plot_exceedence.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xray \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dam_info = pd.read_csv('/raid/yifan/Reservoir_modeling/Run_reservoir/reservoir_to_model_maxFlowFromRVIC_exceed0.2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dam_number</th>\n",
       "      <th>dam_name</th>\n",
       "      <th>xls_name</th>\n",
       "      <th>output_name</th>\n",
       "      <th>dam_lat</th>\n",
       "      <th>dam_lon</th>\n",
       "      <th>grid_lat</th>\n",
       "      <th>grid_lon</th>\n",
       "      <th>year_operated_start_of_Calendar_year</th>\n",
       "      <th>top_vol_acre_feet</th>\n",
       "      <th>bot_vol_acre_feet</th>\n",
       "      <th>max_flow_cfs</th>\n",
       "      <th>min_flow_cfs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Watauga</td>\n",
       "      <td>watauga</td>\n",
       "      <td>Watauga</td>\n",
       "      <td>36.3233</td>\n",
       "      <td>-82.1219</td>\n",
       "      <td>36.3125</td>\n",
       "      <td>-82.0625</td>\n",
       "      <td>1949</td>\n",
       "      <td>677000</td>\n",
       "      <td>0</td>\n",
       "      <td>1132.7</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>South Holston</td>\n",
       "      <td>southholston</td>\n",
       "      <td>South_Holston</td>\n",
       "      <td>36.5233</td>\n",
       "      <td>-82.0889</td>\n",
       "      <td>36.5625</td>\n",
       "      <td>-82.0625</td>\n",
       "      <td>1951</td>\n",
       "      <td>764000</td>\n",
       "      <td>0</td>\n",
       "      <td>1246.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Boone</td>\n",
       "      <td>boone</td>\n",
       "      <td>Boone</td>\n",
       "      <td>36.4400</td>\n",
       "      <td>-82.4378</td>\n",
       "      <td>36.4375</td>\n",
       "      <td>-82.4375</td>\n",
       "      <td>1953</td>\n",
       "      <td>193400</td>\n",
       "      <td>0</td>\n",
       "      <td>3998.4</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Cherokee</td>\n",
       "      <td>cherokee</td>\n",
       "      <td>Cherokee</td>\n",
       "      <td>36.1661</td>\n",
       "      <td>-83.4983</td>\n",
       "      <td>36.1875</td>\n",
       "      <td>-83.4375</td>\n",
       "      <td>1942</td>\n",
       "      <td>1541000</td>\n",
       "      <td>0</td>\n",
       "      <td>7328.4</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>douglas</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>35.9611</td>\n",
       "      <td>-83.5389</td>\n",
       "      <td>35.9375</td>\n",
       "      <td>-83.4375</td>\n",
       "      <td>1943</td>\n",
       "      <td>1461000</td>\n",
       "      <td>0</td>\n",
       "      <td>11029.2</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>Fort Loudoun</td>\n",
       "      <td>fortloudoun</td>\n",
       "      <td>Fort_Loudoun</td>\n",
       "      <td>35.7909</td>\n",
       "      <td>-84.2427</td>\n",
       "      <td>35.8125</td>\n",
       "      <td>-84.1875</td>\n",
       "      <td>1943</td>\n",
       "      <td>393000</td>\n",
       "      <td>0</td>\n",
       "      <td>22085.9</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>Fontana</td>\n",
       "      <td>fontana</td>\n",
       "      <td>Fontana</td>\n",
       "      <td>35.4522</td>\n",
       "      <td>-83.8047</td>\n",
       "      <td>35.4375</td>\n",
       "      <td>-83.8125</td>\n",
       "      <td>1945</td>\n",
       "      <td>1443000</td>\n",
       "      <td>0</td>\n",
       "      <td>6151.8</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>Tellico</td>\n",
       "      <td>tellico</td>\n",
       "      <td>Tellico</td>\n",
       "      <td>35.7778</td>\n",
       "      <td>-84.2597</td>\n",
       "      <td>35.6875</td>\n",
       "      <td>-84.3125</td>\n",
       "      <td>1980</td>\n",
       "      <td>424000</td>\n",
       "      <td>0</td>\n",
       "      <td>8983.3</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>Norris</td>\n",
       "      <td>norris</td>\n",
       "      <td>Norris</td>\n",
       "      <td>36.2242</td>\n",
       "      <td>-84.0914</td>\n",
       "      <td>36.1875</td>\n",
       "      <td>-84.0625</td>\n",
       "      <td>1936</td>\n",
       "      <td>2552000</td>\n",
       "      <td>0</td>\n",
       "      <td>6867.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>Watts Bar</td>\n",
       "      <td>wattsbar</td>\n",
       "      <td>Watts_Bar</td>\n",
       "      <td>35.6213</td>\n",
       "      <td>-84.7816</td>\n",
       "      <td>35.5625</td>\n",
       "      <td>-84.8125</td>\n",
       "      <td>1942</td>\n",
       "      <td>1175000</td>\n",
       "      <td>0</td>\n",
       "      <td>44998.9</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>Chatuge</td>\n",
       "      <td>chatuge</td>\n",
       "      <td>Chatuge</td>\n",
       "      <td>35.0175</td>\n",
       "      <td>-83.7911</td>\n",
       "      <td>35.0625</td>\n",
       "      <td>-83.8125</td>\n",
       "      <td>1942</td>\n",
       "      <td>240500</td>\n",
       "      <td>0</td>\n",
       "      <td>1081.4</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20</td>\n",
       "      <td>Nottely</td>\n",
       "      <td>nottely</td>\n",
       "      <td>Nottely</td>\n",
       "      <td>34.9586</td>\n",
       "      <td>-84.0878</td>\n",
       "      <td>34.9375</td>\n",
       "      <td>-84.0625</td>\n",
       "      <td>1942</td>\n",
       "      <td>174300</td>\n",
       "      <td>0</td>\n",
       "      <td>609.7</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21</td>\n",
       "      <td>Hiwassee</td>\n",
       "      <td>hiwassee</td>\n",
       "      <td>Hiwassee</td>\n",
       "      <td>35.1514</td>\n",
       "      <td>-84.1775</td>\n",
       "      <td>35.1875</td>\n",
       "      <td>-84.1875</td>\n",
       "      <td>1940</td>\n",
       "      <td>434000</td>\n",
       "      <td>0</td>\n",
       "      <td>3101.0</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23</td>\n",
       "      <td>Blue Ridge</td>\n",
       "      <td>blueridge</td>\n",
       "      <td>Blue Ridge</td>\n",
       "      <td>34.8825</td>\n",
       "      <td>-84.2800</td>\n",
       "      <td>34.9375</td>\n",
       "      <td>-84.3125</td>\n",
       "      <td>1931</td>\n",
       "      <td>195900</td>\n",
       "      <td>0</td>\n",
       "      <td>1248.5</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>26</td>\n",
       "      <td>Ocoee 1</td>\n",
       "      <td>ocoeeno1</td>\n",
       "      <td>Ocoee1</td>\n",
       "      <td>35.0947</td>\n",
       "      <td>-84.6478</td>\n",
       "      <td>35.0625</td>\n",
       "      <td>-84.6875</td>\n",
       "      <td>1912</td>\n",
       "      <td>83300</td>\n",
       "      <td>0</td>\n",
       "      <td>2034.6</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27</td>\n",
       "      <td>Chickamauga</td>\n",
       "      <td>chickamauga</td>\n",
       "      <td>Chickamauga</td>\n",
       "      <td>35.1039</td>\n",
       "      <td>-85.2294</td>\n",
       "      <td>35.1875</td>\n",
       "      <td>-85.1875</td>\n",
       "      <td>1940</td>\n",
       "      <td>737300</td>\n",
       "      <td>0</td>\n",
       "      <td>55155.4</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>29</td>\n",
       "      <td>Guntersville</td>\n",
       "      <td>guntersville</td>\n",
       "      <td>Guntersville</td>\n",
       "      <td>34.4211</td>\n",
       "      <td>-86.3931</td>\n",
       "      <td>34.4375</td>\n",
       "      <td>-86.4375</td>\n",
       "      <td>1939</td>\n",
       "      <td>1048700</td>\n",
       "      <td>0</td>\n",
       "      <td>66020.8</td>\n",
       "      <td>3150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>50</td>\n",
       "      <td>Tims Ford</td>\n",
       "      <td>timsford</td>\n",
       "      <td>Tims Ford</td>\n",
       "      <td>35.1969</td>\n",
       "      <td>-86.2786</td>\n",
       "      <td>35.1875</td>\n",
       "      <td>-86.3125</td>\n",
       "      <td>1971</td>\n",
       "      <td>608000</td>\n",
       "      <td>0</td>\n",
       "      <td>1617.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>30</td>\n",
       "      <td>Wheeler</td>\n",
       "      <td>wheeler</td>\n",
       "      <td>Wheeler</td>\n",
       "      <td>34.8069</td>\n",
       "      <td>-87.3819</td>\n",
       "      <td>34.8125</td>\n",
       "      <td>-87.4375</td>\n",
       "      <td>1937</td>\n",
       "      <td>1069000</td>\n",
       "      <td>0</td>\n",
       "      <td>81085.5</td>\n",
       "      <td>3850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>wilson</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>34.8008</td>\n",
       "      <td>-87.6258</td>\n",
       "      <td>34.8125</td>\n",
       "      <td>-87.6875</td>\n",
       "      <td>1924</td>\n",
       "      <td>640200</td>\n",
       "      <td>0</td>\n",
       "      <td>83792.0</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>52</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>bear creek</td>\n",
       "      <td>Bear_Creek</td>\n",
       "      <td>34.3986</td>\n",
       "      <td>-87.9878</td>\n",
       "      <td>34.3125</td>\n",
       "      <td>-87.9375</td>\n",
       "      <td>1969</td>\n",
       "      <td>39700</td>\n",
       "      <td>0</td>\n",
       "      <td>748.9</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>Cedar Creek</td>\n",
       "      <td>cedar creek</td>\n",
       "      <td>Cedar_Creek</td>\n",
       "      <td>34.5289</td>\n",
       "      <td>-87.9267</td>\n",
       "      <td>34.5625</td>\n",
       "      <td>-87.9375</td>\n",
       "      <td>1979</td>\n",
       "      <td>111500</td>\n",
       "      <td>0</td>\n",
       "      <td>678.7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>32</td>\n",
       "      <td>Pickwick Landing</td>\n",
       "      <td>pickwick</td>\n",
       "      <td>Pickwick_Landing</td>\n",
       "      <td>35.0639</td>\n",
       "      <td>-88.2472</td>\n",
       "      <td>35.0625</td>\n",
       "      <td>-88.1875</td>\n",
       "      <td>1938</td>\n",
       "      <td>1332000</td>\n",
       "      <td>0</td>\n",
