plot_time_series_chl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd5747c9-fa84-4e31-9b03-0cfe2c8cdb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>satdate</th>\n",
       "      <th>N</th>\n",
       "      <th>chlmean</th>\n",
       "      <th>pocmean</th>\n",
       "      <th>chlstd</th>\n",
       "      <th>pocstd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20240426</td>\n",
       "      <td>7982</td>\n",
       "      <td>0.253977</td>\n",
       "      <td>76.602640</td>\n",
       "      <td>0.094386</td>\n",
       "      <td>16.689285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20240429</td>\n",
       "      <td>11238</td>\n",
       "      <td>0.298769</td>\n",
       "      <td>81.692890</td>\n",
       "      <td>0.119237</td>\n",
       "      <td>21.280693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20240502</td>\n",
       "      <td>10071</td>\n",
       "      <td>0.275251</td>\n",
       "      <td>77.122925</td>\n",
       "      <td>0.089853</td>\n",
       "      <td>16.160252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20240504</td>\n",
       "      <td>6327</td>\n",
       "      <td>0.256287</td>\n",
       "      <td>72.357200</td>\n",
       "      <td>0.052235</td>\n",
       "      <td>9.771745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20240508</td>\n",
       "      <td>6872</td>\n",
       "      <td>0.256133</td>\n",
       "      <td>70.044280</td>\n",
       "      <td>0.039482</td>\n",
       "      <td>7.693035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>20240513</td>\n",
       "      <td>11066</td>\n",
       "      <td>0.180423</td>\n",
       "      <td>61.662235</td>\n",
       "      <td>0.049762</td>\n",
       "      <td>8.858086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>20240522</td>\n",
       "      <td>5258</td>\n",
       "      <td>0.137189</td>\n",
       "      <td>50.675785</td>\n",
       "      <td>0.042655</td>\n",
       "      <td>7.300459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>20240524</td>\n",
       "      <td>9075</td>\n",
       "      <td>0.120170</td>\n",
       "      <td>47.654730</td>\n",
       "      <td>0.037093</td>\n",
       "      <td>6.868834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>20240601</td>\n",
       "      <td>7761</td>\n",
       "      <td>0.121506</td>\n",
       "      <td>42.229540</td>\n",
       "      <td>0.030649</td>\n",
       "      <td>6.427949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>20240612</td>\n",
       "      <td>5883</td>\n",
       "      <td>0.098091</td>\n",
       "      <td>39.942486</td>\n",
       "      <td>0.013602</td>\n",
       "      <td>2.999338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>20240615</td>\n",
       "      <td>5262</td>\n",
       "      <td>0.092927</td>\n",
       "      <td>39.938900</td>\n",
       "      <td>0.008528</td>\n",
       "      <td>3.634383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>20240616</td>\n",
       "      <td>8335</td>\n",
       "      <td>0.070831</td>\n",
       "      <td>44.480316</td>\n",
       "      <td>0.012155</td>\n",
       "      <td>3.065746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>20240619</td>\n",
       "      <td>6478</td>\n",
       "      <td>0.070648</td>\n",
       "      <td>42.497066</td>\n",
       "      <td>0.015544</td>\n",
       "      <td>4.380224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>20240621</td>\n",
       "      <td>6169</td>\n",
       "      <td>0.072065</td>\n",
       "      <td>34.594000</td>\n",
       "      <td>0.017418</td>\n",
       "      <td>5.288813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>20240622</td>\n",
       "      <td>7472</td>\n",
       "      <td>0.065053</td>\n",
       "      <td>37.512657</td>\n",
       "      <td>0.014449</td>\n",
       "      <td>3.870672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>20240625</td>\n",
       "      <td>7191</td>\n",
       "      <td>0.079851</td>\n",
       "      <td>37.485290</td>\n",
       "      <td>0.018296</td>\n",
       "      <td>3.316251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>20240627</td>\n",
       "      <td>11375</td>\n",
       "      <td>0.070088</td>\n",
       "      <td>37.601765</td>\n",
       "      <td>0.015072</td>\n",
       "      <td>3.185853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>20240630</td>\n",
       "      <td>11475</td>\n",
       "      <td>0.064914</td>\n",
       "      <td>35.510002</td>\n",
       "      <td>0.013186</td>\n",
       "      <td>3.101701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>20240703</td>\n",
       "      <td>6670</td>\n",
       "      <td>0.054381</td>\n",
       "      <td>40.299885</td>\n",
       "      <td>0.009013</td>\n",
       "      <td>3.431242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>20240705</td>\n",
       "      <td>7569</td>\n",
       "      <td>0.062448</td>\n",
       "      <td>33.348373</td>\n",
       "      <td>0.011872</td>\n",
       "      <td>2.696045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>20240706</td>\n",
       "      <td>12312</td>\n",
       "      <td>0.056049</td>\n",
       "      <td>33.211273</td>\n",
