\nvOryvfI9fo1jmQAAYOoKK+/U8HDyvwkODxepsNK5+TluZMbjPXrxmPToS9LOaOLHF485f3tTpjP9\n/qCqq5tVUlIpyaikpFLV1c3y+4OeygSQvX677bJx3/P+dttlnsrMJqncxnWJpAPW2i5r7XFJWyX9\nf84uyz0lb/yvce8XL3njf3kq8/JLnxg38/JLn/BUJgAAOANlQRWU3yMVVcpaIxVVJq7LHHyj7kJm\nPt3e5PcHtXx5RFKrli+PZKTp4kYmgOxUsfDh8eeyLXzYU5nZxFh7+qF3xpj3SLrKWvuRkesPSLrU\nWvvxkx7XIKlBkvx+/7KtW7c6s2KHrfSvljGn1q2VdkbbyMyxzBMdPXpUM2fOdDyHTDLJJJNMMsnM\nlcwdkr4oKX5CrUTSpyQ5tcPajczXePvXk0wyyczWzHx8/+mU1atX77HWXjzpA621p/2Q9B4l5vSM\nXn9A0ldP9zXLli2zuar/SVn79Kkf/U+KzBzMPFFbW1tGcsgkk0wyySSTzNzJPHjwXvvII5W2rc3Y\nRx6ptAcP3uvJzFFe//Ukk0wyszMzH99/OkXS43aSPo61NqXbuHolnXvC9TkjNU+Kv/h2DQ0n14aG\nE3Uycy8TAADgdLi9CQCc13PgbeO+F+w58DZPZWaTVJo9uyX9D2PMecaYsyRdK+kHzi7LPWVX7NDR\nF96uY8cT27uOHZeOvvB2lV2xg8wczAQAAAAAuOv8tf+tZ595W9J7wWefcfZkLDcys8mkR69baweN\nMR+XtF2Jo9fvttb+2vGVuWi0+dDe3q5Vq1ZpBpk5nQkAAAAAcNdok2X0veD5S7yZmS0mbfZIkrX2\nJ5J+4vBaAAAAAAAAME2p3MYFAAAAAACAHEGzBwAAAAAAwENo9gAAAAAAAHgIzR4AAAAAAAAPodkD\nAP9/e/cea1lZ32H8+YaBwgw3EaEglNFWidY0o6BFC6jQGkED4i0aTSRtQzQqDN6iJTGSxkTxmvQP\nGgVLI6iggqWm1REFtG0cMsycGWYcvIXhzgAVb5hMBX79Y71Ht5NRSNi3887zSXbO2uvsc545O2fW\nXufd71pLkiRJkjriYI8kSZIkSVJHHOyRJEmSJEnqSKpq/N80uQ+4dezfePoOBu63adOmTZs2bdq0\nadOmTZs2bdqcA0dV1ZMe7UETGezpRZJ1VXWsTZs2bdq0adOmTZs2bdq0adPmUuFhXJIkSZIkSR1x\nsEeSJEmSJKkjDvb8YZ+yadOmTZs2bdq0adOmTZs2bdpcSjxnjyRJkiRJUkec2SNJkiRJktQRB3t2\nIclnktybZHPnzSOTXJvke0m2JDmn0+beSW5IsrE1z++x2bp7JNmQ5KvT6M2wuS3JTUkWkqzruHlg\nki8luTnJ1iTP762Z5Oj2nC7efp5kdW/N1j23bQ82J/l8kr07bZ7Telum8bxOq7mr1+kkByX5RpIf\nto9P6LT5mvbcPpJk7FcymaPmR9q2b1OSq5Ic2GnzH1tvIcmaJIf32Bz53DuTVJKDe2wm+UCSO0de\n007tsdnWv739f9mS5IIem0kuH3letyVZ6LS5Ksl3W3Ndkuct9eZcqipvO92AE4HnAJs7bx4GPKct\n73yr0RwAAAgMSURBVAf8AHhmh80A+7blPYG1wHG9NVvrHcDngK9O8fdoFs1twMHT6s2w+a/A37fl\nvYADe2yOtPcA7gGO6q0JPBm4Bdin3b8COLPD5rOAzcByYBlwDfBnPTR39ToNXAC8ty2/F/hwp81n\nAEcD1wHHTum5nUXzJcCytvzhKT23s2juP7J8NvDPPTbb+iOBrwO3MubX8HlpAh8A3jXOzpw2X9y2\n73/U7h/SY3Onz38MeH+PTWANcEpbPhW4bqk35/HmzJ5dqKpvAz/ZDZp3V9X6tvwLYCvDHwe9Nauq\nftnu7tluEz1Z1SyaSY4AXgZcNMnOrJu7iyQHMLxQXQxQVf9XVT/trbmTk4EfV9WtnTaXAfskWcYw\nMHFXh81nAGur6ldV9RBwPfDKHpq/53X6dIYBUtrHV/TYrKqtVfX9cXbmtLmm/Q4BfBc4otPmz0fu\nrmDM+yfz0mw+Abxn3L05bE7MHDXfAnyoqna0x9zbaROAJAFeC3y+02YB+7flAxjz/sksmvPIwR4B\nkGQl8GyGGSjdNTMcarQA3At8o6p6bH6SYefikQl3Zt2EYWN9TZIbk5zVafMpwH3Av2Q4TO6iJCs6\nbI56HWPewZiXZlXdCXwUuA24G/hZVa3prckww+aEJE9MspzhnbMjO2wuOrSq7m7L9wCHdtrcHf0t\n8J+9NpN8MMntwBuA9/fYTHI6cGdVbZx0a5bN5u3tMLnPZMyHds5R8+kM2/q1Sa5P8txOm4tOALZX\n1Q87ba4GPtK2CR8F3tdpc6Yc7BFJ9gW+DKze6Z2XbppV9XBVrWJ4x+x5SZ7VUzPJy4F7q+rGSTXm\noTni+PbcngK8NcmJHTaXMUw/vbCqng08yHDIRm9NAJLsBZwGfHEavWk3247w6QwDaocDK5K8sbdm\nVW1lOBRlDfA1YAF4uLfm7/l3FBOewTkPzd1BkvOAh4DLem1W1XlVdWTrva23Zhv4/QemNJA1q2Zz\nIfBUYBXDwP7HOm0uAw4CjgPeDVzRZqL01lz0eqb/Btg0m28Bzm3bhHNps8o7bM6Ugz27uSR7Mgy6\nXFZVV/baXNQOSbkWeGlnzb8CTkuyDfgCcFKSSyfYm1UT+M2MhcXptFcBEz/B2gyadwB3jMwI+xLD\nQExvzUWnAOuravuUetNu/jVwS1XdV1W/Bq4EXtBhk6q6uKqOqaoTgQcYzs3WXbPZnuQwgPZxrFP8\n56i520hyJvBy4A1tMK3L5ojLgFd12PxThoHujW0/5QhgfZI/7qxJVW1vbzA+Anya6ewTTb3JsI9y\nZTtVwg0Ms8rHegLsOWnSDr1+JXD5pFszbL6JYb8EhjfdpvE7NIvmTDnYsxtrI9MXA1ur6uMdN5+U\ndnWLJPsAfwPc3FOzqt5XVUdU1UqGw1K+VVWTfgd/6k2AJCuS7Le4zHByy4lexW4Wzaq6B7g9ydFt\n1cnA93prjuj9HazbgOOSLG/bwZMZzlnWW5Mkh7SPf8Kw4/i5HpvN1Qw7j7SP/9Zpc7eQ5KUMhyaf\nVlW/6rj5tJG7pzPhfaJZNKvqpqo6pKpWtv2UOxguEHJPT034zaDvojOY8P7JrJrAVxhOmEySpzNc\nROL+DpswvFlzc1XdMYXWrJp3AS9syycB0zh0bBbN2ao5OEv0vN0Ydv7vBn7NsKH+u06bxzNM/97E\nMO19ATi1w+ZfABtaczNjPsP8vDRH2i9iilfGmnaTYdrwxnbbApzXY7N1VwHr2u/RV4AndNpcAfwv\ncMA0ntcZNs9n+ANnM/BZ2tU9Omx+h2GQcCNw8pSe24k3d/U6DTwR+CbDDuM1wEGdNs9oyzuA7cDX\nO23+CLid3+6fjPuKUfPS/HLbJmwC/h14co/NnT6/jfFfGWsumm3bflN7bq8GDuu0uRdwafs9Wg+c\n1GOzrb8EePM4W/PWZPib8EaG1+21wDFLvTmPt7QnQ5IkSZIkSR3wMC5JkiRJkqSOONgjSZIkSZLU\nEQd7JEmSJEmSOuJgjyRJkiRJUkcc7JEkSZIkSeqIgz2SJGlJS3Jeki1JNiVZSPKXSVYnWf4YvvZ3\nHpfkP5IcONl/sSRJ0mR56XVJkrRkJXk+8HHgRVW1I8nBwF7A/wDHVtX9j/L12x7L4yRJkpYSZ/ZI\nkqSl7DDg/qraAdAGbV4NHA5cm+RagCQXJlnXZgCd39advYvHbWsDRiR5R5LN7ba6rVuZZGuST7fv\ntSbJPtP+oSVJkv4QZ/ZIkqQlK8m+wH8By4FrgMur6vqdZ+wkOaiqfpJkD+CbwNlVtWkXj9sGHAsc\nBVwCHAcEWAu8EXgA+FH7moUkVwBXV9WlU/qRJUmSHpUzeyRJ0pJVVb8EjgHOAu4DLk9y5i4e+tok\n64ENwJ8Dz3yUb308cFVVPdgaVwIntM/dUlULbflGYOXj+iEkSZLGbNms/wGSJEmPR1U9DFwHXJfk\nJuBNo59P8hTgXcBzq+qBJJcAez+O5I6R5YcBD+OSJElzxZk9kiRpyUpydJKnjaxaBdwK/ALYr63b\nH3gQ+FmSQ4FTRh4/+rhR3wFekWR5khXAGW2dJEnS3HNmjyRJWsr2Bf6pXS79IYbz6ZwFvB74WpK7\nqurFSTYANwO3A/898vWfGn3c4sqqWt9mAN3QVl1UVRuSrJz0DyRJkvR4eYJmSZIkSZKkjngYlyRJ\nkiRJUkcc7JEkSZIkSeqIgz2SJEmSJEkdcbBHkiRJkiSpIw72SJIkSZIkdcTBHkmSJEmSpI442CNJ\nkiRJktQRB3skSZIkSZI68v97PYoDIz2omwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd29496f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 5))\n",
    "ax.plot(max_dsdz, 'o', label = 'CTD')\n",
    "ax.plot(max_dsdz_base, 'ro', label = 'base case')\n",
    "ax.plot(max_dsdz_a, 'yo', label = 'Test A')\n",
    "ax.plot(max_dsdz_b, 'o', color = 'gold', label = 'Test B')\n",
    "ax.set_xlabel('Station')\n",
    "labels = [n//2 + 1 for n in range(36)]\n",
    "plt.xticks(np.arange(36), labels);\n",
    "ax.grid('on')\n",
    "ax.set_title('Maximum ds / dz')\n",
    "ax.legend();\n",
    "print('base case bias =  ' + str(-np.mean(max_dsdz) + np.mean(max_dsdz_base)))\n",
    "print('test a bias =  ' + str(-np.mean(max_dsdz) + np.mean(max_dsdz_a)))\n",
    "print('test b bias =  ' + str(-np.mean(max_dsdz) + np.mean(max_dsdz_b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  0.0572419961294\n",
      "test a bias =  0.0645336972343\n",