       "      <td>89742.6</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>56</td>\n",
       "      <td>Normandy</td>\n",
       "      <td>normandy</td>\n",
       "      <td>Normandy</td>\n",
       "      <td>35.4656</td>\n",
       "      <td>-86.2425</td>\n",
       "      <td>35.4375</td>\n",
       "      <td>-86.1875</td>\n",
       "      <td>1976</td>\n",
       "      <td>134000</td>\n",
       "      <td>0</td>\n",
       "      <td>430.6</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dam_number          dam_name      xls_name       output_name  dam_lat  \\\n",
       "0            1           Watauga       watauga           Watauga  36.3233   \n",
       "1            2     South Holston  southholston     South_Holston  36.5233   \n",
       "2            3             Boone         boone             Boone  36.4400   \n",
       "3            5          Cherokee      cherokee          Cherokee  36.1661   \n",
       "4            7           Douglas       douglas           Douglas  35.9611   \n",
       "5            8      Fort Loudoun   fortloudoun      Fort_Loudoun  35.7909   \n",
       "6           11           Fontana       fontana           Fontana  35.4522   \n",
       "7           16           Tellico       tellico           Tellico  35.7778   \n",
       "8           17            Norris        norris            Norris  36.2242   \n",
       "9           18         Watts Bar      wattsbar         Watts_Bar  35.6213   \n",
       "10          19           Chatuge       chatuge           Chatuge  35.0175   \n",
       "11          20           Nottely       nottely           Nottely  34.9586   \n",
       "12          21          Hiwassee      hiwassee          Hiwassee  35.1514   \n",
       "13          23        Blue Ridge     blueridge        Blue Ridge  34.8825   \n",
       "14          26           Ocoee 1      ocoeeno1            Ocoee1  35.0947   \n",
       "15          27       Chickamauga   chickamauga       Chickamauga  35.1039   \n",
       "16          29      Guntersville  guntersville      Guntersville  34.4211   \n",
       "17          50         Tims Ford      timsford         Tims Ford  35.1969   \n",
       "18          30           Wheeler       wheeler           Wheeler  34.8069   \n",
       "19          31            Wilson        wilson            Wilson  34.8008   \n",
       "20          52        Bear Creek    bear creek        Bear_Creek  34.3986   \n",
       "21          54       Cedar Creek   cedar creek       Cedar_Creek  34.5289   \n",
       "22          32  Pickwick Landing      pickwick  Pickwick_Landing  35.0639   \n",
       "23          56          Normandy      normandy          Normandy  35.4656   \n",
       "\n",
       "    dam_lon  grid_lat  grid_lon  year_operated_start_of_Calendar_year  \\\n",
       "0  -82.1219   36.3125  -82.0625                                  1949   \n",
       "1  -82.0889   36.5625  -82.0625                                  1951   \n",
       "2  -82.4378   36.4375  -82.4375                                  1953   \n",
       "3  -83.4983   36.1875  -83.4375                                  1942   \n",
       "4  -83.5389   35.9375  -83.4375                                  1943   \n",
       "5  -84.2427   35.8125  -84.1875                                  1943   \n",
       "6  -83.8047   35.4375  -83.8125                                  1945   \n",
       "7  -84.2597   35.6875  -84.3125                                  1980   \n",
       "8  -84.0914   36.1875  -84.0625                                  1936   \n",
       "9  -84.7816   35.5625  -84.8125                                  1942   \n",
       "10 -83.7911   35.0625  -83.8125                                  1942   \n",
       "11 -84.0878   34.9375  -84.0625                                  1942   \n",
       "12 -84.1775   35.1875  -84.1875                                  1940   \n",
       "13 -84.2800   34.9375  -84.3125                                  1931   \n",
       "14 -84.6478   35.0625  -84.6875                                  1912   \n",
       "15 -85.2294   35.1875  -85.1875                                  1940   \n",
       "16 -86.3931   34.4375  -86.4375                                  1939   \n",
       "17 -86.2786   35.1875  -86.3125                                  1971   \n",
       "18 -87.3819   34.8125  -87.4375                                  1937   \n",
       "19 -87.6258   34.8125  -87.6875                                  1924   \n",
       "20 -87.9878   34.3125  -87.9375                                  1969   \n",
       "21 -87.9267   34.5625  -87.9375                                  1979   \n",
       "22 -88.2472   35.0625  -88.1875                                  1938   \n",
       "23 -86.2425   35.4375  -86.1875                                  1976   \n",
       "\n",
       "    top_vol_acre_feet  bot_vol_acre_feet  max_flow_cfs  min_flow_cfs  \n",
       "0              677000                  0        1132.7           107  \n",
       "1              764000                  0        1246.0            90  \n",
       "2              193400                  0        3998.4           400  \n",
       "3             1541000                  0        7328.4           325  \n",
       "4             1461000                  0       11029.2           585  \n",
       "5              393000                  0       22085.9           710  \n",
       "6             1443000                  0        6151.8           790  \n",
       "7              424000                  0        8983.3          1000  \n",
       "8             2552000                  0        6867.0           200  \n",
       "9             1175000                  0       44998.9          1200  \n",
       "10             240500                  0        1081.4            60  \n",
       "11             174300                  0         609.7            55  \n",
       "12             434000                  0        3101.0           360  \n",
       "13             195900                  0        1248.5           115  \n",
       "14              83300                  0        2034.6           240  \n",
       "15             737300                  0       55155.4          1500  \n",
       "16            1048700                  0       66020.8          3150  \n",
       "17             608000                  0        1617.0            80  \n",
       "18            1069000                  0       81085.5          3850  \n",
       "19             640200                  0       83792.0          4000  \n",
       "20              39700                  0         748.9            21  \n",
       "21             111500                  0         678.7            10  \n",
       "22            1332000                  0       89742.6          4000  \n",
       "23             134000                  0         430.6            40  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dam_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'list' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-19f330924a7e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'log'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m     \u001b[0mf3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'k-'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Observed Flow from TVA (regulated)'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[0mf4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNatual_flow_TVA\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mNatual_flow_TVA\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNatual_flow_TVA\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'k--'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Observed naturalized flow from TVA (unregulated)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[0mf5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstreamflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstreamflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstreamflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3.28\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'r--'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Modeled naturalized flow from RVIC (unregulated)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'int'"
     ]
    },
    {
     "data": {
      "image/png": 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nZOYfDbnOoXLWsCRJqouRLB/TcbJjgXcCS7p2/Qfw55n5iSoKGSeDoCRJqouRBsGOk+4O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\naiiDoCRJUkP9fwjI63ZjTaCyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b821a0ae7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for i in range(len(Dam_info['dam_name'])):\n",
    "#for i in [0,1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,18,19,22]:\n",
    "#for i in [13,14,15,16,18,19,22]:\n",
    "for i in [4]:    \n",
    "    #Define the period\n",
    "    starting_year = max(Dam_info['year_operated_start_of_Calendar_year'][i],1949)+1\n",
    "    end_year = 2010\n",
    "    starting_time = str(starting_year)+'-10-01 12:00:00' #This is the start of water year----starting_year+1\n",
    "    end_time = str(end_year)+'-09-30 12:00:00'\n",
    "    starting_date = str(starting_year)+'-10-01 00:00:00' #This is the start of water year----starting_year+1\n",
    "    end_date = str(end_year)+'-09-30 00:00:00'\n",
    "    time_index = pd.date_range(start = starting_time,end = end_time ,freq='D')\n",
    "    #read in modeled streamflow using new guide curve -- median of daily data (regulated)\n",
    "    Raw_model = xray.open_dataset('/raid/yifan/Reservoir_modeling/Run_reservoir/result_new_guide_curve.modified_flow.nc')\n",
    "\n",
    "    #read in observed stream flow from TVA(unregulated)\n",
    "    filename_latlon = str(Dam_info['grid_lat'][i])+'_'+str(Dam_info['grid_lon'][i])+'.daily.1903_2013'\n",
    "    dir_latlon = '/raid2/ymao/VIC_RBM_east_RIPS/data/TVA_data/naturalized_flow/downscaled_daily_flow/latlon/'+filename_latlon\n",
    "    Nflow_TVA = pd.read_csv(dir_latlon,sep=' ',header=None,parse_dates=[[0,1,2]])\n",
    "    Nflow_TVA.rename(columns={'0_1_2': 'Time', 3: 'flow(cfs)'}, inplace=True)\n",
    "    Nflow_TVA = Nflow_TVA.set_index('Time')\n",
    "    Nflow_TVA = Nflow_TVA.ix[starting_date:end_date]\n",
    "    Natual_flow_TVA = Nflow_TVA.resample('1W',how = 'mean')['flow(cfs)']\n",