       "      <td>0.005210</td>\n",
       "      <td>1.811287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>20240708</td>\n",
       "      <td>6869</td>\n",
       "      <td>0.056661</td>\n",
       "      <td>30.350573</td>\n",
       "      <td>0.008205</td>\n",
       "      <td>2.643530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>20240711</td>\n",
       "      <td>8802</td>\n",
       "      <td>0.051347</td>\n",
       "      <td>34.617878</td>\n",
       "      <td>0.006385</td>\n",
       "      <td>3.245937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>20240717</td>\n",
       "      <td>5166</td>\n",
       "      <td>0.057710</td>\n",
       "      <td>37.925392</td>\n",
       "      <td>0.011516</td>\n",
       "      <td>3.940214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>20240729</td>\n",
       "      <td>8191</td>\n",
       "      <td>0.090956</td>\n",
       "      <td>42.434060</td>\n",
       "      <td>0.018370</td>\n",
       "      <td>4.666248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>20240808</td>\n",
       "      <td>7216</td>\n",
       "      <td>0.061222</td>\n",
       "      <td>32.812878</td>\n",
       "      <td>0.010121</td>\n",
       "      <td>3.693984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>20240809</td>\n",
       "      <td>10907</td>\n",
       "      <td>0.051816</td>\n",
       "      <td>38.108580</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>3.081469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>20240815</td>\n",
       "      <td>6942</td>\n",
       "      <td>0.075410</td>\n",
       "      <td>41.521454</td>\n",
       "      <td>0.013896</td>\n",
       "      <td>4.149037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>20240826</td>\n",
       "      <td>10479</td>\n",
       "      <td>0.081204</td>\n",
       "      <td>34.766502</td>\n",
       "      <td>0.008404</td>\n",
       "      <td>3.532652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>20240828</td>\n",
       "      <td>6908</td>\n",
       "      <td>0.069370</td>\n",
       "      <td>35.554220</td>\n",
       "      <td>0.008809</td>\n",
       "      <td>3.033818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>20240901</td>\n",
       "      <td>6138</td>\n",
       "      <td>0.068067</td>\n",
       "      <td>39.661750</td>\n",
       "      <td>0.016715</td>\n",
       "      <td>5.353838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>20240906</td>\n",
       "      <td>8890</td>\n",
       "      <td>0.068289</td>\n",
       "      <td>36.222645</td>\n",
       "      <td>0.009656</td>\n",
       "      <td>3.645733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>20240911</td>\n",
       "      <td>5142</td>\n",
       "      <td>0.078660</td>\n",
       "      <td>36.283665</td>\n",
       "      <td>0.010872</td>\n",
       "      <td>5.125101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>20240912</td>\n",
       "      <td>6570</td>\n",
       "      <td>0.061557</td>\n",
       "      <td>42.900208</td>\n",
       "      <td>0.009902</td>\n",
       "      <td>3.174730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>20240920</td>\n",
       "      <td>7156</td>\n",
       "      <td>0.071183</td>\n",
       "      <td>34.976364</td>\n",
       "      <td>0.011800</td>\n",
       "      <td>4.599094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>20241002</td>\n",
       "      <td>8637</td>\n",
       "      <td>0.064548</td>\n",
       "      <td>36.672300</td>\n",
       "      <td>0.013340</td>\n",
       "      <td>4.409662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>20241007</td>\n",
       "      <td>5858</td>\n",
       "      <td>0.082191</td>\n",
       "      <td>33.662548</td>\n",
       "      <td>0.016130</td>\n",
       "      <td>2.979546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   satdate      N   chlmean    pocmean    chlstd     pocstd\n",
       "0            0  20240426   7982  0.253977  76.602640  0.094386  16.689285\n",
       "1            1  20240429  11238  0.298769  81.692890  0.119237  21.280693\n",
       "2            2  20240502  10071  0.275251  77.122925  0.089853  16.160252\n",
       "3            3  20240504   6327  0.256287  72.357200  0.052235   9.771745\n",
       "4            4  20240508   6872  0.256133  70.044280  0.039482   7.693035\n",
       "5            5  20240513  11066  0.180423  61.662235  0.049762   8.858086\n",
       "6            6  20240522   5258  0.137189  50.675785  0.042655   7.300459\n",
       "7            7  20240524   9075  0.120170  47.654730  0.037093   6.868834\n",
       "8            8  20240601   7761  0.121506  42.229540  0.030649   6.427949\n",
       "9            9  20240612   5883  0.098091  39.942486  0.013602   2.999338\n",
       "10          10  20240615   5262  0.092927  39.938900  0.008528   3.634383\n",
       "11          11  20240616   8335  0.070831  44.480316  0.012155   3.065746\n",
       "12          12  20240619   6478  0.070648  42.497066  0.015544   4.380224\n",
       "13          13  20240621   6169  0.072065  34.594000  0.017418   5.288813\n",
       "14          14  20240622   7472  0.065053  37.512657  0.014449   3.870672\n",