      "test b bias =  0.357569111718\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2957f28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 5))\n",
    "ax.plot(depth_of_max_dsdz, 'o', label = 'CTD')\n",
    "ax.plot(depths_base, 'ro', label = 'base case')\n",
    "ax.plot(depths_a, 'yo', label = 'Test A')\n",
    "ax.plot(depths_b, 'o', color = 'gold', label = 'Test B')\n",
    "ax.set_xlabel('Station')\n",
    "labels = [n//2 + 1 for n in range(36)]\n",
    "plt.xticks(np.arange(36), labels);\n",
    "ax.grid('on')\n",
    "ax.set_title('Depth of maximum ds / dz')\n",
    "ax.legend();\n",
    "print('base case bias =  ' + str(-np.mean(depth_of_max_dsdz) + np.mean(depths_base)))\n",
    "print('test a bias =  ' + str(-np.mean(depth_of_max_dsdz) + np.mean(depths_a)))\n",
    "print('test b bias =  ' + str(-np.mean(depth_of_max_dsdz) + np.mean(depths_b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HEB0dLVmwilMM/LE8jlMwb968kUznGxAQgCdPngAAjI2NJbf/7e3tS1w4KLKw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wYNCAC1atWKNm3aJJBxReHwWXlcFr74dv6zs7PRtm1b5OTkYOrUqXBycoKdnZ1k7FPVgODgYPz0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd29427e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colours = cm.tab20(np.linspace(0,1,19))\n",
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "for n in range(19):\n",
    "    station = stationdata.iloc[[n]]\n",
    "    cast = casts[station['Station'].values[0]]\n",
    "    ax.plot(station['LonDecDeg'].values[0], station['LatDecDeg'].values[0], 'o',\n",
    "            color = colours[n], label = 'Station ' + str(station['Station'].values[0]))\n",
    "viz_tools.plot_coastline(ax, Bathymetry, coords = 'map')\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.legend(loc = 'lower left')\n",
    "ax.set_xlim(-124, -123)\n",
    "ax.set_ylim(48.8, 49.5)\n",
    "ax.set_title('Location of Stations');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-123.04      , -123.04      , -123.04      , -123.04      ,\n",
       "       -123.5266    , -123.5266    , -123.46701667, -123.46701667,\n",
       "       -123.40041667, -123.40041667, -123.3755    , -123.3755    ,\n",
       "       -123.35      , -123.35      , -123.32466667, -123.32466667,\n",
       "       -123.309     , -123.309     , -123.25965   , -123.25965   ,\n",
       "       -123.53372   , -123.53372   , -123.46722   , -123.46722   ,\n",
       "       -123.40012   , -123.40012   , -123.37567   , -123.37767   ,\n",
       "       -123.34852   , -123.34852   , -123.3185    , -123.3185    ,\n",
       "       -123.30883   , -123.30883   , -123.03928   , -123.03928   ])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lons = np.array([])\n",
    "for n in range(19):\n",
    "    if n == 13:\n",