    "\n",
    "    #read in TVA observed streamflow (regulated)\n",
    "    xls_path = '/raid/yifan/Reservoir_modeling/TVA_reservoir_data/'+Dam_info['xls_name'][i]+'.xlsx'\n",
    "    Raw = pd.ExcelFile(xls_path)\n",
    "    Total_Q = Raw.parse(sheetname='TotalQ')\n",
    "    year_start = Total_Q.columns[0]\n",
    "    if Dam_info['year_operated_start_of_Calendar_year'][i] <= 1949:\n",
    "        col_del = np.concatenate([np.arange(0,starting_year-year_start,1),np.arange(-1,end_year-2015-1,-1)])\n",
    "    else:\n",
    "        col_del = np.concatenate([np.arange(0,starting_year-year_start,1),np.arange(-1,end_year-2015-1,-1)])    \n",
    "    Observed_temp = Total_Q.ix[1:367]#pd.concat([Total_Q.ix[1:60],Total_Q.ix[62:366]]).dropna(axis=1)\n",
    "    Observed_Q = Observed_temp.drop(Observed_temp.columns[col_del],axis=1)#Observed_temp.resample('1w',how='mean')#groupby(Observed_temp.index.map(lambda t: t.week)).mean()\n",
    "    temp = np.ravel(Observed_Q,'F')\n",
    "    temp1 = temp[274:-92]\n",
    "    temp2 = temp1[~np.isnan(temp1)]\n",
    "    M = pd.DataFrame(temp2,index=Nflow_TVA.index)#Observed_Q.drop(Observed_Q.columns[col_del],axis=1)\n",
    "    M = M.resample('1w',how='mean')\n",
    "    #     M.iloc[10:12,M.shape[1]-1]=np.nan\n",
    "    \n",
    "    #read in modeled streamflow from RVIC (unregulated)\n",
    "    RVIC = xray.open_dataset('/raid2/ymao/VIC_RBM_east_RIPS/RIPS/model_run/output/RVIC/Tennessee_8th_grid/hist/Tennessee_UH_1.hist_1949_2010.calibrated_1961_1970.rvic.h0a.2011-01-01.nc')\n",
    "    RVIC = RVIC.isel(time=slice(0,-1))\n",
    "    RVIC_flow = RVIC.sel(lat = Dam_info['grid_lat'][i],lon = Dam_info['grid_lon'][i])\n",
    "    RVIC_flow = RVIC_flow.sel(time = time_index)\n",
    "    Q = RVIC_flow.resample('1W','time',how = 'mean')\n",
    "    \n",
    "    #read in modeled streamflow using new guide curve -- median of daily data (regulated)\n",
    "    Original_model = xray.open_dataset('/raid2/ymao/VIC_RBM_east_RIPS/RIPS/model_run/output/simple_reservoir/Tennessee/maxFlowFromRVIC_exceed0.2/Tennessee.modified_flow.nc')\n",
    "    Original_streamflow = Original_model.sel(lat = Dam_info['grid_lat'][i],lon = Dam_info['grid_lon'][i])\n",
    "    Raw_streamflow = Raw_model.sel(lat = Dam_info['grid_lat'][i],lon = Dam_info['grid_lon'][i])\n",
    "    Model_Q = Raw_streamflow.sel(time = time_index)\n",
    "    Original_Q = Original_streamflow.sel(time = time_index)\n",
    "    L = Model_Q.resample('1W','time',how = 'mean')\n",
    "    L_original = Original_Q.resample('1W','time',how = 'mean')\n",
    "\n",
    "    #plot\n",
    "    plt.figure(figsize=[10,8])\n",
    "    plt.xlabel('Exceedence',fontsize=16)\n",
    "    plt.ylabel('Thousand CFS',fontsize=16)\n",
    "    plt.xticks(fontsize=16)\n",
    "    plt.yticks(fontsize=16)\n",
    "    plt.yscale('log')\n",
    "\n",
    "    f3, = plt.plot(np.arange(M.size)/M.size,sorted(M.values.ravel(),reverse=True),'k-',label='Observed Flow from TVA (regulated)',linewidth=2)\n",
    "    f4, = plt.plot(np.arange(Natual_flow_TVA.shape[0])/Natual_flow_TVA.shape[0],sorted(Natual_flow_TVA,reverse=True)/,'k--',label='Observed naturalized flow from TVA (unregulated)')\n",
    "    f5, = plt.plot(np.arange(Q.streamflow.values.size)/Q.streamflow.values.size,sorted(Q.streamflow.values*np.power(3.28,3),reverse=True),'r--',label='Modeled naturalized flow from RVIC (unregulated)')\n",
    "    f1, = plt.plot(np.arange(L.streamflow.values.size)/L.streamflow.values.size,sorted(L.streamflow.values,reverse=True),'b-',label='Modeled flow using new guide curve (regulated)',linewidth=2)\n",
    "    f2, = plt.plot(np.arange(L_original.streamflow.values.size)/L_original.streamflow.values.size,sorted(L_original.streamflow.values,reverse=True),'r-',label='Modeled flow using old guide curve (regulated)',linewidth=2)\n",
    "    plt.legend(handles=[f1,f2,f3,f4,f5])\n",
    "    water_year = '(water year:'+str(starting_year+1)+'-'+str(end_year)+')'\n",
    "    title_name = 'Flow duration curve at Dam '+Dam_info['dam_name'][i]+'\\n'\n",
    "    plt.title(title_name+water_year,fontsize=20)\n",
    "    output_path = '/raid/yifan/Reservoir_modeling/Analysis/fig_comparison_modeled_observed/Dam_'+Dam_info['output_name'][i]+'_flow_duration_curve.png'\n",
    "    plt.savefig(output_path,dpi=300)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#read in modeled streamflow using new guide curve -- median of daily data (regulated)\n",
    "Raw_model = xray.open_dataset('/raid/yifan/Reservoir_modeling/Run_reservoir/result_new_guide_curve.modified_flow.nc')\n",
    "    \n",
    "    #read in observed stream flow from TVA(unregulated)\n",
    "filename_latlon = str(Dam_info['grid_lat'][i])+'_'+str(Dam_info['grid_lon'][i])+'.daily.1903_2013'\n",
    "dir_latlon = '/raid2/ymao/VIC_RBM_east_RIPS/data/TVA_data/naturalized_flow/downscaled_daily_flow/latlon/'+filename_latlon\n",
    "Nflow_TVA = pd.read_csv(dir_latlon,sep=' ',header=None,parse_dates=[[0,1,2]])\n",
    "Nflow_TVA.rename(columns={'0_1_2': 'Time', 3: 'flow(cfs)'}, inplace=True)\n",
    "Nflow_TVA = Nflow_TVA.set_index('Time')\n",
    "Nflow_TVA = Nflow_TVA.ix[starting_date:end_date]\n",
    "Natual_flow_TVA = Nflow_TVA.resample('1W',how = 'mean')['flow(cfs)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#for i in range(len(Dam_info['dam_name'])):\n",
    "#for i in [0,1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,18,19,22]:\n",
    "\n",
    "    \n",
    "    #read in TVA observed streamflow (regulated)\n",
    "xls_path = '/raid/yifan/Reservoir_modeling/TVA_reservoir_data/'+Dam_info['xls_name'][i]+'.xlsx'\n",
    "Raw = pd.ExcelFile(xls_path)\n",
    "Total_Q = Raw.parse(sheetname='TotalQ')\n",
    "year_start = Total_Q.columns[0]\n",
    "if Dam_info['year_operated_start_of_Calendar_year'][i] <= 1949:\n",
    "    col_del = np.concatenate([np.arange(0,starting_year-year_start,1),np.arange(-1,end_year-2015-1,-1)])\n",
    "else:\n",
    "    col_del = np.concatenate([np.arange(0,starting_year-year_start,1),np.arange(-1,end_year-2015-1,-1)])    \n",
    "Observed_temp = Total_Q.ix[1:367]#pd.concat([Total_Q.ix[1:60],Total_Q.ix[62:366]]).dropna(axis=1)\n",
    "Observed_Q = Observed_temp.drop(Observed_temp.columns[col_del],axis=1)#Observed_temp.resample('1w',how='mean')#groupby(Observed_temp.index.map(lambda t: t.week)).mean()\n",
    "temp = np.ravel(Observed_Q,'F')\n",
    "temp1 = temp[274:-92]\n",
    "temp2 = temp1[~np.isnan(temp1)]\n",
    "M = pd.DataFrame(temp2,index=Nflow_TVA.index)#Observed_Q.drop(Observed_Q.columns[col_del],axis=1)\n",
    "M = M.resample('1w',how='mean')\n",
    "#     M.iloc[10:12,M.shape[1]-1]=np.nan\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#read in modeled streamflow from RVIC (unregulated)\n",
    "RVIC = xray.open_dataset('/raid2/ymao/VIC_RBM_east_RIPS/RIPS/model_run/output/RVIC/Tennessee_8th_grid/hist/Tennessee_UH_1.hist_1949_2010.calibrated_1961_1970.rvic.h0a.2011-01-01.nc')\n",
    "RVIC = RVIC.isel(time=slice(0,-1))\n",
    "RVIC_flow = RVIC.sel(lat = Dam_info['grid_lat'][i],lon = Dam_info['grid_lon'][i])\n",
    "RVIC_flow = RVIC_flow.sel(time = time_index)\n",
    "Q = RVIC_flow.resample('1W','time',how = 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#read in modeled streamflow using new guide curve -- median of daily data (regulated)\n",
    "Original_model = xray.open_dataset('/raid2/ymao/VIC_RBM_east_RIPS/RIPS/model_run/output/simple_reservoir/Tennessee/maxFlowFromRVIC_exceed0.2/Tennessee.modified_flow.nc')\n",
    "Original_streamflow = Original_model.sel(lat = Dam_info['grid_lat'][i],lon = Dam_info['grid_lon'][i])\n",
    "Raw_streamflow = Raw_model.sel(lat = Dam_info['grid_lat'][i],lon = Dam_info['grid_lon'][i])\n",
    "Model_Q = Raw_streamflow.sel(time = time_index)\n",
    "Original_Q = Original_streamflow.sel(time = time_index)\n",
    "L = Model_Q.resample('1W','time',how = 'mean')\n",
    "L_original = Original_Q.resample('1W','time',how = 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BFgK8gtkWI9sJM9HDPw9YEkD2pzBDxUedvBYBnfyE7eCk83gAmf3Wzwnq+gqMcrvLaa/t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\ni5yXb52lpaV5yR0bG0tubq5Xf6pTp47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U1dUxb948CgsLqa2txd3dvcl5zhfonE9ubm6L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\nioiIiGQoJYIiIiIiGer/A4vnY6GDzsrwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8219c61940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,8])\n",
    "plt.xlabel('Exceedence',fontsize=16)\n",
    "plt.ylabel('Thousand CFS',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.yscale('log')\n",
    "\n",
    "f3, = plt.plot(np.arange(M.size)/M.size,sorted(M.values.ravel()/1000,reverse=True),'k-',label='Observed Flow from TVA (regulated)',linewidth=2)\n",
    "f4, = plt.plot(np.arange(Natual_flow_TVA.shape[0])/Natual_flow_TVA.shape[0],sorted(Natual_flow_TVA/1000,reverse=True),'k--',label='Observed naturalized flow from TVA (unregulated)')\n",
    "f5, = plt.plot(np.arange(Q.streamflow.values.size)/Q.streamflow.values.size,sorted(Q.streamflow.values*np.power(3.28,3)/1000,reverse=True),'r--',label='Modeled naturalized flow from RVIC (unregulated)')\n",
    "f1, = plt.plot(np.arange(L.streamflow.values.size)/L.streamflow.values.size,sorted(L.streamflow.values/1000,reverse=True),'r-',label='Modeled flow(regulated)',linewidth=2)\n",
    "#f2, = plt.plot(np.arange(L_original.streamflow.values.size)/L_original.streamflow.values.size,sorted(L_original.streamflow.values/1000,reverse=True),'r-',label='Modeled flow using old guide curve (regulated)',linewidth=2)\n",
    "plt.legend(handles=[f3,f4,f1,f5])\n",
    "\n",
    "water_year = '(water year:'+str(starting_year+1)+'-'+str(end_year)+')'\n",
    "title_name = 'Flow duration curve at Dam '+Dam_info['dam_name'][i]+'\\n'\n",
    "plt.title(title_name+water_year,fontsize=20)\n",
    "output_path = '/raid/yifan/Reservoir_modeling/Analysis/conference_feb_2016/Dam_'+Dam_info['output_name'][i]+'_flow_duration_curve_simplified.png'\n",
