{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "from salishsea_tools import (tidetools, geo_tools, viz_tools)\n",
    "import numpy.ma as ma\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import pytz\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n",
    "bathy, X, Y = tidetools.get_bathy_data(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HINDCAST_PATH= '/results/SalishSea/nowcast-green/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = pd.read_excel('/ocean/eolson/MEOPAR/obs/PSFCitSci/Chla_2015PSFSalish_Sea_22.01.2018vers_8_CN_edits.xls',header=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = f.drop(f.index[[0]])\n",
    "f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = f.dropna(subset = ['Date sampled (mm/dd/yyyy)', 'Time of Day (Local)', \n",
    "                   'Latitude', 'Longitude', 'Depth', 'Chl a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "local = pytz.timezone (\"America/Los_Angeles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f.loc[f['Date sampled (mm/dd/yyyy)'] == '07/15//2015', 'Date sampled (mm/dd/yyyy)'] = '07/15/2015'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.loc[f['Date sampled (mm/dd/yyyy)'] == '05/23//2015', 'Date sampled (mm/dd/yyyy)'] = '05/23/2015'\n",
    "f.loc[f['Date sampled (mm/dd/yyyy)'] == '05/17//2015', 'Date sampled (mm/dd/yyyy)'] = '05/17/2015'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'44.200'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f['Latitude'].unique()[0][4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Date sampled (mm/dd/yyyy)</th>\n",
       "      <th>Station Name</th>\n",
       "      <th>Time of Day (Local)</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Chl a</th>\n",
       "      <th>Phaeophytin</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Date sampled (mm/dd/yyy)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/16/2015</td>\n",
       "      <td>CBE-2</td>\n",
       "      <td>11:08:00</td>\n",
       "      <td>48° 44.200</td>\n",
       "      <td>123° 34.300</td>\n",
       "      <td>5</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.32</td>\n",
       "      <td>Averaged duplicates</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09/14/2015</td>\n",
       "      <td>LS-2</td>\n",
       "      <td>12:19:00</td>\n",
       "      <td>48° 58.400</td>\n",
       "      <td>123° 46.000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.93919</td>\n",
       "      <td>0.957782</td>\n",
       "      <td>Averaged duplicates</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08/09/2015</td>\n",
       "      <td>CR-8</td>\n",
       "      <td>15:31:00</td>\n",
       "      <td>49° 57.700</td>\n",
       "      <td>125° 08.800</td>\n",
       "      <td>5</td>\n",
       "      <td>0.420117</td>\n",
       "      <td>0.238546</td>\n",
       "      <td>Averaged duplicates</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06/16/2015</td>\n",
       "      <td>CR-8</td>\n",
       "      <td>10:15:00</td>\n",
       "      <td>49° 57.700</td>\n",
       "      <td>125° 08.800</td>\n",
       "      <td>5</td>\n",
       "      <td>0.947415</td>\n",
       "      <td>0.527436</td>\n",
       "      <td>Averaged duplicates</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>06/23/2015</td>\n",
       "      <td>CR-8</td>\n",
       "      <td>10:15:00</td>\n",
       "      <td>49° 57.700</td>\n",
       "      <td>125° 08.800</td>\n",
       "      <td>5</td>\n",
       "      <td>0.493184</td>\n",
       "      <td>0.438475</td>\n",
       "      <td>Averaged duplicates</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date sampled (mm/dd/yyyy) Station Name Time of Day (Local)    Latitude  \\\n",
       "1                09/16/2015        CBE-2            11:08:00  48° 44.200   \n",
       "2                09/14/2015         LS-2            12:19:00  48° 58.400   \n",