    "        lons = np.append(lons, stationdata['LonDecDeg'][n])\n",
    "    elif n == 14:\n",
    "        lons = np.append(lons, stationdata['LonDecDeg'][n])\n",
    "    else:\n",
    "        lons = np.append(lons, stationdata['LonDecDeg'][n])\n",
    "        lons = np.append(lons, stationdata['LonDecDeg'][n])\n",
    "print(lons.shape)\n",
    "lons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  1.98397170126\n",
      "test a bias =  2.25364692297\n",
      "test b bias =  1.71250687659\n"
     ]
    },
    {
     "data": {
      "image/png": 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SpHmqIZXi5Zs2UVpVxTM7dlBaVeXizJIkad4pKyujq6vLwGeGxBjp6uqirKxs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\ngisRURdCUCwifnanIC5E6qYgdNGCiBS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd293ee0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 5))\n",
    "ax.plot(lons[:18], max_dsdz[:18], 'o', label = 'CTD', color = 'darkblue', alpha = 0.3)\n",
    "ax.plot(lons[:18], max_dsdz_base[:18], 'ro', label = 'base case', alpha = 0.3)\n",
    "ax.plot(lons[:18], max_dsdz_a[:18], 'o' ,color = 'maroon',  label = 'Test A', alpha = 0.3)\n",
    "ax.plot(lons[:18], max_dsdz_b[:18], 'o', color = 'deeppink', label = 'Test B', alpha = 0.3)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.grid('on')\n",
    "ax.set_title('Maximum ds / dz - April 10')\n",
    "ax.legend();\n",
    "print('base case bias =  ' + str(-np.mean(max_dsdz[:18]) + np.mean(max_dsdz_base[:18])))\n",
    "print('test a bias =  ' + str(-np.mean(max_dsdz[:18]) + np.mean(max_dsdz_a[:18])))\n",
    "print('test b bias =  ' + str(-np.mean(max_dsdz[:18]) + np.mean(max_dsdz_b[:18])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  -2.04319079964\n",
      "test a bias =  -2.06809140823\n",
      "test b bias =  -1.78867329076\n"
     ]
    },
    {
     "data": {
      "image/png": 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R0UC2lfr6lb1tl7TF7epSz1hr3/kCY2olXW6tvXX4/OOS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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd294003a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 5))\n",
    "ax.plot(lons[18:], max_dsdz[18:], 'o', label = 'CTD', color = 'darkblue', alpha = 0.3)\n",
    "ax.plot(lons[18:], max_dsdz_base[18:], 'ro', label = 'base case', alpha = 0.3)\n",
    "ax.plot(lons[18:], max_dsdz_a[18:], 'o' ,color = 'maroon',  label = 'Test A', alpha = 0.3)\n",
    "ax.plot(lons[18:], max_dsdz_b[18:], 'o', color = 'deeppink', label = 'Test B', alpha = 0.3)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.grid('on')\n",
    "ax.set_title('Maximum ds / dz - May 31')\n",
    "ax.legend();\n",
    "print('base case bias =  ' + str(-np.mean(max_dsdz[18:]) + np.mean(max_dsdz_base[18:])))\n",