    "plt.savefig(output_path,dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BFgK8gtkWI9sJM9HDPw9YEkD2pzBDxUedvBYBnfyE7eCk83gAmf3Wzwnq+gqMcrvLaa/t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\ni5yXb52lpaV5yR0bG0tubq5Xf6pTp47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EoKqqivDwcLKzs6msrMTZ2VmrnsaETmOkp6fX\na096ejoA3bt3Jy4ujitXrnDmzBm2bNnCzJkzyc3N5dixY/Tp0wcACwsLioqK6pVduy+SkpIabX/d\nvruTDR3bt29n9uzZxMXFUV1dTUlJCQEBAQ2mT0pK4umnn0atVmts09fXJysrq55dxsbGWFlZaeUv\nKirCzMzstu2VSP6sSCF4g6vbtlF6/DhIISiR/OlxdXXFw8OD7du315tytba2Rl9fn6SkJNq1awco\nIsLJyQkABwcHUlJSNOmTarmWcnFxwdDQkNzc3JtOebq4uBAaGsrOnTvrXauurkZfX5+UlBTatGkD\ncNs7mgEcHR1JSkrC19dXY3PNKKiRkRHBwcHMmzeP9u3bo6enR/fu3fn888/x9vbWCNWAgAC++OKL\nemXXbufN2u/g4EBqaqrmuG6bWrVqxfXr1zXHmQ048i8vL+eZZ55h1apVPPnkk6jVap5++mnNiKiu\nul1dXVm6dCndu3fXaVfNlDwo0/a5ublaaS5cuEBgYKBOeySShxk5NXwDlYsLgVlZcO3a/TZFIpE0\nA0uXLmXfvn0YGRlpnVer1YwYMYLp06dTXFxMUlISc+fO1WxaGDFiBPPnzyctLY38/Hw+/vhjTV57\ne3sGDBjAlClTNOv2Ll26pFlzV5vBgwcTFxfHqlWrqKyspKKighMnThAbG4tarWbYsGFERERQUlLC\n+fPntTa43CqjR49mzpw55OTkkJOTw/vvv6+1CaNPnz4sWLBAMw0cGhqqdQzQpUsXCgoKyMjIaLCe\nm7V/xIgRzJs3j/T0dAoKCvjvf/+rlT8oKIh169ZRWVmpc/1ljdArLy+nvLwca2trzeaWXbt2adLZ\n2dmRm5tLYWGh5tzEiRN59913NeIzOzubn376CYBnnnmGn3/+mcOHD1NRUcF7771Xb5r9wIEDPP74\n4zfpaYnk4UMKwRusPHeORUDGv/99v02RSCS3Se2RIg8PDzp27Kjz2vz58zE2NsbT05M+ffoQFhbG\n+PHjAcWn3MCBAwkMDKRTp04MHz5cq46VK1dSXl6On58flpaWPPvsszpHtlq3bs2uXbtYt24djo6O\nODo68s4772icNX/55ZcUFRXh4ODAhAkTmDBhQpPbVvf43//+N506dSIgIEBj9/Tp0zXXQ0JCKC4u\n1kwD1xzXFoL6+vqMGzdOs7ZQV503a/+LL77IgAEDCAgIIDg4mEGDBqGnp6eZrn3//feJj4/H0tKS\nWbNm8dxzz+lsU+vWrZk/fz7PPvsslpaWrFu3jieffFKTrm3btowePRpPT08sLS3JzMzk9ddf58kn\nn2TAgAGYmZnRo0cPjh07BoCfnx9fffUVo0ePxtHRESsrK61p+dLSUrZt29akdZoSyf1k//79RDSz\n32PVg+IX6n6iUqlE9bR3WWfYktEzZ8KVK2Bjc7/NkkgeSFQq1QPjT07SvOTk5NCnTx8iIyObxan0\njh07mDRpEpcvX24G6+4eCxYsIDU1lf/85z/32xSJRIuGvm9vnG8WZ5pSCKIIwdJ/vILegnn8r2VL\nus+YQceZM++3WRLJA4kUgpKGKC0t5ZdffmHAgAFkZmbyzDPP0KNHDz777LP7bZpE8qdECsF7hEql\nEskDhuKyczNlZWX3LbySRPJnQApBSUOUlJQQEhJCbGwsRkZGDB48mC+++ILWrVvfb9Mkkj8lUgje\nI1QqlcgxscaqMPt+myKRPPBIISiRSCT3hnshBOVmkRuYXLsK5eX32wyJRCKRSCSSe4YUgjcwqK6A\nS5futxkSiUQikUgk9wwpBGtRlZioeS+E4NyhQ4g333xwwiVIJBKJRCKRNCNSCNYi+bM/IhBUVFTw\n4ksvUbxkCTz/PNSKNCCRSCQSiUTyMCCFYC3MDuzVvDcwMGDBt98SrFaTo1JBcDDcZhxQiUQikUgk\nkgcRKQRvUEwrLCvyID9fc65jx46ETZ7MuNxc+PprePZZ5a9EIvnLkJSUhFqtprq6+qZpV6xYQe/e\nvW+rnjvJe7+p20dPPPGEVoSS5mDWrFlaYfPq8vXXX2Nvb4+pqSl5eXmo1Wou3cV136WlpQwZMgRz\nc3NGjhx51+p5kCkvL8ff35+srKz7bcot07dv33pxyO8WtZ/dK1eu4OfnR0VFxT2puylIIXiDeLwB\nqLhwQev8pEmT2LNnD3l9+yojggMG3A/zJBJJE3B3d8fQ0JC8vDyt8x06dECtVmvi0N4qukKtNUfa\n5szbGHdbFIG27du2bWtUtDVHHbWprKzkzTffZM+ePRQWFmJpaXnX+rKGjRs3kp2dTX5+PuvXr7+r\ndeli1qxZGBgYYGpqiqWlJb169eLo0aMApKeno6+vrzOiy9NPP82//vUvoP5zERcXx4gRI7CxscHC\nwoKgoCDmzp3boLuoxYsXExISgp2d3V1o4YODh4cH+/btu6Myap5HW1tbHn30URYtWtQcpjULUgje\noEYIZtS52TY2NgwePJjTp0+Djw94ed0P8yQSSRNQqVR4eHiwdu1azbno6GhKSkruujB4kLnTtjdl\nNPR+kpmZSVlZGb6+vppzd9vXZVJSEm3atGmwb6uqqu5q/QCjRo2isLCQnJwcQkNDefbZZwFwdHSk\nf//+9UZl8/Pz2b59O+PGjQO0n4uEhAS6deuGm5sb0dHR5Ofns2HDBk6dOkVRUZHO+v/3v//dFcEP\n96b/7hdjxoyRQvBBJIoAAEo2b653bfXq1Tz66KP32iSJRHIbjB07lhUrVmiOV6xYwfPPP6+VprCw\nkPDwcGxtbfHw8OCDDz7QXKuuruatt97CxsYGb29vtm7dWi/vCy+8gKOjIy4uLsyYMaNB0RETE8OA\nAQOwsrLC19eXDRs2aK7l5eUxdOhQzMzM6NatGwkJCQ22qWbqdeXKlbi5uWFra8uHH36ouX78+HF6\n9OiBhYUFTk5O/POf/6SyshKAkJAQhBAEBARgamrKhg0bdE5D1x4dGj9+PC+//DKDBg3CxMSE/fv3\ns23bNjp27IiZmRlubm7MmjWrQXtrT7sFBQVhamqKqakpJiYmqNVqDh48CMDRo0fp2bMnFhYWdOjQ\ngQMHDmjKSExMJDQ0FDMzMwYOHEhOTo7Oui5evEi7du0AsLCwoH///vXSNHa/3d3diYyMBJTverVa\nzYUbM0NLly5l2LBh9cqLiIhg9uzZrFu3DlNTU5YtW8aKFSvo1asXb7zxBtbW1syaNQshBHPmzMHd\n3R17e3vGjRtHYWGh1j1dvnw5rq6uWFlZsWjRIk6cOEFgYCCWlpb885//bLCPa6NWq3nuuedIT08n\nNzcXgPDw8HpCcO3atfj7++Pn5wdoi+WZM2fSs2dPPvnkE80In4+PD99++y2mpqb16kxJSeHy5ct0\n7dpVc67udGvd50ytVrNo0SLatGmDpaUlr776qlbauv1Xcw/8/PywsrLi8ccf1xrV37VrF+3atcPC\nwoJXXnmF0NBQTf11lxI0tsTj0qVL9OvXD2tra2xtbQkLC9Pcp/DwcJKTkxkyZAimpqZ8+umnwJ09\nu127duXSpUukPCibUIUQf/kXIAI4LQSIZBBpKSlCIpHoRvnaeDBxd3cXe/fuFe3atRMxMTGiqqpK\nuLi4iOTkZKFSqURSUpIQQoixY8eKp556Sly7dk0kJiaKNm3aiKVLlwohhPj666+Fr6+vSEtLE/n5\n+aJv375CrVaLqqoqIYQQTz31lJg0aZIoKSkR2dnZomvXrmLx4sVCCCGWL18uevfuLYQQ4tq1a8LF\nxUWsWLFCVFdXi9OnTwtra2tx4cIFIYQQI0eOFCNHjhQlJSUiOjpaODk5afLWJTExUahUKvHSSy+J\nsrIycebMGdGyZUsRExMjhBDi5MmT4vfffxfV1dUiKSlJ+Pn5iXnz5mnyq1QqcenSJc1xbTtrUKvV\nIiEhQQghxLhx44S5ubk4cuSIEEKIsrIyceDAAREdHS2EEOLs2bPC3t5ebN68WWNf7T4KDQ0VS5Ys\nqdeOxYsXC19fX1FUVCRSU1OFlZWV2LFjhxBCiD179ggrKyuRk5MjhBCie/fu4q233hLl5eXi4MGD\nwsTERIwdO7bB/lGr1aK6ulqrzTXtaex+h4eHi88//1wIIcRLL70kvL29xf/+9z/NtS+++EJnnRER\nEVr2LF++XOjp6YmvvvpKVFVVidLSUrFkyRLh4+MjEhMTxbVr18SwYcM0eWru6aRJk0RZWZnYvXu3\nMDQ0FE8//bTIyckRaWlpwtbWVhw8ePCm9ZeVlYm3335b2NjYaO5BSUmJMDc3F4cOHdLk6d69e73n\noqaP7O3txfLly3XWpYutW7eK9u3ba52re9/rPmcqlUoMGTJEFBYWiuTkZGFjYyN27tzZYP/9+OOP\nwsfHR8TGxoqqqirxwQcfiB49egghhMjOzhampqbixx9/FFVVVWLevHnCwMBAU3/d+9PYMxofHy/2\n7NkjKioqRE5OjggJCRFTpkzR5HV3dxf79u3THKelpd3xsxsQECC2bNly035u6Pv2xvlm0UByRPAG\n1z38SccBFyCtqaN/FRXQrRusXSt9DUokNUREgEpV/xUR0fT0DaVtIjWjgrt378bX1xdHR0fNterq\natavX89//vMfjI2NcXNz480339SMnmzYsIHJkyfj6OiIubk506ZN0+TNyspi+/btzJ07F0NDQ6yt\nrZk8ebLWVHQNP//8Mx4eHoSHh6NSqQgMDGT48OFs2LCB6upqfvjhB95//30MDQ3x9/evN2pZF5VK\nRUREBAYGBgQEBBAYGMiZM2cAZWNbly5dUKlUuLq68tJLL2mNUMDNp0rrXn/yySfp1q0boHhR6NOn\nD/7+/gC0b9+eUaNG1aujMX777TdmzJjBli1baN26NatXr2bQoEEMHDgQgH79+tGpUye2bdtGSkoK\nJ06cYPbs2ejr69O7d2+GDBly0zp0tfFm9zskJETTjl9//ZVp06Zpjg8cOEBISEiT2+jk5MTLL7+M\nWq2mZcuWrFmzhjfeeAM3NzeMjY356KOPWLdunWZUSqVS8d5772FgYED//v1p1aoVo0ePxsrKCkdH\nR3r37q0ZrdTF+vXrsbS0xNjYmCVLlrBx40bUauVn3dDQkGeeeYaVK1cCysjpqVOnGDNmjM6ycnNz\ncXBwaHJbCwoKMDExaXL6GqZNm4aJiQkuLi707dtXWXZ1g7r9t2jRIqZNm0abNm1Qq9W88847nD59\nmpSUFLZv30779u158sknUavVvPbaa7e9VtHLy4t+/fqhp6eHlZUVU6ZMafTzs2rVqjt+dk1MTCgo\nKLgte5sbKQRvMOlVPf7JlwB0vniR7P/8p9H01dXVoK8Pn30GM2bAwoX3wkyJ5MEnIkL5x6juqzEh\n2NS0TSQsLIw1a9awfPlywsPDta7l5ORQWVmJq6ur5pybmxtpaWmAstDexcVF61oNycnJVFRU4ODg\ngKWlJRYWFvzjH//QOW2ZlJTE0aNHsbS01KRds2YNWVlZZGdnU1lZibOzs856GqL2D52xsTHFxcWA\n8iM/ZMgQHBwcMDc3Z/r06Q1OpTaV2n0AcOzYMR599FFsbW0xNzdn0aJFTa4jJSWFkSNHsnLlSrxu\nrLNOSkriu+++0+qfQ4cOkZGRQXp6OhYWFhgZGWnKaEr/6OJm9zskJIRff/2VzMxMqqurGTFiBL/9\n9htJSUkUFhYSFBTU5Lrq9ll6erqW3W5ublRWVmrtsrW1tdW8NzIy0rrHRkZGmnusi5EjR5KXl8eV\nK1do3749J06c0Lr+/PPPs2HDBsrLy/n2228ZOHAg1tbWOsuysrIiIyOjaQ1FmYZvaO1gYzT0DEP9\n/ktKSuL111/XPCNWVlaoVCrS0tLqfU4Brc/TrXDlyhVGjx6Ns7Mz5ubmhIWFNfpsN8ezW1RUhLm5\n+W3Z29xIIXiDsDD4geH8yJMAmMyYAaWlOtP+/PPPDB06VBGDPXvCmjUwcyY0s7sEiURye7i6uuLh\n4cH27dvrrfGytrZGX1+fpKQkzbmkpCScnJwAcHBw0Fq7Uzudi4sLhoaG5ObmkpeXR35+PgUFBURF\nRdWzwcXFhdDQUPLy8jRpCwsLWbBgATY2Nujr62vVc7s7mkHxbuDr60tCQgIFBQV88MEHjY4AtmrV\niuvXr2uOMzMz66WpuwlizJgxPPXUU6SlpVFQUMDEiRObtCGjtLSUp59+mjfeeIMBtbwuuLi4EB4e\nrtU/RUVF/Otf/8LBwYH8/HxKSko06W+3f252v728vDAyMuLLL7+kT58+tG7dGnt7exYvXkyvXr1u\nqa66febo6FivXn19/WbfZWtpacmiRYuIiIjQEpm9evXC0tKSH3/8kdWrVzc66ty/f3++//77JtcZ\nEBDA5cuXtdbcNeW5aoy6/efq6sqiRYu0npHi4mK6detW73MKkJqa2qAtjYncd999F7Vazblz5ygo\nKGDVqlVaz3Zdu+702a2qqiI+Pp7AwMAm9MrdRwrBG9jagokJjOA7AAwrK4lbt05n2prFn2PGjOHa\ntWvQpQv89BO89pryVyKR3HeWLl3Kvn37tP4zB2XB+ogRI5g+fTrFxcUkJSUxd+5czcLyESNGMH/+\nfNLS0sjPz+fjjz/W5LW3t2fAgAFMmTKFoqIihBBcunRJs/mhNoMHDyYuLo5Vq1ZRWVlJRUUFJ06c\nIDY2FrVazbBhw4iIiKCkpITz589rbXDRRWOiq6ioCFNTU4yNjYmJieHrOv5O7e3ttdyEBAYGcu7c\nOaKioigrK2PWrFk33VlcXFyMhYUF+vr6HDt2jDVr1jTJvvHjx+Pr68ubb76pdT4sLIwtW7awa9cu\nqqurKS0t5cCBA6Snp+Pq6kqnTp2YOXMmFRUV/Pbbb2zZsqVR+xqq/2b3G5RRwQULFmimgUNDQ7WO\nb5fRo0czd+5cEhMTKS4uZvr06YwaNUozfdsUId1U2rRpw2OPPab1vIKyTOLtt9/m6tWrjU6vz5o1\ni8OHD/P2229rxGR8fDxjx47VbJyojZOTE97e3hw7dkxzLigoiB9++IGSkhLi4+NZsmTJHbVp4sSJ\nfPjhh5w/fx6Aq1evsnHjRgAGDRpEdHQ0P/30E1VVVSxYsEBLBAcFBXHw4EFSUlK4evUq/2lklq+o\nqIjWrVtjYmJCWloan3zyidb1up+fO312jx07hoeHR70RzfuFFII3iIiIoHXr/VRgwG9eHQD4afZs\nysrK6qXV19fn+++/5+rVq7z++uvKyR49YPduuM2haYlEcufUFjMeHh507NhR57X58+djbGyMp6cn\nffr0ISwsjPHjxwPw4osvMnDgQAIDA+nUqRPDhw/XqmPlypWUl5fj5+eHpaUlzz77rM6Rj9atW7Nr\n1y7WrVuHo6Mjjo6OvPPOO5rvlC+//JKioiIcHByYMGECEyZMaHLb6h5/+umnrF69GlNTUyZOnMio\nUaO00kZERBAeHo6lpSUbN27Ex8eH9957j379+tGmTZsmObJeuHAhM2bMwMzMjDlz5tRzolzbntrv\n169fz6ZNmzAxMcHExARTU1MOHTqEs7Mzmzdv5sMPP8TGxgY3Nzc+/fRTzQjT6tWrOXr0KFZWVrz/\n/vtNWkPZ0HFj9xsUIVhcXEyfPn10Ht8uEyZMYOzYsfTp0wcvLy+MjY2ZP39+k2zWdXwz3nrrLb75\n5hutac3w8HBSUlIYNWoU+vr6DZbv6enJkSNHuHz5Mv7+/lhYWPDss8/SuXPnBtcCTpw4UbMGEWDK\nlCno6+tjb2/P+PHjCQsLu6P2PPXUU7zzzjuMGjUKc3NzAgIC2LFjB6BMZW/YsIGpU6dibW1NTEwM\nnTp1omXLloAywjly5EgCAgLo3LlzPRFc25aZM2dy8uRJzM3NGTJkSL3P/DvvvMP777+PpaUln3/+\n+R0/u6tXr+Yf//jHLfVFDfv37yfiDpfO1EXVnP+R/FlRqVRCCMGAAYqWOzxuPt2Xv84m4L327dm2\nbZtO5Z6Xl4etrS0nT558YIZ4JZK7jUqluus+2iQSyYNPeXk5HTt2ZO/evffdqbQQAmdnZ9asWXPH\nI7l3k+zsbEJDQ4mMjMTAwOCm6Rv6vr1xvlmco8oRwVrUDB4ctVTWFA1UqbgQHd2g+ra0tGTatGma\nYesGqbVuQSKRSCSShwEDAwOio6PvmwjctWsXV69epaysTOMbsman+4OKjY0N586da5IIvFdIIViL\nGz5JOZ7hDK6uGAuBl0rF8uXLOXfunM48M2fObDzOZFmZsoYwMFDZWSxHUiQSiUQiuWOOHDmCl5cX\ntra2bN26lc2bN2umhiVNR04N88fU8NGj0L27cq780YHo79vFcW9vusTH88gjj7Br1y7s7e1vvYLS\nUti/H15+WYlV/Pbb4OHRrG2QSO4VcmpYIpFI7g1yavgeExAAVlbK+8NmTwAQnJWFuZkZZ8+epWPH\njmRnZ996wYaG8NhjcPCg8r5jR4iJaUbLJRKJRCKRSG4dKQRrYWz8hx/bZSavQatWqIuKOLZoEZ6e\nnmRkZPDaa6/dNAD7jBkzmDlzJnl5edoXnJ3hiy/g3Dlo2/buNEIikUgkEomkiUghWIfgYOXvF68w\nwwAAIABJREFUb4dUiFGjAfCJjmbz5s3o6+uzbt06XnjhhUbLGDt2LKmpqbRr146FCxdSWtcxtaOj\nEkarLidPwptvwtWrzdEUiUQikUgkkkaRQrAOwcFgbQ0JCRAX8IxycssW2rdvz7obDqaXLVuGj48P\nH330EVVVVfXKaNOmDUuWLGHTpk1s2LChntPNBrG1haIicHOD8eOViCU6/BhKJBKJRCKRNAdyswh/\nbBap4YUXYMkS+M+sMt7+0EwRY/Hx4OXF6tWrmThxohJRBOjcuTM//vijVlD7uqxbt46ioiJefPHF\nphkUHw87dihRSqKi4MQJ6aha8sAgN4tIJBLJvUFuFrlPPPqo8nfl+pZQ41n+nXcAeO6558jLy2Pz\n5s3Y2Nhw/PhxPD09eeutt8jNzdVZ3qhRo5ouAgG8veHVV2HXLvjxR7CwuJPmSCSSOyApKQm1Wn3T\ntcEAK1asaFKUjtvJu2nTJlxdXTE1NeX06dN4eHiwb9++26rrbvLRRx/x0ksv3fN6+/bty9KlS3Ve\nu5V7eKtMmjRJ48NOF2q1Wis82cPAu+++qxUh5V4yfvx43nvvvXtSV+3PZHl5Ob6+vg3+zv+ZkUJQ\nB8OHQ4sWcP48ZP7rc+Xkxo1QUQEoTjSHDh3KyZMneeKJJygrK+Ozzz7D3d2dPXv2NLmeffv2ER0d\n3Xiibt2gVavbbYpE8pfC3d0dQ0PDehu1OnTogFqtrhf8vancSmisWw2j1dS8U6dOZeHChRQWFhIU\nFHTbddxtpk2bxuLFi++3GfW4k/vSGF9//TXTp0+/5/XeL3Jycvj222+ZOHHi/TblpjT2z0FTqbl/\nBgYG/P3vf+ejjz5qDtMeKKQQ1EHLltC+vfL+3VV+issXgOXLtdK5uLiwdetW9u/fT69evSguLmbg\nwIG88sorXLly5ab15Ofn06dPH7777rtbn2qbMgXCw+H775V1hRKJBJVKhYeHB2vXrtWci46OpqSk\n5E//g5yUlISfn9/9NkNyi9zvZRS61rHfCcuXL+eJJ55o0HFzc9f3IDF69GhWrFhBxY1BoYcFKQQb\noCb+e1S0GmqCVf/2m860ISEh/PLLL7z22msIIVi4cCE+Pj689NJLjYafGz58OGvWrCEiIoKOHTuy\ndu3apj9gb7wBXbvC4sXKLuRBg+D332+liRLJQ8nYsWNZsWKF5njFihX1gr4XFhYSHh6Ora0tHh4e\nWlN71dXVvPXWW9jY2ODt7c3WrVvr5X3hhRdwdHTExcWFGTNmNPhjHxMTw4ABA7CyssLX15cNGzZo\nruXl5TF06FDMzMzo1q0bCQkJOssoLy/HxMSE6upqAgIC8PHx0Zlm8uTJODk54ezszJQpUzTfJaGh\noWzatAmAQ4cOoVar2b59O6DMSnTo0EFnvXWn4A4cOKAVc/3jjz/G2dkZU1NTfH19+eWXXwCYNWsW\nY8eOBf6Ykl25ciVubm7Y2try4YcfasooLS3l+eefx9LSEn9/fz755BOdcd1rOHz4MF26dMHCwoKu\nXbty5MgRneludg/rcurUKTp27IiZmRkjRoxg1KhRmrbrmrKvPd1bt58++eQTHB0dcXZ2ZtmyZVr/\ngJSXl/PWW2/h5uaGg4MDL7/8MmWNbAj85ptv8PPzw9TUlPbt23P69Ol69de1oeY+/fe//8XBwYEJ\nEybg5+fHtm3