       "3                08/09/2015         CR-8            15:31:00  49° 57.700   \n",
       "5                06/16/2015         CR-8            10:15:00  49° 57.700   \n",
       "6                06/23/2015         CR-8            10:15:00  49° 57.700   \n",
       "\n",
       "     Longitude Depth     Chl a Phaeophytin             Comments  \\\n",
       "1  123° 34.300     5      3.08        0.32  Averaged duplicates   \n",
       "2  123° 46.000     5   3.93919    0.957782  Averaged duplicates   \n",
       "3  125° 08.800     5  0.420117    0.238546  Averaged duplicates   \n",
       "5  125° 08.800     5  0.947415    0.527436  Averaged duplicates   \n",
       "6  125° 08.800     5  0.493184    0.438475  Averaged duplicates   \n",
       "\n",
       "  Date sampled (mm/dd/yyy)  \n",
       "1                      NaN  \n",
       "2                      NaN  \n",
       "3                      NaN  \n",
       "5                      NaN  \n",
       "6                      NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "list_of_lons = np.array([])\n",
    "list_of_lats = np.array([])\n",
    "list_of_datetimes = np.array([])\n",
    "list_of_cs_chl = np.array([])\n",
    "list_of_model_chl = np.array([])\n",
    "list_of_depths = np.array([])\n",
    "for n in f.index:\n",
    "    decLat = float(f.Latitude[n][:2]) + float(f['Latitude'][n][4:])/60\n",
    "    decLon = (float(f.Longitude[n][:3]) + float(f['Longitude'][n][5:])/60) * -1\n",
    "    Yind, Xind = geo_tools.find_closest_model_point(decLon, decLat, \n",
    "                                                    X, Y, land_mask = bathy.mask)\n",
    "    if f['Depth'][n] == 5:\n",
    "        depth = 4\n",
    "    elif f['Depth'][n] == 20:\n",
    "        depth = 18\n",
    "    elif f['Depth'][n] == 10:\n",
    "        depth = 9\n",
    "    if mesh.variables['tmask'][0,depth,Yind, Xind] == 1:\n",
    "        try:\n",
    "            local_datetime = datetime.datetime.combine(datetime.datetime.strptime(\n",
    "                f['Date sampled (mm/dd/yyyy)'][n], '%m/%d/%Y'),f['Time of Day (Local)'][n])\n",
    "        except (TypeError):\n",
    "            local_datetime = (datetime.datetime.combine(f['Date sampled (mm/dd/yyyy)'][n],\n",
    "                                                        f['Time of Day (Local)'][n]))\n",
    "        date = local.localize(local_datetime, is_dst=True).astimezone(pytz.utc)\n",
    "        if date.minute < 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                               hour = (date.hour), minute = 30) - datetime.timedelta(hours=1)\n",
    "            sub_dir = before.strftime('%d%b%y').lower()\n",
    "            datestr = before.strftime('%Y%m%d')\n",
    "            fname = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr, datestr)\n",
    "            nuts = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir, fname))\n",
    "            after = before + datetime.timedelta(hours=1)\n",
    "            sub_dir2 = after.strftime('%d%b%y').lower()\n",
    "            datestr2 = after.strftime('%Y%m%d')\n",
    "            fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)\n",
    "            nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))\n",
    "            delta = (date.minute + 30) / 60\n",
    "            chl_val = 1.6*((1-delta)*(nuts.variables['diatoms'][before.hour, depth, Yind, Xind] \n",
    "                           + nuts.variables['ciliates'][before.hour,depth,Yind, Xind] \n",
    "                           + nuts.variables['flagellates'][before.hour,depth,Yind,Xind]) + \n",
    "                       (delta)*(nuts2.variables['diatoms'][after.hour, depth, Yind, Xind] \n",
    "                           + nuts2.variables['ciliates'][after.hour,depth,Yind, Xind] \n",