       "15          15  20240625   7191  0.079851  37.485290  0.018296   3.316251\n",
       "16          16  20240627  11375  0.070088  37.601765  0.015072   3.185853\n",
       "17          17  20240630  11475  0.064914  35.510002  0.013186   3.101701\n",
       "18          18  20240703   6670  0.054381  40.299885  0.009013   3.431242\n",
       "19          19  20240705   7569  0.062448  33.348373  0.011872   2.696045\n",
       "20          20  20240706  12312  0.056049  33.211273  0.005210   1.811287\n",
       "21          21  20240708   6869  0.056661  30.350573  0.008205   2.643530\n",
       "22          22  20240711   8802  0.051347  34.617878  0.006385   3.245937\n",
       "23          23  20240717   5166  0.057710  37.925392  0.011516   3.940214\n",
       "24          24  20240729   8191  0.090956  42.434060  0.018370   4.666248\n",
       "25          25  20240808   7216  0.061222  32.812878  0.010121   3.693984\n",
       "26          26  20240809  10907  0.051816  38.108580  0.007059   3.081469\n",
       "27          27  20240815   6942  0.075410  41.521454  0.013896   4.149037\n",
       "28          28  20240826  10479  0.081204  34.766502  0.008404   3.532652\n",
       "29          29  20240828   6908  0.069370  35.554220  0.008809   3.033818\n",
       "30          30  20240901   6138  0.068067  39.661750  0.016715   5.353838\n",
       "31          31  20240906   8890  0.068289  36.222645  0.009656   3.645733\n",
       "32          32  20240911   5142  0.078660  36.283665  0.010872   5.125101\n",
       "33          33  20240912   6570  0.061557  42.900208  0.009902   3.174730\n",
       "34          34  20240920   7156  0.071183  34.976364  0.011800   4.599094\n",
       "35          35  20241002   8637  0.064548  36.672300  0.013340   4.409662\n",
       "36          36  20241007   5858  0.082191  33.662548  0.016130   2.979546"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "dat=pd.read_csv('CHL_TIMESERIES_PACE.csv')\n",
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "11b29bb3-9d29-45ab-94a5-1f67b5f228b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates1=dat.satdate.values\n",
    "# values=dat.chlmean.values\n",
    "# errors=dat.chlstd.values\n",
    "\n",
    "values=dat.pocmean.values\n",
    "errors=dat.pocstd.values\n",
    "\n",
    "dates=[];\n",
    "for IDX in range(0,len(dates1)):\n",
    "    date2 = datetime.strptime(str(dates1[IDX]), '%Y%m%d').strftime('%m/%d/%Y')\n",
    "    dates.append(date2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e91e712f-4376-4370-bb22-3a043913a9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7Cop8luflH9bIOetd00wBAAAAgUQj5SJuGz5XUmr04MItOlGKsmUPLtzieE4AAAAg0GikXMKNw+fW5uxRbv7hCtcbSbn5h7U2Z0/gQgEAAAAuQCPlAm4dPre7sOImqjK3AwAAAEIFjZTD3Dx8rmFcjK23AwAAAEIFjZTD3Dx8rlNqXSUnxMhTwXqPpOSEGHVKrRvIWAAAAIDjaKQc5ubhc5ERHo3PbCtJ5ZqpsuvjM9sqMqKiVgsAAAAITTRSDnP78LmM9GTNGHy2GsZH+yxPSojRjMFnKyM92ZFcAAAAgJOinA4Q7sqGz+XlHz7heVIeHWtanBw+l5GerG6n11e77KWSpFnDz9UFaQ04EgUAAICwxREphwXL8Lnj/36n1LqO5wEAAACcRCPlAgyfAwAAAIILQ/tcguFzAAAAQPDgiJSLMHwOAAAACA40UgAAAABgEY0UAAAAAFhEIwUAAAAAFtFIAQAAAIBFNFIAAAAAYBGNFAAAAABYRCMFAAAAABbRSAEAAACARTRSAAAAAGARjRQAAAAAWEQjBQAAAAAW0UjBESWlxvvvtTl7fK4DAAAAblepRuro0aPauXOnvvrqK+3Zs8fuTAhxizflque0ld7rw2au0/lTPtDiTbkOpgIAAAD853cjtX//fj333HPq0aOHEhIS1Lx5c7Vt21YNGjRQs2bN9Mc//lHr1q2ryqwIAYs35WrknPXaVVDkszwv/7BGzllPMwUAAICg4Fcj9fjjj6t58+Z64YUXdPHFF+vNN9/Uhg0b9NVXX+njjz/W+PHjVVxcrF69eikjI0Pbt2+v6twIQiWlRg8u3KITDeIrW/bgwi0M8wMAAIDrRflzo9WrV2v58uVq167dCdd36tRJN954o2bMmKGXX35ZK1euVFpamq1BEfzW5uxRbv7hCtcbSbn5h7U2Z4+6tKgXuGAAAACARX41Um+88YZfxWJiYjRq1KhTCoTQtbuw4iaqMrcDAAAAnGLbrH1bt27VaaedZlc5hKCGcTG23g4AAABwim2N1JEjR7Rjxw67yiEEdUqtq+SEGHkqWO+RlJwQo06pdQMZCwAAALCM35FCwERGeDQ+s60klWumyq6Pz2yryIiKWi0AAADAHWikEFAZ6cmaMfhsNYyP9lmelBCjGYPPVkZ6skPJAAAAAP/5NdkEYKeM9GR1O72+2mUvlSTNGn6uLkhrwJEoAAAABA2/G6k6derI46n4g25xcbEtgRAejm+aOqXWpYkCAABAUPG7kZo+fXoVxgAAAACA4OF3I9WlSxe1bNmyKrMAlVJSarz/Xpuzh2GCAAAAqHJ+TzbRoUMHtWnTRn/5y1/08ccfV2UmwG+LN+Wq57SV3uvDZq7T+VM+0OJNuQ6mAgAAQKjzu5H69ddfNXXqVP3666/q37+/EhMTddNNN2nBggU6fPhwlQVs3ry5PB5Pucttt90mSTLGKDs7WykpKapRo4Z69OihzZs3V1keuMfiTbkaOWe9dhUU+SzPyz+skXPW00wBAACgyvjdSMXExCgzM1MvvviicnNzNX/+fDVo0EBjx45VvXr1dMUVV+jll1/W7t27bQ24bt065ebmei/Lli2TJF199dWSpKlTp2ratGl6+umntW7dOiUlJalXr14qLCy0NQfcpaTU6MGFW2ROsK5s2YMLt/gM+/PXwSPFaj72XTUf+64OHmESFQAAAJRXqd+R8ng86tq1qx555BFt2bJFGzZs0IUXXqhZs2apSZMmeuaZZ2wL2KBBAyUlJXkv77zzjlq0aKHu3bvLGKPp06frvvvu04ABA5Senq7Zs2fr4MGDmjt3boU1i4qKVFBQ4HNBcFmbs0e5+RUfCTWScvMPa23OnsCFAgAAQNiw5Qd509LSdM899+jDDz/UTz/9pN69e9tRtpwjR45ozpw5uvHGG+XxeJSTk6O8vDyfvxcdHa3u3btr9erVFdaZPHmyEhISvJcmTZpUSV5Und2F/g0n9fd2AAAAgBV+zdq3YMECv4p5PB5lZmaqXr16pxSqIm+99Zb27dunYcOGSZLy8vIkSYmJiT63S0xM1I4dOyqsM27cON19993e6wUFBTRTQaZhXIyttwMAAACs8KuRuvLKK/0q5vF4VFJScip5Tuqll17SpZdeqpSUlHJ/93jGmJP+eHB0dLSio6OrJCMCo1NqXSUnxCgv//AJz5PySEpKiFGn1LqBjgYAAIAw4NfQvtLSUr8uVdlE7dixQ++9955uvvlm77KkpCRJ/zsyVWb37t3ljlIhtERGeDQ+s62kY03T8cquj89sy+9JAQAAoErYco5UIMycOVMNGzZU3759vctSU1OVlJTknclPOnYe1cqVK9W1a1cnYiKAMtKTNWPw2WoY73t0MSkhRjMGn62M9GSHkgEAACDUVaqRWrlypTIzM3X66acrLS1N/fr103/+8x+7s3mVlpZq5syZGjp0qKKi/jca0ePxKCsrS5MmTdL8+fO1adMmDRs2TLGxsRo0aFCV5YF7ZKQn6727u3uvzxp+rlb95WKaKAAAAFQpy43UnDlz1LNnT8XGxuqOO+7Q7bffrho1auiSSy456ZTjp+K9997T999/rxtvvLHcujFjxigrK0ujRo1Sx44d9eOPP2rp0qWKi4urkixwn+OH73VKrctwPgAAAFQ5vyabON7EiRM1depU3XXXXd5ld955p6ZNm6YJEyZUyZGg3r17y5gT/7Cqx+NRdna2srOzbf+7AAAAAHAilo9Iffvtt8rMzCy3vF+/fsrJybElFAAAAAC4meVGqkmTJnr//ffLLX///ff5LSYAAAAAYcHy0L577rlHd9xxhzZs2KCuXbvK4/Fo1apVmjVrlp544omqyAgAAAAArmK5kRo5cqSSkpL02GOP6Z///KckqU2bNnr99dd1xRVX2B4QAAAAANzGciMlSf3791f//v3tzgIAAAAAQaFSjVSZ/fv3q7S01GdZfHz8KQUCAAAAALezPNlETk6O+vbtq5o1ayohIUF16tRRnTp1VLt2bdWpU6cqMgIAAACAq1g+InX99ddLkl5++WUlJibK4+HHTwEAAACEF8uN1H//+1999tlnatWqVVXkAQAAAADXszy079xzz9XOnTurIgsAAAAABAXLR6RefPFFjRgxQj/++KPS09NVrVo1n/Xt27e3LRwAAAAAuJHlRurnn3/WN998o+HDh3uXeTweGWPk8XhUUlJia0AAAAAAcBvLjdSNN96oDh066LXXXmOyCQAAAABhyXIjtWPHDi1YsECnn356VeQBAAAAANezPNnExRdfrC+++KIqsgAAAABAULB8RCozM1N33XWXNm7cqHbt2pWbbKJfv362hQMAAAAAN7LcSI0YMUKS9NBDD5Vbx2QTAAAAAMKB5UaqtLS0KnIAAAAAQNCwfI5URX7++We99tprdpUDAAAAANeyfETqREP6JOmbb77Rm2++qeuuu+6UQwEAAACAm1lupObPn+9zvaSkRDt37lRBQYEmTJhgWzAAAAAAcCvLjdTnn39ebllxcbGysrK0ZcsWW0IBAAAAgJtZbqROWCQqSllZWWrfvr0d5QAAAADA1WybbGLHjh1KTU21qxwAAAAAuJblI1JPPvlkuWV5eXmaOXOmMjMzfdbfcccdp5YOAAAAAFzIciP1+OOPn3B5TEyMli1bpmXLlkk69uO8NFIAAAAAQpHlRionJ6cqcgAAAABA0LDtHCkAAAAACBd+NVIjRozQzp07/Sr4+uuv69VXXz2lUAAAAADgZn4N7WvQoIHS09PVtWtX9evXTx07dlRKSopiYmK0d+9ebdmyRatWrdK8efPUqFEjPf/881WdGwAAAAAc41cjNWHCBP3pT3/SSy+9pGeffVabNm3yWR8XF6eePXvqxRdfVO/evaskKAAAAAC4hd+TTTRs2FDjxo3TuHHjtG/fPu3YsUOHDh1S/fr11aJFC3k8nqrMCQAAAACuYXnWPkmqXbu2ateubXMUAAAAAAgOzNoHAAAAABZV6ogUcKpiq0fpu0f6Oh3jhEpKjfffa3P26IK0BoqMYOgqAAAA/odGCkHPzqZs8aZcjV+w2Xt92Mx1Sk6I0fjMtspIT7blbwAAACD4MbQP+P8Wb8rVyDnrtaugyGd5Xv5hjZyzXos35TqUDAAAAG7jdyN16NAhLViwQIWFheXWFRQUaMGCBSoqKjrBPQH3Kyk1enDhFpkTrCtb9uDCLT7D/gAAABC+/G6knn/+eT3xxBOKi4srty4+Pl5PPvmkXnzxRVvDAYGyNmePcvMPV7jeSMrNP6y1OXsCFwoAAACu5Xcj9eqrryorK6vC9VlZWZo9e7YdmYCA211YcRNVmdsBAAAgtPndSG3fvl1nnnlmhevbt2+v7du32xIKCLSGcTG23g4AAAChze9Gqri4WD///HOF63/++WcVFxfbEgoItE6pdZWcEKOKJjn3SEpOiFGn1LqBjAUAAACX8ruROuOMM/Tee+9VuH7ZsmU644wzbAkFBFpkhEfjM9tKUrlmquz6+My2/J4UAAAAJFlopG688UZNmDBB77zzTrl1Cxcu1MMPP6wbb7zR1nBwj7Lfavrukb6KrR6aPz+WkZ6sGYPPVsP4aJ/lSQkxmjH4bH5HCgAAAF5+fyK+5ZZb9OGHH6pfv35q3bq1WrVqJY/Ho61bt2rbtm0aOHCgbrnllqrMClS5jPRkdTu9vtplL5UkzRp+ri5Ia8CRKAAAAPiw9IO8c+bM0bx589SyZUtt27ZNX375pVq1aqXXXntNr732WpUE/PHHHzV48GDVq1dPsbGxOuuss/TZZ5951xtjlJ2drZSUFNWoUUM9evTQ5s2bqyQLwsPxTVOn1Lo0UQAAACjH8hitgQMHauDAgVWRpZy9e/eqW7duuuiii7Ro0SI1bNhQ33zzjWrXru29zdSpUzVt2jTNmjVLLVu21MMPP6xevXrpq6++OuFvXoWTsuF4AAAAAOxluZH68ccf9X//93/atm2bPB6PWrZsqQEDBqhRo0a2h5syZYqaNGmimTNnepc1b97c+29jjKZPn6777rtPAwYMkCTNnj1biYmJmjt3rm699dYT1i0qKlJRUZH3ekFBge3ZAQAAAIQuS0P7/v73v6tFixbKysrSq6++qldeeUV33nmnWrRoob///e+2h1uwYIE6duyoq6++Wg0bNlSHDh30wgsveNfn5OQoLy9PvXv39i6Ljo5W9+7dtXr16grrTp48WQkJCd5LkyZNbM8OAAAAIHT53Ui9++67uuOOO3T77bfrxx9/1N69e7Vv3z79+OOPGjVqlO688079+9//tjXct99+qxkzZigtLU1LlizRiBEjdMcdd+gf//iHJCkvL0+SlJiY6HO/xMRE77oTGTdunPLz872XnTt32pq7ssJhZjwAAAAgFPj9aX3q1KkaO3asHn74YZ/lycnJmjZtmmJjYzVlyhRddtlltoUrLS1Vx44dNWnSJElShw4dtHnzZs2YMUNDhgzx3s7j8Z0MwBhTbtnxoqOjFR0dXeF6AAAAADgZv49Iff7557rhhhsqXH/DDTdo/fr1toQqk5ycrLZt2/osa9Omjb7//ntJUlJSkiSVO/q0e/fuckepAAAAAMAufjdSpaWlqlatWoXrq1WrJmOMLaHKdOvWTV999ZXPsm3btqlZs2aSpNTUVCUlJWnZsmXe9UeOHNHKlSvVtWtXW7MAAAAAQBm/G6kzzjhDb7/9doXr33rrLZ1xxhm2hCpz1113ac2aNZo0aZK+/vprzZ07V88//7xuu+02SceG9GVlZWnSpEmaP3++Nm3apGHDhik2NlaDBg2yNQsAAAAAlPH7HKlRo0Zp5MiRio6O1i233KKoqGN3LS4u1nPPPaf777/f9pn7zj33XM2fP1/jxo3TQw89pNTUVE2fPl3XX3+99zZjxozRoUOHNGrUKO3du1edO3fW0qVLw/43pAAAAABUHY+xMB5v9OjRmjZtmuLi4tSiRQtJ0jfffKP9+/frjjvu0OOPP15lQatSQUGBEhISlJ+fr/j4eKfjwGEHjxSr7V+XSJK2PNSHGRQBAADCiL+9gaVPiH/729901VVX6bXXXtP27dslSRdeeKGuvfZanXfeeaeWGAAAAACChOWv2s877zyaJgAAAABhze/JJg4ePKjbbrtNjRo1UsOGDTVo0CD98ssvVZkNAAAAAFzJ70Zq/PjxmjVrlvr27atrr71Wy5Yt08iRI6syGwAAAAC4kt9D+95880299NJLuvbaayVJgwcPVrdu3VRSUqLIyMgqCwgEOyavAAAACD1+H5HauXOnLrjgAu/1Tp06KSoqSj/99FOVBAMAAAAAt/K7kSopKVH16tV9lkVFRam4uNj2UAAAAADgZn6PMTLGaNiwYYqOjvYuO3z4sEaMGKGaNWt6l7355pv2JgQAAAAAl/G7kRo6dGi5ZYMHD7Y1DAAAAAAEA78bqZkzZ1ZlDgAAAAAIGn6fIwUAAAAAOIZGCgAAAAAsopECAAAAAItopAAAAADAIhopAAAAALCIRgoAAAAALKKRAgAAAACLaKQAAAAAwCIaKQAAAACwiEYKAAAAACyikQIAAAAAi2ikAAAAAMAiGikAAAAAsIhGCggiB48Uq/nYd9V87Ls6eKTY6TgAAABhi0YKAAAAACyikQIAAAAAi2ikAAAAAMAiGikAAAAAsIhGCgAAAAAsopECAAAAAItopAAAAADAIhopAAAAALAoyukAgNvEVo/Sd4/0dToGAAAAXIwjUgAAAABgEY0UAAAAAFhEIwUAAAAAFtFIAQAAAIBFNFIAAAAAYBGNFAAAAABYRCMFVLGSUuP999qcPT7XAQAAEJxopIAqtHhTrnpOW+m9PmzmOp0/5QMt3pTrYCoAAACcKhopoIos3pSrkXPWa1dBkc/yvPzDGjlnPc0UAABAEKORAqpASanRgwu36ESD+MqWPbhwC8P8AAAAghSNFFAF1ubsUW7+4QrXG0m5+Ye1NmdP4EIBAADANq5upLKzs+XxeHwuSUlJ3vXGGGVnZyslJUU1atRQjx49tHnzZgcTA8fsLqy4iarM7QAAAOAurm6kJOmMM85Qbm6u97Jx40bvuqlTp2ratGl6+umntW7dOiUlJalXr14qLCx0MDEgNYyLsfV2AAAAcBfXN1JRUVFKSkryXho0aCDp2NGo6dOn67777tOAAQOUnp6u2bNn6+DBg5o7d67DqRHuOqXWVXJCjDwVrPdISk6IUafUuoGMBQAAAJu4vpHavn27UlJSlJqaqmuvvVbffvutJCknJ0d5eXnq3bu397bR0dHq3r27Vq9efdKaRUVFKigo8LkAdoqM8Gh8ZltJKtdMlV0fn9lWkREVtVoAAABwM1c3Up07d9Y//vEPLVmyRC+88ILy8vLUtWtX/frrr8rLy5MkJSYm+twnMTHRu64ikydPVkJCgvfSpEmTKnsMCF8Z6cmaMfhsNYyP9lmelBCjGYPPVkZ6skPJAAAAcKqinA5wMpdeeqn33+3atVOXLl3UokULzZ49W+edd54kyePx/UbfGFNu2W+NGzdOd999t/d6QUEBzRSqREZ6srqdXl/tspdKkmYNP1cXpDXgSBQAAECQc/URqd+qWbOm2rVrp+3bt3tn7/vt0afdu3eXO0r1W9HR0YqPj/e5AFXl+KapU2pdmigAAIAQEFSNVFFRkbZu3ark5GSlpqYqKSlJy5Yt864/cuSIVq5cqa5duzqYEgAAAECoc/XQvtGjRyszM1NNmzbV7t279fDDD6ugoEBDhw6Vx+NRVlaWJk2apLS0NKWlpWnSpEmKjY3VoEGDnI4OAAAAIIS5upH64YcfdN111+mXX35RgwYNdN5552nNmjVq1qyZJGnMmDE6dOiQRo0apb1796pz585aunSp4uLiHE4OAAAAIJS5upGaN2/eSdd7PB5lZ2crOzs7MIEAAAAAQEF2jhQAAAAAuAGNFBBESkqN999rc/b4XK+Mg0eK1Xzsu2o+9l0dPFJ8qvEAAADCBo0UECQWb8pVz2krvdeHzVyn86d8oMWbch1MBQAAEJ5opIAgsHhTrkbOWa9dBUU+y/PyD2vknPU0UwAAAAFGIwW4XEmp0YMLt+hEg/jKlj24cMspD/MDAACA/2ikAJdbm7NHufmHK1xvJOXmH9banD2BCwUAABDmaKQAl9tdWHETVZnbAQAA4NTRSAEu1zAuxtbbhSNmJwQAAHajkQJcrlNqXSUnxMhTwXqPpOSEGHVKrRvIWAAAAGGNRgpwucgIj8ZntpWkcs1U2fXxmW0VGVFRqwUA4Ymj0QCqEo0UEAQy0pM1Y/DZahgf7bM8KSFGMwafrYz0ZIeShSc+nAEAgCinAwDwT0Z6srqdXl/tspdKkmYNP1cXpDXgSBQAAIADOCIFBJHjm6ZOqXVpogAAABxCIwUAAAAAFtFIAQAAAIBFNFIAAACAyzCxkfvRSAEAAACARTRSAAAAQY6jF0Dg0UgBAAAAgEU0UgBCXkmp8f57bc4en+sAAACVQSMFIKQt3pSrntNWeq8Pm7lO50/5QIs35TqYCgAABDsaKQAha/GmXI2cs167Cop8luflH9bIOetppgAgyHAuGNyERgpASCopNXpw4RadaBBf2bIHF25hmB8AAKgUGikAIWltzh7l5h+ucL2RlJt/WGtz9gQuFAAACBk0UgBC0u7CipuoytwOAADgeDRSAEJSw7gYW28HAABwPBopACGpU2pdJSfEyFPBeo+k5IQYdUqtG8hYAAAgRNBIAQhJkREejc9sK0nlmqmy6+Mz2yoyoqJWCwAAoGI0UgBCVkZ6smYMPlsN46N9liclxGjG4LOVkZ7sUDK4DVMqA3Abfkze/aKcDgAAJ3LwSLHa/nWJJGnLQ30UW71yb1cZ6cnqdnp9tcteKkmaNfxcXZDWgCNRAADXWrwpV+MXbPZeHzZznZITYjQ+sy1fAroIR6SAKhZbPUrfPdJX3z3St9LNAE7N8U1Tp9S6p9xE8S0hEBzYVxGM+DH54EEjBYQxPmRYt3hTrnpOW+m9PmzmOp0/5QP+YwNchn0VwYgfkw8uNFJAmLL7Q0Y4nGPCt4RAcGBfRbDix+SDC40UEIb4kGEd3xICwYF9FcGMH5MPLjRSQJjhQ0bl8C0hEBzYVxHM+DH54EIjBYQZPmRUDt8SAsEhXPdVznkNDfyYfHChkQLCTLh+yDhVfEsIBIdw3FeZWCN08GPywYVGCggz4fghww58SwgEh3DbVznnNfTwY/LBg0YKCDPh9iHDLnxLCFQ9O2b/DKd9lXNeQ1dGerLeu7u79/qs4edq1V8upolyGRopIMyE04cMu/EtIRAcwmVf5ZzX0Gb3j8nDflFOBwAQeGUfMsYv2OwzHCQpIUbjM9uGzIeMqpCRnqxup9dXu+ylko59S3hBWgP+gwNcJhz2Vc55BZxFIwWEqXD4kFEmtnqUvnukr2313Pwt4cEjxWr71yWSpC0P9VFsdd7mEb7cvK/agXNe3YX33/DD0D4gjIX6hwzAX0wdjUCz43wwznkFnEUjBQAIa0wdjWDFOa9wih1fBIQCGikgiJQNUfvukb6uGzJg9zf6HCFwBzv/s3Tjf7xMHY1gFy4TawBuFFSN1OTJk+XxeJSVleVdZoxRdna2UlJSVKNGDfXo0UObN292LiQQhuz+Rp8jBAgEpo5GqGCqbMAZQdNIrVu3Ts8//7zat2/vs3zq1KmaNm2ann76aa1bt05JSUnq1auXCgsLHUoKhBe7v9HnCAEChamjEUrC5ZxXRivATYKikdq/f7+uv/56vfDCC6pTp453uTFG06dP13333acBAwYoPT1ds2fP1sGDBzV37lwHEwPhwe5v9DlCgEBi6mgguDBaAW4TFI3Ubbfdpr59+6pnz54+y3NycpSXl6fevXt7l0VHR6t79+5avXp1hfWKiopUUFDgcwFgnd3f6HOEAIHE1NFA8GC0AtzI9Y3UvHnztH79ek2ePLncury8PElSYmKiz/LExETvuhOZPHmyEhISvJcmTZrYGxoIE3Z/o88RAgQSU0cDwYHRCnArVzdSO3fu1J133qk5c+YoJqbibwQ9Ht//Bo0x5ZYdb9y4ccrPz/dedu7caVtmIJzY/Y1+uB4hcONsduGAqaOB4FCVoxXc/P7r5pl6cYyrG6nPPvtMu3fv1jnnnKOoqChFRUVp5cqVevLJJxUVFeU9EvXbo0+7d+8ud5TqeNHR0YqPj/e5ALDO7m/0OUKAQGPqaMD9GK1gDzc3jcHK1Y3UJZdcoo0bN2rDhg3eS8eOHXX99ddrw4YNOu2005SUlKRly5Z573PkyBGtXLlSXbt2dTA5EB7s/kafIwRwAlNHA+4WrqMV4H6ubqTi4uKUnp7uc6lZs6bq1aun9PR0729KTZo0SfPnz9emTZs0bNgwxcbGatCgQU7HB8KC3d/oc4QATgiXqaOBYMRoBbhV0A+4HDNmjA4dOqRRo0Zp79696ty5s5YuXaq4uDinowFhIyM9Wd1Or6922UslHftG/4K0BpX+MGp3PQBwm9/+HhLvcRUrG60wcs56eSSfSScYrQAnufqI1ImsWLFC06dP9173eDzKzs5Wbm6uDh8+rJUrVyo9Pd25gECYsvsbfY4QAOEnXH5sld9Dso7RCnCjoGukAABA6AmX5oLfQ6o8zmeE29BIAQACwu6jDcxAFTrCpbkI199DsnNfZbQC3IRGCgBCiN3Nil31wuVoA6wLp+aiKn8PyU58SQH4h0YKACxy648k2t2s2FUvXI42oHKCpbmwA7+HBIQWGikACAF2Nyt21Qunow2onHBqLvg9JCC00EgBQJCzu1mxs144HW0IV6c6DKwqmwu3HT3m95CA0EIjBQBBzu5mxc564XS0AZUTTs1F2e8hSSr3ePk9JASTcPmpgt9DIwUAQc7uZsXO2zGUCb8n3JoLfg8JwY7Jg/6HRgoAgpzdzYqdtwunow2ovHBrLqri95DcNowRoYnJg3zRSAFAkLO7WbGzXrgdbUDlhduPrfJ7SO5g5xC1UB/uxuRB5dFIAbAF34Y6x+5mxe56wXC0ge3XHWguEEh2DlELh+FuTB5UHo0UAIQAu5uVqqgXTkcbALibnUPUwmW4G5MHlUcjBQAhwu5mxe56HG1AMDvVad7hHnYOUQun4W5VOXlQsO5fNFIAXImhVpVjd7Pi5uYn1M9HAEKF2/ZVO4eohdNwNyYPKo9GCgAQdMLhfAQgFLhxX7VziFo4DXdj8qDyaKQAAEElGM5HCNZhKoCd3Lqv2jlELdx+Ky8YJg8KJBopIIwxfA7BJpzORwCCmZv3VTuHqAXTcDe7hlgyedD/0EgBAIJGOJ2PADjFjg/cbt5X7RyiFizD3eweYunm82cDiUYKABzmthOx3SyczkcIFmy/ocWuD9xu31ftHKLm9uFubh1iGQpopADAQW48EdvNwu18BLdj+w0tdn7gDoZ91c4ham4d7ubmIZahgEYKABzCt4TWBdP5CKGO7Te02P2BO1j2VTuHqLlxuJubh1iGAhopAHAA3xJWTrCcj2A3t80CyPZ76tw22Y/dH7jDdV91G7cPsQx2NFIA4AC+Jaw8t5+PEA7YfkNPVXzgZl91XjAMsQxmzn8FAgBhiG8JT01GerK6nV5f7bKXSjp2PsIFaQ34djtA2H5DT1V94GZfdVbZEMu8/MMnPILs0bHG1ukhlr+dtCZYthGOSAGAA8LxW0K7hzK58XyEcBGO22+oq8pzmthXnRMMQyyDedIaGikAcECwnIjttvM4gkWoTwkeLNsv/BcMH7gl3pMqw81DLIN90hoaKQBwQLB8aIF1VfHtqtsas6rafvmQ7Cw3f+DGqXHj9OyhMGkNjRQAOIQPLaGnKr5ddeuwF7bf0OTGD9ywh9uGWIbCpDV83QMADrL7ROyyb/QReL/37apHx75d7dU2ye/Xt6wx+23NssbM6YYlnCYSCNaT4SvDbR+4EZpCYdIajkgBgMP40BIa7P52NViGvYTD9uvWo4JAMAuFSWtopAAAsIHd366GwrCXUBDsJ8MDbhUKk9bQSAEAYAO7v10NhWEvwS5YjgoCwSgUJl2ikQIAwAZ2f7taVcNeDh4pVvOx76r52Hd18EixpfuGG44KAlUr2CetoZECAMAGdn+7GgrDXoIdRwWBqhfMM0XSSAEAYBM7v10NhWEvwS4UTobHyfHbZe4QrJPWsMUAAIKSW6d6t3NK8LLGbPyCzT6THSQlxGh8Ztug+MY2mJUdFczLP3zC86Q8OvZacFTw5Ny6rwKnikYKAACb2fntajj9VpPblB0VHDlnvTySTzPFUUGEM5rjYxjaBwCAywXrsJdQEOwnwwOoOhyRAgAAOAmOCgI4EY5IAQAA/A6OCgL4LRopAAAAALCIoX0AALicm0/sdnM2AKhKNFIAAISRktL/zT23NmcP5/qgHJrjyuF5Cz8M7QMAIEws3pSrntNWeq8Pm7lO50/5QIs35TqYCgCCE40UAABhYPGmXI2cs97nh30lKS//sEbOWU8zBQAWubqRmjFjhtq3b6/4+HjFx8erS5cuWrRokXe9MUbZ2dlKSUlRjRo11KNHD23evNnBxAAA/G+Iz3eP9FVsdedH0ZeUGj24cIvPD8qWKVv24MItPsP+ACBQ3Pae6S9XN1KNGzfWI488ok8//VSffvqpLr74Yl1xxRXeZmnq1KmaNm2ann76aa1bt05JSUnq1auXCgsLHU4OAIB7rM3Zo9z8wxWuN5Jy8w9rbc6ewIUCgCDn6pYvMzPT5/rEiRM1Y8YMrVmzRm3bttX06dN13333acCAAZKk2bNnKzExUXPnztWtt97qRGQAAFxnd2HFTVRlbgcg+DAZhv1cfUTqeCUlJZo3b54OHDigLl26KCcnR3l5eerdu7f3NtHR0erevbtWr1590lpFRUUqKCjwuQAAEKoaxsXYejsAQBA0Uhs3blStWrUUHR2tESNGaP78+Wrbtq3y8vIkSYmJiT63T0xM9K6ryOTJk5WQkOC9NGnSpMryAwDgtE6pdZWcEKOKJjn3SEpOiFGn1LqBjAUAQc31jVSrVq20YcMGrVmzRiNHjtTQoUO1ZcsW73qPx/e/BWNMuWW/NW7cOOXn53svO3furJLsAAC4QWSER+Mz20pSuWaq7Pr4zLb8nhQAWOD6Rqp69eo6/fTT1bFjR02ePFlnnnmmnnjiCSUlJUlSuaNPu3fvLneU6reio6O9MwGWXQAACGUZ6cmaMfhsNYyP9lmelBCjGYPPVkZ6skPJACA4ub6R+i1jjIqKipSamqqkpCQtW7bMu+7IkSNauXKlunbt6mBCAADcKSM9We/d3d17fdbwc7XqLxfTRAFAJbh61r57771Xl156qZo0aaLCwkLNmzdPK1as0OLFi+XxeJSVlaVJkyYpLS1NaWlpmjRpkmJjYzVo0CCnowOA35hJCYF0/PC9Tql1Gc4HAJXk6kZq165duuGGG5Sbm6uEhAS1b99eixcvVq9evSRJY8aM0aFDhzRq1Cjt3btXnTt31tKlSxUXF+dwcgAAEEr4wgPAb3mMMWH/M+YFBQVKSEhQfn4+50sBAELawSPFavvXJZKkLQ/1UWx1V3+nCgAB529vEHTnSAEAAACA02ikAAAAAMAiGikAAAAAsIhGCgAAAAAsopECAAAAAIuYqgcAgDDCNN4AYA+OSAEAAACARTRSAAAAAGARjRQAAAAAWEQjBQAAAAAW0UgBAAAAgEU0UgAAAABgEY0UAAAAAFhEIwUAAAAAFtFIAQAAAIBFNFIAAAAAYBGNFAAAAABYRCMFAAAAABbRSAEAAACARTRSAAAAAGARjRQAAAAAWEQjBQAAAAAW0UgBAAAAgEU0UgAAAABgEY0UAAAAAFgU5XQANzDGSJIKCgocTgIAAADASWU9QVmPUBEaKUmFhYWSpCZNmjicBAAAAIAbFBYWKiEhocL1HvN7rVYYKC0t1U8//aS4uDh5PB5HsxQUFKhJkybauXOn4uPjXVWPbO6oRzbna7m9Htmcr+X2emRzvpbb65HN+Vpur+fmbKfKGKPCwkKlpKQoIqLiM6E4IiUpIiJCjRs3djqGj/j4eFs3Ijvrkc0d9cjmfC231yOb87XcXo9sztdyez2yOV/L7fXcnO1UnOxIVBkmmwAAAAAAi2ikAAAAAMAiGimXiY6O1vjx4xUdHe26emRzRz2yOV/L7fXI5nwtt9cjm/O13F6PbM7Xcns9N2cLFCabAAAAAACLOCIFAAAAABbRSAEAAACARTRSAAAAAGARjRQAAAAAWEQjBQAAAAAW0UgBAAAAgEU0Ui41a9Ys5efnu66W3fXI5nwtt9cjm/O13F6PbM7Xcns9sjlfy+31yIZKMXClatWqmS1btriult31yOZ8LbfXI5vztdxej2zO13J7PbI5X8vt9chWeTNnzjT79u1zbb2qxA/yOqxu3bonXL5v3z7Fx8crIuLYQcM9e/YEtBbZyBboemQLvWx21yNb6GWzux7ZQi+b3fXIVvl6Falevbq++OILtWnT5pTqVFW9qhTldIBwd/ToUXXv3l1XX321d5kxRjfffLPGjBmjRo0aOVKLbGQLdD2yhV42u+uRLfSy2V2PbKGXze56ZKt8vYoas+LiYnXp0sW2Rq+y9RxRpce78Lu2b99uzj33XDNkyBBTWFjoXR4VFWU2b97sWC2ykS3Q9cgWetnsrke20Mtmdz2yhV42u+uRrfL1atWqZfr27WtmzZrlvcycOdNERkaaiRMnepc5Vc8JTDbhsNNPP12rV69WUlKSzjrrLH300UeuqEU2sgW6Htmcr+X2emRzvpbb65HN+Vpur0e2yvv888+1e/duffDBB/rDH/6goUOHatiwYfJ4PLryyis1dOhQDR061LF6jnC6k8P/vP/++6Zp06Zm3Lhxplq1apX6tqAqapGNbIGuRzbna7m9Htmcr+X2emRzvpbb65HNuqNHj5oxY8aYFi1amFWrVhljKn+EqyrqBRpHpFzk4osv1vr16/Xll1+qZs2aioyMdEUtspEt0PXI5nwtt9cjm/O13F6PbM7Xcns9slkXFRWlKVOm6Pnnn9egQYN07733yuPxVDqX3fUCjVn7AAAAAFjy66+/6o9//KOWL1+uNWvWqFWrVq6qFwg0UgAAAABgEUP7XODvf/+7evbsqYEDB+qDDz7wWffLL7/otNNOc6QW2cgW6HpkC71sdtcjW+hls7se2UIvm931yFb5evgNZ0/RwhNPPGFiY2PNbbfdZgYPHmyio6PNpEmTvOvz8vJMREREwGuRjWyBrke20Mtmdz2yhV42u+uRLfSy2V2PbJWvZ4wxzzzzjLnkkkvM1Vdfbd5//32fdT///LNJTU11tF6g0Ug5rG3btubVV1/1Xl+9erVp2LCheeCBB4wx1jZyO2uRjWyBrke20Mtmdz2yhV42u+uRLfSy2V2PbJWvFwyNXqDRSDmsRo0aJicnx2fZpk2bTGJiohk7dqyljcjOWmQjW6DrkS30stldj2yhl83uemQLvWx21yNb5eu5vdFzAo2Uw5o0aWI+/PDDcss3b95sEhMTzQ033OD3RmRnLbKRLdD1yBZ62eyuR7bQy2Z3PbKFXja765Gt8vXc3ug5gUbKYdddd5258847T7hu06ZNpkGDBn5vRHbWIhvZAl2PbKGXze56ZAu9bHbXI1voZbO7HtkqX8/tjZ4TaKQc9sUXX5iXX365wvWbNm0y2dnZAa9FNrIFuh7ZQi+b3fXIFnrZ7K5HttDLZnc9slW+ntsbPSfwO1IAAAAATuq///2vPvvsMw0fPvyE6zdv3qx//etfGj9+vCP1nEAj5RLbt2/X6tWrlZeXJ4/Ho8TERHXt2lVpaWmO1iIb2QJdj2yhl83uemQLvWx21yNb6GWzux7ZKl8Px3H2gBj27dtn+vXrZzwej6ldu7Zp2bKlSUtLM7Vr1zYRERHmiiuuMPn5+QGvRTay8VjJ5rZ6ZAu9bOH0WMnmjnpkq3y9Mtu2bTOzZs0yjzzyiJkyZYqZNWuW2bZtm+U6VVUvkGikHHbDDTeYdu3amTVr1pRbt2bNGtO+fXszZMiQgNciG9kCXY9soZfN7npkC71sdtcjW+hls7se2SpfL1gavUCikXJYQkLCCTfwMh9//LFJSEgIeC2ykS3Q9cgWetnsrke20Mtmdz2yhV42u+uRrfL13N7oOSHK6aGFkDweT6XWVXUtu+uRrXL13JzN7npkq1w9N2ezux7ZKlfPzdnsrke2ytVzcza765GtcvUWLFigJUuWqHPnzuXWde7cWc8995wyMjIcq+cIpzu5cDd48GDTvn17s27dunLr1q1bZ8466yxzww03BLwW2cgW6HpkC71sdtcjW+hls7se2UIvm931yFb5egkJCeaTTz6pcP2aNWssHzGzs54TaKQctnfvXpORkWE8Ho+pU6eOadWqlWndurWpU6eOiYiIMJdeeqnZu3dvwGuRjWw8VrK5rR7ZQi9bOD1WsrmjHtkqX8/tjZ4TmP7cJb788kt9/PHHysvLkyQlJSWpS5cuat26taO1yEa2QNcjW+hls7se2UIvm931yBZ62eyuRzbr9fbt26frrrtOS5YsUe3atdWwYUN5PB7t2rVL+fn56tOnj+bOnavatWs7Us8JNFIAAAAA/OLWRs8JEU4HwDGlpaUVLv/+++8dq0U2sgW6HtlCL5vd9cgWetnsrke20Mtmdz2yVb5e69atNXz4cI0bN07jxo3T8OHDT6npsbteQDk7shD5+fnm6quvNjExMaZhw4bmr3/9qykuLvauz8vLMxEREQGvRTayBboe2UIvm931yBZ62eyuR7bQy2Z3PbJVvl6ZkpKSCpfv2LHD8XqBRCPlsDvuuMO0bNnSvPHGG+aFF14wzZo1M3379jVFRUXGmGMbucfjCXgtspGNx0o2t9UjW+hlC6fHSjZ31CNb5esFS6MXSDRSDmvatKlZvny59/ovv/xiOnfubHr37m0OHz5saSOysxbZyBboemQLvWx21yNb6GWzux7ZQi+b3fXIVvl6bm/0nEAj5bDY2Fjz7bff+iwrKCgwXbp0MRdffLH59ttv/d7I7axFNrIFuh7ZQi+b3fXIFnrZ7K5HttDLZnc9slW+ntsbPScw2YTDmjRpoq1bt/osi4uL09KlS3Xo0CH179/fkVpkI1ug65Et9LLZXY9soZfN7npkC71sdtcjW+Xr/fLLL2rWrJn3er169bRs2TIVFhbqsssu08GDBx2t5wQaKYf16tVLM2fOLLe8Vq1aWrJkiWJiYhypRTayBboe2UIvm931yBZ62eyuR7bQy2Z3PbJVvp7bGz1HOH1ILNzt2bPHbNq0qcL1hYWFZsWKFQGvRTayBboe2UIvm931yBZ62eyuR7bQy2Z3PbJVvt7tt99urrrqqhOuKygoMJ07d7Y0FM/uek6gkXLYddddZ15//XWTn5/vqlp21yOb87XcXo9sztdyez2yOV/L7fXI5nwtt9cjW+W5vdFzAkP7HNaqVStNmTJFDRs2VO/evfXMM89o586djtciG9kCXY9soZfN7npkC71sdtcjW+hls7se2Spf77bbbtPmzZtVUFBwwvW1atVS9+7dHavnCKc7ORyzc+dO88wzz5jevXub6Oho06FDBzN+/Hizfv16R2uRjWyBrke20Mtmdz2yhV42u+uRLfSy2V2PbNbrZWdnm7PPPttER0ebXr16maefftp8//33lcpUFfWcQCPlQgUFBeb11183gwYNMnXq1DFNmzY1t91220kPfwaiFtnIFuh6ZAu9bHbXI1voZbO7HtlCL5vd9chmrZ5bGz0neIwxxumjYqhYSUmJVqxYoQULFqhdu3a6+eabXVGLbGQLdD2yOV/L7fXI5nwtt9cjm/O13F6PbNYUFhZq0aJFevvtt7Vo0SLFxcUpMzNTI0eO1BlnnOF4vapGI+VCRUVF+uGHH9S4cWNFR0dXqkZJSYl++eUXRUZGqn79+jYntM+KFSvUuXNn1ahRw5Z6u3btkjFGSUlJttRzK7uft3ARTvtWqCt7HTwej+rVq6fIyEinI1U5O7bf44X6+2VV7qun+twFw/uI3dub5N5trioeq9sE4j3TjY1elXPuYBiMMWbmzJnm448/NsYYc+jQIXPTTTeZyMhIExERYaKiosytt95qDh8+7He9d955x1xwwQUmOjraREREmIiICJOQkGAGDx5sduzYYTnfhg0bzIQJE8wzzzxjfv75Z591+fn5Zvjw4ZZrHq9atWpmy5Ytlu/366+/mgEDBpimTZuaUaNGmeLiYnPTTTcZj8djIiIiTJcuXcxPP/3kd70XXnjBDBkyxLz88svGGGPmzZtnWrdubVJTU81f//pXy/nc+rz9ni1btpjU1FRL97HrsX711VemtLTUe/0///mPueKKK0zbtm3NJZdcYt566y1Ludy+b52M1dfBzu3N7tchPT3dPPTQQ7aNe3/zzTdN165