{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "base=nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/threemonthsbase_2/06apr17/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "testa=nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/threemonthsa_2/testd/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "testb=nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/threemonthsb_2/testd/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "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",
      "(40, 898, 398) 438 268 4.0\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) 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": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stationdata[['Station', 'Date_UTC', 'Time_UTC_hhmmss']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 11,
   "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/threemonthsbase_2/26may17f/SalishSea_1h_20170526_20170604_grid_T_20170531-20170531.nc')\n",
    "        testa=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsa_2/testi/SalishSea_1h_20170526_20170604_grid_T_20170531-20170531.nc')\n",
    "        testb=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsb_2/testi/SalishSea_1h_20170526_20170604_grid_T_20170531-20170531.nc')\n",
    "    else:\n",
    "        base=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsbase_2/06apr17/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "        testa=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsa_2/testd/SalishSea_1h_20170406_20170415_grid_T_20170410-20170410.nc')\n",
    "        testb=nc.Dataset(\n",
    "            '/ocean/vdo/MEOPAR/completed-runs/threemonthsb_2/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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  -0.475003322603\n",
      "test a bias =  -0.330126925698\n",
      "test b bias =  0.0491614227745\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAAYzS0dmn1hm3qGj4hqmtfr1Zv2KaG1i6XRwYgmRhr7bHvYMzZkp6StMha\n+7Qx5geS3rbW3jTmflWSqiTJ7/cvWL9+vUNDTpyDBw9q+vTpZJJJJplkkkkmmWSSSSaZSZH5zy09\n2tv37tdws3KM/ne587ttePl7SyaZqWDJkiVbrbVnT3pHa+0xPySdLCl01PV5kh491ucsWLDAekFz\nczOZZJJJJplkkkkmmWSSSWbSZAa+8YgtHOcj8I1HEpLv5e8tmWSmAknP2Un6ONbayZdxWWt3S3rd\nGDNy3tzHJP35OJtQAAAAAIDjNGf4pKho6wDSU1RHr0v6kqR6Y8yLkkol3ebckAAAAAAA46leWqzc\nrMyIWm5WpqqXFk/wGQDSUVRHr1tr2yRNviYMAAAAAOCYkVO3Rk/jmpnLaVwA3iWqZg8AAAAAIDlU\nluWrsixfLS0tKi8vd3s4AJJQtMu4AAAAAAAAkAJo9gAAAAAAAHgIzR4ASEb766UdAS32V0g7AuFr\nL2YCAAAAiDv27AGAZLO/XtpdJdkeGSNpoCN8LUl5Qe9kAgAAAHAEM3sAINnsqZFsT2TN9oTrXsoE\nAAAA4AiaPYBXsOzHOwY6p1ZP1UwAAAAAjqDZA3jByBKcgQ4ZY99ZguNk88WNzHQxrWBq9VTNBAAA\nAOAImj2AF7Dsx1tm10rGF1kzvnDdS5kAAAAAHEGzB/AClv14S15QOrlOmlYoa400rTB87eRGyW5k\nAgAAAHAEzR7AC1j24z15QWluSJu6m6S5ocQ0XdzIBAAAABB3NHsAL2DZDwAAQPrgYA4Ak6DZA3gB\ny36QqurrpUBAiysqpEAgfO3FTAAA4oWDOQBEgWYP4BUs+0Gqqa+Xqqqkjg4Za6WOjvC1k80XNzIB\nAIgnDuYAEAWaPQAAd9TUSD1jnjj29ITrXsoEACCeOJgDQBRo9gAA3NE5wRPEieqpmgkAQDyl08Ec\n7E0EHDeaPQAAdxRM8ARxonqqZgIAEE/pcjAHexMBMaHZAwBwR22t5BvzxNHnC9e9lAkAQDyly8Ec\n7E0ExIRmDwDAHcGgVFcnFRbKGiMVFoavgw4+cXQjEwCAeEuHgznYmwiICc0eAIB7gkEpFNKmpiYp\nFEpM08WNTAAAMDXptDcR4ACaPQAAAEACNbR2adHaJq14/JAWrW1SQ2uX20MCkk+67E0EOIRmDwAA\nAJAgDa1dWr1hm7r29UqSuvb1avWGbTR8gLHSZW8il9B09j6aPQCQhPgFDADetK6xXb1HBiNqvUcG\nta6x3aWlS93JAAAgAElEQVQRAUksHfYmcgFN5/RAswcAkgy/gAHAu94Y/tkebR0A4o2mc3qg2QMA\nSYZfwADgXXNm5k6pDgDxRtM5PdDsAYAkwy9gAPCu6qXFys3KjKjlZmWqemmxSyMCkG5oOqcHmj0A\nkGT4BQwA3lVZlq81y0uUP/wzPX9mrtYsL1FlWb7LIwOQLmg6pweaPQCQZPgFDADeVlmWr82rKnTP\nRSdq86oKGj2Yuvp6KRDQ4ooKKRAIX3sxE46g6Zweprk9AABApJFftOsa29W1r1f5M3NVvbSYX8AA\nACDcZKmqknp6ZCSpoyN8LUlBh06OciMTjqosy1dlWb5aWlpUXl7u9nDgAGb2AEAS4l1fAAAwrpoa\nqacnstbTE657KRNATJjZAwAAAACporNzavVUzUwjDa1d78zofqqJGd2IC2b2AAAAAECqKCiYWj1V\nM9NEQ2uXVm/Ypq7hU1e79vVq9YZtamjtcnlkSHU0ewAAAAAgVdTWSj5fZM3nC9e9lJkm1jW2q/fI\nYESt98ig1jW2uzQieAXNHgAAAABIFcGgVFcnFRbKGiMVFoavndwo2Y3MNPHG8IyeaOtAtGj2AAAA\nAEAqCQalUEibmpqkUCgxTRc3MtPAnOHjz6OtA9Gi2QMAAAAAOKaG1i4tWtukFY8f0qK1TQnZU6a7\nu15btgQkVWjLloC6u+s9l1m9tFi5WZkRtdysTFUvLXY0F97HaVwAUgqnFQAAACTWyCbCI3vLjGwi\nLMmx52Hd3fVqb6/S0FD4yPf+/g61t1dJkvx+Z2YVuZE58v0bfX47M5fnt4gLZvYASBmcVgAAAJB4\nbmwivHNnzWjTZcTQUI927qzxVKYUbvhsXlWhey46UZtXVdDoQVzQ7AGQMjitAAAAIPHc2ES4v79z\nSvVUzQScQrMHQMrgtAIAAIDEc2MT4ezsginVUzUTcArNHgApg9MKAAAAEs+NTYSLimqVkeGLqGVk\n+FRUVOupTMApNHsApIx0Oq0gHU6fAAAAqaGyLF9rlpcof/gNtvyZuVqzvMTRvWX8/qCKi+uUnV0o\nySg7u1DFxXWObZTsVibgFE7jApAy0uW0gnQ5fQIAAKSOyrJ8VZblq6WlReXl5QnJ9PuD8vuDamlp\n0cKF3s0EnECzB0BKceOJRqId6yQIpxovbmQCAAAAcAbLuAAgyfT3dUypnqqZAADg+LDcG8BkmNkD\nAEkme2+m+t87OG7dS5kAAGDqWO4NIBrM7AGAJFN016Ay+iJrGX3hupcyAQDA1B1r6bWXMgHEhmYP\nACQZ/45CFX9Hyt4taSj8Z/F3wnUvZQIAgKnr7++cUj1VMwHEhmVcAJBsamvlr6qS/4mj3kHz+aS6\nWm9lAgCAKcvOLlB//7v31MvOLvBUJoDYMLMHAJJNMCjV1UmFhbLGSIWF4eugg2vi3cgEAABTVlRU\nq4wMX0QtI8OnoiLn3qBxIxNAbKJu9hhjMo0xrcaYR5wcEAAcS9qcPhEMSqGQNjU1SaFQYpoubmQC\nAIAp8fuDKi6uU3Z2oSSj7OxCFRfXObpRshuZAGIzlWVcX5b0sqT3ODQWADgmTp8AAAAIPwfx+4Nq\naWnRwoXlns0EcPyimtljjDlF0j9I+omzwwGAiXH6BAAAAABMLtplXN+X9HVJQw6OBQCOidMnAAAA\nAGByxlp77DsY84+SlllrrzfGlEv6mrX2H8e5X5WkKkny+/0L1q9f78BwE+vgwYOaPn06mWSSmTSZ\nl0vqHqful+TUzxw3Mt/h7X9PMskkk0wyySSTTDLJ9GamU5YsWbLVWnv2pHe01h7zQ9IaSbskhSTt\nltQj6d5jfc6CBQusFzQ3N5NJJplJlLl797120yafbW7W6MemTT67e/e9nso8mpf/Pckkk0wyySQz\n3h5+fpf9r2uesIXfeMT+1zVP2Ief35WwbK9/b8kkk8zkIOk5O0kfx1o7+TIua+1qa+0p1tqAwm9x\nN1lrrzjuNhQAHCdOnwAAABNpaO3S6g3b1LWvV5LUta9XqzdsU0Nrl8sjA4DEi/rodQBIBn5/UAsX\nhiQ1aeHCUEKaLm5kuqGhtUuL1jZpxeOHtGhtE0+OAQApZV1ju3qPDEbUeo8Mal1ju0sjQszq66VA\nQIsrKqRAIHztxUzAAVM5el3W2hZJLY6MBADgmpF3Q0eeJI+8GypJlWX5bg4NAICovDE8oyfaOpJc\nfb1UVSX19MhIUkdH+FqSgg698eZGJuAQZvYAXsE7H4gB74YCAFLdnJm5U6ojydXUSD09kbWennDd\nS5mAQ2j2AF4w8i5ER4eMte+8C+Fk88WNTDiGd0MBAKmuemmxcrMyI2q5WZmqXlrs0ogQk87OqdVT\nNRNwCM0ewAt45wMx4t1QAECqqyzL15rlJcof/t2VPzNXa5aXsBw5VRUUTK2eqpmAQ2j2AF7AOx+O\nSoeNi3k3FADgBZVl+dq8qkL3XHSiNq+qoNGTymprJZ8vsubzheteygQcQrMH8ALe+XBMuhzjyruh\nAAAgqQSDUl2dVFgoa4xUWBi+dnKjZDcyAYfQ7AG8gHc+HJNOGxfzbigQnXSY7QcASSEYlEIhbWpq\nkkKhxDRd3MgEHECzB/AC3vlwDBsXAzhausz2AwAAqY1mD+AVvPPhCDYuBnC0dJrtB8SKWXAA4B6a\nPQBwDGxcjHjgBY93MNsPiA6z4ADAXTR7AOAY2LgYseIFj7cw2w+IDrPgAMBdNHsAYBJsXIxY8ILH\nW5jtB0SHWXAA4C6aPQAAOIgXPN7CbD8gOsyCAwB30ewBAMBBvODxHmb7AZNjFhwAuItmDwAADuIF\nD4B0lE6z4NiEH0Aymub2AAAA8LKRFzbrGtvVta9X+TNzVb202JMveAAkr4bWrnd+Dj3VlJCfQ5Vl\n+aosy1dLS4vKy8sdzXLLyCb8I3uzjWzCL4mf8wBcxcwewCO6u+u1ZUtAUoW2bAmou7vek5lAKmLZ\nDwA3cSqgc9iEH0CyotkDeEB3d73a26vU398hyaq/v0Pt7VWONl/cyAQAAFNHQ8I5bMKPuKivlwIB\nLa6okAKB8LUXM5FQNHsAD9i5s0ZDQz0RtaGhHu3cWeOpTAAAMHU0JJzDJvyIWX29VFUldXTIWCt1\ndISvnWy+uJGJhKPZA3hAf3/nlOqpmgkA6YoNYBELGhLOYRN+xKymRuqJfANVPT3hupcykXA0ewAP\nyM4umFI9VTMBIB2x3wpiRUPCOel06hgc0jnBG6UT1VM1EwlHswfwgKKiWmVk+CJqGRk+FRXVeioT\nANIR+60gVjQknMUm/IhJwQRvlE5UT9VMJBzNHsAD/P6giovrlJ1dKMkoO7tQxcV18vuDnsoEgHTE\nfiuIBxoSQJKqrZV8kW+gyucL172UiYSj2QN4hN8f1MKFIUlNWrgwlJCmixuZAJBu2G8FADwsGJTq\n6qTCQlljpMLC8HXQwefVbmQi4Wj2AAAAJDH2WwEAjwsGpVBIm5qapFAoMU0XNzKRUNPcHgAAAAAm\nNrLcZl1ju7r29Sp/Zq6qlxazDAcAAEyImT0AUkt9vRQIaHFFhRQIhK+9mAkAR2G/FQAAMBXM7AGQ\nOurrpaoqqadHRpI6OsLXknN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc525b34400>"
      ]
     },
     "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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  -0.22182059288\n",
      "test a bias =  -0.332375115818\n",
      "test b bias =  -0.272128356828\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5257b6b70>"
      ]
     },
     "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\n00+8+HtHR0f07t2bl+XwW2JiYtC2bVu0adMG169fB4fDEbHFLD8q0SHXEHJkH3LS06Ci1QBdhrvB\npEv3qhvWMT59+sST4A0MDERcXBzs7OwQEhJSLWEefrl27Rp++uknMAyDAQMGYPr06ejZs6fAEgOJ\nQh6XPbRXDVatWgUzMzO0adMGVlZWuHfvHoCaq+X16dMHmZmZtbbr5s2bsLGxgZycHI4fP84rj4iI\nQIcOHXg2Hz16tML2+fn5GDZsGFq0aAF7e3u+0veqqqri5MmTaN68OVasWIFOnTpBS0sL8+bNq/X9\niIMePXrg9u3bmDVrFj58+IDff/8d1tbW2L59e4X1W7VqBR8fH9y5cwdLly4VsbUsPyrRIdcQ4OOF\nnLQPABFy0j4gwMcL0SHXqm5cx1BSUsLAgQOxbds2xMbG4ujRo7h//z4WLlwolPEOHjwIFRUVvH79\nGidPnuQ5f6lC2K8QanIJ4pV+7sP39G7NPUpceJPerblHuQ/fV6v9t9y5c4fat29PX758ISKiDx8+\n8DTj9fX16cOHD1X2wW+96hIfH0+PHz+m0aNH07Fjx3jlL168oJiYGCIievv2LTVp0oQ+fvxYrr23\ntzdNmTKFiL4eSnNxcSlX53vzP3HiRAJADMPw+pF2kpOTqXfv3iQvL//dfbnJkyez+/ksImPn9LG0\nwaVvuWvn9LHiNk0imDVrFgGgkydPCrRfLpdLOjo6NHjwYIH2Wxqwcfg149OjVGSejEVxZj4AoDgz\nH5knY/HpUc1Tw0qjPG6rVq3QsmVLAECzZs3QqFGjCsPpSsvjDhkyBEFBQV8PePBBZmYmfH198csv\nv+DVq1fYsWMHX+0knSZNmmD//v1o2LAh7Ozs0K5dOyxevBjXrl0ro2ewefNmtGnTho3PZxEJOelp\n1Sr/0Vi/fj1sbW0xbtw4xMfHC6zf6OhoJCUliSznvbCokw4/+8prUGHZFLBUyEX2ldc17lPa5XHv\n37+PgoICGBkZlfusInnc9PR0vvoNCgpCcXEx3N3dYWBgUCPbJJUGDRrg1q1b+PPPP1GvXj2sW7cO\nPXr0wODBg3kPRPXr14e/vz/y8/MxYsQINj6fRaioaDWoVvmPhoKCAvbv34+srCwcOHBAYP1evnwZ\nAFiHL4mUrOz5LecHaZbHTU5OxujRo+Hr61vuLUBNISLcunULGzZsgKqqKuzt7QXSr6RhYGCAZcuW\nISQkBBkZGfj7779x9uxZbNq0iVendevW8PHxwe3bt7Fs2TIxWstS1+ky3A1y8gplyuTkFdBluJuY\nLJI8kpKSAKBaeiRVceXKFRgbG0NPT09gfYqDOunwZdUVqlXOd79SKI+bnZ2Nvn37YtWqVWjfvn2F\ndSqSx1VWVkZSUhKePn2K2NhYZGdn49mzZ8jNzcXOnTthZWWFLl26IDo6GmvXrv0hTqmrqqpiyZIl\nGDRoEBYtWoS7d+/yPhsxYgQmT56MtWvXllFIZGERJCZdusNp8kyoNGgIMAxUGjSE0+SZP+Qp/cq4\ncuUKOBwOHBwcBNbngwcPeNuj0kyddPiqvQzAcMreGsORgWovgxr3KY3yuAUFBXB2doabmxuGDBlS\nab0SeVwiwr59+2BnZ4fIyEikpKRAQUEB+fn5+PjxI8zNzaGmpoapU6eCYRj4+Pjg7du3mDZtWq1t\nlRYYhsG///4LXV1dDBs2DBkZGbzPSvbzR48ezVtlsLAIGpMu3THZ2xe/HTmHyd6+rLP/hitXrqBz\n585QVlYWWJ9jxozBuXPnarylKilIZ9B0FShZNwLwdS+/ODMfsuoKUO1lwCuvCbm5uZg1axYyMzMh\nJyeHFi1awMfHB8D/ZW9L9uhL5HF1dXUrlMctqVdCaXlcADx5XH5f34eFhcHZ2RkfP37EuXPnsHz5\ncjx79gz+/v64efMm0tPTedsPe/fuhZWVFZYtWwZbW1sMGDAAY8aMwYgRI6Cvrw8VFRV4eHigSZMm\naNiwIe8NAxFh9+7diI6OxqBBg9ChQwfpC0kREOrq6vD390fHjh0xZswYnD17FgzD8PbzbW1tMXz4\ncFy/fl1q8xKwsEgj7969w9OnT+Hh4SHQfteuXYvbt29j/PjxsLKyqvAslFQg7DCAmlxspj3RUVhY\nSI8ePaKwsDCKjo6mtLS0CnPks/Nfni1bthAA2rhxY5nyQ4cOEQD6/fffxWQZC8uPia+vLwGgiIgI\ngff9+vVrUldXp7Zt2/LCswUJ2LA8FmGTnZ2NoqIitGjRAsbGxtDS0hLYwb66zqxZs+Ds7IyFCxci\nNDSUVz5ixAiMGzcOa9asYV/ts7CIkNTUr6HX/EYZVQd9fX34+fkhPDwcv/32m8D7FwXsN/sPTnZ2\nNmRlZaGmpiZuU6QOhmGwZ88e6OjoYNiwYWW+ZBo0aAAOhwN1dXUxWsjC8mMxffp0tG7dGq6urnj/\n/r3A+x8wYADmzZsHb29v+Pv7C7x/YcM6/B+c3NxcEBHi4+ORlpaGgoICcZskVairq+Po0aNITk6G\njo4OnJycsH79emzYsEHgB4dYWFi+j7KyMo4dO4bMzEy4urqiuLhY4GOsXbsW7du3x8SJExEXFyfw\n/oUJ6/B/cPT09KCuro6cnBy8fv0aT548QWRkJBISEpCZmSmUP5i6hp2dHW7evIkpU6bg7du3WLBg\nAYgII0eOFLdpLCw/HBYWFvDy8kJQUBBWrlwp8P45HA6OHj0KOTk5uLi41CrsWtSwanksAL4e3szL\ny0N2djays7ORk5Pz9ZCHjAxatWqFxMREsc//ly9fcPz4cbx//x7z5s2T2CiBt2/fIjc3F61btxa3\nKSwsPyREhDFjxuDAgQO4evUqevToIfAxzp07hwEDBmDatGnYtm1brfsThVoeGzPEAuDrfrSioiIU\nFRXRpEkTcLlc5Obm4s2bN3j16pXYD/Lt2rULixcv5mkPcDgczJ49W6w2VYa2tra4TWBh+aFhGAbb\ntm1DWFgYRo4ciYiICDRp0kSgY/Tv3x/u7u7YsGEDunXrhmHDhgm0f2HAvtKvBtImj/vmzRvY2NjA\nysoKZmZmlQrbVCSPKyMjA1VVVRgaGqKwsBDp6engcrkVthc2+fn5mDNnDgwNDREQEIABAwbA3d0d\nnp6eePHiBS+vfXp6Ory8vBAeHi4WO1lYWCSHkv387OxsjBw5Uijbk6tXr0bbtm3h7u4u8L6FgrDj\n/mpyCSIOvzApg/KCoynv4hPKC46mwqSMarX/FmmUx83Pz+fZm5OTQ/r6+jybS1OVPO779+/p0qVL\ntG7dOoHbzg9BQUEEgM6cOUNEROnp6WRjY0MACADp6upS3759SUFBgQBQo0aN6N27d2KxlYWFRbLY\ns2cPAaBly5YJpf927dpRx44da90P2Dj8mlH09iOKIt8CX/5TLvtSiKLItyh6+7HGfUqbPG5+fj4Y\nhuHZm5+fX+kKvSp53IYNG0JRURG///477ty5U+M5rCkBAQGQk5PjzZumpibCw8Px8uVL7NixA+3a\ntUNUVBTGjRuHU6dOIScnB0OHDkVwcDDy82sumCQqxPXmhIXlR2Ds2LFwc3PDihUrEBQUJNC+09LS\n8ODBA+lR0RP2E0VNrtqu8Hkr+2+v4Gi++/iWnJwcsrS0pJYtW9K0adPo+vXrvM++Xbmnp6cTEVFR\nURF169aNHj9+XGG9kp8fPHhA5ubmlJubSzk5OWRqakoPHz6k+Ph4kpWVpUePHhER0dChQ2n//v2V\n2jhmzBg6duwYFRUVUXh4OIWFhdHVq1fJ2NiY6tevT1u3bq2wnZmZGSUmJvJ+NjQ0LPcmIjIykgwN\nDUlXV5fS0tL4nTaBsG7dOgJAhw4d4qv+gQMHiMPhEABSVFSk3r170+HDh4VsZc3YsWMHcTgc6tKl\nC61YsYJCQ0OpqKhI3GaxsNQpcnNzycTEhBo1akQpKSkC6zc4OJgA0K5du2rdFyRphc8wjCzDMI8Y\nhjn/38+WDMPcZRjmKcMw5xiGUa2kXW+GYV4wDBPHMMwiQTykVMmXSjTJKyvnA2mSx83OzgaXy0WD\nBg2go6ODAwcO4MSJE9ixYwfu3LmDlJSUaoeSyMjI4OjRo0hJScHYsWPLvAEQNnPmzEGnTp0wefJk\nxMTEVFnf1dUV6enpOHfuHCZMmIC4uDiMGDECFy5cEIG11ePIkSPQ0tLCp0+fsHTpUrRv3x4dOnSo\n8E0OCwtLzVBSUoKnpydSU1Nx8+ZNgfXbsWNHtG3bFvPnz6+RdLmoqc4r/V8BRJf6eTeARURkAeAU\ngPnfNmAYRhaAN4CfAZgCGMEwjGnNzeWTepVItVZWzifSIo+bnZ0NGRkZ6OnpoXXr1rCyskKHDh1g\namqK0NBQJCUlITIyEtnZ2QAqlsfV0tIq16+trS02bNiA8+fPl9GDFzYcDgeHDx+GgoIC33GvKioq\n6NevH/755x88ffoUVlZWcHNz492nJJCbm4vbt2/Dzc0N4eHhSE1NhZeXF8LCwjB37lxxm8fCUqeI\njIwEgEplwmuCgoICjh49Ci6Xi2HDhkl84jK+HD7DMDoA+uKrky+hFYCSR6VAAIMraGoHII6IXhFR\nAYAjAH6pubn8IdeqMSDzTYy2DPO1vIZIizwuESErKwuqqqp49+4d8vLyICsrCyJCREQEHB0d0aZN\nG9SrVw+vXr1CQUEBTx4XAI4fP44ePXpUGuM+a9asCvXghY2uri78/Pzw+PFjzJkzp1pt69WrB39/\nfxQWFmL48OEoLKz5mx5Bcu3aNRQWFvL2/xo2bIgZM2Zg/vz52LlzJ44cOSJmC1lY6g5XrlyBiYkJ\ndHV1BdqvkZER9uzZg/v372PRItG8xK4p/K7wNwNYAKD06aJn+L/zHgqgolnUBlB6SZX0X1k5GIaZ\nzDDMA4ZhHnz48IFPsypGTlsDcuba/1/R1+NAzlwbctoaNe4zNzcXY8aMgampKdq0aYOoqCj8+eef\nAP4ve9u9e3dYWlry5HFHjhxZoTxuyeGzEkrL49rb2/PkcfklLCwMOjo6OHbsGKZOnYqBAwdCVVUV\n0dHRsLe3h6WlJbp16wZ3d3dYWFhAXl4eBw8exLVr1/DkyRN06tQJSUlJMDIywqZNm7B27dpKxyqt\nBz98+PAyevDCpm/fvjxnePTo0Wq1bdmyJXx8fHDnzh0sWbJESBZWj8uXL0NJSanM/yPA1/DPDh06\nYNKkSWUeMllYWGrOp0+fkJWVxTsoLUgGDx6MWbNmwdPTE6dPnxZ4/wKjqk1+AP0AbPvv3w4Azv/3\nb2MAAQDCASwHkF5B2yEAdpf6eTQAr6rGZOVxa05KSgqFhYVRXl5elXU/f/5MSUlJFBUVRWFhYRQW\nFlbm8F5pvp3/+/fvk6ysLP32228CsZtfCgoKqEOHDqSiolJhiGFVTJ06lQDQhQsXhGBd9TAyMqJ+\n/fpV+NmbN29IU1OTLC0t+fpdsrCwfJ+HDx+SvLw89enTRyj9f/nyhWxtbUldXZ1evXpV7faQkEN7\nnQAMYBjmNb6+ku/BMMwBInpORE5E1BbAYQAvK2j7FmVX/jr/lbEIiZIwtKioKMTGxuL9+/eVvsKu\nX78+tLW1YWJiAisrKzRo0AApKSl8JQMyMDAAl8sVucoeh8PBjBkzkJOTUyPpWU9PT5ibm2P06NFI\nTk4WgoX8kZ2djZcvX8LAwKDCz/X09LBv3z48fvyY3c9nYREA1tbWGD9+PC5fviyUbT0FBQX4+/sj\nNzdXpGecqkOVDp+IficiHSIyADAcQDARjWIYphEAMAwjA2AJgIrSuIUBaMkwTHOGYeT/a39WYNaz\nlENHRwdGRkbQ0tLCly9fkJiYiBcvXlSZZUpOTg56enqoX78+4uLi8OzZMyQmJiIrK6vCtoGBgSAi\nscSfBgcHQ11dHTY2NtVql5ycDBcXFzx79gxZWVlISEgQkoVVo6qqChcXF2zfvh23bt2qsE7JFsaO\nHTuqvYXBwsJSnocPH8Le3h4cTu0OcFcGEaGoqAitWrUSSv+1pTaJd0YwDBMD4ELv5jIAACAASURB\nVDmAdwB8AYBhmGYMw1wEACIqAjATwBV8PeHvT0TPamcyy/eQkZGBhoYG9PX1YWFhgZYtW+LLly98\nObcSoRwdHR1wOBykpqYiNjYWEREReP/+PVavXo3bt2/Dw8MDCxYsgJaWFtq2bSuCuypLYGAgunfv\nDjm56klBbNy4EZcvX8bixYsRHx8Pe3t7IVnIH7t27ULz5s0xfPjwSvcV2f18FhbBkJaWhrCwMKEu\nUq5cuQIAkpuIR9h7BjW52D18wfL69WsKCwujwsLCarUrLi6mrKwsSkxM5KW3Lbm6detWJvmQKOnQ\noQNpampSQkJCtdqZm5tTz549hWRVzSjZV/z555+puLi4wjol+/lWVlbsfj4LSw159+4dycnJ0cCB\nA4nL5QpljJkzZ5KcnFylZ6G+ByRkD59FyiksLIS8vDxkZWWr1a5EQEdHRwdNmzbF+/fvcfz4cURG\nRuL69evo1q2bkCz+Pn5+ftUKsSMiBAQEIDIyUuKevK2trbF582ZcunQJ69evr7BOyX5+REQE5s2b\nJ2ILWVjqBk2bNoWHhwdOnz6Nf/75RyhjzJgxAwoKChgxYgRfOVNEjrCfKGpysSt8wcHlcnlpemuD\npM3/kSNHCAAtWLCg0jrZ2dnk7e1NZmZmBIAaNmxY63kQBlwul4YOHUqysrJ069atSuu5u7sTADp6\n9KgIrWNhqTtwuVzq378/cTgcun//vlDGOHjwIAGghQsXVqsdRLDCF7tzr+iSVIe/cuVKMjU1JQsL\nC7K0tKTQ0FAiIvL09KRPnz5V2f7bej///DN9/Pix1nbduHGDrK2tSVZWtoxaHtHXMLZr165RkyZN\naMaMGdVuX4IkzP+3lITYzZw5ky5cuEC5ublERPT06VOaPn06KSsrEwBq27Yt7dmzhz5//ixmiysn\nMzOTDA0NSUdHp1JFxYKCAmrfvj2pqKhQbGysiC1kYakbpKenk56eHhkYGFBGRu1UVCtj8uTJBIAu\nXrzIdxvW4Zeiug7n8ePHtGnTJlq+fDlt2rSJJ2BTU6RRHreEcePGUa9evWj8+PEV7hNX1Z5IMh1+\nXl4eDRkyhCeLWyKYA4AUFBTIzc2N7t27J24z+SY8PJwXJ1zZfv7r169JQ0ODVFRU6JdffiEvLy96\n8eKF0PYkWVjqIqGhoSQnJ0fOzs5C+dv5/Pkz6evrU9euXfluIwqHXyf38J88eYJz584hKysLAJCV\nlYVz587hyZMnNe5T2uRxSwgPD8enT5/QtWtX5OTkICIiolx8/vfaSzL16tXDsWPH8PHjRwQEBGDO\nnDn49ddf4eHhgaSkJPj5+cHOzq5cu5ycHCxatAgHDhxASkoKrzw2Nhbr1q3D+/fvRXkbPGxsbLBx\n40ZcvHgRGzZsqLCOvr4+goKCMHLkSDx+/BgzZ85E69atMX36dBFby8Iivdjb22PdunU4deoUtm3b\nVuYzLpeLwMBA+Pj4ICYm5uvK+D9iYmJw5MiRcpoc2dnZCAgIQEmWWFlZWaSnp8PUVPjSMdVC2E8U\nNblqu8IvWdl/e23atInvPr5FmuRxSyguLqZu3bpRYmIi/fvvvzRx4kR68+YNPX36lMLCwigyMrKM\nFOu37UsjiSv8muLj41Mm4sDCwoK6d+/O+7lr167VjmgQFFwulwYPHkyysrIUEhJSZd3Y2FiaNm0a\nAaC9e/eKyEoWFumHy+VSo0aNaPDgwUT09Xt7w4YNZGRkVOb7QU9PjyZMmECOjo5lylu3bk2zZs2i\nyZMnk5KSEq/c2tqaXF1dCQCdPn2ab3vArvBrRsnKnt9yfpAmedwStm3bhj59+kBHRwcyMjJQUFCA\nnp4ezM3N0bJlS+Tl5UmUepyouHz5MnR0dBAeHo61a9eiYcOGePv2Lf7++29s2bIFN2/e5OkkiJoS\nrQJ9fX106dIFVlZWWLBgAQIDA8tFJDAMgxYtWmDr1q1wcHDA9OnTq/x/jYWF5Svx8fFITU2Fqqoq\nxo0bB21tbbi7u6NJkyY4ePAgnj9/ju3bt6Nt27Y4fvw4oqOjsWLFCoSGhmLTpk1o3rw5du/ejX37\n9sHFxQXnz5/HypUroaqqCn9/fygpKZXTTRE31ctcIiWoqalV6Nxrmwa2RB7XwcEBFhYW8PPzw9ix\nY8vUKZHHDQsLg4aGBsaOHStQedzq6KTfvXsXISEh2LZtG3Jzc1FQUABlZWWsXbsWampqaNKkCVJS\nUqCiolKhHG5dpKioCEFBQRg6dChsbGxgY2ODhQsXlqnz+PFjrF69Gt26dYOjo6PIbVRTU8ONGzew\nb98+BAQEYPPmzVi/fj2cnJxw8eLFcuGVsrKyOHToECwtLeHi4oL79+9DUVFR5HazsEgTJUlyfH19\noaSkhLFjx2LatGlo06YNr07r1q0xdepUcLlcMAzDUxG1t7fH3LlzkZ+fj6KiIigpKQH4mh3zjz/+\nQG5uLnJycqCqqir6G/sOdXKF37Nnz3KpEzkcDnr27FnjPqVFHrc0Bw8eREJCAl6/fo0NGzbAzc2t\njBKetrY2lJWV8ebNG+Tm5tZ6PGng3r17yMrK+m48/tatW2FqagpXV1e8e/dOhNb9Hx0dHSxevBjX\nr19HRkYG1q9fj4CAAKxatarC+k2bNsWBAwcQFRWFmTNnithaFhbpQ0NDA3Z2dvD29sa7d++wffv2\nMs6+NDIyMhVKhisoKPCcfWmUlZXRtGlTgdtcW+qkw2/Tpg369+/PW9Grqamhf//+lf4y+UFa5HGn\nTJkCMzOzKtssW7YM586dg6GhIaKjo2FkZISjR49i0qRJMDY2BpfLrbIPaeTKlSuQkZH57sOfoqIi\n/P398enTJ4wcObJGCTQyMzOxaNEi1FbqGfj65fHbb79h1KhR+PPPPxEcHFxhPScnJyxevBi+vr7Y\nv39/rcdlYanLDB8+HPfu3cP06dMlbiUuLBgqdQJRUrC1taUHDx6UKYuOjoaJiYmYLKrbFBYWIjMz\nE9nZ2cjJyUFRURG0tLRgYGDAe6qtK/Nvb28PGRkZ3L17t8q6+/btw5gxY/DXX39h2bJlfI9Rr149\nnmrhggUL4OHhUWN7S5ObmwtbW1tkZWUhIiICjRs3LlenqKgIPXr0QHh4OB48eFAnfmcsLD8CDMOE\nE5GtMMeokyt8lurB4XDQsGFDGBkZwdLSEk2bNkV6ejo+fPgASXwgrA0NGjRAZGQkYmJiqqzr5uYG\nU1NTXL16tVpjlDj7P/74o8wWSm1RVlbGsWPHkJmZCVdX1wpVDOXk5HD48GEoKirCxcUFnz9/Ftj4\nLCws0g3r8FnKwDAMmjVrBhUVFSQkJCAiIgJxcXHIyclBXFyc1D8A7NixAwoKChg6dGiVByAzMzPx\n4sULdO3atVpjcLlcEBFWrlxZ4b5fbbCwsMDWrVsRFBRU6X6+trY2Dhw4gMjISLRu3RoTJkzAkSNH\nKlXkY2Fh+TFgHT5LORiGQcuWLWFoaAgNDQ18/vwZGRkZvLLJkyfj+PHjvJWsNKGrq4t9+/bhyZMn\nmDt37nfrBgUFobi4GL17967WGIJ28t8yYcIEuLq6Yvny5TA0NMSUKVNw4sSJMpEpvXr1wqlTp2Bv\nb4+TJ09ixIgRaNy4MXx9fYVqGwsLiwQj7ED/mlySmkv/R4XL5dLjx4/Jy8uLfvnlF1JRUSEA1L9/\nf6lN6Tp//nwCQGfPnq20zqlTpwgAeXh4iNAy/sjLy6Nt27aV+X00bty4QlnOoqIiCg0NJQcHB1JQ\nUKCIiAgxWMzCwvI9wObS/z+swxcvpee/oKCA1q5dSwBo48aNYrSq5iQlJREAWrt2baV1uFwuDRky\nhGRlZSksLEyE1lWPgoICCgwMJCUlJercuXOlWQLfv39PTZs2JVVVVRoyZAjt3LmTXr16JWJrWVhY\nKkIUDp99pc9SbTgcDhYsWABnZ2csXLgQoaGh4jap2ly7dg0AvhuexzAM/vrrLxQXF0v0PXI4HPz0\n00/YuXMnbt26VWlEQaNGjXDlyhUMHjwYoaGhmDJlCgwNDTFq1CipP5vBwsJSNazDrwarVq2CmZkZ\n2rRpAysrK9y7dw8AsHnzZr5OQ39br0+fPsjMzKy1XTdv3oSNjQ3k5ORw/Pjxcp9nZ2dDR0en0oQs\nmzZt4uUX6NmzJ968eVPlmAzDYM+ePdDR0cGwYcPKiNBIA4GBgdDU1ISNjc136925cwcA4ODgIAKr\naoerqysmTpyINWvW4PLlyxXWsbCwwJ49e5CQkIDo6GjMnTsXBw8exNatW0VsLQsLi8gR9iuEmlyC\neKWfmppK4eHhdPfuXQoPD6fU1NRqtf8WaZbHnT17No0YMYJmzJhRYfvg4GD69OkTERFt27aNXFxc\nytWpbP7v3btHHA6HGIahdu3a0eLFi6sUfZEEfv/9dwJA58+f/269oUOHkra2ttScVfj06ROZm5vz\nfh9//PEHXb9+nQoKCiqsz+VyacCAASQrK0tOTk60YcMGevz4sdTcLwtLXQHsK/2a8eHDB8THx6Og\noAAAUFBQgPj4+FplPZNmedz379/Dycmp0nvr3r07L/d6+/btkZSUxPe82NnZITw8HMuWLQOHw4GH\nhwe6dOmCGzdu8N2HOFi2bBmsrKzg5ub2XQEhRUVFpKWlISIiQoTW1RxFRUUEBARg+fLl4HA4WLt2\nLU//oeTvoTQMw8DPzw+//vorEhMT4e7uDktLS5iamgokSyALC4sEIewnippctV3hl6zsv73Cw8P5\n7uNbpF0e19fXt9IVfmlmzJhBK1asKFfO7/yvXr2aAPDefkgyMTExpKKiQh07dqx0BfzhwwfS1tam\nFi1aUH5+vogtrD2ZmZnk5eVFAGju3LlV1k9MTCQfHx9SUFCg3r17U3FxsQisZGFhAbvCrxkVrWS+\nV84P0i6Pyw8HDhzAgwcPMH/+fL7H+JarV6/CwsICzZo1q3EfoqJly5bw8fHBnTt34OzsjEOHDiE1\nNbVMnQYNGmDy5MmIi4sTyHmL2vLs2TOkp6fzXV9NTQ0zZszAzJkz4enpCWNjY8yePRvnzp2rUMhJ\nR0cHkyZNgqenJy5fvgx7e3ssW7YMt27dQk5ODvbt24du3bqhR48eWLNmDcLDw+us7gILS12jTsrj\nysvLV+jc5eXla9VvXZLH/ZarV69i1apVuHHjRpkxq0tkZCRMTU1BREJPQCMIhg8fjpiYGGzZsgUX\nLlwAAFhZWcHR0RGOjo7o3LkzQkJCYGZmhkaNGonZWsDc3BwAkJGRAQ0NDb7bbdy4ES1btsTFixex\ne/dubN26FXJycujQoQMcHR3h5OQEW1tbnvTu1KlT8eXLFxw9ehSrVq3CihUreH21atUK9erVw+LF\ni7F48WJoaWlh9+7dGDhwoGBvloWFRaDUyRW+rq5uuf1sGRkZ6Orq1rjPuiiPW8KjR48wZcoUnD17\nttZObcGCBbh+/Tq8vLxq1Y8oWbZsGVJTU3H//n2sWrUK6urq2Lx5M5ycnKCkpISrV69+V06XX8LD\nw3Ho0KFaRTTMnj0bAKCpqVmtB0l5eXnMnj0bly9fRkZGBoKCguDu7o7Pnz9j+fLlaN++PRo0aIDB\ngwdj586diI+Px9y5cxEaGoq0tDQcP34cixYtwtWrV/H8+XM8fvwYKSkpOHDgAHR1deHm5oa4uLga\n3xcLC4sIEPaeQU0uSTyl/+DBA+rQoQOZmJiQhYUFOTs78/bj//nnH2rVqhU5ODgQ0de99JYtW1KP\nHj3I2dmZfH19K6xXek9/48aNZGZmRmZmZuTp6UlEX0/fm5mZ8WxYv349LV++vJxt9+/fJ21tbVJU\nVCRNTU0yNTUtV+fbPfylS5fSmTNniIioZ8+e1KhRI7K0tCRLS0vq379/ufb8zj+Xy6X+/fsTh8OR\n6GQ1VZGTk0MXLlygOXPmkIeHByUnJ9e6TxMTEwJAAMjCwoJ+++03ev36dbX7KekDADk5OdH69evp\n5cuXNbbrw4cPdOTIEZowYQLp6ury+h49ejRfp/XfvHlDGhoapK+vT4sWLaLg4GBeNAsLCwt/QAR7\n+Kw8LgtfVGf+MzIyYGVlBTk5Ody9e7dCGdcfjYSEBOjr62P27Nlo1qwZAgICcOvWLTRv3hwPHjyA\nsrIy330VFBSU23YxMjISyAqbiBATE4Pt27djy5Yt8PLywowZM6psd+PGDSxduhR3795FUVERFBUV\n0bVrV952gZmZmVRs8bCwiAtRyOOKfTVf0cWm1pU8qjv/d+7cITk5uXLx4NJ40l0Q+Pj4EAB69uwZ\nr+zatWskIyNDo0aNqnbce0JCAllZWREAcnNzE3h+/OLiYurTpw/JycmRo6MjrVu3jiIiIqo8tZ+d\nnU1nz56lWbNmkbGxMe9tQdOmTb8bYcLC8qMDNpf+/2EdvnipyfxHRETQ8uXLqWPHjiQrK0sASElJ\nifr06UObN2+m2NhYIVgqmQwePJh0dHTKOfY///yTANC///5bo35zcnIEYV6FZGRk0G+//UZmZmY8\nx21iYkJpaWl895GQkED//vsv2dnZkby8fK1CY1lY6jKswy8F6/DFS23nPzMzk06dOkXTp0+nli1b\nEgCSlZWloKAgAVkouRQWFpKamhpNmDCh3GdFRUXUo0cPqlevHp06dYqX8VDSSEpKIh8fH5KXl6e+\nfftWOz6/JJ+Bjo4O/fnnn3T79u1KRX5YWH5EWIdfCtbhixdBz398fDwZGxtTkyZNKCUlRaB9Sxq3\nbt0iAOTv71/h58nJybzDcvLy8tSjRw/y8PCg3NxcEVtaNVu3biUA1LZtW1qyZAndvHmz0qRF33L/\n/n2ys7MjhmEIAKmqqtLAgQPJ29ubYmNj2XS+LD80onD4dTIsj0XyMTAwgL+/PzIzMzFq1CgUFxeL\n2yShUVRUBAB4+vRphZ83adIEz58/x+XLlzFr1iykpaVh4cKFGDt27NencglixowZ2LRpE+Tl5bF6\n9Wp07doVmpqa6N+/P7Zu3Yrnz59XanO7du1w7949fPjwAf7+/nBxccGjR48wY8YMtGzZEoaGhvDx\n8RHxHbGw/EAI+4miJhe7wpc8hDX/u3btIgAVpvOtS4wdO5YYhqHAwEC+6nt4eBAA+uuvv3ipmiWN\njx8/0smTJ2nq1KlkZGTE2+fX0dGh8ePH0+HDh6sUi+JyuRQTE0Pe3t7UoUMHYhiGgoODRXQHLCyS\nA9hX+v9HEhz+ypUrydTUlCwsLMjS0pJCQ0OJiMjT05Ovvddv6/3888/08ePHWtt148YNsra2JllZ\n2XJqeTIyMt+Nryci+vLlC7m4uJCRkRHZ2dlRfHx8uTrCmn8ul0uurq4kIyNTRp+grpGbm0umpqbU\nuHFjvmL6i4uLacCAAQSAGIYhW1tb+v333ykuLk4E1taMly9f0o4dO2jw4MGkrq7Os71v3758xeXn\n5ORQ69atqVGjRrRmzRoKDw9nc/mz/DCwDr8U1XU475JP061bnelqkBHdutWZ3iWfrlb7b5FWeVwl\nJaUq23t7e9OUKVOIiOjw4cPVkscVBDk5OdSqVStq2rRprRMkSTKRkZFUv3596t69OxUVFVVZv6io\niO7cuUN//vknderUiWRlZUlXV7dap+TFRVFREYWGhvJkiGfNmsVXu8jISLK2tua9LWjQoAFNmzaN\n73MCLCzSCuvwS1Edh/Mu+TQFXzOjq0GGvCv4mlmtnP6JEyeoX79+5cq3bNlCHA6HzM3NeRn0pk6d\nSm3btiVTU1NatmxZpfX4ybRnbGxMEydOJFNTU3J0dKTPnz9XauO3anlE/Dl8JycnunPnDhF9PVGu\npaVV7gCVsN+wREREkIKCAvXq1atOr+p8fX0JQIUZE6siLCyMOBwO9enThxISEgRvnJCYM2cOAaBB\ngwbRzp076dWrV1W2SU5Opv3799Pw4cMJAC1atEgElrKwiA/W4ZeiOg7n68resNx161Znvvv4FmmU\nxyUikpWVJWtra7K3t6dTp05V2M7MzIwSExN5PxsaGpZ7EyGKLZXt27cTAFq5cmWdPrHt5uZGDMPQ\nzZs3q922ROoWALVu3ZpmzpxJZ86coaysLCFYKhjy8/Np5syZpK2tzbPdyMiIpk2bRqdOnaLMzMzv\ntp80aRIBIHd39x86eRNL3UYUDr9OntL/kp9crXJ+kEZ5XAB48+YNHj58iEOHDmHOnDl4+fJltdqL\nkilTpsDFxQVLliyBrq4uxo0bh0OHDiEjI0PcpgkUDw8PEBGuXbtW7bYzZsxAZGQkNm7ciObNm+Pf\nf//FL7/8Ai0tLXTp0gUrVqyQOBEbeXl5bN26FYmJiYiKisKWLVtgbGyMffv2wdnZGVpaWujdu3eF\nwlIAsGXLFvTv3x+enp5wcHCApqYm+vXrhy1btiAqKurryoWFhaVK6qTDr6fQtFrl/FIij/vXX3/B\ny8sLJ06cKFenRB43KCgIT548Qd++fQUqj1sS4sUv2traAABDQ0M4ODjg0aNHFdZJTEwE8DWELCsr\nC1paWjW2uaYwDAM/Pz/s2rULHTt2xJkzZ+Dq6gozMzMkJ9f8YU3SuHfvHgCga9euNWpvZmaGefPm\n4dKlS/j48SOCg4Mxf/585OXl8ZTvkpKSBGmyQGAYBiYmJpg9ezbOnz+PjIwM3LhxAwsWLEBgYCCm\nTJlSofOuX78+zp49i/T0dJw+fRpjx45FTEwM5syZAzMzM+jq6mL69On4/PmzGO6KhUV6qJMO39DI\nHTIy9cuUycjUh6GRe437lEZ53I8fPyI/Px8AkJaWhtu3b8PU1LRcvQEDBsDPzw8AcPz4cfTo0UNs\nQif16tXDxIkT4e/vjw8fPiA4OBjZ2dlwdXWtM7H6ly9fhrKyMjp27FjrvhQUFNC9e3esXr0aDx48\nQGRkJL58+YIRI0YgNTVVANYKD3l5eXTt2hWrV6/G33//jcOHD2Po0KHYt29fhQ94ampq+OWXX+Dl\n5YWYmBjEx8fDx8cHHTp0wPbt2/Hrr7+K4S5YWKQIYe8Z1OSSxFP60iiPe/v2bTI3N6c2bdqQubk5\n7d69m9emtDxuXl4eDRkyhIyMjKhdu3YVSq2KMyxy7969NT7oJmlwuVwyMDCoNERSEBw8eJC3V25p\naUnz58+nW7duCW08QVBcXEy//vorNWzYkGe7ubk5zZ07ly5evFhl1sFFixbx9vnv3r3Lpu1lkTrA\nHtr7P5IQh/8jI+75HzNmDDEMI/W591+8eEEAyNvbW6jjRERE0OrVq8nBwYE4HA4BoJMnTwp1TEFQ\nXFxMDx8+JA8PD/rpp59IQUGBl3L4559/rjRss7CwkJydnXlpe9XV1WnQoEG0ffv2Ch9gWVgkDVE4\nfIYk8MCLra0tPXjwoExZdfTYWQSPuOf/06dPsLW1xcePH/H48WM0btxYbLbUhq1bt2L27Nl4+fIl\nDA0NRTJmbm4uunfvjri4ODx69AgGBgYiGVcQ5OXlISQkBAEBAfDy8oKDgwMuXrwIGZmKdyPT0tIQ\nFBSEwMBABAQE8M6mGBoawsnJCY6OjujRowfU1dVFeRssLFXCMEw4EdkKdRB+nwwAyAJ4BOD8fz9b\nAQgFEAHgAQC7Str9CiASwDMAc/gZi13hSx6SMP9Pnz6l+vXr008//cRX4hpJpE+fPtSiRQuRj/vy\n5UtSU1MjOzs7qQ1rKwnbHDx4MB09erTKBERcLpeeP39O//zzD/Xv35+UlZUJAMnIyFD79u1p6dKl\nFBISUiapz6tXr8jX11dq54hFeoEkvdIHMA/AoVIOPwDAz//9uw+A6xW0Mf/P2SsCkANwFUCLqsZi\nHb7kISnzv3v3bgJAf//9t7hNqTZfvnwhRUVFmjFjhljGP378OAGgefPmiWX82sLlcsnd3Z1UVVV5\naXvbtWtHISEhfLUvKCigmzdv0tKlS6l9+/YkIyNDAEhFRYX69+9Pffr04W0JzJ07V8h3w8JSFolx\n+AB0AAQB6FHK4V8BMOy/f48AcKiCdkMB/Fvq56UAFlQ1HuvwJQ9JmX9pzr0fFBREAHiHJcXBzJkz\nxW5DbSksLKQ7d+7QX3/9Rfr6+tSwYUN69+5dtfvJyMigEydO0NSpU8nQ0JCaNWtGS5cupQkTJhAA\nOn26dgd9WViqgyQ5/OMA2gJwKOXwTQAkAEgE8BaAfgXtTADEAND6b5V/F8DWSsaY/N/WwAM9Pb1y\nkyEpDudHRZLmPzs7m5d7//379+I2h2/CwsIIAE2ePFlsNnz58oVsbGxIQ0ODXr9+LTY7BMWzZ89I\nUVGRbGxsaPfu3fTmzZta9/nlyxdq27Ytqaur14k5YpEOROHwq4zDZximH4BUIgr/5qNpAOYSkS6A\nuQD+/bYtEUUD8Pjv9f9lfN3vrzCYmoh8iMiWiGwbNmxYlVksPzAqKirw9/dHRkYGRo8eDS6XK26T\n+MLW1hYLFiyAj48Pjhw5IhYbFBQU4O/vj+LiYgwfPhyFhYVisUNQmJqaws/PD8nJyZg4cSL09fVh\nbGwMb2/vGvepoKCAo0ePgsvlYtiwYVI/RywsPKp6IgCwBkASgNcAUgB8BnAAQBbAO+XPAMjmo6/V\nAKZXVU9SX+lLozzumzdvyNHRkYyNjcnExKRC6VtxyuPWhh07dhAAWrVqlbhN4ZuCggLq2LEjKSsr\nU0xMjNjs8Pf3JwA0e/bs7woySRKfPn2i3bt304ABA8oJ8HC5XIqMjKRNmzZRx44dCQCdPXu2VuOV\nzJG7u3ut+mFh4QdIyit9XuWyr/SjATj89++eAMIradPov//qAXgOQL2qcQTh8LOyoig+3odiYzdS\nfLwPZWXVzmFJqzxut27dKCAggIi+CgBV9GAibnncmsLlcmnYsGEkKyvL98EtSeDNmzekqalJlpaW\nlJeXJzY7pk+fTgBIQUGBHB0dad26dRIZsx4TE0Nz584ldXV1XlKekoftisjLyyNra2vS0NCo9d/b\njBkzBPLwwMJSFZLu8DsDCAfwGMA9AG3/K28G4GKpNiEAov6r15OfcWrr1Fr94QAAIABJREFU8LOy\noigubgvFxm7kXXFxW2rl9KVRHvfZs2fUqVOnKu9NEuRxa0pWVha1aNGCtLW1hfIwJSzOnz9PAGja\ntGlis6G4uJguXrxIc+bMITMzMwJAqqqqFBcXJzabvmXx4sUEgOTk5GjYsGFkZ2dHGhoaVYZllkRz\nRERE1Gr8vLw83pkHQZwPYGGpDIlz+KK6auvwS1b2317x8T589/Et0iiPe+rUKerbty85OzuTlZUV\nubu7V/hFKSnyuDXl4cOHJC8vT3369JGq+Hx3d3deCNjNmzfLxIOLg+fPn5OGhgbZ2Njw3mSJmyVL\nlhAA2rZtGxER6enp0S+//FJlu1GjRlHDhg2puLi41jbExsaSiooKtW/fXuy/I5a6iygcfp0Uzykq\nqlhms7JyfpBGedyioiKEhIRgw4YNCAsLw6tXryq1WZqxtraGp6cnLl68iIYNG2Lo0KHYtWsXL8ua\npLJ69WoMGjQIW7ZsQdeuXaGpqYkBAwaUEWkSJa1bt4afnx8ePnyI3377TSw2fMuyZctgb2+PRYsW\n4eXLlzAzM0NgYGClf1NcLheBgYG4ePEiHB0dK83IVx1atGiBXbt2ITQ0FH/88Uet+2NhERd10uHL\nyalUq5xfpE0eV0dHB1ZWVjA0NIScnBwGDhyIhw8flqsnKfK4tWHatGk4efIkBg4ciNDQUEyePBmG\nhoYICgoSt2mVwuFwcOLECaSnp+PkyZMYNWoUQkJC4OzsLDap1/79+2PevHnw9vbG/v37xR4BweFw\ncOTIEcjIyMDFxQXbtm2DsrIyhg4dWm6OXr9+DVNTUzg5OUFGRgYzZ84UmB3Dhg3D1KlTsX79epw/\nf15g/bKwiJI66fA1NTuDYeTKlDGMHDQ1O9e4T2mUx23Xrh0yMzPx4cMHAEBwcLDEy+PWFIZh4Ozs\njD179iAhIQFRUVFo2bIlXF1dkZKSUq4+EYndmZWgrq4OZ2dnbN++HUePHkVUVBRmzZolNnvWrFkD\nOzs7uLm5oUmTJnB1dcXevXuRmZkpFnsMDAywd+9ePHz4ECNHjoSVlRWioqLKOfTCwkK8evUKxsbG\nSExMRIcOHQRqh6enJ6ysrDBmzBiJf3vEwlIhwt4zqMkliaf0pVEel4goICCALCwsyNzcnMaMGcPL\nES5N8rg1pST3fv369alXr160YcMGCg0NpZUrV5KOjg6pqKjQgAEDyMvLi168eFHuoKK4+OOPPwgA\n7du3T2w2ZGdnk5+fH7m6ulLjxo15crX8hJ8KCy8vLzI1NeWd1AdQLjxvy5YtBIA2bNggFBtiYmJI\nWVmZ2rdvT/fv3y9zZiQzM5NOnTrF5uFnqRFgD+39H2l0OHUJaZ3/sLAwmj17NpmYmJRxFI6OjjRp\n0iRq3rw5r2zTpk3iNpeIvkZKdOnShRQVFSk6Olrc5hCXy6WTJ08SABo3bpzYD64lJSXR7t27adGi\nReU+43K55OzsTHJycnT37l2hjO/v70+ysrIEgDQ1NWno0KE0fvx4UlRUJAAUHBwslHFZ6jaicPis\nPC4LX9SF+U9KSsLt27dhZWWF1q1b88qvXLmC3r17Y/v27Zg6daoYLfw/b9++hZWVFZo2bYp79+6h\nfv364jYJf/zxB1avXg1lZWU4ODjAyckJ/fv3lzi53czMTFhbW4OIEBcXBzk5uaobVZPU1FQEBQUh\nICAAgYGBSE9Ph7q6OtLS0pCeng5VVVWBj8lStxGFPG6d3MNnYakIHR0dDBs2rIyzB4Dnz58DAHr1\n6iUOsypEW1sb+/fvx9OnT+Hi4oKLFy/i06dPYrVpxYoVOH36NEaNGoXo6GjMnj0bpqamePLkiVjt\n+hZ1dXUMGjQI7969Q0FBgVDGaNSoEUaMGAFfX18kJiYiJycHTZs2RadOnVhnzyKxsA6f5Yfn3r17\nUFNTg7a2trhNKUPv3r2xZs0aXL16FX379oWGhgYcHBxw4cIFsdgjIyODX375Bdu3b0dcXByio6Oh\nrq4OFxcX5ObmisWmyrh69So6d+4MRUXFWvfF5XJx5coVrFmzBiEhIeVy6zMMAzk5OaSmpiIrKwv5\n+fm1HpOFRRiwDp/lh8fFxQVZWVlYuHChuE0px6JFi5CRkYGAgADMmTMHSUlJGDJkCJ4+fSpu02Bs\nbIxDhw4hNjYWU6dOhaRsD75//x5PnjxBYWEhLl26VOM3Ix8/fsSmTZvQunVr9O7dG4sXL+blS+jf\nvz9Onz5dpr6XlxciIiIk8v8jFhYA7KE9Fv6o6/M/a9YsqdBAT0lJoSZNmpCxsbHYD8+V8NdffxEA\n2rFjh0REOuTk5FC/fv1IQUGBAJC8vDw5ODjQ6tWrKSwsrMrsew8ePKDx48dT/fr1CQB16tSJDh06\nRCkpKXTixAmaOnUq6evrk4yMDN24cYPy8vJo37591L59ewJAJiYmVdqYkZFBN27cKJcqOzc3VyIO\narKIHrCn9P9PXXc4kk5dn39p0kAvcbApKSniNoWIvqaQ7tmzJwEgXV1dGj9+PB0+fJgyMzPFatfn\nz58pICCA3N3dqU2bNrxoDC0tLXJxcaHdu3fz8uN//vyZ9u7dS3Z2dgSAlJSUaMqUKZXm4s/OzqZW\nrVoRAN6DQatWrWjz5s18KWCuX7+eJ1zUs2dPWrt2Lf3666+kpqZGAOjixYsCnQsWyYd1+KWQBIcj\nbfK4wcHBZGlpybsUFBTo1KlT5dpLqzyuoImLiyNVVVVSVVWlgQMHkre3N8XGxkrEqrU0Tk5OfK0i\nRUlOTg7t2LGDBg8ezFO1MzIyErvTL01KSgodOHCAxowZQ82aNeM9ALRq1Yo0NTUJABkbG9M///zD\nl92PHz+miRMnkrOzMwUGBlYrb39GRgbp6+vzHpIAEIfDoREjRpC5uTlpaWmV0bdgqfuwDr8U1XU4\nx5PTqe3tSGoS/Ija3o6k48np1Wr/LdIqj1tCeno6aWho1Cl5XGFw//59mjRpEu/LGAAZGBjQpEmT\n6NixYzxhJHHA5XLp5s2bpKCgQHPnzhWbHVVRWFhIZ8+eJVlZWRoyZIjEPTARfZ3LyMhI8vT0pD59\n+pCLiwsFBQWJ1NZLly4RANq/fz8lJydTWloaEX1VuQRAf/75p8hsYRE/rMMvRXUczvHkdDK4HkGN\ngx/xLoPrEbVy+tIoj1uanTt30siRIyv8TJrlcYUFl8ulmJgY8vb2poEDB5KqqioBIFlZ2e8qFgqL\nqKgosrCwIACkoaHBU2CUZEq2HsLCwsRtikSya9cuAkCRkZFlym/dukUAKv1bZqmbsA6/FNVxOG1v\nR5Zx9iVX29uRVTeuBGmUxy1N9+7d6dy5cxV+Ju3yuKKgsLCQbt++TZ07dyZFRUV69uyZSMefMGEC\nKSsr065du0SS3vbz5890/vx5ioqKqvGq9++//yaGYYTyVqsuMHjwYNLR0Sk3v0uWLCFZWVmBbPex\nSA+icPh1MizvbX5htcr5QRrlcUtITk7G06dPJSqxjLQhJyeHjh07wt/fv1K1NmFBRLhy5Qp69eqF\niRMnCiS2vDJyc3OxYMEC6OjooF+/fjA1NYWuri7GjRuHQ4cOITU1le++Ll++DFtbWzRo0EBo9koz\niYmJkJOTKxc2GBsbCyUlpXLx/iwstaVOOnxtBU61yvlF2uRxS/D394ezszM4nErmpQ7I44qKpk2b\n4sCBA4iOjhaZol10dDSSkpJE8sC2YcMGbNiwAT169MCFCxfg4+ODDh064MyZM3B1dUXjxo1hbW2N\nhQsX4v79+5X2Q0R48uQJGjduLHSbpZW1a9ciISEB06ZNK1O+aNEi5OfnY/To0cjIyBCTdSx1kTrp\n8H83bIr6MmXlXevLMPjdsGmN+5RGedwSDh8+jBEjRlT6eV2QxxUljo6OWLx4Mfbs2QN7e3ssWbIE\nN2/eFFoa1ytXrgAAnJychNJ/aS5duoT27dvj2LFj6NOnDyZNmoRjx47hw4cPuP+/9s48vMYzffyf\nJ3uIWIJ8CRpLpyIZMjNR4foiEaUUo0NFq5YZtba2QX4jVXxHg7o6JnYzTFsqHUa0ZQxSS4mSRuxl\nqFrKWBpbbC2R5Ny/P87JmSQSSY7znpPl+VzXeyXvm2e53zvnvPez3O99799PXFwc1atX589//jNt\n27Zl165dAFy5coXjx4+b9wkxR58bP348mzZt4oMPPrBe1/yXyMhIhg4dyurVq/NNCkJDQ5k/fz5J\nSUnUqVPHIZ8xTSXB6D0DW46y6KVfXtPjnj9/XurXr//YK0OVIT2ukWRlZcmcOXOkXbt21sxpVatW\nlZdeeknmz58vJ0+etJvHd5cuXaR58+Z2aetJ3LhxQ5RSJfIOz8jIkOeee07q1asnv/nNb6w68Pf3\nlwEDBsjKlSvl4sWL0rFjR+urZ0OHDpU1a9bInTt3DL+X8kJUVFS+73heUlNTZfr06UV+xi5evOhg\naTVGgnba+y/a4DgXrf+iyc2DPmrUKGnWrJn1db5WrVrJrVu3bG730aNHkpiYKF5eXjJ+/Hg7Slw4\np0+fFkBGjx5dovJHjx4Vb29v8fPzk5iYGPnggw/ktddekzp16lh18Mwzz0iVKlXEw8PDeq1v374G\n34lzOHfunMyZM0eSk5NLFAUxPT1dPDw8ZOLEicWWvX37tnz66af5PmM1a9YsNGaGpnyiDX4etMFx\nLlr/JefcuXOycOFCcXNzk969e9s00z906JAEBARYjaaj9D927FgBZMGCBSUarFy9evWxV0VzcnLk\n0KFD8t5770nnzp2tIW69vLzkt7/9rZw+fdoo8Z1KQkKCdVBTrVo16dmzpyxcuFBOnTqV7zOwf/9+\nGTx4sHh6eoqLi4ukpKSUuq8jR46Ir6+vtGnTRjIzM+15GxonoQ1+HrTBcS5a/6Xn/fffF0Dmz59f\n6rpDhw4VX19f2bhxo2RnZxsgXeE8fPhQ2rZtK4C4uLhImzZtZOrUqbJ7926bDcuPP/4oe/bscWrQ\nIkeQk5Mj3bp1E0A6dOggTZs2tQ4AGjZsKEOGDJGwsDABxMfHR0aNGiXHjh2zub9169YJUKaDMGlK\njjb4edAGx7lo/Zcek8kkPXr0EHd391IFnzGZTBIQECB9+vQxULqiycrKkq+++kqmTZsmbdu2te4f\n+/j4SI8ePWThwoXy4MEDp8hWFOnp6TJr1iwJCgqSZcuWOU2Oo0ePCiBLliwREZGzZ89aQw7XrFlT\nQkJCZPHixXbzY3jzzTcFsPrjaMov2uDnQRsc56L1bxs3btyQhg0bSpMmTUocV/748eMCyPLlyw2W\nrmQU5qMwcODAMhMy99///rd4eXkJIPXq1RN3d3dJTU11iixFRc8zigcPHkizZs2kdevWDulPYxyO\nMPgV8rU8jaas4Ofnx5o1a7hw4QJvvPGGeZRdDI58Da8kVK9end69e7NkyRK+++47ZsyYwccff1xk\n4ClH4+/vT82aNWncuDGpqanUr1+f6Ohobt++7XBZkpKSCAgIoEWLFg7pz93dnYyMDIKCghzSn6Z8\now2+RmMw7dq1Y9asWSQmJvLPf/6z2PJbt24lKCiIRo0aOUC60jN16lQ6derEm2++yYkTJ5wtDrVq\n1eKTTz7hwoULxMbGsnbtWi5dusTvfve7Eg2w7ImLiwu3b9/m22+/dUh/Bw8e5ObNm7z44osO6U9T\nvtEGvxTExcURHBxMy5YtCQ0NJTU1FYD4+PgShVktWK579+52mYUkJyfzy1/+Ejc3NxITE/P9LSYm\nhuDgYIKCghg7dmyhD8DMzEyio6Np1qwZbdq0sSl8r+bJtG7dukTlfvrpJ5KTk8v0A9zV1ZWEhAR8\nfX155ZVXHgsN6wwiIiKYPn06q1ev5sSJE8yZM8cawCouLo60tDRycnIMl2PevHl4e3vTp08fPvvs\nM8NXGU6dOgWYQ39rNMVi9J6BLYc99vDP3PlR1p77QT44fVnWnvtBztx5uoQj5TE97t69e6Vdu3aS\nnZ0t2dnZEh4eLl9++eVj9XV6XOOJiYkRNzc3uXv37hPLbd68WQDZunWrgySzne3bt4tSSoYMGeJs\nUUTEnKwqKipKvL295dixYxIXFyehoaFWT/latWrJK6+8In/961/l+++/N0yObdu2SfXq1a1vOrRt\n21bmz59viM/DvXv3pHnz5uLv7y9Xr161e/sax4Hew7eNs3d/Yu+1O/yYbR7R/5idw95rdzh71/Zk\nJ1evXqV27drW2Pa1a9emfv36LFiwgCtXrhAZGUlkZCQAo0aNIiwsjODgYKZPnw5QaLnAwEBu3LgB\nmGcGISEhhISEEB8fD8D3339PUFAQw4YNIzg4mC5duvDgwYPHZAsMDKRly5a4uOT/dyqlePjwIY8e\nPSIzM5OsrKxCY5tv2LCBwYMHA9C3b1927Njh8KXQis6ePXto0aIF1apVe2K5pKQkvLy86NChg4Mk\ns52oqCimTp3KRx99RHx8vNNn+nlXHqKjoxk3bhyHDx8mPT2dhIQEevXqxb59+xg+fDiBgYH87Gc/\n46233mLDhg3cvXvXbnJ07tyZ69evk5yczNtvv01mZibjxo1jxYoVPHr0iH/84x8sW7aM06dPP/X3\nzMfHh48//pj09HRWrVpVbPn09HT93a7MGD2isOV42hl+7sy+4LH23A8lbqMg5TU97sSJE6V69eri\n6+srsbGxhdbT6XGNZ/r06QLI6tWrn1ju5z//ubRv395BUj092dnZ0qlTJwHEw8NDIiIiJC4uTi5d\nuuQ0mXJXHgYNGvTY30wmk5w4cULi4+Ole/fuUrVqVQHE1dVV+vbtW6IIeaUlOztbXnjhBfHy8pK6\ndetaVxwAadSokTXkcN7vnMlkkl27dsmmTZuKXRVKSkoSQDZv3lysLLn9asoe6Bm+beTO7Et6vSSU\nx/S4Z86csWZau3z5Mjt37rS2q3EcIkKHDh3w8vJixIgRT8yeGBYWxt69e0lOTnaghLbj6urKli1b\n+OKLLxg3bhwZGRm8/fbbREZG2nXWXBqioqJ45513WLVq1WPfUaUULVq0YNy4cfzrX//i1q1b7Nq1\nizFjxpCYmEhsbKzd5XF1dWX16tU8++yztGnThq1bt/Ldd9+xdOlSwsLCSExMpH///tStW5fWrVsz\nduxYgoKCiIiIoEePHtSqVYv27dvzxz/+katXrz7WflJSEm5ubqVaFYqKirLnLWrKCRXS4Fd1cy3V\n9ZJS3tLjfvbZZ4SHh+Pj44OPjw/dunUjJSXlsXI6Pa4x3Lt3j6VLl9KyZUuioqLw8vJiwoQJeHh4\nFFln/vz5NGnShFdffZXr1687UFrb8fDw4IUXXmDu3LkcOXKEXbt2cfbsWUaMGOG05eNp06YRERHB\niBEj6NWrF4sWLeLbb799TB4PDw86duzIzJkzCQsL4/333+frr7+2uzx169bl2LFjbNy4ka5du9Ks\nWTNGjhzJ+vXruXHjBikpKcyYMQMvLy+WLFlCjRo1WLVqFTt37mTy5Mk8fPiQGTNm0LVr18e29U6f\nPk12djZNmjRhwIABrFy5kitXrhQqR2ZmJgBZWVl2v0dN2adCGvxf+VXDtUB6V1el+JXfk/dPn0R5\nTI/bqFEjdu/eTXZ2NllZWezevbvQ93V1elz7cvLkScaMGUNAQACjR4/Gw8ODFStWcPnyZWbOnPmY\nr0VeqlWrxrp167h58yaDBg3CZDI5UHL7kGtA16xZw/Lly50ig6urK2vXrmXo0KGcOHGCMWPG0Lx5\ncwIDA3njjTdYu3at1X9m//79BAQEcODAAUJDQ6lTp45DZXVzcyM8PJxp06axZ88eMjMz+frrrxk4\ncCCRkZHMmjWLtLQ0Nm/ezDfffMPIkSO5du2atf6qVatYunQpnp6eJCYmMmTIEAICAggJCWHChAkk\nJSVZBzoeHh6YTKYnriDlDgo0FRCj9wxsOcqil355TI+bnZ0tw4cPl+bNm0tQUFC+mNs6Pa59ycrK\nkvXr1+fbz3799dclJSXFJu/sJUuWCCCzZ882QFrjycnJkS5duoinp6ds3779sfTMjubMmTOydOlS\nefnll60e9Ln7/Ll5C/bt22c3T3qTySRfffWVTJo0KZ9/zNMSGxubLxvj5MmTZfz48VKjRg3rPXl5\neYm3t7d4e3tbwyJPmTKlxH3ktu/s/1llAx1a979og+NctP6fzJgxY6xOWLNmzZL09PSnas9kMkm/\nfv3E1dVV9uzZYycpHUt6ero145+fn5/069dPVqxY8Vh2PUeTlZUlKSkpMnr0aKtxe/nll+3S9r17\n9+Qvf/mLtGzZ0tp2eHi43ZwBTSaTHDhwQGbNmiURERHi7u4ubm5uEh0dLYmJiTJt2jQJDw8XFxcX\nAcTNzc0qx9///vcS9fHss88KIA0aNLCLzJqSoQ1+HrTBcS5a/0WTk5MjdevWld69e9s1s93t27el\nSZMmUrt2bRkzZoxs3LixWI/tssbNmzdl9erVMnjwYKlXr57VuJaFOPwmk0nat28vgCxatOip21u8\neLH4+voKIKGhobJ8+XL58MMPBZDo6GjZvn27NelQVlaWfPrppzJ79mzZu3evZGVl2dTn/fv3C01j\nfOvWLVm/fr2MHDnSqveCb/AUhclksg4SbMn0qLENbfDzoA2Oc9H6L5pDhw4JICtXrrR728ePH5du\n3bqJt7e3dcY2YMAAh6bMtRcmk0nmzJlj3aooC3ncb9++Lffv37fL8vWwYcMEkPj4+HwDmj/84Q/W\nmba3t7d07tzZuvKRe/j6+sqvf/1rwwIuXbhwoVTlz58/b5Xt3LlzhsikyY8jDH6FdNrTaBzJwYMH\nAWjatKnd2w4ODmbz5s1kZGSwc+dOhg0bRkJCAnFxcXbvy2iUUsTExNCzZ0+mTJlCrVq1eOmll4iP\njy/Sq9xoqlevTtWqVZ/oSFlS5s6dS+PGjZk3bx4ZGRnW67NnzyYjI4NNmzYxbNgwfvjhB4KDg/n8\n88+5du0a69ato3///hw8eJBevXpx+PDhp5alIKXNyxAYGMiiRYsASEtLs7s8GuegzAOLskVYWJgc\nOHAg37WTJ0/SvHlz7T3uBESEU6dO6YxcRXD16lVatWqFv78/qampVKlSxbC+RITBgweTkJDAwIED\n6dKlC507d6Zu3bqG9WlvHj16xJYtW9i2bRvbtm3j9OnTBAQEcOTIEWrXru1s8Z6KAwcO0K5dO7p1\n68bnn39equfVjRs3CA0Nxdvbm4MHD+Lr62ugpCUjMzMz36vBGuNQSh0UkTBDOzF6CcGWo7Al/XPn\nzsn169fLxN5fZcJkMsn169f1sl4xbNq0SQBZvHix4X3du3dPBg0aJDVr1rQuu7Zt27bc7e/nkpKS\nIh4eHtK9e/cK4RkeHx8vgCxbtqzUdZOTk8XV1VWio6P1s66SgQOW9N0MHU3YkQYNGnDp0qVyE4yk\nIuHl5UWDBg2cLUaZxtvbG4CGDRsa3pePjw8rV64kJyeHQ4cOERMTQ3JycqmCMpUlwsPDmTdvHm+