    "                           + nuts2.variables['flagellates'][after.hour,depth,Yind,Xind]))\n",
    "        if date.minute >= 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, day = date.day, \n",
    "                                   hour = (date.hour), minute = 30)\n",
    "            sub_dir = before.strftime('%d%b%y').lower()\n",
    "            datestr = before.strftime('%Y%m%d')\n",
    "            fname = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr, datestr)\n",
    "            nuts = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir, fname))\n",
    "            after = before + datetime.timedelta(hours=1)\n",
    "            sub_dir2 = after.strftime('%d%b%y').lower()\n",
    "            datestr2 = after.strftime('%Y%m%d')\n",
    "            fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)\n",
    "            nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))\n",
    "            delta = (date.minute) / 60\n",
    "            chl_val = 1.6*(delta*(nuts.variables['diatoms'][before.hour, depth, Yind, Xind] \n",
    "                           + nuts.variables['ciliates'][before.hour,depth,Yind, Xind] \n",
    "                           + nuts.variables['flagellates'][before.hour,depth,Yind,Xind]) + \n",
    "                       (1- delta)*(nuts2.variables['diatoms'][after.hour, depth, Yind, Xind] \n",
    "                           + nuts2.variables['ciliates'][after.hour,depth,Yind, Xind] \n",
    "                           + nuts2.variables['flagellates'][after.hour,depth,Yind,Xind]))\n",
    "        list_of_lons = np.append(list_of_lons, decLon)\n",
    "        list_of_lats = np.append(list_of_lats, decLat)\n",
    "        list_of_datetimes = np.append(list_of_datetimes, date)\n",
    "        list_of_cs_chl = np.append(list_of_cs_chl, f['Chl a'][n])\n",
    "        list_of_model_chl = np.append(list_of_model_chl, chl_val)\n",
    "        list_of_depths = np.append(list_of_depths, depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.size'] = 12\n",
    "mpl.rcParams['axes.titlesize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TAuCAAQP01oeEhHDGjBmsV68eFQoFP/30Uw4bNoyNGjWil5cXfXx8OHPmTNao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\n2pWWlpbZDJ9fvnypuJFOnz49k0hTG1gCoK2trc5bJ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ZJjvolnz57xrbfeYp06dZicnKyk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\nEolEIpFIIAWBRCKRSCQSAP8fjh6f1yEeofcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a80de2d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "viz_tools.set_aspect(ax, coords = 'map')\n",
    "ax.plot(list_of_lons, list_of_lats, 'ro')\n",
    "viz_tools.plot_coastline(ax, grid, coords = 'map')\n",
    "ax.set_ylim(48.2, 50.5)\n",
    "ax.set_xlim(-125.5, -122.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_cs_chl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bias =  4.09138969437\n",
      "RMSE = 5.16042682101\n",
      "Willmott = 0.279270060415\n"
     ]
    },
    {
     "data": {
      "image/png": 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EjMJdREQkYBTuIiIiAaNwFxERCRiFu4iISMAo3EVERAJG4S4iIhIwCncREZGAUbhLzDQ0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\nPEgNKSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a80668978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.plot(list_of_cs_chl, list_of_model_chl,  'b.', alpha = 0.5)\n",
    "ax.plot(np.arange(0,10), color = 'grey')\n",
    "ax.grid('on')\n",
    "ax.set_title('Citizen Science Chl 2015, depth = 5m')\n",
    "ax.set_xlabel('Citizen Science')\n",
    "ax.set_ylabel('Nowcast-green');\n",
    "print('bias =  ' + str(-np.mean(list_of_cs_chl) + np.mean(list_of_model_chl)))\n",
    "print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_chl - list_of_cs_chl)**2) /\n",
    "                              len(list_of_cs_chl))))\n",