    "print('test a bias =  ' + str(-np.mean(max_dsdz[18:]) + np.mean(max_dsdz_a[18:])))\n",
    "print('test b bias =  ' + str(-np.mean(max_dsdz[18:]) + np.mean(max_dsdz_b[18:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  -0.611265553368\n",
      "test a bias =  -0.8334933122\n",
      "test b bias =  -0.500131527583\n"
     ]
    },
    {
     "data": {
      "image/png": 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qdxpXCOGtIYT1+321hBCuGY3iJEmSJEmSdGT6HdkTY3wWmAcQQpgEvASsGOG6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd291483390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 5))\n",
    "ax.plot(lons[:18], depth_of_max_dsdz[:18], 'o',color = 'darkblue',  label = 'CTD', alpha = 0.3)\n",
    "ax.plot(lons[:18], depths_base[:18], 'ro', label = 'base case', alpha = 0.3)\n",
    "ax.plot(lons[:18], depths_a[:18], 'o', color = 'maroon', label = 'Test A', alpha = 0.3)\n",
    "ax.plot(lons[:18], depths_b[:18], 'o', color = 'hotpink', label = 'Test B', alpha = 0.3)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.grid('on')\n",
    "ax.set_title('Depth of maximum ds / dz - April 10')\n",
    "ax.legend();\n",
    "print('base case bias =  ' + str(-np.mean(depth_of_max_dsdz[:18]) + np.mean(depths_base[:18])))\n",
    "print('test a bias =  ' + str(-np.mean(depth_of_max_dsdz[:18]) + np.mean(depths_a[:18])))\n",
    "print('test b bias =  ' + str(-np.mean(depth_of_max_dsdz[:18]) + np.mean(depths_b[:18])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  0.725749545627\n",
      "test a bias =  0.962560706668\n",
      "test b bias =  1.21526975102\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd294c4eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 5))\n",
    "ax.plot(lons[18:], depth_of_max_dsdz[18:], 'o',color = 'darkblue',  label = 'CTD', alpha = 0.3)\n",
    "ax.plot(lons[18:], depths_base[18:], 'ro', label = 'base case', alpha = 0.3)\n",
    "ax.plot(lons[18:], depths_a[18:], 'o', color = 'maroon', label = 'Test A', alpha = 0.3)\n",
    "ax.plot(lons[18:], depths_b[18:], 'o', color = 'hotpink', label = 'Test B', alpha = 0.3)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.grid('on')\n",
    "ax.set_title('Depth of maximum ds / dz - May 31')\n",
    "ax.legend();\n",
    "print('base case bias =  ' + str(-np.mean(depth_of_max_dsdz[18:]) + np.mean(depths_base[18:])))\n",
    "print('test a bias =  ' + str(-np.mean(depth_of_max_dsdz[18:]) + np.mean(depths_a[18:])))\n",
    "print('test b bias =  ' + str(-np.mean(depth_of_max_dsdz[18:]) + np.mean(depths_b[18:])))"
   ]
  },
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   "outputs": [],
   "source": []
  }
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