bNOmrqqqwtbXVlHf06FF69uyJhYUFHTp04MCBAw3atH37dq1YvrrqA/j555/p0KED\nFhYW9OrVi7NnzzZLf9emoKCAIUOGYGtri5WVFUOGDCE9PR2Af//73/z666+8+uqrmJqa8tprrwF3\n9pl0cnLC0tKSo0ePNtg/f0akEGyAUaOUvwkJwCuvKAexsQ2m19PTY968eRw/fpzevXtTWFjIN998\ng7+/P+PHjyc/P19nvscee4zo6GimTp3KokWLmDZtWtMMdHFR7Nq5EzIyYPBgZadxSckttFIiaWZU\nquZ73SbdunWjqKiI2NhYqqurWb9+PWFhYVpi7dVXX6WoqIjExET279/PypUrWbZsGQCLFy9m27Zt\nnDlzhhMnTrBx40at8p9//nkMDAy4dOkSkZGR7N69m//7v/+rZ8f169cZMGAAYWFh5OTksG7dOl5+\n+WVibjiTf/nllzE2NiYrK4slS5Y0OIVlYGBAUVERQgjOnj3LxYsX66WZM2cOx44dIyoqijNnznDs\n2DHmzJkDKP+o7t+/H4CDBw/i5eXFwYMHAeVHPDQ0tMl9WyNq4uLi+Oqrrzh58iSFhYXs3LkTd3f3\neulqOHToEBcvXmTPnj3Mnj2b2BvfpRERESQnJ5OYmMju3btZtWpVgyO3+fn5DB48mMmTJ5Obm8uU\nKVMYNGiQzu/Wm93D2lRUVDBs2DAmTJhAXl4eo0eP1gjnhtrTkI07duzg888/Z+/evZr21ubtt98m\nPj6eqKgo4uPjSUtLY/bs2TrL2rBhA7Nnz2bVqlUUFhby008/YXUj4sHNRrczMzMpKCggOTmZxYsX\nM2bMGNasWaNlp42NDUFBQaSlpTF48GDee+898vPz+fTTTxk+fHiDa+HOnj1L2zp+cOvWFxkZyd//\n/ne++eYb8vLymDhxIkOHDqWioqJZ+7u6upoJEyaQkpJCcnIyxsbGvHLj93rOnDn07t2bBQsWUFhY\nyPz585vlM9muXTvOnDnTaP//6RBC/OVfSjdoU10tRKtWQoAQeeczlDfm5sqFm1BdXS0OHz4sunXr\nJgABCCcnJ/HVV1+J7OzsRvNdvXr1puU3UoDu89euCVFaevvlSiS10PV5qXWx+V63gbu7u9i7d6/4\n4IMPxLRp08SOHTvEgAEDRGVlpVCpVCIpKUlUVVUJAwMDERMTo8m3aNEi0bdvXyGEEI8++qhYtGiR\n5tquXbuEWq0WVVVVIjMzU7Rs2VKU1vo8rV27VpN3+fLlonfv3kIIIdavXy/69OmjZd/EiRPF7Nmz\nRVVVldDX1xdxcXGaa++++64mry5UKpVISEio11YhhPDy8hI7duzQXNu5c6fw8PAQQgixd+9eERgY\nKIQQ4rHHHhNLliwR3bt3F0IIERISIjZt2qSzvnHjxokZM2Zojvfv3y9cXFyEEELEx8cLOzs7sWfP\nHlFRUaGVLyIiQowdO1YIIURiYqJQq9UiPT1dc71Lly5i/fr1QgghPD09xe7duzXX/u///k9TR12+\n/fZb0bVrV61z3bt3FytWrBBCCBEaGiqWLFkihGj8Htbl4MGDwtnZWetcr169NG2vfU9rqH0vavfT\nhAkTxLRp0zTp4uLitNK2atVKXLp0SXP98OHDmvtUl4EDB4r58+frvFb3Wahtw/79+0XLli1FeXm5\n5np8fLwwMTERJSUlQgghnnvuOfH+++8LIYT4+OOPRXh4eL26V65cqbNufX19ERsbqznWVd+kSZPE\ne++9p5Wvbdu24uDBg83a33WJjIwUlpaWmuPaz4QQzfOZrN1394KGvm9vnG8WDSRHBBtApQIvL+X9\nT7/bgYkJFBRAEzaDqFQqunfvzpEjR9i9ezcODg6kpaXxyiuvYGNjQ9euXXnjjTfYunWr1iiFSqXC\n1NS0XnmlpaV89913lNxstK+h/xK/+w5MTeGJJ2DzZkhMVH5qJZLmpjml4B0QFhbGmjVrWL58OeHh\n4VrXcnJyqKysxNXVVXPOzc2NtLQ0ANLT07WmJ93c3DTvk5OTqaiowMHBAUtLSywsLPjHP/5BTk5O\nPRuSkpI4evQolpaWmrRr1qwhKyuL7OxsKisrca7lFqp2PbdKenp6vfbUTJF1796duLg4rly5wpkz\nZwgPDyclJYXc3FyOHTtGnxrPCLeAl5cXX3zxBREREdjZ2TFmzBgyMzMbTG9nZ6d5b2xsTHFxscbu\n2n3Q2LRwenp6vT6qfd/qpm3oHupK6+TkpHWuMTsao7F6s7OzuX79OsHBwZpn4vHHH29w5C0lJQWv\nmh+hW8TGxgZ9fX3NsZeXF35+fmzZsoWSkhJ++uknnnvuOUB5Tr/77jut5/TQoUNkZGToLNvCwoKi\nOuvS69aXlJTEZ599plVmamoq6enpzdrfJSUlTJw4EXd3d8zNzQkJCaGgoEDrd7U2zfGZLCoqwtzc\n/LbsfVCRQrARapbiHDiogi5dlIP162+pjP79+3Pu3DmWLFlC//79MTQ05NixY8ydO5fBgwfToUMH\nvvjiC06dOtXgItsrV66wfPlyfHx8WLhwIWfPnr01NwjjxkFhIfTuDYsWQY8e4OCg+CiUSB5CXF1d\n8fDwYPv27QwbNkzrmrW1Nfr6+iQlJWnOJSUlaX6cHBwcSElJ0bpWg4uLC4aGhuTm5pKXl0d+fj4F\nBQVE6fgsubi4EBoaSl5eniZtYWEhCxYs0Pxw1q7ndnc0Azg6OtZrT41vUyMjI4KDg5k3bx7t27dH\nT0+P7t278/nnn+Pt7Y2lpaXOMlu1asX169c1x3WFwahRo/j111819b799tu3bLeDgwOpqama48b6\nwNHRkcTERK1zycnJ9URFTbkN3UNdaeuKydp56/ZDY4JXV70105rW1tYYGxtz7tw5zTNRUFDA1QYi\nSbm4uDS4btTY2LhRm3RNpY4aNYo1a9awefNm/P398fDw0NQTHh6u9ZwWFRXxr3/9S2fdAQEBxMXF\nNVqfi4sL06dP1yqzuLiYkSNHNmt/f/rpp1y8eJHjx49TUFCgWfJQIwR12XWnn8kLFy4QGBjYoE1/\nRqQQbIQZM5S/y5ZB/ss33AMsWaLECr4FLCwsmDBhArt37yY7O5utW7cydepUrK2tOXPmDFOmTCE4\nOBgLCwsGDhzIxx9/rLXuxdXVlW3btrF06VKOHTvG0KFD8fT0vLVt8S1bwrRpsG0bpKcrI4OenrfU\nDonkz8TSpUvZt28fRkZGWufVajUjRoxg+vTpFBcXk5SUxNy5czUbHEaMGMH8+fNJS0sjPz+fjz/+\nWJPX3t6eAQMGMGXKFM26vUuXLml+gGozePBg4uLiWLVqFZWVlVRUVHDixAliY2NRq9UMGzaMiIgI\nSkpKOH/+vNYGl1tl9OjRzJkzh5ycHHJycnj//fc17QHo06cPCxYs0CzyDw0N1TrWRVBQENu2bSM/\nP5/MzEzmzZunuRYXF8cvv/xCeXk5BgYGGBkZoVbr/jlpaHQGlL7+6KOPKCgoIC0tja+++qrBtE88\n8QQXL15k3bp1VFVVsX79ei5cuMCQms18dcpt6B7WpXv37rRo0YKvvvqKqqoqNm/erBUJKjAwkHPn\nzhEVFUVZWRmzZs1qcM3aiBEjWL58ORcuXOD69eta6/9UKhUvvvgikydPJjs7G4C0tDR27dqls6wX\nXniBTz/9lFOnTgGQkJCgESkdOnRgzZo1VFdXs2PHjkY3d9QwatQodu3axddff82YMWM058PCwtiy\nZQu7du2iurqa0tJSDhw4oBlRrssTTzyhWXPaEC+++CL/+9//NP147do1tm3bxrVr15q1v4uLizEy\nMsLU1JS8vDwiIiK0rtvZ2WltMrnTz2R6ejr5+fl069at0fb/6WiuOeY/84sG5uCrq4Xw9VXmqfr1\nKhXVbdoqBz4+QmRl6cxzK5SUlIhvv/1WPP/888LT01OznhAQhoaG4tlnnxULFiwQeXl5WvmqqqrE\n2bNnRUZGxh3bUI8rV4To2FGIN98UYssWIe5kzaLkoaShz8uDgIeHh2bdXG0qKyuFWq0WSUlJQggh\n8vPzRVhYmLCxsRGurq5izpw5WmnfeOMNYWVlJTw9PcXChQu11pcVFhaKSZMmCWdnZ2Fubi46duyo\nWfNWd31TXFycGDRokLCxsRHW1taiX79+4syZM0IIIbKzs8XgwYOFmZmZ6Nq1q3jvvfcaXSOoVqu1\n1oXVbmtpaal4/fXXhYODg3B0dBSTJ08WZWVlmrQ7d+4UarVaHDx4UAghRHR0tFCr1WLDhg0N1lda\nWipGjhwpTE1NRWBgoPjiiy806/eioqJEly5dhKmpqbCyshJDhgzRfB/pWiNYe21e3759Neu2rl27\nJsaOHSvMzc2Fn5+f+OCDD4S3t3eDNh06dEgEBwcLc3Nz0alTJ3H48GGd5d7sHtbl5MmTIigoSJiY\nmIgRI0aI4cOHaz0TH374obC2thaurq5i9erVWvei7pq1jz/+WNjb2wsnJyexbNkyrbRlZWXi3Xff\nFZ6ensLMzEz4+fmJL7/8ssH2Llq0SLRt21aYmJiIRx55RJw+fVoIIcSJEyeEv7+/MDU1FeHh4WLM\nmDFaawQbWmfZr18/YWBgILLq/H4dO3ZMhISECEtLS2FraysGDx4sUlJSdJaRk5MjXFxcNOtkG6pv\n586donPnzsLCwkI4OjqKESNGiOLi4mbt7/T0dBEaGipat24t2rZtKxYvXqx1n48cOSLatGkjLC0t\nxeuvvy6EuLPP5H//+1/x5ptvNni/7gYNfd/SjGsE77sIexBejf2wnTsnhIWF0lPrFhUoIhCEGD++\nwTy3S1pamli3bp3o16+flihUqVRi7Nix4sqVKzct4+rVq2Lx4sUiNTX19owoLxfit9+EmD1biJAQ\nZcdMly5CNLBoWfLX40EWgpI/N19//bUIDQ2932aIrl27iuXLl99vMx5Ypk+fLubNm9ds5f0Z+rus\nrEz4+vo2uuHzbiCF4AMgBIUQIiJC6amXXxZCREYKzXL2o0cbzXcnJCQkiM8++0x0795dIwjVarXo\n2bOnmDp1qjhw4IDOfEVFReLpp58W5ubm4p///GeD6ZpMebkQu3YJsXnznZUjeWiQQlDSXGRkZIhD\nhw6J6upqERMTI7y9vRvcKXs3OXDggMjMzBSVlZVi+fLlwtjYWGRmZt5zO/4qyP5uOlIIPiBC8Ndf\nlZ7y9hbKfHG/fsqJe/Sf686dO7UEYc3L399frF27VmsKqIbU1FQxY8YM0apVK/Hiiy9qpsSanblz\nhfjb34SYNk2I778XIiVFiDruJCQPF1IISpqLpKQk0b59e9G6dWvh7Owspk6dWs8dzb1g8eLFws7O\nTpiYmIjAwECxffv2e27DXwnZ303nXghBlVLeXxuVSiUa64eKCsX7SmkpnDkDAaaJcGPHFfv3QyML\nrpuTzMxMTp8+zZ49e1i2bJkmnqqhoSEdO3akT58+jB49mkceeUSzuLbGMeYzzzxDmzZtmt+o3Fw4\ncgROnIDjx5W/ubnw9dfw4ovNX5/kvqNSqZDfGxKJRHL3aej79sb5ZombKYUgNxeCAAEBcPYs/N//\nwd//DoSFwerVSjSPOwxqfTuUl5ezbNkyvvzyS87V2cXs5uaGj48Prq6udOnShWHDhmFjY1OvjJKS\nEtauXUuvXr3w8fFpvlisVVVQXQ21/EppiIwEe3vFfY3kT4kUghKJRHJvkELwHtEUIfjhhzB9uuJO\n8OhRUJ2LhkceUYYKT5z4w+ngfSAvL4/ff/+dTZs28f3332tGCmvQ09Nj8ODBjBs3jkGDBqGnpwco\nvpumTp3Kr7/+ipubG+PGjWP48OGaMEbNTnExPPecErPZ2FiJldy1qzKi2qkTNOB+QvJgIYWgRCKR\n3BukELwDVCrVv4GxgDfwtBDip0bS3lQI5uUps8GFhbBgAbzysoDhw2HTJmjbFg4cgFre8+8XFRUV\nxMXFkZKSQnx8PNu3b2fnzp0aZ9UODg6EhITQr18/Ro4ciYmJCeXl5WzdupXVq1eza9cuZs+ezeTJ\nk++ekULApUvw++9w6JDi2PrgwTuKLyu5d0ghKJFIJPcGKQTvAJVK1QW4AiwFvrhTIQjw5Zfw2mvK\n+7174dHORdC9u+Jg2tgYTp++ryODDZGRkcHq1atZvHixVsB6fX19PDw86NSpE0OHDqV///4YGRmR\nlZWl8Tpfm/T0dGxtbTUjinedpCSwtYU6DoEl9xd3d/dGIzVIJBKJpHlwc3OrF1EHpBC8JVQq1S/A\n3OYQgkLACy8oSwJbtlQ0oF1JIktOBNCqqojiFqZcNOlYv/ya/Kjqn6xzTeddVdXJWyed5pqOzAJV\nrfqhqqqSiopKysvKqKisqFOuoIW6BXr6+rRs2RJ9AwOtdYOFhYVUVFRg2LIlBoaG6OvpWANYU1Jj\no3uNjvzVunb9GhQVQYsWoKcH+voIfQNUBvqgqj+NXLfOpvZRgxdUdfLqSNVQO1U3uV6TqkULZcmk\nlbXyTGlSOzoq0+Xdu4O3txwtlUgkEomGh04IqlQqJ+AdIBgIBIwAdyFEvUB/KpXKGfgC6I/yu7kH\nmCyESKmb9kb6ZhOCAFlZEBwMtUMlenCJo3TDluwmlSGR3BK+vvDBB/DUU1IQSiQSieShFIIhwDrg\nJNACGAB41BWCKpXKCIgCSoAbwX/5AEU4BgghSnSU3axCEJQ9DydPKhtja2hxvYjWcae0T9amVvn1\nqmqk7hveghpMrkI0XEQD5WpO17GpqrqKrMws4uMvcvLECQquFtTLa2frgKWFORYWFpop4pCQEFq0\nqDVdLG7NpppTNW1pMGlj7RFC2cVdWQF+foj2j2imlFWN2XMTm6htkw57dBapo3/rZq+xKTdX8bqT\nmgY1cdbVVNNWFccQp1N0Sd30RyZHRxgwAIYMUf4bsbMDQ8MGGiWRSCSSh5WHTgjWRqVS/R1YjG4h\n+DrwKdBGCHH5xjl34CIwVQjxhY7yml0I/lU4f/48UVFRJCQkcPz4cbZt20ZFRUW9dPr6+vj7+9Oh\nQweCgoLw9vamrKyMTZs20atXLwIDA+nUqRMtWrS4uwaXlcGOHbBxI/zwgzKS1rYtrFihTC0/wAgB\nly/DL7/Ahg2wc6dy3oYrvGe1kAklCzC+nqudydhY2cEUEKBMIffsCYGBylS6RCKRSB5a/spCcA/Q\nUgjRu875/ShetvvqKE8KwWbi+vXrnDlzhtTUVC5cuMDBgwe5ePEiycn1ZvABsLGxoVWrVhQXF1NV\nVcXQoUMZN24coaGhd9/YsjJl2DY3VxlBq0thobLYs39/8Pd/4KZcY2NhyRL49lvIzAQV1XQgkv7s\nZVSrLXhwGfNrafUztm6tjBZ27QovvQReXvfeeIlEIpHcVf7KQjAD+FEIManO+a+AZ4QQdrXOzQRe\nAKyBIqAU6CaESNdRpxSCd0BRURFRUVFERkZy5swZzQhicXFxvbSenp4EBwfj7u5O+/bt8fb2JjAw\nEH19fQwMDO6d0ampEBEBe/Yoc7J+foqAGjr0nkWKaQqVlYqHogMHlKg2x45BeblyzZpsXEihp/oo\nj5qeoE/lXqyKa31kVCpF5PbsCY8/Dp07K9PJcsRQIpFI/tT8lYVgGfCZEOLdOuffB94WQtyWklCp\nVGLmzJma49DQ0HszavUQU1lZyenTp7l48SIxMTEcOXKEX3/9ldLS0npp1Wo1rVu3xtTUlL/97W90\n6dKFzp0706ZNG0xMTO6uoUJAejqcP694CjcxAV0+FLOylKE5Ly9l1O0+cf06HD6s+DA/elRxwXj5\n8h/XbcmiM8cJN97I0yVr0Bd1pvKNjKBfP+jYEXr1UqaU72N7JBKJRHJz9u/fz/79+zXHs2bNkkKw\nzvk7FoIPWj88jJSUlHDq1CkSExNJSEjg3LlzxMbGcu7cOSorK3Xm8fDwoFu3bnTt2pWOHTvi4eGB\no6Mj6nsdhWTjRpg1S3GE7ewMvXsr069/+xu4u99bW+pw7ZoyWnj4MGzerIR+rqqClpTSiRP0Yy9D\nVVvw0kvGvKLOznZDQxg4UBkJfeopsLS8P42QSCQSSZP5K48IZgKbmjI1fIt1SiF4HyktLSUqKorj\nx49z4cIF0tPTiYqKIjk5WefmFDs7O4yMjHBzcyM4OJguXbrQq1cvHB0dmy9eckNUViqjhwcPKtt9\n+/WD8PD66a5eVUYX70PYvPJyyMiAU6cU/XrgwB/ujhxI52/s4XGXaPpU7sUx49QfGdVqReD27Ame\nnsrftm0fuPWTEolE8lfnrywE9wL6Qog+dc7/AqBrs0gT65RC8AGksrKSM2fOEBkZyW+//UZMTAwX\nL16sF0u5hhYtWvDII48QGhrKwIED8fX1xcXF5d6PHgJMmqTs9PD1VWJSt2+v/O3aVYlPfY/JzFRC\nPG/cCD/+qOylAXAilTGmW5nQ+jvaZB1EXVVnZNbOThGHPXpAnz7KruQHfAe2RCKRPOz8lYXg68An\nKO5jEm+ccwfigH/pch/TxDqlEPyTIITg/PnzxMbGcvbsWU6ePMnly5e5fPky165dq5fe2NgYV1dX\nSktL8fHxoW/fvvTr148OHTqgr99wZJRm4epVJfxgdDScPav8nTNHGWmrS3k53KPNMnl5sHs3bN2q\nrDOsiTpoRgGPso++llF0t7iAX85BjK9mamc2MYFnnlF2YnfrpoRFkSOGEolEck95KIWgSqUafuNt\nf2Ai8DKQDWQLIQ7eSGMMnEZxKD3jRvrZQCsgUAhx/TbrFjNnzpSbRP7ECCHIy8sjOjqaLVu2cOrU\nKS5cuEBmZmaDeYyNjfH29sbJyYl27drRpk0bfHx88PDwwMXF5e4Lxdp06aKIwWHD/vAHaG1916sV\nQpnh3rsX9u1TRg3/2M8jaEss/Q0P8YTFYbpcP4D11QTtAiwslOnjTp0gLExphxSGEolEcleo2TTy\nUG4WUalU1dSP3QBwQAjxaK10zsBc4G/8EWJuiq5wdLdQtxwRfEjJz88nJiaGCxcuaP5GR0frDOJd\nF0NDQ2xtbfH29iYoKIiOHTvStm1b2rZt2/y7mcvKFEX2/ffKluAzZxSRdf48tGrVvHU1QkWF4n7x\nl18UVzXHj2uHU/QnmuF8z+OtfqVD1XFalhZqF+DkpEwlBwUpYrZLF6UdUhxKJBJJs/FQjgjeT6QQ\n/OtRUVFBdnY2WVlZJCUlceHCBS5evEh8fDxxcXFkZWU1mt/BwQFvb28cHBxwcHDAw8MDd3d3XF1d\ncXFxwfpOR/MqKpSFfS4u9a8VFyvh9IKClFfLlndW101IT1e0ac3IYWRkzaihwO6Gu5qWuvyYAAAg\nAElEQVRBbOWZFpuwrrpSvwBra2VtZLduii/DwEBlSlkikUgkt4UUgs2MFIKSulRVVZGbm8vly5eJ\njY3lwoULXLhwgUuXLnHx4kWd/hBrY2ZmhqurK66urtjY2GBpaYmHhwdt27alXbt2ODs73/4O58xM\nePddZVvwxYuKGAwOhscegyeeuL0yb4HcXGUfzPnzijedrCzFjPKyatoTTWeOE8gZOnOcDqrTGAod\nfdW2rTIFHhSkvG/bFlxd5cihRCKRNAEpBJsZKQQlt0J1dTXJyclcunSJzMxMUlJSOH/+PDExMaSm\npnLlypUG/SLW0KpVKzw8PPDw8CA4OBg3NzdcXV1p164d9vb2Td/pXFSkDNVFRiprDKdNq5+msFBx\nJH0X1zxWVMCFC8q08qlTyt+TJ6G8XOBOIl35ne4coXOLSAI4Q+uqwvqF2NoqvgyHDVOcXd/DKXGJ\nRCL5MyGFYDMjhaCkOamuriYrK4vU1FTS09PJzc0lOzubhIQEYmNjOXLkiE7/iDXo6enh4OCAs7Mz\n3t7eeHt74+7ujq2tLTY2Nri4uGBra9t0g9asgb//XQmj5+OjuIHp3VsJP3cXXetcvapMJf/wg7J5\nOjERCgpAjwq6cIwgThPEaXy4SECLc1hW5WjyCj09VF26KOH+evRQ3O+4uMjweBKJRIIUgs2O3DUs\nuZcIIbh8+TKbNm0iMjKShIQEUlNTcXR0JD4+vkE/ibUxNTXF3d0dd3d33Nzc8PT01ExD29ra4uXl\nhV5tf3/XrikubGJiFGfYv/6qiMN33rmLLdVGCMjOVsyIjFTC40VGKiOJVVWCRzjLs2zgcbbTgUha\nUK2dX08Plb8/jBunrDX09JTuayQSyV+Kh3rX8P1EjghKHiRKS0vJyMggKSmJuLg4XnvtNczMzDA2\nNgYgOztbp8/E2hgYGODi4oKbmxteXl60bdsWX19fvL29sbOzw9TUFFV1te4Rts8/V0bfevYER8e7\n0UQtSksVQXj6tKJR9+2D0itX6ckhQtlPMCfx4zwO1HcFVGnnSIvOwaiCOyobUkJC4EY/SSQSycOK\nHBFsZqQQlDzIFBUVceTIEY4dO8axY8c4f/48enp6rF69msTERE3s5vT0dLKzs0lNTSU5uXFvSi1a\ntMDY2Bhzc3NcXFxwcHDA3t4eBwcHeiQn4x8ZieXFi6hbt0YdHAweHhARAWZm96TNxcXKWsPTpyE5\nWfGmE3noOv1KtjCYn2lLLG2Iw5yr9fKW+Adj2Kszqvb+f4TJk+JQIpE8REgh2MxIISj5s1FSUoKR\nkVG987///jthYWG0a9cOBwcHDA0Nqa6uprCwkPT0dBISEpo0oliDDzDY1ZVQKyuinnwSeycnHB0d\ncXZ2xsPDo/n9KTZCWZkiCA8fVma4Y85XU3YuHre8U/TkEI+xA1eSaUl5vbyltq6ogwIw6NkZBg6E\nDh3uWSQXiUQiaW6kEGxmpBCUPCyUlZVx+fJlzdrDmvB77dq1Y+HChZp05eXllJaWkp2dTXp6Ounp\n6WRkZGhemZmZZGRkkJCQQElJSYP1tTc25ruKCmIsLMh0duZ6mza0CAzEzs0NZ2dnnJ2dcXR0pOVd\n9HWYkaHsUI6JgTNHS6jYuQ+74ni6cZSOnMKDyxigvTmnqoU+1z38EY8EYtS7E/oD+kKbNnd1Z7VE\nIpE0F1IINjNSCEr+qnz66acsXLgQf39//Pz8tF6tWrWitLSUo0ePcu7cOY1gTE9PJzk5mcuXL1NV\nVkYQ0B0IBoJQRhE3AWG16rGxscHNzQ1ra2ucnJzw8vLCy8sLDw8PHB0dsbW1bbaQfqWlyi7lixeV\nDSmX46soPR2D/sVzDGQnPThMW2JR1wlkVKnWJ8+2HWVubTEMaodZN18Mhg9R4itLJBLJA4QUgs2M\n3DUs+atSWVlJQkIC58+f13qNHz+e1157rV76a9euYWxsXPMlRGFhIbm5uaSnp5OWlkZKSgoZiYkU\nX77M2fx8UlNTycjI0PhV7AIEADuBlDplm5mZYWlpqYn7bGdnh6urK56enjg7O+Pg4KC9E/oWuXpV\n2bEcHQ1njxQjTp7CKuMsHfL2EUQknlyul6dEZUSaZQDX2nbAqEdHzPt2wCqkPS1aGd62HRKJRHK7\nyF3Ddwk5IiiRNI1Jkyaxfv16XF1d6dq1K97e3vj7+9OjRw/Mzc115qmqqtKE8hMHD2K/di12sbHk\nGBgQqafHyYoKVhcXc/kmn0G1Wo2joyNOTk5aL3d3d1xcXHBycsLBwQGDW1z7V15+QxweLiLvcAyq\nuFhaJMTStWAXXThWL30Felw29CXXtQP6XTrgMDAQ+2AnWrTxkn4OJRLJPUGOCDYzUghKJE1DCEFO\nTg4JCQkcP36cS5cucf78eWbNmkW3bt3qpa+qqqKFLnFUUaEs7IuKgjNnqBo1iqv+/ly5coWoqChS\nU1PJzMwkMTGRS5cukZaWRlZWFjf7nKpUKmxsbHB2dq4nGB0cHDAzM9OE/2vdunWjZV27BrGHc8nY\nfprrhyMxuRiJx9VIvKti6/k4BLiubk1S8NOoe/fCbGA37Pq1R9Xi7jnslkgkf12kEGxmpBCUSO4O\nHTp0IDs7GwcHB1xdXTVOsMPCwrCwsLh5AX36KKHnAgOp8PMjy86ORCFIuzEVnZqayuXLl0lLSyMt\nLY3MzEyqq+uLNF3Y2dnh4+ODm5sbLi4uuLi44OHhQbt27TSCUZeILcq8xqXNZ8ncHgmRkVhknMe+\nIhnXOpPdVzElxrgjUe1GUBXclVZd/LF3a4mDA9jYgIWF3LgskUhuDykEmxkpBCWSu0N5eTlZWVmk\npaWRnJys8Xn4ySefYGpqWi/9Dz/8gJOTE76+vsr18+cVnzE1r6goZS43IwN0rBesrKzUhPerEYc1\nr6ysLAoLC8nPzycpKYmysrJGbdfT06Nt27a0a9cOa2tr7OzscHZ2xt3dnfbt22Nvb4/qRlST1FQ4\nuuQcej9uxDg1Dt+833Cp1vblWIEecbThHP7E0I5Y2nLF1AfjoDZ4dzKnWzfw9QUvLyU0tEQikTSE\nFILNjBSCEsn9RwjBc889R2xsLLGxsTg7OxMcHIynpyezZ8/WiC6uXFFGCeuSlwfTpyujiIGBijuY\nBjaXVFdXk5qaSnx8PCkpKSQnJ5OSkkJsbCyJiYlcvXqVq1frO6uujZWVFQEBATzyyCP4+/tjZWWF\nq6vrjV3QdhRdzKHw+92oNv+IUeJ5bHNjGiwrG2su4sNpgojmEapsHVD5eGPR2Zv2nQxxcwNXVyXQ\nyx3sl5FIJA8JUgg2M1IISiQPFpWVlURHRxMVFUVWVhZTp06tl6aoqIgpU6YQFBREYGAgAe7umK1b\nB7//roQkychQoooMHgyzZ9+yDdeuXeP8+fNcunSJ3NxcMjIySE1NJSEhgbNnz1JQUNBg3hYtWuDq\n6vr/7N13dFVV2sfx704CoffeWyhJqEoHpQgoIhawOzD2MsWZcezjGNvM6BR9VcReUBEQLCAqCBgp\nUiRIaKH33hJIgPT9/rGTEEgCAc7NvXB/n7WycnPOPvvsxLXwWbs8T15pv9atW9OmUSMisrOptW8f\nIWvXYtesIWPVOkI2rCMsvfBcjVmEsIXGrKEVy2nLLyFd2dKgF1Va1SYy0sW6jRpBt25Qo8YZ/4oi\ncp5SIOgxBYIi55/k5GQ+/fRTli5dSnx8PMuXL6dWrVpcfvnlLnl2cjIkJMDhw3DZZQU7sBbM2f07\naq1l+/btLF++nGXLlrF27VoSExPZuHEje/bsYc+ePUU+Gx4eTtOmTalUqRKVK1embu3atKlcmYsq\nVqT1oUNUP5xC9tb9sH4dZXdvItRmFehjLRHMpRc/MID1tGA1rSlToyJNmkDjxq4iYM+e0LdviVUF\nFJESpEDQY8ojKHL+y8rKYv369ezbt49evXoVuL9582Z++eUXOnbsSLNmzQh55BHYtAlGjIBOnaBB\ng7MODE+WmprK5s2bWb16NQkJCaxevZq1a9eyceNG9u7de9rnq1WrRvPmzWlSrx6dq1WjU7lytDxy\nhJobNlE6bjEhRwuWCNxLTXZQn93UYTNN+JbBLKAbYXVqEhUF0dFugrRZM/KWmlWCWeT8ojyCPqIZ\nQZEL3+LFi3n++ef59ddfSUpKon2LFtyRkcHAkBDq7drl1lavvRYefNAd6/WR5ORktm7dSnJyMomJ\niXlpclavXs2aNWtYv379KWtB16pWjcvr1qVfdjYXp6dTNy2NKnv2EJKRUWj7vdRkE01ZSge+5Fpm\ncBlZuI2GtWtDu3YuDu7Y0X1v3hxClPVGJKBpRtBjCgRFgsv+/ftZt24dW7ZsoWbNmvTv1w9+/hm+\n+QYefhiqVSM+Pp6wsDAiIyOPH1QpAdZa9uzZw4YNG9ixYwcJCQl5B2jWrFlTaJAYAtQGWpUrR8eq\nVelZujQdjh2jYWIipU86HZ0SWonVYW2Jy2jLsuxoltOW5bQlCZfOp2JFaNvWTZB26QKdO7u9iLVr\nezZhKiLnSIGgxxQIisjJPv30Ux5//HGSk5PpFxXFK5s2Qfv21LvzTkJ79XInl0s4MrLWsnPnTjZv\n3syuXbtYs2YNK1asYMWKFaxbt65AShwD1AFaAINLleImY2iSnl6g32wTwpYKUazLaEJ8aivW0IqV\nRLGUDqTictlUqgSRkdC/vwsQGzRwE6f16qmgikhJUyDoMQWCIlKUvXv3snThQjZPnEji9Onc3bAh\n1TZsgNRUuOIKmDgRgIyMDEqVKuW3cVprSUpKysvXuHnzZuLi4li2bBkbN24kOTkZcDOH0UDbnO/t\njKEDUKqIfwP3larL+uxmLMuKYhd1SaANs+jHftzyeXg4eYdUGjVyM4cdOkCdOi5WbtFCS80iXlMg\n6DEFgiJyxg4fhqQkF/0A0dHRJCUl0aZNG66qW5drMzOp3LkzFS+7DNO2rV+Hmhskrlq1imXLlpGQ\nkJB3iGX79u1UAprnfLXK+boIiACKCm3XlmnLz6YX8451ZB0RrKUlu6iLm4c8rmpVt++wcWN3YKVT\nJ+jRQ+luRM6FAkGPKRAUkXOVnZ3N1q1bWb16NUvHjaPirFnU2buX66pXxxw54k5l/P73cMMNgAvO\nSnLvYVGSk5NZvXp13snmNWvWsHjxYrZt20Z2Zib1cQFhNFAL6AL0BsoU0ldaqXLsrxLB6vAOLCzd\nm8UHm/FrUhO204DMfCGlMe7P0b27Cw6bN3cpb5o2Vdk9keJQIOgxBYIi4jPWwoEDrkRejRrQvj3b\nt2+ne/fu1K9fn7Zt2zKgXDkaVK1K4yuuoH7Xrv4eMeCWujdu3Jg3e5i7L3HXrl0c3LGDert308ta\nWuMCxZbAqSb5jlatx6oalxKX1YH3tg5gcWZ7LCeuGZcqBa1buyDxoovcXsR27dwBFhE5ToGgxxQI\nikhJstaydetWtm/fzvz582k0ZQoRK1fSLDmZyhERLgF2164weDBUrpx3CCQ8PNzPIz8uMzOTTZs2\nMXv2bBISEli/fj1rFiyg0p49XAK0BxrnfNUHTj5Pklq2IusbXcqSapezIL0zM/e1Y+3WwuYZXVLs\nhg3dV5s2EBHhvjp3dodYRIKNAkGPKaG0iASE7GxYtAjmzHHpbF54ASIjmTp1KsOHDycqKoqrrrqK\ntm3b0rhxY1q1akWFChX8PeoTHD58mJUrV7Js2bK8r+Xx8TRMTqYzbll5ANDgpOeyjGFf1eocqVSP\nlPAG7KYJCYcaMf7gAH5Jb5eX+zC/kBC3vNyhg8uDePXV2nsoFzYllPYRzQiKSKBLS0tj+vTpLJg3\nj1veeYclISGU6t+fm15/PeCjn9wZ0GXLlhEfH8/atWtJW7OGVuvW0TwxkYuB1hScNcyVGhpGcrV6\npDRqz45KHVhpI5mzvw3frm5GYuaJ68Zt2rjqKc2bQ1TU8RlE5UGUC4lmBD2mQFBEzhvWwi+/wA8/\nHJ85rF/fFRZ+7TUIDeXee+8lNTWVOnXq0LJlSxo0aECDBg2IiIigdICdxjh27Bjr1q1jXXw8B+bP\n58Dq1aRs3kzWjh20Tk/nUqDpKZ5Pq1KL/RWbsuJoMxYfbM4yG81CurKFxuQ/wVyzptt3GB3tEmZH\nR7u8iGUKX40WCWgKBD2mQFBEzluZmbBiBWzcCNddB8DKlStZtGgRu3fvJiEhgV27drFt2zYmTpxI\ndHR0gS4C5QRzfrnJs9esXk3CggWs+uYbji1ZQvP0dCKBNrj9h2WLeD6xeguWVe7Fz9ndWbyvMT8e\n6Uwi1U5oU748DBgAl17qZg4jIz0tOS3iMwoEPaZAUEQuWN99B7GxLnlfy5ZuzTTfrKC1lqZNm1K7\ndm2ioqLo168fkZGRNG/enMqVK/tv3IVIT08nLi4ub5/U3NmzqZaaShOgGa6CSrdSpehqLZUyM094\n1oaEcKhxe7ZUbkd8dltmH4zm2+1tC+Q+rFDBnVzODQxz8x4G2FZMCXIKBD2mQFBELljr18O778Ly\n5bB2rUtl07MnPPQQ5ByOO3jwIAkJCSxZsoS5c+eyevVqduzYwd69ewkJ4LIgqampfPvttyxdupSl\nS5cya9Ysjhw5QijkHUxpFxpK+0qViDx8mNCsrIJ9lK/G1krRrDTR/Hy4LT+ntGUlURyiSl6bsDC3\n7/DSS11Q2KuXq5gi4i8KBD2mQFBEgsbu3TB7tpvyKqziibXg/idT6HLxxo0befnllxk6dCh9+/Yl\nLKzgaV5/yV1OXrFiBfPnz+eLL75g+fLlAJQDOgAdQkLoXaUKncuVo/7Bg5Q5erTQvlKqNmRrpWji\n0qL5cU8kG2xT5tOdDNxsauvW0L69y3PYqZOLrZXvUEqKAkGPKRAUEcnRqxfs2uUim0cecacq8tm1\naxdjxoxh/PjxLFu2jGrVqtGwYUOuv/56HnvsMT8Numi7d+9mxowZzJw5k7i4OFauXEl2dnbe/brA\nlQ0bcmm1arQNCaHxoUNU2rGDkJzcjfllhZZie4XW/HIkivjMSH6lI0voxC7qUqmSoW1buOQS96cb\nMEBVUsR3FAh6TIGgiEiOjAxYswa++QZefNFlbO7fH957r8ApiszMTPbv38+2bdsoVaoUHTp0KNDd\n999/z+LFi+natStdunTx+77DI0eOMGPGDJYvX85PP/3Ezz//zNGTZgVDgOgyZRhUvz7dKlakDdAg\nMZGKW7YU2ufBsJoszYxmJVHEcRHbaEhCSDRlGtemWzc3c9iqlTu13LCh739HufApEPSYAkERkUJk\nZ7s9hnFxcPPNBe/nLCOfyuLFi5kwYQILFy4kLi6OevXqERUVxYMPPhgQCfzT09NZunQpK1euZMWK\nFXlfO3fuLNC2PNC3dm361qpFx9KliUxLo9b27ZikpEL73khTZnMJH3A7s7mUkBC3LfOmm1xs3bgx\nhBaVPFHkFBQIekyVRUREzsK778J//gO33ALXXlv4nsN8MjIyWLduHStWrCAqKoqoqKgCbRYuXEid\nOnVo1KiRX1PaJCYmFggO586dS9ZJB04qV6rE9V270qV8eaKzsohISaFaWhomPh5z5Eheu4+bPc1d\nW54iPet45FeqFDRq5BJfR0W5AygREW7msFy5EvtV5TyiyiI+ohlBEZGzkJEBS5bAuHHw6acuiunb\nF+6+2013nYUnn3yS999/n5SUFKKioujcuTPt2rVj2LBhVKtW7fQd+FBycjLr169n06ZNrFy5ksmT\nJ7N48eIC7SpWrEjbyEiubdmSIbt20XrGDACyq9dgVaff8Er6A3y3rgWFTDoCLkDs3t2VnB4wALp0\nceX0RHJpRtBjCgRFRM7R0aOuysmMGXDrraedHTydQ4cOsXTpUpYsWUJ8fDzPPvssjRo1KtBuyZIl\nfs15uGbNGuLj49m0aRPr1q1jzpw5rF279oQ2DwCP4BJgA2Qbw8EePShzx31suegmlieEkZDgcoKv\nXAlLl7pV91xhYdC7t/uzqp6ygAJBzykQFBEpAa1bQ5UqbqNcmzYwcCDUrXvW3WVlZdGzZ09WrFhB\nlSpVaNmyJVdccQU9evSgZ8+e3o37DFhr2bVrF/Hx8SxevJilS5fyyy+/sGP7di6ylr8Cw3EHUgAO\nlirF/F694I476DJoEDVr1uTgQZcD/Lvv4NtvKTBzGBHhAsNevVxg6OeJUvEDBYIeUyAoIlICdu1y\n012LF7upr2nToGtX+P77c+o2OzubrVu3smTJEmbOnMm+ffuYMGFCgXaZmZmEhob6Ze9heno68+fP\nZ+HChexevJjo2FgG79tHnZz7h4G/Aqu6d+emW26hY8eOREVFUaVKFQ4ehMmT4eOPYcECN/maKyzM\nLSHffDNcc4075C0XPgWCHlMgKCLiB8eOuWon7duXyOvmzJnD8OHD6dSpEwMGDGDAgAG0atWK0n5K\n+Ld/xw52/utfVJ84kfq7dwOwCvgYeBs4CERHRzNixAgGDx5MmzZtyMoKYelSmDPHxc+zZkHu+ZWK\nFWHIEBg+3FVBqV7dL7+WlAAFgh5TICgiEmCmTIFff4Urr4QOHTzJs2KtZfv27cTFxTF16lTmzJnD\npk2beOCBB3j55Zc9GPRZDwzefJPs558nJGcdOCUkhAnZ2YwFfgIygUqVKtGmTRt69+7NwIED6dq1\nK+nplfjiC5fmcdGiE7vt1MnF2E2bwg03uFyGcmFQIOgxBYIiIgEmPh7GjHGb5HbvdpviOnZ0Sfja\ntPHsNRkZGSQnJxd6Ijk2NpY9e/Zw5ZVXUqFCBc/eWaSjR+Gzz9zXzJl5l1NKl+bd8HCeT07mQL7m\nxhjatm3LsGHDuP/++0lKqsnkyTBxovvzHTt2YvcdOrgl5EsvdSeR/ZidR86RAkGPKRAUEQlgufWR\nly2Dq65y+wpPdugQeHxy+Ouvv2b06NHMnj2bZs2a0apVKyIiIhg5ciRtPAxGC7ViBXz+uUvLs2ED\nALZsWfZ07szk2rUZt2kTs3/9NS+vYenSpWnZsiUtW7aka9eu9Ot3JQcOtGHz5hCmTIHp0122n1x9\n+sADD7jvNWv69lcR7ykQ9JgCQRGR89wll8C2bS5tTaNGbtawXz9PZg/T09NZuXIl69atY+3atVxz\nzTVEn1SDGWDr1q3UrVuXUqVKnfM782Rnu5Q8r7zijhHnCgsjc+RIxrZsyZtffcX8+fMLPFqlShW6\ndu3K0KFDuf76O5k6NZzvv3eTrMnJx9tFR8P11x+fKSxb1rvhi28oEPSYAkERkfOctZCQ4A6fbN7s\nTiVPn+5OKJfQlNfQoUOZN28eERERNGzYkI4dOxIREcHgwYMpX778ub9g82Z4/31XBzo32WC5cnDV\nVaR0786mNm34dfdupk+fzo8//nhCmbyKFSsyYsQIrr76atq2vZTPPivN11+7P0++AihUrw5PPAEP\nPqjyd4FMgaDHFAiKiFyAiqqFnJrqoh8fHKvduXMnmzdvZsuWLcTFxbFx40befffdQvcgpqSknP3e\nw4QEePxx+Prr49fCwmDQIDcTOmAA26tWZfr06Tz11FMnBIWNGzfmoYceYsiQIdSv35Tp013M/MMP\nsHq1a1OrFgwbBvfff865wcUHFAh6TIGgiEgQmT/fBUyVKrm10E6dXHLrLl1KbAjJyck0adKE7t27\nM3z4cPr06UOTJk3OvKOEBPjxR7fe+/33x3PJgIvgnngCe+ONxMXF8eWXX/L++++zOydVDcANN9zA\nsGHDuOSSS6hduw5ffQUPPQSbNh3vpnVruO02uO8+paQJFH4PBI0xg4FIYBfwpbX26GkeCWgKBEVE\ngoy1bql1wQJXL/mDDyAmBn7/+xIbwtatW/npp5/45ptvmDVrFrVq1eLWW2/liSeeOLsOd+5003rT\np8P48ceDwj594IoroGdPUlu14qNJkxg3bhxz584lMzMTcIFFjx49GDRoECNH/paDBxvy/vvw4YfH\n9xOWK+fK3I0cCT166NSxP5VIIGiM+QNwjbW2f75rBvgWGAjkDmAd0Mtau8+LAfmDMcY+/fTT9OnT\nhz59+vh7OCIiUtJSUtySsZ8K+aanp5OQkMCxY8fo1q1bgfvbtm3jwIEDNGvWjErFKR+SnAwvvQSv\nveZOVOeqVAl+9zu47z62GcPo0aNZsmQJP/74I+np6XnNIiMjGTJkCL169SU1tT8vvliKuLjj3dSv\n7zL63Hmnq2wiJSM2NpbY2FieeeaZEgkEZwGrrbUP5Lt2Fy7h+SfAv4E2wFvAGGvtg14MyB80Iygi\nIoXKyoJHH4W+fWHAAPBTFZI333yTN954g40bN1KmTBmaNWtGs2bNuPvuu+nfv3/RDx44AF984WY+\nf/opLxUN4DYA/vvfUL48hw8fZsaMGbz//vvMnDmT1NTUvGaVK1dm9Og3qVfvar74oixjx8L+/ce7\n+eMf3Z+oXj0f/OJSqJKaEdwJ/NVaOzbftWnARUA9a216zrUngNuttRFeDMgfFAiKiEihUlPdzNoP\nP8Dy5W5d9OmnoZDDHyXBWsu+ffvYuHEjGzduJDo6mnbt2hVoN3fuXKpWrUpkZOSJtZUnTYJXX3V5\nGQEaNoR774XBg10ZkpAQ0tLS+Pnnn5k6dSqfffZZ3kGT8PBwLrroIjp06ETz5lexcmV33n+/IuDK\n2731lktBo4DQ90oqEEwFBlprZ+f8HIqri/2ttfb6fO0uA6ZYa8/bzEMKBEVE5LR27oTnnnMVT/7w\nB/jXv/w9oiK99NJLjB49mszMTHr27MkNN9xA+/btad68udsfOXmyC2jj448/VLWqmyV8/vm8DYCH\nDx9m9OjRfPXVVyxYsKDAe5o2vZ3MzEfZtu14/borr4SHH3ZLxyEhPv9Vg1JJBYJbgIettRNyfu4M\nLMTNEv4vX7u+wFfWWm9TupcgBYIiIlJsqaluD15h+Qk3bnTXK1Ys+XGdxFrLunXrmDlzJlOmTCE+\nPp758+fTqFEj1+DYMRg3DubNw86YgdmyxV3v2hX+/nc3S5jP3r17WbFiBXPnzrA/oI0AACAASURB\nVGXOnDnMmzePY8eOASHA44SHX05mZleyslxC7fr1XRd//KNLWi3eKalAcCJQHRhgrc00xowG7gGi\nrLWr87X7A/CAtdbH9XZ8R4GgiIh44q9/hTffhO7dXV3kQYOgQQN/jwpwgaEp5KhvZmYm9erV4x9N\nmnDbqlWUyc0wHR7uihP/6U9u2fgkaWlpzJ8/n9mzZ/Pee++xdetWoCZVq8YQEjKCAweO50gcORLu\nuAN69lSiai+UVCDYAZgPJOZ8tcGlihl2UrtYYJO19nYvBuQPCgRFRMQzqakwdqzbVzh5MgwfDh99\n5O9RndLOnTv57rvvWDpzJgO++45+SUmckOq6XTu45RaXULB+/QLPHzt2jGeffZaXX36ZtLQ0XGKR\n9lSo8DBHj95AdnYY4GLiJ590OQnl7JVYHsGc5eA/AFWBRcC/rbWp+e7XwZ0ifs1a+4MXA/IHBYIi\nIuIT27e707q33urvkZyR9LQ01n72GdGffAILF7r0OrkGDHBlR7p0gY4dT3juwIEDfPLJJ0ydOpWl\nS5eyb98+3DzS7ZQpczupqS49T40arozd737ntibKmfF7QukLjQJBEREpcZ995g5s3HknjBgBdev6\ne0SFS0tzSarHjIGvvoKcJNQAh/r2pdLbb2NatCjwWGZmJnFxcbz77ru8++67QCjwMMY8irVVAFep\n5F//ckvHpUqV0O9zASippeF+wCJrbUqhDS4gCgRFRKTEZWbCr7/CG2+4AGvoUOjVyy2/lg3QRBy7\nd7PlP/8hZdo0olasAOCYMcy++GI6vP02tTt0KPSxbdu2MW3aNF5//XXi45cBtwN3Aj0AiIiw3H+/\nYcQIlbErjpIKBLOA7tbaRTk/hwCxwJ3W2nVevDxQKBAUERG/2rjRzbp9/z089RRcdJG/R3R6cXHY\nmBjMN98AYENDMYMHw403Qv/+UKdOgUeys7P55JNP+Oijj5g9ew6ZmcOA5wA3o1i2bAaDBoVw3XWh\nXHstVKhQoAuh5ALBbKBbvkAwFMgALrbWLvHi5YFCgaCIiAS83AK/PXu6PXqdOwdGor7p02HUKPj2\n2+PLxqVKwY03sq9XL57/9VcGDBlC//79KZtvpnPPnj3897//Zdy4L9i2rQtwP9A7737Zslm88EIo\n998PZcqU7K8U6BQIekyBoIiIBLx162DXLvjuO3caOSnJnUj+v//z98icvXvhww9h1ix3Yjo7G4DD\nFSowrmZN3k5O5q7nnuOyyy6jadOmhObLI7NhwwYmTpzEu+/OZ/36Zrhl40gAKlfO5I9/DOOWW6BV\nq7xc10FNgaDHFAiKiMh5Z+1aWLLE5SsMNOvXw8SJ8PHHsGoVANmhofync2fe3LmTp2JiuP32wrPO\nxcXFMWbMx7zxRiaZmY8Bx/MwvvhiOo884p96z4GkJAPBYUBu/ZlQYA1wNbDy5PbW2o1eDMgfFAiK\niMgFZepUGD8err0WrrjCf2urmZnwzjvw+ONw6JC71quXOyrcvXuBpe1Ro0ZRt25dLr/8co4dO8YT\nTzzN229vA94C6gDZ9Oo1mXHjulO/fu2S/m0CRkkGgiffNIVcA8Bae97mClcgKCIiF5RNm9zBk88/\ndyeTr7rKHUKJiPDPeFJT4Zln4N13Yf9+d61PHxewliuX12zUqFFMmjSJBQsW0LRpU26++WbCw8PZ\nvXsv773XiUOHbs5peZDHHhvNP/7xRKHVUi50JRUIjjyTjqy1gZ02/RQUCIqIyAVr1y546y0YPRom\nTIBLL/XfWA4cgOeecwHhkSPQrZvbU3hSupy0tDQWLFjApEmT2L9/P2PHjiUrK5vbb9/Ixx/n5izc\nTO3aE7j33mPcc89d1C+k4smFSgmlPaZAUERELnjWFn7SIju75E8f//gj9OvnPrdtC//7H1x2WbEe\nnTMHhg49QlJS+ZwrswgJuYphw65k2LBhREdHExkZeUHPFHoZCJ7yv7wxpq0xpshq2caYBsaYtl4M\nRERERHyoqMBo0iRo2hRefNHN0pWEvn1h3DgXgC5f7srWtW/vkmvn7iUsQu/esHVree66a1XOlX5k\nZ/+Nzz//nJtuuono6GhatGjB888/z7Zt23z/u5znTrU0PBT4HOhqrV1aRJsOwELgWmvttz4bpY9p\nRlBERIKWtfDLL/Df/7qcgFdfDXfd5fIW+nqmcPNmeO89eP11lw4HoGZN2LABKlY87eMffXSY3/62\nEsakER4eRd262SQlJZGYmJjXplOnTgwePJg77riDpk2b+ugXKVklNSN4J/BZUUEgQM69T4G7vRiM\nP8XExBAbG+vvYYiIiJQsY6BLF3fKeNUqaNMG7r/frcH6WpMmbs/g7t0wdqy7tm8f3Hqrq3F8GiNH\nVuLKK8HacOrWTWDkyN+zc+dOPvzwQwYNGkRYWBhLlizh+eefp0OHDmzevNmnv46vxcbGEhMT42mf\np5oR3A38zlo76ZQdGHMdMMpaG6DVsk9PM4IiIiInKWxPYUYGJCdDtWq+eefnn8MNN7jPzZvDmDFu\nZvIUJk92WXJy8lezYwfUq+c+HzlyhC+//JLf/e53HD58mBYtWjBq1CguueQSypzH5UpKakawGrCn\nGH3szWkrIiIiF4rC9hRu3QqNGsGVV8LKlS5Y9NLw4TBtGjRu7JaH+/VzB0tOYehQWLz4+M9vvXX8\nc/ny5bntttt47733AFi/fj2DBg2iTp06/OEPf2B/biqbIHaqQDARl73xdOoASd4MR0RERAJW8+aw\ncaM72NGjhwvYrrvO5Sr0gjEwcCAkJEBUlFse7tcPBg1ygWEROnZ02XEAnn22YOw4fPhw1q1bx913\n302NGjU4dOgQr7/+OlFRUYwaNYqsrCxvxn8eOtXS8BTgqLX2xlN2YMwEoKy19iofjK9EaGlYRETk\nDFnrSsnNmAGDB7ug0Et79sATT7jchykp7vDITz+5qK+I4QwdCt9841auf/kFmjUr2C47O5sFCxYw\nYsQINuQEl9dccw1jx46l7En5DANVSSWUvgKYCvzFWvtKEW3+DPwHuNJa+70XA/IHBYIiIiIeO3IE\njh51p4DPxZ49bvZxY04l2ylTYMiQQptmZ8M117gm3brBvHlFH3zOzs5mzJgx3HfffaSlpdGwYUOe\neOIJ+vXrR2ZmJq1atSI0NDCLppVYQmljzP+APwHLgSnAlpxbjYGrgGjgFWvtQ14Mxl8UCIqIiHjs\n119dvsAHH4Qnn4TSpc++r0OHoGdPty8xPNydEBk4sNCmBw5Agwauqt0dd7jsNKcyd+5cbrzxRnbu\n3AlAjRo1KFOmDOXKleM///kPAwYMCLiDJSVaWcQYcxvwGBB50q0E4B/W2k+9GIg/KRAUERHxgS1b\n3AGQlBRX2u7xx89+CfnIEZdW5uuvXUm6n3+GDh0KbTphAtx4I4SFuUPOp4vjjh07xquvvsorr7zC\n7t27CQkJ4bHHHuPHH39k5cqVjBw5kueee47KlSuf3dg95pcSc8aYukDDnB+3WWt3eTGAQKBAUERE\nxEcyMlxOwlmzXK7A3CXes5GV5ZaFv//ezQhOm1Zk0+hoN4H42mvw+98Xr/uUlBSGDBnCTz/9RL16\n9Zg2bRrlypVj3LhxPPLII4SFhZ392D2kWsMeUyAoIiJSArZvd+u259pH06aQmemOCD/1VKHN/v1v\neOQR9/nzz93EZHEcO3aMhg0bcuDAAQBatGjBRx99RI/T5DMsSSVWa1hERETEM+caBOb28dJL7vPf\n/+5qFRfiL39xh5kBrr8e/vWv40mnT6Vs2bLMnj2bG264gerVq7N+/Xp69uzJ119/XaDtCy+8wDff\nfEN2cToOUAoERURExH8SE9067quvuqXf4vjzn105EYDf/a7QJqGhLpXMyJHu58cfd+dNUlNP331k\nZCTjx49n+/btXHTRRQBce+217N2794R2zZo146GHHmLYsGEcO3aseGMPMAoERURExH8qVYI33zxe\nTu6994pVZ5iXX3a5YebMcZsAN20q0MQY+PBDmDjRHRxZsACefrr4QytTpgzz58+nTp06WGvp0KED\n8+bNy7t/8803ExcXR0pKCr179+Yf//hH3unj84X2CKI9giIiIn6XlgbTp7uZweRk97lSpVM/87//\nwUM5GexCQ93nf/6z0OSBkyYd3ycYFwedOhV/aGvWrGHQoEFs2bKF6tWrs2LFCurUOV58LTs7m7Fj\nx/Lzzz9z2223+Xw/oQ6LeEyBoIiISICwFl54Ae6/H6pXP3372bNh1CiXMwZOmTzwgQdcKbouXWD+\n/KKTTRcmLS2Njh07kpCQQPny5Xn00Ud58sknCTmTTjxSUpVFLjmTjqy1s70YkD8oEBQRETnPjR8P\nN93kPv/3v+60yEkSE6FNG1esZNIkVyb5TOzYsYORI0cyc+ZMAIYNG8aECRNOGwxmZmZ6mnqmpALB\nbCD3psn3uVDW2sCsw1IMCgRFRETOA2vXQkSE2/x3MmvdXsE33nA/f/jh8ZMi+bz6qit2Eh4O8fHQ\nqtWZD+O7777j2muvJS0tje7du/Paa6/lHSopOCxL//79GTNmDA28ODVNyaWP6Qv0y/m6FtgBTANu\nBwbnfJ+ec/0aLwYjIiIiUihr3Yzfrbe6zyczxi0RP/us+/mFFwrt5p573KxgWhpcfjmkp5/5UK64\n4grGjBlD+fLlmT9/Pl26dOHuu+9mw4YNhQzL0LZtW6655ho2nksybR8p1h5BY8yHQKa19q5C7r0H\nhFhrb/d+eCVDM4IiIiLnga1bYdAgqFULvvoKqlYt2CYrC0qVcsHi0qXQvn2BJuvXQ9eucPCgyy/4\n6KNnN5xt27bx17/+lQk5+xNDQ0NZtGgRnU46iZKVlcVjjz3G2LFj+eGHH4iMPLlq75nxR0Lpq4Hx\nRdwbn3NfRERExHcaNXJ14xo0gHr1ji8D5xcaCvfe6z7nlhY5SYsWLmMNwGOPweLFZzechg0bMn78\neH744QeqVatGVlYWPXv2ZOvWrScNKZR///vfPP300/Tv359x48ad3Qt9oLiBYAjQooh7EcB5uz9Q\nREREziMhIfDpp7BoUdGlQv7+d6hY0aWgyT1AcpJhw+Cyy9zn7747tyFddtllLF68mDZt2pCamsqI\nESPIKiQ59j333MOUKVOIiIg4txd6qLhLw2Nx+wLvBr6w1mYZY0KBYcBbwLfW2lt9OlIf0tKwiIjI\nBWbUKHd4BFyumG7dCjQZPdqllGnaFDZsKPwMypnYu3cvbdq04eDBg/Ts2ZNJkyZRu3btc+u0ECWe\nR9AYUwP4EugJZAKJQFUgDJgLXGutPeDFgPxBgaCIiMgF6Ior4PvvYcQI+OijArf374eaNd3nr7+G\noUPP/ZWzZ8/m6quvJikpidq1a7Nu3ToqVqx47h3nU+J7BK21+621vYFBwHPApJzvA621l5zPQaCI\niIhcII4ehb/+1U31HT0KTz3lrn/5JRRSC7hGjePVRv74x8IPI5+pSy65hPj4eCpWrMiePXvo0aMH\nc+fOPeUzq1evZvjw4Rw9evTcB3CGzigdtrX2B2vtc9baB3K+z/DVwERERETOSLlyMHCgqzJy6aVQ\nvjx07uxK1uVWHjnJf/7jvm/ZcvzzuWrUqBELFy6kefPmrFixgt69e/PYY48V2b5p06ZkZ2fzpz/9\nyZsBnIEzLjFnjKkFlDn5urV2ayHN/cYY0wz4CKgFpAD3WGvjimirpWEREZELRXY2fPABPP44REVB\nbKw7ZZyQUGj94n/+E554Atq1c0mmvXL06FGeffZZXnzxRQCSkpKoXLlyoW2Tk5Np0aIFo0aNYnju\nNGUR/LFHsBLwf8CNQHhhbQKtsogxZjowzlr7vjHmMuB1a23rItoqEBQREbnQ7NnjorxvvnGnQQYO\ndPkHy5Y9oVlysosPS5eGI0fAw2pwWGvzStCNGDGCjwrZq5hr8eLFDBw4kOXLl1O/fv0i2/kjEPwY\nd0L4PWA5kHZyG2tt0b9ZCcs53LIBqGatzcq5thq4xVq7pJD2CgRFREQuVPHx0LOni/IeeQRyZujy\na9zY5atevfrsys6dyujRo3nggQcAWLRoEZ07dy6y7R//+EfS0tJ46623imzjj0BwHxBjrR3lxUsL\n6b8+8BhwEdAeKAs0KWy52RjTAHgFuAxXA3kG8Cdr7bZ8bToBY/PPABpjpgGjrbVfFdKnAkEREZEL\n2bvvwt13u2PCmza5/YP5XHklfPstfP758QMkXrHW0rt3b+bNm0eXLl1YuHBhkW1TUlJISko6ZV1i\nf1QWAVjjxQuL0AIYDhwEZgOFRmXGmLLAj0BL4DfAbbiE1rNy7omIiIgUdPvt0Lw57NsH773n9gvm\nc/HF7vvEid6/2hjDt99+S1hYGL/88guJiYlFtq1QocIpg0CvFTcQHAdc5atBWGt/stbWtdYOAU71\nn+AeoAlwtbV2irV2CjA059q9+dptBermJL3O1STnuoiIiASb0FCXIwbgwQddzeL09Lzbd93lipZM\nnFggRvREpUqV6NGjB9Za+vTpQ3q+d/tTcQPB6cAQY8z7xpjhxph+J3/5cpD5XAUssNZuyr1grd0M\nzCNfvWNr7X5gEXA7gDFmQM71AvsDRUREJEj06nX8c0YGDBgAS5cC0LChq0aXlVVkpplz9uijjwKw\nbNkyfvjhB9+85AwVNxD8GmgK/BaYgNuXNwP4Id/3khAFrCjk+kog8qRr9wO3G2PWAC8Ct/h4bCIi\nIhLIOnVyyabDw2H3bleP+NJL3QGSrKy8vYHvvVd0GeNzMXjwYP7yl78AEBsb6/0LzkJxA8G+RXz1\ny/e9JFTDlbc72UFcybs81tr11tqe1tpW1tpOReUQFBERkSBy330wfrz7PHUqfPYZbNsGCxcyaJCL\nDbdty5so9NzAgQMBeOONN4g/TdLC1157jccff9w3A8lRrEw51tqffDqKABATE5P3uU+fPvTp08dv\nYxEREREfGjoULr/c1SEeORJWrYKaNSmHWx5+5x34+GM3gei1gQMHMmLECMaMGcN1113H6tWrKVWq\nVKFtb7vtNho2bEjHjh1ZtWqV94PhLCqL+Jox5k7gbaDpyeljjDG7gS+ttfefdH0UMNxaW/ss36n0\nMSIiIsEkPR169IC4OHjhBVdaBFi82FWlCwuDX3+F6GjvX52amkqzZs3YtWsXU6ZMYciQIUW2ffLJ\nJ1m8eDHTpk3Lu+aX9DHGmChjzMvGmG+NMbNO+prpxWCKYSVun+DJIgHfhMoiIiJy4SldGp56yn1+\n8kk4ehRwaWQGDIDMTHj+ed+8ukyZMtxxxx05r37ylG0ffvhh5s2b57NTxsUKBI0xXYE44ApgEG4/\nXjOgDy4HoCdRaTFMBroZY5rkG1sToCfuQIuIiIhI8VyVLzPezONzWs8+fgyAJT7MNfK3v/2NKlWq\nsGzZMubMmVNkuypVqhAREcEEHx1lLu6M4D+AL3CzcQa401rbBFfdIxQ455jZGDPMGDMMuDjnHYNz\nrl2Sr9k7wGbga2PMUGPMUOArYAtuOVlERESkeEJC4IYb3OcPP8y7HBHmstTt23IE9u/3yavLlCnD\n9ddfD8BLL710yrb/+te/fJZk+kxKzI0Evgcyga7W2l9y7t0P/NZa2/WcBmJMNoVXFPnJWtsvX7sG\nwMvAAI6XmPtzYeXozuDd9umnn9YhERERkWAzZYo7PAKwZQs0akR2NpQta0lPN2ys2ZWmO+ZCEQc6\nzsXatWtp1aoVtWrVYvfu3Rhz6gXW2NhYYmNjeeaZZ0q81vAhYKi19idjzH7gDmvt5Jx7/YAp1try\np+wkgOmwiIiISJDKynKnQ379FQYOdCeJjeGmm1yWmVca/Y8Hb9kHDz0ENWp4+mprLdWrVycxMZE1\na9bQsmXLYj3nj8Mi64FGOZ+XAXcYY0KMMSG46h27vRiMiIiISIkKDYVXX3Wfp0+HWbMA6N3bXYrv\ncjfs3AnLlnn+amMM/fq5Rc/xubkNS1hxA8EpQO5evX/gDo0cxiV3vgX4n/dDExERESkBvXpBTsUP\n/v53ADp0cD8u3VARPvoI+vmmdsZvfvMbAKZOneqT/k/nrPIIGmM6AsOBssD31trpXg+sJGlpWERE\nJMgdOOCWfkuVgpQUDqeWpnJlV43uyBE3cegLKSkp1KhRg7S0NOLj42nXrt1pn/FLHsH8rLW/Wmuf\ntNb+5XwPAnPFxMQETN0/ERERKWHVq0OrVpCRAYsXU6kSNGwIaWmwYYPvXluhQoW8snPPPvtske3G\njh3LX/7ylxMqoXmhuHkEaxhjGp107V5jzGvGmKLTYZ9HYmJidGJYREQkmF2Sswvu9dcBiMopYfHD\nDye1O3rUtfUoyfP//ud22E2ePJnExMRC26SkpJCUlOSfQBB4H3gs9wdjzFPAaNz+wK+NMTd6OioR\nERGRkjZokPs+aRLs3UuvXu7H555zh4vzlC3r1oufe86T17Zo0YL27duTkZHBhiKmHzt27Mi0adPw\neitbcQPBi4H8ZeTuA/5hra0OjAL+4umoRERERErasGFw0UVupu+DD3joIahQAfbsgXXr8rUzxiWg\nfv55WL3ak1dXr14dgF27dhV6/+KLLyYlJaXIGcOzVdxAsBqwB8AYEw3UAT7KufcV0MrTUYmIiIj4\nw6OPuu+PPUaZ5H307+9+LFBurm1bePttt0S8ePE5v7ZWrVoAfPDBB4XeN8ZQrVo1vwWCB4Dc2ib9\ngJ3W2tzYuNQZ9CMiIiISuK6//ngSwZde4uKL3cf58wtpe/fd8MwzLr3MObr22msB+PLLL0lNTS20\nTfXq1dnvccm74gZwM4AYY8zvgYdws4C5WuNq/YqIiIic/x55xH1fuJDcc6RFJha5557jCanPwbBh\nwyhTpgwAixYtKrTNl19+ycW5kalHihsIPgJsA/4JbACeyXfvVmCup6PyA6WPEREREcDtEwwNhTlz\n6LDlawA2by6ibWio2zN4jkJDQ7nrrrsA+POf/8zOnTsLtNmwYQPPeXRAJddZJZQ+oQNjKgGp1lpv\nzlD7gRJKi4iIyAn++U944gns5ZdTcc53HDkC27ZBgwanf/RsLVy4kG7dugEQHh7OO++8k1d5JD+/\nJ5TOz1p7+HwOAkVEREQKuOMOCA3FTJvGbyPdUm18fDGesxbOslxc165dmTdvHn379iUtLY077riD\ntWvXnlVfxVWsGUFjzPunaWKttXd6M6SSpxlBERERKeC+++Ctt/gx8gH6rRrFqFHwwAOneebwYWjc\nGHbtgpw9f2cqOzub3r178/PPP3PrrbfyySefnHDfyxnB4gaCm4GTG1YDKgJJQJK1tpkXA/IHBYIi\nIiJSwJw5cMklHC1XnfJH93HXXYZ33inGc0OHwuDBLpA8SzNnzuSyyy4DKJBEusSXhq21Tay1TU/6\nqgz0AXYDw7wYjIiIiEjA6NULatem3NED3MQ4Jk0q5nM33wzvveeWic/61b3yPucGgtOnT2fEiBFn\n3WdhzmmPoLV2NvAy8Jo3wxEREREJEMbAn/4EwJXmOxITIS2tGM8NHw6HDsGnn571q8PDwyldujQA\n6Tk1jbOzs9mzZ89Z91mYMA/62Ah09KAfv4qJiaFPnz70yU0YJCIiIpJTWqRvSCz3Z73BlwOhbt1i\nPFd2EDz3E3xz+KxffW92LzLJYM7NoylVujQL1y7iWIK3qZvPKX2MMSYM+ADoYq09b8vMaY+giIiI\nFCotDapUgSKqffiDAc/2CBZrRtAYM6uQy6WBlkB14Ox3Q4qIiIgEqvBw+OwzUr6YTvwyyMosuVev\nWbOGjMwMWrZsSelSpY/fWDnas3cU99RwLAVPDafiSsuNs9bGejYiP9CMoIiIiASa1q1bs2bNGubP\nn5+XaBq8PTVcrBlBa20fL14mIiIiIsXTuXNn1qxZw/Lly08IBL10zpVFREREROQ01q0740eaNXMp\nmrdt2+b1aPIUOxA0xrQ1xkw0xuwzxmTmfJ9gjGnrs9GJiIiInO/27IEePWD58jN6rHr16gAcPHjQ\nF6MCihkIGmM6AwuBvsA3wL9zvvcDFhhjLvLZCEVERETOZ7Vrw9/+BiNHQkZGsR+rWrUqAImJib4a\nWbFnBP8JrACaWGtvt9Y+bq29HWiac/2fvhqgiIiIyHnvD39wtYcnTy72I4EUCHYD/mmtTc5/Mefn\nF4HuXg+spMXExBAbG+vvYYiIiMiFKCTEBYNvvFHsR3IDwaSkJABiY2OJiYnxdFjFTR+TDIyw1n5Z\nyL3rgA+ttZU8HVkJUvoYERER8bm0NGjcGObMgYiI0zZfuXIl0dHRgJsVrFKlCuBt+pjizgguBJ4w\nxlTMf9EYUx54FFjgxWBERERELljh