dTfXq1b2vQ/Xq1U3Xrl3N/PnzLdeza9938/Zr9/vl77G6/dq9jdi5r9r93NmZzc5tzu73Szdvc3Y/VjuzGWP/5we73zPhi0bKYaeffrpZt26dMcaY0aNHm+bNm5s333zTbN261bz11lumZcuW5s9//rNftf7xj3+YuLg4k5WVZcaOHWsSExPN2LFjzYwZM0z37t1N/fr1zbZt2/zOtmTJElO9enVzxhlnmKZNm5r69eubDz74wLs+Ly/P7/n9O3TocMKLx+Mxbdq08V731/Dhw016erp56qmnTPfu3c2VV15p2rdvb1atWmVWr15tzj33XDNkyBC/aj3++OOmZs2aZsCAASY5Odk8/PDDpl69eubhhx82Dz30kElISDDPPfec39nc/Lz9ng0bNlj6zQY7H2tERITZtWuXMcaY5cuXm4iICJOZmWkmTpxo/vCHP5iIiAizePFiv7O5ed/6PVZeBztfA2Psfx08Ho+pV6+eiYyMNH369DH/+te/zNGjR/2+//GeffZZU716dTNixAgzf/58s3r1avPRRx+Z+fPnmxEjRpjo6Gjz/PPP+13Pzn3fzduvne+X/rD6PmLnNmL3vmrnc2d3Nju3OTu3N2Pcvc3Z/VjtzGb3+7nd75llj8euRs/uL1GcQCPlsOjoaO+3UC1btjSLFi3yWb9y5UrTtGlTv2q1bt3azJs3z3t93bp1pnHjxt5vrK655hrTv39/v7N16dLF3HvvvcYYY0pLS83UqVNNrVq1vBmt7NBRUVEmIyPDZGdney/jx483ERERZtSoUd5l/kpOTjYfffSRN4fH4zFLly71rl+1apVp1KiRX7Vat25tXn31VWOMMevXrzdRUVHmxRdf9K5/+eWXzTnnnON3Njc/b3fddddJL4MHD7b0Jm3nY/V4PN4PBZdccokZNWqUz/qxY8eaCy+80O9sbt637Hwd7HwNjLH/dfB4PObHH3808+fPN5mZmSYqKso0aNDA3HPPPZaPqrZo0cJn3/ytl156yZx22ml+17Nz33fz9mvn+6Ux9r+P2LmN2L2v2v1/jZ3Z7Nzm7NzejHH3Nmf3Y3Xz+7nd75l2N3p2foniFBophzVr1sy7ETZq1Mj7LUmZLVu2mJo1a/pVq0aNGiYnJ8dnWVRUlPnxxx+NMcZ88sknpnbt2n5ni4+PN19//bXPsrlz55qaNWuaBQsWWNphVq1aZVq0aGH++te/mpKSEp98mzdv9jtTmdjYWPPdd995r1erVs1s3LjRe/3bb7+19LwdP6QiOjraZ/aa7du3h8zzFhERYc4++2zTo0ePE146duxo6U3Qzsd6/IeC5ORks2bNGp/1mzdvNvXq1fM7m5v3LTtfBztfA2Psfx2Or2eMMbm5uWbSpEkmLS3NO8TnpZde8qtWTEyM+fLLLytcv3XrVhMTE+N3Njv3fTdvv3a+Xxpj//uInduI3fuq3f/X2JnNzm3Ozu3NGHdvc3Y/Vje/n9v9nlkVX9zZ9SWKU2ikHHbvvfeaLl26mL1795qxY8eazMxMU1hYaIwx5sCBA2bgwIGmd+/eftVq06aNeeONN7zXP/vsM1O9enVTXFxsjDn2ocDKm0ODBg3Mp59+Wm75vHnzTGxsrJkxY4alHSY/P99ce+21plOnTt43iso2BGeeeaZ5+umnjTHG/Pvf/zZxcXHmscce866fMWOGSU9P96tWvXr1fHbYxo0b+/wHsH37dlOrVi2/s7n5eWvVqpV55ZVXKlz/+eefW8pm52P1eDzm66+/Nvn5+ea0004zn3/+uc/67du3m9jYWL+zuXnfsvN1sHt7s/t1OH740W8tX77cDB482O/n7pxzzjF33313hevvvvtuS0eP7dz33bz92vl+aYz97yN2biN276t2Pnd2Z7Nzm7NzezPG3duc3Y/Vze/ndr9nVuUXd8ac2pcoTqGRclhRUZHp16+fqVOnjunVq5eJiYkxsbGxJi0tzdSsWdM0bdrUfPXVV37Vevrpp01CQoIZM2aM+etf/2pSUlLMTTfd5F0/Z84cS+fT9OrVyzz66KMnXDd37lxTrVo1SztMmZdfftkkJSWZ5557zlSrVq1SDcGcOXNMZGSkOf30001MTIz517/+ZVJSUszAgQPNtddea6pXr+59E/893bp18xlu8VsLFy609Ibv5udt0KBBJisrq8L1GzZsMB6Px+96dj7WspOQIyIijMfjKTcc4a233jJpaWl+Z3PzvmXn62D39mb36/Db/yhPJD8/369aK1asMDVr1jRt27Y1WVlZZvLkyeaRRx4xWVlZ5owzzjC1atUyH374od/Z7Nz33bz92vl+aYz97yN2biN276t2Pnd2Z7Nzm7Nzeyt7LG7d5ux+rG5+P7f7PdPuRs/OL1GcEuX0rIHhrnr16nr77be1ePFiLVy4UJGRkSotLVVycrK6deumQYMGqWbNmn7Vuu222xQREaE5c+aoqKhIw4YN0wMPPOBd36lTJ82dO9fvbCNHjtSHH354wnXXXXedJOn555/3u16Z4cOH6/zzz9f111+v4uJiy/eXpOuvv17NmjXTJ598oq5du6pLly5q06aNHnnkER08eFDPP/+8hg4d6letKVOmnPQ5/v7773Xrrbf6nc3Nz9tjjz2moqKiCtefeeaZKi0t9buenY91+fLlPteTk5N9rn/33Xf64x//6Hc2N+9bdr4Odm9vdr8OQ4cO/d1p+uPj4/2q1b17d23atEkzZszQmjVrlJeXJ0lKSkrS5ZdfrhEjRqh58+Z+Z7Nz33fz9mvn+6Vk//uInduI3fuqnc+d3dns3Obs3N4kd29zdj9WN7+f2/2eedZZZ2n58uU655xzfJZfc801Ki0ttfSaSpI5yS8w9ejRQz169FBBQYGlmoHG70jBMaWlpSosLFR8fLw8Ho/TcYIGzxsAAAi0+fPn68MPP9Tjjz9+wvWvvfaann/++XJNfkWGDx+uJ598UnFxcXbGDCgaKfhlx44dysvLk8fjUWJiopo1a+aKWlVRL1zwvCFUmGPD1BUREVHpGuHyHsd+X3m8Du7A6wBXcXBYIf6/d955x9x0003mz3/+c7lZSvbs2WMuuugiR2oZY8y0adNM48aNvWOwy8ZkN27c2Dz++OOO1aqKehWx+psoxvi+Dlu3bvVZdyqv6anWMiZwz5sxlXvuAlHL7nqhto24td7Ro0fNfffdZy688ELvj+VOnTrVxMbGmurVq5shQ4aYoqIiS9nC5T0uWPf7ytQLp/8HT8bp583N/3e5+XWwu5bdr2uZ7777zqxZs8Z88sknPpP0VJbd9QKFRsphr776qomMjDR9+/Y1559/vomJiTFz5szxrrcyA4qdtYwx5qGHHjLx8fHmkUceMZ9//rn56aefzI8//mg+//xz88gjj5iEhAQzYcKEgNeqinonY/XE6XB5Tf1h9bkLVC2764XSNuLmevfff79JTEw0d999t2nbtq0ZMWKEadKkiZkzZ475xz/+YRo3bmymTJnid7ZweY8L5v3eaj03v2e6+XVw835vTPi8DnbXsvt1MMbdXxg5gUbKYR06dDBPPvmk9/obb7xhatWq5Z19x8pGbmctY45NBTx//vwK17/55psmJSUl4LXsrte/f/+TXi6++GJLz1u4vKbG2Pvc2f06uDmbm7cRN9c77bTTzMKFC40xx6Z2joiI8Jl175///KelGTbD5T3Ozfu93fXc/J7p5tfBzfu9MeHzOrj5/xpj3P2FkVOYtc9h27Zt0+WXX+69ftVVV6l+/frq16+fjh49qv79+ztSS5J+/fVXtWrVqsL1LVu21N69ewNey+56CxcuVK9evZSYmHjC9SUlJX7nksLnNZXsfe7sfh3cnM3N24ib6/30008688wzJUmnn366qlev7r0uSR07dtSOHTv8rhcu73Fu3u/trufm90w3vw5u3u+l8Hkd3Px/jXRsxsDZs2fryiuv9FmekpKis846Sy1bttTtt9+u+++/35F6jnC6kwt3ycnJ5uOPPy63fMWKFaZWrVrmvvvu8/vbAjtrGWNM9+7dzfXXX2+OHj1abt3Ro0fNoEGDTPfu3QNey+567dq1K/f7G8ez+uOS4fKaGmPvc2f36+DmbG7eRtxcLzEx0fz3v//1Xu/atav54YcfvNe3bt1q4uPj/c4WLu9xbt7v7a7n5vdMN78Obt7vjQmf18HN/9cYY0yNGjXKnWd1vE2bNpkaNWo4Vs8JHJFyWKdOnbRo0SKdd955Psu7d++uhQsX+nyTEMhakvTUU0+pd+/eatiwobp3767ExER5PB7l5eXpww8/VHR0tJYtWxbwWnbXO+ecc7R+/XrddNNNJ1wfHR2tpk2b+p0tXF5Tyd7nzu7Xwc3Z3LyNuLle27ZttX79erVr106S9NFHH/ms37hxo9LS0vyuFy7vcW7e7+2u5+b3TDe/Dm7e76XweR3c/H9NWb2JEydq1qxZiorybSGKi4s1adIkderUybF6TmD6c4etXLlSq1ev1rhx4064fsWKFZo9e7ZmzpwZ0FplCgsLNWfOnHI/5NalSxcNGjTI7x9JtLuWnfWKiopUUlKi2NhYS3+/IuH0mtr53Nn9Org5m5u3ETfX27Ztm6pVq6bU1NQTrp87d66ioqI0cOBAv7JJ4fEeZ3ctN++rbn/PdOvr4Ob9vkw4vA5u/r9GOvZlVe/evVVUVHTSJvSMM85wpJ4jnD4kFu6WLFlijhw54rpadtcjm/O13F6PbM7Xcns9sjlfy+31yOZ8LbfXI9upKSgoMH//+9/NkCFDTO/evU3v3r3NkCFDzIwZM0x+fr7j9QKNRsphqampJiEhwQwcONC8+uqrZu/eva6o9dt6c+fONfv27XNFrarMZvfzFsqvqd35wvV1COVsdtdz8/4QLO9xbtvv7a4XrtuIm583XgdnalVFvWBo9AKNRsoFvvjiCzNhwgRz7rnnmujoaHPxxRebJ554wuTk5Dhai2xkC3Q9soVeNrvrkS30stldj2yhl83uemSrXD03f2HkFBopl/nxxx/NjBkzzKWXXmpiYmJM+/btzQMPPGDWrVvnaC2ykS3Q9cgWetnsrke20Mtmdz2yhV42u+uRzVo9Nzd6TqCRcrH9+/ebf/3rX+aGG24w9erVMxMnTnRFLbKFZrbCwkLX1iOb87XcXs/ubG7eV8Mlm931quI984033rBt+7WrVjhlM8bd20ioZ3NjoxdozNoXJEpLS/Xrr7+qQYMGrqpldz2yOV/L7fXI5nwtt9cjm/O13F6PbM7Xcns9sllz4MABLV68WG+