    "xbar = np.mean(list_of_cs_chl)\n",
    "print('Willmott = ' + str(1-(np.sum((list_of_model_chl - list_of_cs_chl)**2)  / \n",
    "                             np.sum((np.abs(list_of_model_chl - xbar) \n",
    "                                     + np.abs(list_of_cs_chl - xbar))**2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6a80836ac8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AYDSmMZhZXEwOH05mZ5N9+5Ll5e75wsLO/7tPmDPestdae9LaI8krkhxP8pTW2he21uZb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\npSAFAAAAwKgUpAAAAAAY1f8DBPX6X1LadDoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a80cfeeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20,8))\n",
    "ax.plot(list_of_lats, list_of_model_chl - list_of_cs_chl, 'ro', alpha =0.5)\n",
    "ax.grid('on')\n",
    "ax.set_xlabel('lat', fontsize = 15)\n",
    "ax.set_ylabel('Model - Observed',fontsize = 15)\n",
    "ax.set_title('Chl', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6a80756a20>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U1TdV1eVVtbeqnpfk6Unefo7LfznJ4ap6bFXNJnlpkjf0WC5MJ80FAQCAcZuf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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a807ffb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20,8))\n",
    "ax.plot(list_of_lons, list_of_model_chl - list_of_cs_chl, 'ro', alpha =0.5)\n",
    "ax.grid('on')\n",
    "ax.set_xlabel('lon', fontsize = 15)\n",
    "ax.set_ylabel('Model - Observed',fontsize = 15)\n",
    "ax.set_title('Chl', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6a806fadd8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mh8naWrK0lAwGNhIAOCD22mXv8Rc9X0ryOUk+uPl4yObj3iT3jKU6AAAONoEB47K6mpw+\nnSwuJseOJevr3fOVFf+OARwAu96y11p78tYjyUuSnEvylNbaQ1try621hyZ5apL7kry0n3IBADgw\ntgKD9fWdgcHq6qQrYxYMh10YtbiYHDp04Xw4nHRlAIxg1B5S/z7J81trv7d9sLX2u0lemOQH9rsw\nAAAOOIEB47S2liws7BxbWOjGAZh6owZSj0zy17u89tdJbtqXagAAmB0CA8ZpaSnZ2Ng5trHRjQMw\n9UYNpP4wyamq+tztg1X1eUlOJblzn+sCAOCgExgwToNBdxvo+npy/vyF88Fg0pUBMIJRA6nb0jUw\nf29V/V5V/UJV/V6S92yO/x/jKhAAgANKYMA4LS93DcwXF5OzZ7ujhuYAB8Zeu+z9jdbaXVX1qCS3\nJnlykocmeVeSn0nyk621j+5nUVV17qKhG5L8WGvtOy5x7S1JXp1kew1f3Vo7s581AQBwhbYCg+27\n7J08KTBg/ywv+/cJ4IAaKZBKktbax5L82Bhr2f5nHdk6r6ojSd6f5E17fMnvt9aeMvbCAAC4MgID\nAOASRr1lL0lSVc+oqhdU1auqamlz7GmbvaTG5VlJPpjk7WP8MwAAAADoyUiBVFUdrao/SPJfkjw3\nyckkD958+RuTvGA85SWbf95Pt9baHtd8SVV9qKrevRmYjbzyCwAAAIB+1d45z+ZFVW9M8rgkX5vk\nvUk+keR4a+0Pq+o5SV7UWnv0vhdX9fAk/yPJ322tvWeXax6ZpCW5Z7PGNyR5XWvtFZe49rZ0Ddpz\n9OjRJ91+++37XXKS5Ny5czly5MjlL2RmmPODzxzOH3M++8zxfDHfs88czx9zPn/M+Wy4+eab72yt\nHb/cdaMGUh9J8tzW2pur6rokn8yFQOrpSX65tXbjNVf9t//c5yf5itba06/ga56d5Htaa0/a67rj\nx4+3O+6441pLvKQzZ87kxIkTY3lvppM5P/jM4fwx57PPHM8X8z37zPH8Mefzx5zPhqoaKZC6kh5S\nn9pl/MHZucPdfvpXSX7qCr+mJakx1AIAAADAPhg1kHp7kn+zuTpqy9bSqluT/Oa+VpWkqr4syedn\n7931thqtH908f0y6flZv2e96AAAAANgfozb//t4kv5PknUnenC6M+qaqelySxyf5B2Oo7blJhq21\n+7YPbu7u9ydJHttaW0vy5UleW1VHknwgyc8kefkY6gEAAABgH4wUSLXW3llVx5O8KMktST6dZJDk\nN5L869baf9/vwlpr37zL+FqSI9ueryRZ2e8/HwAAAIDxGHWFVFprf5bkG8ZYCwAAAABzYKQeUlX1\nkqr6onEXAwAAAMDsG3WF1G1Jvr+q7kpye5I3tNbuHl9ZAMBUWF1NhsNkbS1ZWkoGg2R5edJVAQBw\nwI26y97nJfmKJL+f5DuTvLuq7qiq/3OzyTgAMGtWV5PTp5P19eTYse54+nQ3DgAA12CkQKq1dr61\n9pubjcY/N8lXJVlN8v1J3lNVvzPGGgGASRgOk8XF7nHo0IXz4XDSlXGQrK4mp04lt97aHQWaAEBG\nXyH1N1prn26t/WqSb0nybUnen+Qf7ndhAMCEra0lCws7xxYWunEYhVV2AMAuriiQqqoHVNUzq+pn\nknwwyU8l+ZN0PaYAgFmytJRsbOwc29joxmEUVtkBALsYdZe9Z1TVTyb5QJJfSPKwJP9Xks9vrX1F\na+3VY6wRAJiEwaBb0bK+npw/f+F8MJh0ZRwUVtkBALsYdYXULyX5oiQvSfKw1trTW2s/1lq7d3yl\nAQATtbycrKx0K1rOnu2OKyt22WN0VtkBALs4fLkLquoBSZ6W5D2ttT8ff0kAwNRYXhZAcfUGg65n\nVNKtjNrY6FbZnTw52boAgIkbZYXUp5P8RpIvHHMtAADMEqvsAIBdXHaFVGvtfFX99yQP7aEeAABm\niVV2AMAljNpD6vuTvLCqHj/OYgAAAACYfZddIbXp+Uk+O8kfVdWfp9ttr22/oLX29/a5NgAAAABm\n0KiB1Ds3HwAAAABwTUYKpFpr3zjuQgAAAACYD6P2kEqSVOdhVfVlVXXjuIoCAAAAYHaNHEhV1bcm\n+fMk9yR5e5Iv3BwfVtV3jqc8AAAAAGbNSIFUVX1Pklcm+c9J/nGS2vbymST/Yt8rAwAAAGAmjdrU\n/NuSvLC19oNVdd1Fr70ryaP3tywAAAAAZtWot+w9NMmdu7x2Psn1+1MOAAAAALNu1EDqz5I8fZfX\nnpbkT/anHAAAAABm3ai37P1Qkh+rqk8k+bnNsYdU1ckk353km8ZRHAAAAACzZ6RAqrX2E1W1mOSF\nSV68OfzLSf46yanW2uvHVB8AAAAAM2bUFVJprf2HqvpPSf5hkgcn+XCS32+tbYyrOAAAOFBWV5Ph\nMFlbS5aWksEgWV6edFUAMHVG7SGVJGmt3dda+7XW2utba28VRgEAwKbV1eT06WR9PTl2rDuePt2N\nAwA7jBRIVdWzNvtFbT1/RFX9XlX9VVX9fFV95vhKBACAA2A4TBYXu8ehQxfOh8NJVwYAU2fUFVLP\nT/IZ257/cLrb9v59ki9N8rJ9rgsAAA6WtbVkYWHn2MJCNw4A7DBqIPXIJH+cJFW1kOR/S/JdrbV/\nn+T7kzxzPOUBAMABsbSUbFzU0WJjoxsHAHa4kh5SbfP49CSfTvLrm8/PJvmc/SwKAAAOnMGg6xu1\nvp6cP3/hfDCYdGUAMHVG3WXvHUmeU1X/T5J/neS3Wmsf33xtKckHx1EcAAAcGMvLycrKzl32Tp60\nyx4cdHbPhLEYNZD6viT/Jclzk5xL8hXbXvu6JH+wz3UBALDffKgav+Vl/0xhlmztnrm4uHP3zJUV\n/63DNRopkGqt/U5VLSV5dJK7W2t/te3l1yT5s3EUBwDAPvGhCuDKg/ntu2cmF47Doe+dcI1GXSGV\n1tp9Se68xPgv72tFAMB0sJpmtvhQBcy7qwnm19a6a7ezeybsi5GbmlfV46vq9VX1Z1V1/+bxZ6vK\nTzAAMGu2fmhfX9/5Q/vq6qQr42qtrXUforbzoQqYJ9uD+UOHLpwPh7t/jd0zYWxGCqSq6uvSrY76\nkiQ/l+QFm8cvTXLH5usAwKy4mh/amW4+VAHz7mqCebtnwtiMukLqB5K8JcljW2vPa629srX2vCSP\nTfKLm68DALPCaprZ40MVMO+uJpjf2j1zcTE5e7Y76r0H+2LUHlIPS/JvWmtt+2BrrVXVf07y5n2v\nDACYnKWlLqzY6jOUWE1z0G19qNreF+zkSR+qgPkxGHS3nyfdL1k2Nrr/1508uffX2T0TxmLUQOqO\nJI9L8quXeO2Lk/zhvlUEAEze1f7QznTzoQqYZ4J5mCq7BlJV9Xe2Pf3uJLdX1QOS/EKSDyZ5SJJ/\nmuRfJ3n2OIsEAHrmh3YAZpFgHqbGXiukziXZfoteJXlFkpdfNJYkf5Dkuv0tDQCYKD+0AwAwJnsF\nUrdmZyAFAMAsWV3duQpuMBBCAgC92DWQaq29tsc6AADo0+pq1ydscTE5dqzrEXb6tN2jAIBejNTU\nvKquT/KlST5/c+jPk9zZWvv4uAoDAGAf7LYKajjswqitnRS3jsOhQAoAGLtDe71YVX+nql6Zron5\n25O8YfPx9iT3VtXpi5qfAwAwLbZWQa2v71wFtbraBVQLCzuvX1joxgEAxuxyu+ydSfLEJD+Tbne9\nezZffniSr03yHUmeUlU3t9Y+Ot5SAQC4Inutglpa6gKqrbEk2djoxgEAxmyvFVLfm+SRSZ7UWru1\ntfaLrbV3bD5+sbV2MsmTkjwqyb/to1gAAK7AXqugBoMukFpfT86fv3A+GEymVgBgruwVSD07ycta\na3+82wWttXcmeUWS/32/CwMA4BotLXWrnrbbWgW1vNw1MF9cTM6e7Y4amnMQrK4mp04lt97aHVdX\nJ10RAFdhr6bmS0nuGOE97kjysv0pBwCAfTMYdD2jkm5l1MZGtwrq5MlubHlZAMXBYndIgJmx1wqp\njyQ5OsJ7HN28dl9V1Zmq+lhVndt8vGuPa7+rqt5fVR+pqtdU1QP3ux4AgAPHKihmzfa+aIcOXTgf\nDiddGQBXaK8VUmeSfGdVvbm19ulLXVBV1yX5N0l+awy1Jcm3t9Z+Yq8LquorkzwvyT9O8hdJ3pzk\nxZtjAADzzSooZsnaWrcyartZ2x1ydbUL2NbWuttrBwP/DQMzaa8VUi9J8iVJfqOq/tHFL1bVlyX5\n9c1rXjKe8kby3CSvbq3d1Vpb36zllgnWAwAAjMNefdFmwdYtievrO29J1CcLmEG7BlKttbuSfHWS\nL0jytqr6UFXdufn4UJK3J3l0kq9urf3JmOp7xeaf+7tVdWKXax6X5B3bnr8jydGq+uwx1QQAAEzC\nrO8O6ZZEYI5Ua23vC6puSPLPkzw1yedvDv95krcleVNr7aNjKazq7yf5kySfSLfj348keWJr7e6L\nrrs7ybe11t66+fwBm1/ziNbaey+69rYktyXJ0aNHn3T77bePo/ScO3cuR44cGct7M53M+cFnDueP\nOZ995ni+mO/ZtzXHN959dx789rfn+g98IB87ejQfeupTc/+jHjXp8vbFF/7AD+Tjn/M5XRi15fz5\nPPDee/Ou7/3eyRU2If67nj/mfDbcfPPNd7bWjl/uussGUtOiqt6a5Jdaaz980fg7krystfbGzecP\nTnJvkge31v5yt/c7fvx4u+OOUTYRvHJnzpzJiRMnxvLeTCdzfvCZw/ljzmefOZ4v5nv2zcUcnzrV\nrfhaXLwwtvX81KlJVTUxczHn7GDOZ0NVjRRI7dVDatq0JHWJ8buSPGHb8yck+cBeYRQAwFRYXe0+\nZN56a3fUJwbm26zfkgiwzVQGUlX1mVX1lVV1fVUdrqrnJHlakrde4vKfTnKyqh5bVYtJXpDktT2W\nCwBw5TQvBi62vJysrHQros6e7Y4rK3bZA2bS4UkXsIsHJHlpksck+XSSP03yda21d1fVUrreUo9t\nra211t5aVT+Y5LeS3JDk55O8aEJ1AwCMZnvz4uTCcTj04RPm2fKy7wHAXJjKQKq1dm+SJ+/y2lqS\nIxeNvTLJK3soDQBgf6ytdSujtltY6MYBAGbcVN6yBwAw85aWko2NnWMbG904AMCMu+JAqqoOVdX/\nqKrHjaMgAIC5oHkxADDHruaWvUpyU5IH7m8pAABzZKt58XDY3aa3tJScPKl3zEG3urpzTgcDcwoA\nlzCVPaQAAOaC5sWzZWvnxMXFnTsn2iUNAP4WPaQAAGA/bN858dChC+fD4aQrA4Cpc8WBVGvt00ke\nkeSP978cAAA4oNbWup0St7NzIgBc0lWtkGqt3dNa++R+FwMAAAeWnRMBYGRu2QMAgP1g50QAGJlA\nCgAA9sPWzomLi8nZs91RQ3MAuCS77AEAwH6xcyIAjMQKKQAAAAB6JZACAAAAoFe73rJXVfcmaaO+\nUWvtIftSEQAAAAAzba8eUj+aKwikAAAAAGAUuwZSrbVTPdYBAMCsWV1NhsNkbS1ZWkoGAw2/AYAk\nV9hDqqoWq+qpVfX1VbW4OXZ9VelFBQDABauryenTyfp6cuxYdzx9uhsHAObeSEFSVR2uqh9McjbJ\nbyd5XZJHbL7880leNJ7yAAA4kIbDZHGxexw6dOF8OJx0ZQDAFBh1ZdPLknxTkm9P8sgkte21tyR5\n5j7XBQDAQba2liws7BxbWOjGAYC5N2og9a+SPK+19pNJ3nfRa3enC6kAAKCztJRsbOwc29joxgGA\nubfXLnvbfWa64OlS/pck1+1POQAAzITBoOsZlXQrozY2uj5SJ09Oti4OPs3yAWbCqCuk3pnka3d5\n7RlJ/nB/ygEAYCYsLycrK13fqLNnu+PKysEPDlZXk1Onkltv7Y6atPdLs3yAmTHqCqmXJvn5qroh\nyZuStCRPrKp/muSbk3zNmOoDAOCgWl4++AHUdlthyOLizjBkFoK2g2J7s/zkwnE4NAcAB8xIK6Ra\na29J8vVJ/tckv5KuqflPJLklyTe01n51XAUCAMBUsHPg5GmWDzAzRl0hldbaG5O8saoeneTBST6c\n5F2ttTau4gAAYGqsrXUro7YThvRraalbmba1MirRLB/ggBo5kNrSWnt3knePoRYAgNmmGfPBJgyZ\nPM3yAWZSc2PcAAAfyElEQVTGroFUVb3wSt6otfaSay8HAJgYYcl46T908AlDJm+rWf7271UnT/pv\nCOAA2muF1Hdc9PyGJH9n8/xckiOb53+9+RBIAcBBJSwZP82YDz5hyHSYtWb5AHNq10CqtfY5W+dV\n9Q+T/GyS5yd5c2vto5s77g2S/Lskzxl3oQDAGAlLxk//odkgDAGAfTFqD6n/O8nLW2uv3xporX00\nyc9W1Y1JfjTJl46hPgCgD8KS8dN/CADgbxwa8bovTvIXu7z250m+aH/KAQAmYmmpC0e2E5bsr8Gg\nC6TW15Pz5y+cDwaTrgwAoHejBlLvTvLdVfXA7YNVdX2S707yrv0uDADokbBk/Lb6Dy0uJmfPdkc9\nugCAOTXqLXvfkeSXk5ytqv+W5INJHpLkK9I1On/GeMoDAHqhWXM/9B/ioLMbJwD7ZKRAqrX2tqr6\ngiTfleTJSb4kyfuT/GSSH2qt7XY7HwBwUAhLgL3YjROAfTTqCqm01v5nkn87xloAAIBpZTdOZoWV\nfjAVRu0hlSSpqs+rqmdV1TdV1aCqPm9chQEAAFNkba3bfXM7u3Fy0Gyt9Ftf37nSb3V10pXB3Blp\nhVRVXZfkh5N8U5Lrtr306ap6VZLvaK2dH0N9AADANFha6j68b62MSuzGycFjpR9MjVFXSL04ya1J\nvi/JTUlu2Dx+3+b4qf0vDQAAmBp242QWWOkHU2PUQOpfJXl+a+0/tNbWWmsf3zz+hyQvSHLL2CoE\nAAAmb2s3zsXF5OzZ7qihOQfN0lK3sm87K/1gIkZtav6QJLvdVLu6+ToAADDL7MbJQTcYdD2jkm5l\n1MZGt9Lv5MnJ1gVzaNRA6t1Jnp3k1y7x2rOTvGvfKgIAoGMnKID9tbXSb/v31pMnfW+FCRg1kHpp\nkturainJzyX5QLpVUf8syc3pQikAAPbL1k5Qi4s7d4JyixTAtbHSD6bCSIFUa+2NVfVX6Zqb/8ck\nD0jyySR3JvknrbX/Nr4SAQDmkJ2gAIAZNuoKqbTWfi3Jr1XVoSQPTvKh1tr5sVUGADDP1ta6lVHb\n2QkK4Nq4FRqmxqi77P2N1tr51toHhVEAAGNkJyiA/bV1K/T6+s5boVd3278LGKddV0hV1Quv4H1a\na+3f7UM9AMC08FvkybITFMD+cis0TJW9btk7leSjSe5PUpd5n5ZEIAUAs0JD7cmzExTA/nIrNEyV\nvQKpu5M8PF3j8tuTDFtr9/VSFQAwWX6LPB3sBAWwf5aWul+wbP0/LXErNEzQrj2kWmtfkOTLktyV\nbvXTB6pqWFX/rKpu6KtAAGAC1ta63xpv57fIABxkg0EXSK2vJ+fPXzgfDCZdGcylPZuat9buaK2t\ntNaWkvyTJO9P8iNJPlhVP1tVT+ujSACgZxpqAzBrtm6FXlxMzp7tjm5Fh4nZ65a9HVprb0vytqr6\nziQvS/JdSW5I8rYx1QYATIqG2oxK83vgIHErNEyNPVdIbVdV/6iqfjjJPUm+JcnPJfmP4yiqqh5Y\nVa+uqnuq6r6q+qOqesYu195SVZ+uqnPbHifGURcAzA2/RWYUtlAHAK7SniukqupLkzw7yb9IcjTJ\nW9OtjPrF1tpfj7mu9yV5epK1JF+V5I1V9fjW2nsvcf3vt9aeMsZ6AGD++C3y7Nnv1UxX0/zeiioA\nIHsEUlX1riSPSPKbSV6Ubpe9j/RRVGvt/iSntg3916p6T5InJXlvHzUAAMyUrdVMi4s7VzNdy8q3\nK91CfRw1zBuBHsBsmePv69Vau/QLVeeTfCzJ/UkufdE2rbWH7G9pO2o5mu5WwSe21v70otduSfKj\nST6a5MNJXpfkFa21T13ifW5LcluSHD169Em33377WOo9d+5cjhw5Mpb3ZjqZ84PPHM4fcz77DsIc\n33j33Xnw29+e6z/wgXzs6NF86KlPzf2PetRY/qyHv/a1ueF978sD//Ivc/jcuXzqyJF8/LM/Ox99\n2MNyzy23XPV7PuC++/KpBz3ob8YO33dfPvmgB13yPa/0+itxEOb7Wt1499152BvfmE8dOZJP3Xhj\nDt9/fw6fO5f3/fN/PrZ/b6bJPMwxO5nz+TNvcz6r39dvvvnmO1trxy933V637L14H+u5alX1gCQ/\nm+SnLg6jNr0tyRenC6wel+QNST6V5BUXX9hae1WSVyXJ8ePH24kTJ8ZS85kzZzKu92Y6mfODzxzO\nH3M++6Z+jldXk9e8plsttLmr4SN++7eTJz95PL8Z/aEfSu69N7nhhm4V08c+1j1/0IPyiKv95/RZ\nn9WtcFpYuND8/vz55Lu/O4+41N/hp386edzjkkPb2pieP5+cPXv1NWya+vneD2fOJI95zIVbI5Nk\nfT0Pfd/75mLDgbmY40uZ49UTczvnc2zu5nzOv6/vGki11iYeSFXVoXQrnj6R5NsvdU1r7X9se/rH\nVfWSJN+TSwRSAABT42r6L12Lv/qrLgi64Ybu+Q03JB//eDd+tbaa32//sHzy5O71Ly11t+lt/8F7\nY6Mb5/Ku9BZJDj63ucJsm/Pv63s2NZ+kqqokr07XTP2rWmufHPFLW5IaW2EAAPuh7x9CP/Mzkw9/\nOPnoR5Prr+9WSJ0/341fiytpfj8YdB+mkwsrqtbX5+K3wPtCoDd/+g6u58Ucrzpjysz59/VDl79k\nYn48yRcleWZr7aO7XVRVz9jsMZWqekySFyR5Sz8lAgBcpc3b9HYY5w+hT3xi8vjHdyujPvKR7vj4\nx3fj47S6mpw6ldx6a/cB8Gu+pvvB++zZ7milx+gGg+6Dy/p6FyZunQ8Gk66McVlb68Lb7eZo9cRY\nbK06W1/fuepsdXXSlTGP5vz7+lQGUlX18CTfnOSJSd5fVec2H8+pqqXN862f1r48yWpV3Z/kl5MM\nk7x8MpUDAIyo7x9CB4PkuuuSJzwheeYzu+N11433h95LffD7xV/s/szXvKYLqoRRo9u6RVKgNz/6\nDq7nwfZVZ4cOXTgfDiddGfNozr+vT+Ute621e7L3bXdHtl27kmRl7EUBAOynK+2/dND+vMTtRuNw\nJbdIcvC5zXX/zXnPHqbQHH9fn8pACgBgLvT9Q2jff54PfnBtJhEkz7o579kD00QgBQDAePjgB9du\njldPjIVVZzA1prKHFAAAM2DOm7UCU2jOe/bANLFCCgCA8XC7ETCNrDqDqSCQAgCYlNXVnWHNYDB7\nH5J88AMALkEgBQAwCaurXR+TxcWu8ff6evfcrSMcNPMQrAKw7/SQAgCYhOGwC6MWF5NDhy6cD4eT\nrmy8VleTU6eSW2/tjqurk66Ia7EVrK6v7wxWzSsAlyGQAgCYhLW1boen7RYWuvFZJbyYPfMarAJw\nzQRSAACTsLTUbTe+3cZGNz6rhBezZx6DVQD2hUAKAGASBoNuhdD6enL+/IXzwWDSlY2P8GL2zGOw\nCsC+EEgBAEzC8nLXwHxxMTl7tjvOekNz4cXsmcdgFYB9YZc9AIBJWV6e7QDqYoNB1zMq6VZGbWx0\n4cXJk5Oti6u3Faxu32Xv5Mn5+vca4HLsRnpJAikAAPohvJhN8xasAlyJrQ09Fhd3bugx66uiRyCQ\nAgCgP8ILAObJ9g09kgvH4XDu/3+ohxQAAADAONjQY1cCKQAAAIBxsKHHrgRSAAAAAONgN9Jd6SEF\nAABcPbtHAezOhh67EkgBAABXx+5RAJdnQ49LcsseAABwdbbvHnXo0IXz4XDSlQEw5ayQAgCAg2La\nbo9bW+tWRm1n9ygARmCFFAAAHARbt8etr++8PW51dXI12T0KgKskkAIAgINgGm+Ps3sUAFdJIAUA\nAAfB2lp3O9x2k749bmv3qMXF5OzZ7qihOQAj0EMKAAAOgqWlbvXR4uKFsWm4Pc7uUQBcBSukAADg\nIHB7HAAzRCAFAAAHgdvjAJghbtkDAICDwu1xs291tWtUv7bW3Y45GJjzWWfOmVNWSAEAAEyD1dXk\n9OnuVsxjx7rj6dPdOLPJnDPHBFIAAADTYDjsbsVcXEwOHbpwPhxOujLGxZwzxwRSAAAA02BtLVlY\n2Dm2sNCNM5vMOXNMIAUAADANlpaSjY2dYxsb3TizyZwzxwRSAAAA02Aw6HoIra8n589fOB8MJl0Z\n42LOmWMCKQAAgGmwvJysrHQ9hM6e7Y4rK3Zcm2XmnDl2eNIFAAAAsGl5WRgxb8w5c8oKKQAAAAB6\nJZACAAAAoFcCKQAAAAB6JZACAAAAoFcCKQAAAAB6JZACAAAAoFcCKQAAAAB6JZACAAAAoFcCKQAA\nAAB6JZACAAAAoFcCKQAAAAB6JZACAAAAoFcCKQAAAAB6JZACAAAAoFdTG0hV1WdV1Zur6v6quqeq\nvn6Pa7+rqt5fVR+pqtdU1QP7rBUAAACA0U1tIJXkR5N8IsnRJM9J8uNV9biLL6qqr0zyvCRfnuTh\nSR6Z5MU91gkAAADAFZjKQKqqbkzyrCQvaK2da639TpK3JPmGS1z+3CSvbq3d1VpbT/KSJLf0ViwA\nAAAAV2QqA6kkj07yqdbau7eNvSPJ31ohtTn2jouuO1pVnz3G+gAAAAC4StVam3QNf0tVPTXJm1pr\nD9029k1JntNaO3HRtXcn+bbW2ls3nz8g3a1+j2itvfeia29LcluSHD169Em33377WOo/d+5cjhw5\nMpb3ZjqZ84PPHM4fcz77zPF8Md+zzxzPH3M+f8z5bLj55pvvbK0dv9x1h/so5iqcS/IZF40tJLlv\nhGsXNo9/69rW2quSvCpJjh8/3k6cOHHNhV7KmTNnMq73ZjqZ84PPHM4fcz77zPF8Md+zzxzPH3M+\nf8z5fJnWW/beneRwVX3BtrEnJLnrEtfetfna9us+0Fr7yzHWBwAAAMBVmspAqrV2f5JhkpdU1Y1V\n9ZQkX5PkdZe4/KeTnKyqx1bVYpIXJHltb8UCAAAAcEWmMpDa9K1JbkjywSSvT/ItrbW7qmqpqs5V\n1VKSbPaO+sEkv5XkniTvSfKiCdUMAAAAwGVMaw+ptNY+nOTrLjG+luTIRWOvTPLKnkqbHquryXCY\nrK0lS0vJYJAsL0+6KgAAAIA9TfMKKfayupqcPp2sryfHjnXH06e7cQAAAIApJpA6qIbDZHGxexw6\ndOF8OJx0ZQAAAAB7EkgdVGtrycLCzrGFhW4cAAAAYIoJpA6qpaVkY2Pn2MZGNw4AAAAwxQRSB9Vg\n0PWNWl9Pzp+/cD4YTLoyAAAAgD0JpA6q5eVkZaXrG3X2bHdcWbHLHgAAADD1Dk+6AK7B8rIACgAA\nADhwrJACAAAAoFcCKQAAAAB6JZACAAAAoFcCKQAAAAB6pak5AADAtFhdTYbDZG0tWVpKBgMbGQEz\nyQopAACAabC6mpw+nayvJ8eOdcfTp7txgBkjkAIAAJgGw2GyuNg9Dh26cD4cTroygH0nkAIAAJgG\na2vJwsLOsYWFbhxgxgikAAAApsHSUrKxsXNsY6MbB5gxAikAAIBpMBh0faPW15Pz5y+cDwaTrgxg\n3wmkAAAApsHycrKy0vWNOnu2O66s2GUPmEmHJ10AAAAAm5aXBVDAXLBCCgAAAIBeCaQAAAAA6JVA\nCgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA\n6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQA\nAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBe\nCaQAAAAA6JVACgAAAIBeCaQAAAAA6JVACgAAAIBeCaQAAAAA6NVUBVJV9cCqenVV3VNV91XVH1XV\nM/a4/paq+nRVndv2ONFjyQAAAABcocOTLuAih5O8L8nTk6wl+aokb6yqx7fW3rvL1/x+a+0pPdUH\nAAAAwDWaqkCqtXZ/klPbhv5rVb0nyZOSvHcSNQEAAACwv6bqlr2LVdXRJI9Octcel31JVX2oqt5d\nVS+oqqkK2QAAAADYqVprk67hkqrqAUl+JcndrbVv3uWaRyZpSe5J8rgkb0jyutbaK3a5/rYktyXJ\n0aNHn3T77bePo/ScO3cuR44cGct7M53M+cFnDuePOZ995ni+mO/ZZ47njzmfP+Z8Ntx88813ttaO\nX+66XgOpqjqTrj/UpfzuVi+oqjqU5PVJPiPJ17bWPjni+z87yfe01p50uWuPHz/e7rjjjpHqvlJn\nzpzJiRMnxvLeTCdzfvCZw/ljzmefOZ4v5nv2meP5Y87njzmfDVU1UiDV6+1trbUTl7umqirJq5Mc\nTfJVo4ZRW39Ekrq66gAAAADowzT2kPrxJF+U5JmttY/udWFVPWOzz1Sq6jFJXpDkLeMvEQAAAICr\nNVWBVFU9PMk3J3likv+/vfuPvauu7zj+fLUVscAKyq9OLA1jzKxLweAmIN3KgAUC/1hcWETSzSmb\n20icqbhsZhCSTYedMzPLiBkYsuGaxZWIThEFOnTSTOKPMhg0RErXbUWUUiitCvazPz6n6+3tr+/3\n2+/3nHu+9/lIPun3nnPuuZ/zefXe3r6/n3PO1iQ7mnZNs35R83hR85SLgQ1JXgK+AKwF/ryLvkuS\nJEmSJGliRuqOdKWUpznEKXellM3AsQOPVwGrWuiaJEmSJEmSpslIzZCSJEmSJEnS7GdBSpIkSZIk\nSa2yICVJkiRJkqRWWZCSJEmSJElSqyxISZIkSZIkqVUjdZc9SZIkSYewYQOsXQubN8OiRbBiBSxd\n2nWvJEmaNGdISZIkSX2wYQOsXg3btsFpp9U/V6+uyyVJ6hkLUpIkSVIfrF0LJ5xQ25w5e39eu7br\nnkmSNGkWpCRJkqQ+2LwZFizYd9mCBXW5JEk9Y0FKkiRJ6oNFi2D79n2Xbd9el0uS1DMWpCRJkqQ+\nWLGiXjdq2zbYvXvvzytWdN0zSZImzYKUJEmS1AdLl8KqVfW6UVu21D9XrfIue5KkXprXdQckSZIk\nTdDSpRagJEmzgjOkJEmSJEmS1CoLUpIkSZIkSWqVBSlJkiRJkiS1yoKUJEmSJEmSWmVBSpIkSZIk\nSa2yICVJkiRJkqRWWZCSJEmSJElSqyxISZIkSZIkqVUWpCRJkiRJktQqC1KSJEmSJElqlQUpSZIk\nSZIktcqClCRJkiRJklplQUqSJEmSJEmtsiAlSZIkSZKkVlmQkiRJkiRJUqtSSum6D51I8izw9Azt\n/kTg+zO0b40mM+8/Mxw/Zj77mfF4Me/Zz4zHj5mPHzOfHU4vpZx0uI3GtiA1k5I8XEp5c9f9UHvM\nvP/McPyY+exnxuPFvGc/Mx4/Zj5+zHy8eMqeJEmSJEmSWmVBSpIkSZIkSa2yIDUzPtl1B9Q6M+8/\nMxw/Zj77mfF4Me/Zz4zHj5mPHzMfI15DSpIkSZIkSa1yhpQkSZIkSZJaZUFKkiRJkiRJrRrLglSS\nVye5LcnTSV5M8u0klw+svzjJ40l2JnkgyekD6y5qlm1PsukA+96UZFeSHU279zB9Wdzsb2fzmpcM\nrFuY5O4k/5OkJFk8LQMwhnqU+RVJvpbk+SRbk/xdkuOmaRh6rUcZXpTkkSbDHyS5K8nrp2kYxkpf\nMh/a7vbm8/rMIzj0sdCXfJMsT7J7YF87kqycpmEYK33JvFl/UpJPN6+3Lcmd0zAEs15fMk7yx0Pv\n6V3N+/zEaRqKsdGXzJv11yd5KskLSR5OcuE0DMHY6Uvmqf4kyeYm8zVJfmqahkHTpZQydg04BrgJ\nWEwtyl0JvNg8PhHYDvw6cDTwUWD9wHN/CbgWuA7YdIB9bwIumURfHgI+BrwGuAp4HjipWXcK8HvA\n+UABFnc9dn1tPcr8HcBlwHzgBOCLwK1dj98otB5leArwBiDAq4FbgLu7Hr8+tr5kPrDNhcC/Np/X\nZ3Y9fqPe+pIvsBzY0vV4zYbWl8yb9V9t1i8AXgW8qevx60PrU8ZD294E3N/1+PWx9SVz4C3AS8C5\n1O9o7wWeBeZ2PYZ9az3KfCXwOPV7+bHAZ4E7uh4/21CGXXdgVBqwoflLfB3w9YHlxwC7gDcObX/J\nkb6JgLOAHwHHDSx7EPjdoe3mYUFqrDIfWLcCeKTrsRrVNuoZUgtSHwYe63qsZksb1cybz+lvAUux\nIDWr8sWC1Dhm/mvN/vyP6izNeGjbAN8FVnY9VrOljWLmwNXAvw/1pQALux6v2dBGNPPPADcMrLsA\n+CEwv+vxsu1tY3nK3rAkp1D/Qj8KLAG+s2ddKeUl4Mlm+UTdmeTZJPcmOfsQ2y0BvltKeXFg2Xcm\n+Vqagh5l/stNHzVklDNMsijJ89R/gFdRZ0npCI1y5sAfAg+WUjZM4vU1YMTzPTnJM82pHn+V5JhJ\n9EMHMcKZnwc8AdyReur1N5L8yiT6ocYIZzxoGXAy8M+T6IcOYoQz/yIwN8lbkswF3gV8G9g6ib7o\nAEY48/26Sv1l8c9Ooi+aYWNfkEryKuBO6vS9x6nT+bYPbfYCMNHr+FxDna54OvAA8KUkxx9k2yN9\nLU1BXzJPcil1qumfTrAfY2PUMyylbC6lHE+dtvwh6nRhHYFRzjzJG4DfwffqlI1yvtT37znAQuBX\nqad7fGyC/dBBjHjmp1FnST0AnAr8JfDZeH2hSRnxjAetBD5TStkxwX7oIEY88xepRcevUWfV3Ahc\nV0qdOqOpGfHM7wHe3VxnagHwwWb5/An2RS0Y64JUkjnA3wM/Bv6gWbwDGL7Y2QLqh9hhlVL+rZSy\nq5Sys5TyYep5rMua13t04AJty470tTR5fck8yXnAp4G3l1I2TvgAx0BfMmz2+xxwB/U/MvMmdIDa\nTw8y/zhwcyll+EuRJmDU8y2lbC2lPFZK2V1KeQq4gXpagqZo1DOnzm7dVEq5rZTycillDfBfwFsn\ndaBjrAcZ7+nnfOq1bu6Y8MHpgHqQ+W9TZ0UtAY4C3gl8PslPT+IwNaAHmd8O/COwjjp764Fm+ZaJ\nHaHaMLYFqSQBbqNegPiqUsrLzapHgbMHtjsG+BmmftpUoU4PpJSypJRybNO+2uzzjOx7F7Wzj+C1\ndAh9yTzJm4C7gXeVUu6bYh9mpb5kOGQe9VQA7+oxBT3J/GLgo6l3xtwz9f+hJO+YYl/GRk/yPdC+\nxvb705HqSeYbmucP708T0JOM93gb8Bz1P6yaop5kfg7wuVLKxuYXDPcA/0u9rpAmqQ+ZNznfWEpZ\nXEo5rVn+303TqCgjcCGrLhpwK7AeOHZo+UnUqX9XUe8McAv73hlgTrP8cuDp5uejmnWLqL89O6pZ\n/gHq3Rted4h+rAdWN9uvYP+7vBzN3ovu/RxwdNdj19fWh8yBXwCeAa7uerxGsfUkwxXNe3VO069/\nAr7Z9dj1tfUk85Opp/XsaYV6DZrXdD1+o956ku9F1FMHQr1TzzrgU12PXV9bTzJ/LbCNeirXXODt\n1KLFiV2PXx9aHzIe2OZe6gzXzsetz60PmTfv543AGc3n+aXAToYutm2bVZm/lloMC/DzwH9QT9Ps\nfPxsAxl23YFODrp+sSzUq+zvGGjXNOsvoV4zYhf1i+figecub5472NY165ZQf6v2EvAD4D7gzYfp\ny+LmNXZRL6B5ydD64dcqXY9fH1tfMgc+Bewe6uOjXY/fKLQeZXg98FSzv63AGuD0rsevj60vmR9g\n24J32Zs1+QLvp/42dSf1tK2/ZuCOPrbZl3mzfhnwSNO/h4FlXY9fH1rPMn498Ap+Xo9F5tSixM3A\nZuopXf8JXNv1+PWx9Sjzs5plO6nFr/d3PXa2/VuasCRJkiRJkqRWeA0ESZIkSZIktcqClCRJkiRJ\nklplQUqSJEmSJEmtsiAlSZIkSZKkVlmQkiRJkiRJUqssSEmSJEmSJKlVFqQkSZJmWJKbkpSm7U6y\nLck3kvxZklOnsL8bkiyfga5KkiS1woKUJElSO7YD5wMXAL8BrAWuBR5Jcu4k93UDsHxaeydJktSi\neV13QJIkaUy8UkpZP/D4S0n+FngQWJPkjaWUn3TUN0mSpFY5Q0qSJKkjpZTnqbOdzgQuBUjykSSP\nJNmRZEuSOwdP60uyCXgdcOPAaYDLm3VzkvxRkieT/CjJxiQrWz8wSZKkw7AgJUmS1K11wCvAec3j\nU4G/AK4E3gecAdyfZM/3trdRT/+7jXoK4PnAN5t1nwA+BHwSuAK4C7g9yZUzfhSSJEmT4Cl7kiRJ\nHSql/DDJ94FTmse/uWddkrnAQ8AW4ELgwVLKt5K8AmwZPAUwyZnAe4HfKqXc0Sz+SpKFwI3A59s4\nHkmSpIlwhpQkSVL38v8/JJcn+XqS7dSZU1uaVWcdZh8XA7uBu5LM29OA+4BzmuKWJEnSSHCGlCRJ\nUoeSHE29JtQzSX4RuJt6qt1HgO8BBVgPHH2YXZ0IzKWezncgC9lb3JIkSeqUBSlJkqRuXUT9TvYQ\n9fpQzwJXl1IKQJLTJ7if56gzqt5KnSk17HtH3lVJkqTpYUFKkiSpI0mOp17A/EngK8BlwMt7ilGN\naw7w1B+z/4yp+6kzpBaUUr48A92VJEmaNhakJEmS2jEvyZ476R0HnEu9CPl84LJSyk+SfBl4X5KP\nA58DLgDeeYB9PQ5ckeQeYAfwRCnliSS3AmuS3AI8TC1aLQHOKqW8eyYPTpIkaTIsSEmSJLVjAfW0\nvAK8QJ0V9Q/AJ0opWwFKKV9I8kHgeuA9zfZXAhuH9vUB4G+Af6EWtC4C1gG/32z7HuDm5nUeA26b\nweOSJEmatOw7I1ySJEmSJEmaWXO67oAkSZIkSZLGiwUpSZIkSZIktcqClCRJkiRJklplQUqSJEmS\nJEmtsiAlSZIkSZKkVlmQkiRJkiRJUqssSEmSJEmSJKlVFqQkSZIkSZLUKgtSkiRJkiRJatX/AbqZ\nmyfXfdT1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a8071a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20,8))\n",
    "ax.plot(list_of_datetimes, list_of_model_chl - list_of_cs_chl, 'ro', alpha =0.5)\n",
    "ax.grid('on')\n",
    "ax.set_xlabel('Date', fontsize = 15)\n",
    "ax.set_ylabel('Model - Observed',fontsize = 15)\n",
    "ax.set_title('Chl', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}