4TBjBrRoUazmrVu3plUrV7ht8hksKZ+J4gaCTwBRwBZjzBhj\nzIvGmI+AzUA08KRPRiciIiJyIYmOBlO8ybzQ0FCGDh0KwM6dO30ynOImlF5kjOkG/B0YBFQDDgI/\nAs9Za8/sPLSIiIiInFaNGjUA2L9/v0/6L1YgCGCtXQYM98koRERERKSA8uXLA3D06FGf9K/KIiIi\nIiL+kJEBp0kWHRoaCkBWVpZPhlDsGUFjzKXAzUAjoMxJt621tr+XAxMRERG5oL37LsycCRMnFtnE\n14FgcSuL3IvbDzgcqAKYk740sygiIiJyJm67DWbNgvXri2wSKDOCDwFjgTustek+GYmIiIhIMKlY\nEX7/e/jTn+CbbwptEiiBYH3gAwWBIiIiIh56+GGoWRO2b4cGDQrcDoilYSAOaOaTEQQIlZgTERGR\nElexIvToAatWFXo7fyDozxJzHYBPgfuttbM9HUEAUIk5ERER8ZvYWGja1JWfO8mECRO48cYbGT58\nOJ9//jngbYm5IpeGjTHbgPzRUWXgR2PMUSDxpObWWltw9CIiIiJyan36FHnLn3sEZ3JiICgiIiIi\nJchvgaC19rc+eaOIiIiIFIvfDosYYzYaY9r75K0iIiIiclr+PDXcBAj3VjAeRAAAFrVJREFUyVtF\nRERE5ER79kB29gmXAiV9jIiIiIj40uDBMGnSCZf8HQjqsIiIiIhISXjwQfjooxMu5QaCmZmZPnnl\n6SqLPGOM2V+Mfqy1dqQXAxIREREJSgMHwt13Q1YW5ASA/i4x1wFIK0Y/mjkUERERORd16kClSvDd\ndzBkCABhYS5U81cgeI21dpFP3iwiIiIiJ7r++hMCQX/PCIqIiIhISfnzn+HAgbwfFQiKiIiIBIuI\nCPeVw9+nhoNGTEwMsbGx/h6GiIiISJ78gWBsbCwxMTGe9m+s1TkPY4zV30FEREQCzcqVK4mOjqZN\nmzasWrUKAGMM1lrjRf+aERQREREJUFoaFhEREQk2OaXmFAiKiIiIBJt+/WDqVAWCIiIiIkFn+HAY\nN06BoIiIiEjQ6dgR1q5VICgiIiISdNq0gdWrCU1PBxQIioiIiASPatWgVSvK5KSMUSAoIiIiEky6\ndyc0NRXwXSCohNIoobSIiIgEpqSkJKpWrUrlypVJSkoClFBaREREJCiEhLhQLTsnr6Dn/fukVxER\nERE5ZwoERURERIKUMW4FWIGgiIiISJDRjKCIiIhIkAqNi+MKwFeHWhUIioiIiASokNWruR7NCIqI\niIgEHVOrFrVRICgiIiISdELq1KEWCgRFREREgo6pXZtaOZ99sU8wzPMez1MxMTH06dOHPn36+Hso\nIiIiIk7NmnmB4KxZs5gzZ46n3avEHCoxJyIiIoHryZAQXrSWY+nplCpVytMScwoEUSAoIiIigat0\n6dJkZGSQmppKeHi4ag2LiIiIBIvcpNK+mLRSICgiIiISwHxZXUSBoIiIiEgAUyAoIiIiEqQUCIqI\niIgEqZCQEMYCdvdu7/v2vEcRERER8UxISAhRADt2eN+35z2KiIiIiGdCQkJIAMy6dd737XmPIiIi\nIuIZYwz7ALN/v+d9KxAUERERCWAhISEcAMyBA9737XmPIiIiIuKZkJAQkgGOHPG+b897FBERERHP\nhISE8BmQMmKE93173qOIiIiIeKZs2bLsBBKrVvW8bwWCIiIiIgGscePGAGzdutXzvhUIioiIiASw\n8uXLA5Camup53woERURERAJYWFgYAJmZmZ73rUBQREREJICVKlUK8E0gGOZ5jyIiIiLimdwZwS6v\nvgotWnjat2YERURERAJYbiBYKjkZdu3ytG8FgiIiIiIBLHdp+Fj58nDwoKd9KxAUERERCWC5M4LH\nypQBj8vMKRAUERERCWC5geDRsmU1IygiIiISTHKXho+UKwd79nja9wUbCBpj/maMWWOMyTLGDPX3\neERERETORu6M4IroaHj+eU/7vmADQWA6MAj4yd8DERERETlbeTOCpUtDrVqe9n3B5hG01i4CMMYY\nf49FRERE5GxdcJVFjDH1jTGvGWN+NsYcMcZkG2MaFdG2gTFmojEmyRhzyBgzyRjTsKTHLCIiIuIP\nuYFgRkaG5337a2m4BTAcOAjMBmxhjYwxZYEfgZbAb4DbgAhgVs49ERERkQuaL0vM+SUQtNb+ZK2t\na60dAkw8RdN7gCbA1dbaKdbaKcDQnGv35jYyxvzGGPOrMWaJMeZ+Hw5dREREpET5cmk40PcIXgUs\nsNZuyr1grd1sjJkHXA28knPtY+Bj/wxRRERExHfyAsG0NNiwwdO+A/3UcBSwopDrK4HIUz1ojHna\nGLMN6Aa8a4zZaoyp54MxioiIiPhM7tJwRnY2NG/uad+BPiNYDUgs5PpBoOqpHrTWPgM8U9wXxcTE\n5H3u06cPffr0Ke6jIiIiIj6zceNGABYsWHBCvOKFQA8ES4zXf1gRERERL0RGukXQ9u3bExMTwzPP\nFHue67QCfWk4kcJn/oqaKRQRERG5oFxwp4bPwErcPsGTRQKrSngsIiIiIiXuQswjWFyTgW7GmCa5\nF3I+9wS+9suIREREREpQbpE0awtNu3xO/BYIGmOGGWOGARcDBhicc+2SfM3eATYDXxtjhhpjhgJf\nAVuAt70cT0xMDLGxsV52KSIiInLOQkJcuLZnzx7PzzQYX0SXxXqxMdkUXlHkJ2ttv3ztGgAvAwNw\nAeMM4M/W2q0ejsX66+8gIiIicirjx4/npptu4vrrr2fChAkYY7DWGi/69tupYWttsWYjrbXbget9\nPBwRERGRgHRBLg2LiIiIyOnlLg0rEBQREREJMrkzgtnZ2Z73rUBQREREJIBpabgE6NSwiIiIBKLc\npeG9e/deOKeGA4lODYuIiEig+vrrr7nmmmu46qqrmDx5sqenhjUjKCIiIhLAdFhEREREJEjpsIiI\niIhIkNJhEREREZEgpaXhEqBTwyIiIhKIcmcE9+/fr1PDvqBTwyIiIhKopk2bxuWXX86AAQOYPn26\nTg2LiIiIBAstDYuIiIgEKZ0aFhEREQlSOjUsIiIiEqS0NCwiIiISpLQ0LCIiIhKktDRcApRHUERE\nRAJR7tJwYmKi8gj6gvIIioiISKCaO3cuvXv3pkePHsybN095BEVERESChZaGRURERIKUTg2LiIiI\nBCmdGhYREREJUloaFhEREQlSWhoWERERCVJaGhYREREJUloaLgFKKC0iIiKBKHdp+PDhw0oo7QtK\nKC0iIiKBaunSpXTs2JF27doRHx+vhNIiIiIiwUKHRURERESClA6LiIiIiAQpHRYRERERCVJaGhYR\nEREJUloaFhEREQlSWhoWERERCVJaGhYREREJUloaFhEREQlSWhouASoxJyIiIoEod2n46NGjKjHn\nCyoxJyIiIoFq06ZNNGvWjMaNG7N582aVmBMREREJFloaFhEREQlSOjUsIiIiEqR0alhEREQkSGlp\nWERERCRIaWlYREREJEhpaVhEREQkSGlpWERERCRIaWlYREREJEhpaVhEREQkSGlpWERERCRIaWlY\nREREJEhpabgExMTEEBsb6+9hiIiIiJwgd0YwIyODmJgYT/s2vphmPN8YY6z+DiIiIhKIDh8+TOXK\nlalQoQLJyckYY7DWGi/61oygiIiISADTYRERERGRIKXDIiIiIiJBSodFRERERIKUloZFREREgpSW\nhkVERESClJaGRURERIKUloZFREREgpSWhkVERESClJaGRURERIJUbiDoCwoERURERAJY/kDQ6+Vh\nBYIiIiIiQUqBoIiIiEiQUiAoIiIiEqQUCIqIiIgEKQWCIiIiIkFKgaCIiIhIkFIgmCMmJobY2Fh/\nD0NERESkSDExMZ72Z3xRruR8Y4yx+juIiIhIoMpfXSQkJARrrSdZpjUjKCIiIhKkFAiKiIiIBCkF\ngiIiIiJBSoGgiIiISJBSICgiIiISpBQIioiIiAQpBYIiIiIiQUqBoIiIiEiQUiAoIiIiEqQUCIrI\n/7d370FzFXUax78PQXJRV5RULAQiQVAS71WocVMERDBASbBEvHLR4rIsK4irETYoKOhqjEqUsoII\nqEQEAauACCJ3UioKuitgVkIUCLIqFyFRyQ2Sn390jxwmM+87ec9MZt5znk9V17zTp8853W9n5v3l\nnNPdZmZWUw4EzczMzGrKgaCZmZlZTTkQNDMzM6spB4JmZmZmNeVA0MzMzKymHAiamZmZ1ZQDQTMz\nM7OaciBoZmZmVlMOBM3MzMxqyoGgmZmZWU05EDQzMzOrKQeCZmZmZjXlQNDMzMysphwImpmZmdVU\nJQNBSdtKWizpHkn/K+laSS/rd73MzMzMBkklA0EggLMiYveIeD1wNXBen+tkZmZmNlAqGQhGxKqI\nuKmQ9TPgpf2qj5mZmdkg6ksgKGkHSWdL+pmkJyVtlDS5TdkdJV0uaaWkVZJ+IGmnzTzlScAV5Wtu\nZmZmVh39uiK4K/Au4HFgCelW7iYkjQduBl4OHA4cBuwG3JS3DUvS6cAUYG75atugueWWW/pdBSvB\n/Td6ue9GN/efNfQlEIyIWyNi+4h4O3D5EEWPBXYGDo6IxRGxGJid8/6tUUjS4XlQyP9I+vdC/ieB\n/YH9I2JtD5pifeYvs9HN/Td6ue9GN/efNWzd7woM4yDg5xFxfyMjIh6Q9FPgYGBBzlsELCrumK8E\n7g+8LSL+vuWqbGZmZjY6DPpgkVcCv2mRvxSY1m4nSdOA04HtgFvz1cLbe1NFMzMzs9FJES0fz9ty\nFZCOAs4FpkTEg03b1gFfjoi5TflnAidHxDZdqkN/fwlmZmZmmyEi1I3jDPqt4S2iW79MMzMzs9Fk\n0G8NPwG8sEX+i/I2MzMzMxuhQQ8El5KeE2w2Dfi/LVwXMzMzs0oZ9EDwKmC6pJ0bGfnnGcCVfamR\nmZmZWUX0LRCUdIikQ4A9AAEH5ryZhWLfBB4ArpQ0W9Js0gohK0gDTIY6/ohXJJE0VtJ8SX+UtDqv\ngLLnSNppIzPS/pO0h6TzJN2bV61ZIem7xf9MWG91aTUgJJ2SVx1a0ot6Wmtl+0/SVEmXSno0f3/e\nI+mEXtbZkpJ/93aS9J38nbla0jJJZ0qa0Ot6W7I5q6612HfEcUvfRg1L2kjrFUVujYh9CuV2BM4C\n9iMFjDcAH20eYdx07PHAXcAa4NSc/TlgPPCaiFgzTN0uAg4APg7cD3w4v58eEXd11EAbsTL9J2k+\n6Yrxd0lTD70EOA2YBLw2Iv6/h1WvvbKfvcJxdgHuBP4OLI+ImcPsYl3Qhe/OPYAbSStCXQCsIq0G\n9byIWNCrelvp780JwK+BMaSp1/4AvAE4A7gyIt7Xw6pbJmkv4BLgV6S+eBstZlRps+/I45aIqFwC\nPgI8lX+Bjbydc95Jw+z7WmAjcEQhbwxwD3BFv9tWh1Sy/ya2yJsMbAA+3e+2VT2V6bum41wLLCQF\nFEv63a66pJKfPZGe67683+2oYyrZd/vl78h9m/I/D6wHxvW7fXVLwFG5TyZ3ULZU3DLozwiOVMsV\nSYDGiiRDmU36h39pYd8NpCh9lqTndL221mzE/RcRj7XIexB4FNihu9W0Fsp89gCQ9H7g9cB/9aKC\nNqQy/fcWYHfgKz2rnQ2lTN815uRd1ZS/ivQImadYG2yl4paqBoIjWpEkmwbcH5uuTbyU9GHZtXz1\nbBhl+m8TkqaSbg17pHnvleo7SduSAok5EbGyy3Wz4ZXpvxn5dYKk2yStl/SwpK9KGtfVWlorZfru\nBmA58MX8jOdzJe0DnAgsjA4f6bC+KRW3VDUQbDfP4OO0npew030b2623yvTfs0gaA5wDPEJ6Zsl6\nq2zffQlYFhEXdrVW1qky/fcS0pWjS0i39vcF5gFHAxd1sY7W2oj7LiLWAXuSbicuBf4GXA8sjggP\n9Bl8peIWryxiVfd1YDpwYEQ03/awAZJHuB1Gui1so89WpAGAiyLiMzlviaStgc9LekVELOtf9awd\nSWNJtxUnAR8gDRZ5I3C6pA0RcXw/62e9VdVAsMyKJE+QBhe02heeibCtd7qyooykL5CuRhwRETd2\nqW42tDJ9dw5wPvBHSS8gXV3aGtgqv18TEeu7WVnbRJn++0t+vaEp/zrgC8DrAAeCvVOm744GZgK7\nFp4x/ImkvwLfkLQwIu7uXlWty0rFLVW9NVxmRZKlwJQWz7S8kvQw5u/KV8+GUXpFGUmnAnOAEyLi\ne12smw2tTN9NBY4jfak9QfrymgG8Of98XPeqaW2U/e60/inTd68CVhYHmmS3k/5DNrV89ayHSsUt\nVQ0Ey6xIspj0cOWhhX3HAO8GfhwRT3W3qtZCqRVlJJ0InAnMjYiFPamhtVOm7/YmjTzdu5DuBO7O\nP1/exXpaa2X670ekPzqzmvIPIN0yvqNLdbTWyvTdn4Ft8/ydRdNJfef5Vwdbubil33Pl9Gj+nQnA\nvaQ/IrNz+jVpVNSEQrnJwNPAJ5v2v5h0m+MoYB/SH6DVpAmJ+96+qqcy/Qe8lzT30tXAm5rS1H63\nreqp7GevxfE8j+Ao6j/S5O3rSRMZvxU4JX93nt/vtlU9lfzefCmwkjTv3BGk/3jNIU0f84t+t61O\nCTgkp4WkuQGPy+9ntuu/nD/iuKWSzwhGxOo89P0s4EKevSLJ6kJRFVLRB0lfZGcC25I+WLMi4s4e\nV90o3X+NqxH751R0K+kDYj3Shc9ey8N2vaLWUtn+i4gz8nNlxwMfA/5EGjn82S1Q/Vor03cRsULS\ndODTpL97E0kDRs4B/nuLNMAaLuOZ77wgDXiEZ/5+dT1u6dsSc2ZmZmbWX1V9RtDMzMzMhuFA0MzM\nzKymHAiamZmZ1ZQDQTMzM7OaciBoZmZmVlMOBM3MzMxqyoGgmZmZWU05EDSzgSbpSEkb26QhF1Mf\nVJK+Lal5XVczsy2ukiuLmFnlBPAuNl3z9Ok+1KUbAq+YYmYDwIGgmY0Wd0bEff2uhJlZlfjWsJmN\nakpulnS/pOcX8l8tabWkeU3lj5H0q7zt8bzv9ML28ZLmSbpP0rr8OleSmo4zUdI5kh6StFbSbyUd\n06J+b83nWyNpuaRj27Rj2PNK2ivfEj9I0tmSHs1pkaR/aTreGEknS1qaz/2IpGskvXxz22Bm1eUr\ngmY2WoyRNKYpb2NEhKTDSIusfwN4v6RxwCXA3cCpjcKSvgT8J/BN4DRgIzAdmAz8PB//OmB34Azg\nN3n7acALgTn5OM8HfgqMzdseAGYBCyVtExFfz+WmAlcDtwPvBsYBnwGeR+G2dqfnLVgA/BB4H/AK\nYH4+3ocKZb4PzAbOAm7M554JbA/c22kbzKziIsLJyclpYBNwJClga5WuKpR7B7AB+CBwLrAK2KWw\n/WWkYGn+EOc6PB9jRlP+XGAtMDG//xSwunj8nH8u8AiwVX5/UX4/rlBmR2AdcN8IzrtXbvcFTeXO\nBlYX3u+Ty/3HEG3tqA1OTk7VTr41bGajQQAHA3s0pZP+WSDiCtIVwYXAUcAJ8exnCvcFRLoa2M4s\nYAX56mAjAdcD25Cu0jXK/QJY0VTuOmAiMC2Xmw5cExFrC/V8iHQlbiTnbbim6f3dwFhJk/L7/UiB\n4HnDtLVdG7YrtMHMKsy3hs1stFgaww8WuRA4DngYuLhp23b59aEh9p8E7Aw81WJbFI4xiXSFcbhy\n2+e6NHs4n2dzz9vQPG3Ouvw6Lr9uBzweEetor9M2mFmFORA0s0qQNB74Funq2G7APNLzgA2P5dcd\ngOVtDvMX4D7gUNLVw2YPFMo9DJzYptyy/Pon4MUttjfndXreTj0GvEjS2CGCwU7bYGYV5kDQzKri\na6QrcK8DDgIWSPpRRFyft99AutJ1LJsOvmi4Fngn8GRE3DvEua4FPgz8ISIeG6LcbcCBksZHxBoA\nSTsBM3j2nIidnhc6m3/wOuAU4Gig3aCPTttgZhWmCM9pamaDS9KRpCt9h9L6tu4vSQNFLgM+EBEX\n5/0Wk54jfE1EPJrz5gMfBc4HriIN0Hgj8NuIuEzS1qTn8nYDvkwaibwNsCspuDw4ItbmqVpuA8aQ\nRuUuA55LGvW7Z0S8I59v93yMO0gje8cCp5NGDW+IiF1yuU7PuxdwM7BvRNzU9Du6AJgSEQ/mvMtI\no4YXADcBzyGNGv5hRCzptA1mVm2+Imhmo0EAl7bZNpk00nVRIwjMPkQKqL4FvB0gIuZIWg4cDxwB\nPAncBfw4b39a0izS1bRjgCm5zO9J07Wsz+X+KulfSdOufIJ0u3klKZj6wT8rHXGPpANIQeAlpKuA\n84A3A3sXynV03sLvohPvAU4mjbr+CGkU9R3kwTKdtsHMqs1XBM3MzMxqytPHmJmZmdWUA0EzMzOz\nmnIgaGZmZlZTDgTNzMzMasqBoJmZmVlNORA0MzMzqykHgmZmZmY15UDQzMzMrKb+AYIsTmpq2x52\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b821cddba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,8])\n",
    "plt.xlabel('Exceedence',fontsize=16)\n",
    "plt.ylabel('Thousand CFS',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.yscale('log')\n",
    "\n",
    "f3, = plt.plot(np.arange(M.size)/M.size,sorted(M.values.ravel()/1000,reverse=True),'k-',label='Observed Flow from TVA (regulated)',linewidth=2)\n",
    "f4, = plt.plot(np.arange(Natual_flow_TVA.shape[0])/Natual_flow_TVA.shape[0],sorted(Natual_flow_TVA/1000,reverse=True),'k--',label='Observed naturalized flow from TVA (unregulated)')\n",
    "f5, = plt.plot(np.arange(Q.streamflow.values.size)/Q.streamflow.values.size,sorted(Q.streamflow.values*np.power(3.28,3)/1000,reverse=True),'r--',label='Modeled naturalized flow from RVIC (unregulated)')\n",
    "f1, = plt.plot(np.arange(L.streamflow.values.size)/L.streamflow.values.size,sorted(L.streamflow.values/1000,reverse=True),'b-',label='Modeled flow(regulated)',linewidth=2)\n",
    "f2, = plt.plot(np.arange(L_original.streamflow.values.size)/L_original.streamflow.values.size,sorted(L_original.streamflow.values/1000,reverse=True),'r-',label='Modeled flow using old guide curve (regulated)',linewidth=2)\n",
    "plt.legend(handles=[f3,f4,f1,f5,f2])\n",
    "\n",
    "water_year = '(water year:'+str(starting_year+1)+'-'+str(end_year)+')'\n",
    "title_name = 'Flow duration curve at Dam '+Dam_info['dam_name'][i]+'\\n'\n",
    "plt.title(title_name+water_year,fontsize=20)\n",
    "output_path = '/raid/yifan/Reservoir_modeling/Analysis/conference_feb_2016/Dam_'+Dam_info['output_name'][i]+'_flow_duration_curve.png'\n",
    "plt.savefig(output_path,dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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