9\n9RZ/+tOfmDx5srNFeipGjhzJ5MmTi42wWRjt27dn5syZxMbGEhERwciRIw2QUFNpKenIAHAFDgOb\nLOehwNfAEeAA8HwR9SYAJ4DjwN8Br+L6KmyGr9GUZWJiYsTd3V3u3bvn8L6ff/55CQ8Pd3i/9sRk\nMkmfPn3E1dVV9u3b52xxnoodO3YIYI1zUVpycnKka9eu4unpac2jURa5fv26zJ07V5o2bSr+/v4y\nYMAA+eijj6xZRDWlgzLmtDcOOJnnfC7wfyISCkyznOdDKRUAjAXCRCTEMmjoX4o+NZpyweHDh/H3\n939i2FyABw8e8OGHH9K6dWt8fX3p0aMHCxYs4OTJk7kD5FJx584d0tLSePjwIWvWrCm3K2BKKf72\nt7/xzDPPEB0dza1bt5wtks188cUXAPznP/+xyRnRxcWFVatW4efnR79+/QqNzulsTCYTISEhxMTE\nEBAQQFRUFNu2bcsX5e/3v/89W7ZscXoWRU0eSjIqABoAO4BO/HeGnwREW35/FfikkHoBwH+AWoAb\nsAnoUlx/eoavKW8kJiYKIBMmTCh07/Xs2bMyadIkqVWrlgDSokULGTp0qDRr1sy6D//iiy+W+n3s\nzMxMGTRokDXSGpb9/PI6y0pLSxN3d3fp2bOnU1ZL7EFCQoLUqVPH+v8IDg4ucdCbvOzevVtcXFzk\n1VdfLZP7+f379xcXFxdrYKicnBw5fPiwvPfee9K5c2fx9PS0Rp2MjIyU2bNny4EDByqEn4YRUFbe\nwwcSgV8BEXkMfhBw0WLQLwPPFFF3HHAfuA4klKQ/bfA15ZHcyG0BAQEyZMgQSUhIkA0bNkj37t1F\nKWVNwfrll1/me4CfP39eZsyYIYC88847NvWdnZ0tqampMnPmTKlSpYp07NjR5mAuzibX6c3d3V06\ndOgg7777rqSmppar2AO5xm/u3LnSqlUrcXd3l7S0tFK38+677wogsbGxcubMGQMktZ07d+5I48aN\n5Re/+EWhf//pp58kKSlJJk6cmC/yoJ+fn0RHR8uKFSvk4sWLDpa67OIIg1/sa3lKqR5AdxEZrZSK\nACaJSA+l1AJgt4isV0r1A4aLSOcCdWsC64Fo4DawDkgUkdWF9DMcGG45DcG8568xjtrADWcLUcHR\nOjYerWPHoPVsPM+JiO0Z3kpASQz+bGAgkA14Ab7Ap0BPoIaIiDK/bHpHRHwL1H0FeFFEhlrOBwHh\nIjK6mD4PiNHvI1ZytI6NR+vYeLSOHYPWs/E4QsfFOu2JyBQRaSAigZgd7naKyOvAFaCjpVgn4LtC\nql8EwpVSVSyDgijyO/5pNBqNRqNxAE/zHv4wYL5Syg14iGU5XilVH1ghIt1FJFUplQgcwrxCcBj4\n61PKrNFoNBqNppSUyuCLyC5gl+X3rzA78hUscwXonud8OjC9lHLpQYHxaB0bj9ax8WgdOwatZ+Mx\nXMdlMpa+RqPRaDQa+6Kz5Wk0Go1GUwlwmMFXSr2ilDqhlDIppcKfI36jAAAEK0lEQVTyXH9BKXVQ\nKfWN5WenQupuVEoV+ZqeUmqKUuqMUupbpVRXo+6hrGOLjpVSW5VSRy31limlXAtp110ptdJS/6RS\naoqj7qksYpSeLeVaKqVSLOW+UUp5OeKeyhpG6thStpFS6r5SapLR91JWMfB5UewzvTJh8POidLbP\n6Bf9cw/MgXqew+wDEJbn+i+A+pbfQ4DLBer9BvgEOF5Euy2Ao4An0Bg4C7g66r7K0mGLjgFfy0+F\nOWZC/0LafQ1YY/m9CvA9EOjs+62AenYDjgGtLOd++rNsXx3nKZuIOS7IJGffa0XTcXHP9Mp2GKjn\nUts+h2XLE5GTwGP5oUXkcJ7TE4C3UspTRDKVUj7A7zG/AfCPIpr+NWZjlAmcV0qdAZ4HUux8C2Ue\nW3QsInct190AD8zRsB5rGqhqeSPDG3gE3C2kXKXAQD13AY6JyFFLezftLXt5wUAdo5TqDZwHKnWQ\nd6N0/KT69pS/vGDgZ7nUtq+s7eH3AQ7l+WDMBP4E/PSEOrnx+nO5ZLmmKZyCOkYplQRcA+5hnvkU\nJBHzw/Eq5tgK74tI+c1u4hhs0fPPAFFKJSmlDimlYhwjarml1Dq2TCL+H/B/jhKynGPL5/iJ9TWF\nYoueS2377DrDV0ptB/6nkD+9LSIbiqkbDLyHeZaDUioUaCoiE5RSgfaUszxjTx3nIiJdLXvFCZiD\nKG0rUPV5IAeoD9QE9iiltovIOdvuouzjJD27Af8LtMY8yN2hlDooIjtsu4uyjZN0PAP4s4jcLzjj\nqog4ScdPrF8RcaaeS4NdDb4UiKVfUpRSDYDPgEEictZyuS0QppT6HrOcdZVSu0QkokD1y0DDPOcN\nLNcqJHbWcd52HyqlNmBeJir4wXoN2CoiWcA1pdReIAyosAbfSXq+BCSLyA1LW5uBX2LOVFnhcJKO\n2wB9lVJzgRqASSn1UEQW2SJLWcdJOi62fkXDSXoute1z+pK+UqoG8C/gDyKyN/e6iCwVkfpiDun7\nv8DpQow9wEagv1LKUynVGHgW2G+85OWHonSslPJRStWz/O4GvAScKqSJi5hHmCilqgLhRZSr1NhB\nz0nAz5U5FLUb5tDV/zZe8vLD0+pYRNqLSKDluRIPzKqoxt5WnlbHRdXX5McOz4vS2z6jPBML8Sh8\nGfMMJhNIB5Is16di3h8+kueoW6BuIHm89IFewB/znL+N2UPxW6Cbo+6prB2l1THgD6Rh9gw/DiwE\n3ArqGPDB7NF8ArMBmuzse62Ieracv27R83FgrrPvtSLqOE8fM6jcXvpGPS+KfaZXpsPg50WpbJ+O\ntKfRaDQaTSXA6Uv6Go1Go9FojEcbfI1Go9FoKgHa4Gs0Go1GUwnQBl+j0Wg0mkqANvgajUaj0VQC\ntMHXaDQajaYSoA2+RqPRaDSVAG3wNRqNRqOpBPx/G48Y9zDuAfEAAAAASUVORK5CYII=\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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18,)\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": 17,
     "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[:18].shape)\n",
    "lons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  1.10840564258\n",
      "test a bias =  1.56264322288\n",
      "test b bias =  2.16262231886\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5254bee80>"
      ]
     },
     "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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  -2.05841228778\n",
      "test a bias =  -2.22289707427\n",
      "test b bias =  -2.06429947332\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAACMg2IPAOCog91S0byjx9E+yZcrdfYdbSua55wHAAAAYE6i2AMA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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5254a6518>"
      ]
     },
     "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  -0.833480781979\n",
      "test a bias =  -0.722380664614\n",
      "test b bias =  -1.05571789212\n"
     ]
    },
    {
     "data": {
      "image/png": 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T0qDTuEIIbwghbBzw1RpC+Mx4FCdJkiRJkqRjM+jInhjjU8ASgBBCPvA8cM8Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc525343be0>"
      ]
     },
     "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base case bias =  0.389839596219\n",
      "test a bias =  0.0576304329766\n",
      "test b bias =  0.511461178462\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5253d0a20>"
      ]
     },
     "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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   "cell_type": "code",
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   "outputs": [],
   "source": []
  }
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