//bb+/e9/6+6779a9997rmnpVhUbKpU477TQtWbLE0m+iVHWto0eP6t1339X27duVnJys/v37q2bNmo7XIpv/tX744QfFxMSofv36kqT//Oc/evbZZ/X999+rWbNmuu2229SlSxdH6pEt9LK5/bE+9thjuuqqq9SsWTO/7xOIWnbXc3M2u+vZnU2SFi5cqE8//VQZGRnq0qWLPvjgA/3tb39TaWmpBgwYoFtuucWRWuGUTZIOHTqk1157TatWrVJubq4iIyOVmpqqK6+8UpdccoljtcIpm7/c2OhVGWcPiOGJJ5444SUyMtKMGzfOez3QtYwxpkuXLt4ZXnbv3m3atWtnqlevbtLS0kxMTIxp2rSp+eGHHwJei2ynlu3f//63McaYt956y0RERJh+/fqZv/zlL6Z///6mWrVqZuHChY7UI1voZXP7Y/V4PCYyMtL07NnTzJs3zxQVFfl936qsFU7Z7K5nd7YZM2aYqKgoc84555j4+HgzZ84cExcXZ26++WZz6623mho1apjp06cHvFY4ZTPGmO3bt5tmzZqZevXqmeTkZOPxeEzfvn1N586dTWRkpLn66qvN0aNHA14rnLKV2b9/v3n++efNsGHDTEZGhrn00kvNsGHDzAsvvGD2799vqVZV1As0GimHeTwe07hxY9O8eXOfi8fjMY0aNTLNmzc3qampAa9VVm/Xrl3GGGP++Mc/mrPOOsvk5uYaY4z55ZdfTNeuXc2NN94Y8Fpkq3y2uLg47wmcnTt3No888ojP+qeeesp06NDBkXpkC71sdtezO5vH4zEzZ840V1xxhalWrZqpV6+eufPOO83GjRv9rlEVtcIpm9317M7Wpk0b8/zzzxtjjPnggw9MTEyMeeaZZ7zrZ86cadq0aRPwWuGUzRhjLr30UnPrrbeakpISY4wxkydPNpdeeqkxxpht27aZ5s2bm/Hjxwe8VjhlM8aYzZs3m5SUFFO7dm1zxRVXmFtuucX88Y9/NFdccYWpXbu2adSokdm8ebNj9ZxAI+WwW265xZx11llmy5YtPsujoqIsbzx21jLG90N8y5YtzTvvvOOzfvny5aZ58+YBr0W2ymdLSEgwX3zxhTHGmIYNG3r/Xebrr782sbGxjtQjW+hls7ue3dmO37927dplpkyZYlq3bm0iIiLMueeea55//nlTUFAQ8FrhlM3tj7VGjRpmx44d3uvVqlXzacpycnL83ubsrBVO2YwxJjY21mzbts17vaioyFSrVs388ssvxphjR6j9/b/QzlrhlM0YY3r06GGuvfbaEx7pLSoqMtddd53p0aOHY/WcEOH00MJw99xzz2n8+PHq06ePnn76adfUKuPxeCRJ+/btU2pqqs+61NRU5ebmOlKLbJWr1b17d7322muSpA4dOmjFihU+65cvX65GjRo5Uo9soZfN7np2Zztew4YNNWbMGG3dulUrVqxQ27Ztdddddyk5OdnRWuGUze56dtSqV6+eduzYIUn66aefVFxcrO+//967fseOHapbt27Aa4VTNkmqXbu2CgsLvdcPHjyo4uJiVa9eXZLUvn17v/8vtLNWOGWTpE8++UQPPPCA9/7Hq169uu6991598sknjtVzhNOdHI754YcfzMUXX2wyMjJMbm5upY8i2VnL4/GYyy67zPTv39/UqVPHe25CmY8//tgkJiYGvBbZKp9ty5Ytpl69embIkCFmwoQJplatWmbw4MFm4sSJZsiQISY6OtrMnDnTkXpkC71sbn+sERER3qMXJ5Kfn+8dnhTIWuGUze56dme77bbbTFpamnn44YdNp06dzNChQ03r1q3NokWLzOLFi027du38Hl5tZ61wymaMMUOHDjXdu3c3W7duNd9++6255pprfIbxrlixwjRp0iTgtcIpmzHGpKSkmLfeeqvC9fPnzzcpKSmO1XMCjZSLlJaWmkmTJpmkpCQTGRl5SuNC7ag1bNgwn8s///lPn/WjR482ffr0CXgtslU+mzHHhj9dc801Ji4uzng8HuPxeEy1atVM165dzfz58y3Vsrse2UIvm9317Kx1/DCwU2VnLbvruTmb3fXszrZ//35z8803m/T0dDNixAhz5MgR8+ijj5rq1asbj8djevTo4fffs7NWOGUz5tgwzfPOO8+7zzdv3tx89tln3vVvvPGGefLJJwNeK5yyGWPM+PHjTUJCgnn00UfNhg0bTG5ursnLyzMbNmwwjz76qKlTp4558MEHHavnBKY/d6H169frP//5j4YMGaI6deq4ptZvHThwQJGRkYqJiXFVLbvrhWo2Y4x2796t0tJS1a9fX9WqVTulLHbWI5vztdxez+5sgBWHDx/W0aNHFRcX56padtdzW7bt27erqKhIbdq0UWRk5CllsbNWOGWbMmWKnnjiCeXl5XlPPTDGKCkpSVlZWRozZoyj9QKNRsoFfvjhB82YMUOrV6/2bkiJiYnq2rWrRowYoSZNmjhSi2zhla1bt2669dZbHa1HttDLFqyP1c37aqhlC9bH2q1bN40YMUKNGzd2pFY4ZTtZPTdvI6GW7Xg5OTnKy8uTJCUlJZU7h9vpeoFCI+WwVatW6dJLL1WTJk3Uu3dvJSYmer9pXbZsmXbu3KlFixapW7duAa1FNrLxWMnmtnpkC71s4fRYyeaOemSrfL3fs3PnTo0fP14vv/yyK+tVCVsHCsKyjh07mqysrArXZ2VlmY4dOwa8FtnIFuh6ZAu9bHbXI1voZbO7HtlCL5vd9chW+Xq/Z8OGDSYiIsK19aoCjZTDYmJizJdfflnh+q1bt5qYmJiA1yIb2QJdj2yhl83uemQLvWx21yNb6GWzux7ZKl/v7bffPunl8ccft9T42F3PCfyOlMOSk5O1evXqCtd//PHHfv/mhZ21yEa2QNcjW+hls7se2UIvm931yBZ62eyuR7bK17vyyivVv39/XXnllSe83H333X7Xqop6TohyOkC4Gz16tEaMGKHPPvtMvXr1UmJiojwej/Ly8rRs2TK9+OKLmj59esBrkY1sPFayua0e2UIvWzg9VrK5ox7ZKl8vOTlZzzzzjK688soTrt+wYYPOOeccx+o5wulDYjBm3rx5pnPnziYqKso7139UVJTp3Lmzef311x2rRTayBboe2UIvm931yBZ62eyuR7bQy2Z3PbJVrl5mZqZ54IEHKly/YcMG4/F4HKvnBGbtc5GjR4/ql19+kaRT/l0UO2uRjWyBrkc252u5vR7ZnK/l9npkc76W2+uRzZr//Oc/OnDggDIyMk64/sCBA/r000/VvXt3R+o5gUYKAAAAACxisgkAAAAAsIhGCgAAAAAsopECAAAAAItopAAAAADAIhopAAAAALCIRgoAEFKGDRsmj8cjj8ejatWqKTExUb169dLLL7+s0tJSv+vMmjVLtWvXrrqgAICgRiMFAAg5GRkZys3N1XfffadFixbpoosu0p133qnLL79cxcXFTscDAIQAGikAQMiJjo5WUlKSGjVqpLPPPlv33nuv3n77bS1atEizZs2SJE2bNk3t2rVTzZo11aRJE40aNUr79++XJK1YsULDhw9Xfn6+9+hWdna2JOnIkSMaM2aMGjVqpJo1a6pz585asWKFMw8UAOAYGikAQFi4+OKLdeaZZ+rNN9+UJEVEROjJJ5/Upk2bNHv2bH3wwQcaM2aMJKlr166aPn264uPjlZubq9zcXI0ePVqSNHz4cH300UeaN2+e/vvf/+rqq69WRkaGtm/f7thjAwAEnscYY5wOAQCAXYYNG6Z9+/bprbfeKrfu2muv1X//+19t2bKl3Lo33nhDI0eO1C+//CLp2DlSWVlZ2rdvn/c233zzjdLS0vTDDz8oJSXFu7xnz57q1KmTJk2aZPvjAQC4U5TTAQAACBRjjDwejyRp+fLlmjRpkrZs2aKCggIVFxfr8OHDOnDggGrWrHnC+69fv17GGLVs2dJneVFRkerVq1fl+QEA7kEjBQAIG1u3blVqaqp27Nihyy67TCNGjNCECRNUt25drVq1SjfddJOOHj1a4f1LS0sVGRmpzz77TJGRkT7ratWqVdXxAQAuQiMFAAgLH3zwgTZu3Ki77rpLn376qYqLi/XYY48pIuLY6cL//Oc/fW5fvXp1lZSU+Czr0KGDSkpKtHv3bl1wwQUByw4AcB8aKQBAyCkqKlJeXp5KSkq0a9cuLV68WJMnT9bll1+uIUOGaOPGjSouLtZTTz2lzMxMffTRR3r22Wd9ajRv3lz79+/X+++/rzPPPFOxsbFq2bKlrr/+eg0ZMkSPPfaYOnTooF9++UUffPCB2rVrp8suu8yhRwwACDRm7QMAhJzFixcrOTlZzZs3V0ZGhpYvX64nn3xSb7/9tiIjI3XWWWdp2rRpmjJlitLT0/Xqq69q8uTJPjW6du2qESNG6JprrlGDBg00depUSdLMmTM1ZMgQ3XPPPWrVqpX69eunTz75RE2aNHHioQIAHMKsfQAAAABgEUekAAAAAMAiGikAAAAAsIhGCgAAAAAsopECAAAAAItopAAAAADAIhopAAAAALCIRgoAAAAALKKRAgAAAACLaKQAAAAAwCIaKQAAAACwiEYKAAAAACz6f8l62pksFv1kAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "df = pd.DataFrame({'date': dates, 'value': values, 'error': errors})\n",
    "\n",
    "# Plot the time series with error bars\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.xticks(rotation=90)\n",
    "plt.errorbar(df['date'], df['value'], yerr=df['error'], fmt='o')\n",
    "plt.xlabel('Date')\n",
    "# plt.ylabel('chl-a (mg/m^3)')\n",
    "plt.ylabel('POC (µmol/L)')\n",
    "plt.title('CHL time series in Edward')\n",
    "plt.title('POC time series in Edward')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd20a76-9de1-4036-b9fd-e619342286a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}